Bi-level stackelberg game-based distribution system expansion planning model considering long-term renewable energy contracts

Institute of Electrical and Electronics Engineers (IEEE) - Tập 8 Số 1 - 2023
Hongjun Gao1, Renjun Wang1, Shuaijia He1, Zeqi Wang1, Junyong Liu1
1College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Tóm tắt

AbstractWith 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 different risk preferences. The effectiveness of the proposed model and method is verified by studies using the Portugal 54-bus system.

Từ khóa


Tài liệu tham khảo

Gao, H., Wang, R., Liu, Y., Wang, L., Xiang, Y., & Liu, J. (2020). Data-driven distributionally robust joint planning of distributed energy resources in active distribution network. IET Generation, Transmission and Distribution, 14(9), 1653–1662.

Roy Ghatak, S., Sannigrahi, S., & Acharjee, P. (2020). Multiobjective framework for optimal integration of solar energy source in three-phase unbalanced distribution network. IEEE Transactions on Industry Applications, 56(3), 3068–3078.

Ehsan, A., & Yang, Q. (2019). Coordinated investment planning of distributed multi-type stochastic generation and battery storage in active distribution networks. IEEE Transactions on Sustainable Energy, 10(4), 1813–1822.

He, Y., Yang, N., Dong, B., Ding, L., Qin, T., Huang, Y., & Chen, C. (2019). Incremental distribution network source-load collaborative planning method considering uncertainty and multi-agent game. Proceedings CSEE, 39(09), 2689–2702.

Munoz-Delgado, G., Contreras, J., & Arroyo, J. M. (2019). Distribution system expansion planning considering non-utility-owned DG and an independent distribution system operator. IEEE Transactions on Power Systems, 34(4), 2588–2597.

Huang, C., Wang, C., Xie, N., Sun, K., Chen, F., & Huang, J. (2019). Distribution expansion planning based on strong coupling of operation and spot market. Proceedings CSEE, 39(16), 4716–4731.

Yang, N., Li, X., Liu, Y. et al. (2023) Research on distribution network planning method based on multi-lateral incomplete information evolutionary game in power market, Power System Technology. https://doi.org/10.13335/j.1000-3673.pst.2022.2502.

Nourollahi, R., Gholizadeh-Roshanagh, R., Feizi-Aghakandi, H., Zare, K., & Mohammadi-Ivatloo, B. (2022). Power distribution expansion planning in the presence of wholesale multimarkets. IEEE Systems Journal, 17(1), 1684.

Alotaibi, M. A., & Salama, M. M. A. (2018). An incentive-based multistage expansion planning model for smart distribution systems. IEEE Transactions on Power Systems, 33(5), 5469–5485.

Hu, J., Yang, G., Ziras, C., & Kok, K. (2019). Aggregator operation in the balancing market through network-constrained transactive energy. IEEE Transactions on Power Systems, 34(5), 4071–4080.

Attarha, A., Scott, P., & Thiébaux, S. (2020). Affinely adjustable robust ADMM for residential DER coordination in distribution Networks. IEEE Transactions on Smart Grid, 11(2), 1620–1629.

Han, L., Morstyn, T., & McCulloch, M. (2019). Incentivizing prosumer coalitions with energy management using cooperative game theory. IEEE Transactions on Power Systems, 34(1), 303–313.

Lu, T., Wang, Z., Wang, J., Ai, Q., & Wang, C. (2019). A data-driven stackelberg market strategy for demand response-enabled distribution systems. IEEE Transactions on Smart Grid, 10(3), 2345–2357.

Fattaheian-Dehkordi, S., Tavakkoli, M., Abbaspour, A., Fotuhi-Firuzabad, M., & Lehtonen, M. (2021). An incentive-based mechanism to alleviate active power congestion in a multi-agent distribution system. IEEE Transactions on Smart Grid, 12(3), 1978–1988.

Ming, H., Xia, B., Lee, K. Y., Adepoju, A., Shakkottai, S., & Xie, L. (2020). Prediction and assessment of demand response potential with coupon incentives in highly renewable power systems. Protection and Control of Modern Power Systems., 5(1), 1–14.

Valinejad, J., Marzband, M., Korkali, M., Xu, Y., & Al-Sumaiti, A. S. (2020). Coalition formation of microgrids with distributed energy resources and energy storage in energy market. Journal of Modern Power Systems and Clean Energy, 8(5), 906–918.

Chinmoy, L., Iniyan, S., & Goic, R. (2019). Modeling wind power investments, policies and social benefits for deregulated electricity market: A review. Applied Energy, 242, 364–377.

Anwar, M. B., Stephen, G., Dalvi, S., Frew, B., Ericson, S., Brown, M., & O’Malley, M. (2022). Modeling investment decisions from heterogeneous firms under imperfect information and risk in wholesale electricity markets. Applied Energy, 306, 117908.

Tekiner-Mogulkoc, H., Coit, D. W., & Felder, F. A. (2015). Mean-risk stochastic electricity generation expansion planning problems with demand uncertainties considering conditional-value-at-risk and maximum regret as risk measures. International Journal of Electrical Power and Energy Systems, 73, 309–317.

Salci, S., & Jenkins, G. P. (2018). An economic analysis for the design of ipp contracts for grid-connected renewable energy projects. Renewable and Sustainable Energy Reviews, 81, 2410–2420.

Xuan, A., Shen, X., Guo, Q., & Sun, H. (2021). A conditional value-at-risk based planning model for integrated energy system with energy storage and renewables. Applied Energy, 294, 116971.

Miranda, V., Ranito, J. V., & Proenca, L. M. (1994). Genetic algorithms in optimal multistage distribution network planning. IEEE Transactions on Power Systems, 9(4), 1927–1933.

Yao, W., Zhao, J., Wen, F., Dong, Z., Xue, Y., Xu, Y., & Meng, K. (2014). A multi-objective collaborative planning strategy for integrated power distribution and electric vehicle charging systems. IEEE Transactions on Power Systems, 29(4), 1811–1821.

Thông tin
Thông tin xuất bản

Institute of Electrical and Electronics Engineers (IEEE)

Tập 8 Số 1

Thông tin tác giả