Optimal Energy Management Algorithm for Smart Cities Using Online Energy Trading Framework. Issue 14 (8th December 2020)

• Record Type: Journal Article
• Authors: Rehman, Ubaid ur
• Is Part Of: Electric power components and systems. Volume 48:Issue 14/15(2020)
• Page Start: 1660
• Page End: 1672
• Publication Date: 2020-12-08
• Subjects: battery storage -- electric vehicles -- online energy trading -- real time pricing; Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2020.1857474
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: The latest and advance renewable power equipment being deployed in smart cities have increased the overall power generation efficiency. Due to the penetration of internet of things (IoT), the online energy trading (OET) methods in Smart Grids (SG) have significantly enhanced the power generation from multiple renewable resources and enabled the bidirectional flow of power between distributed generators (DGs). Previous research works on OET mainly focus on the optimization of central grid or enhancing the decisional capabilities of central OET operators. However, few of them developed the optimal decision taking methods for a single consumer in the OET structure, but these methods could not be implemented practically. In this paper we have proposed a mixed-integer-linear programming' (MILP') method for a consumer who have domestically installed renewable power plant, owns electric vehicle (EV), battery storage (BS) system, and uses OET based energy purchasing and sealing structure to minimize energy loss. In this method we have considered an actual power network for optimizing the EV and BS charging as well as managing different power utility facilities on the consumer end by OET, which enable a consumer to buy and sale electricity through OET network. In this method the energy cost is pre-set by the power suppliers for all OET platforms. Practically, the MILP' is NP' complete therefore, the proposed method is comprised of several problems and variables. To cope this issue, in this paper we have proposed a genetic algorithm (GA) this GA consists of a novel repairing framework to tackle the optimal solution infeasibilities for all contingencies. The simulation results show the proposed algorithm can efficiently reduce power loss on the consumer end.