Demand-side management in residential community realizing sharing economy with bidirectional PEV while additionally considering commercial area. (March 2020)

• Record Type: Journal Article
• Authors: Cheng, Pei-Hsuan; Huang, Tzu-Han; Chien, Yi-Wei; Wu, Chao-Lin; Tai, Chia-Shing; Fu, Li-Chen
• Is Part Of: International journal of electrical power & energy systems. Volume 116(2020)
• Publication Date: 2020-03
• Subjects: Demand-side management -- V2G -- Energy trading -- Sharing economy -- Plug-in electric vehicle -- Smart grid; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.105512
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A demand-side management system using particle swarm optimization. Calculates the optimal activity schedule based on user preferences profile. Considers selling energy back to grid, home energy storage, renewable energy and EV. Helps the community sharing PEV's energy within a profitable and fair strategy. The problem of multiple PEVs charging in the commercial building is also solved. Abstract: In smart grids, demand-side management (DSM) is one of the important mechanism since it can reduce the total electricity cost of each customer, meanwhile alleviates the aggregate peak-to-average ratio (PAR) subject to real-time pricing (RTP) policy. On the other hand, with the growth of bidirectional charging/discharging Plug-in Electric vehicles (PEV), the capability of storing electrical energy for load shifting and energy sharing among users may take smart grid to a next level. In the viewpoint of a community, we design a fairness strategy and apply particle swarm optimization (PSO) to get the near optimal sharing schedule within community and share PEV's battery with neighbors to reduce the total electricity cost and peak-to-average ratio (PAR). On the other hand, we try to utilize the PEVs which park at the commercial building during working hour to be involved with part of the building's power consumption. In the simulation, we have collected ten daily activity profiles from ten different people to represent the residents in the community and use the hourly load profile data from the Office of Energy Efficiency & Renewable Energy (EERE) for a commercial sector. The results show that the proposed DSM system not only reduces the electricity cost for each household but also reduces the PAR of the community. Moreover, for the commercial building, the proper rearrangement of PEVs' behavior also helps to reduce the total electricity cost and PAR.