A Two-Stage Sustainable Optimal Scheduling Strategy for Multi-Contract Collaborative Distributed Resource Aggregators

Lei Su, Wanli Feng, Cao Kan, Mingjiang Wei, Rui Su, Pan Yu and Ning Zhang ()
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Lei Su: State Grid Hubei Electric Power Research Institute, Wuhan 430000, China
Wanli Feng: State Grid Hubei Electric Power Research Institute, Wuhan 430000, China
Cao Kan: State Grid Hubei Electric Power Research Institute, Wuhan 430000, China
Mingjiang Wei: State Grid Hubei Electric Power Research Institute, Wuhan 430000, China
Rui Su: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Pan Yu: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
Ning Zhang: School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China

Sustainability, 2025, vol. 17, issue 15, 1-23

Abstract: To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for distributed resource aggregators. A phased multi-contract collaborative scheduling model oriented toward sustainable development is proposed. Through intelligent algorithms, the model dynamically optimises decisions across the day-ahead and intraday phases: During the day-ahead scheduling phase, intelligent algorithms predict load demand and energy output, and combine with elastic performance-based response contracts to construct a user-side electricity consumption behaviour intelligent control model. Under the premise of ensuring user comfort, the model generates a 24 h scheduling plan with the objectives of minimising operational costs and efficiently integrating renewable energy. In the intraday scheduling phase, a rolling optimisation mechanism is used to activate energy storage capacity contracts and dynamic frequency stability contracts in real time based on day-ahead prediction deviations. This efficiently coordinates the intelligent frequency regulation strategies of energy storage devices and electric vehicle aggregators to quickly mitigate power fluctuations and achieve coordinated control of primary and secondary frequency regulation. Case study results indicate that the intelligent optimisation-driven multi-contract scheduling model significantly improves system operational efficiency and stability, reduces system operational costs by 30.49%, and decreases power purchase fluctuations by 12.41%, providing a feasible path for constructing a low-carbon, resilient grid under high renewable energy penetration.

Keywords: distributed resource aggregators; artificial intelligence dispatch; energy storage capacity contracts; flexible energy efficiency response contracts; dynamic frequency stability contracts; sustainable energy systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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