It is important in the LT Plan formulation that the model does not inappropriately consider the end of the planning horizon to also be the 'end of time'. In the above basic formulation we have implicitly assumed that LT Plan has an infinite horizon. In practice, the model will have a finite horizon such as 20 or 30 years. We therefore have to take account of so-called end year effects. Without some method for dealing with end-year effects, the model will in the last years of the horizon only build generators with low build costs (even if their marginal generation costs are high). This is because the average cost of generation between build year and the end of the planning horizon - including build costs - will be lower for such generators. We therefore assume that the last year of the horizon is repeated an infinite number of times. The objective function is expanded by the cost of the years after the final horizon year, which equals the sum of the present values of generation costs. Solution approach Problem Size Reduction The capacity expansion 'sub-problem' uses annual build/retire decisions. Thus the number of these elements in the formulation is relatively small. No problem size reduction is required to these elements. However, if all decisions are integer (as controlled by the option), then the problem can become very hard to solve. You can reduce the number of integers by selectively turning off integer optimization at the level using the property. LT Plan (by default) spans the whole simulation planning horizon in a single optimization 'step'. For realistically-sized power systems and long planning horizons this may lead to extremely big problems in the production cost sub-problem. To make this problem tractable, two problem size reduction techniques can be used: The production cost subproblem's chronology is reduced using the load duration curve (LDC) methodology (as in ). The optimization can be split into multiple steps using the option. LT Plan always uses a LDC approach. The controls for the level of detail in this method are , and .
It is important in the LT Plan formulation that the model does not inappropriately consider the end of the planning horizon to also be the 'end of time'. In the above basic formulation we have implicitly assumed that LT Plan has an infinite horizon. In practice, the model will have a finite horizon such as 20 or 30 years. We therefore have to take account of so-called end year effects. Without some method for dealing with end-year effects, the model will in the last years of the horizon only build generators with low build costs (even if their marginal generation costs are high). This is because the average cost of generation between build year and the end of the planning horizon - including build costs - will be lower for such generators. We therefore assume that the last year of the horizon is repeated an infinite number of times. The objective function is expanded by the cost of the years after the final horizon year, which equals the sum of the present values of generation costs. Solution approach Problem Size Reduction The capacity expansion 'sub-problem' uses annual build/retire decisions. Thus the number of these elements in the formulation is relatively small. No problem size reduction is required to these elements. However, if all decisions are integer (as controlled by the option), then the problem can become very hard to solve. You can reduce the number of integers by selectively turning off integer optimization at the level using the property. LT Plan (by default) spans the whole simulation planning horizon in a single optimization 'step'. For realistically-sized power systems and long planning horizons this may lead to extremely big problems in the production cost sub-problem. To make this problem tractable, two problem size reduction techniques can be used: The production cost subproblem's chronology is reduced using the load duration curve (LDC) methodology (as in ). The optimization can be split into multiple steps using the option. LT Plan always uses a LDC approach. The controls for the level of detail in this method are , and .
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