基于服务商引导的综合能源系统源-荷协同优化方法

doi:10.12204/j.issn.1000-7229.2022.01.005

电力建设 ›› 2022, Vol. 43 ›› Issue (1): 38-48.doi: 10.12204/j.issn.1000-7229.2022.01.005

• 综合多元能源及信息技术的能源互联网规划与运行·栏目主持刘洋副教授、韩富佳博士· • 上一篇下一篇

基于服务商引导的综合能源系统源-荷协同优化方法

廖宗毅(), 万文略, 陈曦, 陈岚

重庆理工大学电气与电子工程学院,重庆市 400054

收稿日期:2021-07-22 出版日期:2022-01-01 发布日期:2021-12-21
通讯作者: 廖宗毅 E-mail:1056406531@qq.com
作者简介:万文略(1964),男,硕士,教授,主要研究方向为嵌入式开发;
陈曦(1986),男,博士,讲师,主要研究方向为电力设备在线监测与智能诊断技术、能源互联网、能源经济与市场;
陈岚(1998),女,硕士研究生,主要研究方向为大功率变换器控制、新能源发电并网控制。
基金资助:
重庆市人工智能技术创新重大主题专项重点研发项目(cstc2017rgzn-zdyf0120);重庆理工大学研究生创新重点项目(clgycx20201004)

Source-Load Collaborative Optimization Method of Integrated Energy System Based on Service Provider Guidance

LIAO Zongyi(), WAN Wenlue, CHEN Xi, CHEN Lan

School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China

Received:2021-07-22 Online:2022-01-01 Published:2021-12-21
Contact: LIAO Zongyi E-mail:1056406531@qq.com
Supported by:
Major Project of Artificial Intelligence Technology Innovation of Chongqing(cstc2017rgzn-zdyf0120);Key Project of Graduate Innovation of Chongqing University of Technology(clgycx20201004)

摘要/Abstract

摘要：

随着能源市场的逐步开放,大量市场因素涌入传统集中式优化调度策略。在此背景下,以园区综合能源系统(community integrated energy system,CIES)优化运行为应用场景,提出了基于综合能源服务商(integrated energy services provider,IESP)引导的源-荷协同优化运行模型。在需求侧,考虑园区可转移电、气负荷以及建筑物维护结构虚拟储热特性建立综合需求响应(integrated demand response,IDR)策略。在供给侧引入综合能源服务商以代替能源网络引导多能协同灵活交易,综合考虑园区用能需求、响应反馈以及主网与园区联络线电交互功率变化情况优化电-气联合价格信号。通过粒子群结合混合整数线性规划的双层优化算法对上层服务商的价格信号及下层园区需求响应、经济调度等行为进行分层优化,考虑供需双侧互动过程中的相互影响,循环迭代求解出各方追求利益目标时的交互策略。算例仿真表明所提模型可在保障园区的参与满意度、用能经济性的基础上挖掘其响应潜力,对园区与主网间电交互功率进行“削峰填谷”,优化配置系统能源资源,提高系统整体经济性。

关键词: 综合能源服务商(IESP), 综合需求响应(IDR), 价格机制, 双层优化, 削峰填谷

Abstract:

As the energy market gradually opens up, multiple market factors have been involved in the traditional centralized optimal dispatching approach. In this context, the paper takes the optimal operation of community integrated energy system (CIES) as an application scenario and proposes a source-load collaborative optimization model under the guidance of an integrated energy service provider (IESP). On the demand-side, an integrated demand response (IDR) strategy is established according to the transferable electricity load and gas load as well as the virtual heat storage characteristics of the enclosed structures in the buildings. On the supply-side, an integrated energy service provider is introduced to replace the energy networks and lead the collaborative and flexible trade of multiple energies. Besides, it is able to comprehensively evaluate the community’s energy demand, respond feedback and the situations of interactive power of major grid and tie lines in the community so as to optimize the electricity-gas combined price signals. The bi-level optimization algorithm of particle swarm optimization combined with mixed integer linear programming is used to optimize the price signal of the upper service provider and the demand response and economic dispatching of the lower community. Considering the mutual influence in the process of bilateral interaction between supply-side and demand-side, the interactive strategy of all parties in pursuit of interest goals is solved by cycle iteration. Numerical simulation shows that the model can be used to exploit response potential in the participation satisfaction of the community and energy economy, realize“peak-shaving and valley filling”in the community and the main network, optimize the configuration of energy resources, and increase the overall economy of the system.

Key words: integrated energy service provider (IESP), integrated demand response (IDR), price mechanism, bi-level optimization, peak-shaving and valley filling

中图分类号:

TM734

廖宗毅, 万文略, 陈曦, 陈岚. 基于服务商引导的综合能源系统源-荷协同优化方法[J]. 电力建设, 2022, 43(1): 38-48.

LIAO Zongyi, WAN Wenlue, CHEN Xi, CHEN Lan. Source-Load Collaborative Optimization Method of Integrated Energy System Based on Service Provider Guidance[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(1): 38-48.

图/表 20

图1 CIES架构

Fig.1 Architecture of CIES

图2 IESP引导源-荷交互

Fig.2 IESP guidance of source-load interaction

图3 PSO-MILP双层优化求解流程

Fig.3 Solution flow of PSO-MILP bi-level optimization

表1 设备出力范围与运行参数

Table 1 Equipment output and operation parameters

表2 设备单位出力运维成本

Table 2 Operation and maintenance cost per unit output of equipment

表3 建筑维护结构参数

Table 3 Structural parameters of building maintenance

表4 场景信息

Table 4 Scene information

图4 PSO-MILP可行解收敛情况

Fig.4 Convergence of PSO-MILP feasible solution

表5 不同引导方式结果对比

Table 5 Comparison of the results of different guidance modes

图5 场景D下IESP价格信号

Fig.5 IESP price signal in scenario D

图6 场景D下IESP引导效果

Fig.6 IESP guidance effect in scenario D

图7 电能供需平衡

Fig.7 Balance of power supply and demand

图8 天然气供需平衡

Fig.8 Balance of natural gas supply and demand

图9 热能供需平衡

Fig.9 Heat energy supply and demand balance

图10 冷能供需平衡

Fig.10 Cooling energy supply and demand balance

图11 CIES室内外温度

Fig.11 Indoor and outdoor temperature in CIES

图A1 场景A下IESP价格信号

Fig.A1 IESP price signal in scenario A

图A2 场景B下IESP价格信号

Fig.A2 IESP price signal in scenario B

图A3 场景C下IESP价格信号

Fig.A3 IESP price signal in scenario C

图A4 场景A、B、C下IESP引导效果

Fig.A4 IESP guidance effect in scenario A, B and C

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基于服务商引导的综合能源系统源-荷协同优化方法

Source-Load Collaborative Optimization Method of Integrated Energy System Based on Service Provider Guidance

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