| Citation:Hongjun Gao,Renjun Wang,Shuaijia He,Zeqi Wang,Junyong Liu.Bi-level stackelberg game-based distribution system expansion planning model considering long-term renewable energy contracts[J].Protection and Control of Modern Power Systems,2023,V8(4):1031-1045[Copy] |
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| Abstract: |
| With the deregulation of electricity market in distribution systems, renewable distributed generations (RDG) are being invested in by third-party social capital, such as distributed generations operators (DGOs) and load aggregators (LAs). However, their arbitrary RDG investment and electricity trading behavior can bring great challenges to distribution system planning. In this paper, to reduce distribution system investment, a distribution system expansion planning model based on a bi-level Stackelberg game is proposed for the distribution system operator (DSO) to guide this social capital to make suitable RDG investment. In the proposed model, DSO is the leader, while DGOs and LAs are the followers. In the upper level, the DSO determines the expansion planning scheme including investments in substations and lines, and optimizes the variables provided for followers, such as RDG locations and contract prices. In the lower level, DGOs determine the RDG capacity and electricity trading strategy based on the RDG locations and contract prices, while LAs determine the RDG capacity, demand response and electricity trading strategy based on contract prices. The capacity information of the DRG is sent to the DSO for decision-making on expansion planning. To reduce the cost and risk of multiple agents, two long-term renewable energy contracts are introduced for the electricity trading. Conditional value-at-risk method is used to quantify the RDG investment risk of DGOs and LAs with diferent risk preferences. The efectiveness of the proposed model and method is verifed by studies using the Portugal 54-bus system. |
| Key words: Distribution system expansion planning, Distributed generation, Distribution system operators,
Distributed generation operator, Prosumers, Load aggregators |
| DOI：10.1186/s41601-023-00335-5 |
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| Fund:This work was supported by the National Science Foundation of China under
Grant 52077146 and 52307123, the Sichuan Science and Technology Program
under Grant 2023NSFSC1945 and 2023YFSY0032. |
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