Multi-time Scale Thermoelectric Joint Optimal Dispatching of Virtual Power Plant With Solar-thermal Power Station

ZHANG Guobin; ZHU Cen; YUAN Guili; FU Yu; LI Hongbo; HANG Chenhui

doi:10.19725/j.cnki.1007-2322.2023.0380

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Multi-time Scale Thermoelectric Joint Optimal Dispatching of Virtual Power Plant With Solar-thermal Power Station

更新时间：2026-04-02

- Multi-time Scale Thermoelectric Joint Optimal Dispatching of Virtual Power Plant With Solar-thermal Power Station
- Vol. 43, Issue 2, Pages: 265-275(2026)
- 作者机构：
  
  1. 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司, 内蒙古自治区,呼和浩特市,010020
  2. 华北电力大学控制与计算机工程学院, 北京市昌平区,102206
  3. 内蒙古电力(集团)有限责任公司电力调度控制分公司, 内蒙古自治区,呼和浩特市,010010
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2023.0380
  CLC： 2
- Published：2026
- 稿件说明：
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ZHANG Guobin, ZHU Cen, YUAN Guili, et al. Multi-time Scale Thermoelectric Joint Optimal Dispatching of Virtual Power Plant With Solar-thermal Power Station[J]. 2026, 43(2): 265-275.
DOI：

ZHANG Guobin, ZHU Cen, YUAN Guili, et al. Multi-time Scale Thermoelectric Joint Optimal Dispatching of Virtual Power Plant With Solar-thermal Power Station[J]. 2026, 43(2): 265-275. DOI： 10.19725/j.cnki.1007-2322.2023.0380.

摘要

为提高虚拟电厂调度计划的精确性，充分挖掘虚拟电厂内灵活性资源的调节潜力，该文提出一种日前–日内多时间尺度优化调度方法，对风光出力和负荷分别进行日前预测和日内预测。采用储能电池对日内风光出力波动进行平抑。利用光热电站对热电机组解耦，降低热电机组最小出力，考虑不同响应速度的需求响应资源，建立含光热电站的虚拟电厂多时间尺度热电联合优化调度模型，采用自适应遗传算法求解。算例仿真结果表明：相较于传统日前调度，多时间尺度优化调度能够获得更加精细的调度计划，进一步促进风光消纳，降低虚拟电厂出力偏差，提高虚拟电厂经济性。

Abstract

To enhance the accuracy of the virtual power plant scheduling plan and fully exploit the adjustment potential of flexible resources in the virtual power plant

in this paper we propose a day-ahead and intraday multi-time scale optimization scheduling method that makes both day-ahead and intraday forecast of wind and solar output as well as load. Additionally

we utilize energy storage batteries to smooth the fluctuations of intraday wind and solar output

employ solar-thermal power station to decouple the thermoelectric units with the aim of mitigating their minimum output

and consider demand response resources with varying response speeds. A multi-time scale thermoelectric joint optimization scheduling model for virtual power plants containing solar-thermal power stations is established

and the adaptive genetic algorithm is employed to solve it. The simulation results of the example show that

compared to traditional day-ahead scheduling

the multi-time scale optimal dispatching enables a more refined dispatching plan

thereby further promoting wind and solar consumption

reducing the output deviation of the virtual power plant

and improving the economy of the virtual power plant.

关键词

Keywords

references

WAGNER M J, NEWMAN A M, HAMILTON W T, et al . Optimized dispatch in a first-principles concentrating solar power production model[J ] . Applied Energy, 2017, 203: 959−971.

DU E, ZHANG N, HODGE B M, et al . Economic justification of concentrating solar power in high renewable energy penetrated power systems[J ] . Applied Energy, 2018, 222: 649−661.

KARDAKOS E G, SIMOGLOU C K, BAKIRTZIS A G. Optimal offering strategy of a virtual power plant: a stochastic bi-level approach[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 794−806.

WANG Zhenhao, XU Jingjian, TIAN Chunguang, et al . Combined thermal power dispatching of wind power system including carbon trading cost[J ] . Journal of Solar Energy, 2020, 41(12): 245−253(in Chinese).

WANG Z H, XU J J, TIAN C G, et al . Combined heat and power scheduling strategy considering carbon trading cost in wind power system[J ] . Acta Energiae Solaris Sinica, 2020, 41(12): 245−253.

NOSRATABADI S M, HOOSHMAND R A, GHOLIPOUR E. A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems[J]. Renewable and Sustainable Energy Reviews, 2017, 67: 341−363.

NAVAL N, SANCHEZ R, YUSTA M J. A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation[J]. Renewable Energy, 2020, 151: 57−69.

ZHI X W, SHI T S, XIANG N L, et al . A remote integrated energy system based on cogeneration of a concentrating solar power plant and buildings with phase change materials[J ] . Energy Conversion and Management, 2019, 187: 472−485.

YU C F, SHU Q Z. Risk-constrained optimal scheduling with combining heat and power for concentrating solar power plants[J]. Solar Energy, 2020, 208: 937−948.

SUN Shitong, KAZEMI-RAZI S M, KAIGUTHA L G, et al . Day-ahead offering strategy in the market for concentrating solar power considering thermoelectric decoupling by a compressed air energy storage[J ] . Applied Energy, 2022(305): 117804.

ZHAO S H, WANG J, GONG L W, et al . Economic dispatch of virtual power plant contained wind-photothermal considering integrated demand response[C ] //2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2021.

CUI Yang, YANG Zhiwen, ZHONG Wuzhi, et al . The joint output dispatch of thermal storage station and thermal power unit[J ] . Electric Power Automation Equipment, 2019, 39(02): 71−77(in Chinese).

JU Liwei, YU Chao, TAN Zhongfu. Two-stage scheduling optimization model and solution algorithm of wind power energy storage for demand response[J]. Power System Technology, 2015, 39(5): 1287−1293(in Chinese).

HAN K H, KIM K H. Genetic quantum algorithm and its application to combinatorial optimization problem [C]//Proceedings of IEEE International Conference on Evolutionary Computation. California, USA: IEEE, 2000: 1354−1360.

ZHANG Qin, WANG Xifan, WANG Jianxue, et al . Review of demand response research in the electricity market[J ] . Automation of Electric Power Systems, 2008, 32(3): 97−106(in Chinese).

NING Yangtian, LI Xiangjun, DONG Dehua, et al . Overview of the research method of energy storage system to suppress the output fluctuation of solar power generation[J ] . Distribution Utilization, 2017, 34(04): 2−11(in Chinese).

LU Zongxiang, LIN Yisha, QIAO Ying, et al . Flexibility of supply and demand balance in an extremely high proportion of renewable energy power systems[J ] . Automation of Electric Power Systems, 2022, 46(16): 3−16(in Chinese).

YUAN Guili, WANG Linbo, WANG Baoyuan. Load optimization scheduling and economic benefit analysis based on the “thermoelectric decoupling” of the virtual power plant[J]. Proceedings of the CSEE, 2017, 37(17): 4974−4985(in Chinese).

LI Hui. Research on low-carbon economic scheduling of electric-thermal integrated energy system containing solar-thermal-thermoelectric[D]. Jilin: Dongbei Dianli University, 2020(in Chinese).

YU Jianfang. Active multi-source collaborative optimization dispatching of power distribution network [D]. Beijing: North China Electric Power University, 2022(in Chinese).

GB/T 19963—2011, Technical regulations on wind farm access to power system[S]. Beijing: China Standard Publishing House, 2011(in Chinese).

GB/T 19964—2012, Technical regulations on photovoltaic power station access to power system[S]. Beijing: China Standard Publishing House, 2011(in Chinese).

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