Adetunji, KEHofsajer, IWAbu-Mahfouz, Adnan MICheng, L2023-04-172023-04-172022-09Adetunji, K., Hofsajer, I., Abu-Mahfouz, A.M. & Cheng, L. 2022. An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks. <i>Applied Energy, 322.</i> http://hdl.handle.net/10204/127460306-26191872-9118https://doi.org/10.1016/j.apenergy.2022.119513http://hdl.handle.net/10204/12746In developing a sustainable and efficient power systems network while reducing carbon footprint, renewable energy (RE)-based Distribution Generation (DG) units are highly recommended. Furthermore, Battery Energy Storage Systems (BESS) and other passive electronic units are adopted to improve grid performance and mitigate the effects of high variability from RE power. Hence, planning frameworks are developed to optimally allocate these units to distribution networks. However, current planning mechanisms do not consider the relative effect of different allocated units in planning frameworks. To bridge this gap, this paper presents a novel comprehensive planning framework for allocating DG units, BESS units, and Electric Vehicle Charging Station (EVCS) facilities in a distribution network while optimizing its technical, economic, and environmental benefits. The proposed framework uses a recombination technique to generate more solutions by dynamically updating the DG and BESS units’ locations in one iteration. A Reinforcement Learning (RL)-based algorithm is introduced to coordinate EV charging that suggests the optimal EVCS location in relation to other units’ locations. To cope with the complexity ensuing from searching a larger solution space, a multi-stage, hybrid optimization scheme is developed to produce optimal allocation variables. A category-based multiobjective framework is further developed to simultaneously optimize many objective functions — power loss, voltage stability, voltage deviation, installation and operation cost, and emission cost. Through numerical simulations on the IEEE 33- and 118-bus distribution network, it is shown that the proposed optimization scheme achieves higher metric values than the adopted benchmark optimization schemes. A validation process was also carried out on the proposed multiobjective optimization approach, comparing it with other approaches. Using the Spacing metric, the proposed approach proves to be efficient, depicting a good spread of Pareto optimal solutions.AbstractenBattery energy storage systemsDistributed generationElectric vehiclesHybrid optimization algorithmMultiobjective optimizationPareto optimal solutionsReinforcement learningArticleAdetunji, K., Hofsajer, I., Abu-Mahfouz, A. M., & Cheng, L. (2022). An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks. <i>Applied Energy, 322</i>, http://hdl.handle.net/10204/12746Adetunji, KE, IW Hofsajer, Adnan MI Abu-Mahfouz, and L Cheng "An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks." <i>Applied Energy, 322</i> (2022) http://hdl.handle.net/10204/12746Adetunji K, Hofsajer I, Abu-Mahfouz AM, Cheng L. An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks. Applied Energy, 322. 2022; http://hdl.handle.net/10204/12746.TY - Article AU - Adetunji, KE AU - Hofsajer, IW AU - Abu-Mahfouz, Adnan MI AU - Cheng, L AB - In developing a sustainable and efficient power systems network while reducing carbon footprint, renewable energy (RE)-based Distribution Generation (DG) units are highly recommended. Furthermore, Battery Energy Storage Systems (BESS) and other passive electronic units are adopted to improve grid performance and mitigate the effects of high variability from RE power. Hence, planning frameworks are developed to optimally allocate these units to distribution networks. However, current planning mechanisms do not consider the relative effect of different allocated units in planning frameworks. To bridge this gap, this paper presents a novel comprehensive planning framework for allocating DG units, BESS units, and Electric Vehicle Charging Station (EVCS) facilities in a distribution network while optimizing its technical, economic, and environmental benefits. The proposed framework uses a recombination technique to generate more solutions by dynamically updating the DG and BESS units’ locations in one iteration. A Reinforcement Learning (RL)-based algorithm is introduced to coordinate EV charging that suggests the optimal EVCS location in relation to other units’ locations. To cope with the complexity ensuing from searching a larger solution space, a multi-stage, hybrid optimization scheme is developed to produce optimal allocation variables. A category-based multiobjective framework is further developed to simultaneously optimize many objective functions — power loss, voltage stability, voltage deviation, installation and operation cost, and emission cost. Through numerical simulations on the IEEE 33- and 118-bus distribution network, it is shown that the proposed optimization scheme achieves higher metric values than the adopted benchmark optimization schemes. A validation process was also carried out on the proposed multiobjective optimization approach, comparing it with other approaches. Using the Spacing metric, the proposed approach proves to be efficient, depicting a good spread of Pareto optimal solutions. DA - 2022-09 DB - ResearchSpace DP - CSIR J1 - Applied Energy, 322 KW - Battery energy storage systems KW - Distributed generation KW - Electric vehicles KW - Hybrid optimization algorithm KW - Multiobjective optimization KW - Pareto optimal solutions KW - Reinforcement learning LK - https://researchspace.csir.co.za PY - 2022 SM - 0306-2619 SM - 1872-9118 T1 - An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks TI - An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks UR - http://hdl.handle.net/10204/12746 ER -26406