Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach
The increasing occurrence of extreme weather events has severely compromised the resilience of power distribution systems, resulting in widespread outages and substantial economic losses. This paper proposes a novel solution to enhance the resilience of distribution networks without the need for sig...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524006227 |
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| author | Farid Hamzeh Aghdam Aleksandr Zavodovski Adeleye Adetunji Mehdi Rasti Eva Pongracz Mohammad Sadegh Javadi João P.S. Catalão |
| author_facet | Farid Hamzeh Aghdam Aleksandr Zavodovski Adeleye Adetunji Mehdi Rasti Eva Pongracz Mohammad Sadegh Javadi João P.S. Catalão |
| author_sort | Farid Hamzeh Aghdam |
| collection | DOAJ |
| description | The increasing occurrence of extreme weather events has severely compromised the resilience of power distribution systems, resulting in widespread outages and substantial economic losses. This paper proposes a novel solution to enhance the resilience of distribution networks without the need for significant infrastructure upgrades. We introduce a bilevel optimization framework that integrates Demand Response Programs (DRPs) to strategically manage electricity consumption and mitigate the impact of system disruptions. The approach fosters collaboration between Distribution System Operators (DSOs) and Demand Response Aggregators (DRAs), optimizing both operational resilience and economic efficiency. To solve the bilevel problem, we employ a Mathematical Program with Equilibrium Constraints (MPEC), transforming the bilevel model into a single-level problem by utilizing the Karush–Kuhn–Tucker (KKT) conditions. This method is applicable when the lower-level problem is convex with linear constraints. The model also incorporates Long Short-Term Memory (LSTM) neural networks for wind generation forecasting, enhancing decision-making precision. Furthermore, we conduct multiple case studies under varying severities of incidents to evaluate the method’s effectiveness. Simulations performed on the IEEE 33-bus test system using GAMS and Python validate that the proposed method not only improves system resilience but also encourages active consumer participation, making it a robust solution for modern smart grid applications. The simulation results show that by performing DRP to handle the contingencies in a high-impact incident, the resilience of the system can be improved by 5.3%. |
| format | Article |
| id | doaj-art-d706b264d2314b6a856d5cc88db7de19 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-d706b264d2314b6a856d5cc88db7de192025-01-19T06:23:54ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-03-01164110399Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approachFarid Hamzeh Aghdam0Aleksandr Zavodovski1Adeleye Adetunji2Mehdi Rasti3Eva Pongracz4Mohammad Sadegh Javadi5João P.S. Catalão6Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FI-90014, Oulu, Finland; Corresponding author.Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FI-90014, Oulu, Finland; Centre for Wireless Communication, University of Oulu, P.O. Box 4300, FI-90014, Oulu, FinlandWater, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FI-90014, Oulu, FinlandWater, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FI-90014, Oulu, Finland; Centre for Wireless Communication, University of Oulu, P.O. Box 4300, FI-90014, Oulu, FinlandWater, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FI-90014, Oulu, FinlandInstitute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, PortugalResearch Center for Systems and Technologies (SYSTEC), Advanced Production and Intelligent Systems Associate Laboratory (ARISE), Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalThe increasing occurrence of extreme weather events has severely compromised the resilience of power distribution systems, resulting in widespread outages and substantial economic losses. This paper proposes a novel solution to enhance the resilience of distribution networks without the need for significant infrastructure upgrades. We introduce a bilevel optimization framework that integrates Demand Response Programs (DRPs) to strategically manage electricity consumption and mitigate the impact of system disruptions. The approach fosters collaboration between Distribution System Operators (DSOs) and Demand Response Aggregators (DRAs), optimizing both operational resilience and economic efficiency. To solve the bilevel problem, we employ a Mathematical Program with Equilibrium Constraints (MPEC), transforming the bilevel model into a single-level problem by utilizing the Karush–Kuhn–Tucker (KKT) conditions. This method is applicable when the lower-level problem is convex with linear constraints. The model also incorporates Long Short-Term Memory (LSTM) neural networks for wind generation forecasting, enhancing decision-making precision. Furthermore, we conduct multiple case studies under varying severities of incidents to evaluate the method’s effectiveness. Simulations performed on the IEEE 33-bus test system using GAMS and Python validate that the proposed method not only improves system resilience but also encourages active consumer participation, making it a robust solution for modern smart grid applications. The simulation results show that by performing DRP to handle the contingencies in a high-impact incident, the resilience of the system can be improved by 5.3%.http://www.sciencedirect.com/science/article/pii/S0142061524006227Demand Response AggregatorResilienceDay-ahead operationConsumer participationBilevel optimizationLong short-term memory |
| spellingShingle | Farid Hamzeh Aghdam Aleksandr Zavodovski Adeleye Adetunji Mehdi Rasti Eva Pongracz Mohammad Sadegh Javadi João P.S. Catalão Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach International Journal of Electrical Power & Energy Systems Demand Response Aggregator Resilience Day-ahead operation Consumer participation Bilevel optimization Long short-term memory |
| title | Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach |
| title_full | Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach |
| title_fullStr | Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach |
| title_full_unstemmed | Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach |
| title_short | Co-optimization of Demand Response Aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting: A bilevel approach |
| title_sort | co optimization of demand response aggregators and distribution system operator for resilient operation using machine learning based wind generation forecasting a bilevel approach |
| topic | Demand Response Aggregator Resilience Day-ahead operation Consumer participation Bilevel optimization Long short-term memory |
| url | http://www.sciencedirect.com/science/article/pii/S0142061524006227 |
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