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Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids

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  • Yin, Linfei
  • Luo, Shikui
  • Ma, Chenxiao

Abstract

The uncertainty of renewable energy sources (RESs) puts pressure on the economic dispatch of smart grids. However, many traditional multiple time-scales dispatching approaches lead to problems such as inverse regulation and cost increase. To optimize the economic dispatch of smart grids, firstly, a three-state energy prosumers (TSEPs) model is proposed to facilitate flexible dispatching of RESs. Secondly, an economic smart generation control (ESGC) framework is designed to replace the traditional multiple time-scales framework. The ESGC allows TSEPs to enter and exit at any time and improves the economy of economic dispatch. Furthermore, an expandable depth and width adaptive dynamic programming (EDP) algorithm is proposed. The EDP can match the entering and exiting characteristics of TSEPs, and has high algorithm accuracy and calculation speed with reducing the network redundancy of the EDP. Finally, compared with compared algorithms, the EDP reduces 69.559% and 61.2% in |Δf| and improves 1.783% and 0.668% in profit under a two-area smart grid and a modified 1888-bus French based smart grid, respectively. The simulation results verify the feasibility and reliability of the EDP for the ESGC of smart grids.

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  • Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:energy:v:232:y:2021:i:c:s0360544221012123
    DOI: 10.1016/j.energy.2021.120964
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    1. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).

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