A Long-term Capacity Expansion Planning Model for an Electric Power System Integrating Large-size Renewable Energy Technologies

The recent interest in reducing greenhouse gas emissions has facilitated the integration of renewable energy technologies (RETs) into the electricity sector around the world. Despite the fact that renewable energy provides substantial benefits for climate and economy, the large size deployment of RETs could possibly hurt the level of power system reliability because of their technical limitations, intermittency and non-dispatchability. Many power system planners and operators consider this to be a critical problem. This paper proposes a possible solution to this problem through the design of a new stochastic optimization model for the long-term capacity expansion planning of a power system that explicitly incorporated the uncertainty associated with RETs, and developed its solution by using the sample average approximation method. A numerical analysis followed to emphasize the effects of the large scale integration of RETs on not only an example at the power systems reliability level but also, consequentially, its long-term capacity expansion planning. From the results of the numerical analysis, we can show that our proposed model can develop a long-term capacity expansion plan that is more robust with respect to the uncertain RETs and is able to quantify how much capacity the system requires to be reliable.1

Multi-objective Analysis of Sustainable Generation Expansion Planning based on Renewable Energy Potential: A case study of Bali Province of Indonesia

This article analyzes the role of renewable energy in producing sustainable generation expansion planning. The generation expansion planning is carried out using an optimization model which has two objective functions, namely the objective function of planning costs and the objective function of emissions. Multi-objective analysis was performed using the epsilon constraint method to produce the Pareto set. Solution points are selected from the Pareto set generated using the fuzzy decision making method. The process of determining the best solution points is based on three scenarios. Furthermore, calculations were carried out to obtain 7 indicators of sustainability covering economic, social, and environmental aspects. The sustainability index is calculated based on several predetermined policy options. The model is implemented using data obtained from the electricity system in Bali Province, Indonesia. From the analysis, the planning scenario by implementing renewable energy sources in the generation of electrical energy, namely scenario 3, results in an increase in the sustainability index with the highest value during the planning period. However, scenario 3 produces two sustainability indices from the economic aspect, namely the unit cost of generation and shared electricity cost to GDP, which is the lowest when compared to other scenarios

Adaptive Two-stage Stochastic Programming with an Application to Capacity Expansion Planning

Multi-stage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that are fully adapted to the uncertainty. Often such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a number of planning periods. Two-stage stochastic programming might be better suited to such settings, where the decisions for all periods are made here-and-now and do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, where the stages are not predetermined but part of the optimization problem. Each component of the decision policy has an associated revision point, a period prior to which the decision is predetermined and after which it is revised to adjust to the uncertainty realized thus far. We motivate this setting using the multi-period newsvendor problem by deriving an optimal adaptive policy. We label the proposed approach as adaptive two-stage stochastic programming and provide a generic mixed-integer programming formulation for finite stochastic processes. We show that adaptive two-stage stochastic programming is NP-hard in general. Next, we derive bounds on the value of adaptive two-stage programming in comparison to the two-stage and multi-stage approaches for a specific problem structure inspired by the capacity expansion planning problem. Since directly solving the mixed-integer linear program associated with the adaptive two-stage approach might be very costly for large instances, we propose several heuristic solution algorithms based on the bound analysis. We provide approximation guarantees for these heuristics. Finally, we present an extensive computational study on an electricity generation capacity expansion planning problem and demonstrate the computational and practical impacts of the proposed approach from various perspectives