Abstract:With the spread of distributed power generation and the popularization of electric vehicles, power storage technology will be further developed on the demand side. Focusing on the benefit analysis and economic operation of user side energy storage, the status of its research in terms of life loss cost modelling,multiple revenue stream profit models, and joint optimization of multiple application scenarios is sorted out, and typical cases of actual operation at home and abroad are introduced. Finally, the development prospects of user side energy storage are summarized in terms of technology, policy and market, and possible future research directions are foreseen. It is hoped that the work can provide useful references for relevant research and industrial development in domestic user-side energy storage.

Abstract:With the scaled development of electric vehicle, it is urgent to make reasonable site selection planning for charging stations to meet the actual needs. Electric vehicle traffic trajectory data are modeled and analyzed, and spectral clustering method is adopted to realize rapid clustering of electric vehicle driving data. According to the results of rapid clustering, the optimal location planning of charging stations is realized with the goal of minimizing the cost of construction economic operation and maintenance after the location of charging stations. The result of the calculation example shows that the charging station established can satisfy the user’s convenience and minimize the annual economic cost.

Abstract:Orderly control of electric vehicle load can improve load characteristics of the regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangement for the access of electric vehicles. Aiming at this problem, a real-time energy management system and optimization strategy for electric vehicles based on deep long short-term memory neural networks are proposed. Firstly, a three-tier management architecture for electric vehicles including the grid layer, regional energy management system and charging station energy management system is constructed, and arge-scale electric vehicles are managed hierarchically and partitioned；Then a district-station two-level interaction strategy based on deep long short-term memory neural network is proposed, and the historical optimal solution obtained by historical load information is used to train the learning network to guide new real-time optimization；The proposed strategy can further reduce the charging cost and improve the area under the premise of ensuring the users’charging demand load peak and valley characteristics. Finally, asimulation example verifies the effectiveness and superiority of the proposed layered architecture and management strategy.

Abstract:Aiming at the challenges brought to the distribution network after the massive access of renewable distributed generation（RDG）and electric vehicle（EV）, a robust optimal scheduling model considering the randomness of EV access and the uncertainty of RDG output is proposed. Firstly, the collaborative characteristics of RDG and EV load are analyzed. Combined with EV charging and discharging management method and the stopping characteristics of all kinds of EV, an estimation model of EV-VES capacity for dispatching is established. Then, the robust stochastic optimization theory is used to describe the uncertainty of wind and solar power, and the minimum net load fluctuation and the mini-mum energy cost of EV are taken as the objective functions to construct the optimal scheduling model and algorithm. Finally, a distribution network is taken as an example to verify the proposed model and algorithm. The results show that centralized optimization control of charging and discharging price, power and time of EV can achieve the purpose of coordinating renewable energy output and suppressing load fluctuation, and the effect of peak load reduction and valley filling is significant.

Abstract:Buildings are characterized by clusters. Through the interaction of buildings and optimization of energy equipment,the energy efficiency can be further improved and the environmental pollution would be much reduced. Firstly, a typical energy inter-connected building is constructed, and a multi-building joint operation structure is proposed. Then, the cooling load demand mode land electric vehicle model are mainly discussed. The room temperature is optimized by appropriate temperature limits and temperature penalties. Taking the total operating cost of the whole building group as the optimization objective, a multi-building combined optimal scheduling model considering the power interaction between buildings is proposed. The simulation of building independent operation is set up to enhance the persuasiveness. The economic cost of the two operation modes is compared through a calculation example. The influence of electric vehicle and cooling load requirements on the system operation is analyzed. The results show that the electric vehicle and cold load can affect the building micro-source out-put and the economy, which can further improve the utilization of renewable energy and reduce system operating costs.

Abstract:How to orderly charge electric vehicles with randomness is an urgent problem to be solved in“green travel”period today. Firstly, a mathematical model of electric vehicles based on advanced data mining technology is established. To grasp the load characteristics of the electric vehicle, the fuzzy C-means clustering algorithm is used to analyze the driving behavior of the owner and the initial battery state of the incoming electric vehicle taking the historical data of electric vehicle travel in a community in Beijing as an example. Based on the idea of Markov chain, a model consisting of several typical electric vehicle load mode transition probability matrices is established to describe the stochastic dynamic process of electric vehicles. Multiple population GA is applied to solve the orderly charging problem of electric vehicles.Taking the community charging station as an example and comparing with other methods, the effectiveness of the model and method is verified.

Abstract:Reasonable control of charging and discharging for electric vehicles（EVs）can improve the safety and economy of electric power system, and significantly reduce charging costs for EV users. The demand response strategy is one of the key technologies for orderly charging and discharging of EVs, which can solve a serious of problems caused by large-scale EVs interfacing with the grid and improve the consumption ratio of renewable energy in the grid. It has been widely studied by scholars at home and abroad.Firstly, the background and technology of demand response are illustrated. Then, aiming at the characteristics of demand response,the price-based and incentive-based demand response strategies are analyzed and compared respectively. Finally, relative suggestions are put forward for the domestic development of demand response for charging and discharging of EVs.

Abstract:With the rapid development and industrialization of the new energy storage technology, smoothing the fluctuations of nenewable power output by integrated mass storage unit becomes possible. Because of the allocation and internal resistance of battery energy storage system, a fully distributed optimization scheduling strategy based on multi-agent consensus algorithm which considered the internal resistance of the battery energy storage unit has been designed. By using a fully distributed algorithm, the power balance control of the power grid is realized by the real-time power allocation of the energy storage unit. The verification of each distributed strategy is realized through simulation analysis. The effectiveness and plug-and - play characteristic of the distributed consensus algorithm are verified by simulation results. The distributed consensus algorithm has good applicability for different communication topology.

Abstract:Hunan, Jiangxi and Zhejiang province have experienced“power curtailment”in the winter of 2020. On the one hand,it is due to excessive power demand. On the other hand, due to the unreasonable power supply structure, and the development of industrial user-side energy storage can help solve the issue of“power curtailment”to a certain extent. The deployment of energy storage on the industrial user side can not only alleviate the problem of grid power supply shortage, promote the consumption of renewable energy, but also reduce the industrial user’s electricity cost and obtain the peak-to-valley price yield. The development of user-side energy storage in Jiangsu province and related policies to promote the development of energy storage are studied firstly. Then, referring to the current industrial consumer electricity prices and peak shaving auxiliary service policies in Jiangsu province, the economics of battery energy storage of industrial user-side configuration of lithium-ion battery and full vanadium liquid flow battery are studied. Finally, some suggestions are provided for the development of user-side energy storage in Jiangsu province.

Abstract:The heat storage capacity of the building can be regarded as a virtual energy storage device, which can participate in demand response to reduce the operating costs and peak load of the system. Accordingly, the optimization model of a combined cooling, heating and power (CCHP) -ground source heat pump(GSHP) coupling system with virtual energy storage (VES) is established and an economic dispatch strategy from the perspective of electrical load and thermal load is proposed. Firstly, a charging and discharging energy model of VES is established, based on the comfortable temperature range of human body and building performance. Secondly, an optimization model for the CCHP-GSHP coupling system is formed to minimize the daily operating costs of the system. The VES affects the operating costs through the connection with thermal load, whose power is restricted by the rate of temperature change. Finally, taking the winter scene as an example, the optimal dispatch analysis of two typical buildings is carried out with respect to electrical load and thermal load, and the simulation results are analyzed in terms of economic efficiency. The simulation results validate that the adoption of virtual energy storage can reduce the operating cost.

Abstract:Residents have a wide variety of household electrical appliances with similar characteristics, which brings problems to non-intrusive identification such as uncertainty in the types of appliances and the need to improve the accuracy of identification.In response to this problem, a cloud-based collaborative load identification method based on multi - type feature interaction is proposed. Firstly, the end-side performs feature extraction and load identification based on high-frequency sampling, to improve the detection sensitivity of small offset events in the detection process based on the CUSUM event detection method, using the light-weight proximity recognition method to perform basic electrical appliance identification and upload the spatial-temporal characteristics of uncertain electrical appliances to the cloud. Secondly, the cloud side recognition ability is improved by constructing a 16-dimensional cloud-side historical feature database consisting of inherent features, spatio - temporal features and statistical features.An optimized identification technology based on the nearest neigh-bor principle for multi-dimensional spatio-temporal features is proposed. Finally, a closed - loop cloud upgrade mechanism is built.The cloud side sends back the different characteristics to the terminal to improve the terminal electrical feature library, comprehensively realizing the improvement of the ability to identify uncertain electrical appliances. Taking a user in Nanjing province as an example, the recognition rate of cloud collaboration has increased from 67% to 91% compared with that of the terminal. The recognition rate of unrecognized appliances has been realized, effectively verifying the effectiveness of the algorithm.

Abstract:As the terminal user of power system, the model accuracy of power load has profound influence on the accuracy of power system operation analysis. In recent years, a large amount of steady-state data has been collected by measuring devices in distribution networks. How to realize the parameters identification for static load with these measured data becomes a new research problem. A static load parameters identification method based on distribution network measured data is proposed. Firstly, the measured active power data is clustered to obtain the similar load curves with similar load characteristic. Then, under the assumption that similar load characteristic corresponds to similar load parameters, a model for static load parameters identification is established, and solved by optimization algorithm to get the parameters of daily static load.Lastly, case studies demonstrate the feasibility and effectiveness of the proposed methodt extent.

Abstract:As the penetration rate of distributed energy increases gradually and the load fluctuation on the user side becomes larger, it is difficult for the power grid to maintain its original better operation mode all the time. With the maturity of battery energy storage technology, the application is extensive, and the benefits of peak cutting and valley filling are also recognized. The application of battery energy storage in distribution network is considered, and the future distribution network is dynamically reconstructed. Taking the minimum network loss as the objective function and considering the network topology constraint, node voltage constraint, branch current constraint and power flow balance constraint, an upper optimization model is established for network reconfiguration. Taking the maximum profit as the objective function and considering the energy storage charge and discharge power constraints, the lower optimization model is established to formulate the charge and discharge strategy. The two-layer optimization model is solved by the genetic algorithm embedded with fmincon function. Then a mathematical model is established. Genetic algorithm is solved by embedding graphminspantree function. Finally,a modified IEEE-33 node system is used as an example to verify the effectiveness of the proposed method.

Abstract:During the 14th Five-Year Plan period, the gap between power supply and demand in Henan province will continue to increase. The government and State Grid put forward clear requirements for gradually forming a 3%~5% demand response capability, so as to guide the adjustable resources to favor regions with power supply shortage. In order to ensure the steady progress, the power supply and demand of the whole province are considered,and the decomposition optimization of the provincial mand response reserve target in each municipality are achieved based on the assessment of the demand response capability of 18 cities. Relevant methods and conclusions can be applied to the formulation of demand response plan, which is conducive to the integration of demand side adjustable resources, so as to better play the role of guaranteeing the balance of supply and demand and mitigating power grid investment.

Abstract:The continuous growth of loads such as electric vehicles（EVs）and air conditioners（ACs）has made transformer overload and accelerated aging problems more and more serious.An optimal coordination method for residential loads that dynamically determines the maximum load is proposed based on the hot-spot temperature（HST）of the transformer winding. In order to meet the scale requirements of loads, the duality principle is used to decompose the original problem into a distributed optimization problem of each flexible load. The simulation case considers flexible loads such as EVs and ACs, and compares four schemes：dynamic congestion management, static congestion management, self-interested mode, and free power consumption. Results show that this method can effectively implement congestion management and control the HST while significantly reducing electricity costs.

Abstract:With the increasing penetration rate of new energy in distribution network, it is urgent to conduct a comprehensive evaluation on the operation flexibility of distribution network. However, most of the discussions on flexibility are focused on the transmission system, and the evaluation scenario is single and lack of discussion on the interruptible load and other flexibility resources.Based on the clustering of flexibility scenarios, a distribution net-work flexibility evaluation method is proposed. Firstly, the nearest neighbor propagation algorithm is used to cluster the typical distribution network operation scenarios. Then, considering the uncertainty of wind power and load output, nonparametric model is used to fit the probability distribution of prediction error. Then, combined with Monte Carlo simulation and distribution network economic dispatch model, a practical calculation method of distribution network flexibility evaluation index is proposed. An actual distribution network data is used to verify the proposed algorithm, and the impact of interruptible load on flexibility evaluation is studied.

Abstract:In the electricity marketing business, electricity tariffs are the core of business development. As the real-time data requirements continue to increase, the pressure on cloud master stations for electricity tariff calculation services is increasing due to the increase in the amount of data. The timeliness of electricity tar-iff data provided by the system is generally low. In order to solve this problem, this paper proposes a research on the real-time calculation system of electricity charges based on edge computing technology. The more mature EdgeX Foundry framework was selected as the edge computing gateway. The low-occupancy and lightweight MQTT protocol was used as the secure cooperative transmission and control protocol. The indirect access method was selected as the device integrated access technology. Corresponding real -time computing services based on the three goals of the edge computing gateway’s data collection capabilities, data aggregation computing capabilities and early-warning capabilities were developed and deployed. Finally, the results of simulation experiments verify the advancement and effectiveness of the real-time electricity tariff system based on edge computing technology.

Abstract:The deregulation of power retail market has a great impact on the management of power quality. Different types of power selling companies have participated in the market and intensified the management dilemma of power quality. Firstly, the impact of the deregulation of electricity retail market on power quality management is analyzed. The differences of power quality management between domestic and foreign power enterprises are comparatively analyzed. Then, suggestions are proposed for power quality management in the deregulation of electricity retail market from the aspect of index management, development trend of power selling business and price management, so as to manage power quality in the power market environment, which can be the reference to the development of power market in China.