Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors
In recent years, the power market and regional distributed energy systems (RDES) in China have experienced considerable growth. However, the critical issue of how multi-stakeholder parties within the distributed energy system evaluate risk preferences in order to develop scientifically sound trading...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-04-01
|
| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1173981/full |
| _version_ | 1797843252496302080 |
|---|---|
| author | Jun Dong Xihao Dou Dongran Liu Aruhan Bao Dongxue Wang Yunzhou Zhang Peng Jiang |
| author_facet | Jun Dong Xihao Dou Dongran Liu Aruhan Bao Dongxue Wang Yunzhou Zhang Peng Jiang |
| author_sort | Jun Dong |
| collection | DOAJ |
| description | In recent years, the power market and regional distributed energy systems (RDES) in China have experienced considerable growth. However, the critical issue of how multi-stakeholder parties within the distributed energy system evaluate risk preferences in order to develop scientifically sound trading strategies remains unclear. To address this problem, this study constructs a multi-agent assisted decision-making model that incorporates the critical features of a regional distributed energy system. By simulating various calculation scenarios using this model, the study aims to provide a better understanding of the system’s multi-agent interactions and decision-making processes. First, different types of stakeholders and risk preferences in RDES are delineated. Second, supply and demand fluctuations in RDRS are treated and the impact of wholesale market price volatility risk on distributed energy system aggregators (DERA) decisions is fully considered. Meanwhile, a multi-stakeholders DERA transaction decision-making model in the day-ahead market considering risk preference behaviors is constructed based on information gap decision theory (IGDT) and solved by the Opposition Learning Grey Wolf Optimizer (OLGWO). The mathematical analysis conducted in this study indicates that the approach proposed could provide an effective trading scheme and operational strategy for multi-interest entities participating in the market of RDES. Therefore, incorporating the proposed approach would be beneficial in enhancing the performance and effectiveness of such systems. |
| first_indexed | 2024-04-09T17:02:10Z |
| format | Article |
| id | doaj.art-839c9da12d8d4fe2bcf8f098bc8dda46 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-09T17:02:10Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-839c9da12d8d4fe2bcf8f098bc8dda462023-04-21T04:39:05ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-04-011110.3389/fenrg.2023.11739811173981Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviorsJun Dong0Xihao Dou1Dongran Liu2Aruhan Bao3Dongxue Wang4Yunzhou Zhang5Peng Jiang6School of Economics and Management, North China Electric Power University, Beijing, ChinaSchool of Economics and Management, North China Electric Power University, Beijing, ChinaSchool of Economics and Management, North China Electric Power University, Beijing, ChinaSchool of Economics and Management, North China Electric Power University, Beijing, ChinaSchool of Economics and Management, Wuhan University, Wuhan, ChinaSchool of Economics and Management, North China Electric Power University, Beijing, ChinaSchool of Economics and Management, North China Electric Power University, Beijing, ChinaIn recent years, the power market and regional distributed energy systems (RDES) in China have experienced considerable growth. However, the critical issue of how multi-stakeholder parties within the distributed energy system evaluate risk preferences in order to develop scientifically sound trading strategies remains unclear. To address this problem, this study constructs a multi-agent assisted decision-making model that incorporates the critical features of a regional distributed energy system. By simulating various calculation scenarios using this model, the study aims to provide a better understanding of the system’s multi-agent interactions and decision-making processes. First, different types of stakeholders and risk preferences in RDES are delineated. Second, supply and demand fluctuations in RDRS are treated and the impact of wholesale market price volatility risk on distributed energy system aggregators (DERA) decisions is fully considered. Meanwhile, a multi-stakeholders DERA transaction decision-making model in the day-ahead market considering risk preference behaviors is constructed based on information gap decision theory (IGDT) and solved by the Opposition Learning Grey Wolf Optimizer (OLGWO). The mathematical analysis conducted in this study indicates that the approach proposed could provide an effective trading scheme and operational strategy for multi-interest entities participating in the market of RDES. Therefore, incorporating the proposed approach would be beneficial in enhancing the performance and effectiveness of such systems.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1173981/fullregional distributed energy systemmulti-stakeholdersrisk preferencedecision supportinformation gap decision theoryopposition learning grey wolf optimizer |
| spellingShingle | Jun Dong Xihao Dou Dongran Liu Aruhan Bao Dongxue Wang Yunzhou Zhang Peng Jiang Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors Frontiers in Energy Research regional distributed energy system multi-stakeholders risk preference decision support information gap decision theory opposition learning grey wolf optimizer |
| title | Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors |
| title_full | Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors |
| title_fullStr | Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors |
| title_full_unstemmed | Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors |
| title_short | Energy trading support decision model of distributed energy resources aggregator in day-ahead market considering multi-stakeholder risk preference behaviors |
| title_sort | energy trading support decision model of distributed energy resources aggregator in day ahead market considering multi stakeholder risk preference behaviors |
| topic | regional distributed energy system multi-stakeholders risk preference decision support information gap decision theory opposition learning grey wolf optimizer |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1173981/full |
| work_keys_str_mv | AT jundong energytradingsupportdecisionmodelofdistributedenergyresourcesaggregatorindayaheadmarketconsideringmultistakeholderriskpreferencebehaviors AT xihaodou energytradingsupportdecisionmodelofdistributedenergyresourcesaggregatorindayaheadmarketconsideringmultistakeholderriskpreferencebehaviors AT dongranliu energytradingsupportdecisionmodelofdistributedenergyresourcesaggregatorindayaheadmarketconsideringmultistakeholderriskpreferencebehaviors AT aruhanbao energytradingsupportdecisionmodelofdistributedenergyresourcesaggregatorindayaheadmarketconsideringmultistakeholderriskpreferencebehaviors AT dongxuewang energytradingsupportdecisionmodelofdistributedenergyresourcesaggregatorindayaheadmarketconsideringmultistakeholderriskpreferencebehaviors AT yunzhouzhang energytradingsupportdecisionmodelofdistributedenergyresourcesaggregatorindayaheadmarketconsideringmultistakeholderriskpreferencebehaviors AT pengjiang energytradingsupportdecisionmodelofdistributedenergyresourcesaggregatorindayaheadmarketconsideringmultistakeholderriskpreferencebehaviors |