Microgrid System Energy Storage Capacity Optimization Considering Multiple Time Scale Uncertainty Coupling

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Abstract
In this paper, we propose an energy storage capacity optimization (ESCO) method for grid-connected microgrid systems (MSs) considering multiple time scale uncertainty coupling. First, an envelope model and a box uncertainty model are, respectively, employed to characterize the stochastic uncertainties and the forecasting error uncertainty of the renewable energy (RE) power supply and load demand. Based on the above two uncertainty characterization models, the energy balance ability indicator and the robustness coordination cost indicator are proposed, which are used to quantitatively analyze the MS energy balance state probability for different ESCO schemes and the economic cost of improving the robustness of the MS optimal operation, respectively. Then, an ESCO model, where the stochastic uncertainty and forecasting error uncertainty of the RE power supply and load demand are, respectively, considered in the long-time-scale investment decision of the energy storage capacity and the short-time-scale operation optimization of the energy storage system, is established, with the goal of realizing an MS that operates in a grid-friendly and economical manner. Furthermore, a new multi-objective compound differential evolution (MO-CDE) algorithm is designed to solve the energy storage capacity collaborative optimization model efficiently. Finally, simulations are conducted to verify the rationality and effectiveness of the proposed model and method.
Funding Information
  • National Natural Science Foundation of China (51577073)
  • Natural Science Foundation of Guangdong Province (2017A030313288)

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