Abstract:With the continuous clarification of China’s requirements for the construction of new power system, the construction of VPP has been widely concerned by the industry. However,in the process of large-scale construction and operation of VPP, the factors influencing the economic benefits under the market environment have not been fully studied. Aiming at this problem, the construction investment costs, operation costs and incomes of VPP are sorted out, and the economic benefit model of VPP is constructed. Furthermore, from the perspective of market income, the sensitive factors are selected and the economic benefit analysis method of VPP participating in different market trading varieties is propose. Finally, example varifications are given, and the variation trend of economic benefit of VPP under different sensitive factors is discussed. The results can provide objective evaluation method support for VPPs to recover costs reasonably and effectively and participate in market transactions under new power system market environment.

Abstract:Peak load greatly reduces the installed utilization rate and investment benefit of power supply. The participation of virtual power plant in peak shaving can effectively delay the construction of power supply. Firstly, a fuzzy interval estimation method considering multi-domins and multiple evaluation indexes is proposed to solve the problem that the schedulable capacity of urban virtual power plants cannot be calculated accurately. This method decomposes the schedulable capacity estimation task into the fields of industry, construction and transportation, and constructs a fuzzy interval evaluation model considering user participation, revenue sensitivity, resource controllability and controllable scale. Then, based on fuzzy estimation of schedulable capacity, a power capacity planning method considering the participation of virtual power plant in peak shaving is proposed. Under the condition of meeting the power reserve capacity, the power capacity planning model is constructed by taking the minimum power expansion installation capacity as objective function, considering various factors such as different power structures, new energy output uncertainty, virtual power plant schedulable capacity, and so on. Finally,a city in East China is taken as an example to verify the effectiveness of the proposed method.

Abstract:Low carbon energy transformation under the dualcarbon target will promote wind power, photovoltaic and other new energy gradually become the main power supply in China. But intermittent output of new energy needs enough system regulation ability to support its stable power supply, which brings great challenges to the flexibility of new power system. Based on the operation requirements of Beijing -Tianjin -Tangshan new power system,some flexibility improvement strategies are put forward, such as the flexibility transformation of thermal power units, the comprehensive demand response based on virtual power plants and the response mechanism based on flexible loads. Secondly, a full time series system operation model considering the flexibility resource constraints and multi-day coordinated operation is constructed. Finally, an empirical simulation is carried out through the Beijing -Tianjin-Tangshan planning system to realize the flexibility improvement scheme selection considering the operation economy, environmental protection and efficiency multi-dimensional benefits, which provides a reference for the future planning and construction of new power systems.

Abstract:Electric vehicles have developed rapidly in recent years due to their advantages in energy saving and emission reduction, but their disorderly charging will lead to“peak on peak”in system load. Under the incentive of time -of -use price, the orderly charging and discharging of electric vehicles will also cause newpeak - valley difference. In order to solve the problem, the idea of master - slave game is used and an optimization model of virtual power plant with electric vehicles based on dual incentives of time-of - use price and carbon quota is proposed. Firstly, the concept of equivalent load, and formulates dual incentive policies are introduced based on the equivalent load to guide electric vehicles to discharge at peak times and charge at valley times. The virtual power plant is in the main position as the maker of incentive policies, the maximum benefit of virtual power plant are taken as the optimization objectives. The electric vehicle as the recipient of the incentive policy is in the subordinate position, the minimum cost of electric vehicle are taken as the optimization objectives. Finally, finding the Nash equilibrium solution of the established optimization model, and compared with only considering the time -of -use priceincentive. The calculation example shows that use of dual incentive policies can improve the overall benefits of virtual power plants and electric vehicles, and it can avoid the occurrence of newpeak-valley difference.

Abstract:In order to analyze the impact of electric vehicleaccess to virtual power plant on virtual power plant scheduling, the space-time distribution characteristics of electric vehicle are analyzed by fitting the starting time of electric vehicle charging with three peak Gaussian distribution function and the mileage of electric vehicle with lognormal distribution. Then based on the limitedcharge and discharge power, an orderly charge and discharge strategy is formulated. Secondly, the EVPP scheduling optimization model is constructed by maximizing the EVPP comprehensive in-come and minimizing the deviation rate of the declaration, and using the improved NSGA-II algorithm that introduces the lion selection method to search for the optimal solution. Finally, an example of EVPP including gas turbine, wind turbine, photovoltaic unit,and electric vehicle is analyzed. The analysis results of the calculation examples show that on the one hand, the improved NSGA-II algorithm has higher solution efficiency and better optimization effect than the traditional NSGA-II algorithm. On the other hand, the power control charging and discharging strategy can not only reduce the peak-valley difference, but also realize electric the car’speak and valley time changes smoothly.

Abstract:The reliability issues of the smart distribution system are studied when multiple distributed energy resources（DERs）are managed via virtual power plant（VPP）. Firstly, a VPP management strategy is proposed for power supply reliability, which includes VPP operation scheme and optimization models for each DER group. Then, reliability assessment method is developed for this cyber-physical power distribution system and the influence of the proposed strategies on power supply reliability is qualified. It is learned from case study based on IEEE 33 bus system that distribution network with faults can operate more economically and reliably via optimal dispatch of VPP. Therefore, VPP is one of the most potential platforms for the improvement of power supply reliability in the future.

Abstract:Central air conditioning load has become the main component of peak load, alleviating peak load by central air conditioning has become an effective mean. Firstly, the central air conditioning system is analyzed, the control mode combining with the start-stop control and flexible control of the central air conditioning is established, and the potential of the central air conditioning is analyzed combined with the indoor thermal process model. The parameters of virtual charge- discharge power, virtual energy storage capacity and virtual energy storage state are obtained. The differences between different control modes are described by virtual energy storage charge-discharge efficiency, and the central air conditioning model is transferred to virtual energy storage model.Based on the virtual energy storage model, a peak adjustment strategy for virtual energy storage group is proposed to reduce the peak load as far as possible to the planned load reduction. The simulation results verify the effectiveness of the peak adjustment strategy.

Abstract:Multi- region integrated energy system can improve energy efficiency, promote the absorption of renewable energy, and reduce energy waste and emission effectively. Multi-region integrated energy system involves several kinds of energy systems and is difficult to operate of cooperative optimization. So the article establishes the framework of integrated energy system and multi-region integrated energy management system. The functions of the component are analysed in the article. On this basis, the architecture of multi- region integrated energy management system based on multi-agent technology is established. The framework of energy management system includes energy management center generalagent of multi- region integrated energy system, learning center agent, solution center agent and sub- agent of energy management system. The functions of each agent and the working flow of the system are introduced in detail. At last, the JADE platform is built to realize the run of the system.

Abstract:In order to increase the accuracy and improve the forecasting system of electricity sales, a combined forecasting model of Elman neural network combined with historical similar monthis proposed. Combined with the characteristics of rapid identification among historical data, a set of historical data similar to the forecasted month is found by analyzing and processing the detailed data and external influencing factors of electricity sales objects.This set of historical data is used as the input data of Elman neural network to complete the prediction of such sales objects. Then, the forecast data of each electricity sales object is combined to get the total monthly forecast electricity sales. The simulation result shows that compared with the single Elman neural network, the combined prediction method has higher prediction accuracy, better convergence performance and a good application prospect.

Abstract:Electrical demand prediction is an important issue in power system planning. An improved electricity prediction method is put forward, which looks out the electricity consumption level in long term future first, then utilizes Logistic curve to fit the electricity consumption growth with saturation process. The future developing scenario constrains are taken into account in the parameter identification step of Logistic curve, which makes the curve beable to not only reflect the historical changing trend but also be in accordance with the future scenario. This method is applied to predict the electricity consumption of China and Guangxi Province in case studies respectively, and shows feasible and significant to other power demand prediction tasks.

Abstract:The development of integrated energy system is the only way to realize the energy Internet. The coordinated operation of integrated energy in the region is the key to realize the complementary of electricity, heat and gas. Firstly, the control and operation characteristics of wind turbine, gas turbine and energy storage device are analyzed. Taking the minimum operation cost as the optimization objective and considering the constraints of safety and stability, the comprehensive energy scheduling model of electricity heat gas area is established. Then, the proposed model contains avariety of complex operation constraints of electricity, heat and gas,while the variables are coupled with each other. It means that traditional mathematical optimization methods is difficult to solve this model. Therefore, particle swarm optimization algorithm is used. Finally, the simulation results show that the proposed model and algorithm are correct and effective.

Abstract:Air conditioning load has become one of the main loads of summer electricity consumption in China, and chiller is the largest part of the central air conditioning system power consumption. For a system with multiple units operating together, each chiller often operates under partial load. Based on the user-side demand response, the quaternary polynomial regression model of COP-PLR is established by polynomial regression method. In order to minimize the operating energy consumption of the chiller, the objective function is established, and the control strategy of optimizing PLR of the chiller with improved hybrid genetic algorithm is studied.The overall COP of the chiller is improved by optimizing PLR of each chiller.Compared with the original control method, the typical daily energy consumption is reduced by 702 kW, and the energy saving rate is 8.48%, which verifies the feasibility of the optimization method.

Abstract:The traditional power system optimization model usually use the inherent technical output of the thermal power unitas the output constraint of the unit, in actual operation, the unit’sdis patching output range is also affected by the net load and theunit’s output during the previous period, there is a large optimization space for output constraints. In order to effectively reduce the feasible range of unit output variables, a net load incremental indexis proposed, and the index and the output state of the unit in the previous period are used to determine the dispatch able space of eachunit. With the scope of schedulable space as a constraint, an improved dispatching model for improved power systems is established. A hybrid particle swarm optimization algorithm combining standard particle swarm optimization and simulated annealing algorithm is used. The example results show that the hybrid particles warm optimization algorithm can effectively improve the shortcomings of the standard particle swarm optimization algorithm to fall into the local optimum, and improve the accuracy of the model solution. In addition, compared with the traditional optimization scheduling model, the improved optimization scheduling model of power system that introduces schedulable space constraints, while ensuring the accuracy of the solution, the calculation amount is greatly reduced, and it is not easy to fall into a local optimum. the improved ideas and methods can also be applied to the optimization scheduling model of other energy systems.

Abstract:The treatment and settlement mechanism of deviation power is on important standard to judge whether the transaction mechanism is fair and reasonable. Whether the treatment and settlement mechanism is appropriate will directly affect the economic interests between market subjects and the promotion degree of market reform. Based on the summary of the current treatment and settlement mechanism of deviation power in the domestic power market, taking Guangxi as an example, the current deviation treatment, settlement mechanism and existing problems in the power market are dnalyzed, two deviation treatment and settlement mechanisms based on the idea of uncoupling plan and market and monthly pre-listing are designed, and the safety of the implementation of the monthly pre-listing mechanism constrained unit commitment and safety constrained economic scheduling model are corstructed.Finally, through the comparative analysis of the two deviation han-dling mechanisms, the phased implementation suggestions are putforward from the idea of no decoupling between plan and market to the idea of monthly pre-listing, and to the market transaction deviation handling and settlement mechanism under the spot market mechanism.

Abstract:In order to reduce the risk of large losses caused by power deviation assessment, a piecewise linear power assessment mechanism is proposed. Considering the four power purchase modes of bilateral contract market, centralized bidding market, independent power generation and demand side response, the power purchase and sales optimization model of power sales companies are put forward. Using the conditional value-at-risk method, a riskaversion optimization model is proposed to analyze the optimal power purchase combination of power sales companies considering risk aversion factors.

Abstract:With the development and improvement of the electricity competition market, the interests of power grids have gradually diversified. Under the background of active participation of users in the grid, it is of great significance to conduct research on consumer power consumption behavior. an optimization algorithm is proposed for residential electricity consumption behavior based on the characteristics of household appliances. First, the analysis of the electricity consumption characteristics of household appliances was carried out, and the use utility and schedulable potential of consumer electrical equipment were studied to achieve an assessment of the typical household’s responsiveness. Then the household households considering user comfort were given. An optimization model for the comprehensive management of electrical appliances.This model uses time expectation, user effect expectation and electricity charge change expectation as constraints, and restricts the use of household appliances and the existence time of regulation.Finally, based on tests of actual examples, the method of this paperis verified effectiveness.

Abstract:The electric load of Zhejiang Province is growing rapidly, and peak-valley difference is increasing by years. The duration of peak load only lasts for a short time.As energy storage technology is developing rapidly and the cost of it is falling, energy stor-age gradually becomes an important measure of reducing peak-valley difference. Aiming at the reality of Zhejiang power grid, the costbenefit analysis of electrochemical energy storage is modelled. This model of peakanalyses the econmic benefit, calculates the price difference- valley which can bring benefits and the dynamic paybackperiod under current electricity price policy. Finally, the prospect of energy storage technology is forecasted.

Abstract:Aiming at the problems of high labor cost, poor efficiency, and small coverage in the current error verification of smart meters, an out- of- tolerance smart meter screening method based on line loss iteration is proposed. Firstly, the daily electricitydata of the distribution station is collected and screened over a period of time to identify the data of light and no-load meters. Then,the fixed loss, line loss and error coefficient of meters are calculated iteratively by using the error estimation model. Through iteration, the data with high and low line loss rate are gradually eliminated, so that the line loss rate of effective data is concentrated inan interval. This not only improves the accuracy of error estimation, but also prevents valid data from being deleted by mistake. Finally, a non- light and no- load meter whose error coefficient exceeds the standard value is labeled as an out- of- difference one.The validity of the proposed method is verified by the data of electricity meters in three different sizes of distribution stations, and the experimental results show that the proposed method can detectout-of-tolerance electricity meters accurately. Comparing with the traditional least-squares method, the detection accuracy of the proposed method is obviously better than it.