dc.contributor.author |
Adetunji, KE
|
|
dc.contributor.author |
Hofsajer, IW
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|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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|
dc.contributor.author |
Cheng, L
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dc.date.accessioned |
2023-04-17T06:13:57Z |
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dc.date.available |
2023-04-17T06:13:57Z |
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dc.date.issued |
2022-09 |
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dc.identifier.citation |
Adetunji, 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/10204/12746 |
en_ZA |
dc.identifier.issn |
0306-2619 |
|
dc.identifier.issn |
1872-9118 |
|
dc.identifier.uri |
https://doi.org/10.1016/j.apenergy.2022.119513
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|
dc.identifier.uri |
http://hdl.handle.net/10204/12746
|
|
dc.description.abstract |
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. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.sciencedirect.com/science/article/pii/S0306261922008339 |
en_US |
dc.source |
Applied Energy, 322 |
en_US |
dc.subject |
Battery energy storage systems |
en_US |
dc.subject |
Distributed generation |
en_US |
dc.subject |
Electric vehicles |
en_US |
dc.subject |
Hybrid optimization algorithm |
en_US |
dc.subject |
Multiobjective optimization |
en_US |
dc.subject |
Pareto optimal solutions |
en_US |
dc.subject |
Reinforcement learning |
en_US |
dc.title |
An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
15pp |
en_US |
dc.description.note |
© 2022 Elsevier Ltd. All rights reserved. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://www.sciencedirect.com/science/article/pii/S0306261922008339 |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Adetunji, 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/10204/12746 |
en_ZA |
dc.identifier.chicagocitation |
Adetunji, 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/10204/12746 |
en_ZA |
dc.identifier.vancouvercitation |
Adetunji 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. |
en_ZA |
dc.identifier.ris |
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 - |
en_ZA |
dc.identifier.worklist |
26406 |
en_US |