Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
The demand response (DR) market, as a vital complement to the electricity spot market, plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events. While the DR market offers the load aggregator (LA) additional profitable opportunities beyond the electricity spot market, it also introduces new trading risks due to the significant uncertainty in users’ behaviors. Dispatching energy storage systems (ESSs) is an effective means to enhance the risk management capabilities of LAs; however, coordinating ESS operations with dual-market trading strategies remains an urgent challenge. To this end, this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market, which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market. First, the intrinsic coupling characteristics of the LA participating in the dual market are analyzed, and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed. Second, an uncertain user response model is developed based on price‒response mechanisms, and actual market settlement rules accounting for under- and over-responses are employed to calculate trading revenues, where possible revenue losses are quantified via conditional value at risk. Third, by imposing these terms and the capacity allocation mechanism of ESS, the risk-aware stochastic coordinated trading model of the LA is built, where the bidding and pricing strategies in the dual model that trade off risk and profit are derived. The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.
Ferreira, P. D. F., Carvalho, P. M. S., Ferreira, L. A. F. M., Ilic, M. D. (2013). Distributed energy resources integration challenges in low-voltage networks: Voltage control limitations and risk of cascading. IEEE Transactions on Sustainable Energy, 4: 82–88
Mohajeryami, S., Doostan, M., Moghadasi, S., Schwarz, P. (2017). Towards the interactive effects of demand response participation on electricity spot market price. International Journal of Emerging Electric Power Systems, 18: 20160158.
Guo, Y., Han, X., Zhou, X., Hug, G. (2023). Incorporate day-ahead robustness and real-time incentives for electricity market design. Applied Energy, 332: 120484.
Li, S., Zhang, L., Nie, L., Wang, J. (2022). Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game. Energy, 249: 123678.
Huang, C., Zhang, M., Wang, C., Xie, N., Yuan, Z. (2022). An interactive two-stage retail electricity market for microgrids with peer-to-peer flexibility trading. Applied Energy, 320: 119085.
Vahid-Ghavidel, M., Javadi, M. S., Santos, S. F., Gough, M., Mohammadi-Ivatloo, B., Shafie-Khah, M., Catalão, J. P. S. (2021). Novel hybrid stochastic-robust optimal trading strategy for a demand response aggregator in the wholesale electricity market. IEEE Transactions on Industry Applications, 57: 5488–5498.
Hosseini, S. M., Carli, R., Dotoli, M. (2021). Robust optimal energy management of a residential microgrid under uncertainties on demand and renewable power generation. IEEE Transactions on Automation Science and Engineering, 18: 618–637.
Li, K., Li, Z., Huang, C., Ai, Q. (2024). Online transfer learning-based residential demand response potential forecasting for load aggregator. Applied Energy, 358: 122631.
Afzalan, M., Jazizadeh, F. (2019). Residential loads flexibility potential for demand response using energy consumption patterns and user segments. Applied Energy, 254: 113693.
Huang, C., Li, K., Zhang, N. (2025). Strategic joint bidding and pricing of load aggregators in day-ahead demand response market. Applied Energy, 377: 124552.
Wang, Q., Huang, C., Wang, C., Li, K., Xie, N. (2024). Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions. Applied Energy, 360: 122810.
Salah, F., Henríquez, R., Wenzel, G., Olivares, D. E., Negrete-Pincetic, M., Weinhardt, C. (2019). Portfolio design of a demand response aggregator with satisficing consumers. IEEE Transactions on Smart Grid, 10: 2475–2484.
Dey, B., Misra, S., Garcia Marquez, F. P. (2023). Microgrid system energy management with demand response program for clean and economical operation. Applied Energy, 334: 120717.
Ruggiero, S., Kangas, H. L., Annala, S., Lazarevic, D. (2021). Business model innovation in demand response firms: Beyond the niche-regime dichotomy. Environmental Innovation and Societal Transitions, 39: 1–17.
Zhao, H., Wu, Q., Hu, S., Xu, H., Rasmussen, C. N. (2015). Review of energy storage system for wind power integration support. Applied Energy, 137: 545–553.
Zhang, W., Wei, W., Chen, L., Zheng, B., Mei, S. (2020). Service pricing and load dispatch of residential shared energy storage unit. Energy, 202: 117543.
Lu, X., Ge, X., Li, K., Wang, F., Shen, H., Tao, P., Hu, J., Lai, J., Zhen, Z., Shafie-khah, M., et al. (2021). Optimal bidding strategy of demand response aggregator based on customers’ responsiveness behaviors modeling under different incentives. IEEE Transactions on Industry Applications, 57: 3329–3340.
Wang, F., Ge, X., Yang, P., Li, K., Mi, Z., Siano, P., Duić, N. (2020). Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing. Energy, 213: 118765.
Zhang, T., Qiu, W., Zhang, Z., Lin, Z., Ding, Y., Wang, Y., Wang, L., Yang, L. (2023). Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets. Applied Energy, 329: 120158.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).