Electric Power

Distributionally robust optimization of park-level integrated energy systems considering uncertainties in power generation and carbon emissions

ZHANG Xiaolin, DU Ershun, ZHANG Guangdou, WANG Jiaxu, SONG Liang, LIU Yuliang

2026, 59(2): 1-12. DOI: 10.11930/j.issn.1004-9649.202511033

Asbtract ( )

PDF (2351KB) ( )

Figures and Tables | References | Related Articles | Metrics

Under the "dual carbon" goals, the park-level integrated energy system (PIES), as an important carrier for achieving multi-energy complementarity and low-carbon transition, has attracted extensive attention. However, its operation is affected by multiple sources of uncertainty, such as wind power output fluctuations and indirect carbon emission intensity of the power grid, which poses challenges to both economic efficiency and carbon performance of the system. To this end, this paper proposes a distributionally robust optimization method for PIES considering the uncertainty of power generation and carbon emissions. A hybrid fuzzy set is constructed based on the Wasserstein distance and moment information, while chance constraints are employed to handle wind power uncertainty. Meanwhile, a polyhedral uncertainty set is used to characterize the fluctuations in carbon emission intensity, and user-side demand response is incorporated to enhance system flexibility. The proposed model is transformed into a solvable mixed-integer linear programming (MILP) problem through the column-and-constraint generation (C&CG) algorithm and Karush–Kuhn–Tucker (KKT) conditions. Case study results demonstrate that the proposed method enhances economic efficiency while ensuring system robustness, and effectively coordinates the relationship between renewable energy accommodation, carbon emission constraints, and economic operation.

Analysis and outlook on national power supply-demand situation for 2026 and the 15th Five-Year Plan period

JI Guoqiang, DUAN Jinhui, WU Shanshan, HAN Xinyang, YOU Peipei, ZHANG Lili, ZHANG Yu

2026, 59(2): 13-23. DOI: 10.11930/j.issn.1004-9649.202512007

Asbtract ( )

PDF (1342KB) ( )

Figures and Tables | References | Related Articles | Metrics

The 15th Five-Year Plan period is a critical stage for consolidating the foundation and advancing in an all-round way in the process of China basically realizing socialist modernization. The external environment is intricate and complex, with an increasing number of disturbing factors, and the power supply and demand will still face a series of risks and challenges. Taking into comprehensive consideration the influencing factors such as economic situation, key industries, meteorological factors and primary energy, this paper conducts an analysis and forecast of the national power demand and supply in 2026 and the 15th Five-Year Plan period, makes an assessment of the national power supply and demand situation, and puts forward countermeasures and suggestions for ensuring the balance between power supply and demand. The research shows that supported by factors such as steady economic growth and accelerated construction of a modern industrial system, the national social electricity consumption will maintain rapid growth during the 15th Five-Year Plan period; driven by the "dual carbon" goals, the proportion of newly installed renewable energy capacity will keep rising, and the power source structure will continue to shift toward cleanliness and low carbonization. The overall power supply and demand situation will be characterized by "loose in the early stage and tight in the later stage". It is necessary to promote the commissioning of guaranteed and supportive power sources, guide renewable energy to play a supporting role in power supply guarantee, strengthen inter-provincial and cross-regional mutual assistance and support, and tap the potential of demand-side regulation, so as to ensure the balance of power supply and demand during the 15th Five-Year Plan period.

District cooling demand response strategy based on tripartite Stackelberg game

YU Junyi, LIAO Siyang, KE Deping

2026, 59(2): 24-36. DOI: 10.11930/j.issn.1004-9649.202503035

Asbtract ( )

PDF (3991KB) ( )

Figures and Tables | References | Related Articles | Metrics

To address such issues as low user participation and coarse incentive strategies in demand response of air-conditioning loads in commercial complexes, a dynamic pricing model integrating tripartite Stackelberg game and deep learning is proposed. Firstly, a three-level hierarchical decision framework of power grid-aggregator-user is designed, and a neural network is used to mine the nonlinear function of user load reduction and incentive electricity price. Secondly, an aggregator profit-risk equilibrium model is constructed, and a penalty mechanism and power grid cost function under the elastic constraint of compliance rate are introduced. And then, the optimal subsidy price and load reduction are identified to optimize the subsidy strategy of power grid demand response. Finally, taking a commercial complex as an empirical case, the results show that the proposed model achieved a 94.2% load reduction compliance rate, reduced the user comfort deviation to 0.86, and optimized the peak-shaving cost of the power grid by 57.14% respectively. This study provides a decision-making tool that integrates game-theoretic equilibrium and behavioral interpretability for demand response in energy-intensive buildings, facilitating coordinated resource regulation in new-type power systems.

Optimization scheduling of virtual power plants with collaborations of energy storage devices and multi-type power-to-hydrogen units

HUANG Songtao, ZHAO Xuenan, SHANG Guozheng, ZHAO Pengyu, DONG Wenjing, ZHANG Yajian, YANG Yi

2026, 59(2): 37-46. DOI: 10.11930/j.issn.1004-9649.202507028

Asbtract ( )

PDF (2103KB) ( )

Figures and Tables | References | Related Articles | Metrics

To address the challenges posed by the volatility of wind and solar power output on virtual power plant scheduling and renewable energy consumption, a coordinated optimization scheduling method for energy storage and multi-type power-to-hydrogen (P2H) facilities is proposed. Firstly, the output of wind and solar power is decomposed into low-, medium-, and high-frequency components using empirical mode decomposition, which are then matched with the respective characteristics of alkaline electrolyzer, proton exchange membrane electrolyzer, and energy storage systems to achieve frequency-division and collaborative consumption of renewable energy. Secondly, an optimization model is constructed to minimize the configuration and operation costs, considering such constraints as electrolyzing powers, state-of-charge of energy storage devices, and power balances. Finally, a three-stage algorithm is designed to determine the optimal scheduling scheme. Simulation results demonstrate that, compared to the collaborative dispatch strategy utilizing a single-type electrolyzer or the strategy combining energy storage with a single-type electrolyzer, the proposed method reduces the curtailment rate to 0.14%, significantly enhancing the consumption efficiency of renewable energy and improving system economics.

Active-reactive power optimization of active distribution networks considering dynamic wind-solar renewable uncertainty and demand response

JI Hongli, XU Lei, YANG Jiahui, DOU Chunxia

2026, 59(2): 47-60. DOI: 10.11930/j.issn.1004-9649.202501063

Asbtract ( )

PDF (2632KB) ( )

Figures and Tables | References | Related Articles | Metrics

To address the impact of peak loads on voltage security in active distribution networks, an active-reactive power optimization strategy is proposed based on price-guided demand-side response. Firstly, this strategy incorporates multi-scenario stochastic optimization and proposes a dynamic renewable scenario generation method based on nonparametric kernel density estimation and sequential sampling from standardized multivariate normal distributions, yielding a probabilistic set of typical daily wind-solar power profiles. Secondly, considering demand-side response and grid interaction to mitigate grid operational risks, this study proposes an optimization strategy coordinating time-of-use price-guided load shifting and reactive power compensation. The optimization objective is constructed based on multiple scenario probabilities to minimize voltage deviation and economic operation costs in the distribution networks. Finally, to address the complex characteristics of this model, a multi-objective hybrid multi-population collaborative optimization algorithm is proposed, yielding optimal reactive power compensation output and demand-side load scheduling strategy. Verification results show that the proposed strategy can effectively reduce the risk of voltage violations, minimize network losses, and achieve safe and economic operation of the distribution networks.

Economic analysis method for park-level source-grid-load-storage integration project under independent investment operation mode

WU Danman, DING Yucheng, XIE Guanglong, JIN Xiaoling

2026, 59(2): 61-70. DOI: 10.11930/j.issn.1004-9649.202502013

Asbtract ( )

PDF (1014KB) ( )

Figures and Tables | References | Related Articles | Metrics

Given that the impact of initial investment in energy storage as well as its operational costs and revenues on the overall economic benefits of the integrated project are of vital importance, this paper proposes an investment benefit analysis method in which the operational profit of energy storage for park-level projects is taken into full consideration under the user's independent investment operation mode. Firstly, based on the operating characteristics of energy storage, quantitative models are constructed for typical-day residual photovoltaic (PV) charging/discharging volumes and peak-valley arbitrage electricity quantities in park-level energy storage. Secondly, models for various revenue sources within the project are presented. Then combined with the analysis of the investment and operating costs, a comprehensive investment return analysis model that accounts for the operating benefits of energy storage is proposed. Finally, the effectiveness of the proposed model and methodology is validated through the case study of a comprehensive park project in Shanxi Province. Simulation results show that with the internal power source primarily photovoltaic, considering the optimization objective of the overall system benefit, the optimal source-to-load ratio for the project should fall within the range of 2∶1 to 3∶1, and the energy storage configuration should account for 10% to 25% of the total installed PV capacity.

Method for quantifying the average carbon footprint of coal-fired power based on non-random samples

WANG Zhixuan, ZHANG Jingjie, SHI Lina, FENG Tianfeng, WANG Chenlong, DU Xinxin, LEI Yuwei, GU Erxue

2026, 59(2): 71-80. DOI: 10.11930/j.issn.1004-9649.202510069

Asbtract ( )

PDF (1376KB) ( )

Figures and Tables | References | Related Articles | Metrics

To address the challenge of selecting representative units for quantifying the average carbon footprint per unit of electricity generation of coal-fired units nationwide or in a specific region, and to provide scientific support for the quantification of the average carbon footprint factors of coal-fired power across the country, it is necessary for the research to focus on the representativeness analysis method of non-random samples for the overall carbon footprint of coal-fired power. As the dominant source of carbon emissions in China's power industry (accounting for approximately 88%), coal-fired power, due to its large installed capacity and complex influencing factors, makes analyzing overall characteristics through representative samples a feasible approach. Firstly, it is clarified that the core links of coal-fired power carbon footprint are coal combustion (accounting for 93.0%) and coal acquisition (accounting for 6.5%), jointly contributing to over 99% of carbon emissions. The coal consumption level and the carbon emission factor of power coal are the essential factors affecting the carbon footprint. Secondly, three types of methods for constructing new representative samples based on over a hundred existing quantitative samples are proposed, including indirectly proving representativeness through consistency verification of key parameters, supplementing missing samples by multi-dimensional stratification, and conducting weighted resampling to match the overall distribution. Finally, 171 sample datasets are generated by combining existing data. The deviations between their power generation coal consumption (286.9 g/(kW·h)) and carbon content per unit calorific value (26.39 t/TJ) with the corresponding indicators of the overall population composed of 1964 power plants (286.7 g/(kW·h) and 26.28 t/TJ) are only –0.07% and –0.415%, respectively, verifying the effectiveness of the proposed methods.

Two-stage voltage optimization control method for hydrogen-production-assisted distribution networks

LI Xutao, ZHOU Yang, CHANG Qicheng, Alexis P. ZHAO, GU Wenbo, MA Xin, LI Hongqiang, WANG Weijie, ZHANG Yajian, BAO Shuxin

2026, 59(2): 81-89. DOI: 10.11930/j.issn.1004-9649.202601035

Asbtract ( )

PDF (1355KB) ( )

Figures and Tables | References | Related Articles | Metrics

The large-scale application of renewable energy in the distribution network poses a challenge to the voltage stability of the distribution network. This article proposes a two-stage voltage optimization control method considering the assistance of electric hydrogen production equipment. In the recent optimization phase, a global voltage regulation strategy was developed using an optimization model that includes dynamic constraints on hydrogen production equipment, with the goal of minimizing distribution network operating losses and hydrogen production costs. During the real-time control phase within the day, a bounded adaptive event triggered control method is adopted to dynamically adjust the triggering frequency of control instructions, in order to solve the voltage deviation problem caused by short-term power fluctuations with a lower response frequency of the hydrogen production equipment. Simulation examples show that the proposed method can reduce the voltage deviation amplitude of the distribution network by more than 10.24%, and the cost of voltage regulation can be reduced by more than 4.89%, demonstrating high voltage stability and good operational economy.

Power grid load restoration strategy considering optimization of mobile emergency power sources under heavy rainstorm disasters

YANG Tingtian, LIN Xingxin, ZENG Kehan, ZHANG Caibin

2026, 59(2): 90-103. DOI: 10.11930/j.issn.1004-9649.202502050

Asbtract ( )

PDF (1577KB) ( )

Figures and Tables | References | Related Articles | Metrics

Utilizing various forms of mobile emergency power sources to flexibly dispatch electrical energy is an important technical approach to rapidly restore the important loads of power grids and enhance the resilience of urban distribution networks under rainstorm disasters. Therefore, a collaborative optimization strategy for rapid recovery of distribution networks after rainstorm disasters based on mobile emergency power sources is proposed. Firstly, a spatio-temporally coupled mobile behavior model for mobile emergency power sources is developed, and linear constraints characterizing the movement of mobile emergency power sources in post-disaster power grids are constructed. Then, relevant constraints characterizing the output operation of mobile emergency power sources are constructed to address the two main categories—mobile energy storage and mobile generators—while accounting for the charging and discharging behaviors of mobile energy storage systems. Meanwhile, by integrating with constraints characterizing power grid operations, an objective function is constructed based on the principle of maximizing restored load energy while considering dispatch costs, and a mixed-integer second-order cone optimization model for post-disaster grid load restoration is established, which incorporates the collaborative optimization of mobile emergency power sources. Finally, by setting up different case scenarios, this study analyzes the dispatch of mobile emergency power sources and the load recovery results after rainstorm disasters, verifying the accuracy and effectiveness of the proposed model.

Data driven planning and optimization of high penetration electric vehicle charging

QI Chengfei, WANG Yachao, LI Wenwen, ZHANG Wei, ZHAO Peng

2026, 59(2): 104-113. DOI: 10.11930/j.issn.1004-9649.202504013

Asbtract ( )

PDF (2121KB) ( )

Figures and Tables | References | Related Articles | Metrics

In the context of "double high" penetration of renewable energy and electric vehicles, the uncertainty of power grid supply and demand has significantly increased, urgently demanding planning and scheduling strategies to ensure stable operation. To address this, a data-driven multi-source fusion method is proposed to construct a charging demand prediction model, achieving joint optimization of facility layout and dynamic charging and discharging strategies. The Open Distribution System Simulator (OpenDSS) platform is used as a carrier to model and simulate a typical distribution network. results show that the proposed method can effectively reduce the peak-valley difference of the power grid, enhance the stability of power grid operation and the utilization rate of charging facilities, reduce user charging waiting time.

Evaluation model for setting TOU electricity pricing peak and valley periods considering multi-dimensional value of power regulation

LI Junlong, ZHANG Chao, JIANG Yitian, YAO Li, YOU Peipei, GUO Sen

2026, 59(2): 114-126. DOI: 10.11930/j.issn.1004-9649.202502042

Asbtract ( )

PDF (3115KB) ( )

Figures and Tables | References | Related Articles | Metrics

Time-of-use (TOU) pricing policy serves as a crucial pricing mechanism for power systems to adapt to high penetration of clean energy and enhance security of power supply, and the rationality of peak-valley period setting has become a key evaluation metric for the ongoing adjustments of policies in recent years. However, limited by data availability and timeliness, the comparative analysis-based traditional evaluation methods are inadequate for the new evaluation demands. Based on a review of the latest trends in TOU pricing policy adjustments, this paper proposes an evaluation model for setting TOU pricing peak and valley periods incorporating the multidimensional value of power regulation. The model adopts the confusion matrix method to reduce its excessive reliance on historical data. In addition, it establishes a multidimensional value system for power regulation to avoid inefficient "whack-a-mole" approaches for peak-shaving and valley-filling. Meanwhile, designed with flexible and tunable parameter weights, it is applicable to diverse assessment scenarios with varying expectations and priorities, thereby providing a viable solution for evaluating TOU pricing periods under data constraints. Finally, the evaluation model is validated using real load data, renewable energy output data, and spot market price data. The results demonstrate that the proposed model achieves better alignment with current policies and possesses the flexible, adjustable capability to conduct forward-looking assessments based on parameter weight adjustments.

Equilibrium analysis of generator bidding in the electricity spot market considering medium- and long-term transactions

LI Xiaogang, LIU Qiyuan, FENG Yuanhao, WU Min, CHEN Zhongyang, FENG Donghan

2026, 59(2): 127-137. DOI: 10.11930/j.issn.1004-9649.202507063

Asbtract ( )

PDF (2578KB) ( )

Figures and Tables | References | Related Articles | Metrics

To examine how medium- and long-term (MLT) market transactions affect spot-market operations and to analyze generators' bidding strategies in the spot market, this paper proposes a bilevel optimization model and a multi-agent deep reinforcement learning (MADRL) algorithm to simulate the bidding equilibrium of generators in the electricity spot market. A supply–demand ratio is introduced to characterize spot-market tightness, and the prospect theory is employed to capture generators' bounded-rational behavior, thereby analyzing the impact of MLT transactions on bidding strategies of generators in the spot market. In the MADRL solution process, generators are modeled as agents and market clearing is modeled as the environment; iterative training yields equilibrium bidding strategies for each generator and the corresponding spot-market clearing prices. A case study on an actual power system in Eastern China involving eight generators demonstrates that the proposed MADRL approach effectively computes generators' bidding strategies and accurately simulates the influence of different MLT market settings on spot-market operations. The findings provide quantitative guidance for power trading institutions to assess strategic bidding and to design coordinated rules for the joint operation of MLT and spot markets.

Multi-time scale bidding strategy for virtual power plant markets based on hierarchical partition control

GAO Feng, HUANG Lili, CHEN Jie, LU Song, LI Mingnan

2026, 59(2): 138-147. DOI: 10.11930/j.issn.1004-9649.202506053

Asbtract ( )

PDF (2192KB) ( )

Figures and Tables | References | Related Articles | Metrics

To enhance the economic efficiency and coordination capability of virtual power plant (VPP) in electricity markets, this paper proposes a multi-timescale bidding strategy based on hierarchical and zonal regulation. Firstly, virtual power units are defined as unified modeling units for dispatch and trading. A zonal aggregation strategy is developed based on grid topology and overload risk, and external characteristic models of virtual power units are constructed to represent the regulation capability of VPP. Secondly, a multi-timescale bidding model covering both day-ahead and real-time markets is established, and the bi-level optimization problem is transformed into a single-level model via Karush-Kuhn-Tucker (KKT) conditions to reduce computational complexity. Finally, case studies are conducted to validate the effectiveness of the proposed model. Results demonstrate that the proposed strategy can effectively reduce modeling complexity while ensuring network security and privacy protection. It enables VPP to coordinate bidding in multi-time scale markets, with VPP revenue increasing by 15.8% and 17.2% compared to fixed bidding and the day-ahead market, respectively, thus enhancing its market adaptability and profitability.

Virtual inertia control technology and application of energy storage power station

HUANG Yingfeng, WANG Feng, CAI Desheng, LI Hucheng, ZHAO Jingbo

2026, 59(2): 148-153. DOI: 10.11930/j.issn.1004-9649.202506023

Asbtract ( )

PDF (931KB) ( )

Figures and Tables | References | Related Articles | Metrics

To address the issues of inertia reduction and frequency stability caused by high-penetration renewable energy access, a virtual inertia control system architecture for energy storage stations is proposed. Verified by the actual measurement of an energy storage power station in Shandong, this method can achieve rapid response under ±1.0 Hz/s frequencyances, with a control error of no more than 0.4%, which is conducive to enhancing the power grid's ability to suppress power disturbances. The method can not only improve the real-time and stability of frequency change rate measurement, but also effectively coordinate local response and dispatching instructions, providing support for inertia support and frequency stability power grids with high renewable energy penetration.

Reliability assessment and optimal operational sequence strategy allocation for next-generation smart substation secondary system safety operations based on fuzzy synthetic evaluation

YE Yuanbo, SHAO Qingzhu, SUN Zhenxing, ZHOU Peng, SUN Hongyi, WANG Yibo

2026, 59(2): 154-162. DOI: 10.11930/j.issn.1004-9649.202506012

Asbtract ( )

PDF (1912KB) ( )

Figures and Tables | References | Related Articles | Metrics

Current solutions lack comprehensive reliability assessment for secondary safety measures in next-generation smart substations, particularly regarding operational sequence strategy. This paper addresses this by, first, building an secondary safety measures linkage matrix and using fuzzy synthetic evaluation (FSE) to quantify and integrate multi-dimensional reliability data (execution & isolation verification) into a single metric; second, quantifying secondary safety measures operation reliability and complexity. An improved greedy algorithm, weighting these factors, then calculates a comprehensive score for each linkage's disconnection method. Secondary safety measures execution sequences are prioritized based on these scores. Case verification shows that under identical maintenance conditions, the optimal secondary safety measures strategy can reduce link disconnection failures by 20% compared to typical secondary safety measures strategies, demonstrating higher reliability.

Table of Content