A novel dynamic planning mechanism for allocating electric vehicle charging stations considering distributed generation and electronic units. (November 2022)

• Record Type: Journal Article
• Authors: Adetunji, Kayode E.; Hofsajer, Ivan W.; Abu-Mahfouz, Adnan M.; Cheng, Ling
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 14658
• Page End: 14672
• Publication Date: 2022-11
• Subjects: Battery energy storage systems -- Computational intelligence -- Distributed generation -- Electric vehicles -- Electric vehicle charging station -- Hybrid optimization algorithm -- Planning mechanism -- Reinforcement learning; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.10.379
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Achieving a sustainable and efficient power systems network and decarbonized environment involves the optimal allocation of multiple distributed energy resource (DERs) unit types and flexible alternating current transmission systems (FACTS) to distribution networks. However, while the most focus is on optimization algorithms and multi-objective techniques, little to no attention is paid to the underlying mechanisms in planning frameworks. This paper goes beyond existing literature by investigating the impact of planning mechanisms in smart grid planning frameworks when considering the allocation of PV distributed generation units, battery energy storage systems, capacitor banks, and electric vehicle charging station facilities. First, a single- and multi-objective planning problem is formulated. Then, we propose a novel adaptive-dynamic planning mechanism that uses a recombination technique to find optimal allocation variables of multiple DER and FACTS types. To cope with the additional complexity resulting from the expanded solution space, we develop a hybrid stochastic optimizer, named cooperative spiral genetic algorithm with differential evolution (CoSGADE) optimization scheme, to produce optimal allocation solution variables. Through numerical simulations, it is seen that the proposed adaptive planning mechanism improves achieves a 12% and 14% improvement to the conventional sequential (multi-stage) and simultaneous mechanisms, on small to large scale distribution networks.