Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current
Changes are emerging that will significantly alter structure and operation of this century’s distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical load...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/5587904 |
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| _version_ | 1849397919746097152 |
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| author | Ibrahim Cagri Barutcu |
| author_facet | Ibrahim Cagri Barutcu |
| author_sort | Ibrahim Cagri Barutcu |
| collection | DOAJ |
| description | Changes are emerging that will significantly alter structure and operation of this century’s distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation. |
| format | Article |
| id | doaj-art-a67490da8e384460abefbce0ad355b91 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-a67490da8e384460abefbce0ad355b912025-08-20T03:38:48ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/5587904Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line CurrentIbrahim Cagri Barutcu0Çölemerik V.H.S.Changes are emerging that will significantly alter structure and operation of this century’s distribution networks, and photovoltaic (PV) systems will play greater role in energy sector, with implications for power system reliability. Considering uncertainties in solar irradiance and electrical loads and incorporating them into the optimization problem within an appropriate methodology is becoming increasingly important in reshaping distribution networks. In this paper, uncertainty scenarios are handled with Monte Carlo Simulation (MCS) under genetic algorithm (GA) and differential evolution- (DE-) based optimization, and probability distribution functions (pdf) of bus voltages and line current are obtained to be used in chance-constrained stochastic programming. This present study focuses on investigating impact of uncertainties in PV system operating under different irradiance scenarios on power loss with probabilistic constraints in distribution networks instead of precise deterministic limits to contribute more efficient and reliable use of energy. By combining meta-heuristic optimization and MCS technique under one framework, this paper contributes to knowledge base of how to allocate PV plants within distribution networks under chance-constrained strategy. In order to show the effectiveness of the proposed methodology, obtained optimization results are tested using MCS under set of uncertainty conditions and network constraints are evaluated for limit violation probabilities. The effectiveness of this method is investigated based on comparative results of two different optimization methods through probabilistic analysis and simulation.http://dx.doi.org/10.1155/2023/5587904 |
| spellingShingle | Ibrahim Cagri Barutcu Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current International Transactions on Electrical Energy Systems |
| title | Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current |
| title_full | Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current |
| title_fullStr | Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current |
| title_full_unstemmed | Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current |
| title_short | Chance-Constrained Optimization of Photovoltaic System Allocation considering Power Loss, Voltage Level, and Line Current |
| title_sort | chance constrained optimization of photovoltaic system allocation considering power loss voltage level and line current |
| url | http://dx.doi.org/10.1155/2023/5587904 |
| work_keys_str_mv | AT ibrahimcagribarutcu chanceconstrainedoptimizationofphotovoltaicsystemallocationconsideringpowerlossvoltagelevelandlinecurrent |