Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm. (1st November 2020)

• Record Type: Journal Article
• Authors: Fan, Shuai; Liu, Jiang; Wu, Qing; Cui, Mingjian; Zhou, Huan; He, Guangyu
• Is Part Of: Applied energy. Volume 277(2020)
• Publication Date: 2020-11-01
• Subjects: Virtual power plant -- Online distributed optimization -- Power internet of things -- Photovoltaics -- Electric vehicles; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.115583
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A comprehensive optimal coordination model of VPP and a novel distributed online algorithm are proposed. The presented method significantly reduces the computational and communication complexity. The VPP operator can generate the inventive signals based on privacy-protected measurement feedback. Each device uses a closed-form strategy to update its setpoint locally based on currently observable information. The optimality of the proposed algorithm is characterized and guaranteed mathematically. Abstract: This paper presents a temporally coupled distributed online (TDO) algorithm to aggregate and coordinate numerous networked distributed energy resources (DERs) as a virtual power plant (VPP). A centralized stochastic optimization problem is formulated to minimize the long-term social utility loss while satisfying the voltage security, operational requirements of DERs, and VPP service requests. After that, we propose the TDO algorithm to reformulate the primary problem as an adaptation of online convex optimization (OCO). In particular, the temporally coupled constraints are well separated to each timeslot. In real-time operation, the VPP operator updates the incentives according to the measurement feedback. The smart energy gateways (SEGs) equipped at each node maximize their income and utility based on the received incentive signals through adjusting the setpoints of the governed photovoltaics (PV) inverters and electric vehicles (EVs). Unlike conventional distributed optimization algorithms where complicated iterative procedures between agents are unavoidable, the proposed TDO algorithm is computation- and communication-efficient since the SEG can directly employ the closed-form optimal setpoints without iterative communications once receiving the incentives. Furthermore, we design an incentive scheme to coordinate the SEGs based on the privacy protected nonintrusive measurements instead of direct control. Optimality and convergency of TDO are analyzed mathematically. Finally, the proposed method is corroborated numerically on a modified 33-node test feeder. A larger system is tested to validate the computational time performance.