text:
\section*{Nomenclature}
\subsection*{Abbreviations}
\begin{notationlist}{MAXIMUMSPC}

\item[PDS] Power distribution system

\item[UTS] Urban transportation system

\item[CDS] Charging/discharging station

\item[SUE] Static user equilibrium

\item[DUE] Dynamic user equilibrium

\item[OPF] Optimal power flow

\item[SDP] Semidefinite relaxation programming

\item[ADMM] Alternating direction method of multiplier

\item[EV] Electrical vehicle

\item[SOC] State of charge

\end{notationlist}

\subsection*{Functions}
\begin{notationlist}{MAXIMUMSPC}

\item[$F^t_{UTY}$] Objective function of the utility and customer in time interval $t$, $t \in D_t$

\item[$F^t_{CSO}$] Objective function of the customer in time interval $t$, $t \in D_t$

\item[$F^t_{CDS}$] Objective function of the smart charging/discharging in a CDS

\item[$f^t_{PS}$] Cost function of PDS in time interval $t$

\item[$f^t_{UTS}$] Cost function of UTS in time interval $t$

\item[$f^t_{WT}$] Cost function of charging/discharging waiting time

\item[$Q^t_{PDS}$] Netload of PDS in time interval $t$

\item[$Q^t_{DPDS}$] Total charging/discharging power of all CDS

\item[$Q^t_{DUTS}$] Total EVs for charging/discharging

\item[$F^t_{PDS}$] Objective function of OPF in PDS

\item[$f_{k_a}(x^t_{k_a})$] Travel time function on link $k_a$ in time interval $t$, where traffic flow is $x^t_{k_a}$

\end{notationlist}

\subsection*{Parameters}
\begin{notationlist}{MAXIMUMSPC}

\item[$\mathcal{G}^{UTS}$] The graph for UTS with a node set and a link set: $\mathcal{G}^{UTS} = [\mathcal{V}^{UTS}, \mathcal{E}^{UTS}]$, where $\mathcal{V}^{UTS}$ = $\{1,2,\cdots, n_{\mathcal{V}^{UTS}}\}$, and $\mathcal{E}^{UTS} = \{1,2,\cdots, m_{\mathcal{E}^{UTS}}\}$

\item[$P_{r_us_u}$] A set of path used to connect the OD pair, which is defined as $r_u \in \mathcal{V}^{UTS}_{r_u}$ and $s_u \in \mathcal{V}^{UTS}_{s_u}$, $\mathcal{V}^{UTS}_{r_u}$ and $s_u \in \mathcal{V}^{UTS}_{s_u}$ are the original node set and destination node set, respectively

\item[$C_{k_a}$] The traffic flow capacity on link $k_a$

\item[$\mathcal{G}^{PDS}$] The graph for PDS with a node set and a link set: $\mathcal{G}^{PDS} = [\mathcal{V}^{PDS}, \mathcal{E}^{PDS}]$, where $\mathcal{V}^{PDS}$ = $\{1,2,\cdots, n_{\mathcal{V}^{PDS}}\}$, and $\mathcal{E}^{PDS} = \{1,2,\cdots, m_{\mathcal{E}^{PDS}}\}$

\item[$\underline{\Upsilon},  \overline{\Upsilon}$] The upper bound and lower bound of SOC

\item[$\gamma_1$] Weight factor between PDS and UTS

\item[$\gamma_2$] Iteration step coefficient for gradient descent

\item[$\varrho_1, \varrho_2$] The electrical power price and the congestion fee

\item[$\varrho_3, \varrho_4$] The EV parking ratios to the CDSs

\item[$\chi_{i_1}$] The capacity of CDS $i_1$
\end{notationlist}

\subsection*{Variables}
\begin{notationlist}{MAXIMUMSPC}
\item[$\theta^t_{k_a}$] The traffic flow on link $k_a$ in time interval $t$

\item[$\pi^{*t}$] The optimal charging/discharging price in time interval $t$

\item[$q^{dt}_{r_us_u}$] Number of vehicles from $r_u$ to $s_u$ departing in time interval $d$ via any path, $d \in D_t$, $D_t$ is the time interval set

\item[$h^{d_1}_{p_{r_us_u}}$] Number of vehicles assigned to path $p_{r_us_u}$ with departing time interval $d_1$

\item[$\delta^{d_1t}_{p_{r_us_uk_a}}$] A 0-1 variable to indicate in time interval $t$, whether the trip from $r_u$ to $s_u$ is assigned to path $p_{r_us_uk_a}$ via link $k_a$ departing in time interval $d_1$

\item[$V^{\phi t}_i$] Complex voltage on bus $i$ with phase $\phi $, $\phi $ $\subseteq$ $\{a,b,c\}$

\item[$I^{\phi t}_i$] Complex current on bus $i$ with phase $\phi $, $\phi $ $\subseteq$ $\{a,b,c\}$

\item[$s^{\phi t}_i$] Power injection on bus $i$, where $s^{\phi t}_i$ = $p^{\phi t}_{i}$ + $\textbf{i}q^{\phi t}_{i}$

\item[$z^{\phi }_{i}$] The complex impedance matrix $z^{\phi }_{i}$ =  $r^{\phi }_{i}$ + $\textbf{i} x^{\phi }_{i}$

\item[$S^{\phi t}_i$] The complex power from bus $i$ to bus $i^1$, where bus $i^1$ is the ancestor of bus $i$, $S^{\phi t}_i$ = $V^{\phi t}_i (I^{\phi t}_i)^H$, $H$ indicates the hermitian transpose

\item[$v^{\phi t}_{i}$, $i^{\phi t}_{i}$] $v^{\phi t}_{i}$ = $V^{\phi t}_{i} (V^{\phi t}_{i})^H$, $l^{\phi t}_{i}$ = $I^{\phi t}_{i} (I^{\phi t}_{i})^H$

\item[$x^{t,k}, y^{t, k}, \lambda^{t, k}$] The $k$th iterative variables of ADMM for distributed OPF computation

\item[$C^{t_1}_{ev, i_3}$] The discharging (positive) and charging (negative) speed of EV $i_3$ at time $t_1$

\end{notationlist}
\section{Introduction}\label{sec:introduction}
\begin{figure*}[!t]
	\begin{center}
		\includegraphics[width=1.90\columnwidth, angle=0]{Idea1.eps}
		\caption{The main idea and the bi-peak and bi-ramp problems in a power distribution system and a transportation system in a urban area.}\label{fig:idea1}
	\end{center}
\end{figure*}

Rooftop photovaltaic (PV) gained a foothold in many power systems because of its continuously declining cost~\cite{chen12g2015seeds,cui2015sol12ar,gu2016kno12wledge}. It brings many benefit to costumers such as reduced energy cost; however, it also presents unprecedented challenges to power systems operation and control. The upper right-hand plot in Fig.~\ref{fig:idea1} shows the so-called ``duck curve", which has been recorded in some high-distribution PV regions such as California~\cite{katsigiann123is2010novel, tian2013re1view, connolly2010review}. The peak solar generation in the middle of the day sinks the netload to lower valley and then when the peak load occurs right after sunset, a huge volume of energy demand ramps up in a short time frame. This creates the artificial peak and ramping that are costly to balance using the current power system assets.

It is not surprising that other man-made complex systems also suffer similar issues. As shown in the lower right-hand plot in Fig.~\ref{fig:idea1}, the transportation system also has high peaks and steep ramps due to fairly constrained traffic patterns at the rush hours in urban areas~\cite{gonzal65es2013evening,cheng2015urb1an,huang2017para123meterized}. Thanks to the spawn of EVs and the widely located CDSs, the two originally independent systems: the PDS and the UTS can be coupled together.

Specifically, the increasing number of EVs can be seen as a significant amount of distributed and highly manipulatable small reserves that can be used to provide demand response, enhance the system reliability, and provide other services for the power systems~\cite{fol1ey2013impacts, liu2012asse234ssment, wu2012loa234d,sortomme2012opt234imal,synchro2015jiangf234s,heymans2014econo123mic,geng2017learn89ing}. In these studies, the geographical information and transportation information are ignored, because the EVs are treated as the aggregated loads/reserves. In~\cite{sadeghi2014opt234imal}, the optimal placement of the charging stations is studied with the geographical information and transportation information. In~\cite{he2013op234timal}, based on locational marginal pricing (LMP), an optimal deployment of charging stations is proposed for EVs. In~\cite{wei2017opt456imal}, an optimal traffic-power flow is proposed with the wireless charging technology and congestion toll manipulation.

In this paper, a two-step power-traffic coordinated operation approach is proposed to address the bi-peak and bi-ramp problems for both PDS and UTS. It also provides a multifunction platform for the researches such as emission reduction, multisystem integration, and future city planning. The \textbf{main contributions} of this paper are:
\begin{enumerate}
	\item Considering the complexity of the power-traffic system, a two-step coordinate approach is designed from the higher level to lower level to operate the system in spatial and temporal domain hierarchically. In the higher level, both the PDS and the UTS are regulated and treated together as an utility to minimize the social cost. The EVs and CDSs are treated as customers to minimize the expenditure. Then, an equilibrium exists between the utility and the customers to determine the operation variables such as optimal charging/discharging price, total electrical demand, and total available EVs. In the lower level, the detailed models of PDS, UTS, EVs \& CDS are considered to specifically determine these variables in spatial and temporal domains.

	\item In the lower level, the PDS is built with the branch flow model, which is equivalent to the classic bus-injection model and more suitable for the PDS with radial topology~\cite{suh20va09lnc}.  There are several developed approaches for solving an optimization problem associated with a PDS, usually called OPF such as DC OPF~\cite{st456ott2009dc}, genetic algorithm~\cite{b45tzs2002optil,yigu2014statistical1}, and OPF relaxation with the second-order cone program (SOCP)~\cite{pe78ng2014distributed}. Based on~\cite{pe015dis456ed,dall2013di23stributed}, we relax an OPF with the SDP for the three-phase unbalanced PDS. Meanwhile, several distributed algorithm are designed to solve the OPF such as dual decomposition~\cite{lam212dti123bud} and predictor corrector proximal multiplier (PCPM)~\cite{li2012dem45and}. Based on~\cite{pe015dis456ed,boyd2011d45istributed}, the ADMM is applied to decompose the OPF problem and reduce computation time.

	\item In the lower level, for the UTS, the DUE is widely used to estimate the traffic volume, congestion time, and travel cost for each link~\cite{pat015fic}. Based on the assumption that all travelers can choose the best route to minimize the cost of transportation, in the Wardrop user equilibrium (UE), the travel times of all paths are equal for each original-destination (OD) pair and less than any unused paths~\cite{jia2056514nk,sheffy1985ur123ban}. In~\cite{wei2017opt456imal}, a static user equilibrium assignment is used to integrate with the PDS. Considering that the EV behaviors can be impacted by the charging/discharging prices, a DUE is applied to keep the temporal domain continuous for the UTS.

	\item In this paper, the EVs and the CDSs are designed as the ``reserves" for both PDS and UTS, respectively. Considering the SOC~\cite{he2013n1ew,pattipati2011sy11stem}, an optimal charging/discharging schedule is designed to meet the requirements of PDS and UTS and reduce the impacts to PDS. The test bench consists of real systems, the PDS is the IEEE 8,500-bus distribution system, and the UTS is the Sioux Falls transportation system with about 10,000 EVs, to demonstrate the proposed approach.
\end{enumerate}

 Based on Fig.~\ref{fig:idea1}, we acknowledge that cybersecurity is a critical aspect to consider in the proposed power-traffic system operation. Also, the large volume of data generated by the proposed power-traffic system requires a high-speed, flexible and reliable communication network. Because the proposed power-traffic system contains two complex systems, many industrial communication network infrastructures for PDS and UTS operations need to be attached, such as 3G, 4G, and WiFi~\cite{sed12jelmaci2017hierarchical}. The multi-network integration brings a lot of challenge for the 1). real-time monitoring \& anomaly detection~\cite{jiang2014fault,jiang2016spatial1}, 2). data transmission, storage, \& security, 3). attack analysis \& mitigation~\cite{ten2010cyber12security, islam2012wirel12ess,geng2015da12ta,jiang2017big}. In this paper, we assume that all the messages such as electrical power price, traffic congestion information, and various control signals can be transmitted correctly in real-time without any cybersecurity issues.

The paper is organized as follows. In Section~\ref{sec:formulation}, the flowchart of the proposed approach is introduced. In Section~\ref{Sec:Equilibrium}, the equilibrium between the utility and the customers is designed to determine the optimal system variables. In Section~\ref{Sec:UTS}, based on SUE, a DUE is applied to model the UTS and compute the dynamic traffic flow. In Section~\ref{sec:PDS}, based on the branch flow model, the OPF problem is relaxed with SDP and solved with ADMM.  In Section~\ref{Sec:EVs}, considering the SOC and the EVs behaviors, an optimal charging/discharging schedule is designed to reduce the impacts to the PDS. In Section~\ref{sec:Implementation}, the numerical results are presented to validate the proposed approach. The conclusion is presented in Section~\ref{Sec:Concl}.

\section{The Flowchart of the Proposed Approach}\label{sec:formulation}
\begin{figure*}[t!]
	\begin{center}
		\includegraphics [width=1.25\columnwidth, angle=90]{Architecture4_flowchart1.eps}
		\caption{Flowchart of the proposed power-traffic coordinate operation approach.}\label{fig:flowchart}
	\end{center}
\end{figure*}

The proposed two-step power-traffic operation approach is shown in Fig.~\ref{fig:flowchart}. In the higher level, the power-traffic system consists of two major parts: the utility part (taking the PDS and the UTS as a whole) shown in green, and customer part (taking the CDSs and related EVs) shown in yellow. At time interval $t$, the duck curve of the netload is recorded with the renewable energy generation data and end user load data in PDS. Meanwhile, the travel data is collected with the traffic monitoring data and congestion model in UTS. Then, as shown in the two green blocks, the PDS and UTS are treated as a regulated utility to minimize the social cost. In the yellow block, the EVs and CDSs are treated as the customers to minimize their expenditures. There exists an equilibrium to determine the system operation variables such as optimal charging/discharging prices, total demand electrical power, and total required EVs. These variables are fitted into the lower level as inputs.

In the lower level, the power-traffic system consists of three major parts: the PDS part, the EVs \& CDSs part, and the UTS part, which determine the system operation variables in spatial and temporal domains with detailed models. First, in the UTS, to accurately simulate the dynamic traffic system in spatial and temporal domain, the DUE is built based on SUE with temporal continuous constraints. The proposed DUE problem can be transfered into a convex problem and solved with optimal results. Second, in the PDS, the objective function $F_{PDS}$ is designed to minimize the cost of the PDS, which is built with a three-phase unbalanced branch flow model. Based on the SDP relaxation, $F_{PDS}$ can be minimized in an OPF problem, which is solved with ADMM. Third, with the specified electrical power demands and available EVs for each CDS, an objective function $F_{CDS}$ is designed to minimize the charging/discharging impacts to the PDS while considering the SOC.

In addition, in this paper, it is assumed that all the drivers know the traffic information very well, and can get the optimal charing/discharging price through the wireless communication immediately. The user-equilibrium in UTS network can be reached in a very short time.

\section{Equilibrium Design Between Utilities and Customers (Higher Level)}\label{Sec:Equilibrium}

\subsection{Equilibrium Design}
In this paper, we focus on a small city with tens of thousands of cars, which means any single user behavior has a very weak impact on the equilibrium design in the higher level. The utility consists of PDS and UTS. A customer consists of a CDS and related EVs.

\textbf{Utility Objective (Minimize Social Cost)}
\begin{figure*}[!t]

	\begin{equation}\label{equ:ISO}

		F^t_{UTY} = \min_{P^t_{i_1}, N^t_{i_1}} \bigg\{ f^t_{PS} (Q^t_{PDS} - Q^t_{DPDS} (P^t_{i_1}))
		  + \gamma_1 f^t_{UTS}(Q^t_{UTS} - Q^t_{DUTS} (N^t_{i_1})) + f^t_{WT}(\tau_1 Q^t_{DUTS} (N^t_{i_1})) \bigg\}

	\end{equation}

	\vspace*{0.0in}
\end{figure*}

As shown in (\ref{equ:ISO}), $F^t_{UTY}$ is the objective function of the utility, which is designed to minimize the social cost with the variables $P^t_{i_1}$ and $N^t_{i_1}$, $t$ is a time interval $t\in D_t$. $P^t_{i_1}$ is the electrical power charging (negative) or discharging (positive) at CDS $i_1$. $\gamma_1$ is the weight coefficient of UTS. $N^t_{i_1}$ is the number of available EVs on CDS $i_1$.
The constraints of (\ref{equ:ISO}) are illustrated as follows:
\begin{subequations}\label{equ:ISOcon}
	\begin{align}
	& \underline{P^t_{i_1}}\leq P^t_{i_1} \leq \overline{P^t_{i_1}},  \label{equ:ISO1} \\
	& 0 \leq \gamma_1 \leq \overline{\gamma_1} \label{equ:ISO2} \\
	& 0 \leq N^t_{i_1} \leq \overline{N^t_{i_1}} \label{equ:ISO3}
	\end{align}
\end{subequations}

$f^t_{PS}$ is defined as a convex function~\cite{park2005particle}, which determine the PDS cost in time interval $t$,  $Q^t_{PDS}$ is the netload of PDS, and $Q^t_{DPDS} (P^t_{i_1})$ is the total charging/discharging power of all CDSs, which can be defined as

\begin{equation}
Q^t_{DPDS} (P^t_{i_1}) = \sum_{i_1 \in N_{CDS}}P^t_{i_1} \label{equ:EV1}
\end{equation}
where $N_{CDS}$ is a set of CDS. $f^t_{UTS}$ is defined as a convex function, which determines the UTS cost in time interval $t$, $Q^t_{UTS}$ is the traffic load, and $Q^t_{DUTS} (N^t_{i_1})$ is the total EVs for charging/discharging. $f^t_{WT}(\tau_1)$ is the time cost with charging/discharging time $\tau_1$. Considering the number of EVs is very large, $Q^t_{DUTS} (N^t_{i_1})$ can be estimated as

\begin{equation}
Q^t_{DUTS} (N^t_{i_1}) = \sum_{i_1 \in N_{CDS}}N^t_{i_1} = \sum_{i_1 \in N_{CDS}}\frac{P^t_{i_1}}{C^t_1} \label{equ:EV2}
\end{equation}
where $C^t_1$ is the average charging/discharging speed of all the EVs. $f^t_{WT}$ is the waiting cost when an EV is parking for charging/discharging, which is also defined as a convex function.
In summary, the proposed utility objective function $F^t_{UTY}$ is convex, which indicate an optimal point existed.

\textbf{Customer Objective (Minimize Own Expenditure)}
Based on (\ref{equ:EV2}), the objective function of a customer (a CDS and related EVs) can be designed as follows:
\begin{subequations}\label{equ:obj_cus}
	\begin{align}
	& F^t_{CSO} = \min_{P^t_{i_1}}f^t_{WT}(\tau_1\sum_{i_1} \frac{P^t_{i_1}}{C^t_1}) - \pi^t P^t_{i_1} \label{equ:cus1}\\
	& s.t.\ \ (\ref{equ:ISO1}),  \underline{\pi^t}\leq \pi^t \leq \overline{\pi^t} \label{equ:cus2}
	\end{align}
\end{subequations}
where the objective function of the customer consists of two parts: the time consumption in the CDSs and the benefit/cost in charging/discharging. $\pi^t$ is the electrical power price, and $\pi^t P^t_{i_1}$ is the benefit (by discharging, positive) or cost (by charging, negative) at CDS $i_1$. As discuss above, the objective function of the customer $F^t_{CSO}$ is also convex.

\subsection{Solutions}
An equilibrium $\pi^{*t}$ and $P^{*t}_{i_1}$ exists, and the optimal price $\pi^{*t}$ can be computed as~\cite{boyd2004conv456ex,li2011opt789imal}
\begin{equation}\label{equ:opti}
\pi^{*t} = f'^t_{PS}(Q^t_{PDS} - \sum_{i_1}P^{*t}_{i_1}) + \frac{\gamma_1}{C^t_1} f'^t_{UTS}(Q^t_{UTS} - \sum_{i_1} \frac{P^{*t}_{i_1}}{C^t_1})
\end{equation}
where the optimal price depends on the PDS part $f'^t_{PS}$ and the UTS part $f'^t_{UTS}$. Based on the gradient descent, a distributed algorithm is proposed to optimize the utility and the customers jointly the equilibrium. Specifically, the objective function of each customer can be computed independently to the optimal price $\pi^{*t}$.
At $k_1$-th iteration:

\begin{subequations}\label{equ:equ_iter}

	\begin{align}
	& \pi^{k_1,t} = \nonumber\\
	& f'^t_{PS}\bigg(Q^{t}_{PDS} - \sum_{i_1}P^{k_1,t}_{i_1}\bigg) + \frac{\gamma^1_1}{C^t_1} f'^t_{UTS}\bigg(Q^{t}_{UTS} - \sum_{i_1} \frac{P^{k_1,t}_{i_1}}{C^t_1}\bigg) \label{equ:priceup}\\
	& P^{k_1+1, t}_{i_1} = P^{k_1, t}_{i_1} + \gamma_2\bigg(\frac{\tau_1}{C^t_1}f'^t_{WT}(\tau_1\sum_{i_1}\frac{P^t_{i_1}}{C^t_1})-\pi^{k_1,t}\bigg) \label{equ:powerup}\\
	& (\pi^{k_1,t}, P^{k_1+1, t}_{i_1}) = [\overline{\pi^{k_1,t}}, \overline{P^{k_1+1, t}_{i_1}}]^{\varpi}
	\end{align}
\end{subequations}
where the optimization stepsize can be set as $0 \leq \gamma_2$, and the algorithm can converge when $\gamma_2$ is small enough~\cite{bert123sekas1989parallel}. $[\cdot]^{\varpi}$ means that the results are projected onto the set $\varpi$ defined by (\ref{equ:cus2}).

In summary, in this section, the optimal price $\pi^{*t}$ can be computed in the higher level, and the optimal results $P^{*t}_{i_1}$ gives the upper bound of power injection in (\ref{equ:constain_PDS_s}). In the following sections, the models of the UTS, PDS, and CDS are built to provide detailed information in both temporal and spatial domains about how to operate the power-traffic system in the lower level.

\section{Dynamic User Equilibrium Assignment for UTS (Lower Level) }\label{Sec:UTS}
Compared with the SUE, the DUE can accurately describe the traffic dynamics in a set of successive time intervals. In this paper, the DUE is based on the SUE with a temporal generalization~\cite{ja123nson1991dynamic,sheffy1985ur123ban}.
\subsection{Basic Model of UTS}\label{Sec:UTS1}
The UTS can be represented in a graph with a node set and a link set: $\mathcal{G}^{UTS} = [\mathcal{V}^{UTS}, \mathcal{E}^{UTS}]$, where $\mathcal{V}^{UTS}$ = $\{1,2,\cdots, n_{\mathcal{V}^{UTS}}\}$, and $\mathcal{E}^{UTS}$ = $\{1,2,\cdots, m_{\mathcal{E}^{UTS}}\}$. The origin and destination (OD) pair can be defined as $r_u \in \mathcal{V}^{UTS}_{r_u}$ and $s_u \in \mathcal{V}^{UTS}_{s_u}$, a set of paths $P_{r_us_u}$ is used to connect the OD pair, and the traffic flow between the OD pair can be represented as $q^d_{r_us_u}$ with departing time $d_t \in D_t$, where $D_t$ is a set of all time intervals. Then, the congestion model can be presented as follows~\cite{manual1964b123ureau}:

\begin{equation}\label{equ:cong}
f_{k_a}(\theta^t_{k_a}) = f^0_{k_a}\bigg[1+0.15(\frac{\theta^t_{k_a}}{C_{k_a}})^4\bigg]
\end{equation}
where $f_{k_a}(\theta^t_{k_a})$ is the travel time (travel impedance) when the traffic flow at link $k_a \in K_A$ is $\theta^t_{k_a}$ in time interval $t\in D_t$, $f^0_k$ is the initial travel time, and $C_{k_a}$ is the traffic flow capacity.
\subsection{Dynamic User Equilibrium}
Based on the SUE, the DUE can be built as follows~\cite{ja123nson1991dynamic}:

\begin{subequations}\label{equ:due_equa}
	\begin{align}
	& J_{DUE} = \min_{k_a, \theta^t_{k_a}} \sum_{k_a\in K_A}\sum_{t\in D_t} \int\limits^{\theta^t_{k_a}}_{0}f^t_{k_a}(w)dw  \label{equ: DUE1} \\
	s.t.\ \  & \theta^t_{k_a} = \sum_{p_{r_us_u}\in P_{r_us_u}}\sum_{d_1\in D_t} h^{d_1}_{p_{r_us_u}} \delta^{d_1t}_{p_{r_us_uk_a}}  \label{equ:DUE2} \\
	& q^{d_1}_{r_us_u} = \sum_{p_{r_us_u}\in P_{r_us_u}} h^{d_1}_{p_{r_us_u}}  \label{equ:DUE3} \\
	& b^{d_1}_{p_{r_us_u}} = \sum_{t\in D_t}\sum_{k_a \in K_a}f^t_{k_a}(\theta^t_{k_a})\delta^{d_1t}_{p_{r_us_u}k_a} \label{equ:DUE4}
	\end{align}
\end{subequations}
where $h^{d_1}_{p_{r_us_u}}$ is the number of vehicles in path $p_{r_us_u}$ with departing time interval $d_1$, $b^{d_1}_{p_{r_us_u}}$ is the travel time from $r_u$ to $s_u$ via path $p_{r_us_u}$, and $\delta^{d_1t}_{p_{r_us_uk_a}}$ is a 0-1 variable and can be defined as

\begin{equation}\label{equ:OSS01}
\delta^{d_1t}_{p_{r_us_u}k_a} =\left\{ \begin{array}{ll}
1    & \mbox{If in time interval $t$, link $k_a$ is in } \\
& \mbox{path $p_{r_us_u}$ with departing time $d_1$}  \\
0    & \mbox{otherwise}
\end{array}
\right .
\end{equation}

In time interval $t$, the traffic flow $\theta^t_{k_a}$ on link $k_a$ is given as in (\ref{equ:DUE2}), which equals to the sum of the traffic flows $h^{d_1}_{p_{r_us_u}} \geq 0$ via link $k_a$ for any departing time interval $t \in D_t$ and any path $p_{r_us_u}\in P_{r_us_u}$. The number of vehicles from  $r_u$ to $s_u$ is given as in (\ref{equ:DUE3}), which equals to the sum of traffic flows $h^{d_1}_{p_{r_us_u}}$ on any assigned path $p_{r_us_u}\in P_{r_us_u}$. The total time consumption of path $p_{r_us_u}$ from $r_u$ to $s_u$ is given as in (\ref{equ:DUE4}), which keeps the temporal continuous traffic flow for all time intervals $t\in D_t$ with a temporal unique constraint:

\begin{equation}\label{equ:sum}
\sum_{t\in D_t}\delta^{d_1t}_{p_{r_us_u}k_a} = 1
\end{equation}
which ensures that a vehicle cannot be assigned on two links in a certain time interval $t$.

\subsection{Solutions}
Considering the temporal continuous traffic flow, in time interval $t$, take the initial state $f^0_{k_a}$ in (\ref{equ:cong}) as $f^{t-1}_{k_a}$. In this paper, the test bench is based on Sioux Falls network, and the departing time is the same during each time interval $t$. Then, in time interval $t$, the DUE problem can be transfered as a SUE problem. The SUE is based on (\ref{equ: DUE1}), (\ref{equ:DUE2}), and (\ref{equ:DUE3}), and the Lagrangian can be generated as (\ref{eq:big_lagrangian_UTS}).
\begin{figure*}[!t]

	\begin{equation}\label{eq:big_lagrangian_UTS}

	 L_{1}(\theta,\lambda_1,\mu_1) = \sum_{k_a\in K_A}\sum_{t\in D_t} \int\limits^{\theta^t_{k_a}}_{0}f^t_{k_a}(w)dw +
	 \lambda^t_{1,k_a}\bigg[\theta^t_{k_a} - \sum_{ P_{r_us_u}}\sum_{d_1\in D_t} h^{d_1}_{p_{r_us_u}} \delta^{d_1t}_{p_{r_us_uk_a}}\bigg] + \mu^d_{1,r_us_u} \bigg[q^{d_1}_{r_us_u} - \sum_{ P_{r_us_u}} h^{d_1}_{p_{r_us_u}}\bigg]

	\end{equation}

	\vspace*{0.0in}
\end{figure*}

The Hessian matrix of (\ref{equ: DUE1}) is positive definite, which indicate the convexity. The optimal travel flow $\theta^{*t}_{k_a}$ can be computed with the KKT conditions~\cite{boyd2004conv456ex, sheffy1985ur123ban}. Therefore, the solution of the UTS in time interval $t$ can be designed as in \textbf{Algorithm 1}.
\begin{algorithm}\label{algorithm1}
	\caption{DUE solution}
	\label{alg11}
	\begin{algorithmic}
		\STATE $\mathbf{Step\ 1}$: Initialization. Set the iteration number $n_{1} = 1$, the threshold $\varepsilon_{1}$. Update the new demand as $q^{dt}_{r_us_u}$ = $q^{dt-1}_{r_us_u}$. Then using \textit{all-or-nothing} assignment~\cite{sheffy1985ur123ban,chekuri200412all} to generate $\theta^t_{k_a}$.
		\STATE
		\STATE $\mathbf{Step\ 2}$: Update the travel time with $\theta^t_{k_a}$: $f^{t, n_{1}}_{k_a}$ = $f^{t}_{k_a}(\theta^{t, n_{1}}_{k_a})$, which is the beginning of the loop (Step 2 to Step 6).
		\STATE
		\STATE $\mathbf{Step\ 3}$: Determine the descend direction. Find the shortest path with $f^{t, n_{1}}_{k_a}$ using \textit{all-or-nothing} assignment to generate $\phi^{t,n_{1}}$, which is the shortest route pattern.
		\STATE
		\STATE $\mathbf{Step\ 4}$: Determine the iteration step $\zeta^{n_1}$, which can be designed as:
		\begin{subequations}
			\begin{align}
			& min_{\zeta^{n_1}}\sum_{k_a}\int\limits_{0}^{\eta}f^{t}_{k_a}(\omega)d\omega \label{equ:step41} \\
			s.t. \ \ & \eta = \theta^{t, n_{1}}_{k_a} + \zeta^{n_1} (\phi^{t,n_{1}} - \theta^{t, n_{1}}_{k_a})  \label{equ:step42} \\
			& 0\leq \zeta^{n_1} \leq 1 \label{equ:step43}
			\end{align}
		\end{subequations}
	    The Golden-section search is employed to determine $\zeta^{n_1}$ in a short time.
		\STATE
		\STATE $\mathbf{Step\ 5}$: Update $\theta^{t, n_{1}+1}_{k_a}$ with
		\begin{equation}
		\theta^{t, n_{1}+1}_{k_a} = \theta^{t, n_{1}}_{k_a} + \zeta^{n_1} (\phi^{t,n_{1}} - \theta^{t, n_{1}}_{k_a})
		\end{equation}
		\STATE
		\STATE $\mathbf{Step\ 6}$: Compare the result to the threshold $\varepsilon_{1}$ as:
		\begin{equation}
		\frac{\sqrt{\sum_{k_a}(\theta^{t, n_{1}+1}_{k_a} - \theta^{t, n_{1}}_{k_a})^2}}{\sum_{k_a}\theta^{t, n_{1}}_{k_a}} \leq \varepsilon_{1} \label{equ:DUE_thres}
		\end{equation}
		where if (\ref{equ:DUE_thres}) is fulfilled, the result of traffic flow in time interval $t$ is $\theta^{t, n_{1}+1}_{k_a}$; otherwise, go back to step 2, with $n_{1} = n_{1} + 1$.
	\end{algorithmic}
\end{algorithm}

\section{Optimal Power Flow of Power Distribution System (Lower Level)}\label{sec:PDS}
\subsection{Branch Flow Model}\label{sec:BFM}

The PDS can be represented in a radial graph as: $\mathcal{G}^{PDS} $= $[\mathcal{V}^{PDS}, \mathcal{E}^{PDS}]$, where $\mathcal{V}^{PDS}$ = $\{1,2,\cdots, n_{\mathcal{V}^{PDS}}\}$, and $\mathcal{E}^{PDS}$ = $\{1,2,\cdots, m_{\mathcal{E}^{PDS}}\}$. Similar as UTS, the PDS also contains the temporal characteristics, the superscript $(\cdot)^t$, $t\in D_t$. Considering the three-phase unbalanced system, the phase configuration is defined as $\phi $ $\subseteq$ $\{a,b,c\}$. For a radial distribution system, there is an unique $i^1$ as the ancestor bus for bus $i$, and the branch between bus $i^1$ and $i$ can be named as $i$~\cite{pe015dis456ed,low201454convex}. The branch flow model in time interval $t$ can be defined as:

\begin{subequations}\label{equ:constraint_PDS_BFM}
	\begin{align}
	s^{\phi t}_i &= \text{diag}(S^{\phi t}_i - \sum_{j \in C_i}(S^{\phi t}_j-z^{\phi t}_jl^{\phi t}_j)),  \label{equ:eps1} \\
	v^{\phi t}_{i^1} &= v^{\phi t}_i-2(r^{\phi }_{i}P^{\phi t}_{i} + x^{\phi }_{i}Q^{\phi t}_{i}) + |z^{\phi }_{i}|^2l^{\phi t}_{i}, \label{equ:eps2}
	\end{align}
\end{subequations}
where $s^{\phi t}_i$ = $p^{\phi t}_{i}$ + $\textbf{i}q^{\phi t}_{i}$ indicates the power injection on bus $i$, $S^{\phi t}_i$ = $V^{\phi t}_i (I^{\phi t}_i)^H$ is the complex power from bus $i$ to bus $i^1$, $v^{\phi t}_{i}$ = $V^{\phi t}_{i} (V^{\phi t}_{i})^H$, $l^{\phi t}_{i}$ = $I^{\phi t}_{i} (I^{\phi t}_{i})^H$, $z^{\phi }_{i}$ =  $r^{\phi }_{i}$ + $\textbf{i} x^{\phi }_{i}$, $C_i$ is the set of buses that have a common ancestor bus $i$, and $H$ indicates the Hermitian transpose.

In addition, according to~\cite{gan2014con123vex}, the matrix $\mathbf{M}^t$ is defined as

\begin{equation}
\mathbf{M}^t =
\begin{bmatrix}
v^{\phi t}_{i} & S^{\phi t}_i \\[0.3em] \label{equ:constraint_matrx}
(S^{\phi t}_i)^H & l^{\phi t}_{i}
\end{bmatrix}
\end{equation}
where in (\ref{equ:constraint_matrx}), $\mathbf{M}^t\succeq0$ ( $\mathbf{M}^t$ is positive semidefinite) and $\mathbf{Rank}({\mathbf{M}^t})$ = 1.

\subsection{Objective Function and SDP Relaxation of OPF in PDS}\label{sec:OPF}
The objective function contains two major parts: the generation cost and system line loss, which can be designed as follows:
\begin{equation}
F^t_{PDS} = \sum_{i\in\mathcal{V^{PDS}}}\sum_{\phi_i}[\alpha_{1} (p^{\phi t}_{i})^2 + \beta_{1}(p^{\phi t}_{i})] \label{equ:constraint_PDS_obj}
\end{equation}
where $\alpha_{1}$ and $\beta_{1}$ are two weight coefficients for the generation cost and system line loss, respectively.

The constraints of the voltage $v^{\phi t}_{i}$ and current $l^{\phi t}_{i}$ are illustrated as follows:

\begin{subequations}\label{equ:constraint_PDS_v_l}
	\begin{align}
	\underline{v^{\phi }_{i}} \leq v^{\phi t}_{i} \leq \overline{v^{\phi }_{i}},  \label{equ:constraint_PDS_v} \\
	\underline{l^{\phi }_{i}} \leq l^{\phi t}_{i} \leq \overline{l^{\phi }_{i}}, \label{equ:constraint_PDS_l}
	\end{align}
\end{subequations}

In this paper, the injection power $s^{\phi t}_{i}$ plays an important role in active control and reactive control for the OPF.
\begin{equation}
\underline{s^{\phi }_{i}} \leq s^{\phi t}_{i} \leq P^{*t}_{i}, \label{equ:constain_PDS_s}
\end{equation}
where $P^{*t}_{i}$ is the optimal power injection in (\ref{equ:powerup}). Considering the relationship with the UTS, the optimal injection power $s^{*t}_{i_1}$ decides the number of EVs in CDS $i_1$ (Here for CDS, $i_1 = i$ for the PDS and UTS, which can be explained as Fig.~\ref{fig:testbench} with yellow circles).

According to the SDP relaxation introduced in~\cite{gan2014con123vex,pe015dis456ed}, the constraint $\mathbf{Rank}({\mathbf{M}^t})$ = 1 of (\ref{equ:constraint_matrx}) can be removed, and the relaxed objective problem is shown as follows:

\begin{subequations}\label{equ:relax_obj_PDS}
	\begin{align}
	&\min_{s^{\phi t}_{i}, v^{\phi t}_{i}, l^{\phi t}_{i}, S^{\phi t}_{i}} F^t_{PDS} \\
	s.t.\  &(\ref{equ:constraint_PDS_BFM}), (\ref{equ:constraint_PDS_v_l}), and\  \mathbf{M^t}\succeq0
	\end{align}
\end{subequations}
where (\ref{equ:relax_obj_PDS}) is a convex problem and can be solved with ADMM.

\subsection{OPF Solution with ADMM}
In the power-traffic system, the OPF computation of the three-phase unbalanced PDS is the bottleneck of the whole system operation. Firstly, because the real PDS usually contains a lot of feeders, which brings high computation loads for the system. Secondly, the PDS is a highly dynamic system, which requires a short computation time. Therefore, the ADMM, a distributed algorithm is proposed to solve this problem in a short time, which is based on Lagrange multiplier with an additional penalty term (augmentation term).

The standard form of the ADMM can be formulated as follows:

\begin{subequations}\label{equ:relax_org_ADMM}
	\begin{align}
	&\min_{x,y} f_{A}(x) + g_{A}(y) \\
	s.t. &\  x \in \mathcal{K}_x, y \in \mathcal{K}_y, and \ x = y
	\end{align}
\end{subequations}
where $f_{A}$ and $ g_{A}$ are convex, and $\mathcal{K}_x$ and $\mathcal{K}_y$ are convex sets. With an additional penalty term $\rho/2 ||x-y||^2_2$, the efficient augmented Lagrangian function is shown as follows~\cite{boyd2011d45istributed}:

\begin{equation}\label{equ:relax_aug_ADMM}
L_{\rho}(x,y,\lambda) = f_{A}(x) + g_{A}(y) + <\lambda,x-y> + \frac{\rho}{2} ||x-y||^2_2,
\end{equation}
where $0\leq \rho$ is a coefficient for convergence, and $\frac{\rho}{2} ||x-y||^2_2$ is the quadratic penalty term to increase the converge speed.

Therefore, the relaxed objective function (\ref{equ:relax_obj_PDS}) can be formulated as

\begin{subequations}\label{equ:ADMM_advance}

	\begin{align}
	& \min_{S^t_i, s^t_i, v^t_i, l^t_i} F^t_{PDS} = \min_{S^t_i, s^t_i, v^t_i, l^t_i} \sum_{i \in \mathcal{V^{PDS}}} F^t_{PDS,i} \label{equ: ADMM_ad1}\\
	& s.t. \ (\ref{equ:constraint_PDS_BFM}), (\ref{equ:constraint_PDS_v_l}), and\  \mathbf{M}\succeq0 \label{equ: ADMM_ad2}\\
	& S^{\phi t,(x)}_{i,i^1} = S^{\phi t,(y)}_{i}, l^{\phi t,(x)}_{i,i^1} = l^{\phi t,(y)}_{i}, v^{\phi t,(x)}_{i,i^1} = v^{\phi t,(y)}_{i} \label{equ: ADMM_ad3}\\
	& S^{\phi t,(x)}_{i} = S^{\phi t,(y)}_{i},  l^{\phi t,(x)}_{i} = l^{\phi t,(y)}_{i},  v^{\phi t,(x)}_{i} = v^{\phi t,(y)}_{i} \label{equ: ADMM_ad4}\\
	& s^{\phi t,(x)}_{i} = s^{\phi t,(y)}_{i} \label{equ: ADMM_ad5}
	\end{align}
\end{subequations}

where the variables $(S^t_i, s^t_i, v^t_i, l^t_i)$ is maintained in bus $i$, which can be seemed as an local agent. In the ADMM, the $x$-update and $y$-update indicate as $( \cdot )^{(x)}$ and $( \cdot )^{(y)}$.

Then, as in~\cite{pe015dis456ed,boyd2011d45istributed} the augmented Lagrangian of (\ref{equ:ADMM_advance}) can be derived as (\ref{eq:big_lagrangian}).

\begin{figure*}[!t]

	\begin{subequations}\label{eq:big_lagrangian}
		\begin{align}
		L^t_{\rho}(x^t,y^t,\lambda^t)
		& = \sum_{i \in \mathcal{V^{PDS}}}F^t_{PDS,i} + <\lambda^t, x^t_i-y^t_i> + <\mu^t_i, x^t_{i,i^1} - y^t_i> + \sum_{j \in C_i}<\gamma^t_j, v^{(x)t}_{i,j} - v^{(y)t}_i> + \frac{\rho}{2} f^t_{PDS,i,y}\\
		& = \sum_{i \in \mathcal{V^{PDS}}}F^t_{PDS,i} + <\lambda^t, x^t_i-y^t_i> + \sum_{j \in C_i} <\mu^t_j, x^t_{j,i} - y^t_j> + <\gamma^t_i, v^{(x)t}_{i^1,i} - v^{(y)t}_{i^1}> + \frac{\rho}{2} f^t_{PDS,i,x}
		\end{align}
	\end{subequations}

	\vspace*{0.0in}
\end{figure*}
The detail formulation of $f^t_{PDS,i,y}$ and $f^t_{PDS,i,x}$ can be found in~\cite{pe015dis456ed}, and at iteration $k$, the variables $x$, $y$, and $\lambda$ are illustrated as follows:

\begin{subequations}\label{equ:ADMM_updates}
	\begin{align}
	& x^{t,k+1} \in \arg\min_{x \in \mathcal{K}_x} L_{\rho}(x^t,y^{t,k},\lambda^{t,k}) \label{equ:ADMM_updates_x} \\
	& y^{t,k+1} \in \arg\min_{y \in \mathcal{K}_y} L_{\rho}(x^{t, k+1},y^t,\lambda^{t,k}) \label{equ:ADMM_updates_y} \\
	& \lambda^{t,k+1} = \arg\min_{x \in \mathcal{K}_x} \lambda^{t,k} +\rho(x^{t, k+1} - y^{t, k+1}) \label{equ:ADMM_updates_ru}
	\end{align}
\end{subequations}
Considering the time intervals of the UTS, the ADMM-based distributed OPF computation architecture is built to dramatically reduce the time consumption to the proposed power-traffic coordinated operation approach.

Therefore, in addition to the designed user equilibrium in the higher level, the detail models of UTS and PDS are build to provide the spatial and temporal information in the lower level. In the next section, a smart charging/discharging strategy is proposed for the EVs and bidirectional CDSs to further reduce the impact to PDS.

\section{Smart EV Charging/discharging (Lower Level)}\label{Sec:EVs}

In this paper, the EVs and CDSs act as the reserves for both PDS and UTS. In this section, we take CDS $i_1$ as an example, other CDSs contain the same performances. In time interval $t$, $n^t_{i_1}$ EVs are parked in CDS $i_1$ with parking time $\tau_2$, which is defined in (\ref{equ:ISO}). In real world, $n^t_{i_1}$ depends on many factors, for example, different habits of the drivers, which are beyond the scope of this paper. Here, $n^t_{i_1}$ is determined as follows:
\begin{equation}\label{equ:park_EV}
\begin{split}
& n^t_{i_1} =  \\
&\left\{ \begin{array}{ll}
\varrho_3 *\min\{s^{*t}_{i_1}/C^t_{1}, \theta^t_{k_a}\} & \mbox{If $\pi^{*t} \geq \varrho_1 + \varrho_2$} \\
\varrho_4 * \min\{s^{*t}_{i_1}/C^t_{1}, \theta^t_{k_a}\}  & \mbox{If $ \varrho_1 \leq \pi^{*t} \leq \varrho_1 + \varrho_2$}  \\
0    & \mbox{If $\varrho_1 \geq \pi^{*t}$}
\end{array}
\right.
\end{split}
\end{equation}
where $ s^{*t}_{i_1}$ is the optimal power injection, which is determined in the OPF solution (\ref{equ:ADMM_updates}). $C^t_{1}$ is the average discharging/charging speed of EVs. $\varrho_1$ is the electrical power price, $\varrho_2$ is the congestion fee, $\varrho_3$ is the ratio for EV parking to the CDSs. $n^t_{i_1} < \chi_{i_1}$, $\chi_{i_1}$ is the capacity of CDS $i_1$.

During the parking time $\tau_2$, the stochastic EVs charging/discharging can impact the stability of the PDS. Considering the SOC, a smart EVs charging/discharging approach is proposed to reduce the impact to PDS as follows:

\begin{subequations}\label{equ:smart_EV}

	\begin{align}
	F^t_{CDS} = & \min_{C^{t_1}_{ev, i_3}} \sum_{t_1\in \tau_2} \bigg[P^{t_1+1}_{CDS} (C^{t_1}_{ev, i_3}) - P^{t_1}_{CDS} (C^{t_1}_{ev, i_3}) \bigg]^2  \label{equ: CDS1}\\
	s.t.\ \  & \underline{C_{ev}} \leq C^{t_1}_{ev, i_3} \leq \overline{C_{ev}} \label{equ: CDS2}\\
	& P^{t_1}_{CDS} (C^{t_1}_{ev, i_3}) = Q^{t_1}_{PDS} - \sum^{n^t_{i_1}}_{i_3} C^{t_1}_{ev, i_3} \label{equ: CDS3}\\
	& Q^{\tau_2}_{ev, i_3} = \sum_{t_1\in \tau_2}C^{t_1}_{ev, i_3}, \ 0 \leq i_3 \leq n^t_{i_1}  \label{equ: CDS4}\\
	&  \underline{\Upsilon} \leq SOC \leq  \overline{\Upsilon}, \  \label{equ: CDS5}
	\end{align}
\end{subequations}
where $C^{t_1}_{ev, i_3}$ is the discharging (positive) and charging (negative) speed of EV $i_3$ at time $t_1$. In (\ref{equ: CDS3}), $P^{t_1}_{CDS} (C^{t_1}_{ev, i_3})$ is the netload at CDS $i_1$, which equals to original load of PDS $Q^{t_1}_{PDS}$ minus the total charging/discharging power. In (\ref{equ: CDS4}), $Q^{\tau_2}_{ev, i_3}$ is the total demand of charging/discharging of EV $i_3$, which contains a SOC constraint as (\ref{equ: CDS5}),  $\Upsilon$ is the ratio of SOC.

\section{Numerical Simulation and Results}
\label{sec:Implementation}
\begin{figure*}[t!]
	\begin{center}
		\includegraphics [width=1.00\columnwidth, angle=90]{power_traffic_fig1.eps}
		\caption{The test bench consists of a PDS and a UTS.}\label{fig:testbench}
	\end{center}
\end{figure*}
As shown in Fig.~\ref{fig:testbench}, the test bench is based on the IEEE 8,500-bus PDS and the Sioux Falls UTS~\cite{IEEE8500PDS,lam2008model123ing}, which cover the similar geographical areas. It is assumed that a small city with 10,000 EVs has the bi-peak and bi-ramp problem, the "duck curve" data is based on~\cite{Duck2017c} and the traffic behavior data is based on~\cite{gonzal65es2013evening}. 11 CDSs are located in node 2, 3, 5, 8, 10, 11, 17, 18, 20, 22, 23, which are shown with yellow circles and illustrated in both PDS and UTS, respectively. The time interval $t$ is 15 minutes for the power-traffic systems. The PDS cost function $f^t_{PDS}$ is defined as quadratic function as in~\cite{park2005particle}, and the UTS cost function $f^t_{UTS}$ is defined similarly. Both $f^t_{PDS}$ and $f^t_{UTS}$ are convex, and the quadratic form indicates the utility cost increase fast with the increasing of PDS load and UTS load. As discussed above, the utility objective function $F^t_{UTY}$ is convex.  The weight factor $\gamma_1$ equals to 1, which indicates the same importance of PDS and UTS. Similarly, the customer objective function $F^t_{CSO}$ is also set as a convex function. The electrical power price $\varrho_1$ = $45 \ \$/MWh $, and the congestion fee $\varrho_2$ = $2 \ \$/h$ \cite{EIA2017132US,de2011traf78fic}. The EVs parking ratio $\varrho_3$ = $0.8$, $\varrho_4$ = $0.3$. The parking capacity for each CDS is $\chi_1$ = 100. The simulations are executed using a server with 3.60 GHz Intel Xeon CPU and 32 GB RAM, and the software are Python, MATLAB, MATLAB global optimization toolbox, and parallel computing toolbox. The communication network is good enough to transmit all the information in real-time without any cybersecurity issues.

\subsection{Flowchart of the Simulation}
The simulation flowchart is shown in Fig.~\ref{fig:simuflow1}, and the corresponding description is shown in \textbf{Algorithm 2}. In Step 2, a distributed algorithm is used to reduce the time consumption of the equilibrium computation. In Step 3, for each time interval $t$, the DUE problem is transferred as a SUE problem. The the Golden-section search is employed to determine the iteration step $\zeta^{n_1}$ in a short time for the UTS with about 10,000 EVs and 11 CDSs. In Step 4, because the test bench is based on the IEEE 8,500-bus PDS, the ADMM is implemented to computed the OPF in distributed manner with the constraints from Step 3. In Step 5, for each CDS, the charging/discharging are independent, the smart EVs charging/discharging can be computed in parallel, which also helps to reduce the computation time. The computation time analysis is shown in Fig~\ref{fig:Duck_curve2}. The two curves indicate the average computation times of the proposed approach are less than 50 s with 100 scenarios. Compared with the time interval 15 minutes, the proposed approach is quick enough to support continuously operations for the power-traffic systems.
\begin{figure}[h!]
	\begin{center}
		\includegraphics [width=0.80\columnwidth]{flowchart_num1.eps}
		\caption{The test bench consists of a PDS and a UTS.}\label{fig:simuflow1}
	\end{center}
\end{figure}
\begin{algorithm}\label{algorithm2}
	\caption{Simulation Process}
	\label{alg2}
	\begin{algorithmic}
		\STATE $\mathbf{Step\ 1}$: Initialization and data collection. Collect the data and parameters of the PDS, UTS, CDSs, and EVs, such as the topology information, electrical power price $\varrho_1$, congestion fee $\varrho_2$, set the simulation time interval $t$ = 15 min, etc.
		\STATE
		\STATE $\mathbf{Step\ 2}$: Higher level: equilibrium computation for the defined utility part (PDS \& UTS) and customer part (CDSs \& EVs). Build the utility and customer objective function as (\ref{equ:ISO}) and (\ref{equ:obj_cus}). Then, compute the equilibrium $\pi^{*t}$ and $P^{*t}_{i_1}$ with (\ref{equ:opti}) and (\ref{equ:equ_iter}).
		\STATE
		\STATE $\mathbf{Step\ 3}$: Lower level: DUE in UTS. Receive the information such as $\pi^{*t}$, $N^t_{i_1}$, $q^{d_1t}_{r_us_u}$ and $P^{*t}_{i_1}$, build the DUE objective function with constraints as in (\ref{equ:due_equa}), solve it with \textbf{Algorithm 1}, and generate the traffic information such as $\theta^t_{k_a}$ and $b^{d_1}_{r_us_u}$.
		\STATE
		\STATE $\mathbf{Step\ 4}$: Lower level: OPF in PDS. Receive the information such as $\pi^{*t}$, $\theta^t_{k_a}$, and $P^{*t}_{i_1}$, build the OPF objective function as in (\ref{equ:constraint_PDS_obj}) with the constraints (\ref{equ:constraint_PDS_v_l}) and (\ref{equ:constain_PDS_s}), relax it as (\ref{equ:relax_obj_PDS}), solve it as (\ref{eq:big_lagrangian}) and (\ref{equ:ADMM_updates}). Then, generate the $s^{*t}_{i_1}$ to CDS.
		\STATE
		\STATE $\mathbf{Step\ 5}$: Lower level: smart EVs charging/discharging. Receive the information such as $s^{*t}_{i_1}$, $\pi^{*t}$, and $N^t_{i_1}$, compute the parking EVs for each CDS as (\ref{equ:park_EV}), build the smart charging/discharging as (\ref{equ:smart_EV}), then generate the feedback information $n^t_{i_1}$ for UTS and $\sum^{n^t_{i_1}}_{i_3} C^{t_1}_{ev, i_3}$ for PDS.
		\STATE
		\STATE $\mathbf{Step\ 6}$: The power-traffic system update with the new UTS and PDS information, then back to Step 2 with $t$ = $t+1$.
	\end{algorithmic}
\end{algorithm}
\begin{figure}[t!]
	\begin{center}
		\includegraphics [width=0.8\columnwidth]{Computation_time.eps}
		\caption{ Computation time comparison with different traffic and load factor.}\label{fig:Duck_curve2}
	\end{center}
\end{figure}

\subsection{Bi-Peak Shaving and Bi-Ramp Smoothing}
As shown in Fig.~\ref{fig:Duck_curve3}, the blue curve is the netload peak of PDS from 17:00 to 22:00, and the orange curve is the result of proposed approach. The peak load decreases from 78 MW to 72 MW with 15\% EVs discharging. The yellow curve is the traffic delay peak in UTS, and the purple curve is the reduced traffic delay with the proposed approach. The total peak traffic delay time of UTS decreases from about 4,100 hours to 2,900 hours. In Fig.~\ref{fig:Duck_curve4}, it is also clear that from 8:00 to 15:00, the netload of PDS increases from 48 MW to 51 MW, and the total traffic delay of UTS decreases from 3,500 hours to 2,800 hours. From Fig.~\ref{fig:Duck_curve5}, it is clear that the proposed approach can benefit both PDS and UTS to reduce the bi-peak and smooth the bi-ramp.

\begin{figure}[!t]
	\begin{center}
		\subfigure[]{ \label{fig:Duck_curve3}
			\resizebox{2.8in}{!}{\includegraphics{Evening_curve.eps}}}
		%
		\subfigure[]{ \label{fig:Duck_curve4}
			\resizebox{2.8in}{!}{\includegraphics{Day_curve.eps}}}
		%
		\caption{(a) The bi-peak shaving and bi-ramp smoothing for PDS and UTS from 17:00 to 22:00, (b) The peak-shaving and ramp-smoothing for UTS and over-generation compensation for PDS from 8:00 to 15:00.}\label{fig:Duck_curve5}
	\end{center}
\end{figure}
\begin{figure}[!t]
	\begin{center}
		\subfigure[]{ \label{fig:Duck_curve9}
			\resizebox{2.8in}{!}{\includegraphics{Traffic_congestion1.eps}}}
		%
		\subfigure[]{ \label{fig:Duck_curve10}
			\resizebox{2.8in}{!}{\includegraphics{Traffic_congestion2.eps}}}
		%
		\caption{(a) The traffic congestion scenario in 18:30, (b) The traffic congestion scenario with proposed approach in 18:30.}\label{fig:Duck_curve8}
	\end{center}
\end{figure}

In Fig.~\ref{fig:Duck_curve8}, the detailed traffic congestion information of a congestion scenario is shown with the traffic maps. The OD pair of this scenario is shown in Table~\ref{Table: OD pair}. The red and pink arrows indicate the traffic congestions in the roads. The traffic congestion information without the proposed approach is shown in Fig.~\ref{fig:Duck_curve9} in 18:30. The red arrows indicate that the traffic delays on these roads are larger than 15 minutes. As shown in Fig.~\ref{fig:Duck_curve10}, with the proposed approach, the pink arrows indicate that the traffic delays on these roads are less than 15 minutes. The number of pink arrows (6 pink arrows) is much less than the number of red arrows (16 red arrows), which also indicates the alleviation of traffic congestion with the proposed approach. The total congestion delay reduces from 4100 hours to 2900 hours.

In summary, from Fig.~\ref{fig:Duck_curve5} and Fig.~\ref{fig:Duck_curve8}, the bi-peak and bi-ramp problems can be reduced and smoothed by the proposed approach, which benefits both PDS and UTS.
\begin{center}
	\begin{table}[h]

		\caption{The OD pair of UTS}\label{Table: OD pair}\vspace{-0.1in}
		\begin{center}
			\begin{tabular}{ | c | c | c | c | c | c | c |}

				\hline
				Nodes & 13  & 14  &  18 &  20 &  21 & 23  \\ \hline
				1 	  & 200 & 350 & 240 & 300 & 256 & 345 \\ \hline
				2 	  & 270 & 210 & 360 & 200 & 200 & 310 \\ \hline
				3 	  & 200 & 300 & 220 & 345 & 270 & 345 \\ \hline
				5 	  & 250 & 320 & 260 & 450 & 230 & 345 \\ \hline
				6 	  & 300 & 210 & 270 & 250 & 300 & 300 \\ \hline
				10 	  & 200 & 200 & 345 & 345 & 200 & 300 \\ \hline
			\end{tabular}
		\end{center}
	\end{table}
\end{center}

\subsection{Social Cost Analysis}
In Fig.~\ref{fig:Duck_curve1}, the blue curve with circle and green curve with square are in one group, which indicates the social costs with different number of EVs on the road. The load of PDS is 1.0 P.U., which means the total load of PDS is 60 MW. From Fig.~\ref{fig:Duck_curve1}, the cost of the proposed approach is lower, and it is clear that the proposed approach can benefit more for the social cost with the increasing number of EVs. In Fig.~\ref{fig:Duck_curve1}, the red curve and cyan curve indicate the social cost with different power factors. The EV number is 8000 in the UTS. From Fig.~\ref{fig:Duck_curve1}, it is also clear that the proposed approach can benefit more for the social cost with the increasing power factor. In summary, the prosed approach can benefit both PDS and UTS with different traffic scenarios and load factors. In addition, the social cost increases more with the increasing of PDS load or EV number, which indicates the quadratic objective function of PDS and UTS. This design benefits the power-traffic system to reduce the total social cost.
\begin{figure}[t!]
	\begin{center}
		\includegraphics [width=0.80\columnwidth]{Social_cost.eps}
		\caption{The different social costs comparison with different traffic and load factors.}\label{fig:Duck_curve1}
	\end{center}
\end{figure}

\subsection{Smart EVs Charging/Discharging}
\begin{figure}[!t]
	\begin{center}
		\subfigure[]{ \label{fig:smart_EVs1}
			\resizebox{2.8in}{!}{\includegraphics{Flat_discharging.eps}}}
		%
		\subfigure[]{ \label{fig:smart_EVs2}
			\resizebox{2.8in}{!}{\includegraphics{Smart_discharging.eps}}}
		%
		\caption{(a) The normal EV discharging in CDS 3 without considering the impacts to the PDS, (b) smart EV discharging in CDS 3 considering the impacts to the PDS.}\label{fig:smart_EVs}
	\end{center}
\end{figure}
To test the robustness of the proposed approach, we select two continuous intervals load data, which contains the biggest deviations within the 30 minutes. In Fig.~\ref{fig:smart_EVs}, EV discharging is taken as an example and 50 EVs are employed to test the proposed approach. As shown in Fig.~\ref{fig:smart_EVs1}, without the proposed approach, the stochastic EV discharging behaviors increase the deviations of the netload. In Fig.~\ref{fig:smart_EVs2}, with the proposed smart charging/discharging approach, the deviations of the netload are smoothed, which decreases the impacts to the PDS during the EV discharging. At the same time, the discharging speeds are controlled to meet the constraint (\ref{equ: CDS2}).

\section{Conclusion}\label{Sec:Concl}
In this paper, a hierarchical approach is proposed to shave the bi-peak and smooth the bi-ramp problem in the power-traffic system from higher level to lower level. The higher level gives an ``overview" for the whole system, and the lower level gives the detailed description for each part. The EVs and CDSs function as reserves for both the PDS and UTS to utilize the flexibility and optimize the operations of the power-traffic system.  In the higher level, the PDS and UTS are treated together to minimize the social cost, and the EVs and CDSs are treated as customers to minimize their expenditure. Then, an equilibrium is designed to determine the optimal charging/discharging prices, total demand electrical power, and total required EVs. In the lower level, considering the spatial and time domain, the detail models of PDS and UTS are built to specifically determine the power injection and EVs behaviors. In each CDS, a smart EVs charging/discharging approach is proposed to further reduce the impacts to the PDS. In the numerical results, the test bench consists of the IEEE 8,500-bus PDS and Sioux Falls UTS with about 10,000 EVs and 11 CDSs, which are used to demonstrate the feasibility and effectiveness of the proposed approach.

In real-world implementation, the weather, human behavior, and social issues bring the stochastic impacts to the power-traffic system, which increase the uncertainties and bring more challenges for system operation. In addition, the other systems such as the natural gas delivery system and the water system can also impact the proposed system and result in nonnegligible consequences. In the next step, other factors such as stochastic of renewable energies, the departure time of EVs, cybersecurity, multi-energy system, and human behaviors will be taken into consideration.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


The Cannabis College is located in the Red Light District of Amsterdam on the main canal of the Oudezijds Achterburgwal. They offers free advise on the safe use of cannabis, both for recreational and medicinal use.
They also offer the abbility to test cannabis, so you get to know exactly what is in the cannabis you bought. You can even have your space cake, brownies or muffins tested here, so you know what you're taking in.
So if you're really interested in getting to know more about cannabis, or you're planning to use cannabis but want to know if it's safe to use, this is definetely the place to go to.


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Tuesday Must Reads: Unions Say BART Is Overstating Costs of Family Leave; Growth of Oakland Charter Schools Damages School District
Tue, Nov 19, 2013 at 9:59 AM
Education / Health & Medicine / Transportation
Stories you shouldn't miss:
1. BART unions say the transit agency is overestimating the costs of a disputed family medical leave benefit by perhaps tens of millions of dollars, the Chron reports. The BART board directors has effectively rejected a tentative deal its management team reached with the unions because of the six-week paid family leave benefit — a move that could prompt another strike. BART officials estimate that the benefit, which they contend they agreed to by mistake, will cost the agency up to $44.2 million over the life of the four-year contract. But the unions say that the estimate is deeply flawed, and say they have no intention of renegotiating the deal.
Oaklandwiki.org
2. The growth of charter schools in Oakland is damaging the public school district because charters are siphoning off students who do not have special needs and thus are cheaper to educate, the Chron reports. As a result, the beleaguered school district is left with a disproportionately high number of special needs children who are bankrupting the system. Oakland has the most charter schools per capita in the state by far — about forty, serving about 13,000 students. Charters divert about $75 million away from the school district each year.
3. California officials may decide to ignore President Obama's request that consumers be able to keep health insurance plans that were slated to be cancelled by the end of this year, the LA Times$ reports. Officials for Covered California — the state's version of Obamacare — are concerned that too many young healthy people will choose to keep their inferior, cheaper plans and thus will cause insurance rates to skyrocket for everyone else because of the costs of treating people with serious chronic illnesses.
4. Oakland City Auditor Courtney Ruby says the fire department has done a poor job protecting the city from another major wildfire like the deadly 1991 firestorm, the Chron and Trib report. Ruby's audit found the fire department has routinely failed to inspect private property, clear away fire fuels, and require homeowners to pay for cleanups as required by law. The audit comes less than a week after hills residents rejected a proposed renewal of the city's wildfire tax.
5. A federal judge cleared the way for the City of Albany to evict a longtime homeless encampment at the Albany Bulb to make way for a new park, the Chron reports. Attorneys for the homeless had filed suit to block the planned eviction.
6. And Bay Area News Group, which includes the Trib, the CoCo Times, and the Mercury News, announced that it is instituting a website paywall that will require regular users to pay for online content. The announcement means that more than 40 percent of the nation's daily newspapers now have paywalls.
« Alternative to Governor's Gian… | Bay Area Air Quality Deteriora… »

redpajama_set_name: RedPajamaCommonCrawl


For startups to thrive everywhere, they need capital and revenue that reaches them, wherever they are. GAN Accelerators do that every day. And, we're doing it with GAN Ventures. But we knew that—if we were going to help all GAN Members have at least 10 startups that are making $10M or more in revenue by 2020 (our BHAG)—we needed to find a way to create more substantial access to capital and customers for startups everywhere. And that's where Corporate Connect comes in.


Modaka and Avi kadubu (kolukattai) are the traditional sweet which we offer to lord Ganesha. We are celebrating Ganesha festival this week. So I thought of sharing this recipe with you all.
First to make a coconut jaggery filling, place a pan on the stove and add jaggery.
Add 2 tbs of water to dissolve all the jaggery.
Once the jaggery has melted, add coconut and mix it well.
Stir often until the water evaporates (~5 minutes). Make sure it does not burn.
Once it is ready, add cardamom powder and stir it well.
Once they start popping, switch off the stove and let it cool.
Powder the jaggery and keep it aside.
Powder the sesame seeds and then mix the jaggery to this powder. Mix this well.
Now our sesame seeds filling is ready.
Boil 1 1/2 cups of water on medium high heat.
Add salt and oil, and reduce the heat. Add rice flour and mix it well so that there are no lumps.
Close the lid and leave it for few seconds. Then switch off the stove and let it cool.
Once it has cooled (make sure you can handle the dough for kneading), take a small amount of dough and knead it.
Use the kadabu mold to shape the dough into small, flat circles.
Take each of these round pieces of dough, place some of the mixture on the dough, and close the mould. If you don't have the mould you can press it together using your finger.
Place some of the coconut filling onto the center of the flattened dough.
Bring the sides together towards the top, and twist it shut. It looks somewhat like a garlic shape.
Repeat the process for all the pieces.
Take an idli stand and grease the plates with a little oil.
Place the filled dough (all three varieties) on the plates.
Add 2 cups of water to a pressure cooker.
Place the stand inside, close the lid and steam cook 10 to 12 minutes.
If you don't have a kadabu mold, make small balls from the dough and flatten them into a round shape.


Dds/rsa Randolph-sheppard Vending Facility Program (rsvfp) is business with Basic Business Licenses (BBL) issued by the Department of Consumer and Regulatory Affairs (DCRA), District of Columbia. The customer number is 930513000109. The address is 320 21st Street Nw, Washington, DC 20451. The zip code is 20451.	The license category is Delicatessen.


text:
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Ponzi Perspectives
Analysis, Impact, and Outlook on Ponzi Scheme Litigation
Home » Minnesota Court Untangles Who Owns What Claim in the Fallout of a Ponzi Scheme
Minnesota Court Untangles Who Owns What Claim in the Fallout of a Ponzi Scheme
By Alexander Madrid on July 12, 2021
Posted in Case Analysis
The collapse of a Ponzi scheme usually follows a familiar pattern. When the scheme is exposed, the company created by the schemer—which is usually little more than a sham entity—is placed into receivership or declares bankruptcy (or both). A receiver or bankruptcy trustee is then tasked with recovering any funds belonging to the estate so that they may be distributed to creditors. As part of this process, these court-appointed parties step into the shoes of the company and may bring any litigation that the company itself could have brought. Bankruptcy trustees are also granted the exclusive right to bring "general claims" on behalf of the entities' creditors.
This process creates a thorny question: who may seek recovery from a third party alleged to have been involved in the fraud? Creditors that lent funds to sham companies often pursue claims against financial institutions that banked the schemers on aiding-and-abetting theories. Yet receivers and trustees also often bring these claims, leading to duplicative litigation and the question of who properly "owns" the claim.
A recent decision by the U.S. District Court for the District of Minnesota provides important guidance on this question. Ritchie v. JPMorgan Chase & Co., No. 14-cv-04786, 2021 WL 2686079 (D. Minn. June 30, 2021) untangles who has standing to bring claims against a third party alleged to have aided and abetted a Ponzi scheme. As the Court explains, "general" claims for loss of funds belong exclusively to court-appointed bankruptcy trustees. Third parties may only bring particularized claims that arise from injuries "directly traceable" to the defendant's conduct. Ritchie thus serves as a touchstone in disputes over standing in Ponzi litigation.
I. The Petters Ponzi Scheme and Plaintiffs' Attempt to Recover From Third Parties.
Ritchie involved a Ponzi scheme run by Thomas Petters through various sham companies. Following the collapse of the scheme, Petters and his companies were placed into receivership and a number of the companies declared bankruptcy. Bankruptcy trustees and a receiver were appointed and pursued hundreds of claims in an attempt to recover creditors' funds. Among these claims were adversary proceedings against JPMorgan Chase; as part of the settlements for these proceedings, the court entered bar orders permanently enjoining Petters's creditors from asserting any claims that belonged to the trustees or receivers or were derivative of such claims.
The Ritchie plaintiffs, certain Cayman Island-based special purpose entities known collectively as "Ritchie," loaned hundreds of millions of dollars to Petters and one of his companies. Ritchie alleged JPMorgan Chase, which also lent funds to Petters's company and provided other banking services, knew of the scheme but consciously avoided informing Ritchie so the scheme would continue operating long enough for it to be reimbursed. Ritchie asserted claims for aiding-and-abetting fraud or conversion, fraudulent transfer, negligence, breach of fiduciary duty, and unjust enrichment against various JPMorgan entities as well as a financial consultancy named Richter that allegedly performed due diligence on PCI.
II. The Ritchie Court Holds Aiding-and-Abetting, Fraudulent Transfer, and Unjust Enrichment Claims Are "General Claims" Belonging to Trustees.
On June 30, the Court issued an opinion dismissing Plaintiffs' claims in full. The crux of the Court's decision concerns who has standing to bring claims arising from a creditor's losses against a third-party financial institution: the creditor itself or the court-appointed trustee. As the Court explained, "general claims—those with no particularized injury arising from them—that can be brought any creditor of the debtor, must be brought by the trustee, for the benefit of all creditors. In contrast, particularized claims are specific to a plaintiff, in that the injury is 'directly traced to' the non-debtor's conduct."
As to the aiding-and-abetting claims, plaintiffs argued these claims never belonged to PCI and were "direct and particularized" because no other creditor lent funds based on the specific misrepresentations at issue. Defendants, on the other hand, argued the claims were generalized and "derivative of the Petters bankruptcy estate."
The Court held the aiding-and-abetting claims "were the property of the bankruptcy estates." The Court noted plaintiffs "largely take issue with misrepresentations made by Petters to Ritchie and the allegations do not concern particularized conduct by JPMC or Richter toward Ritchie or any unique harmed suffered by Ritchie from interactions with JPMC or Richter." Indeed, "the alleged loss of Ritchie's funds was a result of the collapse of Petters's Ponzi scheme," not from any defendants' specific conduct, and thus "Ritchie's claims allege the same kind of harm—a loss of funds—that affected all creditors of the Petters entities." As a result, the Court held the aiding-and-abetting claims "are derivative claims, over which the Trustees had exclusive standing to pursue" and, in fact, had pursued previously against JPMorgan Chase.
The Court reached the same conclusion with respect to the fraudulent transfer and unjust enrichment claims. Ritchie sought to "reclaim" an alleged $6.5 million payment made from one of the Petters entities to JPMorgan Chase on the theory that Ritchie lent the funds to the Petters entity, which then transferred the money to JPMorgan Chase. The Court explained that "the allegations demonstrate that the money was presumptively the property" of the Petters company which, "in turn, is one of the debtors." Thus, the Court held "Ritchie cannot bring a claim that is based on the transfer of the debtor's property" because "the Trustees had exclusive standing" to bring such claims.
III. Even Where a Plaintiff Has Standing, Allegations Against Third Parties Face a High Hurdle to Survive Motions to Dismiss.
The Ritchie Court's decision is most notable for its careful parsing of who has standing to bring what claim when creditors and court-appointees bring parallel litigation. But the decision also provides a reminder of the pleading standards applicable to claims against third parties and the high hurdle plaintiffs face when attempting to hold third party financial institutions liable for providing banking services to individuals and entities later discovered to be fraudsters. Following its discussion of standing and a separate analysis of New York's statute of limitations, the Court detailed why—even if Ritchie had standing and brought timely claims—the complaint warranted dismissal for failure to state a claim.
The Court prefaced its discussion with a reminder of the heightened requirements for pleading fraud and deception. Additionally, aiding-and-abetting claims require allegations that give rise to a plausible inference of actual knowledge of the fraud, not merely awareness of "red flags" or constructive knowledge. The Court held there were "no specific allegations that JPMC had actual knowledge" of Petters's fraud, and as a result the aiding-and-abetting claims against it failed. As for Ritchie's negligence and breach of fiduciary duty claims, the Court held Ritchie failed to allege a special relationship that would give rise to a duty to warn of a suspected Ponzi scheme. The Court confirmed that typical bank-depositor and creditor-borrower relationships did not suffice to form a special relationship, which would create a duty to warn. The Court further dismissed Ritchie's fraudulent transfer claims because the complaint did "not sufficiently allege a lack of good faith or that JPMC was motivated by an intent to defraud creditors in furtherance of Petters' fraudulent scheme." Lastly, the Court dismissed the unjust enrichment claim as duplicative because it sought "redress for the same injury allegedly caused by JPMC's fraudulent transfer."
IV. Ritchie's Impact
Ritchie provides a useful guidepost in untangling who has standing to bring a claim following the collapse of a Ponzi scheme. Where the claim is "general" in nature, such as the loss of funds that all creditors suffer, it belongs exclusively to the court-appointed bankruptcy trustee. Only when a claim arises an injury "directly traceable" to the defendant's particular conduct can another party bring the claim. Counsel defending financial institutions and other third parties that are inevitably swept into the tide of litigation that follows a collapsed Ponzi scheme should pay careful attention to the types of claims being brought and consider whether the party bringing the claims has standing to do so.
Tags: Aiding and Abetting, Breach of Fiduciary Duty, financial institution, Fraud, Fraudulent Transfer, Negligence, Schemer
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Индустриальные субкультуры (от  — деятельность, усердие, — то же, что «промышленность») или  урбанистические субкультуры  (, от латинских слов urbanus — городской, urbs — город) — современные молодёжные субкультуры сложившиеся под влиянием дадаизма, в мировоззрении которых большое место занимает город  и городская индустриальная (промышленная) и техническая среда.

Основное

С развитием промышленности, и в особенности появления компьютерных технологий в части молодёжной культуры стала развиваться тенденция противопоставления человека и Урбаноса (города), что привело к мифологизации города и техники и к образованию индустриальных субкультур.

Некоторые из индустриальных субкультур имеют корни в группах поклонников индастриал, но большую часть субкультуры составляют поклонники компьютерных игр.

В патримониальном обществе индустриальная субкультура  имеет тесную связь с традиционными религиями, без которых она не сумела бы устояться в общественном сознании не европейских стран.

Характерными чертами мировоззрения этих субкультур является:
 Конфликт (борьба) города и человека
 Город, как среда (Каменные джунгли).
 Гиперболизация роли техники.
 Постапокалиптический настрой.
 Футуристические тенденции.

Пик этих субкультур пришёлся на конец 20-го начало 21 века. Индустриализация и передовые технологии, вместе с технологическим прогрессом, предоставляют в тоталитарном обществе благодатную почву для формирования субкультур индустриального характера, позволяя им дробиться и обретать неповторимые черты. Однако, чёткие следы индустриализации субкультур на данное время можно встретить далеко не у всех групп.

Урбанистическая субкультура может включать себя городское дно.

Список индустриальных субкультур
 Руферы
 Риветхед
 Кибер-готы
 Диггеры
 Хулиганы
 Сталкеры

См. также
 Индастриал
 Индустриальный туризм
 Прогулки по крышам

Примечания

Литература
 Лосинская А. Ю. Формы фольклора в ролевой субкультуре: ироническое и запредельное // Исторические, философские, политические и юридические науки, культурология и искусствоведение. Вопросы теории и практики. — Тамбов: Грамота, 2011. — № 5 (11): в 4-х ч. Ч. III. C. 100-103. ISSN 1997-292X.
 Дарья Саркисян  Субкультуры-fresh: Индустриальщики. Высокие технологии, штрихкоды на теле, железо и Шопенгауэр // Частный корреспондент, 27.11.2009 г.
 Kristina Paytasheva Industrial Subculture – the Mutated Child of Punk // , issue 1, August 2007

Индустриальные субкультуры
Субкультуры

redpajama_set_name: RedPajamaWikipedia


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Glee's Matthew Morrison Reveals Why He Loves Being Married to an Interior Designer
The actor's wife is quite crafty with furniture and a budget
Matthew Morrison and his wife, Renee Puente, own homes in upstate New York and Los Angeles.Photo: Tolga Akmen/Getty Images
As a nature lover, former Glee star Matthew Morrison wanted his 18-month-old son, Revel, to grow up experiencing wildlife and greenery firsthand. Which is why he recently sold his Manhattan apartment and purchased a home in upstate New York. It's also why he played the role of "minister" at the world's first Funeral for a Tree presented by Tushy on April 25. He hopes to inspire a wake-up call to the wasteful ways in which humans are destroying forests. Through this partnership, Architectural Digest was able to chat with the actor about the simple change he made in his bathroom to reduce his carbon footprint and the perks of being married to an interior designer.
Architectural Digest: OK, so you're playing the role of minister at the world's first Funeral for a Tree. What does that mean?
Matthew Morrison: I'm really good friends with Miki Agrawal, who is the founder of Tushy. It's a company that's environmentally conscious and wants us to realize what we're doing to our planet. As Americans, we're flushing away 15 million trees down our toilets every single year and spend $500 per household on paper each year. That can all be remedied by installing an attachable bidet that Tushy sells for $69. And we're doing this funeral to bring awareness to this issue, and that there are many ways people can reduce their carbon footprint.
AD: So, do you have a bidet in your New York apartment?
MM: We do!
AD: Besides the bathroom, what's the design style like in the rest of your home?
MM: We are bicoastal. So, we have an amazing home in Los Angeles, an apartment here, and a little lake house in upstate New York. My wife is an interior designer, so our homes are completely different from one another. Our apartment in New York has a very Balinese theme; there's a lot of pillows on the floor and deep, rich colors. But we just sold it!
AD: Why did you decide to sell?
MM: Because of the carbon footprint. Why do we need so many homes? We're just trying to downsize everything.
AD: And how was the selling process?
MM: Well, we had it on the market for a year, and it didn't sell. Then Hudson Yards opened, and we put it back on the market, and it sold immediately.
AD: So, will you just stay in hotels when you're in New York then?
MM: When we come back to New York, we're just going to rent on VRBO and get to try out different neighborhoods. So, we'll rent for a month on the Upper West Side and just use it as a chance to explore other areas if we ever want to buy again. I want to be around more parks.
AD: Now, how does the style of your New York home compare to the design styles of your other homes?
MM: Our L.A. home is very rustic. It's almost like a rustic farmhouse style.
AD: And how about your upstate home?
MM: That one needs the most work. So, we're kind of figuring out what that is. But we actually took out the existing fireplace and put in a modern-looking one with a wall behind it that has Aztec Mexican tiles. It's such a statement piece that I think the rest of our house is going to find inspiration from that.
AD: Now, your wife is an interior designer. But did you have a say in the design process at all?
MM: She definitely included me on every single phase. But, I don't see the big picture like she does. So, there were a couple of times I kind of fought back and she said just to trust her. Of course, everything worked out great.
AD: Do you have any favorite pieces?
MM: The thing I love about my wife is she's an interior designer, but she does it on the cheap. So, she'll go to Home Goods and scour everything to find really cool stuff. Then she comes home and paints it or tweaks it to make it look one of a kind. I love that about her. I appreciate what she does for our home and my wallet.
Explorecelebrity styleCelebrity Homes
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College of Arts & Sciences professor emeritus of philosophy, on August 12, 2015.
One of the world's eminent philosophers and logicians, Hintikka was born in Vantaa, Finland, in 1929, and educated at the University of Helsinki, where he earned a PhD in philosophy in 1956.
In 1990, he joined the BU philosophy faculty, where his expertise in game-theoretical semantics and epistemic logic (the logic of knowledge and belief) brought students from many disciplines to his classes.
Hintikka earned numerous honors during his career, including a 1979 Guggenheim Fellowship and the 2005 Rolf Schock Prize in logic and philosophy from the Swedish Academy, an award comparable to the Nobel Prize. He published more than 30 books and hundreds of scholarly articles.
College of Arts & Sciences professor emeritus of English, on March 12, 2015.
Saitz earned an AB from BU in 1949, an MA from the University of Iowa in 1950, and a PhD in English and linguistics from the University of Wisconsin, Madison, in 1955.
Saitz was dedicated to teaching English to non-native speakers, and was the founder and first president of the professional organization Massachusetts Educators of English Language Learners (MATSOL), as well as a founding member of Teachers of English to Speakers of Other Languages (TESOL).
At BU, Saitz created the program that became the School of Education master's Teaching English as a Second Language program, and he founded and directed the Boston Area Seminar for International Students, an intensive English program that is now BU's Center for English Language & Orientation Programs.
College of Arts & Sciences professor emeritus of philosophy and of physics, on August 8, 2015.
Shimony's research transcended disciplinary boundaries and literary genres. He made lasting contributions to the areas of inductive logic, the philosophy of C. S. Peirce, the quantum measurement problem, and Bell's theorem.
Shimony earned a joint degree in philosophy and mathematics at Yale, a master's in philosophy at the University of Chicago, and a PhD in philosophy at Yale. He earned a second doctorate, in physics, at Princeton University.
After arriving at Boston University, Shimony published arguably the most important physics article of his career, "Proposed experiment to test local hidden-variable theories." The article, coauthored by his BU graduate student Michael Horne (GRS'67,'70) and two other graduate students (John Clauser at Columbia and Richard Holt at Harvard), derived a new form of Bell's inequality.
Among his many contributions to the field of philosophy, Shimony was one of the most persuasive proponents of epistemological naturalism. He also introduced the term "experimental metaphysics" (the use of scientific experiments to investigate metaphysical questions) and contributed to its study.
Read more at bu.edu/cphs/about/history/abner-shimony. Alisa Bokulich is a CAS associate professor of philosophy, and Don Howard is a University of Notre Dame professor of philosophy.
A dear friend of mine. You will be always be remembered.


Modern society is built on secure and reliable access to energy.
We are in the middle of a profound transformation in the way our energy system works. Advanced and distributed energy markets are growing exponentially, creating substantial opportunities for new energy technologies, business models, and system architectures.
At the same time, we are experiencing more threats to secure energy access than ever before. Determined adversaries are actively targeting our critical infrastructure through cyberspace. Dozens of physical attacks are perpetrated on unprotected utility sites across the country every year. More frequent and severe weather events exacerbate threats to our energy security. Governments, private companies, the military, and civilian populations each face significant risks in this heightened threat environment.
Converge Strategies works to accelerate energy innovation to keep America secure.
We envision a future in which the military, private sector, and government work in concert to advance energy solutions for American security and prosperity.


While there isn't a cyberpunkCon (yet), here are a few places where you chummers and net-runners can find a few days of adventure and entertainment if you happen to find yourself outdoors.
Gather round, gather round. It's storytime. This is the tale of the Great Reply-All Stampede of 2018—an epic email chain of nightmare proportions involving mysterious surveillance devices, a mouthy pirate, and far more "Reply All" magic than you ever imagined possible.


This very similar story, about an eight year old girl lured up to an apartment by a young boy she knew, killed, and thrown into a dumpster, is a harder story to find. It's the same theme, though - humans luring trusting innocents away from safety so they can torture and murder. Wouldn't one think that the story about the human child should be the one that outrages and inflames the public? Why aren't we knocking down barriers and demanding to know who that killer kid is?
The dentist who killed this lion is, for sure, lacking some sort of empathy gene. But so is this teenager who lured away the little girl.. If I were that girl's mother I'd be beside myself with fury at the whole world right now, being so angry about the death of a beloved lion and totally ignoring the fact that people get hunted and brutally murdered like that right here in our own towns, and no one seems to be outraged about that.
I think we are desensitized by the media. We are so used to shootings, kidnappings, abuse, and killing of each other that we don't get upset anymore. It's the overspread of news that is doing it - we can only take so much awfulness. We just can't handle being horrified at human abuse anymore, our psyches can't take it. For our own emotional protection, we have had to put up a wall to avoid being shocked and aghast at what the news reporters tell us happens every day in our cities and neighborhoods. We've reached our shock limit.
Okay that's all I have to say about that, have to go - homework to do.


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Tag Archives: Alma Sports Club
OPEN SPACE – THE CONTINUING ALMA CLUB SHAM
Posted on August 28, 2012 | Comments Off on OPEN SPACE – THE CONTINUING ALMA CLUB SHAM
The Alma Sports Club was just sold at auction for $7.94m. Glen Eira has just lost a unique opportunity to acquire significant open space.
Following on from GERA's previous posting re the sale and probable $100m redevelopment of the Alma Sports Club, residents should note that the potential sale/leaseback agreement between Council and the Alma Sports Club (a land locked site of 7100+ square metres, valued at $8m for $3m) was again not openly discussed at the last Council Meeting (14/8/2012).
The Alma Club
Although the potential Alma Sports Club sale/leaseback was mentioned in response to a public question raised by a resident, the public question (presented below – (Council Meeting Minutes, 14th August, 2012 – Section 11.4) did not engender any discussion – it comprised a formal reading of the question and answer. Cr. Penhalluriack dissented from the response.
Public Question
"In considering an offer from the Alma Sports Club to sell their property, did Council submit a proposal to the Club that would be acceptable to Council I.E. did Council submit a counter offer? Did Council consider utilising funds from Open Space contributions to fund the purchase?"
Public Question Answer*
The Mayor read Council's response. He said:
"The Alma club proposed that Council buy, and then leaseback, the land to the club so that the club would continue to operate as before. Council did not accede to that proposition. Council spends far more each year on improvements to public open space than it receives in open space contributions. Council's 2012-13 Budget and ten year Strategic Resource Plan already commit Council to spend the open space contributions expected to be received. A priority is to convert the former Glenhuntly reservoir to public open space. The reservoir is on two main roads and a tram line and is very accessible to the community.
Council has limited capacity for further borrowings. Council is budgeting to repay the GESAC loan over 14 years. Council also has to address the defined benefit superannuation shortfall which was notified to all Councils earlier this month. In those circumstances, Council has not put a counter
proposal to the Club.
Council understands that negotiations are underway for the proceeds from sale of the site to be used to improve community recreation facilities in Glen Eira.
In the event of future subdivision of the land, a public open space contribution would be involved and Council would propose to take that in the form of land to be used to provide a park for the local community."
GERA believes that Council is not acting in the best interests of, or in accordance with the expressed open space wishes of the community by
not entering into formal discussions of the potential sale/leaseback agreement. Any agreement would involve a myriad of details, proposals and counter proposals yet none of this has occurred. Council's seemingly dismissive attitude appears to be summed up in the February, 2012, response to a public question which was attached, without any preamble, to the last page of the Council Meeting Minutes, 20th March, 2012.
"There are dozens of clubs within Glen Eira. There is no prospect of Council using ratepayers' funds simply to provide financial support to the operation of any private club".
As a result, Council is showing a lack of will and vision, a continued unwillingness to actively address the well documented and long standing residents' concerns re a need to increase the amount of open space – hence the end result will be that a site with enormous potential for providing passive open space will be lost forever.
failing to discuss the potential sale/leaseback agreement openly at the Councillor level, Council has failed to act in accordance with the principles of good governance (openness, transparency and accountability) and has hence
restricted ratepayers awareness of the potential increase in open space
prevented ratepayers from understanding the pros and cons of the potential access of additional open space
failed to provide for any community participation or community input (despite much lauded claims of welcoming and encouraging community participation)
As surmised in our previous posting on this issue, we repeat that GERA believes that Council (Administration and Councillors) have a lot to answer for the handling of the Alma Club issue. Discussions/negotiations have never gone beyond the initial proposal, yet this proposal is not dissimilar to various other proposals where private club facilities have been built on public land. GERA believes Council has not acted impartially, responsibly or prudently by not formally discussing the sale/leaseback agreement and re-iterates that in doing so Council has shown a lack of vision and a failure to invest in the future of Glen Eira.
* GERA's comments on Council's response to the public question are:
Sale and Lease Back Proposal
Private sporting/social club facilities on public land is an accepted concept – it represents a subsidy to the club which enables facilities, that benefit the community, to be built which might otherwise not be built. Providing such "subsidies" is one of the essential roles of government.
Usually, the public land exists prior to the building of the facilities (eg. the most recent example being the 2008 Maccabi Victoria Sports Foundation building of the Leon Haskins Tennis Centre in Bignell Road, Bentleigh East), however, in the Alma Sports Club instance the process differs. The facilities have been built on private land that, under the proposed sale/leaseback agreement, would later become public land. Regardless of the process, the end result is the same – a fact which Council has failed to recognise or take into consideration in it's decision to "not accede" to, or even discuss, the proposal.
Open Space Levy
The Open Space Contribution Levy, is a State Government legislated levy, payable (in the form of land or dollars) to Council, by developers when a site is subdivided. It is intended to provide Councils with funding for the acquisition of parkland, however, unlike other Councils, Glen Eira Council does not segregate these funds for the specific purpose of the acquisition of new parkland. Glen Eira's interpretation of the legislation is such that funds raised from the levy may be used to maintain existing parkland. As a result, when rare opportunities arise to acquire open space, such as the unique Alma Sports Club opportunity, this Council does not have the funding readily available to take advantage of the opportunity and can, with questionable validity, claim it does not have the funds. Council's 2010-2011 Annual Accounts states that in 2010 the Open Space Levy revenue was $1.630m and in 2011 it was $1.664m.
For the past 15 years Glen Eira residents have repeatedly and vocally expressed a need for more open space yet Council persistently uses the Open Space Contribution Levy for general parkland maintenance rather than the acquisition of future parkland. Residents are now asking why this is so.
Glen Huntly Reservoir is a priority
Unfortunately Council's performance on the Glen Huntly Reservoir (also known as the Booran Road Reservoir) does not support a "priority" status being given to this site. As mentioned in our 15th June, 2012, posting on the Booran Road Reservoir, in 2006/8 Council knew that the site would revert to Council (the site was officially reverted to Council in 2010). Despite two public consultations (March, 2008 and June, 2012), Council is yet to develop any substantive plans for the site. Council's 2012 Strategic Resources Plan provides for conversion work on the reservoir to commence in 2016/7 and estimated a completion time of 2 years. Given the well documented and long standing open space issues within Glen Eira, the fact that it has taken/will take 13 years (from unofficial knowledge of the site reversion in 2006 to actual park completion in 2018/9) raises a question about Council's definition of "priority".
GESAC loan and the Defined Superannuation Shortfall
Council's well publicised statements re GESAC are that it was under budget (under budget construction costs being further offset by liquidated damages) and financially successful – GESAC was not only self-funding but would also generate a profit. Now, in what GERA predicts will be first of many instances, the GESAC loan commitments are a major reason Council is unable to take advantage of this unique opportunity to valuable open space.
The inclusion of the Defined Superannuation Shortfall would be a valid consideration except that
As per Council's Minutes, the potential sale/leaseback proposal (February, 2012) was rejected well before the superannuation shortfall was known (July, 2012)
Of the reported $7.1m shortfall, $3m is already budgeted for and Council has 15 years to budget for the remaining $4.1m (Glen Eira Leader article – Superannuation Debt).
No Counter Proposal
As mentioned in our previous posting and confirmed by the above answer to the public question, discussions with the Alma Sport Club, have been minimal. Council has not reviewed or considered the proposal in any detail or reviewed the proposal to the extent that a counter proposal could be made. The advertisements re the sale indicate that generous interest free vendor terms are available – a consideration that Council has failed to take into account. Not only would be the club owned land be available to Council at a heavily discounted price, the lease back agreement would provide an income for Council.
Proceeds of Sale
The Alma Sports Club constitution clauses related to the dissolution of the Club precludes individual members benefitting from the sale of club assets. In the event of dissolution, any profit made on the disposal of club assets must be returned to the community via distribution to a similar organisation/s or Council. Hence, as mentioned in the above response, "Council understands that negotiations are underway for the proceeds from sale of the site to be used to improve community recreation facilities in Glen Eira".
Land as Open Space Contribution
Given Glen Eira's aversion to pocket parks (most, along with small community centres, having been sold to developers pre 2004), Council's statement that it "would propose to take the open space contribution in the form of land to be used to provide a park for the local community" is inconsistent with Council's track record. 5% of the land value is the maximum open space contribution that can be levied on developments – simplistically, that means that Council would gain approximately 356 sqm of the site as parkland.
Comments Off on OPEN SPACE – THE CONTINUING ALMA CLUB SHAM
Posted in Alma Club
Tagged Alma Club, Alma Sports Club, glen eira open space, Wilks Street


We source qualified tutors to deliver the classes in our timetable. Courses are run termly in a structured and student-centred manner. Inspired has recently established a transition programme for prospective school leavers to attend the project on a part time basis. The aim of this programme is to support the young people through the transition from secondary school to further education or employment. We have collaborated with special education schools in Kerry in order to roll out this programme.
Participants work with our Job Coach to identify the jobs they would like to pursue. The Job Coach then arranges an appropriate placement with a local business. These placements are part of a long-term process that is centred on the individual's ongoing development. Businesses are supported by Inspired to understand the young person's abilities and ambitions and to establish a structured programme of training that may lead to formal employment. To date, participants have undertaken placements in hairdressers and barbers, bookshops, charity shops, supermarkets and hardware stores.
We believe the visible and valuable contributions that people with intellectual disabilities make to the community are important in reducing the stigma that persists around disability. Meaningful participation in the community also gives patricipants a sense of purpose and self-worth. To that end, a major part of our work is helping in the local community. To date, particpants have helped with fundraising for other charities, worked in a community garden, contributed to the Tralee Tidy Towns effort, and taken part in a local football club's Strictly Come Dancing event.


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Q: Wicket basic implementation of wizard steps is there any basic implementation wicket 6.20 provides for a step overview functionality like in this picture or like this if the other won't work?
When looking at the documentation I couldn't find anything close to it, so I started by doing my own implementation like
public List<String> getSteps(WizardModel model){
    Iterator<IWizardStep> iterator = model.stepIterator();
    List<String> steps = new ArrayList<String>();

    for(int i = 1; iterator.hasNext(); i++){
        steps.add(String.valueOf(i));
        iterator.next();
    }

    //model.getActiveStep(); unnecessary in this context
    return steps;
}

to get all possible steps in a List. And now I would go on by getting the index of the current panel (if possible) and get it's state by isColmplete(); to mark it in a different color. But I can't believe, that I'm the first one with this problem.
Should I go on with my idea or is there a better option?

A: You can (have to) implement a wizard yourself, it is not too hard.
I would use a AjaxTabbedPanel as basis. You just have to add a 'next', 'back' and 'finish' bar below, and do some CSS styling

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