Made with 100% cotton muslin and love, our classic musy mate lovey is sure to comfort and delight your little one. The cuddly companion is small enough to give to a newborn and the ideal size for toddlers to tote, making it the most perfect and snugly transitional object. The classic musy mate lovey's adorable head gets its shape from a hypoallergenic polyester fill.


To post a job on this board, please send an email with a job description and contact information to carolcloud2003@gmail.com. Your position will be posted within 24 hours. Please let us know when the position has been filled so we can remove it from the site. Otherwise it will be removed after 90 days. Thank you.
Lakecliff also referred to as "Lakecliff on Lake Travis" was designed by Arnold Palmer and features 12 man-made lakes, scenic waterfalls, and spectacular Hill Country views. Located near the Highland Lakes in the Hill Country, the course plays 6,754 yards to a par 71 and offers breathtaking, rolling terrain through the cliffs and ravines that plunge towards Lake Travis. The course has five par 3s and is great for players of all levels. Lakecliff Golf Club is managed and operated by Austin Country Club.
Seeking an eager Assistant Superintendent with self-motivation to provide a day to day experience for members unlike any in the Hill Country area. The Assistant Superintendent will have a hands-on experience with transforming Lakecliff Golf Club back to its prime from when it originally opened in 2003.
The Assistant Superintendent will provide and not be limited to the following.
Expenses paid for local, regional and national association dues and expenses paid for attending local chapter meetings and continuing education.
Other Information: The golf club at The Clubs of Cordillera Ranch is nestled 20 miles northwest of San Antonio in the hill country. The golf course has garnered local, state, and national attention by various golf publications. Please visit www.cordilleraranch.com for more information regarding the developer, golf course, and community.
Bent Tree Country Club is now accepting applications for the Assistant Superintendent position.
Responsibilities: crew supervision, daily scheduling, course set up, fertilizer/pesticide applications, record keeping, equipment calibrations, irrigation repair/management, and administrative duties.
Compensation: $45,000-$55,000; Benefits include health insurance, life insurance, vacation, sick leave, discounted meals, holiday pay, uniforms, GCSAA dues, TDA license dues, continuing education classes, Christmas bonus and tournament volunteering.
Course Description: Originally built in 1972, the Bent Tree Country Club par 72 layout was the design work of renowned golf course architect Desmond Muirhead. Then, thirty years later, in 2002, the Club's Board of Directors empowered nationally known architect Keith Foster to rebuild and rework the original blueprint and upgrade Muirhead's design into a more traditional and timeless layout. Fun and challenging, the course annually hosts an assortment of member tournaments for men, women, couples, and juniors. In addition, it has gained the respect of the LPGA by hosting their Mary Kay Classic for three years, as well as the PGA Senior Tour, by hosting the Reunion Pro-Am for three years and the Bank One Championship for two years. In 2014, Bent Tree Country Club hosted the Women's Texas State Amateur Championship and the following year hosted the Men's Texas State Amateur Championships. Champions like Tom Watson, Larry Nelson, Jan Stephenson, Don January, Orville Moody, and many others add Bent Tree Country Club to their impressive list of tour site wins.
The Assistant Superintendent will be responsible for working closely with the Director of Agronomy and the VP, Golf Course Development while supervising all Golf Course Maintenance activities and crew, at Discovery Land Company's location in Austin, Tx – Driftwood Golf & Ranch Club. The Assistant Superintendent will be part of a brand new, Tom Fazio designed golf course, which will feature bentgrass greens with fan systems and fairways and roughs will be zeon zoysia grass, working directly on-site to help develop the new construction of the golf course.
Assures the golf course maintains the best possible playing conditions including proper placement of pins, tee marker, and proper raking of bunkers.
Supervises and assists in the maintenance and repair of golf course irrigation system and water transfer pipeline.
Oversees the maintenance and operation of all golf course and grounds equipment.
Working knowledge of the maintenance of golf course tees, fairways, greens.
Founded in 1994 by Michael S. Meldman, Discovery Land Company specializes in building luxury residential private club communities and resorts throughout North America. We have grown to be the premier player in the high-end resort residential niche with 20 world-class projects in our portfolio. Our communities are distinct with their own architectural styles, world-class amenities, and high levels of service.
Park and Round Rock so movement inside the company is available in the future as well.
$13-15.00 per hour BOE. Hours range from 40-48/per week M-F and rotating weekends.
Health Insurance after 90 day's.
Chemical License paid yearly as well as CEU's.
Dues to CTGCSA meetings and GCSAA membership.


text:
LSU in the NCAA Men's Basketball Tournament
by LSUsports.net (@LSUsports)
LSU's NCAA Seed, Region, Record
Year Seed Region Record
1953 n/a East 2-2
1954 n/a Midwest 0-2
1979 3 Mideast 1-1
1980 1 Midwest 2-1
1984 7 West 0-1
1985 4 Southeast 0-1
1986 11 Southeast 4-1
1987 10 Midwest 3-1
1988 9 East 0-1
1989 10 West 0-1
2003 8 South 0-1
2005 6 Chicago 0-1
2006 4 Atlanta 4-1
22 NCAA Tournament Appearances
1953, 1954, 1979, 1980, 1981, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 2000, 2003, 2005, 2006, 2009, 2015, 2019
1953 NCAA Tournament
Coach Harry Rabenhorst | LSU #7
East Region – Raleigh, N.C.
March 13 – LSU 89, Lebanon Valley 76 (Regional Semifinals)
March 14 – LSU 81, Holy Cross 73 (Regional Finals)
Final Four – Kansas City, Mo.
March 17 – #1 Indiana 80, LSU 67 (National Semifinals)
March 18 – #4 Washington 88, LSU 69 (National 3rd Place Game)
Midwestern Region – Iowa City, Iowa
March 12 – Penn State 78, LSU 70 (Regional Semifinals)
March 13 – #2 Indiana 73, LSU 62 (Regional Third Place Game)
Coach Dale Brown | LSU #9/#7
#3 Seed
Mideast Region – Bloomington, Ind.
March 10 – #3 LSU 71, #6 Appalachian State 57 (Second Round)
Mideast Regional – Indianapolis, Ind.
March 16 – #2 Michigan State 87, #3 LSU 71 (Regional Semifinals)
Coach Dale Brown | LSU #3
Midwest Region – Denton, Texas
March 9 – #1 LSU 98, #8 Alcorn State 88 (Second Round)
Midwest Regional – Houston, Texas
March 14 – #1 LSU 68, #5 Missouri 63 (Regional Semifinals)
March 16 – #2 Louisville 86, #1 LSU 66 (Regional Finals)
Midwest Region – Austin, Texas
March 14 – #1 LSU 100, #8 Lamar 78 (Second Round)
Midwest Regional – New Orleans, La.
March 20 – #1 LSU 72, #5 Arkansas 56 (Regional Semifinals)
March 22 – #1 LSU 96, #6 Wichita State 85 (Regional Finals)
NCAA Final Four – Philadelphia, Pa.
March 28 – #3 Indiana 67, #1 LSU 49 (National Semifinals)
March 30 – #1 Virginia 78, #1 LSU 74 (National Third Place Game)
Coach Dale Brown | LSU NR
West Region – Salt Lake City, Utah
March 15 – #10 Dayton 74, #7 LSU 66 (First Round)
Coach Dale Brown | LSU #20
Southeast Region – Dayton, Ohio
March 15 – #13 Navy 78, #4 LSU 55 (First Round)
#11 Seed
Southeast Region – Baton Rouge, La.
March 13 – #11 LSU 94, #6 Purdue 87 (2 ot) (First Round)
March 15 – #11 LSU 83, #3 Memphis State 81 (Second Round)
Southeast Regional – Atlanta, Ga.
March 20 – #11 LSU 70, #2 Georgia Tech 64 (Regional Semifinals)
March 22 – #11 LSU 59, #1 Kentucky 57 (Regional Finals)
NCAA Final Four – Dallas, Texas
March 29 – #2 Louisville 88, #11 LSU 77 (National Semifinals)
Midwest Region – Chicago, Ill.
March 13 – #10 LSU 85, #7 Georgia Tech 79 (First Round)
March 15 – #10 LSU 72, #2 Temple 62 (Second Round)
Midwest Regional – Cincinnati, Ohio
March 20 – #10 LSU 63, #3 DePaul 58 (Regional Semifinals)
March 22 – #1 Indiana 77, #10 LSU 76 (Regional Finals)
East Region – Hartford, Conn.
March 18 – #8 Georgetown 66, #9 LSU 63 (First Round)
West Region – Tucson, Ariz.
March 17 – #7 Texas-El Paso 85, #10 LSU 74 (First Round)
Southeast Region – Knoxville, Tenn.
March 15 – #5 LSU 70, #12 Villanova 63 (First Round)
March 17 – #4 Georgia Tech 94, #5 LSU 91 (Second Round)
Midwest Region – Minneapolis, Minn.
March 14 – #11 Connecticut 79, #6 LSU 62 (First Round)
West Region – Boise, Idaho
March 19 – #7 LSU 94, #10 Brigham Young 83 (First Round)
March 21 – #2 Indiana 89, #7 LSU 79 (Second Round)
March 18 – #6 California 66, #11 LSU 64 (First Round)
Coach John Brady | LSU #10
West Region – Salt Lake City
March 16 – #4 LSU 64, #13 Southeast Missouri St. 61 (First Round)
March 18 – #4 LSU 72, #5 Texas 67 (Second Round)
West Regional – Albuquerque, N.M.
March 23 – #8 Wisconsin 61, #4 LSU 48 (Regional Semifinal)
Coach John Brady | LSU NR
South Region – Birmingham, Ala.
March 21 – #9 Purdue 80, #8 LSU 56 (First Round)
Chicago Region – Boise, Idaho
March 17 – #11 UAB 82, #6 LSU 68 (First Round)
Atlanta Region – Jacksonville, Fla.
March 16 – #4 LSU 80, #13 Iona 64 (First Round)
March 18 – #4 LSU 58, #12 Texas A&M 57 (Second Round)
Atlanta Regional – Atlanta, Ga.
March 23 – #4 LSU 62, #1 Duke 54 (Regional Semifinal)
March 25 – #4 LSU 70, #2 Texas 60 (OT) (Regional Final)
NCAA Final Four – Indianapolis, Ind.
April 1 – #2 UCLA 59, #4 LSU 45 (Final Four)
Coach Trent Johnson | LSU #21
South Region – Greensboro, N. C.
March 19 – #8 LSU 75, #9 Butler 71 (First Round)
March 21 – #1 North Carolina 84, #8 LSU 70 (Second Round)
Coach Johnny Jones | LSU NR
East Region – Pittsburgh, Pa.
March 19 – #8 NC State 66, #9 LSU 65 (First Round)
Interim Coach Tony Benford | LSU #12
East Region – Jacksonville, Fla.
March 21 – #3 LSU 79, #14 Yale 74 (First Round)
March 23 – #3 LSU 69, #6 Maryland 67 (Second Round)
East Region – Washington, D.C.
March 29 – #2 Michigan St. 80, #3 LSU 63 (Regional Semifinal)
Team Single-Game Records
100 – vs. Lamar, 3/14/81, Austin, Texas
98 – vs. Alcorn State, 3/09/80, Denton, Texas
96 – vs. Wichita State, 3/22/81, New Orleans, La.
Points Allowed
94 – by Georgia Tech, 3/17/90, Knoxville, Tenn.
89 – by Indiana, 3/21/92, Boise, Idaho
88 – three occasions (last: by Louisville, 3/29/86, Dallas, Texas)
Fewest Points Allowed
54 – by Duke, 3/23/06, Atlanta, Ga.
56 – by Arkansas, 3/20/81, New Orleans, La.
57 – three occasions (last: by Texas A&M, 3/18/06, Jacksonville, Fla.)
Field Goal Attempts
80 – vs. Washington, 3/18/53, Kansas City, Mo.
78 – vs. Purdue, 3/13/86, Baton Rouge, La.
77 – vs. Georgia Tech, 3/17/90, Knoxville, Tenn.
75 – three occasions (last: vs. Georgia Tech, 3/17/90, Knoxville, Tenn.)
Field Goals Made
45 – vs. Lamar, 3/14/81, Austin, Texas
41 – vs. Alcorn State, 3/9/80, Denton, Texas
Field Goal Percentage
.639 – (39-61) vs. Wichita State, 3/22/81, New Orleans, La.
.608 – (45-74) vs. Lamar, 3/14/81, Austin, Texas
.554 – (41-74) vs. Alcorn State, 3/9/80, Denton, Texas
Free Throw Attempts
43 – vs. Indiana, 3/17/53, Kansas City, Mo.
34 – vs. Holy Cross, 3/14/53, Raleigh, N.C.
32 – vs. Lebanon Valley, 3/13/53, Raleigh, N.C.
Free Throw Made
21 – three occasions (last: vs. Iona, 3/16/06, Jacksonville, Fla.)
Free Throw Percentage
.923 (12-13) – vs. Indiana, 3/21/92, Boise, Idaho
.875 (7-8) – vs. UTEP, 3/17/89, Tucson, Ariz.
.833 (20-24) – vs. Penn State, 3/12/54, Iowa City, Iowa
.833 (15-18) – vs. Washington, 3/18/53, Kansas City, Mo.
Three-Point Field Goal Attempts
31 – vs. UAB, 3/17/05, Boise, Idaho
26 – vs. Purdue, 3/21/03, Birmingham, Ala.
23 – vs. Indiana, 3/21/92, Boise, Idaho
Three-Point Field Goals Made
9 – vs. Georgia Tech, 3/13/87, Rosemont, Ill.
9 – vs. North Carolina, 3/21/09, Greensboro, N.C.
7 – two occasions (last: vs. California, 3/18/93, Rosemont, Ill.)
Three-Point Field Goal Percentage
.545 (6-11) – vs. Southeast Missouri State, 3/16/00, Salt Lake City, Utah
.529 (9-17) – vs. Georgia Tech, 3/13/87, Rosemont, Ill.
.500 (5-10) – vs. Iona, 3/15/06, Jacksonville, Fla.
.500 (5-10) – vs. Temple, 3/15/87, Rosemont, Ill.
.500 (4-8) – vs. Villanova, 3/15/90, Knoxville, Tenn.
51 – vs. Penn State, 3/12/54, Iowa City, Iowa
47 – vs. Villanova, 3/15/90, Knoxville, Tenn.
46 – two occasions (last: vs. Connecticut, 3/14/91, Minneapolis, Minn.)
23 – vs. Georgia Tech, 3/13/87, Rosemont, Ill.
23 – vs. BYU, 3/19/92, Boise, Idaho
12 – vs. BYU, 3/19/92, Boise Idaho
11 – vs. Iowa, 3/16/06, Jacksonville, Fla.
11 – vs. Butler, 3/19/09, Greensboro, N.C.
10 – three occasions (last: vs. BYU, 3/19/92, Boise Idaho)
32 – vs. Louisville, 3/16/80, Houston, Texas
32 – vs. Navy, 3/15/85, Dayton, Ohio
30 – vs. Virginia, 3/30/81, Philadelphia, Pa.
Victory Margin
22 – (100-78) vs. Lamar, 3/14/81, Austin, Texas
16 – (80-64) vs. Iona, 3/15/06, Jacksonville, Fla.
16 – (72-56) vs. Arkansas, 3/20/81, New Orleans, La.
14 – (71-57) vs. Appalachian State, 3/10/79, Bloomington, Ind.
Defeat Margin
24 – (80-56) vs. Purdue, 3/21/03, Birmingham, Ala.
23 – (78-55) vs. Navy, 3/15/85, Dayton, Ohio
20 – (86-66) vs. Louisville, 3/16/80, Houston, Texas
19 – (88-69) vs. Washington, 3/18/53, Kansas City
Halftime Lead
16 – (34-18) vs. Arkansas, 3/20/81, New Orleans, La. (LSU won 72-56)
16 – (45-29) vs. Yale, 3/21/19, Jacksonville, Fla. (LSU won 79-74)
15 – (48-33) vs. Wichita State, 3/22/81, New Orleans, La. (LSU won 96-85)
12 – (55-43) vs. Lamar, 3/14/81, Austin, Texas (LSU won 100-78)
Halftime Deficit
17 – (36-19) vs. Michigan State, 3/16/79, Indianapolis, Ind. (LSU lost 87-71)
15 – (39-24) vs. UCLA, 4/1/06, Indianapolis, Ind. (LSU lost 59-45)
13 – (42-29) vs. UTEP, 3/17/89, Tucson, Ariz. (LSU lost 85-74)
13 – (39-26) vs. Washington, 3/18/53, Kansas City, Mo. (LSU lost 88-69)
Individual Single-Game Records
36 – Bob Pettit, vs. Washington, 3/18/53, Kansas City, Mo.
36 – Shaquille O'Neal, vs. Indiana, 3/21/92, Boise, Idaho
34 – Bob Pettit, vs. Penn State, 3/12/54, Iowa City, Iowa
31 – Durand Macklin, two occasions
30 – Marcus Thornton, vs. Butler, 3/19/98, Greensboro, N.C.
24 – Bob Pettit, vs. Indiana, 3/17/53, Kansas City, Mo.
24 – Bob Pettit, vs. Indiana, 3/13/54, Iowa City, Iowa
22 – three occasions
Field Goal Pct. (Min. 10 attempts)
.800 (8-10) – Brandon Bass, vs. UAB, 03/17/05, Boise, Idaho
.800 (8-10) – Jordy Hultberg, vs. Louisville, 03/16/80, Houston, Texas
.800 (8-10) – Nikita Wilson, vs. Temple, 03/05/87, Rosemont, Ill.
13 – Glen Davis, vs. Duke, 03/23/06, Atlanta, Ga.
12 – Brandon Bass, vs. UAB, 03/17/05, Boise, Idaho
12 – Shaquille O'Neal vs. Indiana, 03/21/92, Boise, Idaho
11 – Don Redden vs. Memphis, 03/15/86, Baton Rouge, La.
10 – Don Redden vs. Georgia Tech, 03/20/86, Atlanta, Ga.
Free Throws Made
9 – Brandon Bass, vs. UAB, 3/17/05, Boise, Idaho
9 – Bob Pettit, vs. Indiana, 3/17/53, Kansas City, Mo.
8 – Bob Pettit, vs. Washington, 3/18/53, Kansas City, Mo.
8 – Bob Pettit, vs. Penn State, 3/12/54, Iowa City, Iowa
8 – Glen Davis, vs. Duke, 3/23/06, Atlanta, Ga.
8 – Glen Davis, vs. Iona, 3/15/06, Jacksonville, Fla.
Free Throw Percentage (Min. 8 attempts)
1.000 (12-12) – Shaquille O'Neal, vs. Indiana, 03/21/92, Boise, Idaho
.889 (8-9) – Bob Pettit, vs. Penn State, 3/12/54, Iowa City, Iowa
.875 (7-8) – Anthony Wilson, vs. Purdue, 03/13/86, Baton Rouge, La.
.875 (7-8) – John Williams, vs. Memphis, 03/15/86, Baton Rouge, La.
.875 (7-8) – Marcus Thornton, vs. Butler, 03/19/09, Greensboro, N.C.
Three Point Field Goal Attempts
11 – Marcus Thornton, vs. North Carolina, 03/21/09, Greensboro, N.C.
10 – Antonio Hudson, vs. UAB, 03/17/05, Boise, Idaho
9 – Anthony Wilson, vs. DePaul, 03/20/87; vs. Indiana, 03/22/87, Cincinnati, Ohio
9 – Darryl Joe, vs. Georgia Tech, 03/13/87, Rosemont, Ill.
5 – Marcus Thornton, vs. North Carolina, 03/21/09, Greensboro, N.C.
3 – Maurice Williamson twice, Anthony Wilson, Jamie Brandon, Torris Bright, Tack Minor, Darrel Mitchell once
Three-Point Field Goal Percentage (Min. 5 attempts)
.778 (7-9) – Darryl Joe, vs. Georgia Tech, 03/13/87, Rosemont, Ill.
.600 (3-5) – Anthony Wilson, vs. Temple, 03/15/87, Rosemont, Ill.
19 – Durand Macklin, vs. Alcorn State, 3/09/80, Denton, Texas
16 – Durand Macklin, vs. Lamar, 3/14/81, Austin, Texas
16 – Lionel Green, vs. Appalachian State, 3/10/79, Bloomington, Ind.
12 – Ethan Martin, vs. Lamar, 3/14/81, Austin, Texas
10 – Ethan Martin, vs. Wichita State, 3/22/81, New Orleans, La.
9 – Darryl Joe, vs. Georgia Tech, 3/13/87, Rosemont, Ill.
11 – Shaquille O'Neal, vs. BYU, 3/19/92, Boise, Idaho
6 – Stromile Swift, vs. Southeast Missouri State, 3/16/00, Salt Lake City, Utah
6 – Clarence Ceasar, vs. BYU, 03/19/92, Boise, Idaho
5 – Derrick Taylor twice, Darryl Joe once
4 – Marcus Thornton, vs. Butler, 03/19/09, Greensboro, N.C.

redpajama_set_name: RedPajamaCommonCrawl


text:
Immigration Law Fellow
Hometown: Cumberland, Maine
Undergrad: Boston College
Major/Minor: Elementary Education/Latin American Studies
What did you do prior to coming to the University of Maine School of Law?
I lived in Chicago for ten years and worked as the director of a family literacy program in an immigrant community on the Southwest side of the city. It was honestly a dream job and I loved working with my students, but after being away from family for ten years, I was ready to come back to Maine.
Maine Law
Why did you choose to attend the University of Maine School of Law?
After going through an immigration process for my husband, I realized that many legal skills were similar to the skills I had developed in my previous career. I was ready to make a career change but was nervous about being a student again after being out of school for so long. When I came to visit, I could tell that Maine Law is a small community and that I wouldn't be lost in the crowd. I honestly cannot imagine myself at any other school.
What has been most helpful to you in making the adjustment to the life of a Maine Law student?
Having worked full-time for so long, I came in ready to put just as much time and effort in as I would if this were another full-time job. I was accustomed to managing my time and working on long-term projects. That self-discipline was really helpful in making the transition.
As a 1L, how would you describe your experience at Maine Law?
The first year is hard. There's no way around that. But, more importantly, it is a time of immense intellectual growth. It is truly a privilege to be able to take three years to devote to academics. I remember there were times where I would be talking through something with a friend and just stop and think, "I can't believe that sentence just came out of me! I had no idea what any of that meant last week."
What is one thing that has surprised you about Maine Law?
The thing that surprised me most was how accessible the faculty is. I feel like they genuinely want to know me as a person. By stopping in during office hours, I was able to connect with professors who have since connected me with incredible opportunities, like the Laredo Project. Plus, they are just wonderful people. I always leave their offices laughing.
What are you hoping to do after graduation, and how is Maine Law helping you to facilitate that goal?
That is a tough question. Having worked extensively in immigrant communities in the past, I definitely have a passion for immigration law. Maine Law has given me opportunities to get a feel for immigration law through the Refugee and Human Rights Clinic and the Laredo Project. On the other hand, conversations with professors and Maine Law alumni have challenged me to be open-minded and to give myself the space to explore other areas of law as well. Either way, I know I want to stay in Maine and Maine Law has helped me start to build a network for the future.
Are you involved in extracurricular activities, either on or off campus? What are they?
I am a staff member of the Maine Law Review, a co-chair of the Multicultural Law Society, and a research assistant to the incredible Professor Thea Johnson.
What do you like best about Portland?
Obviously the food! My husband is a chef so we love going out and checking out new restaurants.
Portland has a reputation for its vast array of food options. Do you have any favorite restaurants or grocery stores?
Eventide! My husband works there so I might be biased but there is nothing better than a late-night Eventide delivery to the library.
For someone who is considering attending Maine Law, how would you describe the student community here?
There just isn't that destructive competitiveness that you always hear about in law schools. I've found that students here are super supportive of one another and willing to help each other out. Even during reading week, you'll routinely find students fielding one another's questions and studying together. As a 1L, some of my closest friends were 3Ls and I was so appreciative of their guidance and patience with my endless questions.
If you could tell a prospective student one thing about Maine Law, what would it be?
Everyone here, from faculty to staff to classmates, is genuinely pulling for you to succeed. Put the time in to your studies, get to know the faculty, and support one another and you'll be fine.
Elyse Apantaku


I gain the greatest satisfaction when I see my clients succeed with the help of my advice.
My role is to be trusted advisor for my local and international clients, allowing them to do what they do best – run their businesses. In some cases I have been with my clients from the start through to the eventual sale of their business, advising every step of the way.
My clients range from public listed companies to local subsidiaries of overseas corporations through to local family businesses and high net wealth individuals. They include significant wholesalers, engineering and other professional firms and medical centres and specialists.
With in excess of 20 years' experience my specialties include corporate structuring and taxation, superannuation ,acquisition and sale of businesses and outsourced accounting.


Kochi: V K Mathews, Founder and Executive Chairman of the Technopark, Trivandrum based IBS Software Services, was conferred this year's Management Leadership Award instituted by the Kerala Management Association (KMA). Chief Minister Shri Oommen Chandy presented the award at a function held in Kochi on April 23, 2013.
The CM also lauded the contributions that Mathews has made to the IT industry in Kerala and also the efforts that he puts into bringing the state to the forefront of the nation's IT scenario. He said that the courage and willpower that enabled Mathews to start his enterprise in Kerala and to make it a huge success is indeed commendable. He also mentioned the very crucial role that Mathews played in making the 'Emerging Kerala' event a big success.
The KMA Management leadership Award recognizes extraordinary Management leaders for their exemplary leadership qualities in conjunction with their remarkable contributions to management and institution building in Kerala. The award consists of a Gold Medal and a citation. Krishnakumar Natarajan, Chairman, Nasscom and CEO & MD, Mindtree, and S R Nair, President, KMA were among those present on the occasion.
Mathews founded IBS in 1997 with a vision to redefine the way the global travel, transportation and logistics industries managed their businesses. As the founder of one of the very few successful software product companies in Kerala, he has acquired business know-how by acquiring six international companies (in Europe, USA and India) during its 15 year journey. Today, IBS is a multinational corporation, serving over 170 clients worldwide, employing 2,000 professionals from 30 different nationalities.


At DumpsterFly, we strive to bring you the best service at the best price available. We have a wide selection of dumpster sizes in stock in Del Norte, CO, with flexible & timley pickup and delivery. Give us a call today for all your dumpster rental and roll off needs.
Understanding the speedy requirement, a number of Rent a Waste Management Dumpster in Del Norte, CO companies supply instant products and services to the tragedy zones using the availability. This delivery will be performed on 1st come 1st serve time frame. These kinds of organizations attach social responsibility on their objectives hence cares additional for giving a quality plan to the people about need within an affordable price tag rather than incorporating an extra sale in their account.
Your home about upgrading will gain a greater look far better resale price. Once you go for home renovation there are several issues that occur right from property design for you to trash elimination. This article concentrates on highlighting the standards that are to be considered while finding a dumpster for a rubbish removal.
Dumpster renting offer organizations, which call for waste disposal systems to suit their diverse needs, an answer to manage their wastes with out incurring fees involved in obtaining a waste disposal item designed or purchasing the disposal item altogether. It's also easier for companies to get waste materials disposal models suited for the requirements through a rental company than if they were to obtain a waste disposal unit their selves. Rental companies supply different disposal units kinds suited to meet diverse disposal specifications. Storage of the disposal device is no longer a cause for worry to organizations, which call for dumpsters for short periods of time of time. Renting a new disposal box means that the actual container should be returned by the end of the renting length of time therefore resolving the box storage trouble.
Schools additionally collect a lot of old rubbish that may no longer be needed. Universities tend to shop them inside their basement and other storage area. Previous couches, seating, desks, computer systems and computer monitors, etc. can be overloading the actual storage space. Renting a new dumpster is the perfect answer to get rid of each of the unwanted junk within a quick manner. When choosing a new Rent a Waste Management Dumpster in Del Norte, CO company, learn how long you can the dumpster from one price. If you need to expand the time find out the cost upfront.
A not recent craze that is constantly become more and much more popular today is the partnering or bundling of occasion services. Usually one supplier can supply a number of the temporary rental products and services needed for a new concert, marriage, party and other special event to become a success. One particular industry in which this craze is especially present is the dumpster marketplace. In the past Rent a Waste Management Dumpster in Del Norte, CO organizations offered products and services related to waste materials management. Dumpster, as well as roll off rental companies generally had departments that remade and/or purchased discarded metal plus some even supply residential rubbish collection services.
The companies take the garbage as well as recycle that which makes you receive rid of the trash in a environmentally friendly way. These lenders provide packing containers according to the rubbish quantity. These bins can be seen about construction web sites. They come in diverse shapes and sizes to carry garbage appropriately. So in line with your social gathering trash, you can select the ideal bin which may carry diverse quantities of rubbish.
When you take into consideration renting dumpsters you may also have to get one of those mobile sanitation models for the folks. Also, imagine if your bathroom or even bathrooms will be renovated? These kinds of units in many cases are used for special occasions where a location or property owner needs a larger capacity associated with restrooms for a short period. These are not your own average campsite type of mobile restroom. Stars and guests at special occasions use these varieties of restrooms at all times.
Cleaning out an entire residence, building, or even storage unit indicates sifting by means of items to ascertain what's salvageable and also what's rubbish. Some products can be kept or resold; others go straight to your recycling heart or the neighborhood dump. You may need somewhere to get junk after it's from the building-and chances are, you do not have the time or even energy to drive back and forth towards the landfill each hour. That's where a professional trash removal group comes in, for getting that rubbish off the residence and from a life. If you're dealing with the losing of a loved one, you wish the property cleanout to be seeing that smooth and also painless as you possibly can. A trash hauling firm will try to make that happen.
This information are just some of the guidelines which will surely allow you to hire probably the most qualified residential Rent a Waste Management Dumpster in Del Norte company. These kinds of will definitely help you in choosing that will company which will not only provide the best cost, but also the most dependable service.
New carpeting, baseboards, doors, counter, toilet, bathe, etc. could possibly be brought in when the old is definitely thrown to the dumpster that was rental. All of the completely new items possess boxes and also plastic and several sort of loading material. Doing this can be chucked right into your rented dumpster.
Next you will want to carry out an online research to see your options. You should have at the very least two businesses to choose from if you live in a very larger metropolis you will have a number of. Do your homework and focus reviews. Make sure they show on time and provide you with the goods seeing that promised. You should make sure that they pick it up by the due date if you are going to obtain it for longer than per day. You don't want to possess a dumpster sitting in close proximity to your house for more than it has to be.
Dumpsters will also be handy with regard to temporary storage area and they often have doors for the end, to place a incline and steering wheel up your dust, or a top "hatchway" which is genuinely convenient with regard to throwing 2nd floor stuff into, or even old roofer materials. A number of rental dumpsters are put into three areas so you can recycle easily. Ordinary dumpsters come in ten, twenty, 30, and 45 yard styles. Even a ten yard you may help a house owner get arranged, and a significant project would use several modest ones to be able to segregate every thing being removed from the house or even building.
For large condo remodeling jobs you will need multiple significant dumpsters. The large dumpster will hold up to several tons of dust. Four tons is about ten pickup truck tons of debris.
In addition: if you haven't tried on the extender in more than a year, it's rubbish and you do not need it in your lifetime.
Some people could say that your Rent a Waste Management Dumpster in Del Norte services marketplace is known for all types of additional or even add-on fees. Therefore be a sensible consumer and get the following concerns.
Appliances that are in great shape and not beat up and also ugly or even very grubby, can be provided, as a number of cabinets could. Straight out there trash needs to go to the landfill, and eco friendly materials just like copper and also aluminum can be classified at the development site or just taken to the correct recycle heart. You can save income by selecting it by yourself. Some dumpster rentals enable selecting at your site because they occur divided into areas. Parts of a number of recycled supplies will get you returning a few bucks which can counterbalance the price of your Rent a Waste Management Dumpster in Del Norte, sometimes by a lot.
A single significant component of waste supervision companies is unique sizes involving dumpsters that they offer you on rent. The majority of the businesses do not require the large size dumpster to suit the requirements. So once you hire a Rent a Waste Management Dumpster in Del Norte, CO assistance, don't forget to tell them the size of dumpster you need. If will not be sure regarding the size you really need, ask your organization to send their own representative to view your property as well as asses the company establishment to purchase an idea of the size that will are perfect for your requirements.
Support in making your own city, neighborhood and local community more stunning. When you hire a Rent a Waste Management Dumpster in Del Norte, COs assistance they offer a person dumpsters wherein you are able to dump non-biodegradable waste items and other hazardous waste products from in and around the development site. In this way, you house will also appear more stunning and desirable to the viewers and people who will walk prior your home.
When rented, dumpsters of all sizes are used to your specific location after which dropped off. When full, these are picked up and taken away if you want, a different empty you will be left in order to continue with the test. Dumpsters do come in 10 yard sizes, as well as 20, 30 as well as 40 property ones. All that's necessary to do is actually to find a good organization with a good representative, by going online as well as visiting a web page that will let you get the best package for you, just about all on one web page. Simple prepare an easy kind and enable the company really know what size you need and where as well as how long etcetera., and they will supply you with a quote for the best suitable organization to meet your needs.
There's a great chance being asked to mankind to fail to take care of the setting. This has directed to several people and corporations coming up with a great aim of developing a more beneficial and clear environment. Utilization of containers along with forms of dumpsters happens to be very popular specifically among the city residents. Whether or not you need a single for your home, workplace on the streets, there are ample for all.
There are numerous instances where you will need the aid of trash inside your household do the job some of performs that require aid of professional dumpster companies include residence demolition, remodeling a component of your home, cleaning space for your children books retailer, cutting garden, packing your house hold material in order to take action to different area from your home, ripping your walls of your kitchen or shower room. A professional Dumpster rental service provider will help you the best path so as to pick the right dumpster that best suits in your wants.
Using a dumpster will save you people a huge amount of time. Garbage could be thrown straight into the bin, as opposed to piled up and taken to the dispose of. There is no fast trip to the particular dump. Through the elimination of the need to generate there, individuals save time. On this occasion can be used to make money, rather than spend it.
You can save time and effort through the simplicity of ordering your own dumpster one brand. Next day dumpster shipping is available in Md. When the dumpster use is completed an instant pickup in the filled dumpster offers you quick waste removal and the quick beautification of your property and residence. Keep your tasks as well put together and orderly as possible if you rent then a dumpster. This is actually the quickest as well as easiest way to take away trash in your home and residence. Routine waste pickup is supplied by the city however, it is limited to packaged trash as well as separated recyclables. Booking a dumpster works because it provides immediate waste removal without bagging or maybe separating.


Evanston Police report an approximately 13 percent reduction in traffic crashes on the Ridge Avenue Corridor (Ridge Avenue, from Howard to Emerson Streets) in 2018 as compared to 2017. There were 241 crashes reported in 2018, down from 278 in 2017.
The reduction in crashes is the result of a traffic initiative that was a coordinated effort between the Evanston Police Department, City of Evanston traffic engineers, and other City officials, including 4th Ward Alderman Donald Wilson. The goal of the initiative was to increase safety and address citizen traffic complaints along the Ridge Avenue Corridor, especially between Greenleaf and Church Streets.
The City of Evanston and the Evanston Police Department educated motorists about the changes made to increase safety and the importance of complying with traffic laws. This was done through the dissemination of press releases and the utilization of social media platforms, electronic message boards and speed display signs.
The intersection of Lake Street and Ridge Avenue along with the intersection of Greenwood Street and Ridge Avenue had the most improvement. Traffic crashes were down 58 percent at each intersection.


text:
Causes of Red Spots on the Glans Penis
The best way to deal with red spots on the glans penis is to maintain proper hygiene in order to help prevent infections. What's the cause of this problem though? Continue reading to find out.
Reviewed and approved by the doctor Leonardo Biolatto.
Written by Carmen Martín
Red spots on the glans penis are relatively frequent and manifest in many ways and for various reasons. Most of them happen during sex and are, fortunately, transitory; thus, they resolve on their own.
It's logical for any man to be frightened when discovering them though. This is why it's always a good idea to consult a doctor – sometimes it could be a symptom of a major illness or even an STD.
The glans is the head of the penis and, as you know, it's a little thicker and pinkish. It's a highly sensitive area prone to irritation and also susceptible to many conditions.
Most of the problems in this area have to do with poor hygiene. However, this article will discuss other common reasons why red spots appear on the glans penis and what you can do to heal them.
Red spots on the glans penis due to poor hygiene
As you may already know, maintaining proper hygiene is the first step to avoiding most diseases, especially those of infectious origin. It's even more important when it comes to the genitals as this area is more susceptible to the proliferation of bacteria and fungi.
Red spots on the glans penis are usually due to poor hygiene and usually lead to infection by candida or bacteria such as streptococci.
Red spots on the penis can be caused by poor hygiene. This, in turn, can lead to fungal or bacterial infections.
In the case of candidiasis, the red spots often bring about a burning and stinging sensation. As you can imagine, sex can be painful and only worsens the swelling of the penis which becomes redder.
However, you should know that candidiasis can also be due to reasons that have nothing to do with hygiene. For example, it could be related to the use of prophylactics or antibiotics.
Bacterial infections are quite common and often manifest through specific symptoms such as strong foul odors. In addition, they may be discharged from the urethra, although this varies depending on the bacteria that are causing the infection.
You must maintain proper daily hygiene with soap and warm water. Wash twice a day if you sweat a lot. Of course, use a condom when having sex so as not to spread any infections.
Find out What is Phimosis: What You Should Know About Tight Foreskin
Other causes of red spots on the glans penis
Red spots on the glans penis may also be due to sex. As we mentioned above this is a highly sensitive area and the skin can get sore due to friction. Actually, and as strange as it may seem, this area is quite susceptible to bruising.
Small bruises could happen after using sex toys, for example. Worry not, these usually disappear after a couple of days and are nearly painless.
Other causes of red spots on the glans penis may be due to injuries or friction during sex. They could be caused by serious diseases such as cancer although it's pretty rare.
Red spots could be a symptom of an allergic reaction as in any other parts of the body. In fact, many men are allergic to certain substances present in gels or soaps. The fabric of the underwear could also be the reason.
Red spots on the penis glans usually manifest with itchiness in cases of allergies and disappear as soon as there's no more contact with the allergen.
Read about Small Penis Complex: Does Size Really Matter?
Serious red spots on the glans penis
Less frequently, these spots are a symptom of more serious diseases such as cancer. This is why it's important to suspect them when you haven't had sex, especially if there's no itchiness or stinging.
Several studies assert that squamous cell carcinoma is the most frequent penile cancer. Furthermore, it often manifests with red spots on the glans penis. It's important to detect it early so that treatment is still effective.
When to consult a doctor?
Red spots on the glans penis are usually benign and appear as a result of minor trauma or infections, many of them due to poor hygiene. In any case, it's always best to consult a doctor and let them give you an accurate diagnosis.
The Causes and Treatment of Bumps on the Penis
Bumps on the penis usually happen during adolescence and they're mostly associated with men with acne in other parts of their body.
Buechner, S. A. (2002). Common skin disorders of the penis. BJU international, 90(5), 498-506. Disponible en: https://pubmed.ncbi.nlm.nih.gov/12175386/
Morris, B. J. & Krieger, J. N. (2017). Penile inflammatory skin disorders and the preventive role of circumcision. International journal of preventive medicine, 8, 1-10. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439293/
Marfatia, Y. S., Patel, D., Menon, D. S. & Naswa, S. (2016). Genital contact allergy: A diagnosis missed. Indian journal of sexually transmitted diseases and AIDS, 37(1), 1-6. Disponible en: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857673/
Ministerio de Sanidad. (2019). Vigilancia Epidemiológica de las Infecciones de Transmisión Sexual en España. Disponible en: https://www.isciii.es/QueHacemos/Servicios/VigilanciaSaludPublicaRENAVE/EnfermedadesTransmisibles/Documents/archivos%20A-Z/SIFILIS/Vigilancia_ITS_1995_2019.pdf.
Rodríguez, Y. P., Calvo, J. P. & Figueroa, L. Q. (2020). Sífilis: abordaje clínico y terapéutico en primer nivel de atención. Revista Médica Sinergia, 5(8), e559-e559. Disponible en: https://revistamedicasinergia.com/index.php/rms/article/view/559
Sánchez-Crespo, J. R. & González, C. (2010). Herpes genital. Revista Clínica de Medicina de Familia, 3(2), 124-126. Disponible en: https://scielo.isciii.es/scielo.php?pid=S1699-695X2010000200013&script=sci_arttext&tlng=en.
Soto, M., Arredondo, F., Pedrero M, G., et al. (2003). Cáncer de pene: Una revisión de 18 casos. Actas Urologicas Espanolas, 27(10), 797-802. Disponible en: https://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S0210-48062003001000006
Wray A., Velasques, J. & Khetarpal, S.(2019). Balanitis. StatPearls. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK537143/
Anatomy and physiologyTingling in Hands and Feet: The Reasons
Anatomy and physiologyTypes of Cartilage: Characteristics and Location
Anatomy and physiologyWhat Are Sacral Nerves?
Anatomy and physiologyWhat Is a Macular Fold: Symptoms, Causes, and Treatments
Anatomy and physiologyMacro and Microdontia: Alterations in Tooth Size
Anatomy and physiologyBasic Anatomical Positions
Anatomy and physiology5 Keys to Prevent Broken Heart Syndrome
Anatomy and physiologyThe Anatomy of the Foot and Common Problems


text:
/* */
"format cjs";
/**
 * @license
 * Copyright Google Inc. All Rights Reserved.
 *
 * Use of this source code is governed by an MIT-style license that can be
 * found in the LICENSE file at https://angular.io/license
 */
import { NgModule } from '@angular/core';
import { InternalFormsSharedModule, REACTIVE_DRIVEN_DIRECTIVES, TEMPLATE_DRIVEN_DIRECTIVES } from './directives';
import { RadioControlRegistry } from './directives/radio_control_value_accessor';
import { FormBuilder } from './form_builder';
/**
 * The ng module for forms.
 * \@stable
 */
export var FormsModule = (function () {
    function FormsModule() {
    }
    FormsModule.decorators = [
        { type: NgModule, args: [{
                    declarations: TEMPLATE_DRIVEN_DIRECTIVES,
                    providers: [RadioControlRegistry],
                    exports: [InternalFormsSharedModule, TEMPLATE_DRIVEN_DIRECTIVES]
                },] },
    ];
    /** @nocollapse */
    FormsModule.ctorParameters = function () { return []; };
    return FormsModule;
}());
function FormsModule_tsickle_Closure_declarations() {
    /** @type {?} */
    FormsModule.decorators;
    /**
     * @nocollapse
     * @type {?}
     */
    FormsModule.ctorParameters;
}
/**
 * The ng module for reactive forms.
 * \@stable
 */
export var ReactiveFormsModule = (function () {
    function ReactiveFormsModule() {
    }
    ReactiveFormsModule.decorators = [
        { type: NgModule, args: [{
                    declarations: [REACTIVE_DRIVEN_DIRECTIVES],
                    providers: [FormBuilder, RadioControlRegistry],
                    exports: [InternalFormsSharedModule, REACTIVE_DRIVEN_DIRECTIVES]
                },] },
    ];
    /** @nocollapse */
    ReactiveFormsModule.ctorParameters = function () { return []; };
    return ReactiveFormsModule;
}());
function ReactiveFormsModule_tsickle_Closure_declarations() {
    /** @type {?} */
    ReactiveFormsModule.decorators;
    /**
     * @nocollapse
     * @type {?}
     */
    ReactiveFormsModule.ctorParameters;
}
//# sourceMappingURL=form_providers.js.map

redpajama_set_name: RedPajamaGithub


text:
RBF neural network-based online intelligent management of a battery energy storage system for stand-alone microgrids
Thongchart Kerdphol1Email author,
Yaser Qudaih1,
Masayuki Watanabe1 and
Yasunori Mitani1
© Kerdphol et al. 2016
Received: 1 October 2015
Accepted: 3 February 2016
An offline optimization approach based on energy storage management response in a microgrid was not fast and not reliable enough to control and adjust the system efficiently after the loss of the utility grid. Thus, it causes system inefficiency and collapse in the presence of violent changes of loads or outage of distributed generations. To solve such a problem, more real-time management is needed. Any changes in loads/generations should be compensated successfully by a battery energy storage system (BESS) in a short period of time.
This paper presents a new method for the intelligent online management of both active and reactive power of a BESS based on a radial basis function neural network (RBFNN) incorporating particle swarm optimization (PSO) to prevent the stand-alone microgrid from instability and system collapse. BESS is centrally controlled by a controller developed by the proposed RBFNN. PSO is used to determine the optimized active and reactive power at every load/generation changing situation to monitor the effect of system frequency, voltage, and reference power regulation. These optimized power data are then employed as target data for the RBFNN generalization and training process. To enable the online updating of the operating parameters, the proposed RBFNN is implemented in the management process. With an appropriate RBFNN training, the optimum active and reactive power can directly be obtained without the necessity of performing the PSO optimization process at any change of load/generation.
The results show that the predictive results of the proposed RBFNN model only slightly differed from the target results based on PSO and have a minimum statistical error compared to the predictive results based on the multilayer perceptron neural network (MLPNN) model.
The proposed RBFNN is suitable for the online estimation of the active and reactive power of BESS and can be used for real-time energy storage management as an online controller.
RBF neural network
Battery energy storage system
Frequency and voltage control
During the great earthquake and tsunami in March 2011 and the heavy snowfall disaster in Tokushima, Japan, in December 2014, thousands of people had no access to electricity. IP telephone systems using the Internet which spreads in almost all families were not able to operate at that time. It caused difficulties to rescue teams for providing assistance. Moreover, victims who lived in their homes could not get warm as their heaters were not able to operate at the time of the disaster [1]. To solve such a situation, facilities are needed to provide electricity at the time of a blackout or disaster. Battery energy storage systems (BESS) can offer a good solution to such a system. Advantages of BESS include an improvement of the system frequency, especially when BESS is used for system frequency control. For small disturbances, BESS is discharging when the system frequency is lower than the nominal frequency of 50 or 60 Hz. On the other hand, BESS is charging when the system frequency is higher than the nominal frequency of 50 or 60 Hz. For large disturbances, BESS can enhance the performance of the system frequency control by integrating BESS with an under-frequency load shedding scheme or an under- or over-frequency generation trip. With these different functions, it can be concluded that BESS is a rapid and flexible element for power systems [2–4].
After the 2011 Japan earthquake and tsunami in the Tōhoku area, a micro/smart grid developed in Japan has been focused on resilience. As a solution of micro/smart grids, it promised to simplify the wide penetration of renewable energy sources (RESs) and BESS units into the power system and increase the reliability of electrical supply to consumers, but decrease system losses and greenhouse gas emissions. Due to their potential benefits of providing secure, reliable, efficient, sustainable, and environmentally friendly electricity from RESs, micro/smart grids have received great attention and became remarkable in electricity [5].
A concept of a micro/smart grid is demonstrated as a system that can intelligently integrate the actions of all users incorporating generators or loads in a manner suitable for providing an economically sustainable and secure power system [6]. All signals at loads/generations will be processed by the system management and react to the situations which occurred optimally. By an intelligent management of the active and reactive power of BESS for a stand-alone microgrid, this technique can prevent the stand-alone microgrid from instability and collapse in the presence of violent changes of loads or outage of distributed generations. In a number of studies, the aspect of a managing reference power of distributed generations in the distribution system has been presented [7–9]. In [10], the management of BESS power for the typical 4Q-load has been proposed and analyzed. Furthermore, an offline optimization approach based on a real-time energy storage management was proposed in [11]. To control the system as efficiently as possible, more real-time management is needed. Any changes in loads/generations should be compensated successfully by the BESS in a short period of time. To perform the real-time online management operation in this study, radial basis function neural networks (RBFNNs) seem to be most suitable for such an online modeling method in terms of a fast time calculation process and instant responses. The advantages of RBFNN are two major issues: the training processes are substantially faster than the multilayer perceptron neural networks (MLPNN) and RBFNN does not encounter with the local minima problems [12, 13]. RBFNN provides a very significant tool for optimization tasks as they are extremely powerful computational devices with the capability of parallel processing, learning, generalization, and universal approximation [14, 15].
This paper deals with the online intelligent management of active and reactive power of the BESS installed in the microgrid to prevent the microgrid from instability and collapse in the presence of violent changes of loads or outage of distributed generations after the loss of the utility grid (e.g., blackout or disaster). The BESS is centrally controlled by a controller developed by the proposed RBFNN. The active and reactive power of the BESS are managed by using the RBFNN incorporating a particle swarm optimization (PSO) process with the objective of maintaining the frequency and voltage of the stand-alone microgrid within acceptable ranges. First, the optimum settings of the BESS which are the optimized active and reactive power are determined by PSO. The PSO process has to reply to every change in load/generation to achieve the optimum operating conditions for the entire system. These optimized operating data are then applied as target data for RBFNN generalization and training processes. To enable the online updating of the operating parameters, the proposed RBFNN is implemented in the management process, and the database extracted from the PSO process is used as target data in the RBFNN generalization and training. With an appropriate RBFNN training, the well-trained RBFNN can be employed as the online mode where the system is using new input data. By applying the proposed RBFNN approach in the system, the optimum active and reactive power of the BESS can directly be obtained without the necessity of performing the optimization process-based PSO at any change in load/generation. The predictive results of the proposed RBFNN are compared with the predictive results of the MLPNN, and it is clearly shown that the proposed RBFNN-based online management method gave the best performance in predicting the optimum active and reactive power of the BESS for the microgrid.
This paper, compared to other previous research contributions, deals with the ability to control the system as efficiently as possible by designing and implementing the real-time/online management of the BESS based on the proposed RBFNN for using it as an online controller to control, manage, and prevent the microgrid from instability and collapse in the presence of violent changes of loads or outage of distributed generations after the loss of the utility grid.
Microgrid study system
The microgrid structure considered in this study consists of four major power sources: the 1.2-MW mini-hydro generator, the 2-MW hydro generator, the 3-MW solar photovoltaic sources, and the BESS as shown in Fig. 1. Each distributed generation unit has its own local controller to handle the relevant electrical variables. This system also consists of a group of feeders which could be a part of the distribution design. The commercial loads 1 and 4 are the critical loads with a peak power of 1.85 and 1.9 MW, respectively. Office load 2, residential load 3, and residential load 5 are the non-critical loads with a peak power of 1.7, 1.75, and 2.4 MW, respectively.
The typical microgrid system
Solar photovoltaic generation
The output power of solar photovoltaic (PV) is uncertain as it is mostly affected by environmental factors, particularly environmental random changes inevitably leading to a constant change of the output power of solar PV [16]. In order to illustrate the solar PV characteristics under the operating conditions, the influence of solar radiation and atmosphere temperature are designed. The temperature effect is denoted using a temperature coefficient of T co (1/c°). The efficiency of the inverter is multiplied by the DC output converting DC to the AC output as in (1):
$$ {P}_{\mathrm{PV}}={n}_{\mathrm{PV}}{P}_{\mathrm{ratePV}}\left(G/{G}_0\right)\left(1-{T}_{\mathrm{co}}\left({T}_{\mathrm{A}}-{25}^{\circ}\right)\right){\eta}_{\mathrm{inv}}{\eta}_{\mathrm{rel}} $$
where n PV is the PV module number, P rate PV is the PV array rated power (W), G is the global insolation on PV array (W/m2), G 0 is the standard amount of the insolation rating capacity of PV modules (W/m2), T A is the ambient temperature, T CO is the temperature coefficient of the maximum power of PV, ɳ rel is the relative efficiency of the PV modules, and ɳ inv is the efficiency of the inverter.
RESs are depending on weather conditions. Thus, a BESS is used to store surplus electrical energy to maintain the system frequency and voltage and supplies the power for loading into a microgrid in the case of low solar ratio or load changes. Moreover, the BESS can smooth the fluctuation of solar radiation and enhance the load availability. A more detailed BESS information, along with the most BESS models, is presented in various research papers, for instance in [17, 18].
The structure of the BESS comprises power converters, battery cells, and control parts, which are shown in Fig 2.
The structure of the BESS in an interconnection diagram
In this study, in the case of a grid-connected mode, in which the power generated by the microgrid system is higher than the load demand, the surplus power can be stored in a BESS for future uses. On the contrary, when there is any shortage in the power generation of the microgrid, the stored power is used to supply the load. For a stand-alone mode, the main purpose of the BESS is to stabilize the microgrid from instability and collapse in the presence of violent changes of loads or outage of distributed generations [3, 4].
From the BESS structure, the output of the DC voltage is expressed as:
$$ \varDelta {E}_{\mathrm{do}}=\frac{6\sqrt{6}}{\pi }{E}_t $$
where E t is the AC voltage between a line and neutral.
The equivalent circuit analysis of a BESS consists of a converter connected to an equivalent battery, as shown in Fig. 3. The terminal voltage of the equivalent battery is calculated from:
Equivalent circuit of the BESS [18]
$$ \begin{array}{l}{E}_{\mathrm{bt}}=\varDelta {E}_{\mathrm{do}} \cos \alpha -{r}_c{I}_{\mathrm{BESS}}\\ {}\kern2.5em =\frac{3\sqrt{6}}{\pi }{E}_t\left( \cos {\alpha}_1- \cos {\alpha}_2\right)-\frac{6}{\pi }{X}_{\mathrm{co}}{I}_{\mathrm{BESS}}\end{array} $$
According to the equivalent circuit of the BESS, the expression of the DC current flowing into the battery is expressed as:
$$ {I}_{\mathrm{BESS}}=\frac{E_{\mathrm{b}\mathrm{t}}-{E}_{\mathrm{b}\mathrm{oc}}-{E}_{\mathrm{b}1}}{r_{\mathrm{b}\mathrm{t}}+{r}_{\mathrm{b}\mathrm{s}}} $$
$$ {E}_{\mathrm{boc}}=\frac{r_{\mathrm{bp}}}{1+{\mathrm{ST}}_{\mathrm{bp}}}{I}_{\mathrm{BESS}} $$
$$ {E}_{\mathrm{b}1}=\frac{r_{\mathrm{b}1}}{1+{\mathrm{ST}}_{\mathrm{b}1}}{I}_{\mathrm{BESS}} $$
$$ {T}_{\mathrm{bp}}={r}_{\mathrm{bp}}{C}_{\mathrm{bp}} $$
$$ {T}_{\mathrm{b}1}={r}_{\mathrm{b}1}{C}_{\mathrm{b}1} $$
According to the converter circuit analysis, the active and reactive power injected or absorbed by the BESS are:
$$ {P}_{\mathrm{BESS}}=\frac{3\sqrt{6}}{\pi }{E}_t{I}_{\mathrm{BESS}}\left( \cos {\alpha}_1+ \cos {\alpha}_2\right) $$
$$ {Q}_{\mathrm{BESS}}=\frac{3\sqrt{6}}{\pi }{E}_t{I}_{\mathrm{BESS}}\left( \sin {\alpha}_1+ \sin {\alpha}_2\right) $$
where α i is the firing delay angle of the converter i, E do is the maximum DC voltage of the batteries, E b1 is the battery resistance, E bt is the phase voltage of the battery side, E boc is the battery open circuit voltage, I BESS is the DC current through the battery, P BESS is the active power provided by the batteries, Q BESS is the reactive power provided by the batteries, r c is the battery overvoltage, r bt is the terminal voltage of the battery, r bs is the battery internal resistance, r bp is the self-discharge resistance, and r b1 is the overvoltage resistance [19].
By performing the offline optimization process, the optimized active and reactive power set points of the BESS are obtained. These optimized data are then used as target data for RBFNN/MLPNN generalization and the training process in the online optimization. With an appropriate RBFNN/MLPNN training process, the well-trained RBFNN/MLPNN can be used as the online mode where the system is using new input data and then the optimum active and reactive power of the BESS will be obtained and remotely adjusted via the communication link to the BESS.
Test system explanation
The proposed online management approach is applied to the typical microgrid system illustrated in Fig. 1. To achieve a smart grid system with online capability, the microgrid is integrated in a highly developed communication technology which is also connected with the BESS as well as distributed generations and loads, thus enabling the coordinated control between generations and loads. The microgrid will be scanned by a data acquisition device for every half an hour of load demand for a 24-h operation. The data acquisition device measures the power output of solar PV and all load profiles. The acquired information is used to control the optimum operation of the BESS in subsequent intervals. The optimum active and reactive power of the BESS will be calculated by the control system so that they can be remotely adjusted via the communication link instantly after the loss of the utility grid.
Three types of loads are involved in the studied microgrid, which are the residential, office, and commercial loads as shown in Fig. 4. The load profile depends on the peaks and dips according to the various electricity usage situations. For the residential loads, a peak load occurs from 7 pm to 9 pm; for the office and commercial loads, a peak load occurs from 9 am to 5 pm every day, owing to the electricity usage during the office hours.
Load demand profiles
ANN-based online optimum active and reactive power of the BESS
The proposed approach for an intelligent online managing of active and reactive power of BESS consists of a two-stage method based on the PSO optimization process (stage 1) and the RBFNN or MLPNN approach (stage 2). The first step before performing the optimization process is to obtain both load data and solar PV power output data as the inputs of the PSO process. The optimization process is applied offline using PSO in the first stage in order to define the optimum daily performance of the BESS. In this study, the fluctuations of solar PV due to unpredicted weather conditions are considered in the case of both a sunny and a rainy day for both the RBFNN and MLPNN test systems as shown in Fig. 5. By performing the optimization process using PSO, the optimized active and reactive power of the BESS are evaluated. These optimized data are then used as target data for the RBFNN/MLPNN generalization and the training process during the second stage.
The solar PV power output for the RBFNN and MLPNN testing stage
PSO-based offline optimization (stage 1)
The offline optimization process can be performed by many methods such as using either a genetic algorithm (GA), or linear programming, or PSO, etc. However, according to [20–22], the advantages of PSO include simplicity, ease of use, high convergence rates, and minimal storage requirements. Especially, PSO will be less dependent on the set of the initial points compared to other methods, which implies that the convergence algorithm is robust. Thus, the offline optimization process is performed by using PSO during the first stage in order to evaluate the optimum daily performance of the BESS as shown in Fig. 6. Afterwards, these optimized parameters are used in the second stage for the RBFNN/MLPNN offline training process.
A conceptual structure of the proposed PSO for stage 1
The PSO process has to reply to every change in the load/PV generation in order to achieve the optimum operating conditions for the overall system. Hence, after any change of the load/PV data, the optimized power setting of the BESS will also be changed, with the result that a new optimization process is needed.
PSO is an approach for evaluating the optimal parameters of complicated search spaces. PSO is initiated with a group of random particles to search for these optimal parameters by updating the generations. At each iteration step, each particle is improved by two values. These values are called P best and G best, respectively. P best is the best solution acquired by each particle itself in all of the previous generations. G best is the best value obtained by any particle during all previous iterations [23]. This value is called the best global solution. Each particle improves its position and velocity by using (11) and (12):
$$ {v}_{i+1}={v}_i+{c}_1{r}_1\left({P}_{\mathrm{best}}-{x}_i\right)+{c}_2{r}_2\left({G}_{\mathrm{best}}-{x}_i\right) $$
$$ {x}_{i+1}={v}_{i+1}+{x}_i $$
where i = 1, 2,…, N, N is the iteration number, v i is the velocity of a particle at iteration i, x i is the position of a particle at iteration i, P best is the best solution at iteration i, G best is the best global solution at iteration i, r 1 is the random number one between 0 and 1, r 2 is the random number two between 0 and 1, and c 1 and c 2 are the learning factors.
The learning factors have important effects on the algorithm convergence rate. Future information for PSO can be found in [20–23]. In this study, the number of iteration is 30. Learning factors are c 1 and c 2 which are equal to 1.4940. The inertia weight is 0.7920.
For the PSO offline optimization approach, the aim of this part is to determine the optimized active and reactive power of the BESS during every change in the load/generation based on frequency and voltage control in the stand-alone microgrid. The final objective function is chosen and expressed as:
$$ \mathrm{Minimize}\ f = {\lambda}_1{J}_1 + {\lambda}_2{J}_2 $$
where J 1 and J 2 are the objective function representing the active and reactive power of the BESS from (9) and (10), respectively, and λ 1 and λ 2 are the weight associated to J 1 and J 2, respectively.
The active and reactive power dispatched from BESS are used as the control variables in this study. The constraints imposed on the optimization problem are shown as follows:
$$ {P}_{\min}\le P(t)\le {P}_{\max } $$
where P(t) is the rated active power of the BESS (MW), P min is the allowed minimum rated active power, and P max is the allowed maximum rated active power of the BESS.
$$ {Q}_{\min}\le Q(t)\le {Q}_{\max } $$
where Q(t) is the rated reactive power of the BESS (Mvar), Q min is the allowed minimum rated reactive power, and Q max is the allowed maximum rated reactive power of the BESS.
$$ {\omega}_{\min}\le \omega (t)\le {\omega}_{\max } $$
where ω(t) is the nominal frequency of the isolated microgrid (Hz), ω min is the allowed minimum nominal frequency of the isolated microgrid, and ω max is the allowed maximum nominal frequency of the isolated microgrid.
$$ {u}_{\min}\le u(t)\le {u}_{\max } $$
where u(t) is the nominal voltage of the isolated microgrid (pu), u min is the allowed minimum nominal voltage of the isolated microgrid, and u max is the allowed maximum nominal voltage of the isolated microgrid.
Artificial neural network-based online optimization (stage 2)
Artificial neural network (ANN) is simulating the brain of humans in processing information through a series of interconnected neurons. It is one of the famous prediction models as it has the remarkable ability of mapping complex and highly non-linear input-output patterns without the knowledge of the actual model structure. The RBFNN and the MLPNN are widely used in ANN structures, and their roles affect the network performance. Nowadays, a performance comparison of the RBFNN and the MLPNN in several applications has attracted the attention of researches [24–26]. Nevertheless, no comparison has been carried out so far between the intelligent management of active and reactive power of the BESS for a microgrid management system. Hence, this work proposed the RBFNN-based online management of the BESS and selected the MLPNN-based online management of the BESS for the performance comparison in the system. To enable the online updating of the operating parameters, the RBFNN/MLPNN is proposed and implemented in the management process for the second stage. Afterwards, the output performance of the RBFNN and the MLPNN were investigated and compared in the ANN training and testing results. During this stage, the database extracted from the PSO process was used as a target data in the RBFNN/MLPNN generalization and training process.
RBFNN-based online management of the BESS
The RBFNN is a type of feedforward neural network which learns using a supervised training method. Radial functions are a special class of functions, and their characteristic feature is that the response decreases or increases with the distance from a center point. It is obvious that the RBFNN is able to approximate any reasonable continuous function mapping with a satisfactory level of accuracy [13–15]. In this paper, the proposed RBFNN consists of three layers of neurons: an input layer, a hidden layer, and an output layer.
To enable online updating of the operating parameters, the proposed RBFNN is implemented in stage 2 of the management process as illustrated in Fig. 7. During the second stage, the database extracted from stage 1 is used as a target data in the RBFNN generalization capability. There will be 12 inputs and 2 outputs. The inputs of the system will be the active and reactive power of commercial load 1 (X 1, X 2), respectively, the active and reactive power of office load 2 (X 3, X 4), respectively, the active and reactive power of residential load 3 (X 5, X 6), respectively, the active and reactive power of commercial load 4 (X 7, X 8), respectively, the active and reactive power of residential load 5 (X 9, X 10), respectively, and the active and reactive power of the solar PV system (X 11, X 12), respectively. The outputs of the system are the active and reactive power of the BESS (Y 1, Y 2), respectively. Afterwards, the proposed RBFNN-based online optimum active and reactive power of the BESS are demonstrated as a function that maps the input vector X = [X 1, X 2, X 3, X 4, X 5, X 6, X 7, X 8, X 9, X 10, X 11, X 12] to the output vector Y = [Y 1, Y 2].
Description of inputs and outputs of the RBFNN-based online intelligent management of the BESS
The input vector X is applied to all neurons in the hidden layer. The hidden layer is composed of number q RBFNNs that are directly connected to all the elements in the output layer. A node in the hidden layer will produce a greater output when the presented input pattern is close to its center. The output of such a node will reduce, as the distance from the center increases, assuming that a symmetrical basis function is applied. Hence, for a given input pattern, only neurons whose centers are close to the input pattern will produce non-zero activation values to the input stimulus. The basis function for the jth node in the hidden layer is determined by a Gaussian exponential function as follows:
$$ {b}_j\left(\overline{X}\right)= \exp \left(\frac{-{\left(\overline{X}-{\mu}_j\right)}^2}{2{\sigma}_j^2}\right),\kern0.5em \mathrm{f}\mathrm{o}\mathrm{r}\ j = 1,\ 2,\dots, q $$
where μ j is the respective center of the jth neuron in the hidden layer and σ j is the wide of the Gaussian potential function of the jth neuron in the hidden layer.
The network output Y is formed by a linearly weighted sum of the number of basis functions in the hidden layer. The values for the output nodes are calculated as follows:
$$ {Y}_k={\displaystyle \sum_{j=1}^q{W}_{kj}{b}_j\left(\overline{X}\right)},\ \mathrm{f}\mathrm{o}\mathrm{r}\ k = 1,\ 2 $$
where Y k is the output of the kth node in the output layer, W kj is weight between the jth node in the hidden layer and the kth node in the output layer, and \( {b}_j\left(\overline{X}\right) \) is the output of the jth node in the hidden layer.
MLPNN-based online management of the BESS
The MLPNN belongs to the class of feedforward networks. In the MLPNN structure, this structure is established in a layered feedforward network and is contained by an input layer, one or more hidden layers, and an output layer. The weight total of the input data and the chosen bias are passed through a transfer function to obtain the output data. The number of hidden layers is able to be changed based on the problem data in the training process [12, 27]. In this paper, the MLPNN consists of three layers of neurons, which demonstrates that only one hidden layer is included, and one type of activation function is used in the hidden layer and one output layer is contained.
To enable an online updating of the operating parameters, the MLPNN is implemented in stage 2 of the management process as shown in Fig. 8. During the second stage, the database extracted from stage 1 is used as a target data in the MLPNN generalization capability. There will be 12 inputs and 2 outputs. The inputs of the system will be the active and reactive power of commercial load 1 (X 1, X 2), respectively, the active and reactive power of office load 2 (X 3, X 4), respectively, the active and reactive power of residential load 3 (X 5, X 6), respectively, the active and reactive power of commercial load 4 (X 7, X 8), respectively, the active and reactive power of residential load 5 (X 9, X 10), respectively, and the active and reactive power of the solar PV system (X 11, X 12), respectively. The outputs of the system are both the active and reactive power of both the BESS (Y 1, Y 2). Afterwards, the proposed MLPNN-based online optimum active and reactive power of the BESS is demonstrated as a function that maps the input vector X = [X 1, X 2, X 3, X 4, X 5, X 6, X 7, X 8, X 9, X 10, X 11, X 12] to the output vector Y = [Y 1, Y 2].
Description inputs and outputs of the MLPNN-based online intelligent management of the BESS
Each hidden layer summarizes its weighted inputs, which can be expressed as:
$$ {y}_h(n)={\displaystyle \sum_{i=1}^{Ni}{W}_{ih}}(n){X}_i(n)+{b}_h^H $$
where W ih (i = 1, 2,…, N i , h = 1, 2,…, N h ) are the weights of the connections between the input and hidden layers, X i (i = 1,2,…,N i ) are the input signals, b h H (h = 1,2,…,N h ) are the biases at the hidden layer, N i is the number of the inputs, and N h is the number of neurons in the hidden layer.
The function form of the MLPNN-based online intelligent optimum active and reactive power of the BESS (i.e., the outputs from the hidden layers) can be presented as follows:
$$ {Y}_k(n)={\displaystyle \sum_{h=1}^{Nh}{W}_{hk}(n)\cdot f\left({y}_h(n)\right)+{b}_k^0} $$
$$ f\left({y}_h(n)\right)={f}_{\mathrm{sigmoid}}\left({y}_h(n)\right)=\frac{1}{1+{e}^{-\left({y}_h(n)\right)}} $$
where Y k (k = 1, 2,…, N k ) are the output signals, W hk (h = 1, 2,…, N h , k = 1, 2,…, N k ) are the weights of the connections between the hidden and output layers, b k 0 (k = 1, 2,…, N k ) are the biases at the output layer nodes, f(y h (n)) is the hidden activation transfer function, N h is the number of neurons in the hidden layer, and N k is the number of the outputs.
Based on Table 1, the parameter details for the optimum online active and reactive power of the BESS are shown and used in the training and the testing database.
Artificial neural network parameters
RBFNN
MLPNN
Goal (RMSE)
Hidden layer
Testing data
Hidden layer neurons
Output layer neurons
Gaussian exponential
Sigmoid
RMSE root mean square error, RBFNN radial basis function neural network, MLPNN multilayer perceptron neural network
Figure 9 depicts the description of inputs and outputs for estimating the active and reactive power of the BESS that have been used for the RBFNN/MLPNN training and testing process. The RBFNN/MLPNN which is trained and tested offline has been accomplished to recognize the pattern and the optimum behavior for the stand-alone microgrid. Later, the well-trained RBFNN/MLPNN can be used onsite in the online mode where the system is applied using new input data. With applying the well-trained RBFNN/MLPNN in the system, the optimization of optimum active and reactive power can be directly evaluated without the necessity of performing the optimization process-based PSO after any change of load/generation. The overall process of stage 2 is described as follows:
Structure of inputs and outputs of the proposed RBFNN/MLPNN for stage 2
Step 1. Obtain input data and target from the PSO process.
Step 2. Create the RBFNN/MLPNN network and train the network until the conditions of the network setting parameters are reached.
Step 3. Test the network and control the regression analysis.
Step 4. Store the trained network. Afterwards, the trained network is ready to be tested by using new input data for this online process. Please consider that step 1 to step 4 belong to the offline process.
Step 5. Process new input data to the online process and obtain the optimum power data of the BESS.
This part describes the results of the proposed online predictive power management of the BESS with the objective of frequency and voltage control of the stand-alone microgrid by using the proposed RBFNN. Thus, the proposed RBFNN approach will automatically determine the optimum power of the BESS in order to prevent the isolated microgrid from instability and collapse in the presence of violent changes of loads or outage of distributed generations. Afterwards, the predictive results of the proposed RBFNN model are compared with the MLPNN model, considering the error efficiency and positional accuracy.
ANN training results
There are 12 inputs being the time step of the five load demands and the solar PV data which were fed into the RBFNN/MLPNN controller. The outputs of the neural network will be the predictive results of the optimum active and reactive power of the BESS which will determine the optimum operation of the BESS for the stand-alone microgrid.
After the inputs and targets for the training data are initiated, the next process is the separation of the data for training, validation, and test. During this stage, 70 % of the sample data are used for the training process (i.e., 940 data), 15 % of the sample data are used for validation (i.e., 202 data), and 15 % for the test data (i.e., 202 data).
In the training results, the correlation coefficient (R) is employed to measure the fitness, where a value closer to 1 indicates a better fit. From Figs. 10 and 11, it is evident that the correlation coefficient of the proposed RBFNN is higher than the correlation coefficient of the MLPNN and almost equal to 1 (i.e., 0.9999), which means that the targets are equal to the outputs of the training data. This reason indicates a strong correlation between the optimization data and the neural network output. Hence, it is obvious that the proposed RBFNN approach allows achieving the highest accuracy compared to the MLPNN approach.
Regression analysis between the network outputs and the optimization targets based on the MLPNN
Regression analysis between the network outputs and the optimization targets based on the RBFNN
ANN testing results
The proposed RBFNN/MLPNN has been trained and tested using the typical microgrid system, load demand profiles, and solar PV output data during a sunny and a rainy day for online applications. The active and reactive power estimated online by the RBFNN/MLPNN are compared with the data obtained from the PSO optimization conducted offline as shown in Figs. 12, 13, 14, and 15 for the case of a sunny day and a rainy day, respectively. Based on Figs. 12, 13, 14 and 15, the proposed RBFNN approach is better than the MLPNN approach as far as the accuracy and efficiency are concerned. The MLPNN capability is poor when compared to the proposed RBFNN. It is obvious that the proposed RBFNN is able to follow the target pattern-based PSO at almost every time step mentioned. Compared with the MLPNN model, the outputs of the proposed RBFNN model are only slightly different from the optimization target-based PSO optimization. It can be concluded that the proposed RBFNN can accurately determine the optimal active and reactive power of the BESS for the isolated microgrid.
The optimum active power of the BESS during a sunny day
The optimum reactive power of the BESS during a sunny day
The optimum active power of the BESS during a rainy day
The optimum reactive power of the BESS during a rainy day
In this study, three adopted error indexes are used to estimate the RBFNN/MLPNN training performance, including the mean absolute error (MAE), the mean relative error (MRE), and the root mean square error (RMSE). These indexes show the learning and generalization error of the normalized values of the online intelligent management of the BESS. The computation of these three error indexes can be expressed as:
$$ \mathrm{M}\mathrm{A}\mathrm{E}=\frac{1}{n}{\displaystyle \sum_1^n\left|{T}_i-{O}_i\right|} $$
$$ \mathrm{M}\mathrm{R}\mathrm{E}=\frac{1}{n}{\displaystyle \sum_1^n\frac{\left|{T}_i-{O}_i\right|}{T_i}} $$
$$ \mathrm{RMSE}=\sqrt{\frac{1}{n-1}{\displaystyle \sum_1^n{\left({T}_i-{O}_i\right)}^2}} $$
where T i is the target vector, O i is the output vector, and n is the number of training data or the data for each test in the testing data set.
Compared with the MLPNN model, Table 2 demonstrates that the proposed RBFNN model has a minimum statistical error in terms of MAE, MRE, and RMSE. It is concluded that the proposed RBFNN is an effective way to improve the prediction accuracy. In addition, the proposed RBFNN is able to accurately predict the optimum active and reactive power of the BESS with minimal errors.
Error indexes for online optimum BESS management
RMSE
Y 1-based RBF (active power)
9.947 × 10−7
Y 2-based RBF (reactive power)
Y 1-based MLP (active power)
Y 2-based MLP (reactive power)
MAE mean absolute error, MRE mean relative error, RMSE root mean square error, RBF radial basis function, MLP multilayer perceptron
Frequency and voltage of stand-alone microgrids
Frequency and voltage profiles are important criteria which have to be monitored to ensure that the frequency and voltage of the isolated microgrid are satisfied within the required limit in order to prevent the isolated microgrid from instability and collapse in the presence of violent changes of loads or outage of distributed generations. Looking at the frequency and voltage variation evident in Figs. 16, 17, 18 and 19, it can be seen that the frequency and voltage variation of the proposed RBFNN are within acceptable ranges from 49.95 to 50.05 Hz for frequency regulation and from 0.99 pu to 1 pu for voltage regulation. On the other hand, the frequency variation of the MLPNN exceeds the acceptable range of frequency regulation in some parts of time duration. It is obvious that the proposed RBFNN approach would achieve better performance in terms of frequency and voltage regulation compared to the MLPNN approach.
Frequency variation of the stand-alone microgrid during a sunny day
Frequency variation of the stand-alone microgrid during a rainy day
Voltage variation of the stand-alone microgrid during a sunny day
Voltage variation of the stand-alone microgrid during a rainy day
In this paper, a novel method for the optimum online intelligent management of active and reactive power of the BESS for the isolated microgrid is proposed. The entire BESS is centrally controlled by a controller developed using the proposed RBFNN model. The results show that the proposed RBFNN is able to follow the optimum target-based PSO at almost every time step mentioned under the changes of typical loads and solar PV generation with the profiles of a sunny and a rainy day. Compared with the MLPNN model, the proposed RBFNN model provides superior performance, when the error efficiency and positional accuracy are considered. It can be summarized that the proposed RBFNN model is appropriate for the online prediction of active and reactive power of the BESS differing only slightly from the optimal target result-based PSO and can consequently be used for real-time energy storage management as an online controller.
The authors are grateful for the feedback of two anonymous reviewers and the editors of this journal; their comments helped us a lot in improving the quality of this paper. We would like to thank Dagmar Fiedler, Editorial Manager for the revisions.
TK developed the optimization modeling part of this research and also trained and tested the proposed model. YQ conceived of study and participated in its design and coordination and helped to draft the manuscript. MW and YM critically reviewed the paper and gave final approval for the paper to be published. All authors read and approved the final manuscript.
Thongchart Kerdphol is a Ph.D. student sponsored by the Japan Student Services Organization (JASSO honors scholarship) and Kyushu Institute of Technology (KIT) at MITANI laboratory, KIT. His research interests are in the area of the application of artificial intelligence in power systems, power system stability, smart/microgrids, renewable energy, and power system dynamics and controls.
Yaser Qudaih is currently an assistant professor at the Department of Electrical Engineering and Electronics, Kyushu Institute of Technology (KIT), Japan. His area of interest including power system is renewable energy and Smart Grid applications.
Masayuki Watanabe is an associate professor at the Department of Electrical Engineering and Electronics, Kyushu Institute of Technology (KIT), Japan. His research interests are in the areas of analysis and control of power systems
Yasunori Mitani is a professor at the Department of Electrical Engineering and Electronics, Kyushu Institute of Technology (KIT), Japan. His research interests are in the areas of analysis and control of power systems.
Department of Electrical and Electronics Engineering, Kyushu Institute of Technology, 1-1, Sensui-cho, Tobata-ku, Kitakyushu-shi, Fukuoka 804-8550, Japan
Marnay C, Aki H, Hirose K, Kwasinski A, Ogura S, Shinji T (2015) How two microgrid fared after the 2011 earthquake. IEEE Power Energy Magazine 13(3):44–57View ArticleGoogle Scholar
Wu D, Tang F, Dragicevic T, Vasquez J, Guerrero J (2014) Autonomous active power control for islanded AC microgrid with photovoltaic generation and energy storage system, IEEE Trans. Energy Conversion 29(4):882–892View ArticleGoogle Scholar
Moghadam MRV, Zhang R, Ma RTB (2013) Randomized response electric vehicles for distributed frequency control in smart grid. In: proceeding of IEEE International conf. on Smart Grid Communications., pp 139–144Google Scholar
Nan Z, Qian L, Li H (2014) Auxiliary frequency and voltage regulation in microgrid via intelligent electric vehicle charging. In: proceeding of IEEE International conf. on Smart Grid Communications., pp 662–667Google Scholar
Inthamoussou F, Pegueroles-Queralt J, Bianchi F (2013) Control of a supercapacitor energy storage system for microgrid application, IEEE Trans. Power Systems 28(3):690–697Google Scholar
Cecati C, Mokryani G, Piccolo A, Siano P (2010) An overview on the smart grid concept. In: Proceeding of Conf. on 36th annual IEEE Industrial Electronics Society., pp 3322–3327Google Scholar
Toma S, Senjyu T, Miyazato Y, Yona A (2008) Decentralized voltage control in distribution system using neural network. In: proceeding of Conf. on 2nd IEEE International Conference., pp 1557–1562Google Scholar
Hatta H, Kobayashi H (2007) A study of centralized voltage control method for distribution system with distributed generation. In: proceeding of Conf. on 19th CIRED International Conference on Electricity Distribution., pp 1–4Google Scholar
Marcela M, Sumper A, Gomis O, Sudra A (2011) Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search. Appl Energy 1:4678–4686Google Scholar
Baalbergen F, Baber P, Ferreira J (2009) Energy storage and power management for typical 4Q-load. IEEE Trans on Industrial Electronic 56(5):1485–1498View ArticleGoogle Scholar
Rahbar K, Xu J, Zhang R (2015) Real-time energy storage management for renewable integration in microgrid: an off-line optimization approach. IEEE Trans on Smart Grid 6(1):124–134View ArticleGoogle Scholar
Li G, Na J, Stoten D, Ren X (2014) Adaptive neural network feedforward control for dynamically substructured systems, IEEE Trans. Control System Technology 22(3):944–954View ArticleGoogle Scholar
Bianchini M, Frasconi P, Gori M (1995) Learning without local minima in radial basis function networks, IEEE Trans. Neural Netw 6(1):749–756View ArticleGoogle Scholar
Park J, Sandberg IW (1991) Universal approximation using radial basis functions network. Neural Comput 3(2):246–257View ArticleGoogle Scholar
Tang Y, Wong WK (2015) Distributed synchronization of coupled neural networks via randomly occurring control. IEEE Trans on Neural Networks and Learning Systems 24(3):435–447View ArticleGoogle Scholar
Sitbon M, Leppaho J, Suntio T, Kuperman A (2014) Dynamic of photovoltaic-generator-interfacing voltage-controlled buck power stage. IEEE Journal of Photovoltaics 5(2):633–640View ArticleGoogle Scholar
Kottick D, Blau M, Edelstein D (1993) Battery energy storage for frequency regulation in an island power system, IEEE Trans. Energy Convers 8(3):455–458View ArticleGoogle Scholar
Kerdphol T, Qudaih Y, Mitani Y (2015) Optimal battery energy storage system size using particle swarm optimization for microgrid system. International Review of Electrical Engineering (IREE) 10(2):277–285View ArticleGoogle Scholar
Aditya SK, Das D (1999) Application of battery energy storage system to load frequency control of an isolated power system. Int J Energy Res 23(1):247–258View ArticleGoogle Scholar
Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system, IEEE Trans. Energy Conversion 19(2):284–391Google Scholar
Esmin AA, Lambert-Torres G, Zambroni de Souza AC (2005) A hybrid particle swarm optimization applied to loss power minimization, IEEE Trans. Power Systems 20(2):859–866View ArticleGoogle Scholar
Gaing ZL (2003) Particle swarm optimization to solving the economic dispatch considering the generator constraints, IEEE Trans. Power Systems 18(3):1187–1195View ArticleGoogle Scholar
Kennedy J, Eberhart R (1995) Particle swarm optimization in Proc IEEE Int. Conf. Neural Netw 4:1942–1948Google Scholar
Zyandehroodi H, Mohamed A, Shareef H, Mohammadjafari M (2010) Performance comparison of MLP and RBF neural networks for fault location in distribution networks with DGs, Proceeding of IEEE Int Conf Power Energy., pp 342–345Google Scholar
Sereno F, de Sa JP M, Matos A, Bernades J (2000) A comparative study of MLP and RBF neural nets in the estimation of the foetal weight and length. In: Proceeding of RECPAD., pp 1–9Google Scholar
Memarian H, Balasundram SK (2012) Comparison between multi-layer perceptron and radial basis function networks for sediment load estimation in a tropical watershed. Journal of Water Resource and Protection 1:870–876View ArticleGoogle Scholar
Karami H, Mousavi MF, Shamsipur M, Riahi S (2006) New dry and wet Zn-polyaniline bipolar batteries and prediction of voltage and capacity by ANN. J Power Sources 154(1):298–307View ArticleGoogle Scholar


Best offers on North India Tours Travel packages with Car rental services.
India's largest Car Rental/Hire Company offers cab or car rental services for Ahmedabad, Bengaluru,... Outstation and Local Car Rental Service.
Hire Tata Indica taxi in India from amongst widest ranges of options available.
Responsibility were just a bunch of people who believe there are crazy, "Outstation Car Rental".
The "Rent A Car" enables our customers that it is amazing what the power of a safe, reliable, and get clean cabs? Well, we have our top secret technology that accurately as possible with the most convenient cab, is used to match our headquarters, you can relay critical data back to the client with the tools we have established All drivers are capable of. Local car rental, outstation car rental or airport taxi service outside India with ant cities.


text:
Team behind $1 billion Bitcoin ETP to list the first Litecoin exchange traded product on Deutsche Börse's XETRA
03-04-2021 company
The LTCetc – Physical Litecoin ETC lists on April 14th on Deutsche Börse XETRA
This is the third 100 % physically-backed cryptocurrency ETP launched by ETC Group
ETC Group pioneered listings of cryptocurrency ETPs on XETRA with the launch of its Bitcoin ETC (BTCE) in June 2020
ETC Group is set to list the ETC Group Physical Litecoin ETC (Ticker: ELTC) on 14th of April, the first Litecoin exchange traded product (ETP) to be listed on the Deutsche Börse
New launch follows the huge success of ETC Group's first product BTCetc - ETC Group Physical Bitcoin,
which listed on Deutsche Börse's XETRA in June 2020 has amassed over $1 billion in AUM and the launch of the ETC Group Physical Ethereum ETC , the first Ethereum exchange traded product in March 2021
The new Physical Litecoin ETC will be a central counterparty cleared exchange traded product and will track the price of the Litecoin cryptocurrency, with the security, safety, and liquidity of trading on regulated markets
The Litecoin, Bitcoin and Ethereum products by ETC Group are distributed on the HANetf platform alongside its existing range of innovative ETFs and ETPs
ETC Group, the specialist provider of innovative, digital asset-backed securities, has announced that it will list LTCetc – ETC Group Physical Litecoin ETC (Ticker: ELTC) on Deutsche Börse's XETRA platform on Wednesday 14th April 2021. The exchange traded cryptocurrency is structured as an asset-backed debt security issued in Germany, and is marketed and distributed by HANetf.
ETC Group´s Physical Litecoin ETC (Ticker: LITE) will track the price of Litecoin providing investors with an institutional grade product to gain exposure to the Litecoin cryptocurrency. ELTC is the first Litecoin product to be listed on Xetra and will be central counterparty cleared[1].
The launch follows the huge success of ETC Group's first product - BTCetc (Bitcoin Exchange Traded Crypto, ticker BTCE) – launched on XETRA last June, which has seen its assets under management grow to over $1 billion in just seven months[2]. BTCE had a turnover of 925million EUR on Xetra in February with a market share of 79.4% of the ETN segment of Xetra and was the most traded ETP across the whole of Xetra[3].
It's the third crypto ETC from ETP Group – in March 2021 it listed ETC Group Physical Ethereum ETC (Ticker: ZETH) which tracks the price of Ether and was the first Ethereum ETP to be listed on the Deutsche Börse. ZETH traded over $1million in its first week of launch[4] and had spreads of just 23 basis points[5].
Bradley Duke, CEO of ETC Group said:
Litecoin has grown by 342% [6] in the past 12 months and is the nineth largest cryptocurrency by market capitalisation at $14.6 billion [7] with growing interest from institutional and retail investors. It's often talked about as the silver to Bitcoin's gold.
The current economic uncertainty has underlined the attraction of crypto assets for investors as they offer opportunities for diversification from traditional assets such as equities and bonds, and strong hedging qualities against inflation. But it is a crowded market with more than 8,700 cryptocurrencies [8], making it difficult for investors to select assets and to store and manage them.
Our physically backed Bitcoin ETC (BTCE) was designed to provide investors with a safer and more transparent way to gain exposure to Bitcoin and we are now able to offer the same benefits to Litecoin with the listing of ETC Group Physical Litecoin ETC in addition to the ETC Group Physical Ethereum ETC ,which launched in March 2021.
Institutional investors are demanding regulated crypto products that are secure, liquid and central counterparty cleared and ETC Group is building products that adhere to the complex and exacting standards of the institutional investor.
Traditional markets will remain volatile for some time, fuelling demand for alternatives such as cryptocurrencies. In contrast to previous rallies in cryptocurrencies, the current one is the first to be supported by both retail and institutional investors, making this the ideal time to launch ELTC.
Past performance is no guarantee of future performance. Cryptocurrencies are highly volatile, and your capital is at risk.
Hector McNeil, co-Founder and co-CEO at HANetf, said:
We are excited to be supporting the ongoing expansion at ETC Group and the launch of their third crypto ETC with the listing of ETC Group Physical Litecoin ETC. ETC Group is firmly established as a market leader in offering investment products focused on crypto assets. The huge success of their first product, BTCE, which reached over $1 billion in assets in just over seven months since listing3 demonstrates the growing demand for products which enable investors to avoid concerns about the technical challenge of buying and storing cryptocurrencies. ETPs are listed on a regulated Exchange and investors buy them through regulated brokerages. This is arguably the most robust way to trade and invest in crypto currencies.
LTCetc – ETC Group Physical Litecoin (ELTC)
Approved by BaFin, the German financial regulator, each unit of ELTC is physically backed by Litecoin stored in regulated, institutional-grade safe custody. This gives the investor a claim on a predefined amount of Litecoin - a structure very similar to physically backed gold exchange traded commodities and allows investors the option for redemption in Litecoin. The ETC structure is based on a transparent and verifiable process to ensure that the provenance of the Litecoin in custody has been strictly vetted. Investors do not need to engage with the technical challenges of purchasing and storing the cryptocurrencies such as setting up a cryptocurrency wallet or trading on unregulated crypto exchanges and there is no need to manage cryptographic keys or engage with blockchain technology at all. Units of ELTC are safely held with an investor's securities broker or bank, thus removing the risk of losing the investment because of, for instance, lack of knowledge of how cryptographic keys work.
ELTC will be supported by a network of world class Authorised Participants (APs) and Market Makers such as Flow Traders, experienced in cryptocurrency markets as well as the ETP markets. The APs ensure on-exchange liquidity and tight spreads, enabling traders to purchase in all sizes without having to worry about market impact.
Trading will be centralised on XETRA, drawing liquidity from the many fragmented crypto exchanges. ETPs are at their most powerful when they democratise access to difficult-to-access asset classes and can harness global liquidity in a robust, and tried and tested product structure.
[1] Source: ETC Group
[2] Source: BTCE AUM $1.12billion as of 22.02.2021
[3] Source: Deutsche Boerse Group Turnover Report -February 2021
[4] Source: Bloomberg as of 12.03.21. Trading volume 1,377,438 EUR
[5] Source: Bloomberg as of 12.03.21
[6] Source: Bloomberg/HANetf as of 24.03.21
[7] Source: https://www.forex-ratings.com/cryptocurrencies-rating/
[8]Source: https://coinmarketcap.com/all/views/all/ >
ETC Group to launch new ETP based on upcoming Ethereum hard fork.
ETC Group Appoints Blockchain Analytics Leader, Crystal, To Provide Chain Analysis Services