Low-carbon optimal scheduling of wind-solar-thermal-storage combined power generation systems based on APO-PSO

doi:10.3969/j.issn.2097-0706.2026.03.004

Abstract

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

CLC Number:

TK 01：TM 61

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