A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty
A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/6/1/23 |
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| author | Amrutha Raju Battula Sandeep Vuddanti Surender Reddy Salkuti |
| author_facet | Amrutha Raju Battula Sandeep Vuddanti Surender Reddy Salkuti |
| author_sort | Amrutha Raju Battula |
| collection | DOAJ |
| description | A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management. |
| first_indexed | 2024-03-11T08:09:21Z |
| format | Article |
| id | doaj.art-eb6c279d2f8d4a22b2af4769e1757eee |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-11T08:09:21Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-eb6c279d2f8d4a22b2af4769e1757eee2023-11-16T23:15:14ZengMDPI AGSmart Cities2624-65112023-02-016149150910.3390/smartcities6010023A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with UncertaintyAmrutha Raju Battula0Sandeep Vuddanti1Surender Reddy Salkuti2Department of Electrical Engineering, National Institute of Technology Andhra Pradesh (NIT-AP), Tadepalligudem 534101, Andhra Pradesh, IndiaDepartment of Electrical Engineering, National Institute of Technology Andhra Pradesh (NIT-AP), Tadepalligudem 534101, Andhra Pradesh, IndiaDepartment of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Republic of KoreaA microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management.https://www.mdpi.com/2624-6511/6/1/23microgriddemand schedulingpoint estimate methodenergy managementmixed integer linear programminggenetic algorithm |
| spellingShingle | Amrutha Raju Battula Sandeep Vuddanti Surender Reddy Salkuti A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty Smart Cities microgrid demand scheduling point estimate method energy management mixed integer linear programming genetic algorithm |
| title | A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty |
| title_full | A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty |
| title_fullStr | A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty |
| title_full_unstemmed | A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty |
| title_short | A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty |
| title_sort | day ahead demand schedule strategy for optimal operation of microgrid with uncertainty |
| topic | microgrid demand scheduling point estimate method energy management mixed integer linear programming genetic algorithm |
| url | https://www.mdpi.com/2624-6511/6/1/23 |
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