Multi-agent-based Rolling Optimization Method for Restoration Scheduling of Distribution Systems with Distributed Generation

Abstract

Resilience against major disasters is the most essential characteristic of future electrical distribution systems (EDSs). A multi-agent-based rolling optimization method for EDS restoration scheduling is proposed in this paper. When a blackout occurs, considering the risk of losing the centralized authority due to the failure of the common core communication network, the available agents after disasters or cyber-attacks identify the communication-connected parts (CCPs) in the EDS with distributed communication. A multi-time interval optimization model is formulated and solved by the agents for the restoration scheduling of a CCP. A rolling optimization process for the entire EDS restoration is proposed. During the scheduling/ rescheduling in the rolling process, CCPs in EDS are re-identified and the restoration schedules for CCPs are updated. Through decentralized decision-making and rolling optimization, EDS restoration scheduling can automatically start and periodically update itself, providing an effective solution for EDS restoration scheduling in a blackout event. A modified IEEE 123-bus EDS is utilized to demonstrate the effectiveness of the proposed method.

Resilience of the electric distribution systems: concepts, classification, assessment, challenges, and research needs

“…[65] incorporated the communication network failure after a natural disaster and employ a global information discovery algorithm to collect operation parameters of distribution systems for the optimal formation of microgrids to serve critical loads. In [66], the authors employ a multi‐agent‐based rolling optimisation method for scheduling restoration by allowing agents to identify communication connected parts in the distribution system with distributed communication.…”

Dynamic battery loss evaluation and its application for optimal online wind‐storage integrated scheduling

“…In (25), P BESS dis (t)/P BESS ch (t) is the discharge/ charge power of the BESS. In (26) and (27), P BESS dis, max /P BESS ch, max is the BESS discharge/charge power upper bound and v dis (t)/v ch (t) is the binary discharge/charge state variable. Constraint (28) avoids the BESS to be in charging and discharging modes simultaneously.…”

“…China, for example, has clearly specified the maximal error limitation for the short-term (the timescale is less than 4 h) wind power forecast, and the accuracy of the short-term wind power forecast output can be obtained from [3]. In order to ensure the effectiveness of the optimised results, a shorter advance control horizon H (compared to the short-term forecast timescale) is considered, and the matured receding horizon optimisation process [26] is used to conduct the real-time implementation of the proposed model. Fig.…”

Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network

“…But, it is noted that this capability of EVs is not considered in different researches such as [12–32]. In the many types of research, the energy management and restoration process of ADN have been investigated, separately [12–40]. In other words, the coordination of zone agents and RESs, as well as FSs with the distribution system operator (DSO), has not been considered such as works in [12–40].…”

“…In contrast, this coordination can obtain the high advantage and optional to ADN from reliability, security, stability, flexibility, and operationally viewpoints. For example, it is forecasted that the load not supplied and the total number of switches operation at fault condition will be reduced if the restoration process is coordinated with FEM, in comparison with the cases including only the self‐healing method. Moreover, the restoration approach needs to calculate the network variables before fault occurrence, where research related to this approach such as [33–40] uses power flow analyses for this purpose. But, noted that the power flow method is not suitable for ADN that includes different sources and active loads; for this reason, it needs optimal operation formwork to obtain the network variables.…”