Abstract
With the large-scale integration of renewable generation, energy storage system (ESS) is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty. This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load. A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS. Specifically, the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making. Then, an easy-to-implement variant of Benders decomposition (BD) algorithm is developed to solve the resulting mixed integer nonlinear programming problem. Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power. In addition, the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.
Highlights
Nowadays, many countries are committed to promoting the development of renewable power generation to cope with global warming and fossil energy crisis
The main contributions of this paper are threefold: À A scenario-based chance-constrained model is proposed to achieve flexible adjustment of the risk level of wind power curtailment and the wind power utilization rate in the energy storage system (ESS) planning under uncertainty; ` In addition to consideration of wind power uncertainties, the modeling of storage portfolio problem takes into account a number of factors that reflect differences between different storage technologies, including the lifetime, the investment costs per unit power/energy capacity, the typical energy/power ratio of energy storage and the storage loss during the charging and discharging; ́ According to the problem structure, a modified Benders decomposition (BD) algorithm is developed to improve the computational efficiency of solving this kind of chance-constrained programming problem
To avoid overinvestment in ESS, this paper extends conventional ESS planning models by adding scenario-based chance constraints on wind power utilization, where a proper amount of wind curtailment is allowed over the planning period
Summary
Many countries are committed to promoting the development of renewable power generation to cope with global warming and fossil energy crisis. The main contributions of this paper are threefold: À A scenario-based chance-constrained model is proposed to achieve flexible adjustment of the risk level of wind power curtailment and the wind power utilization rate in the ESS planning under uncertainty; ` In addition to consideration of wind power uncertainties, the modeling of storage portfolio problem takes into account a number of factors that reflect differences between different storage technologies, including the lifetime, the investment costs per unit power/energy capacity, the typical energy/power ratio of energy storage and the storage loss during the charging and discharging; ́ According to the problem structure, a modified BD algorithm is developed to improve the computational efficiency of solving this kind of chance-constrained programming problem.
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