Table of Content

25 March 2026, Volume 48 Issue 3

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Power System Modeling and Control Low-carbon Optimization for Energy Systems Low-carbon Technical Economy

Power System Modeling and Control

Research progress on modeling， control， and source-load prediction of new-type power systems

DING Xinyu, ZHOU Qingcai, CHI Yaodan, ZHANG Yao, WANG Junxi, WANG Chao, JIA Hongdan, LIN Guoxiong

2026, 48(3):  1-14.  doi:10.3969/j.issn.2097-0706.2026.03.001

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Currently， the global energy structure is undergoing a profound transformation. Renewable energy sources represented by wind and solar power are increasingly becoming key components of power supply due to their cleanliness and sustainability. However， the inherent intermittency and volatility of these energy resources pose significant challenges to the frequency， voltage， and overall stability of power systems， threatening the security and reliability of power supply. To address these challenges effectively， it is crucial to integrate advanced technologies and methods to ensure reliable and efficient operation of power systems. The roles of simulation technologies， frequency regulation strategies， and artificial intelligence in new-type power systems are systematically analyzed. The evolution of hybrid simulation， from traditional serial methods to high-performance parallel and intelligent approaches， is analyzed in depth. The key roles of virtual synchronous generators and multi-type energy storage in mitigating inertia reduction and frequency regulation are examined. Additionally， recent advances and potential of artificial intelligence in renewable energy power generation prediction， load prediction， and smart microgrid scheduling are comprehensively evaluated. It is emphasized that the supporting roles of energy storage technologies and artificial intelligence should be fully leveraged， and more flexible market mechanisms and resource allocation systems should be established， thereby laying a foundation for the stable operation of new-type power systems.

Optimization operation of multi-energy complementary system based on multi-agent reinforcement learning

CHEN Feng, LU Xiaomin, LI Mengyang, ZHANG Tao, YANG Fan

2026, 48(3):  15-26.  doi:10.3969/j.issn.2097-0706.2026.03.002

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To address the dynamic coordinated optimization challenges of multi-energy complementary systems under high renewable energy integration and the limitations of traditional centralized methods in multi-agent interest coordination and real-time response， dynamic optimization modeling research was conducted. A three-layer multi-agent reinforcement learning （MARL） framework—consisting of a physical layer， decision layer， and coordination layer—was developed， with energy producers， consumers， and system schedulers classified as independent agents. Based on the improved proximal policy optimization algorithm， a dynamic reward function integrating economic efficiency， environmental friendliness， and stability was designed， and distributed decision-making with global coordination was achieved through a centralized training-decentralized execution mechanism. A typical park-level multi-energy complementary system was used as a case study. The results showed that the proposed MARL model increased the renewable energy consumption rate to 92.3%， reducing the unit electricity cost by 28.9% compared to the traditional mixed integer programming （MIP） method. Under a 50% load abrupt change scenario， the system recovery time was shortened to 90 s， which was 900% faster than the MIP method. Even with ±20% wind and solar forecasting errors， the load satisfaction rate remained at 98.7%. This dynamic optimization model effectively addressed the multi-agent coordination and uncertainty adaptation challenges in multi-energy complementary systems， providing technical support for the real-time optimization and scheduling of high-penetration renewable energy systems.

Modeling and stability analysis of grid-forming energy storage systems based on dynamic phasors

ZENG Yunrui, WANG Zeqi, XU Bo, JIANG Tianyu, LI Xijun, LI Tingyi

2026, 48(3):  27-36.  doi:10.3969/j.issn.2097-0706.2026.03.003

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With the increasing penetration of new energy in the power grid， the parallel operation of grid-forming energy storage （GFS） devices has become crucial for ensuring power system stability. However， the parallel operation of traditional GFS devices mainly relies on the droop control characteristics of controllers， and droop control alone is insufficient to ensure system stability when the system experiences disturbances. Therefore， a small-signal modeling method based on dynamic phasors was proposed， combined with eigenvalue analysis to identify key factors affecting the interactions among parallel GFS devices. Compared with traditional single frequency-domain or time-domain methods， this method could more precisely characterize the high-frequency dynamic response characteristics of inverters and quantify the coupling effects of control and network parameters on system stability， thereby expanding the stable operation region of the system without introducing additional damping. Moreover， the modular design of this method facilitated adaptation to different system topologies， significantly improving the adaptability of the model and the accuracy of stability prediction. Simulation verification using Matlab/Simulink showed that the proposed method could effectively evaluate system stability under various operating conditions， offering higher accuracy and flexibility compared with traditional methods.

Low-carbon Optimization for Energy Systems

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

MU Yutong, WANG Wei

2026, 48(3):  37-46.  doi:10.3969/j.issn.2097-0706.2026.03.004

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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.

Distributed robust low-carbon optimization model for integrated energy systems driven by historical data

WANG Zixuan, HAO Yu, LIU Xingchen, SUN Weinan, LIU Lin, ZHANG Yi

2026, 48(3):  47-55.  doi:10.3969/j.issn.2097-0706.2026.03.005

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To effectively improve the economic benefits of integrated energy systems （IES）， reduce carbon emissions， promote the efficient utilization of hydrogen energy， and mitigate the uncertainty of renewable energy output， a distributed robust low-carbon optimization model for IES driven by historical data was proposed. A data-driven uncertainty quantification method was used to construct a probability distribution ambiguity set with joint constraints of 1-norm and ∞-norm based on historical renewable energy output data. By identifying the worst-case probability distribution scenario， a min-max-min three-layer distributed robust optimization framework was constructed， ensuring robustness while reducing scheduling conservatism. An integrated tiered carbon trading mechanism and power-to-gas （P2G） technology were adopted to incentivize carbon reduction through phased carbon pricing， achieving closed-loop utilization of carbon elements within the system while balancing environmental benefits and economic costs. The distributed robust model was solved using the column-and-constraint generation （C&CG） algorithm. The results showed that the daily carbon trading cost obtained based on the hybrid-norm ambiguity set was approximately 1.6% lower than that derived from the single-norm ambiguity set， further reducing the conservatism of the renewable energy output model. Meanwhile， with a reasonable division of tiered intervals， carbon emissions and carbon trading costs were reduced by 16.35% and 22.35%， respectively， achieving a relative balance between carbon emissions and carbon trading costs. The experimental results verified the advantage of the model in reducing carbon trading costs. The proposed model provides theoretical support and practical reference for the planning and operation of IES.

Carbon pathway analysis and integrated energy management strategy considering rural energy consumption diversity

ZHANG Yang, TAO Shenghu, LIU Qi

2026, 48(3):  56-64.  doi:10.3969/j.issn.2097-0706.2026.03.006

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The proactive utilization of self-built biomass reactors by rural residents， photovoltaic equipment， and solar thermal equipment in energy supply can facilitate the development of low-carbon micro-integrated energy systems with islanded autonomous operation capabilities for individual users， thereby reducing their reliance on centralized power supply. To address the ambiguity in carbon emission pathways resulting from the diversified green energy production equipment and complex energy consumption behaviors in rural areas， a multi-layer physics-informed convolutional neural Network （PI-CNN） embedded with primary and secondary knowledge was proposed. Through deep interaction between primary and secondary knowledge bases， the model uncovered the mapping relationships between user energy consumption behaviors and carbon emissions， enabling precise tracking of carbon emission paths. Based on the learning outcomes of the PI-CNN network， appropriate weights were assigned to carbon emission pathways， radial distribution network power flows， and time-of-use heat and electricity pricing. An energy management strategy evaluation model was then designed by considering operational integrated carbon emissions， global power flow uniformity of the grid， and total benefits of user energy consumption， to quantify the quality of energy solutions. To address the challenges in solving centralized heterogeneous multi-objective optimization problems， an integrated energy management strategy was developed to balance economic efficiency， low-carbon performance， and robustness by combining adaptive gradient algorithms for mathematical problem solving. Simulation analysis conducted in a village in southern China verified the feasibility of PI-CNN and demonstrated the advantages of the proposed energy management strategy.

Low-carbon Technical Economy

Research on construction pathways for zero-carbon parks driven by data flywheel

XIA Xiaoping, YAO Dehua, SONG Zongtao, WANG Yueyue, TAN Yifan, FANG Yiming, ZHANG Jun

2026, 48(3):  65-75.  doi:10.3969/j.issn.2097-0706.2026.03.007

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Zero-carbon parks serve as key carriers for achieving the "dual-carbon" goals. The core of such parks lies in realizing efficient energy utilization through the construction of integrated energy systems. Activating the data flywheel in zero-carbon parks helps drive efficient coordination and continuous optimization of park-level integrated energy systems， thereby addressing current development challenges such as "overemphasis on construction and neglect of operation" and the fragmented "source-grid-load-storage" chain. Based on the data flywheel theory and typical zero-carbon park construction cases， construction pathways for zero-carbon parks driven by the data flywheel were explored. The integrated energy system， energy consumption scenarios， and carbon asset management were proposed as the three core components of zero-carbon parks. The data flywheel theory was introduced as an analytical perspective to examine the synergistic mechanisms and data flow logic among these three components. The feasibility of the proposed strategies was verified through quantitative comparative analysis of cases such as Xiamen ABB Industrial Center， Shenzhen Virtual Power Plant Management Center， and Bo'ao Dongyu Island Zero-Carbon Demonstration Zone. Furthermore， the construction pathways for zero-carbon parks driven by the data flywheel were deduced. The spatiotemporal misalignment between the energy system and energy consumption scenarios， the asset mismatch between the energy system and carbon assets， and the resource misallocation between energy consumption scenarios and carbon asset management together constituted the three core bottlenecks in the development of zero-carbon parks. Activating the five-stage data flywheel of "acquisition—storage—entry—acceleration—adaptation" can inject core momentum for continuous optimization into the integrated energy system. This can be transformed into the system's self-optimization capability in operation， a leap in overall efficiency， and the intrinsic driving force for value creation. It is recommended to establish unified data standards， develop park-level data middle platforms and platform operators， explore market-based allocation pathways for energy data elements， and design data-driven business closed loops to empower zero-carbon park construction.

Extraction of active components from fly ash and preparation of a carbon adsorbent doped with carbide slag： Performance evaluation

WU Jie, ZHANG Chao, LIANG Xiaolong

2026, 48(3):  76-84.  doi:10.3969/j.issn.2097-0706.2026.03.008

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Industrial calcium carbide slag has advantages such as low cost and good CO₂ adsorption performance. However， it tends to sinter after multiple cycles at high temperatures， directly resulting in low CO₂ adsorption capacity. Enhancing the activity and specific surface area of calcium carbide slag to improve its CO₂ adsorption-desorption performance has become an urgent challenge. Amorphous aluminum hydroxide was extracted from fly ash by mixed calcination of fly ash， Na₂CO₃， and CaCO₃， and then incorporated into carbide slag to prepare aluminum-calcium-based composite adsorbents. Four samples with different doping ratios of the modified adsorbents were prepared and tested for adsorption performance and cyclic stability under various adsorption temperatures. The results showed that during the rapid reaction phase， the CA91（$ m_{\mathrm{Ca}(\mathrm{OH})_{2}}: m_{\mathrm{AlO}(\mathrm{OH})}$=9∶1） and CA73（$ m_{\mathrm{Ca}(\mathrm{OH})_{2}}: m_{\mathrm{AlO}(\mathrm{OH})}$=7∶3） samples exhibited the fastest adsorption reaction rates. After the adsorption reaction was completed， the adsorption capacities of four doping ratios followed the order： CA73 > CA91 > CA82（$ m_{\mathrm{Ca}(\mathrm{OH})_{2}}: m_{\mathrm{AlO}(\mathrm{OH})}$=8∶2） > CA64（$ m_{\mathrm{Ca}(\mathrm{OH})_{2}}: m_{\mathrm{AlO}(\mathrm{OH})}$=6∶4）. In terms of cyclic performance， pure carbide slag showed a significant decrease in stability after 13 cycles， with its adsorption capacity reduced by 15%. In contrast， CA73 exhibited only a 5% decrease in adsorption capacity after 20 cycles， making it the optimal adsorbent. Further adsorption-desorption experiments on CA73 at constant and varying temperatures identified the optimal adsorption temperature as 750 ℃. The local disposal and resource utilization of carbide slag and fly ash are achieved，and a cost-effective adsorbent for CO₂ emission reduction is also provided.

Research on impacts of carbon trading policies on the value of wind power enterprises based on the mediating effects of innovation investment and carbon price

LIAO Zhigao, GENG Nanfang

2026, 48(3):  85-95.  doi:10.3969/j.issn.2097-0706.2026.03.009

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To accelerate the achievement of "dual carbon" goals，significant attention has been directed towards developing renewable energy.The "14th Five Year Plan" further proposes accelerating the implementation of renewable energy development strategy.Currently，wind power has become the main force in new energy generation，showing extensive development potential.As a key measure to promote economic growth and carbon reduction，carbon trading policies require further research on whether they can promote the value of wind power enterprises and the pathways through which this occurs.The carbon emission trading pilot in China was used as a quasi-natural experiment，with data from listed companies between 2010 and 2022 selected to construct a double difference-in-differences（DID）model to analyze the impact of carbon trading on the value of wind power enterprises.The mediating effects of innovation investment and carbon prices in this relationship were also explored. Findings indicated that carbon trading policies significantly enhanced the market value of wind power enterprises，with the conclusions remaining robust after conducting parallel trend tests，placebo tests，and other robustness checks.Further analysis revealed that innovation investment and carbon prices significantly promoted the positive effect of carbon trading on enterprise value.Heterogeneity tests demonstrated that the effects were more pronounced among state-owned enterprises，those with low financing constraints，and small to medium-sized enterprises. Based on these findings， suggestions were made from both government and enterprise perspectives： the government should improve carbon market construction，fully leverage the vitality of the carbon market，appropriately expand the scope of government subsidies，and support corporate technological innovation；enterprises should innovate in fixed asset management，broaden financing channels，and promote the digital and intelligent transformation of wind power.