A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems. (15th January 2018)

• Record Type: Journal Article
• Authors: Kandil, Sarah M.; Farag, Hany E.Z.; Shaaban, Mostafa F.; El-Sharafy, M. Zaki
• Is Part Of: Energy. Volume 143(2018)
• Page Start: 961
• Page End: 972
• Publication Date: 2018-01-15
• Subjects: Charging stations -- Distribution system resource allocation -- Electric vehicles -- Energy storage systems -- Genetic algorithms -- Monte Carlo simulation -- Renewable energy; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.11.005
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The massive deployment of plug-in electric vehicles (PEVs), renewable energy resources (RES), and distributed energy storage systems (DESS) has gained significant interest under the smart grid vision. However, their special features and operational characteristics have created a paradigm shift in distribution network resource allocation studies. This paper presents a combined model formulation for the concurrent optimal resource allocation of PEVs charging stations, RES and DESS in distribution networks. The formulation employs a general objective function that optimizes the total Annual Cost of Energy (ACOE). The decision variables in the formulation are the locations and capacities of PEVs charging stations, RES, and DESS units. A Markov Chain Monte Carlo (MCMC) simulation model is utilized to account for the uncertainties of PEVs charging demand and output generation of RES units. Also, in order to enhance the accuracy of the resource allocation problem, the coordinated control of PEVs charging, RES output power, and DESS charging/discharging are incorporated in the formulated model. The formulation is decomposed into two interdependent sub-problems and solved using a combination of metaheuristic and deterministic optimization techniques. A sample case study is presented to illustrate the performance of the algorithm. Highlights: Combined resource allocation framework of PEVs, RES and DESS is proposed. Unified scenario-based model is developed for PEVs demand and RES production. Operation scheduling is incorporated in the framework to enhance its accuracy. The proposed formulation is decomposed into two interdependent sub-problems. Useful analysis tool for LDC planners, policy makers and private investors.