基于综合优化目标的低碳园区能源系统规划配置

doi:10.11930/j.issn.1004-9649.202306117

摘要/Abstract

摘要：

低碳园区复合式能源系统合理的规划与配置对提升系统能效、降低成本、减少污染物排放等具有重要意义。首先，基于园区用能需求快速预测方法和能源系统动态仿真平台，提出了以综合评价指标为导向的低碳园区复合式能源系统优化规划配置方法，建立了虎克-捷夫算法和粒子群算法相融合的混合优化算法。然后，以典型低碳园区冷热电三联供耦合地源热泵、蓄能与燃气锅炉的多能系统为例，对比分析了不同优化算法的配置结果和优化速度，研究了综合优化目标最优、全生命周期成本、能耗最低等不同优化目标对容量优化配置结果和系统典型日运行情况的影响。结果表明，本文提出的优化配置方法和混合优化算法可以保证寻优能力、优化精确度，同时提高了优化计算性能。以综合评价目标最优的能源系统配置结果可以兼顾系统成本、经济运行、环境影响及低碳排放等多方因素，更有利于构建低碳园区最优能源形态。

关键词: 低碳园区, 复合式能源系统, 优化配置, 综合优化目标, 混合优化算法

Abstract:

The reasonable planning and configuration of composite system in low-carbon park is of great significance for improving system energy efficiency and reducing costs and pollutant emissions. Firstly, based on the energy-demand fast prediction method for park and the composite energy system dynamic simulation platform, this paper proposed an optimal planning and configuration method for low-carbon park composite energy system, and established a hybrid optimization algorithm combining Hook-Jeff algorithm and particle swarm optimization. Secondly, by taking a multi-energy system with triple supply of cooling, heating and power system coupled with ground source heat pump, energy storage, and gas boiler in a typical low-carbon park as an example, a comparative analysis was made on the configuration results and optimization speed with different optimization algorithms, and a study was conducted on the impacts of different optimization objectives such as optimal comprehensive optimization objective and lowest whole life-cycle cost and energy consumption on the capacity optimization configuration results and typical daily operation situation. The results show that the proposed optimal configuration method and hybrid optimization algorithm can ensure the optimization capacity and accuracy with higher calculation performance. The energy system configuration results with optimal comprehensive evaluation objectives can take into account multiple factors such as system cost, economic operation, environmental impacts and low-carbon emissions, which can effectively realize the optimal energy form of low-carbon parks.

Key words: low-carbon park, composite energy system, optimal configuration, integrated optimization objective, hybrid optimization algorithm

王扬, 路菲, 李骥, 谈竹奎, 孙宗宇, 徐伟, 宋子宏, 刘振鹏. 基于综合优化目标的低碳园区能源系统规划配置[J]. 中国电力, 2024, 57(4): 14-24.

Yang WANG, Fei LU, Ji LI, Zhukui TAN, Zongyu SUN, Wei XU, Zihong SONG, Zhenpeng LIU. Multi-energy System Planning and Configuration Study for Low-Carbon Parks Based on Comprehensive Optimization Objectives[J]. Electric Power, 2024, 57(4): 14-24.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202306117

https://www.electricpower.com.cn/CN/Y2024/V57/I4/14

图/表 14

图 1 问卷调研对象组成及信息

Fig.1 Composition and information of the survey respondents

图 2 低碳园区复合式能源系统类型调研结果

Fig.2 Investigation results of the typical types of composite energy system in low-carbon parks

图 3 TRNSYS典型复合式能源系统能耗模拟计算平台示例

Fig.3 Example of a typical composite energy system simulation platform based on TRNSYS software

表 1 低碳园区综合评价指标体系[25]

Table 1 Comprehensive evaluation index system for low-carbon parks

一级指标	二级指标	指标定义
系统成本指标A₁	机房设备费用A₁₁	复合式能源系统设备购置费用及设备安装费用
	管道安装A₁₂	能源系统机房至建筑的管道费用和安装费用
	机房建设费用A₁₃	机房土建及装修相关的费用
	折旧费用A₁₄	设备、管网及土建的折旧费用
经济运行指标A₂	电力价格A₂₁	电力费用
	燃气/热力价格A₂₂	燃料费用
	综合能效A₂₃	设备/系统供能量累计值与输入能量累计值之比
	维护管理成本A₂₄	日常维护、维修和运行管理费用
环境影响指标A₃	可再生能源利用率A₃₁	可再生能源消纳量与系统能源消费总量之比
环境影响指标A₃	碳减排量A₃₂	二氧化碳排放量减少值

图 4 复合式能源系统一体化规划与配置方法

Fig.4 Integrated planning and configuration method for composite energy systems

图 5 HJ-PSO混合优化算法计算流程

Fig.5 Calculation flowchart of HJ-PSO hybrid optimization algorithm

图 6 园区设计日动态负荷曲线

Fig.6 Designed daily dynamic load curve of the park

图 7 不同优化算法下系统供热、冷容量配置占比

Fig.7 Proportion of system heating/cooling-supply capacity with different optimization algorithms

表 2 不同优化算法的优化结果

Table 2 Optimization results with different optimization algorithms

优化算法	迭代步长/步	优化时长/min	供冷容量配置/kW				供热容量配置/kW
优化算法	迭代步长/步	优化时长/min	三联供	地源热泵	蓄能系统	燃气锅炉	三联供	地源热泵	蓄能系统	燃气锅炉
虎克-捷夫算法	75	220	2800.62	7806.29	5347.45	8655.34	3429.08	10605.73	9244.85	18084.34
粒子群算法	98	292	2795.70	7934.26	5593.85	8286.19	3400.12	11222.05	9422.72	17319.11
混合优化算法	50	150	2721.87	7437.14	5798.12	8652.88	3263.62	10113.50	9943.91	18042.98

图 8 不同优化目标下复合式能源系统供热容量优化配置结果

Fig.8 Heating-supply capacity optimal configuration results of composite energy systems under different optimization objectives

图 9 不同优化目标下复合式能源系统供冷容量优化配置结果

Fig.9 Cooling-supply capacity optimal configuration results of composite systems under different optimization objectives

图 10 综合指标最优的容量配置下供暖季典型日运行模式

Fig.10 Typical daily operation mode in heating season under the capacity configuration with optimal comprehensive evaluation index

图 11 综合指标最优的容量配置下供冷季典型日运行模式

Fig.11 Typical daily operation mode in cooling season under the capacity configuration with optimal comprehensive evaluation index

表 3 复合式能源系统优化容量配置下经济性和环境影响结果

Table 3 Economy and environmental impacts under the optimal capacity configuration of composite energy system

优化目标	单位面积初投资/ (元·m^–2)	供冷运行费用/ (元·m^–2)	供暖运行费用/ (元·m^–2)	可再生能源利用率/ %	年度运行总能耗折算耗电量/ ((kW·h)·(m²·a)^–1)	碳减排量/ (kg·(m²·a)^–1)
综合指标最优	293.5	9.9	17.7	17.9	20.4	38.0
全生命周期成本最优	281.6	10.1	17.8	17.8	20.6	35.0
运行能耗最优	342.6	10.2	16.9	16.9	18.0	45.0

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