Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm
As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach op...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/21/5278 |
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| _version_ | 1846173449404284928 |
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| author | Md Jamal Ahmed Shohan Md Maidul Islam Sophia Owais Md Omar Faruque |
| author_facet | Md Jamal Ahmed Shohan Md Maidul Islam Sophia Owais Md Omar Faruque |
| author_sort | Md Jamal Ahmed Shohan |
| collection | DOAJ |
| description | As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications. |
| format | Article |
| id | doaj-art-537e345744b34810822554a9cfa4a4c3 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-537e345744b34810822554a9cfa4a4c32024-11-08T14:35:10ZengMDPI AGEnergies1996-10732024-10-011721527810.3390/en17215278Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search AlgorithmMd Jamal Ahmed Shohan0Md Maidul Islam1Sophia Owais2Md Omar Faruque3Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USADepartment of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USADepartment of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USADepartment of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USAAs the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.https://www.mdpi.com/1996-1073/17/21/5278electric vehiclesgraph-search algorithmcharging-discharging schedulingenergy storagedigital real-time simulation |
| spellingShingle | Md Jamal Ahmed Shohan Md Maidul Islam Sophia Owais Md Omar Faruque Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm Energies electric vehicles graph-search algorithm charging-discharging scheduling energy storage digital real-time simulation |
| title | Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm |
| title_full | Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm |
| title_fullStr | Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm |
| title_full_unstemmed | Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm |
| title_short | Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm |
| title_sort | optimal energy management of evs at workplaces and residential buildings using heuristic graph search algorithm |
| topic | electric vehicles graph-search algorithm charging-discharging scheduling energy storage digital real-time simulation |
| url | https://www.mdpi.com/1996-1073/17/21/5278 |
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