Abstract:In view of the high cost and insufficient resource utilization of energy storage invested independently by users, the introduction of shared energy storage in the park is conducive to reducing the power cost of users in the park and promoting the consumption of distributed renewable energy. In order to achieve a win-win situation between shared energy storage service provider and park users, a Stackelberg game model in which shared energy storage service provider dominates and park users follow is established. The shared energy storage service provider sets the capacity price and power price and uses conditional value-at-risk to evaluate the income risk caused by the uncertainty of photovoltaic output. According to the price of shared energy storage and the forecast of their own load and photovoltaic output, park users decide the purchased energy storage capacity and charging and discharging power strategy. Through KKT optimality condition and the dual theorem of linear programming, the above Stackelberg game problems can be transformed into mixed integer linear programming problems. Finally, based on the example of multiuser and multi-scenario, the influence of energy storage price on the game result is analyzed, and the economy of shared energy storage is compared with that of fixed energy storage configuration and no energy storage confiodel.

Abstract:The flow of virtual power plant（VPP）with generalized energy storage（GES）participating in dispatching optimization and power market as a“virtual”integrated entity is analyzed.A two-stage sharing strategy and a two-level coordinated optimal dispatching model of park-level VPP-GES based on hybrid game are proposed. First stage：according to the response characteristics of various energy storage resources in the park, a GES sharing model composed of actual energy storage, demand side flexible load and EV is established, and then the main structure of VPP aggregating multiple energy storage resources is constructed. Second stage：a two-tier hybrid game model with VPP operators as the upper leaders and park energy storage service providers, load aggregators and energy suppliers as the lower followers is established. The stackelberg game is used between the upper and lower levels to share and interact the purchase and sale price and GES, so as to ensure the win-win interests of leaders and followers. Cooperative game is adopted among the followers of the lower level to realize multi-agent real-time collaborative optimization. The example analysis shows that the proposed not only maximizes the prs.

Abstract:Under the background of low-carbonization of distribution networks, an active distribution network expansion planning model that considers low-carbon and flexible loads is proposed. The objective is to provide the investment strategy with the minimum total cost under the premise of satisfying network operation constraints and CO2 emission limits. Decision variables include replacing overloaded lines, constructing new energy and energy storage devices,and constructing voltage control devices such as stabilizers and capacitor banks. The model proposes a polynomial form of voltage-related flexible load characteristics. To address the non-linear constraints in the flexible load model and network reconstruction constraints, the paper proposes a segmented McCormick envelope method and a virtual demand method to transform them into mixed-integer second-order cone optimization. Considering the uncertainty of new energy output,load, and energy prices, a two-stage stochastic optimization method based on scenario clustering is used for solving. The proposed model is tested on a 69-node system, and theions.

Abstract:In the multi-station integration scenario of five stations in one of“new energy station- electric vehicle charging station- 5G station- data center- substation”, making full use of the backup energy storage capacity of 5G base station and data center is an important way to improve the economy of multi-station integration. A multi-time scale trading mechanism for sharing the backup energy storage capacity of 5G base stations and data center under the multi-station integration mode is proposed. Firstly, 5G base station and data center call energy storage capacity model based on the“energy storage spare time”is set up, and then the shared energy storage problem is transformed into electricity trade problem to established the internal power price trading model, which can dynamically adjust the power price, make more station system internal trading more economical according to the internal power trading demand. Then, a multi-time scale trading mechanism of pre-day declaration- day adjustment is established. In the day- daytrade, the electricity trade are cleared through the internal electricity price trading model, and a compensation scheme based on the deviation of the contact line is established in the intra-day trading.Finally, the validity of the proposed trading mechanism is verified by example.

Abstract:With the proportion increase of renewable energy in the power system, the fluctuation, randomness and intermittency of its power generation output will bring greater operating pressure to the power system. Hence, the power system needs to have a certain flexible adjustment and response ability to deal with the potential situations of wind, light and load loss caused by intermittent renewable energy, which has higher requirements for system flexibility. At present, China has not systematically carried out research on the market mechanism to improve the flexibility of power system,and there is still a large degree of imbalance between the supply and demand of power system flexibility. Considering the current situation that China’s power system flexibility needs to be improved,model first summarizes and analyses the market mechanism construction experience and research status of the United States and British on improving power system flexibility, and then analyzes the progress and challenges of China’s power system flexibility construction. Finally, putting forward relevant suggestions to promote the improvement of China’s power system flexibility.

Abstract:In the open electricity market environment, distributed generation has developed rapidly. Traditional centralized transactions face great challenges in meeting the transparency, timeliness and data security of distributed power transactions, in order to help park microgrid operators to achieve multi- energy complementarity and collaborative optimization, and help campus microgrids to be digitalized, economical and low-carbon operations. In this context, model analyzes the fit between blockchain technology and distributed electricity market, and constructs a distributed transaction framework model based on blockchain-based microgrid based on distributed power trading and blockchain. Secondly, the implementation mechanism of blockchain-based multi-distributed power transaction (including smart contract, consensus mechanism and cross- chain technology) is analyzed, and the risk challenges faced by different implementation mechanisms are proposed.

Abstract:Integration of power generation, transmission, distribution, consumption, and energy storage is crucial in facilitating the goals of carbon peaking and carbon neutrality and promoting the green and low-carbon transformation of the power system. The core sub-project of the Ulanqab demonstration project, new-generation grid-friendly green power station demonstration project, is taken as a case study to provide an overview of the design concept of source- grid- load- storage integration project. The demonstration project achieves grid- friendly functionality through the design of large-scale equipped storage and integrated intelligent regulation and control. In terms of energy storage, the project adopts lithiumion battery energy storage systems to support the transformation and application of energy storage technologies and their large-scale development. In terms of intelligent control, the project designs different centralized dispatch and operation modes to address the randomness and fluctuation issues of conventional renewable energy power plants. The first units of the demonstration project had successfully integrated into the grid, and the project is still under active construction. The project will have demonstration significance in ensuring the safe and stable operation of the power system, exploring new models for the development of renewable energy, and conducting large-scale energy storage technology innovation.

Abstract:Under the“double carbon”target, pumped storage will bring new opportunities to participate in carbon trading market.Combined with the existing Chinese certified emission reduction project methodology in the electric power industry, a quantitative model for carbon emission reduction of pumped storage based on emission factor method is established. Firstly, the existing CCER projects in the power industry are investigated, and the key elements of the methodology and calculation criteria of CCER projects are analyzed. Then, based on the carbon emission reduction mechanism of pumped storage during pumping period and peak load period, a quantitative carbon emission reduction model of pumped storage project is constructed. Finally, based on the analysis of a regional power grid,the carbon emission reduction of pumped storage is calculated. Examples analysis shows that the model can effectively calculate the carbon emission reduction of pumped storage, and the carbon emission reduction benefit is significant.

Abstract:In the context of low-carbon development, the study of regional carbon emission prediction models is of great significance in guiding the formulation and implementation of future dual carbon target tasks. The ElasticNet- XGBRegressor model, which combines the ElasticNet model as a feature selection model and the XGBRegressor model for regional carbon emission prediction, is a type of ensemble learning model. Based on the principles of the STIRPAT model and the IPCC emission factor method, an original dataset containing 25 features is constructed for the study of regional carbon emission prediction. To validate the effectiveness of the proposed model, an empirical controlled experiment was conducted,with the ElasticNet- XGBRegressor model as the experimental group, and Spearman feature selection and common machine learning methods as the control group. The results showed that the ElasticNet- XGBRegressor model out performed the control group in terms of model evaluation metrics such as RMSE, MAPE, and R2,demonstrating the superiority of the ElasticNet- XGBRegressor model in regional carbon emission prediction. Regression models are innovatively combined with decision tree-based ensemble learning models, leveraging the feature selection capability of the ElasticNet model and the high accuracy and robustness of ensemble learning to improve the accuracy and stability of the prediction model.

Abstract:In order to realize the economic operation of the micro-energy network, an economic optimization scheduling method of the building micro-energy network with heat pump and electro-thermal hybrid energy storage is proposed. Firstly, based on the ETP model combined with Monte Carlo simulation, an aggregated air-conditioning load model with on-off regulation is established to determine the hourly cooling load. Then, a micro- energy network optimal dispatch model is constructed considering the temperature change characteristics of phase-change cold storage and the refrigerating characteristics of heat pumps. Finally, taking the minimum life cycle cost of the micro- energy network as the objective function, the model is solved using Python and Gurobi. The simulation results show that the configuration of electric-thermal hybrid energy storage has the effect of peak-load shifting of electricity and cooling loads, which can reduce the initial investment cost and operating cost, and the economy and flexibility of the system are better.

Abstract:In view of different demands for power supply reliability of multi-type end users in different regions and the limitation of measuring user power supply reliability based on a single power supply reliability index at the distribution network side, a power supply demand division method for end users based on big data mining is proposed. Firstly, the differentiated power supply demand of endusers is quantified, and the information model of end-users’powersupply demand is constructed. Then, using integrated K-means and density- based spatial clustering of applications with noise big data mining to cluster end users, to achieve user classification. Finally,improved grey relational degree is used to divide the end user reliability level within the region. Through the simulation analysis of the power supply area with multi-type end users,combined with the analysis of comparative schemes,the validity of the proposed method based on big data mining is further verified.

Abstract:Aiming at the problem that the disorderly charging behavior of electric vehicles affects the operating economy of the integrated energy system, an integrated energy system optimal dispatch strategy that takes into account the elastic electricity price demand response of electric vehicles is proposed. This strategy first analyzes the charging behavior of electric vehicles, introduces incentive demand response, and considers the elastic electricity price based on the time-of-use electricity price, and establishes an electric vehicle elastic electricity price demand response optimal dispatch model；secondly, under the premise of meeting the normal operation of the system. The goal is to minimize the operating cost of the integrated energy system. A regional integrated energy system model with electric vehicle demand response is established and solved by hybrid linear programming. Finally, model sets three scenarios and four different flexible electricity prices based on whether electric vehicles participate in demand response. Make comparisons and analyze the equipment output and system operating costs under different scenarios and different electricity prices. The simulation results show that it is feasible and economical to guide electric vehicles to participate in the demand response of the integrated energy system through flexible electricity prices.

Abstract:In order to reduce the impact of wind power and load forecasting errors on the output plan adjustment of each unit of intergrated energy system（IES）, two- stage（day- ahead and intraday）optimal scheduling model of IES based on the Stackelberg game real-time pricing mechanism is established. In the day ahead dispatching plan, the output plan of each unit for the next day is determined based on the optimal economic cost of the integrated energy system. In the intra day scheduling plan, taking the maximum income of comprehensive operators and the maximum user satisfaction as the objective function, a master-slave game model is established to adjust the current energy price through the two-party game, so as to guide users to actively participate in the demand response.Through the simulation analysis of actual examples, compared with the energy price determined by the price elasticity coefficient matrix method, the real-time energy price determined by the Stackelberg game has a higher degree of user participation in the demand response, which can not only reduce the adjustment of the output plan formulated by each unit in the past, but also promote the consumption of wind power. The model effectively improves the economic benefits of IES and improves the satisfaction of the user side.

Abstract:Microgrid is an effective solution to solve the problem of grid connection of distributed power generation by effectively using renewable resources such as local scenery. The configuration of microgrid directly determines its economy and power supply reliability. Considering the influence of wind speed and light intensity with strong randomness and load fluctuation on the microgrid,based on the theory of multi-state system, the multi-state modeling of wind power output and load at each time of typical day in each month is carried out. Aiming at economic and environmental protection, an improved non inferior sorting genetic algorithm is introduced to solve the multi-objective optimal allocation model. The rationality and effectiveness of the optimal allocation model based on the multi-state system theory are verified by an example of integrated energy system engineering in a province of East China.

Abstract:Orderly power utilization is an important means of power demand side management to ease the tension between power supply and demand during peak power consumption. Rotation is one of the important measures for orderly power utilization. Transferring users’power load to the low power consumption period can not reduce users’power consumption. When formulating the traditional orderly power utilization rotation scheme, extensive measures such as one size fits all rotation according to the power load and power rationing are generally adopted, ignoring the impact of some enterprises’rotation on the coordinated operation of the industrial chain. To this end, a strategy for formulating an orderly electricity rotation scheme considering the industrial chain is designed. Users in the same industrial chain are aggregated into a user instance to jointly participate in the preparation of the rotation scheme. Users participating in the orderly electricity rotation are selected according to the rotation value index, and the rotation grouping is optimized to minimize the peak load of the rotation, so as to reduce the impact of the rotation on normal production and life. The results show that the above scheme can enable users in the same industrial chain to implement the same rotation strategy, and reduce the impact of partial rotation on production and operation of users in the industrial chain. It is more fair and reasonable to determine the users participating in the rotation according to the rotation value index.

Abstract:Pumped storage power stations play a positive role in peak load regulation, new energy consumption and the realization of the“double carbon”goal. In order to study the cost diversion of pumped storage power stations in the process of power market, firstly the historical evolution of the provisions of national policies on the price mechanism of pumped storage power stations are combed systematically, and the cost recovery methods of foreign pumped storage power stations are investigated. On the premise of understanding the existing policies and practices, combined with the construction process of China’s power market, it is divided into three stages：the initial stage, the transition stage, and the mature stage. According to the construction situation of each market in different stages, the price mechanism of pumped storage power stations in different market stages is clearly given, and the analytical mathematical model of electricity price is established to effectively guide the costs incurred by pumped storage power stations. Considering economic development, price level, affordability and other factors, three principles for cost diversion of pumped storage power stations are determined, and six single and combined modes of cost allocation are proposed,which has certain theoretical and practical significance for the development of pumped storage power stations in China.

Abstract:In the process of digital transformation, electric power enterprises can effectively reduce their operating costs and provide customers with more convenient and efficient services by building a multi- channel service system and making full use of the Internet + physical channels. Under the above background, a power marketing channel diversion strategy based on improved collaborative filtering algorithm is proposed. Firstly, the customer attribute data matrix is constructed, and the matrix decomposition algorithm is used to recover the missing data in the original customer attribute matrix, and the K-means algorithm is used to cluster the customer attributes. Then, using the customer mixed type attribute dissimilarity measure, through the user based collaborative filtering recommendation algorithm, the target customer’s K- nearest neighbor matrix is found, and the diversion strategy of travel alienation is formulated.Finally, taking 100 000 payment work order data as an example, the influence of customer attribute matrix filling, different measurement methods and the number of nearest neighbors on the drainage accuracy are analyzed, and the effectiveness and feasibility of the proposed algorithm are found.

Abstract:With the deepening of power market reform in the east of Mengdong region, the scale of power trading has been gradually expanded, and power settlement errors and deviation handling have been the focus and difficulty of power trading settlement in east of Mengdong. To address the above problems, a mathematical model of user electricity bill settlement based on the settlement rules of Mengdong electricity trading center is constrcted. Sensitivity analysis is conducted on the issue of the transmissibility of meter reading errors, the link to the transmission effect of meter reading error is determined. According to this impact link, two measures such as“early warning”and“spread income analysis”are proposed to deal with the deviation of bill settlement and profit loss caused by meter reading errors of customers, the effectiveness of the measures are verificated by simulation of arthmetic cases.