Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges
In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10018371/ |
| _version_ | 1797894941633937408 |
|---|---|
| author | Amira Noor Farhanie Ali Mohamad Fani Sulaima Intan Azmira Wan Abdul Razak Aida Fazliana Abdul Kadir Hazlie Mokhlis |
| author_facet | Amira Noor Farhanie Ali Mohamad Fani Sulaima Intan Azmira Wan Abdul Razak Aida Fazliana Abdul Kadir Hazlie Mokhlis |
| author_sort | Amira Noor Farhanie Ali |
| collection | DOAJ |
| description | In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme’s optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demand-side management solutions is also illustrated congruently. |
| first_indexed | 2024-04-10T07:18:34Z |
| format | Article |
| id | doaj.art-7650391084bd4e90a7b0a05f12b83179 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T07:18:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7650391084bd4e90a7b0a05f12b831792023-02-25T00:02:32ZengIEEEIEEE Access2169-35362023-01-0111169071692210.1109/ACCESS.2023.323773710018371Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and ChallengesAmira Noor Farhanie Ali0Mohamad Fani Sulaima1https://orcid.org/0000-0003-1600-9539Intan Azmira Wan Abdul Razak2Aida Fazliana Abdul Kadir3Hazlie Mokhlis4https://orcid.org/0000-0002-1166-1934Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, MalaysiaFaculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, MalaysiaFaculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, MalaysiaFaculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, MalaysiaFaculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaIn recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme’s optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demand-side management solutions is also illustrated congruently.https://ieeexplore.ieee.org/document/10018371/Artificial intelligence (AI)demand response (DR)demand side management (DSM)optimization algorithms |
| spellingShingle | Amira Noor Farhanie Ali Mohamad Fani Sulaima Intan Azmira Wan Abdul Razak Aida Fazliana Abdul Kadir Hazlie Mokhlis Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges IEEE Access Artificial intelligence (AI) demand response (DR) demand side management (DSM) optimization algorithms |
| title | Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges |
| title_full | Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges |
| title_fullStr | Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges |
| title_full_unstemmed | Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges |
| title_short | Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges |
| title_sort | artificial intelligence application in demand response advantages issues status and challenges |
| topic | Artificial intelligence (AI) demand response (DR) demand side management (DSM) optimization algorithms |
| url | https://ieeexplore.ieee.org/document/10018371/ |
| work_keys_str_mv | AT amiranoorfarhanieali artificialintelligenceapplicationindemandresponseadvantagesissuesstatusandchallenges AT mohamadfanisulaima artificialintelligenceapplicationindemandresponseadvantagesissuesstatusandchallenges AT intanazmirawanabdulrazak artificialintelligenceapplicationindemandresponseadvantagesissuesstatusandchallenges AT aidafazlianaabdulkadir artificialintelligenceapplicationindemandresponseadvantagesissuesstatusandchallenges AT hazliemokhlis artificialintelligenceapplicationindemandresponseadvantagesissuesstatusandchallenges |