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An analytical target cascading method-based two-step distributed optimization strategy for energy sharing in a virtual power plant

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  • Yan, Xingyu
  • Gao, Ciwei
  • Meng, Jing
  • Abbes, Dhaker

Abstract

Large-scale distributed renewable energy sources as well as emerging controllable loads such as electric vehicles connected to the distribution grids have posed challenges to the efficient management of these distributed energy resources. Promoting energy-sharing of distributed energy resources through economic incentives is a cost-effective and equitable solution to the challenge. This paper proposes a two-stage transactive energy control mechanism by the virtual power plant (VPP) for energy-sharing of multiple prosumers. Firstly, a VPP internal price mechanism based on the supply-demand ratio of prosumers in the energy-sharing alliance is proposed. Then, a two-stage economic optimization model is developed. Trading prices between VPP and prosumers are established in the first stage, and prosumers make operating plans based on those prices in the second stage. Then, considering that prosumers and VPP are independent stakeholders, a decentralized optimization algorithm based on the analytical target cascading method is developed. The upper-level VPP coordinates the energy-sharing alliance and sets the transaction price following the overall supply and demand ratio. Lower-level prosumers independently make decisions and feedback on the trading power of the VPP. Then, iterative calculations provide a distributed and independent solution. Finally, the case study verifies that the proposed energy-sharing model can reduce the operating cost of prosumers by 14.29 %. Moreover, prosumer privacy protection is achieved by the analytical target cascading-based distributed model at a cost of less than 1.5 % accuracy.

Suggested Citation

  • Yan, Xingyu & Gao, Ciwei & Meng, Jing & Abbes, Dhaker, 2024. "An analytical target cascading method-based two-step distributed optimization strategy for energy sharing in a virtual power plant," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s0960148123018323
    DOI: 10.1016/j.renene.2023.119917
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    References listed on IDEAS

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    1. Sun, Lingling & Li, Haibin & Jia, Qingquan & Zhang, Gong, 2024. "Optimization method of dynamic reconfiguration in virtual power plants," Renewable Energy, Elsevier, vol. 228(C).

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