Abstract:

With the increasing penetration of renewable energy sources, there has been a profound structural transformation in electricity markets, particularly in the operational paradigm where demand-side resources participate in market mechanisms. This evolution underscores the critical role of demand-side flexibility in enhancing grid resilience and accelerating low-carbon transition pathways. The traditional supply-side regulation is gradually shifting towards a multi-agent decision-making model dominated by demand-side management, involving complex interactions among government, electricity suppliers, grid operators, and consumers. Evolutionary game theory (EGT), as an important tool for studying multi-agent dynamic games and strategy evolution, demonstrates unique advantages in analyzing demand-side participation in the electricity market. Unlike traditional game theory, EGT accounts for bounded rationality, incomplete information, and dynamic strategy adaptation, enabling effective analysis of market equilibrium and stability under different incentives and policy frameworks. A comprehensive review of the applications of EGT in the electricity market is provided in this paper, focusing on the strategy evolution and equilibrium mechanisms of demand-side participation. It explores how EGT helps to understand and predict strategy adjustments and the impact of policy incentives on market stability in multi-agent games. The paper also looks forward to future research directions. By organizing and analyzing the existing literature, this review offers a theoretical framework and practical reference for policymakers, market regulators, and researchers to facilitate the collaborative evolution and sustainable development of the electricity market under the context of renewable energy.

Abstract:

To improve the power quality of wind energy, increase the participation of wind farms in the electricity market, and achieve reasonable energy storage allocation, wind farm energy storage optimization strategy based on information gap decision theory (IGDT) is proposed under the Guangdong electricity market trading rules, considering the uncertainty in electricity energy and frequency regulation ancillary service market prices. In the configuration stage, the configuration incorporates operational considerations and proposes a two-level optimization model for wind farm with integrated energy storage. The upper level optimizes energy storage allocation by maximizing the annual net revenue of the wind-storage system, while the lower level optimizes the system's operation based on actual operating scenarios, aiming to maximize daily operational revenue. To address the price uncertainty in the lower-level model, a price deviation factor is introduced using IGDT. Based on the two-level model, an IGDT-based energy storage optimization configuration model is constructed with the goal of maximizing the price deviation factor. The energy storage configuration is jointly optimized. Simulation results demonstrate that the proposed strategy can achieve economically feasible energy storage configurations under electricity market price fluctuations.

Abstract:

In the power market environment, participation of wind-storage system in both the energy market and the frequency regulation market is essential to enhance economic efficiency and support grid frequency regulation and peak shaving. However, key issues such as formulating bidding strategies for wind-storage systems in energy-frequency regulation dual markets need to be addressed. A bidding model driven by deep reinforcement learning is proposed in this paper to formulate bidding strategies in an incomplete information market environment. Firstly, a framework for wind-storage systems participating in the energy and frequency regulation markets is established to clarify the bidding operation strategies of each market entity. Then, a real-time frequency regulation performance scoring model is introduced to address the differences in frequency regulation response capabilities among various resources. Based on this, a bidding model for wind-storage systems is developed. Finally, a multi-agent deep reinforcement learning method with strong model-free learning capabilities is employed to solve the stochastic game problem in an incomplete information market environment and to handle the multi-agent bidding game relationship. Simulation results indicate that the proposed method can effectively formulate bidding strategies for wind-storage systems participating in the energy and frequency regulation markets. The method achieves high returns while ensuring high convergence stability. As a result, the economic efficiency of wind-storage systems is enhanced, and grid frequency regulation and peak shaving are effectively supported.

Abstract:

The integrated energy system (IES), known for its flexibility and environmental friendliness, is a promising solution for low-carbon economies and energy efficiency. However, existing IES modeling methods are limited in their ability to explore the synergistic coupling of hydrogen energy across multiple devices, and the scheduling strategies lack market mechanism support. To address this, an optimal scheduling strategy of hydrogen-containing IES is proposed, which comprehensively considers green certificate trading, tiered carbon emission trading, and demand response. Firstly, a hydrogen multi-utilization model based on two-stage power-to-gas is established to promote the use of renewable energy. Secondly, a green certificate-carbon joint trading mechanism is developed to reduce reliance on fossil fuels through market incentives. Finally, an IES optimal scheduling model is constructed with the objective of minimizing economic operating costs, incorporating comprehensive demand response to optimize user-side energy consumption. The model is solved using the CPLEX solver. The results demonstrate that the proposed model effectively achieves multi-energy coupling within IES, enhances the absorption capacity of renewable energy, and reduces carbon emissions.

Abstract:

In order to face the challenge of large-scale integration of new energy into the power grid with uncertainty to the traditional electricity market, and to address the problems of long time scales and low returns of new trading entities in the clearing strategy of the traditional electricity market, a clearing strategy considering the uncertainty of wind-solar-thermal-storage combined power generation system is proposed to participate in the joint trading of electricity spot market and frequency regulation auxiliary service market. Firstly, the sample scenes are processed using Latin hypercube sampling and Kantorovich distance reduction to generate typical wind solar power output scenarios. A wind-solar-thermal-storage combined power generation system with the goal of minimizing power generation costs is proposed to participate in the joint trading clearing strategy of electricity spot market and frequency regulation auxiliary service market, taking the generated typical wind and solar output scenarios as the research object. The model is solved by alternating direction method of multipliers (ADMM). Finally, a corresponding mathematical model is built in the IEEE 39-node system, and simulation verification is carried out using wind and solar data from a certain location in northwest China as an example. The results show that the strategy can allocate energy output reasonably and improve energy utilization efficiency and the profits of various trading entities. The results verify the effectiveness of the strategy.

Abstract:

Although numerous studies have investigated low carbon optimization for park integrated energy system (PIES) under dual-carbon goals, few have proposed strategies to enhance carbon efficiency from carbon flow analysis. Therefore, a cloud platform-based low carbon economic operation strategy for PIES is proposed, considering carbon flow optimization. Firstly, a two-layer collaborative optimization architecture is constructed, where the upper-layer cloud platform designs a carbon flow optimization module to provide suggestions to the lower-layer PIES, then the lower-layer cloud platform adjust their energy consumption plans for collaborative optimization. Secondly, the load carbon potential indicator is developed to measure the carbon flow of PIES, guiding them through the cloud platform to reduce load carbon potential and improve system carbon emission efficiency. Additionally, a Nash bargaining module incorporating carbon efficiency is designed to maximize participants' benefits of multi-subject transactions. Then the target cascade analysis method is applied to solve the two-layer model. Finally, simulation results verify that the PIES operation strategy considering carbon flow optimization can reduce carbon emissions, improve carbon efficiency, and enhance economic benefits while promoting energy trading between PIES and increasing carbon profits.

Abstract:

As China's electricity and carbon markets undergo coordinated reforms, the growing integration of distributed resources within virtual power plants (VPPs) is shifting their role in market participation. To explore bidding strategies that balance economic efficiency and carbon reduction under the electricity-carbon joint market, this paper models VPPs as price makers and proposes a bi-level bidding framework considering wind and solar uncertainty. The upper level maximizes the VPP's profit, while the lower level maximizes social welfare in the joint market. To handle renewable generation uncertainty, robust optimization is employed to transform the bi-level model into a two-stage robust problem. The model is then solved as a mixed-integer linear program using the column-and-constraint generation algorithm, strong duality and the Big-M method. Case studies confirm the feasibility and effectiveness of the proposed strategy.

Abstract:

To improve the low-carbon efficiency and economy of the integrated energy system, while fully utilizing demand-side flexibility, a hydrogen-containing integrated energy system (HIES) based on green certificate-carbon trading and flexible output of heat and power is proposed. Firstly, the traditional combined heat and power (CHP) is coupled with organic Rankine cycle (ORC) and electric boiler (EB) to allow flexible operation, enabling the power generated by gas turbine (GT) to be supplied simultaneously to the power grid and EB. The heat energy released is simultaneously used for ORC and EB, alleviating the constraints of thermoelectric coupling. Secondly, a green certificate-carbon trading model is constructed by integrating green certificate trading with tiered carbon emission trading, which reduces the system's carbon emissions and improves the consumption of renewable energy. The influence of the green certificate unit price and the renewable energy quota coefficient on the system's optimal scheduling is analyzed. Additionally, the model includes electrolyzers and hydrogen fuel cells, fully considering the diversified utilization of hydrogen energy, thereby improving the system's low-carbon efficiency. Lastly, a day-ahead optimal scheduling model is constructed with the goal of minimizing the total cost of the system. The results demonstrate that this strategy can enhance renewable energy consumption while reducing carbon emissions and total system costs.

Abstract:

In the emerging electricity market, bilateral transactions are conducted between distribution-grid operators and micro-grid operators. Under the uncertainty caused by the large-scale integration of wind and solar generation, the cooperation game between the distribution-grid and the micro-grid is analyzed at the planning level. The impact of multi-agent bilateral energy transactions is considered in this approach. To address the multiple non-convexities in traditional coordinated planning of distribution-grid and micro-grid, an alternating optimization-based strategy is proposed. Through the alternating optimization process, the discontinuous coordinated planning problem is transformed into a bi-level iterative solving process. In the upper level, Nash bargaining is applied to determine the traded electricity and payment strategies within a convex subset generated by the lower level. In the lower level, local individual planning problems are solved using trading variables obtained from the upper level. The market-clearing problem is decomposed into two subproblems, electricity transaction and payment settlement. The alternating direction method of multipliers is used sequentially to solve these subproblems. Case simulations on the IEEE 33-bus system demonstrate that the proposed model effectively enhances the operational benefits for both trading parties.

Abstract:

Based on the background of the current electricity market and carbon trading market, aiming at the problems of insufficient consumption capacity and difficult peak shaving of regional power grids with new energy access, an optimal scheduling strategy of regional power grid electricity-carbon joint multilateral trading based on Nash negotiation is proposed. Firstly, considering improving the flexibility of multilateral trading in regional power grids, combined with carbon trading and power generation rights trading mechanisms, an electricity-carbon joint trading model is constructed. Secondly, considering the volatility of wind power, the demand method of wind power flexibility adjustment is adopted. According to the wind power curtailment and time period of day-ahead dispatching, a multi-agent peer-to-peer trading model of electricity-carbon joint is constructed. And then the model is decoupled into two sub-problems: maximizing the benefits of regional power grid alliance and reasonably allocating the benefits of electricity-carbon joint multilateral trading. The alternating direction method of multipliers is used for interactive decoupling to ensure the privacy security of each subject. In addition, in the fair distribution of benefits, the asymmetric bargaining method is selected to quantify the contribution of each subject in peer-to-peer transactions as a bargaining factor to achieve the purpose of fair distribution of benefits within the alliance. Finally, several different scenarios are set up to verify the low carbon and economy of the proposed method and model. The results show that the proposed method can promote the consumption of new energy and the improvement of flexible resource collaborative scheduling ability, and achieves the goal of energy saving and emission reduction.

Abstract:

In response to the difficulties in carbon trading cost-sharing and the insufficient motivation for emission reduction in the integrated energy system under the dual-carbon targets, a low-carbon economic scheduling strategy based on a multi-principal carbon trading cost-sharing mechanism is proposed. Firstly, a multi-principal carbon trading cost-sharing model is designed, considering the flexible adjustment and response mechanisms on both the source and load sides. Secondly, a master-slave game low-carbon economic dispatch model is constructed, with the energy seller as the leader and the energy supplier and load aggregator as followers. The leader formulates dynamic time-sharing carbon prices to guide the energy supplier in optimizing equipment output strategies and the load aggregator in adjusting energy consumption strategies. Finally, several scenarios are compared and analyzed through simulation experiments. Results demonstrate that, compared with the average of three mechanisms where a single subject bears the carbon trading cost, the proposed multi-principal carbon trading cost sharing mechanism increases the system's overall total benefit by 3.71%, reduces total costs by 11.97%, cuts carbon trading costs by 21.06%, and decreases total carbon emissions by 19%. This model effectively realizes the sharing of carbon trading costs among multiple subjects, promotes cooperation in emission reduction among all parties, and achieves a win-win situation in economic and environmental benefits.

Abstract:

Time-of-day division is an important step in the formulation of time-of-use electricity prices. The minimum and maximum continuous duration in each period have an important impact on the adjustment effect of time-of-use electricity prices. However, at present, previous studies on the time-of-use electricity price division have not taken into account the time preference of the policymaker. The different needs of policynakers for the total duration of the session, the minimum continuous duration of the session, and the maximum continuous duration within the session are considered in this paper. On the basis of cluster analysis, a time-of-use electricity price time division model considering the preference of policymakers is constructed. The proposed model calculates the distance between load values through affiliation function and Euclidean distance, with the objective function of minimizing the total similarity within each cluster. In this paper, the typical daily load data of a region in China is used to verify the rationality and effectiveness of the proposed model. The results show that the proposed method can obtain the time division results that meet the preferences of decision makers. It is found that the clustering analysis of time period using 0-1 integer linear programming is an effective method to solve the problem of policymakers' preference for duration. Under the traditional time-of-use electricity price time division model, the modified load is inconsistent with the time period electricity price. However, the time-of-use electricity price time division model considering the time preference of decision-makers has a significant corrective effect on this phenomenon.

Abstract:

The open-loop mode resonance theory is an important theoretical achievement to explain the mechanism of system instability caused by the grid connection of permanent magnet synchronous generator (PMSG), but the determination of the resonance risk of open-loop mode based on the black box measurement model is rarely introduced in its theoretical research. In the past, the determination of resonance risk in open-loop mode relied on accurate parameterization models. However, in practical applications, it is often difficult to obtain the detailed parameters of PMSG, so it is impossible to establish an accurate parameterization model of the system. Therefore, a method for determining the risk of open-loop mode resonance based on the measurement model is proposed in this paper. Firstly, the PMSG to be studied is regarded as a black box model, and its frequency response is measured by injecting voltage disturbance using frequency sweep method. The impedance transfer function matrix is obtained after fitting the frequency response of the measurement model by vector fitting technology, which is transformed into a time-domain state-space model and connected to the closed-loop system. Then, the general analysis method of open-loop mode resonance based on the measurement model is used to determine the system instability risk. Finally, a simulation example is used to demonstrate the analysis and determination process of resonance risk in open-loop mode. The results show that the resonance risk of open-loop mode can also be determined based on the measurement model. The proposed method does not need to obtain the detailed internal parameters of the PMSG, and has high engineering practicability.

Abstract:

The all-DC system of onshore wind power can effectively solve the problems of harmonic resonance and reactive power transmission，which is the development direction of wind power generation system. The all-DC system of onshore wind power has a low-voltage ride-through (LVRT) capability and can guarantee voltage stability. Based on the topology and operation control strategy of the onshore wind power all-DC power generation system，the surplus power gathered in the DC link of the system is analyzed during the grid-side voltage dips and the applicability of conventional LVRT strategies is analyzed in the wind power all-DC system. Considering the grid requirements for wind power system energy storage configuration and the self-starting characteristics of wind turbines，a LVRT control strategy based on battery energy storage is proposed. This strategy can store the surplus power of the DC bus when low voltage faults occur. The all-DC power generation system of onshore wind power is built by PSCAD/EMTDC，and the proposed strategy is verified by simulation. The results show that the proposed control strategy can improve the low-voltage ride-through capability of the all-DC system of onshore wind power，and promote the rapid recovery of DC bus voltage. Battery storage absorbs surplus energy during faults and provides energy during self-start of the wind turbine. This strategy can improve the utilization of energy and storage.

Abstract:

As a fault occurs on lines of a two-terminal low-frequency transmission system, fault currents will be suppressed when frequency converters adopt negative sequence current suppression strategy. Under such a circumstance, currents on both sides of the line show weak feed and traversal characteristics, leading to poor sensitivity or even failure to operate of the traditional phase-based line differential protection. To solve the above problem, the two-terminal low-frequency transmission system based on modular multilevel matrix converter (M3C) topology is constructed. And the fault electrical characteristics and phasor differential protection adaptability of single-phase grounding and two-phase short-circuit faults of low-frequency lines are analyzed. Then, a fault control strategy is proposed to highlight fault characteristics by suppressing the positive sequence current output of the power control station, thereby improving the braking characteristics and sensitivity of differential protection. Finally, a real-time digital simulator (RTDS) model is built based on a low-frequency transmission project, and the proposed control strategy is simulated and verified against typical faults. The results show that the proposed fault control strategy can effectively solve the problem of insufficient sensitivity of phasor differential protection for low-frequency line fault, being of well value in engineering application.

Abstract:

As wind power integration increases, its unpredictability challenges the integrated energy system (IES). A low-carbon, economic dispatch method for IES using an enhanced wind power scenario reduction algorithm is introduced in this paper. It employs an improved iterative self-organizing data analysis technique algorithm (ISODATA) for clustering historical wind power scenarios, addressing the limitations of traditional clustering algorithms in determining cluster centers and analyzing inherent data features. Then, an integrated energy model is established and it optimized using an improved stepwise carbon trading and power to gas and carbon capture system (P2G-CCS) coupling model. Finally, an IES model is developed with the goals of improving economic efficiency, reducing carbon emissions. Simulation results demonstrate that this model reduces comprehensive operational costs while ensuring low carbon emissions in the system.

Abstract:

Currently, the distribution grid is substantially pressured due to the charging requirements of electric vehicles during peak hours with the rapid growth of electric vehicles. Existing studies indicate that the power supply pressure on the distribution grid can be effectively mitigated by the orderly charging and discharging scheduling of electric vehicles. However, the disparities in charging and discharging needs among different electric vehicle users are not considered by the majority of electric vehicle charging station operators, which treat the charging and discharging scheduling of electric vehicles uniformly, thus increasing grid pressure. To address this, an optimization approach for electric vehicle charging scheduling based on electricity price guidance is proposed in this paper, for the game between electric vehicle operators and users under a cooperative game framework. Additionally, a dynamic time-of-use optimized charging and discharging simulation model for electric vehicles is constructed. In the solution process, an improved fruit fly optimization algorithm (FOA) is uesd to plan the charging periods of electric vehicles. Eventually, the feasibility and economic advantages of the proposed strategy are verified through case study simulation analysis. Compared to the existing fixed electricity price strategy, the proposed strategy not only effectively reduces the peak-to-valley differences of the grid load and prevents new load peaks but also improves the benefits for both electric vehicle operators and users.

Abstract:

High penetration of distributed photovoltaic causes voltage violation problem in active distribution network. In order to solve this problem, a voltage optimization strategy based on distributed photovoltaic cluster control is proposed for the active distribution network. Firstly, with economic operation as the objective,shiftable load dispatch, the tap positions of on-load tap-changing transformers, and the output of capacitor banks are determined in the day-ahead stage. Subsequently, the approximate voltage sensitivity is calculated based on the day-ahead scheduling results, and clusters are partitioned using the K-means algorithm according to a comprehensive clustering index. Finally, in the intra-day stage, the cluster self-regulation is carried out based on the cluster adjustment characteristics, aiming at minimizing the internal network losses or the node voltage deviations. The inter-cluster coordination optimization is then performed based on the alternating direction multiplier method. Deploy this strategy on the IEEE 33-node system, and conduct a comparative analysis of voltage regulation effectiveness under various weather conditions and different scheduling strategies. The results show that after the day-ahead centralized optimization, the node voltages are within the limit range. After the intra-day rolling optimization, the voltage deviation is further reduced, and the deviation does not exceed 3% on sunny days. The case study results verify that the proposed optimization strategy guarantees voltage quality and meanwhile improves operational efficiency of distribution network.

Abstract:

Exposed conductors in 10 kV distribution line are one of the major causes with operational faults in distribution lines, continuously affecting the safe and stable operation of the distribution network. Traditional manual inspection methods often fail to detect such defects in a timely manner. A detection method for exposed conductors in 10 kV distribution lines is proposd based on an improved YOLOv8 algorithm,which is designed to assist power grid maintenance personnel detecting conductor exposed defects quickly and efficiently. The algorithm replaces the original convolution with omni-dimensional dynamic convolution in the backbone network, enhancing the features of exposed conductors through multi-dimensional feature extraction. In the neck network, the connection between high-level and low-level features is enhanced by combining attention embedding module with the cross stage feature fusion module of the original network, thereby analyzing both the overall shape and local details of exposed conductors. For the loss function, distance intersection over union with normalized wasserstein distance is combined to increase focus on cases where targets are small or background interference exists in drone inspection photographs. The experimental results demonstrate that the improved algorithm achieves increases of 4.8 percentage points, 4.2 percentage points, and 5.2 percentage points in precision, recall, and mean average precision, respectively, compared to the original algorithm. This effectively enhances the detection capability for exposed distribution conductors, providing a new technical approach for ensuring the safe and stable operation of power systems.

Abstract:

To address the issue of low signal-to-noise ratio in disturbance localization on power communication optical cables caused by environmental noise in phase sensitive-optical time domain reflectometry (Φ-OTDR) systems, the Φ-OTDR localization technique is investigated based on moving variance averaging and spectral subtraction. A coherent detection Φ-OTDR system is built to connect optical fibers and optical cables to carry out galloping simulation experiments. The backward Rayleigh scattering signal is acquired by using the system and the scattering signal is demodulated to obtain the amplitude signal curve. By selecting an appropriate number of curves and an optimal step size, the amplitude signal curves are processed using moving variance average to generate a preliminary disturbance localization map. To suppress ambient noise outside the localization region, noise reduction using spectral subtraction is performed to achieve enhanced localization. A comparative analysis is conducted between spectral subtraction and two alternative noise reduction methods-wavelet decomposition and empirical mode decomposition. The results demonstrate that spectral subtraction significantly outperforms the other two algorithms in localization effectiveness, achieving an SNR improvement exceeding 20 dB while maintaining a computational time as low as 0.58 s.

Abstract:

Aiming at the characteristics of large-scale data in the distribution network with distributed generation, as well as the limitations of the traditional grid partitioning method based on complex network theory, such as irrational partitioning and insufficient reactive power regulation capability, a two-stage division method based on complex network theory is proposed in this paper. In the first stage, based on mapping partition, load nodes are initially partitioned according to the principle of "minimum electrical distance" so that the reactive power resources can better coordinate and control the load nodes in the region. At the same time, the initial association size in the second stage is reduced. In the second stage, the Louvain community discovery algorithm is used for partition aggregation, and the initial partition is adjusted by improving the partition modularity function to ensure the reactive power adjustment capability of the partition. The IEEE 69-node system is used as an example to verify the effectiveness of the proposed method in this paper. The number of partitions and the amount of inter-area reactive power transfer after partitioning are reduced compared with the traditional method. The results show that the proposed partitioning method has stronger reactive power regulation capability and is more suitable for autonomous control of regional voltage and reactive power in distribution networks.