Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions
This paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9468665/ |
| _version_ | 1818601169789911040 |
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| author | Xin Wen Dhaker Abbes Bruno Francois |
| author_facet | Xin Wen Dhaker Abbes Bruno Francois |
| author_sort | Xin Wen |
| collection | DOAJ |
| description | This paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into a two-stage unit commitment optimization. The planning objective consists in minimizing operating costs and/or equivalent carbon dioxide (CO<sub>2</sub>) emissions. Based on distributions of forecasting errors of the net demand, a LOLP-based risk assessment method is proposed to determine an appropriate amount of operating reserve (OR) for each time step of the next day. Then, in a first stage, a deterministic optimization within a mixed-integer linear programming (MILP) method generates the unit commitment of controllable generators with the day-ahead PV and load demand prediction and the prescribed OR requirement. In a second stage, possible future forecasting uncertainties are considered. Hence, a stochastic operational planning is optimized in order to commit enough flexible generators to handle unexpected deviations from predic-tions. The proposed methodology is implemented for a local energy community. Results regarding the available operating reserve, operating costs and CO<sub>2</sub> emissions are established and compared. About 15% of economic operating costs and environmental costs are saved, compared to a deterministic generation planning while ensuring the targeted security level. |
| first_indexed | 2024-12-16T12:47:07Z |
| format | Article |
| id | doaj.art-12f0af6e7e3b4b7f836e1171977e87bc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T12:47:07Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-12f0af6e7e3b4b7f836e1171977e87bc2022-12-21T22:31:15ZengIEEEIEEE Access2169-35362021-01-019970399705210.1109/ACCESS.2021.30936539468665Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon EmissionsXin Wen0https://orcid.org/0000-0001-6479-0126Dhaker Abbes1Bruno Francois2https://orcid.org/0000-0002-9717-5004Centrale Lille, Cité Scientifique, Villeneuve d’Ascq, FranceJunia Hei Lille, Lille, FranceCentrale Lille, Cité Scientifique, Villeneuve d’Ascq, FranceThis paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into a two-stage unit commitment optimization. The planning objective consists in minimizing operating costs and/or equivalent carbon dioxide (CO<sub>2</sub>) emissions. Based on distributions of forecasting errors of the net demand, a LOLP-based risk assessment method is proposed to determine an appropriate amount of operating reserve (OR) for each time step of the next day. Then, in a first stage, a deterministic optimization within a mixed-integer linear programming (MILP) method generates the unit commitment of controllable generators with the day-ahead PV and load demand prediction and the prescribed OR requirement. In a second stage, possible future forecasting uncertainties are considered. Hence, a stochastic operational planning is optimized in order to commit enough flexible generators to handle unexpected deviations from predic-tions. The proposed methodology is implemented for a local energy community. Results regarding the available operating reserve, operating costs and CO<sub>2</sub> emissions are established and compared. About 15% of economic operating costs and environmental costs are saved, compared to a deterministic generation planning while ensuring the targeted security level.https://ieeexplore.ieee.org/document/9468665/Decision makinggenerator schedulingprobabilistic modelingrenewable energyreserve allocationstochastic optimization |
| spellingShingle | Xin Wen Dhaker Abbes Bruno Francois Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions IEEE Access Decision making generator scheduling probabilistic modeling renewable energy reserve allocation stochastic optimization |
| title | Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions |
| title_full | Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions |
| title_fullStr | Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions |
| title_full_unstemmed | Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions |
| title_short | Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions |
| title_sort | stochastic optimization for security constrained day ahead operational planning under pv production uncertainties reduction analysis of operating economic costs and carbon emissions |
| topic | Decision making generator scheduling probabilistic modeling renewable energy reserve allocation stochastic optimization |
| url | https://ieeexplore.ieee.org/document/9468665/ |
| work_keys_str_mv | AT xinwen stochasticoptimizationforsecurityconstraineddayaheadoperationalplanningunderpvproductionuncertaintiesreductionanalysisofoperatingeconomiccostsandcarbonemissions AT dhakerabbes stochasticoptimizationforsecurityconstraineddayaheadoperationalplanningunderpvproductionuncertaintiesreductionanalysisofoperatingeconomiccostsandcarbonemissions AT brunofrancois stochasticoptimizationforsecurityconstraineddayaheadoperationalplanningunderpvproductionuncertaintiesreductionanalysisofoperatingeconomiccostsandcarbonemissions |