Considering the combined heat and power synergy of advanced adiabatic compressed air energy storage system in multi-objective optimal scheduling of virtual power plant

HAN Aiming; CHEN Xiaotao; RUAN Yu; HAI Xin; ZHU Yuhao

doi:10.19799/j.cnki.2095-4239.2026.0322

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Considering the combined heat and power synergy of advanced adiabatic compressed air energy storage system in multi-objective optimal scheduling of virtual power plant

更新时间：2026-05-14

- Considering the combined heat and power synergy of advanced adiabatic compressed air energy storage system in multi-objective optimal scheduling of virtual power plant
- Energy Storage Science and Technology Pages: 1-13(2026)
- 作者机构：
  
  青海大学能源与电气工程学院，青海西宁 810016
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2026.0322
  CLC： TM 73
- Received：15 April 2026，
  
  Revised：2026-05-09，
  
  Online First：14 May 2026，
- 稿件说明：
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韩爱明, 陈晓弢, 阮雨, 等. 计及先进绝热压缩空气储能系统热电协同的虚拟电厂多目标优化调度研究[J]. 储能科学与技术, XXXX, XX(XX): 1-13.

HAN Aiming, CHEN Xiaotao, RUAN Yu, et al. Considering the combined heat and power synergy of advanced adiabatic compressed air energy storage system in multi-objective optimal scheduling of virtual power plant[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-13.
韩爱明, 陈晓弢, 阮雨, 等. 计及先进绝热压缩空气储能系统热电协同的虚拟电厂多目标优化调度研究[J]. 储能科学与技术, XXXX, XX(XX): 1-13. DOI： 10.19799/j.cnki.2095-4239.2026.0322.

HAN Aiming, CHEN Xiaotao, RUAN Yu, et al. Considering the combined heat and power synergy of advanced adiabatic compressed air energy storage system in multi-objective optimal scheduling of virtual power plant[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-13. DOI： 10.19799/j.cnki.2095-4239.2026.0322.

摘要

针对现有虚拟电厂在热电协同调度方面的欠缺导致的运行成本与碳排放高等问题，本文提出了含先进绝热压缩空气储能系统（AA-CAES）的虚拟电厂热电协同调度模型，通过挖掘其“储能—产热—用热”的闭环耦合特性，旨在降低系统的运行成本与碳排放。模型以光伏、风电、微型燃气轮机、蓄电池、储热罐与AA-CAES为核心设备，构建电-热-气多能流协同框架，详细刻画压缩储能过程中热回收与放热需求的交互约束，并以系统运行总成本与碳排放量最小化为双目标。选取青海某地工业园区典型日数据为算例，设置有无AA-CAES的对比场景，采用增广ε-约束与熵权-TOPSIS贯序决策算法分别求解出有无先进绝热压缩空气储能的虚拟电厂模型对应的总成本与碳排放的双重最优解进行对比。结果表明，配置AA-CAES后，最优折中方案在总成本降低5.1%、购电成本优化5.0%的同时实现碳排放下降15.8%，夜间弃风电量得到全额消纳，燃气轮机供热高峰负荷占比大幅下降，实现了经济性与低碳性的协同提升，为青海工业型虚拟电厂的高效低碳调度提供理论与工程参考。

Abstract

Addressing the deficiencies in existing virtual power plants regarding coordinated heat-power dispatch

which lead to high operational costs and carbon emissions

this paper proposes a coordinated heat-power dispatch model for virtual power plants incorporating an advanced adiabatic compressed air energy storage (AA-CAES) system. By exploring the closed-loop coupling characteristics of "energy storage–heat production–heat utilization

" the model aims to reduce system operational costs and carbon emissions. The model takes photovoltaic systems

wind turbines

micro gas turbines

batteries

thermal storage tanks

and AA-CAES as core components

establishing an electricity-heat-gas multi-energy flow coordination framework. It elaborately characterizes the interactive constraints between heat recovery and heat release demand during the compression energy storage process and adopts the minimization of total operational cost and carbon emissions as dual objectives. Using typical daily data from an industrial park in Qinghai Province as a case study

comparative scenarios with and without AA-CAES are established. The augmented ε-constraint method combined with the entropy-weighted TOPSIS sequential decision-making algorithm is employed to derive the dual-optimal solutions for total cost and carbon emissions in VPP models with and without AA-CAES

respectively

for comparison. The results indicate that after configuring AA-CAES

the optimal compromise solution achieves a 5.1% reduction in total cost and a 5.0% optimization in electricity purchase cost

while carbon emissions decrease by 15.8%. Nighttime curtailed wind power is fully absorbed

and the share of gas turbine peak heating load decreases significantly

achieving a synergistic improvement in both economic efficiency and low-carbon performance. This provides theoretical and engineering references for the efficient and low-carbon dispatch of industrial virtual power plants in Qinghai.

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