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Exploring multiple investment strategies for non-utility-owned DGs: A decentralized risked-based approach

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  • Yao, Haotian
  • Xiang, Yue
  • Liu, Junyong

Abstract

The development of distributed generator (DG) and energy market has facilitated the investment in non-utility-owned DGs, leading to the necessity of decentralized optimization and finical risk management due to multiple uncertainties. To cope with these problems, this paper addresses the planning framework of non-utility-owned DGs considering multiple investment strategies, from the perspective of risk and profit. Initially, the autonomous planning and operation strategy (APOS), and leasing planning and operation strategy (LPOS) are proposed, considering the different ownership of DG investment/operation rights and pricing mechanism. Then the DG planning problem is modeled as the independent decision-making stage of multiple DG investors and the global coordination stage of distribution system operator (DSO). Furthermore, in the DSO coordination problem, to accurately model the real-time uncertainties in DGs, load demand and main grid price, the conditional value at risk (CVaR) is adopted to manage the risk in profit (RIP). The effect of multiple investment strategies on the tradeoff between RIP and expected profit is analyzed. The planning problem is solved by a decentralized optimization approach that ensures the privacy protection and autonomous optimization of investors. Finally, results from the case study of the IEEE 33-bus system and IEEE 123-bus system demonstrate the superiority and effectiveness of the proposed method in dealing with the planning problem for multiple DG investors.

Suggested Citation

  • Yao, Haotian & Xiang, Yue & Liu, Junyong, 2022. "Exploring multiple investment strategies for non-utility-owned DGs: A decentralized risked-based approach," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s030626192201193x
    DOI: 10.1016/j.apenergy.2022.119936
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    References listed on IDEAS

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    Cited by:

    1. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).

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