Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies
Demand response (DR) flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a demand response optimization dispatch control strategy for flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs) with st...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Fractal and Fractional |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3110/5/4/140 |
| _version_ | 1797504586412457984 |
|---|---|
| author | Jianqiang Hu Jinde Cao |
| author_facet | Jianqiang Hu Jinde Cao |
| author_sort | Jianqiang Hu |
| collection | DOAJ |
| description | Demand response (DR) flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a demand response optimization dispatch control strategy for flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs) with stochastic renewable power injection. Firstly, a chance constraint look-ahead programming model is proposed to maximize the social welfare of both units and load agents, through which the optimal power scheduling for TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed, respectively, based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. It has been shown that the proposed dispatch and control strategy can coordinate the flexible load agents and the renewable power injection. Finally, the simulation results on a modified IEEE 39 bus system demonstrate the effectiveness of the proposed demand response strategy. |
| first_indexed | 2024-03-10T04:06:40Z |
| format | Article |
| id | doaj.art-cce13dac21b44a56acf94d7866141425 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-10T04:06:40Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-cce13dac21b44a56acf94d78661414252023-11-23T08:22:48ZengMDPI AGFractal and Fractional2504-31102021-09-015414010.3390/fractalfract5040140Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable EnergiesJianqiang Hu0Jinde Cao1Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing 211189, ChinaJiangsu Provincial Key Laboratory of Networked Collective Intelligence, School of Mathematics, Southeast University, Nanjing 211189, ChinaDemand response (DR) flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a demand response optimization dispatch control strategy for flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs) with stochastic renewable power injection. Firstly, a chance constraint look-ahead programming model is proposed to maximize the social welfare of both units and load agents, through which the optimal power scheduling for TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed, respectively, based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. It has been shown that the proposed dispatch and control strategy can coordinate the flexible load agents and the renewable power injection. Finally, the simulation results on a modified IEEE 39 bus system demonstrate the effectiveness of the proposed demand response strategy.https://www.mdpi.com/2504-3110/5/4/140chance-constraint programmingsource–load systemsdemand response controlthermostatically controlled loads (TCLs)plug-in electric vehicles (PEVs) |
| spellingShingle | Jianqiang Hu Jinde Cao Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies Fractal and Fractional chance-constraint programming source–load systems demand response control thermostatically controlled loads (TCLs) plug-in electric vehicles (PEVs) |
| title | Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies |
| title_full | Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies |
| title_fullStr | Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies |
| title_full_unstemmed | Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies |
| title_short | Demand Response Optimal Dispatch and Control of TCL and PEV Agents with Renewable Energies |
| title_sort | demand response optimal dispatch and control of tcl and pev agents with renewable energies |
| topic | chance-constraint programming source–load systems demand response control thermostatically controlled loads (TCLs) plug-in electric vehicles (PEVs) |
| url | https://www.mdpi.com/2504-3110/5/4/140 |
| work_keys_str_mv | AT jianqianghu demandresponseoptimaldispatchandcontroloftclandpevagentswithrenewableenergies AT jindecao demandresponseoptimaldispatchandcontroloftclandpevagentswithrenewableenergies |