Abstract:With the high proportion of new energy access and the growth of power demand during peak hours, the balance of power supply and demand is increasingly hard to maintain. It is urgent to explore the potential of demand-side resource, establish complete power market mechanism and transform the“source follows load”to“source-load interaction”under the new-type power system. Firstly, a multi-dimensional review of demand-side resources is introduced and the market types in which they participate are summarized. Then, the technical progress in assessment and regulation of demand-side resource are analyzed in 3 aspects：demand-side resource quantitative assessment of adjustable potential, demand-side resource aggregation and regulation and demand-side management system equipment. Finally,combined with the requirements of new-type power systems, some suggestions are put forward for demand-side resource utilization in the market environment.

Abstract:Monthly load forecasting is the basis for medium and long-term operation of power system and development of marketing work,and probabilistic power load forecasting can portray medium and long-term uncertainty, and better support the new type of power system load assessment and regulation strategy development. In this context, the medium and long-term probabilistic forecasting method is studied with the system load as the research object, and the medium and long-term probabilistic forecasting method based on fine-grained data fusion is proposed. Firstly, an hourly multiple linear regression model is established to model the fine-grained loads based on the influencing factors, and then the fine-grained forecasts under different scenarios are generated based on the different predicted values of the influencing factors. Secondly, according to the“bottom-up”temporal hierarchy coordination strategy, monthly aggregation is performed for each scenario, and monthly load forecasts are generated for different hierarchical regions to form probabilistic forecasts. Finally, the effectiveness of the method is verified by taking load data of a region in eastern China and its subordinate areas as an example.

Abstract:With the gradual advancement of electricity market reform, demand response will play an increasingly important role in future new power systems. Considering the problems of cumbersome process of DR potential calculation and lack of detailed data on user electricity consumption process, a group users DR potential prediction and classification method based on historical load, temperature, and electricity price data is proposed. First, data processing and information extraction are carried out on the user’s daily electricity load curves.Three characteristics, monthly load regularity, daily load fluctuation and peak-valley consistency, are calculated, which form an index system of physical regulative potential evaluation. Further, nonlinear au-to-regressive model with exogenous inputs neural network is applied to the prediction of group users’daily load and DR potential. Finally, taking industrial users as examples, Meanshift algorithm is used to partition user clusters, and DR regulation power of general component manufacturing industry is predicted. By comparing and analyzing with actual data, the effectiveness of the proposed method in this paper is verified.

Abstract:Aiming at the problem that the extensive regulation of the current central air-conditioning system cannot accurately release the potential of regulation, a technical scheme for refined regulation and control of central air-conditioning load is proposed based on bilevel optimization after elaborate analysis of the operating principle and accommodation mode of each subsystem of central air-conditioning.Firstly, a upper-level control model based on the objective function of comfort and economy optimization is established. Secondly, it is supposed to establish the optimization objective function of amenity and economy, and allocate the temperature and humidity setting points by the Shapley value method. Then, a lower regulation mode aiming at the lowest energy consumption of the optimal combination of subsystems is established, and ant colony algorithm is used to solve the optimization objective function. Then, by solving bilevel optimization, the refined regulation strategy including end fan, chilled water pump and chiller is formulated. Finally, numerical examples are used to verify the correctness and feasibility of the proposed technical scheme for refined regulation.

Abstract:With the promotion of clean energy, capacity adequacy is playing a more important role in the grid. However, the capacity market mechanism has not been fully developed in China. By analysing the current status of capacity market research at home and abroad, the definition and rules of capacity adequacy are determined, and the clearing model of capacity market is established with reference to foreign practical experience of PJM. In order to play the advantages of demand response in the market, the characteristics of the capacity market is considered and a bidding strategy for demand response in the capacity market is designed.

Abstract:With the rapid growth of wind and photovoltaic power, the power system is facing issues of insufficient adequacy. To ensure an ample capacity supply, it is necessary to introduce the capacity market mechanism. Compared to the large-scale thermal power plants with long construction periods and high investment costs, virtual power plants（VPP）that aggregate distributed adjustable resources has a shorter construction period, lower investment, and quicker effectiveness. VPPs serve as effective resources for providing generation capacity to the system, they can be used to manage the peak demand. Therefore, a VPP unforced capacity（UCPA）formula and a comprehensive capacity market clearing model which is designed to consider capacity offers including VPP is proposed. The proposed VPP UCAP computation formulas consider power capacity, energy capacity and operational attributes. The proposed capacity market clearing model introduces the participation of VPPs with the consideration of wind power, photovoltaic power and conventional generation units. The presented capacity market clearing model considers system constraints such as peak demand, base demand, energy requirement, off-peak demand, and ramp power demand. Finally, the impact of UCPA of VPP, total capacity demand and renewable energy installed capacity on market cleared results is analyzed by conducting the study cases, it shows the validity of the proposed VPP UCPA formulas and the capacity market clearing model.

Abstract:Virtual power plants can aggregate adjustable load resources at the end of distribution network to participate in power system calls to alleviate the problems faced by the supply and demand balance. At the same time, the power regulation of the end resources affects the operation quality of the distribution network, and the invocation of load-side resources can reduce the peak-to-valley difference in the distribution network area and reduce the system network loss. To this end, a virtual power plant with the distribution network operator as the main body is constructed, and a multi-objective optimization model of distribution network virtual power plant based on AUGMECON2 is proposed, which invokes the end load resources of distribution network to participate in the demand response with the operation benefit,distribution network peak-valley difference and distribution network loss as the objective functions, and improves the operation quality of distribution network while improving the operation benefit. The Pareto frontier of the proposed multi-objective optimal problem is obtained by AUGMECON2, and a compromise solution is obtained by considering fairness. Finally, simulation analysis based on the modified IEEE 33-node distribution network system is conducted to verify the effectiveness of the proposed model.

Abstract:Promotion and use of new energy power generation has aggravated the contradiction between supply and demand of power grid during peak hours. Identification of load patterns of power users can provide support for load participation in peak regulation decisions. In order to improve the accuracy of power load pattern recognition, a power load pattern recognition model based on improved particle swarm optimization（IPSO）algorithm to optimize long short-term memory（LSTM）neural network is proposed. By introducing diversified initial parameters, dynamic nonlinear weights and elimination mechanism, the optimization ability of PSO algorithm is improved, the key parameters of LSTM are optimized, and optimal parameter combination of LSTM neural network is determined. Experimental results show that this method can effectively improve the accuracy of the model and save the training time of the model.

Abstract:As a high proportion of distributed renewable energy is connected to the power grid, prosumers gradually become an important role in demand-side coordination and optimization. Meanwhile, the problem of response delay of massive resources becomes more obvious in demand-side real-time regulation. Therefore, combined with 5G technology enabling, a real-time regulation and optimization strategy is proposed for prosumers considering 5G communication and shared energy storage. Firstly, the real-time regulation architecture of prosumers based on 5G cloud-edge collaboration is designed. Secondly, the regulation service delay model based on 5G is constructed from two aspects of transmission delay and calculation delay. Then, a real-time operation optimization model considering delay compensation and energy-capacity sharing storage service is established, where the upper objective function is to maximize the revenue of the edge layer aggregator, and the lower objective function is to minimize the total operating cost of all prosumers in the end layer；Finally, simulation results show that the proposed strategy can effectively reduce load deviation caused by delay, improve the new energy consumption capacity, and improve the operational revenue of aggregators, reduce energy consumption costs of prosumers, achieving rapid and precise regulation of demand side resources.

Abstract:Incorporating carbon inclusive mechanism into the carbon market trading system under the dual carbon target is an important means to enhance public carbon perception. It is noteworthy that how to carry out the application of carbon inclusion credits. To this end,an integral formation method for electric vehicle charging is constructed using carbon integral as the carrier of carbon inclusion mechanism；Considering the demand for aggregation trading, a double-layer trading model is designed for carbon aggregators and emission control enterprises to participate in the regional carbon inclusion market. Upper layer is an economic model for carbon aggregators and emission control enterprises, forming a supply-demand relationship between the trading parties. Lower layer is a regional carbon trading price decision-making model based on supply-demand ratio, and an improved particle swarm optimization method is used to solve the problem；Simulation results indicate that the proposed method establishes a feasible trading scheme for electric vehicle charging carbon inclusion to participate in regional carbon markets based on the aggregation of scattered carbon inclusion resources, effectively expanding the application scenarios and value of carbon inclusion.

Abstract:The introduction of 5G into the electric power 4G wireless private network and the integration of 4 great significance for improving the efficiency of electric power business communication. Firstly application of G and 5G networks are of 5G in the power system and 4G and 5G networking methods is descirbed. Then, comprehensively considering the power own business, power business communication requirements, power base station construction costs, and base station operating costs, the power wireless private network 4G and 5G base station layout optimization models are constructed, and then optimize the layout of 4G and 5G base stations. Secondly, optimize the design and improvement of the coding and initialization population operations in the genetic algorithm, which greatly reduces the number of algorithm iterations, improves the convergence speed, and reduces the complexity of the model. Finally, the improved model solves the optimization model of power 4G and 5G base station layout. It can quickly give 4G and 5G base station optimization solutions to meet the requirements of power communication coverage and economy. Rationality and effectiveness of the proposed model and improved algorithm are verified by simulation examples.

Abstract:Integrated energy service provider, as a multi-energy supply entity, can participate in market transactions. To explore new profit sources for integrated energy service provider in the electricity market environment, a multi-agent interactive mechanism considering demand response has been constructed. This mechanism takes into account the uncertainties of photovoltaic output, diverse energy demand power, market electricity price, and demand response resource potential. By employing interval optimization methods, a mixed-integer linear programming model is developed to represent the day-ahead operational optimization problem for integrated energy service provider,yielding strategies for energy procurement, equipment operation, and demand response resource utilization. Case studies are verified that demand response mechanism can effectively reduce purchased cost and increase the operational revenue of integrated energy service provider.

Abstract:Heating, ventilating and air condition（HVAC）energy consumption on load side accounts for a large proportion of total energy consumption of the building. Adopting appropriate control scheme can effectively reduce energy consumption of energy consumption system. At present, the methods of energy saving regulation and control of HVAC on load side mostly focus on load forecasting control, which requires the combination of multiple model prediction results and high accuracy requirements. However, association rule analysis method can directly learn the law of historical operation data, and can more easily obtain more energy-saving regulation strategies. Energy consumption system of a shopping mall in Wuhan is taken as the research object, and operating data of the system load side in June are collected for research. After the data is cleaned and discretized, Apriori algorithm is used to calculate the association rules between power and device start-stop and frequency, and the energy-saving control scheme is presented based on this. Research results show that Apriori algorithm can learn the law of historical operation data of energy consumption system well. By analyzing the relationship between system power and adjustable parameters such as frequency, start and stop, the energy-saving control strategy can be obtained more quickly, conveniently and directly in actual use.

Abstract:The development of an integrated energy system is a key initiative to economic development, energy conservation and emission reduction. Firstly, to improve the peaking capacity of the units and reduce CO2 emissions, carbon capture technology is introduced into the integrated energy system, and its role in the flexibility, economic, and low-carbon operation of the integrated energy system is studied.Then, to reduce the pressure on the system caused by fluctuations in load and wind power peaks and valleys, the role of demand response of electric and thermal loads is considered to achieve healthy and sustainable operation of the integrated energy system. Finally, the role of energy coordination and coupling is considered, and a low-carbon economic dispatch model for the integrated electricity-thermal energy system is constructed to minimize the comprehensive cost. The results of the analysis show that the combined effect of carbon capture and demand response reduces the total operating cost and carbon emission cost of the system, promotes the consumption of wind power from both the source and load sides, and facilitates the flexible dispatch and low carbon operation of the integrated electricity-thermal energy system.

Abstract:With the rapid development and wide application of new energy technologies, power system is facing the trend of high proportion of new energy and multi-agent interconnection. However, the dimensional explosion caused by solving the scheduling target of unit subject diversity in the whole period has become the main problem affecting the normal operation of the system. Therefore, a collaborative management method is proposed low-voltage station area based on microgrid carbon metering data. According to the dynamic cohesion of the system, the time range of optimal scheduling by the agent is divided to balance the solution dimension. At the same time, in order to solve the problems of economic operation, environmental benefit and consumption of high proportion of new energy system, a multi-objective scheduling model is constructed, which combines its dynamic cohesion and dynamic multi-objective co-evolution solution model. The results of the final example show that the model and solution method proposed can effectively reduce the computational dimension of the solution and realize the fine scheduling and management of the low-voltage station area, and improve the operation efficiency and stability of the system.

Abstract:In order to promote clean energy heating and improve heating quality and efficiency of municipal heating in northern China, an optimal operation strategy of heat pump system in urban heat exchange station is put forward. Firstly, the equipment of heat pump system in urban heat exchange station is modeled；Secondly, the interactive scene between heat pump supplementary heating system and power grid is constructed, including responding to peak-valley average electricity price and participating in peak-shaving auxiliary service market；Then, a double-objective optimization model with the lowest operating cost and the best stability of heating network is established,which is transformed into a single-objective optimization problem and solved by particle swarm optimization algorithm；Finally, four heating schemes are comparatived and analysed through an example, and the economy and low carbon of proposed model are verified model,which provides a new idea for the low carbon transformation of urban heating.

Abstract:As the construction of 5G base stations continues to spread, the issue of high energy consumption and high cost of base stations is a major concern. Through the configuration and optimal operation of photovoltaic（PV）and energy storage, and further participation in peak regulation auxiliary services, the economy of 5G base station energy use can be effectively improved, and the scale application of green energy in 5G base stations can be realized. Therefore, the economics of 5G base station PV and energy storage systems participating in peak regulation auxiliary services are studied. Firstly, an optimization goal model considering the participation of PV and energy storage systems in peak regulation auxiliary services is established according to the load characteristics of base stations and different peak regulation periods；Then, CPLEX optimization solver is used to solve the optimization model and obtain the optimal capacity and operation strategy of energy storage.Finally, the scenarios of energy storage independently participating in peak regulation and PV and storage jointly participating in peak regulation are constructed with different peaking modes are built and cases show that greatly affect the optimal configuration capacity of energy storage and the energy cost of 5G base stations. A reference for the optimal operation of 5G smart base station optical storage system participating in peak regulation auxiliary services can be provided.

Abstract:Currently, new energy storage has become necessary supporting of constructing new power system, and reasonable energy storage policies and market mechanisms will have a profound impact on the healthy development of new energy storage. Therefore,it’s necessary to learn from advanced experience of countries with better energy storage development. Firstly, research on the planning and development status of new energy and new energy storage in Germany is carried out, and internal factors of the growth of new energy storage are analyzed from the aspects of economy, policy incentives and subsidy policies, and the mechanism of German energy storage’s participation in power market and main income model are further studied. Then, the development status, market mechanism and invocation mode of Jiangsu new energy storage planning are analyzed. Lastly, differences of energy storage development between Jiangsu and Germany are compared, and relevant experience of new energy storage development for Jiangsu power grid is summarized.