Electric Power

Impacts and Countermeasures of Generators' Strategic Bidding on Inter-Provincial and Intra-Provincial Spot Markets

SHANG Jingyi, ZHANG Yihan, YANG Fuwang, XIANG Mingxu

2025, 58(11): 1-13. DOI: 10.11930/j.issn.1004-9649.202503108

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Electricity spot-market price serve as a crucial signal that reflects supply-demand dynamics and guides optimal allocation of resources. However, when market resources become scarce, some generators may exploit their market power to secure higher profits, resulting in distorted market price signals and excessive costs for end users. Establishing a robust mechanism for monitoring and constraining market power, and fostering vigorous competition among market participants, is therefore essential. Accordingly, based on the operational realities of the two-tier electricity market in China, a competitive equilibrium model is first established to represent generators' participation in the two-level spot markets. Next, based on the correlation of generators' strategic behaviors during resource scarcity, their strategic bidding practices are scanned, the scope of their bidding strategies is analyzed, the impacts of market-power abuse are quantified, and a simulation analysis is conducted on the problems existing in the current market mechanism under extreme supply-security scenarios. Finally, based on the case study results, the mechanisms of generators' strategic behavior is analyzed, and recommendations are proposed to improve the two-tier spot market trading mechanism from three aspects: optimizing the market bidding mechanism, enhancing the market supervision and penalty system, and establishing a bilateral bidding-guided market competition equilibrium.

Design of Dynamic Pricing Strategy for Electric Vehicles Charging in Smart Communities

PAN Tingzhe, JIN Fengyuan, LU Yonghao, CAO Wangzhang, YANG Hao, YU Heyang, ZHAO Boyang

2025, 58(11): 14-24, 37. DOI: 10.11930/j.issn.1004-9649.202503024

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Smart communities utilize demand response to achieve integrated management of electric vehicles (EVs) and photovoltaic-storage systems. However, traditional static pricing, which disregards actual load responses, tends to induce new load peaks and increases the volatility of the equivalent load. To address these challenges, this paper proposes a dynamic pricing strategy, wherein the price not only varies over time but is also correlated with the internal net load of the community. Firstly, the operator forecasts the photovoltaic generation and the baseline load within the scheduling horizon, and establishes the initial model of dynamic pricing. Secondly, a stackelberg game framework is constructed: the operator, as the leader in the upper-level model, aims to minimize the volatility of the equivalent load by formulating and publishing the dynamic prices as well as scheduling the energy storage output; EVs, as followers in the lower-level model, respond to the dynamic prices and optimize their charging strategies with the objective of minimizing charging costs. Furthermore, in the lower-level model, the introduction of dynamic pricing makes the charging decision of EVs mutually dependent, thereby forming an aggregative game structure, in which the optimal charging load of each EV is determined through the Nash equilibrium. Finally, the optimal dynamic pricing is determined through a genetic algorithm. Simulation results demonstrate that the proposed model effectively reduces load volatility, avoids new load peaks, and achieves the management objectives of the community operator while safeguarding the economic interests of EV users.

Time-of-Use Pricing Model Considering the Risk of Cost Transmission in Multi-level Market Agent Electricity Purchase

WANG Siqi, CAO Fang, YAO Li

2025, 58(11): 25-37. DOI: 10.11930/j.issn.1004-9649.202503013

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Aiming at the current issue that the intra-provincial time-of-use (TOU) electricity pricing mechanism overlooks the impact of transaction costs in electricity markets above the provincial level and ignores the risk of cost transmission for agent purchasers, this paper proposes a TOU electricity pricing model that considers the risk of cost transmission in multi-level market for agent electricity procurement. Firstly, a multi-level market electricity procurement decision-making model is established based on the objective of minimizing electricity procurement costs. Then, probabilistic scenarios are used to describe prediction deviations of spot market prices and user responses. Conditional value at risk (CVaR) is adopted as the risk assessment indicator for cost transmission. A TOU electricity pricing model is constructed with the dual objectives of maximizing the transmission of procurement cost fluctuations from upper-level markets and minimizing the transmission risk for agent electricity purchasers. Finally, k-means and particle swarm optimization (PSO) are used to solve the model. Case study shows that the proposed model can more accurately reflect the cost and risk of multi-level markets, deliver integrated price signals to users, and provide risk analysis for electricity retailers under multi-level market transactions.

Distributionally Robust Optimization-Based Decomposition Method for Medium- and Long-Term Contracts of Large-Scale Energy Bases

SUN Tian, WANG Guoyang, LI Yinxiao, FAN Menghua, TANG Chenghui, GUO Hongye

2025, 58(11): 38-48. DOI: 10.11930/j.issn.1004-9649.202504023

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With the gradual construction and commissioning of large-scale energy bases, the medium- and long-term contracted energy of their supporting thermal power units needs to be decomposed into hourly scales for dispatching plans. To address the challenge of coordinating the decomposition of contract energy with inter-provincial power transmission requirements, a distributionally robust optimization-based decomposition method for medium- and long-term contracts of large-scale energy bases is proposed. Firstly, a deterministic optimization model for medium- and long-term contract decomposition is developed to maximize thermal power contract fulfillment and minimize generation costs while complying with standard transmission curve requirements. Secondly, a fuzzy set for renewable energy output uncertainty is constructed based on the empirical distribution formed from historical samples and a Wasserstein ball, and accordingly a distributionally robust optimization model is established. Finally, the model is transformed into a mixed-integer linear programming problem based on the strong duality theorem and affine adjustment strategy. The case study results demonstrate that the proposed method effectively balances the aggressiveness and conservatism of decisions, supporting the optimal dispatch of large-scale energy bases in electricity market environment.

Market Participation of Integrated Source-Grid-Load-Storage Projects: Status and Prospects

DONG Xiaoliang, WANG Yuxuan, ZHANG Shengnan, LI Guodong, YONG Pei

2025, 58(11): 49-61. DOI: 10.11930/j.issn.1004-9649.202503047

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Under the drive of the "Dual Carbon" goals, integrated source-grid-load-storage projects, with their capacity for localized renewable energy consumption, have garnered significant attention. However, the inherent source-load balance within these projects conflicts with existing electricity pricing mechanisms, posing risks such as underpayment of transmission and distribution tariffs and mismatched system operating costs, which may compromise grid revenues. To address these challenges, this paper first systematically examines the construction and operational mechanisms of integrated source-grid-load-storage projects, distinguishing their similarities and differences from other aggregated entities. Secondly, it summarizes their market participation mechanisms and proceeds to dissect the shortcomings present in existing market access regulations and market-based trading systems. Furthermore, the study analyzes existing benefit distribution models among internal stakeholders. Finally, guided by national and regional policy directions, a design framework for market access mechanisms is proposed, offering theoretical foundations and practical pathways for standardizing the development of integrated source-grid-load-storage projects and refining electricity market mechanisms.

Comprehensive Evaluation of Flexibility Resource Regulation Capability in New Energy Power Systems Based on Hybrid Multi-attribute Decision-making Methods

CAO Shuyi, TAO Hongzhu, WANG Qiang, LI Xiaofei, WANG Leibao, GUO Sen

2025, 58(11): 62-71, 87. DOI: 10.11930/j.issn.1004-9649.202504063

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A reasonable assessment of the regulation capability of flexibility resources is of importance for enhancing the flexibility of power systems and constructing new-type power systems with high proportions of renewable energy integration. An evaluation indicator system for the regulation capability of flexibility resources in renewable energy power systems is established from both economic and technical dimensions. Furthermore, a comprehensive evaluation model for the regulation capability of flexibility resources is constructed based on the entropy weight method and the MARCOS method. Finally, four typical flexibility resources are evaluated. The results indicate that ramp rates, timescale regulation capability, service life, and unit regulation range are key factors influencing the flexibility resource regulation capability; electrochemical energy storage demonstrates the best regulation performance, while gas-fired power generation exhibits the poorest. Empirical research and sensitivity analysis validate the effectiveness and feasibility of the proposed model.

A Stackelberg Game-based Optimization Strategy for Integrated Energy Systems Incorporating Wind-solar Power Scenario Generation

WEN Sihai, XIAN Yuesheng, HAN Yang, LIU Qunying, CHEN Shuheng

2025, 58(11): 72-87. DOI: 10.11930/j.issn.1004-9649.202502017

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In view of the characteristics of multi-physical system coupling and multi-stakeholder involvement in the operation of integrated energy systems (IES), this paper conducts a study on the optimization strategy of the leader-follower integrated energy systems considering the uncertainty and correlation of wind and solar output, aiming to enhance the energy utilization rate and operational efficiency of the IES with high permeability of new energy while taking into account the operational demands of various entities involved. Firstly, a scenario generation method based on mixed Copula function theory is presented to obtain typical daily scenarios of wind and PV power generation. Secondly, a multi-user integrated energy service provider model is developed and an integrated demand response model for electricity and heat loads is established. And then, the integrated energy sales department, acting as the leader, publishes energy selling prices and demand response compensation rates to users; the park energy aggregation department and energy storage stations, acting as followers, regulate the output of energy coupling conversion equipment, electricity exchange between parks, and the charging and discharging of energy storage stations. Finally, case studies demonstrate that the proposed methods and strategies can effectivey enhance the economic benefits of the integrated energy system.

Collaborative Optimization of Multi-agent Integrated Energy Based on Asymmetric Nash Bargaining

LU Yiwei, SONG Xiaotong, ZHANG Jiahui, LI Huajian, SU Jia, JU Yuntao, JIA Xuwen

2025, 58(11): 88-100. DOI: 10.11930/j.issn.1004-9649.202502002

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The multi-agent integrated energy system (IES) faces many problems such as conflict of interest in peer-to-peer (P2P) transactions among members and privacy data protection. In the context of the ladder carbon trading mechanism, a collaborative optimization strategy for multi-agent IES based on asymmetric Nash bargaining is proposed. Firstly, a collaborative optimization model for electricity, heat and hydrogen energy sharing among multiple agents in IES is established by introducing such decarbonization measures as carbon capture power plant and hydrogen-blending equipment. Secondly, based on the Nash bargaining theory, the collaborative optimization problem is equivalently decomposed into two sequential subproblems: minimizing the operational costs of multi-agent IES and maximizing transaction payments, thereby achieving a fair and reasonable distribution of cooperative benefits. Finally, the alternating direction multiplier method (ADMM) is employed to solve these problems, determining the trading power and trading prices. The case studies demonstrate that this strategy achieves collaborative optimization of multiple heterogeneous energy flows (including electricity, heat, and hydrogen), reduces system operating costs, and ensures equitable benefit distribution among stakeholders.

Two-stage Robust Low-carbon Optimal Scheduling for Integrated Energy Systems Considering for Multiple Uncertainties

YANG Huping, GONG Jianing, CHENG Ming, LI Xiangjun, LIU Changlu, ZHANG Yang

2025, 58(11): 101-110, 121. DOI: 10.11930/j.issn.1004-9649.202507022

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To address the challenges of electricity-heat-gas coupling in integrated energy systems under multiple uncertainties, a two-stage robust optimization scheduling model is constructed that considers the multiple uncertainties from both renewable energy and equipment. Firstly, an integrated energy system model encompassing electricity, heat, and gas is established. Subsequently, with the objectives of system economy, robustness, and low-carbon performance, a two-stage robust low-carbon optimal scheduling model is established by introducing a tiered carbon trading mechanism to constrain carbon emissions. Finally, five scenarios are designed and solved using the column-and-constraint generation algorithm. Simulation results demonstrate that the proposed model effectively reduces carbon emissions of the system scheduling scheme while ensuring system robustness, thereby significantly enhancing the rationality of the scheduling scheme.

Impacts of Diverse Operational Modes and Flexibility of Distributed Energy Systems within Electricity Market

SONG Zhuoran, ZHANG Yanni, WANG Yang, JIANG Haiwei, LI Jiayu, GAO Hongchao

2025, 58(11): 111-121. DOI: 10.11930/j.issn.1004-9649.202501049

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Improving demand side collaboration capability is one of the key measures to accelerate the construction of new power systems. Unlike traditional resources, the flexibility of distributed resources is limited by the bounded rationality of users and the value transmission mechanism of the retail market. Exploring the interaction modes and flexibility impact mechanism between distributed energy systems and power grids has become a key issue. To this end, based on the user's perspective, we firstly sorted out the typical interaction modes between distributed resources and power systems, and analyzed the selection logic of users' different behavior patterns. Secondly, we analyzed the market transaction models available to resource users under current market mechanisms and their impact mechanism on users' behaviors. It was found that the direct transmission of spot price signals will maximally incentivize strategic behaviors among resource users, while their bounded rationality in decision-making simultaneously introduces operational risks to market stability. To mitigate these compounded effects, we proposed a split electricity volume bidding mechanism for resource users participating in spot markets. Finally, based on the real data of market price signals in a province in China, the rationality of the proposed method and the improved mechanism was demonstrated.

Spatio-temporal Guidance Strategy for Electric Vehicle Loads with Energy Recovery under Dynamic Pricing

YANG Xinqiao, JIA Heping, LI Peijun, LI Shun, LONG Yi, LIU Dunnan, HUANG Hui

2025, 58(11): 122-134. DOI: 10.11930/j.issn.1004-9649.202501053

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With the large-scale development of electric vehicles, the disorderly access of high-uncertainty electric vehicles to the grid will exacerbate the peak-valley difference of the grid, and it is necessary to improve the flexible adjustment ability of electric vehicles through the guidance of dynamic pricing. In addition, the energy recovery technology widely used in electric vehicles helps to reduce energy loss and carbon emission. This paper proposes a spatio-temporal guidance strategy for electric vehicle loads considering energy recovery under dynamic pricing. Firstly, based on the topological map of urban transportation network, an electric vehicle travel chain model considering urban functional areas is established. Secondly, based on Monte Carlo simulation method, a spatio-temporal distribution model of electric vehicle load considering electric vehicle braking energy recovery is constructed; and by introducing a charging utility function that considers users' sequential charging behavior, a Stackelberg game model is established to minimize distribution grid load fluctuations and electric vehicle user charging costs, which achieves the spatio-temporal guidance for electric vehicle loads considering energy recovery under a dynamic pricing mechanism. Finally, a case study is used to verify the effectiveness of the proposed method in guiding the spatio-temporal distribution of electric vehicle loads.

Information Transmission Optimization for Distributed Energy Systems via Edge Collaboration and Relay Assistance Joint Architecture

ZHANG Mingrui, HU Xuguang, WANG Jingyu, PAN Huilin, DU Hangyuan

2025, 58(11): 135-145. DOI: 10.11930/j.issn.1004-9649.202505024

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With the surge in data transmission volume from physical terminals in distributed energy systems, the accuracy of data transmission and the rationality of communication task allocation face significant challenges. To address these issues, this paper proposes an optimization method for information transmission in distributed energy systems based on joint edge collaboration and relay assistance architecture. Firstly, a joint edge collaboration and relay-assistance communication network architecture for distributed energy systems is proposed, mitigating communication resource location imbalance by having relay assistance undertake tasks and enabling task collaboration between adjacent energy bodies. Secondly, a five-slot information transmission architecture based on non-orthogonal multiple access (NOMA) technology and an energy consumption cost representation method are proposed to solve the quantification problem of communication costs by modeling the overall transmission process of the five-slot information. Thirdly, a collaboration assistance computing and resource allocation algorithm is proposed to minimize communication costs by optimizing relay power, execution time, and task allocation. Finally, the proposed communication framework and optimization strategy are tested on a distributed energy system in China. The results demonstrate that the proposed approach can effectively promote the reasonable allocation of communication resource and can significantly reduce communication costs.

Analysis of the Spain 4·28 Major Power Blackout and Its Implications on China's Power Development

WANG She, LIANG Shuang, LIU Siwei, HUANG Linghui

2025, 58(11): 146-155. DOI: 10.11930/j.issn.1004-9649.202507011

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On April 28, 2025, a nationwide blackout occurred in Spain, affecting Portugal, France, Belgium and other countries with a population of more than 50 million, of which Spain and Portugal lost about 55% and 98% of the load respectively. The event lasted for 26 hours, resulting in direct economic losses exceeding €20 billion. This paper begins by outlining the composition of the power system, covering its generation grid, and load profiles. It then details the pre-incident operational status, the process of blackout itself, and the restoration process, followed by an analysis of both the direct and root causes. By examining this event alongside other major global blackouts in recent years, the paper identifies four key risks to the secure operation of the power systems with a high penetration of renewable energy: "high volatility, susceptibility to oscillations, weak system support, and difficult restoration". Finally, based on the reality of China's power system, the paper proposes relevant measures and recommendations for building a new type of power system.

A Power Text Classification Model Based on Dual-layer Attention Mechanism

WU Tongxin, JI Xin, YANG Chengyue, CHEN Yiting, YANG Zhiwei

2025, 58(11): 156-163. DOI: 10.11930/j.issn.1004-9649.202411021

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There are a large amount of Chinese text data in the electric power field, traditional text mining methods are faced with problems such as difficulty in word segmentation, limitations in text feature representation, and poor performance in handling complex relationships in text, which limit the deep understanding and classification of power information. This paper proposes a power text classification model that combines text convolutional neural networks (TextCNN) and Attention mechanism. A hierarchical optimization design was carried out for the input layer, TextCNN layer, first attention layer, pooling layer, second attention layer, and output layer, with experimental validation conducted to verify the model's performance. The results show that the proposed TextCNN-Attention model achieved a text classification accuracy of 96.8%, with a precision of 86.3%, a recall of 90.3%, and a F1 score (comprehensive evaluation metric) of 88.2% on the power text dataset, demonstrating the superior performance of the TextCNN-Attention model in processing power texts. This study can provide valuable experiences for application of deep learning in power text classification.

Optimization Model of Distributed Multi Energy Fusion System Considering the Mobile Energy Storage Characteristics of Electric Vehicles

WANG Jinkun, XUE Xian, WU Huimin, QIN Keyuan

2025, 58(11): 164-174. DOI: 10.11930/j.issn.1004-9649.202412068

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Electric vehicles (EVs) bring a large number of mobile energy storage resources to the distributed multi-energy complementary system (DMS). Considering mobile storage characteristics of EVs, an optimization model for DMS is constructed based on the fusion of non-dominated sorting genetic algorithm and mixed-integer linear programming with system cost and emission as the optimization objective, and the influence of EV storage characteristics on the system is analyzed. The effectiveness of the proposed method is verified by a simulation example. The results show that the storage characteristics of EVs can effectively promote the mutual cooperation among the main bodies and improve the economic performance of the system when designing the system.

Identify the Participating Active Distribution Network Fault Recovery Strategy Based on the Nature of the Fault

YE Yuanbo, WANG Jiwen, SHAO Qingzhu, PING Xia, XU Ming, LI Wenzheng

2025, 58(11): 175-185. DOI: 10.11930/j.issn.1004-9649.202503051

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The high proportion of new energy access brings a large impact on the operation mode of the distribution network, and also puts forward higher requirements for the fault strategy of the distribution network. The traditional reclosing device can not effectively judge the nature of the fault, which may cause the power system to suffer unnecessary secondary fault impact during the re operation. The adaptive reclosing is proposed, and the adaptive reclosing is involved in the fault rapid recovery strategy of the active distribution network. Firstly, the fault characteristics of the island after the circuit breaker tripping are analyzed, and the two criteria of fault nature identification are constructed; secondly, the time sequence coordination problem of fault detection time and re time is analyzed, and the distribution network fault recovery strategy applicable to the high proportion of new energy access is proposed; finally, the effectiveness of the proposed method is verified by simulation examples and the proposed method can speed up the system power supply recovery under the premise of ensuring that the distributed power supply does not fall off the network.

A Data-driven Adaptive Testing Method for Flexible DC Relay Protection

LIU Liping, LIU Dekun, DENG Bo, FAN Dengbo, XIN Guangming, ZHANG Xinyuan, LI Botong, REN Xiang, LIN Mengyuan

2025, 58(11): 186-192, 204. DOI: 10.11930/j.issn.1004-9649.202412036

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The flexible direct current transmission system's complex operation environment has put forward higher requirements for relay protection performance testing. In order to improve the testing efficiency, data-driven self-adaptive testing method for flexible direct current relay protection is proposed. The key testing scenarios for protection evaluation are tested by mining. The mean drift method is used to the historical testing data, and a testing prediction model based on typical scenarios and neural networks is established. The effectiveness of the proposed method is verified by simulation examples. At the same, the simulation results show that the proposed method is conducive to improving the efficiency and accuracy of testing, and effectively reducing the time and resource cost of testing.

Islanded Microgrid System Modeling Based on Kuramoto Coupled Oscillator Theory

LI Cheng, ZHANG Haiyu, SONG Huihui, QU Yanbin, ZHANG Xin, LI Yingying

2025, 58(11): 193-204. DOI: 10.11930/j.issn.1004-9649.202404032

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To further explore the implicit collective dynamics within the synchronization phenomenon of various complex systems, the Kuramoto model is introduced to describe the evolution of globally sinusoidal coupled phase oscillator systems with natural frequency distributions. This paper proposes a Kuramoto modeling method for island microgrid systems from the perspective of oscillator synchronization, clarifying the transient characteristics of frequency synchronization among different units in the microgrid. Firstly, a deep analysis is conducted to explore the similarities between the operational characteristics of each unit in the microgrid and the dynamic characteristics of Kuramoto oscillators, and a Kuramoto model is established based on the differences in operational characteristics. Then, the dynamic equations of Kuramoto oscillators classified by inertia are derived according to their inertial properties, and a first- and second-order mixed inhomogeneous Kuramoto model is constructed to represent the entire system. Finally, a 3-machine 5-node microgrid system is built on the Simulink simulation platform to verify the applicability of the Kuramoto synchronization theory in microgrid modeling. The similarity between the frequency synchronization of the Kuramoto model and the transient-to-steady-state process of the microgrid is revealed, laying a theoretical foundation for quantitative analysis of transient stability and the construction of transient energy functions.

Bypass Heating System with Integrated Steam Ejector and Performance Analysis

XU Jidong, MEI Long, SONG Yajun, WANG Zhengming, SI Paiyou, ZHAO Ning, LIU Shuangbai

2025, 58(11): 205-213. DOI: 10.11930/j.issn.1004-9649.202406101

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In order to solve the problem of residual energy loss of bypass steam in the process of temperature and pressure reduction in the normal pressure bypass heating system, a new bypass heating system connected to the bypass heating system was proposed. By adding a steam ejector, the bypass steam is injected into the steam turbine exhaust steam to provide heat, which not only reduces the energy cascade utilization, but also causes a loss of cold source in the central air conditioner. Taking a 300MW combined heat and power central air conditioner as the research object, a thermodynamic calculation model of air conditioner central air conditioner was established using EBSILON to conduct a comprehensive analysis of the peak shaving capacity, thermal performance and economy of the new bypass heating system. The results show that compared with the conventional bypass system, the heating capacity of the new bypass system increases by 97.26 MW, and the minimum electric load rate decreases by 0.14. Under the same electric heating load conditions, the new system's power generation thermal efficiency increased by 19.4%, saving 9.2 tons of standard coal per hour, and the annualized net present value of income increased by 2.16 million yuan, with higher economic benefits and promotion value.

Optimization of Heating Operating Mode for Ground Source Heat Pump Coupled with Seasonal Solar Heat Storage

RAN Yujin, PENG Jia, TIAN Xiaolin, MA Lei, SHAN Qiang, YANG Xufei, ZHANG Wei

2025, 58(11): 214-224. DOI: 10.11930/j.issn.1004-9649.202503025

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To address the challenges of the long-term decline of geothermal temperature in ground source heat pump (GSHP) systems and the efficiency reduction caused by the coupling of seasonal solar heat storage (SSHS), this study proposes a novel operational mode—the 'store-then-supply' (SSHS+GSHP) mode—to enhance the overall efficiency of the coupled heating system. Based on greenhouse heating experimental data, a simulation model was developed using TRNSYS to analyze the geothermal temperature variation and heating efficiency under three different modes: no-storage mode, supply-then-store mode, and store-then-supply mode. The proposed 'store-then-supply' mode leverages seasonal solar heat storage before the heating period, achieving efficient coupling of solar and shallow geothermal energy. This approach not only mitigates geothermal imbalance, but also significantly improves heating efficiency, maintaining a stable performance over a 20-year operational period. Compared with the 'supply-then-store' mode, the proposed mode improves the annual energy efficiency by approximately 4.2% to 4.4%, achieving a cumulative electricity saving of about 1.07 × 10⁴ kW·h over 20 years and an average annual saving of 534 kW·h, directly reducing the operating energy costs by 4.2%, effectively lowering system energy consumption and enhancing heating cost-effectiveness.

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