Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation
Abstract The economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainab...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13077 |
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| author | Aamir Ali Sumbal Aslam Sohrab Mirsaeidi Noor Hussain Mugheri Riaz Hussain Memon Ghulam Abbas Hammad Alnuman |
| author_facet | Aamir Ali Sumbal Aslam Sohrab Mirsaeidi Noor Hussain Mugheri Riaz Hussain Memon Ghulam Abbas Hammad Alnuman |
| author_sort | Aamir Ali |
| collection | DOAJ |
| description | Abstract The economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs. |
| format | Article |
| id | doaj-art-d29cf709a0a34fe58172628fc6859e51 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-d29cf709a0a34fe58172628fc6859e512025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163903392210.1049/rpg2.13077Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generationAamir Ali0Sumbal Aslam1Sohrab Mirsaeidi2Noor Hussain Mugheri3Riaz Hussain Memon4Ghulam Abbas5Hammad Alnuman6Department of Electrical Engineering Quaid‐e‐Awam University of Engineering Science and Technology Nawabshah Sindh PakistanDepartment of Electrical Engineering Quaid‐e‐Awam University of Engineering Science and Technology Nawabshah Sindh PakistanSchool of Electrical Engineering Beijing Jiaotong University Beijing ChinaDepartment of Electrical Engineering Quaid‐e‐Awam University of Engineering Science and Technology Nawabshah Sindh PakistanDepartment of Electrical Engineering Quaid‐e‐Awam University of Engineering Science and Technology Nawabshah Sindh PakistanSchool of Electrical Engineering Southeast University Nanjing ChinaDepartment of Electrical Engineering, College of Engineering Jouf University Sakaka Saudi ArabiaAbstract The economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.https://doi.org/10.1049/rpg2.13077distribution networkseconomic forecastingoptimisationPareto optimisationrenewable energy sources |
| spellingShingle | Aamir Ali Sumbal Aslam Sohrab Mirsaeidi Noor Hussain Mugheri Riaz Hussain Memon Ghulam Abbas Hammad Alnuman Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation IET Renewable Power Generation distribution networks economic forecasting optimisation Pareto optimisation renewable energy sources |
| title | Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation |
| title_full | Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation |
| title_fullStr | Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation |
| title_full_unstemmed | Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation |
| title_short | Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation |
| title_sort | multi objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar pv generation |
| topic | distribution networks economic forecasting optimisation Pareto optimisation renewable energy sources |
| url | https://doi.org/10.1049/rpg2.13077 |
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