Abstract:The fault distribution network with a high proportion of distributed power supply access possesses active recovery and self-governance capabilities, which is conducive to enhancing the power system's adaptability to climate change and disaster risk management, aligning with the development direction of distribution network technology. Currently, research on active islanding self-recovery and stable operation of distribution networks has received widespread attention. This paper begins with an overview of the connotations of active islanding in multi-source distribution networks, as well as the distinctions between multi-source distribution network islands and microgrid islands. It then analyzes and summarizes the key issues that arise in the stable operation of active islands in distribution networks with a high proportion of distributed power supply integration. Subsequently, the paper elaborates on the current research status from three aspects: active islanding partitioning, fluctuation mitigation control for island self-networking, and small signal stability control for active islands after networking completion, followed by a summary. Finally, based on the internet of things for power, small signal suppression in the context of high power electronics, and active islanding solutions within the framework of the energy internet, the paper provides an outlook and summary on the development prospects of active islanding in multi-source distribution networks.

Abstract:With the widespread popularization of electric vehicles (EVs) and the increasing level of electrification in transportation, there is a growing trend of deep coupling between the distribution grid and the transportation network. Achieving efficient interaction between EVs and the power-transportation coupled network can effectively enhance the stability of grid operation and alleviate congestion in the transportation network, thus becoming a research hotspot in the fields of power systems and EVs. This paper provides a comprehensive introduction to the basic concept of the power-transportation coupled network and delves into three key areas: modeling and state prediction of the power-transportation coupled system, EV charging station planning under the coupling of the two networks, and optimal dispatch and control under the coupling of the two networks. Additionally, it analyzes the status of the development of its standard system and offers forward-looking prospects for its future direction.

Abstract:To address the nonlinear output characteristics of photovoltaic (PV) cells and enhance their utilization efficiency, it is crucial to track their maximum power output in real-time. Starting from an analysis of PV cell output characteristics and based on an explanation of the maximum power point tracking (MPPT) control principle, this paper reviews the algorithms for MPPT in photovoltaic power generation systems. It discusses tracking speed, control accuracy, and issues that need to be addressed in future research and applications. Through experimental comparisons, the paper analyzes differences between various algorithms and between different parameters within the same algorithm, as well as the distinctions between voltage control and direct duty cycle control. Based on the experimental results, the paper summarizes various MPPT algorithms, including their tracking time, tracking efficiency, and voltage fluctuations. Recommendations are provided for suitable applications in engineering, offering a reference for the design and implementation of key components in PV grid-connected controllers. Finally, the paper outlines future development trends.

Abstract:Transmission line ice-covering accidents occur frequently in winter in China, and the high-frequency ice-melting method based on dielectric loss and skin effect has significant advantages over traditional AC-DC ice-melting heat generation. Aiming at the design problems of frequency and voltage of ice melting power supply in high frequency and high voltage online ice melting, a NSGA-Ⅱ based high-frequency ice melting power supply parameters design method considering line constraints is proposed. Firstly, based on the uniform transmission line theory, the equivalent transmission line circuit model of the ice-covered wire is established; secondly, from the engineering reality and combined with the principle of high frequency online ice melting heat balance, derive the high frequency ice melt objective function and constraints; the NSGA-Ⅱ algorithm is then used to obtain the Pareto optimal solution set and to select the ice melt power parameters according to the TOPSIS method; finally, simulations using comsol based finite element methods were used to verify the correctness of the ice melt model and the validity of the parameters selected in this paper.

Abstract:Carbon emissions from electricity production activities account for a significant proportion of total global emissions. Therefore, the power industry has become a key stakeholder in achieving "carbon emission reduction" goals. By employing a quantitative comprehensive evaluation approach to assess national power development levels, we can not only clearly delineate the development trajectory of various countries in the power sector but also more accurately identify the gaps between China and other countries in power development. This paper conducts a comprehensive evaluation and research on national power development levels and proposes an evaluation method for power development levels based on an improved multi-objective particle swarm optimization (MOPSO) algorithm to optimize the projection pursuit model. Firstly, an improved MOPSO algorithm is proposed. Secondly, two projection pursuit models are established, and further optimized using the improved MOPSO algorithm to obtain the optimal Pareto solution set for the projection vectors. Finally, a fuzzy comprehensive evaluation is used to obtain the optimal weight compromise solution, which is then substituted into the prospect theory model to derive comprehensive scores for the power development levels of various countries. Based on these scores, an objective ranking of the power development levels of various countries is conducted. The proposed method is validated using an actual dataset of national power development levels. The experimental results demonstrate that this method can effectively rank national power development levels, with evaluation accuracy superior to existing evaluation methods for power development levels.

Abstract:To effectively address the uncertainty and volatility brought by the large-scale integration of renewable energy into the grid and achieve optimal operation of the distribution network, a dynamic network reconfiguration optimization method considering the time-varying nature of distributed generation (DG) and load demand characteristics is proposed. Firstly, to tackle the non-convexity of the traditional static branch power flow model, slack variables are introduced to transform it into second-order cone constraints, establishing an optimal power flow model based on second-order cone relaxation. Then, considering the constraints of active management elements in the active distribution network environment, a multi-period distribution network reconfiguration model under high penetration of DG is constructed with the optimization objectives of minimizing network losses and electricity purchases while ensuring voltage stability. Finally, the Yalmip toolbox and Gurobi solver are employed for modeling and solving, and the IEEE33-node model is adapted for verification and analysis. The results of the case study demonstrate that the proposed scheme reduces the expected network loss value from 0.176 MW to 0.097 MW, a decrease of 44.89%, and improves the voltage level by 1.40%, thereby validating the effectiveness of the model.

Abstract:With the digitization and intellectualization of power systems, the prediction of distribution transformer overload has become one of the key technologies for realizing intelligent condition-based maintenance. In real-world scenarios, the spatiotemporal factors of distribution transformer overload often exhibit a biased distribution, among which some high-risk and rare (HRR) factors, once occurred, can cause irreversible damage to transformers and should not be ignored. Therefore, this paper proposes a prediction method for distribution transformer overload based on the improved association rules-criticality importance (IAR-CI ) model. Firstly, considering both internal and external factors, multiple data sources are collected to establish a database of distribution transformer operating states, and ICA is used to identify rare high-risk periods and HRR factors that are strongly associated with severe transformer overload. Secondly, based on the criticality importance (CI) metric calculation, a factor weighting method is designed to accurately measure the risk weight of each factor. Finally, the TBFP-Growth algorithm is applied to enhance the operational efficiency of the model. Simulation analysis conducted in a region in southern China demonstrates that the proposed method can improve the prediction performance of severe distribution transformer overload, facilitating the reasonable planning and scientific scheduling of subsequent inspection and testing strategies. This reduces the operation and maintenance costs of power equipment while enhancing the reliability of power supply.

Abstract:Electrical distance (ED) is the primary basis for dividing voltage control areas in power grids. Both classic methods and their improved versions for calculating electrical distance rely on the power flow Jacobian matrix, which may lead to frequent adjustments in partitioning schemes and increase the difficulty of reactive power and voltage management as the randomness of power sources and loads continues to grow. Therefore, this paper first reviews the classic definition of electrical distance and then demonstrates that the ratio of voltage changes at a pair of nodes in a power system after a small disturbance can be approximately obtained using network topology parameters. Next, following the classic formula for calculating electrical distance, a new method for computing electrical distance based on network topology is presented. Finally, this method is applied to the NE-39 and NE-68 node systems, and the results are compared with those obtained using the classic method in terms of electrical distance and partitioning schemes, validating the effectiveness of the proposed approach.

Abstract:A harmonic responsibility estimation method based on the Gaussian mixture model (GMM) is proposed for partially observable systems. This method estimates the harmonic responsibility of each harmonic load based on the probabilistic distribution characteristics of measured harmonic voltages, circumventing the difficulties in quantifying harmonic responsibility due to the introduction of unmeasurable line parameters. Specifically, the process begins by training a GMM using the measured harmonic voltage samples. Then, the number and range of Gaussian components in the mixture model are determined based on the Bayesian information criterion and the Kullback-Leibler divergence ratio. Additionally, anomaly detection of harmonic voltage samples is achieved through the Z-test principle. Finally, the effectiveness of the proposed method is verified using the IEEE 14-node test system.

Abstract:This paper investigates the planning of wireless charging lanes and further explores the impact of electric vehicles (EVs) charging on the power grid while using these lanes. Firstly, a novel road network traffic model is established based on the distribution of roads, charging stations, and traffic conditions. By analyzing the congestion coefficients of various roads, an optimal planning for wireless charging lanes is proposed. Subsequently, a Stackelberg game model is formulated, where the grid operator acts as the leader and EV users, whose charging behavior aligns with an improved demand response function, act as followers. Finally, the particle swarm optimization algorithm is employed to solve the model, yielding optimal time-of-use electricity prices and charging strategies that maximize the grid operator's revenue while minimizing the users' charging costs. The research findings indicate that the proposed method addresses the issues of low utilization and profitability of wireless charging lanes and provides a theoretical basis for EVs to participate in grid scheduling through these lanes.

Abstract:The new power system with a high proportion of renewable energy grid integration serves as a pivotal platform for achieving the "dual carbon" goals. However, the uncertainty in renewable energy output inevitably poses challenges such as difficulty in peak load regulation and insufficient flexibility to the power system. To effectively balance the economy and security of the system, several measures are taken. Firstly, the improved worst case conditional value at risk (WCVaR) is introduced to assess the risks posed by the uncertainty of renewable energy output to the power system. Secondly, price-based demand response and low-carbon flexibility retrofits for thermal power units are introduced to enhance the level of renewable energy absorption while mitigating excessive carbon emissions caused by deep peak load regulation of thermal power units. Thirdly, a low-carbon economic optimization scheduling model for the power system is established, with the objective function of minimizing the total system operation cost and operation risk value. Finally, the effectiveness of the model is verified through practical examples.

Abstract:Non-invasive load monitoring (NILM) technology can obtain the electricity consumption information of various electrical devices of users without intruding into their premises, solely through the analysis of data from their electricity meters. NILM has been extensively researched and applied in residential load disaggregation, but its application in industrial loads is limited. On one hand, industrial loads differ significantly from residential loads in terms of load characteristics and data distribution, leading to a noticeable performance decline when methods designed for residential scenarios are applied to industrial settings. On the other hand, industrial users, concerned about privacy protection, are reluctant to disclose their electricity consumption data, making it highly challenging to effectively learn about industrial load equipment using limited data. To address these issues, an industrial load disaggregation method based on the factorial hidden Markov model (FHMM) is proposed. This method utilizes multiple independent hidden state chains of the FHMM to simulate the operational state transition process of industrial load equipment. By determining the state of the equipment at each moment, the electricity consumption of the equipment can be predicted in conjunction with state-specific energy consumption information. Finally, the proposed method is tested using on-site energy consumption monitoring data from a factory, and the results demonstrate its effective load disaggregation performance.

Abstract:Considering the characteristics of numerous wind power units, variable operating conditions, and complex collection grids as well as topological wiring in large-scale centralized wind farms, a data-driven similarity method is proposed to realize the equivalent modeling of such wind farms. Firstly, similarity is introduced to characterize the data features in the operating states of wind power generation units, and through similarity, data-driven clustering of wind power generation units is achieved. Secondly, the generation units within the same cluster are aggregated to obtain the equivalent parameters of the equivalent units, ultimately leading to the equivalent model of the wind farm. Finally, a case study of an offshore wind farm is used to simulate and verify the proposed method. The research results indicate that this method can effectively enhance the modeling efficiency and accuracy of wind farms.

Abstract:The integration of a large number of renewable energy grid-connected converters into the power grid can easily trigger small-signal instability, posing a serious threat to the safety and stability of the system. The existing small-signal stability analysis using the impedance method, based on a multi-machine equivalent mechanism, can only provide an analysis of the factors influencing stability and cannot identify the key weak grid-connected units that affect stability. This paper proposes a method for identifying small-signal stability weak grid-connected units in a renewable energy multi-site system based on the generalized Nyquist criterion (GNC). Firstly, based on the stability analysis approach of GNC, multiple renewable energy sites with different control parameters are decoupled into multiple single-site subsystems. Then, a stability criterion is constructed for each subsystem to analyze the small-signal stability of each subsystem. Finally, the subsystem with the weakest stability is identified based on the small-signal stability of each subsystem. The research results show that, compared with existing methods, this method can more accurately and quickly identify small-signal stability weak grid-connected units, providing targeted guidance and suggestions for improving system stability

Abstract:Optimizing the allocation of energy storage resources within wind farms can effectively mitigate the negative impacts of wind power induced strong randomness and volatility on the power system, promoting the construction of grid-friendly wind farms. Currently, wind farm energy storage systems face issues such as single application functions, low equipment utilization rates, and poor profitability, which constrain the large-scale application of energy storage systems in wind farms. To address these issues, this paper proposes a method for optimizing the capacity allocation of a multifunctional electrical-hydrogen hybrid energy storage system in wind farms, incorporating various energy storage application functions such as primary frequency regulation, power prediction compensation, and renewable energy absorption. Firstly, an operational strategy for the wind farm energy storage system is proposed, considering the three functions of primary frequency regulation, power prediction compensation, and renewable energy absorption. Furthermore, an energy management strategy for the electrical-hydrogen hybrid energy storage system, taking into account the characteristics of the energy storage equipment, is proposed. Secondly, based on this, a wind farm multifunctional hybrid energy storage system optimization allocation model is established with the goal of maximizing net revenue, using a time-series production simulation method. Finally, a case study is conducted on the optimization and allocation of a hybrid energy storage system in a wind farm in Hebei province. The research results show that the capacity allocation method under this strategy, which considers the characteristics of energy storage equipment, can meet the multi-time scale demands of the power system and effectively improve the economy of the power system.

Abstract:In recent years, the large-scale integration of wind turbines, characterized by strong uncertainty and weak support capability, has posed significant challenges to the frequency security of power systems. To enhance the stable operation capability of power systems with a high proportion of wind power, this paper proposes an optimal energy storage allocation strategy considering frequency security constraints. Firstly, the multi-agent frequency response expression for power systems with a high proportion of wind power is derived, and a dynamic frequency response model for the entire system is established. Secondly, aiming to minimize the annual total cost of the power system, an upper-level optimal energy storage capacity allocation model is formulated. With the objective of minimizing the day-ahead scheduling cost of the system, a lower-level typical daily optimal scheduling model considering frequency security constraints is constructed. An improved particle swarm optimization algorithm is adopted to solve this bi-level model. Finally, a case study is conducted based on a modified IEEE 39-bus system. The research results indicate that this strategy can ensure stable and adequate frequency regulation resources for the power system, effectively improving the economic performance of power system operation while satisfying frequency security constraints.

Abstract:To enhance the accuracy and reliability of short-term photovoltaic (PV) output power forecasting, a hybrid model is proposed, which integrates principal component analysis (PCA), variational mode decomposition (VMD), and multi-verse optimizer (MVO) to optimize a support vector machine (SVM) for PV output power prediction. Initially, PCA's data analysis capabilities and VMD's data decomposition performance are leveraged to reduce the dimensionality and decompose the multidimensional training data. Subsequently, the extracted dataset is fed into an SVM prediction model optimized by the MVO algorithm to obtain PV output power forecast components for different intrinsic modes. Finally, the results of these forecast components are aggregated. The research findings indicate that the proposed model achieves mean absolute percentage errors (MAPEs) of 0.7453%, 0.5105%, and 1.0156% for sunny, partly cloudy, and rainy days, respectively. Taking partly cloudy weather as an example, the MAPE of the proposed model is reduced by 3.8207%, 2.9173%, and 1.8438% compared to the MVO-SVM, VMD-MVO-SVM, and PCA-MVO-SVM models, respectively.

Abstract:The output power of photovoltaic (PV) arrays exhibits strong randomness and volatility. In the event of a fault, it can severely impact the safety and stable operation of the power system. Addressing the challenges of low accuracy and slow convergence in current PV fault diagnosis, this paper proposes a PV array fault diagnosis method based on the grasshopper optimization algorithm-support vector machine (GOA-SVM) model. Firstly, an equivalent circuit model of the PV array is established to analyze the variation characteristics of the PV array's voltage-current curve. Secondly, considering environmental factors and the nonlinear changes in the scale of the PV array, feature quantities reflecting different fault characteristics are extracted, and the data is mapped into a high-dimensional space for nonlinear processing. Finally, an improved method for optimizing the nonlinear support vector machine using GOA is proposed, and a GOA-SVM PV array fault diagnosis model is established, with simulations conducted using practical examples. The research results indicate that this method can be applied to various PV array models of different scales and effectively diagnose faults in PV arrays. For a 4×3 PV array scale, the data simulation classification accuracy can reach 99.8088%. When validated using the publicly available dataset from the national institute of standards and technology (NIST), the fault diagnosis accuracy achieves 92.3682%. Compared with other methods, this approach demonstrates significant improvements in recall rate and F1-Score.

Abstract:Assessing the clustered benefits of various resources in parks based on electrical and carbon synergy is crucial for achieving comprehensive benefit evaluation of the parks. Firstly, key factors influencing the benefits of each cluster within the park are analyzed from three aspects: economic, environmental, and technical benefits. A comprehensive index system and a calculation model for evaluating the overall benefits are constructed, encompassing the target level, foundation level, and indicator level. Relevant economic, environmental, and technical experts score each indicator, and the fuzzy analytic hierarchy process is used to determine the importance and weights of each indicator in the index system. Secondly, an investment evaluation model is employed to determine the benefit scores for each cluster's indicators. Finally, the overall benefit scores for each project are calculated by weighting the scores of each indicator with their corresponding weight coefficients. The simulation analysis of the case study demonstrates that this evaluation method has practical significance for the comprehensive benefit evaluation of distributed resource clusters in parks with electrical and carbon synergy. Through this evaluation system, park investors can select renewable energy technologies that provide the best balance in terms of financial profitability, low-carbon sustainable development capabilities, and technical feasibility.

Abstract:With the continuous development of the integrated energy trading market, the issue of uncontrollable carbon emissions has become increasingly prominent. To minimize carbon emissions in the integrated energy trading market, this paper first constructs a system configuration for an electricity-heat-cooling-gas integrated energy virtual power plant, and establishes a demand conversion model and a carbon emission production priority index for it. Next, a comprehensive energy trading allocation model based on carbon emission production priority is developed. Then, based on the determined energy trading volumes, the pricing and demand response strategies for various energies are optimized with the goal of maximizing the benefits of integrated energy suppliers, shared energy storage operators, and users. Finally, the optimized energy trading strategy is compared with three other energy trading strategies, verifying its effectiveness. The research results indicate that this strategy reduces carbon emissions by 23.4%, 30.1%, and 21.1% compared to the other three strategies, respectively. While the revenue is slightly lower than that of strategies 2 and 4, the difference is around 5%, and it is even higher compared to strategy 3. This strategy effectively promotes low-carbon operation in the integrated energy trading market while also balancing economic benefits.

Abstract:With the continuous advancement of new power systems and energy interconnection, building an efficient, low-carbon, and economical energy supply system is crucial for the development of the dual carbon strategy. To this end, this paper proposes an optimized operation strategy for an integrated energy system (IES) that considers multi-type demand response and diverse utilization of hydrogen under a joint mechanism of green certificate and carbon trading. Firstly, to fully leverage the regulatory capabilities of demand-side resources, a multi-type demand response model is constructed, encompassing price-based, incentive-based, and substitution-based responses. Secondly, considering the clean nature of hydrogen, a diverse utilization model for hydrogen is established, including power-to-hydrogen, hydrogen-to-methane, hydrogen-to-heat/electricity, and blending hydrogen with natural gas. Lastly, by combining green certificate trading with carbon trading, a joint green certificate-carbon trading mechanism is proposed, and an IES low-carbon economic operation model that takes into account this joint trading mechanism is constructed. The simulation examples, which compare different operational scenarios, verify the effectiveness of the proposed model in enhancing renewable energy integration, improving energy utilization efficiency, and reducing carbon emissions.

Abstract:Single-phase photovoltaic inverters are widely used in distribution systems, and studying their harmonic models is of great significance for harmonic power flow analysis, harmonic interaction effects, and power system stability. Firstly, by analyzing the interaction mechanism between the grid and the inverter, as well as the harmonic conduction on the AC- and DC-side of the inverter, the impedance formulas of the single-phase photovoltaic inverter under different harmonics are derived. Secondly, the frequency coupling mechanism of harmonic impedance is analyzed in conjunction with the control loop, and the harmonic coupling impedance matrix model and equivalent Thévenin circuit of the single-phase inverter under different harmonics are established. Furthermore, based on the proposed model, a harmonic analysis of the system is conducted, revealing that the model significantly simplifies the harmonic power flow analysis of the single-phase inverter, improves the calculation accuracy of the system's harmonic power flow, and can effectively assess the impact of connecting the single-phase inverter on power quality. Finally, time-domain simulations are conducted to verify the correctness and effectiveness of the established model.

Abstract:To enhance the system performance of the modular multilevel converter (MMC) under unbalanced grid conditions, this paper proposes a fractional order integral sliding mode control (FO-I-SMC) strategy. Firstly, the topology of the MMC is analyzed, and the fundamental frequency external characteristic equations of positive- and negative-sequence voltages and output currents, as well as the double frequency internal characteristic equations of positive-, negative-, and zero-sequence circulating currents, are derived. Secondly, combining the control objectives with the mathematical model of the MMC, a fractional order sliding mode controller is designed for application in unbalanced grid voltages. This controller aims to reduce the harmonic content in the AC-side output current and the DC-side circulating current. Finally, a corresponding model is established on the Matlab/Simulink simulation platform to verify the effectiveness of the proposed algorithm. The research results demonstrate that the system performance of the MMC using the FO-I-SMC strategy is significantly better than that using the proportional integral (PI) control strategy and the integral sliding mode control (ISMC) strategy.

Abstract:The energy router is a core device in the field of the energy internet, and its circuit topology enables unified distribution of new energy sources, energy storage, and various loads. Currently, energy routers lack the capability to restore power distribution networks after faults. A multi-port energy router (MP-ER) is proposed in this paper, which achieves fault regulation and new energy accommodation. Firstly, the topology and principles of the MP-ER are introduced, and based on its overall structure, a control strategy is proposed that utilizes the DC bus voltage as the primary signal and implements decentralized control for each port. Secondly, by considering the DC bus voltage and the zero-sequence voltage of the distribution network, the operating modes of the MP-ER are divided into normal mode and fault-tolerant flexible arc suppression mode. Under the proposed control strategy, stable and efficient operation is achieved within each mode. Finally, for the connected microgrid and distribution network systems, MATLAB numerical software is used to simulate the model and verify the rationality of the proposed topology and its functions. This research introduces a new topology and model for the study of energy routers.

Abstract:Energy storage operators can enhance the reliability of power supply and mitigate the losses caused by power outages. To further expand the application scenarios of energy storage for improving power supply reliability and increase the profits of energy storage operators, a profit allocation strategy based on insurance actuarial theory is proposed. Firstly, the B?hlmann premium model is constructed by fitting the distribution of power outage losses, integrating credibility theory with the pure premium model. Then, a corrected failure rate model based on a health index is established, and the minimum path method is employed to calculate the insurance indemnity probabilities for various users in the event of a power outage. Lastly, by combining power outage insurance with peak-shaving and valley-filling arbitrage, a profit model for energy storage operators is constructed to maximize their profits over the entire lifecycle, which is then compared with their profits without participating in power outage insurance. The research findings indicate that collaborating with insurance companies can significantly boost the profits of energy storage operators and accelerate the recovery of investment costs.

Abstract:The increasing number of electric vehicles presents opportunities for investment in public charging stations, but uncertainties such as competition and electricity price fluctuations elevate the risks associated with these investment projects. Considering the random fluctuations in the marginal contribution per unit of charging and the utilization rate of charging stations, a real options model is constructed. By solving this model, the investment threshold for charging stations under the condition of maximizing expected investment value is obtained. Using this investment threshold condition, investment strategies are discussed in two scenarios: random fluctuations in charging service fees and the integration of energy storage systems. The research findings indicate that: 1) Uncertainty in the external environment significantly delays investment in charging stations, highlighting the importance of policies to ensure relative stability in the investment environment; 2) The waiting time for charging station investment is determined not only by external environmental uncertainty but also by initial returns, suggesting that ensuring a minimum return for charging stations is an effective way to incentivize investment; 3) Whether energy storage investment is advantageous depends on the additional investment amount and the marginal contribution per unit of electricity. Specifically, when the proportion of energy storage investment is relatively small, increasing such investment may help promote investment in charging stations.

Abstract:With the high proportion of renewable energy generation such as wind, solar, and hydro power integrated into the power system, the low cost of renewable energy generation poses a significant challenge to the cost recovery of traditional thermal power units, resulting in an increasing number of thermal power units exiting the capacity market. Additionally, as renewable energy generation primarily depends on weather conditions, it cannot guarantee sufficient generation capacity during peak load periods. A reasonable pricing mechanism in the capacity market is crucial to address this issue. In response, a multi-energy trading model that considers both the capacity market pricing mechanism and electricity demand is proposed. Firstly, a pricing model that accounts for the load demand and capacity characteristics of traditional thermal power units, wind turbines, photovoltaic (PV) power generation, hydropower, and energy storage at different time intervals is presented for the capacity market level, serving as the main problem. Secondly, a joint optimization scheduling model for thermal power, wind power, PV power, hydropower, and energy storage in the daily spot market is introduced at the electricity trading level, serving as the sub-problem. Then, based on this, through iterative solutions to the main and sub-problems, a capacity pricing mechanism for thermal power, wind power, PV power, hydropower, and energy storage is proposed. Finally, multi-scenario simulations are conducted using an actual power grid as an example, demonstrating the rationality and effectiveness of the proposed scheme.

Abstract:The physicochemical properties and dielectric properties of cross-linked polyethylene (XLPE) insulated cables can change under long-term thermal aging, affecting their electrical behavior, such as the growth process of electrical trees. This paper investigates the impact of thermal aging on the growth characteristics of electrical trees in XLPE cable insulation. Electrical tree growth tests were conducted on accelerated thermally aged samples, and differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and broadband dielectric spectroscopy tests were used to study changes in the microstructure and dielectric properties of XLPE materials. The results indicate that recrystallization reactions and thermal-oxidative reactions are the main causes of changes in crystallinity (Jc) and carbonyl index (Q), which subsequently lead to changes in polar molecules within XLPE. This results in a trend where dielectric parameters initially decrease slowly and then increase rapidly. The study also found significant correlations between the thermal aging evaluation indicators (Jc, εr, and tanδ) and the growth rates of electrical trees at 10kV (G10) and 12kV (G12). Equivalent relationships between G10, G12, and the thermal aging evaluation indicators were established through polynomial fitting analysis. Given the known physicochemical properties and dielectric properties of XLPE materials, the degree of electrical tree aging in XLPE cable materials can be determined, providing a reference for cable insulation assessment and lifespan prediction.