Abstract:In recent years, there has been a rapid development of demand-side flexibility resources, such as flexible loads, energy storage,electric vehicles, etc. These resources can flexibly regulate the consumption and storage of electricity without affecting the quality of electricity consumption and user experience, and can better maintain the stable operation of the power grid. However, given that most of the demand-side resources have small capacity, different characteristics and are scattered at the bottom layer, their integration into an aggregated model is usually considered in practical scheduling. For this, a feasible region approximation and efficient aggregation method for demandside resources based on improved zonotope is proposed. Firstly, the feasible region of individual demand-side resources is approximated by the zonotope based on the bottom-up idea, and the general construction form of the zonotope generator is proposed to further improve the approximation accuracy, and finally the aggregation model of demand-side resources is obtained based on the Minkowski sum and the validity of the model proposed in terms of computational accuracy and speed is verified through the analysis of the calculation examples.

Abstract:With China vigorously promoting the“Dual Carbon”targets of“striving to peak CO2 emission by 2030 and achieving carbon neutrality by 2060”, renewables such as wind power and solar power have been widely used, but at the same time it also leads to the continuous reduction of thermal power utilization hours represented by coal-fired power generation, and it is difficult to recover the cost in the electricity energy market. According to the current situation of power structure and market in Zhejiang Province, a direct capacity compensation mechanism is selected to help recovering coal-fired power’s costs, and a series of realistic compensation calculation process for Zhejiang Province is designed to calculate the effective capacity, various costs, market revenue and compensation price required for the unit.Combining the real unit data and future development forecast of Zhejiang Province, the direct capacity compensation price for 1 000 MW and 600 MW units in 2025 and 2030 with different coal prices and on-grid prices are calculated and their characteristics and differences are analyzed. The results provide reference for Zhejiang and other provinces who want to establish direct capacity compensation mechanism, so as to effectively protect the revenue of thermal power enterprises and improve grid security and flexibility.

Abstract:The high penetration of distributed generations will lead to more severe security problem in the process of reconfiguration, and the closing loop current may not meet the requirements of distribution network. New-type phase shifting transformer can adjust the amplitude and phase simultaneously, which is an important means to effectively solve the problem of loop closing or opening. Therefore, a newtype phase shifting transformer is proposed to be installed in the closed-loop line, and a double-layer optimization model for distribution network reconfiguration is designed. The upper layer of the model performs distribution network reconfiguration to optimize system network losses, while the lower layer optimizes the order of the closing loops with the objective of minimizing the closing loop impulse current. The case results demonstrates that the proposed strategy of reconfiguration can greatly reduce the closing loop current and improve the safety margin of closing loop operation while reducing the loss of distribution network.

Abstract:With a high proportion of new energy access, the deployment of energy storage can assist the power system to cut peaks and fill valleys and smooth out fluctuations. However, current energy storage systems are costly and require government support. To this end, a profitability strategy for energy storage to maximise operating profit in an electricity market consisting of the grid is proposed, storage operators and customers. A profitability strategy that takes into account incentives in combination with an intelligent algorithm that provides different weighted reward allocations to the storage system operator for each peak hour is proposed. On the one hand, the algorithm is based on deep learning of least square support vector machine to establish price and load forecasting models. On the other hand, deep reinforcement learning is used to determine the optimal charging and discharging strategy considering the peak state of power grid, user load demand and the profits of energy storage system operators. Finally, a case study is conducted to verify that the strategy can significantly improve the profitability of the energy storage system operator and reduce the pressure on the grid.

Abstract:To realize friendly interaction between building heating, ventilation and air conditioning（HVAC）and the power grid, a demand response cost assessment method combining model and data driven is proposed. Firstly, the fitting ability of artificial neural network（ANN） is used to automatically learn from the physical model and build a dynamic model of HVAC. Then, considering the impact of temperature changes on the personnel efficiency of building, an optimization model of the HVAC setting temperature is established and solved based on particle swarm optimization（PSO）algorithm. Finally, additional cost incurred by users due to deviation from the optimal set temperature is defined as response cost, and demand response cost assessment for HVAC is proposed accordingly. Simulation results show that by setting optimal temperature of HVAC, total cost of users can be significantly reduced. The response cost curve can reflect users’productivity loss factors, thereby helping users determine the bid price rationally in demand response.

Abstract:A region-based commercial building load forecasting method based on electricity consumption behavior patterns is proposed to fully exploit the fine-grained load data collected by smart meters and to improve the accuracy of regional commercial building load forecasting. Firstly, the mean-variance normalization method is used to standardize the collected load data. Then, to extract different electricity consumption behavior patterns in regional commercial building loads, the elbow method is used to determine the number of clusters, followed by k-Shape clustering. Next, an improved Informer model is introduced to address the challenge of predicting large-scale commercial building loads within a region, which often requires significant memory resources while struggling to achieve high accuracy. This model uses clustering algorithms to identify commercial buildings with similar electricity consumption patterns and accounts for the impact of anomalous load data collected by smart meters on the training results. The proposed model effectively addresses the problem of low accuracy in predicting load for large commercial buildings. Finally, experiments are conducted using commercial building loads in California.The experimental results demonstrate the effectiveness of our proposed method in the improvement of the accuracy of regional commercial building load forecasting.

Abstract:The participation of high-energy load in demand side response has become an important measure to promote the consumption of new energy. In the future, the new power system will face the situation of increasing the proportion of new energy and large-scale access of high-energy load. It is urgent to study the impact of large-scale access of high-energy load on the power grid, so as to reasonably plan the location and capacity of new energy units and high-energy load in the distribution network. Therefore, silicon carbide load is taken as the research object, and an IEEE33 node system with high proportion of wind power is built based on MATLAB/Simulink simulation platform.Based on the parameter perturbation method, the impact of different access locations of wind turbines and silicon carbide loads on the power grid is evaluated from three aspects of power quality, technical economy and safety. The entropy fuzzy analytic hierarchy process is used to evaluate the simulation results；The evaluation results are consistent with the theoretical analysis, which verifies the effectiveness of entropy fuzzy analytic hierarchy process.

Abstract:To improve the accuracy of power system load forecasting and maintain the safety and stability of power system operation, a combination of self-organizing maps（SOM）clustering based on feature vector and improved radial basis function（RBF）neural network for power load forecasting model is proposed. The samples are clustered by extracting feature vectors that reflect the characteristics of the daily electric load. Data with similar features are used as training samples for the neural network to improve sample regularity. To overcome the effects of gradient descent and local optimum on the network prediction accuracy, the particle swarm optimization（PSO）algorithm is used to modify the neural network particle swarm velocity and position. The validity and good adaptability of the proposed model are verified based on power load data of distribution network in an area.

Abstract:In recent years, the rapid growth of air-conditioning load has become the main reason for seasonal power shortage. Using air-conditioning demand response strategies to adjust air-conditioning load is of great significance for achieving grid peak reduction and valley filling. Taking the air conditioning system in the form of fan coil units and fresh air as the object, the TRNSYS simulation model is established. Based on the time-of-use electricity price policy, the temperature reset demand response strategy is studied, and the refrigeration zone temperature and the thermal comfort index are two strategies which aim to compare the peak load reduction during the demand response period and the overall system operating costs. The results show that the peak load transfer rate of the demand response strategy based on temperature reset is about 30.3%, and the peak load transfer rate of the demand response strategy based on the thermal comfort index control is about 22.7%. However, two strategies cannot save electricity bills. Therefore, in addition to the preferential policies for time-of-use electricity prices, corresponding incentives must be given.

Abstract:As a key strategy to achieve China’s dual carbon targets, electric vehicles（EVs）can not only reduce CO2 emissions and improve air quality but also utilize renewable energy for enhanced energy security. EVs can also help balance and stabilize the power grid through smart charging systems. To maximize EVs’role in carbon reduction, it’s vital to establish a carbon inclusion mechanism for quantifying and rewarding low-carbon behavior. Existing models, often based on macro-data or typical experiences, don’t accurately reflect individual variations. So that, a new model considering factors like air conditioning, driving modes, road conditions and load is proposed based on the mechanics of EV systems. This model provides an improved method for calculating equivalent mileage between fuel and electric vehicles, and a carbon reduction calculation model for EVs, effectively reflecting the impact of individual driving habits on carbon reduction.By analyzing long- term driving data of various EVs under different conditions, the model’s accuracy has improved from 82.47% to 96.33%, demonstrating its effectiveness.

Abstract:Enhancing the flexibility of the power system to ensure stable electricity supply is a crucial concern amid the development of renewable energy. Coal-fired power plants, renewable energy sources, electrochemical energy storage, and electrolysis-based hydrogen production systems are considered. Using mixed-integer linear programming, the research investigates the optimal deployment of electrochemical energy storage and electrolysis-based hydrogen production systems as flexibility resources. The goal is to promote renewable energy development and improve system economics. The impact of carbon trading prices and variations in wind and solar resources on the allocation of flexibility resources in microgrid systems are analyzed. Results demonstrate that leveraging flexibility resource synergy can reduce microgrid system costs by approximately 22.17%. Increasing carbon trading prices stimulates the development of electrolysis-based hydrogen production.

Abstract:The application of various types of renewable energy on the building side is becoming more and more popular. Forecasting of building electricity consumption plays an increasingly important role in the balance of energy supply and demand, stable grid operation,and peak demand response. Although many data-driven models have been widely used in energy consumption prediction, there is still a lack of short-term prediction models with high prediction accuracy and strong generalization ability. In order to solve this problem, a classification and integration energy consumption prediction method based on the characteristics of building energy consumption and combined with data mining technology is proposed. Firstly, the recursive feature elimination method is used to screen the features of the data, and the fuzzy C-means clustering algorithmisused to cluster the training set data, meanwhile, K-nearest neighbor methodis used to classify the validation set and test set data. Then, five hybrid data-driven models combined with intelligent optimization algorithms areselected as sublearners, and each type of data is predicted respectively. Finally, multiple linear regression method is used to integrate the results. The accuracy of the ensemble prediction model is better than that of single sub-model, and it has potential to predict different building types and energy use scales.

Abstract:Photovoltaic power is an important part of the new energy system, the installed capacity of photovoltaic power is growing rapidly during the 14th Five-Year Plan in China. Photovoltaic power has strong randomness and volatility because of the strong correlation between photovoltaic power and weather, the high proportion of photovoltaic grid- connected increases the difficulty of power balancing in power system. The output characteristics of photovoltaic power under high temperature climate are analyzed in detail, and the view that photovoltaic power generation has a higher daytime peak capacity in high temperature weather is pointed out, and then the rationality of improving the credible capacity of photovoltaic power generation in extreme high temperature weather to participate in power balance is demonstrated. Finally, the calculation verified that increasing the credible capacity of photovoltaic power generation in extreme high temperature weather could reduce the daytime power balance gap, relevant work suggestions about photovoltaic participating in power balance are proposed.

Abstract:Green certificate trading（GCT）and carbon emission trading（CET）are effective means to promote low-carbon operation of power system economy at present stage. However, current green card system does not perform well in encouraging grid connection of renewable energy and easing pressure of financial subsidies, and current carbon market has no obvious effect on promoting emission reduction, and generally presents the characteristics of“isolated operation”. Therefore, in order to break the barriers between traditional green certificates and carbon market transactions and effectively link up the market mechanism, a block-chain based carbon rights green certificates joint trading market framework and incentive mechanism were designed. Firstly, smart contracts are compiled to run power purchase/sale, carbon rights and green card transactions on the Ethereum platform, ensuring the safe, efficient, automatic execution and information transparency of joint market transactions. Secondly, concept of carbon token is introduced, and DPoS-CT consensus algorithm considering carbontoken is proposed to encourage market players to make more contributions to carbon emission reduction. Finally, proposed mechanism can effectively stimulate renewable energy trading and limit the carbon emissions of thermal power units through an example analysis, providing new ideas for the construction of the GCT and CET market.

Abstract:With the widespread application of intelligent energy meters, the demand for energy meters is becoming increasingly large. Using empirical manual estimation can easily lead to temporary shortages and inventory backlog of energy meters. Based on the historical installation data of electricity meters in the metering system, simple seasonal model, Winters addition model, and Winters multiplication model are used. And LSTM model is combined to compare and analyze the results, compare the advantages and disadvantages of different models, and determine the optimal method for predicting electricity meter demand. The empirical results indicate that the Winters multiplication model has the best prediction effect, with an average error of 0.96% in the predicted values, and the predicted trend is in line with the actual situation. Winters multiplication index smoothing method can scientifically and reasonably predict the trend of electricity meter demand changes, assist power supply companies in more efficient operation and management of electricity meters, and improve the efficiency of measuring asset utilization.

Abstract:With the dual carbon construction target of“carbon peak, carbon neutralization”proposed in our country, energy saving and carbon reduction has become a hot issue instantly. User portraits are of great significance for power companies to analyze the power usage behavior of residents and formulate reasonable regulation measures for energy carbon reduction. For this, user carbon portrait label system and portrait method based on power usage behavior are studied. Firstly, collect and filter user multidimensional behavior data. Secondly,according to the filtered data of user’s electricity usage behavior, combined with the purpose of portrait, the label system of user’s carbon portrait is designed from three dimensions：carbon load reduction characteristics of users, low-carbon electricity consumption characteristics of users, carbon production and consumption characteristics of users’electric energy. The data of the sublabels are processed and the multidimensional label data is obtained by analyzing various comprehensive indexes. Finally, by using the k-means clustering algorithm to identify the user’s cluster, and displayed with the three-dimensional scatter plot, the user’s comprehensive carbon reduction index is calculated according to the label system and displayed in a column chart, realizes the visual presentation of the user’s carbon portrait, and reflects the comprehensive ability of the user’s electric energy carbon reduction. The effectiveness of the proposed method is demonstrated by comparing the carbon emissions of typical users before and after the implementation of carbon reduction measures.

Abstract:The National Development and Reform Commission has recently issued the provincial-level transmission and distribution tariffs for the third regulatory period and adjusted the scope of the two-part tariff implementation, granting eligible users the choice to adopt either a single flat-rate tariff or a two-part tariff pricing strategy. However, the selection of a pricing strategy that is more economically viable has become a challenge for electricity consumers. For this, the key factors influencing the selection of pricing strategies for users based on the current transmission and distribution tariffs and relevant electricity cost calculation rules are analyzed. By defining relevant parameters as variables and calculating the economic critical curve of pricing strategies based on the existing transmission and distribution tariffs, a model for comparing and selecting pricing strategies for transmission and distribution tariffs is constructed. The model is validated using typical user scenarios, providing a feasible solution for users to scientifically and rationally choose pricing strategies for transmission and distribution tariffs.

Abstract:Aiming at the issue of low fault identification rate in existing power load management terminals, an operating fault diagnosis method is proposed for the power load management terminals based on auto-encoder multilayer perception. Firstly, operational status indicators and its related faults in the power load management terminals are analyzed. Secondly, the fault diagnosis method’s related model is depicted including its implementation method. Furthermore, the model obtains a candidate set of features representing the terminal operating status through equidistant sliding window operations on the timing data of terminal operating technical indicators. The auto-encoder feature selection sub-model is used to extract the optimal feature subset, and the multi-layer perceptron fault diagnosis identification submodel is used to achieve terminal operating fault classification and diagnosis. Finally, the effectiveness of proposed method is verified through experiments. Experimental results illustrate that the proposed method can improve accuracy, recall, and identification efficiency of power load management terminal operating fault identification, which can achieve refined diagnosis performance for power load management terminal operation faults.