基于APO-PSO的风光火储联合发电系统低碳优化调度

doi:10.3969/j.issn.2097-0706.2026.03.004

摘要/Abstract

摘要：

联合发电系统可以对分布式电源进行有效管理，降低温室气体排放。然而，现阶段多数研究依旧以经济性为核心目标，对节能减排及相关市场化机制的考虑较为有限，且未能根据最新政策变化对现有机制进行适配性建模，同时，可再生能源的随机性和波动性是亟待解决的问题。因此，提出一种考虑绿证-阶梯碳联合交易机制的“风-光-火-储”联合发电系统优化调度策略，机制设计与模型构建深度耦合：在市场化机制层面，将绿证划分为可交易与不可交易证书，并将其与绿证-阶梯碳联合交易框架深度耦合；在模型构建层面，引入随机机会约束规划刻画可再生能源出力不确定性，构建包含燃气轮机、光伏、风力与储能系统的联合调度模型；在求解层面，提出人工原生动物优化器与粒子群算法融合的APO-PSO混合算法，以提升求解精度与收敛速度。结果表明，所提方法可显著提高可再生能源的利用率，减少碳排放，充分调动储能系统平抑功率波动，削峰填谷，提高电力系统运行的稳定性和经济性。

关键词: 绿色证书交易, 阶梯型碳交易, 碳排放, 优化调度, 随机机会约束规划方法, 人工原生动物优化器, 风-光-火-储联合发电

Abstract:

Integrated power generation systems effectively manage distributed energy resources and reduce greenhouse gas emissions. However， current research predominantly prioritizes economic viability， with limited consideration given to energy conservation， emission reduction， and related market-oriented mechanisms， and lacks adaptive modeling of existing mechanisms in response to recent policy changes. Additionally， the stochasticity and volatility of renewable energy sources remain critical challenges to be addressed. Consequently， an optimal dispatch strategy for a wind-solar-thermal-storage combined power generation system considering a combined renewable energy certificate and stepped carbon trading mechanism was proposed， where mechanism design and model construction were deeply coupled. From the perspective of market-oriented mechanisms， renewable energy certificates were categorized into tradable and non-tradable types， followed by their deep integration into the trading framework. By leveraging stochastic chance-constrained programming to characterize renewable energy output uncertainty， a joint dispatch model comprising gas turbines，photovoltaics，wind power，and energy storage systems（ESS） was established. Furthermore，a hybrid artificial protozoa optimizer（APO）-particle swarm optimization（PSO） algorithm merging the APO and PSO was developed to enhance solution accuracy and convergence speed. The results demonstrated that the proposed method significantly enhanced renewable energy utilization and reduced carbon emissions. Moreover， the strategy fully mobilized the ESS to smooth power fluctuations， achieved peak shaving and valley filling， and exhibited improved stability and economic efficiency in power system operations.

Key words: renewable energy certificate trading, stepped carbon trading, carbon emissions, optimal scheduling, stochastic chance-constrained programming, artificial protozoa optimizer, wind-solar-thermal-storage combined power generation

中图分类号:

TK 01：TM 61

穆雨彤, 王巍. 基于APO-PSO的风光火储联合发电系统低碳优化调度[J]. 综合智慧能源, 2026, 48(3): 37-46.

MU Yutong, WANG Wei. Low-carbon optimal scheduling of wind-solar-thermal-storage combined power generation systems based on APO-PSO[J]. Integrated Intelligent Energy, 2026, 48(3): 37-46.

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参数	参数值	功能说明
种群规模N	100	平衡计算效率与搜索能力
最大迭代次数I_max	500	确保充分收敛
初始惯性权重ω	0.9	控制历史速度影响
个体学习因子c₁	2.0	调节个体经验导向
群体学习因子c₂	2.0	调节群体经验导向
伪足延伸比例	0.4	每代执行延伸操作的粒子比例
延伸强度系数	0.3	控制伪足搜索范围
重组比例N_worst	0.15	淘汰劣质粒子比例
重组周期	10	维持种群多样性间隔

时段类型	时段	购电电价	售电电价
峰	10:00 —15:00；17:00 —22:00	1.2	0.6
平	07:00 —10:00；15:00 —17:00 22:00 —24:00	0.8	0.4
谷	00:00 —07:00	0.5	0.3

场景	光伏发电场景概率	风力发电场景概率
1	22.3	15.2
2	34.7	31.6
3	17.6	23.5
4	15.3	12.4
5	10.1	17.3

情景	CET	SCT	RECT
1	×	×	×
2	√	×	×
3	×	√	×
4	√	×	√
5	×	√	√

求解方法	净收益/元	可再生能源发电弃电率/%	碳排放量/kg
APO-PSO	693.57	1.74	280.48
APO	453.14	3.44	539.29
PSO	517.16	2.65	417.50
GA	488.64	3.01	440.11