106 results about "Electricity pricing" patented technology

The invention relates to the field of multi-energy collaborative scheduling of an electric power system, and particularly discloses a wind-light-fire-hydrogen coupling system electric quantity balance method considering an electric power price factor, which is realized based on an optimization model construction module, a scene generation module, a scene reduction module, a dynamic adjustment module and a solving module. Comprising the steps that S1, a wind-light-fire-hydrogen multi-energy coupling system joint optimization hydrogen storage model is constructed, and a dynamic electricity-hydrogen conversion and virtual bidding arbitrage mechanism is combined; s2, an initial scene set is generated through Latin hypercube sampling according to photovoltaic Beta distribution, wind power Weibull distribution and real-time electricity price logarithmic normal distribution; s3, performing staged mixed reduction to extract a typical scene set; s4, executing two strategies to adjust the bidding scale and the electricity purchasing and selling behavior; and S5, converting a KKT condition by using a nonlinear complementary function, and solving a Nash equilibrium solution to realize market clearing. According to the method, price fluctuation is effectively stabilized, the new energy consumption capability is improved, and an efficient and stable electric quantity balance solution is provided for a high-proportion new energy power system.

The invention relates to the technical field of electric power, in particular to an electricity price fluctuation quantification method, device, equipment and medium based on regression analysis, and the method comprises the steps: taking electric power price fluctuation in peak load of electric power as a dependent variable, and taking new energy power generation, thermal power generation, electric power demand, generating capacity and capacity electricity price as independent variables; establishing a multiple linear regression model; verifying whether the independent variable and the dependent variable have a linear relationship or not, preliminarily carrying out multiple linear regression analysis, finding an optimal regression coefficient by minimizing a residual sum of squares, and carrying out multiple linear problem judgment; a ridge regression analysis method is adopted, a regularization item is added into a loss function to limit the size of a regression coefficient, overfitting of the model is reduced, and a ridge function is confirmed through cross validation; performing overall significance test and regression coefficient significance test on the multiple linear regression model, and analyzing the fitting degree; and performing quantitative analysis on electricity price fluctuation by using the fitted multiple linear regression model.

The invention discloses an urban user power utilization efficiency collaborative optimization analysis method and system based on the Internet of Things, and relates to the technical field of power analysis, and the technical scheme is characterized in that a multi-source heterogeneous data pool containing timestamp alignment is constructed; generating a user electricity consumption characteristic matrix through a space-time dynamic weight fusion algorithm; calculating a frequency modulation potential value through an entropy-TOPSIS combinatorial algorithm based on the user power consumption characteristic matrix; inputting the frequency modulation potential value into an LSTM-GAN confrontation generative network, and dynamically generating a three-dimensional efficiency parameter vector containing a user optimization degree, a good power grid contribution degree and an environmental benefit index; and based on the three-dimensional efficiency parameter vector, an optimization target of a user and a power grid is coordinated through a Stackelberg game model, and a dynamic electricity price strategy and a load adjustment strategy are output. The method has the advantages of being low in response delay, high in energy efficiency optimization rate, good in environmental benefit and the like when being used for coping with complex power consumption scenes in urban areas.

The invention belongs to the technical field of power dispatching. The invention provides a user side energy flexible scheduling management method and system. The method comprises the following steps: acquiring first electricity demand historical data and second electricity demand historical data of each user in a target area; performing prediction processing on the first electricity demand historical data by using a first prediction model to obtain a first predicted electricity demand; using a second prediction model to perform prediction processing on the second power consumption demand historical data to obtain a first power consumption demand fluctuation coefficient; and calculating a second predicted power consumption demand based on the first predicted power consumption demand and the first power consumption demand fluctuation coefficient, and determining an energy flexible scheduling management scheme according to the second predicted power consumption demand. According to the method, the historical electricity utilization rule of the user and the influence on fluctuation of the electricity price and the electricity supply and demand condition are considered at the same time, and the accuracy and rationality of the determined energy flexible scheduling management scheme can be remarkably improved.

The invention discloses an electric vehicle and building energy management optimization method based on a digital twin technology, and belongs to the field of building energy management and electric vehicle integration. The electric vehicle and building energy management optimization method based on the digital twin technology comprises the following steps: acquiring real-time data related to a building energy system, and performing short-term prediction; an energy management optimization model is constructed by considering the vehicle state, the electric power price and the photovoltaic power generation factor; performing dynamic optimization solution on the energy management optimization model based on real-time data and a prediction result, sending an optimization result to charging equipment and a battery management system through a communication interface, and adjusting an operation strategy in real time through a feedback mechanism to obtain a final scheduling result; the energy management method is based on the digital twin technology and is combined with the dynamic optimization model, and the energy management problem of the electric vehicle and building integrated system can be better solved.

The invention discloses an electric power price prediction method and device based on time dimension, equipment and a storage medium, and relates to the technical field of electric power systems, and the method comprises the steps: carrying out the preprocessing of a multi-source data set comprising historical electric power transaction data, a load demand curve, energy production data and fuel cost, performing feature extraction on the obtained processed data set to obtain each data feature, and determining a training set and a verification set based on the data features by using Bernoulli distribution; constructing an initial power price prediction model based on a multi-head attention mechanism, a convolutional layer, a preset activation function, a bidirectional long-short-term memory network and integration of a low-rank adaptation technology, and training and verifying the initial power price prediction model by using the training set and the verification set respectively; and performing multi-scale prediction by using the obtained target power price prediction model to obtain prediction results of the power price in different time dimensions. And the real-time, day-ahead and weekly prediction accuracy of the electric power price is improved.

This prediction device comprises: a verification prediction unit that predicts electricity prices in a predetermined verification period, on the basis of each of a plurality of trained prediction models; a model selection unit that selects a prediction model having high accuracy for predicting electricity prices among the prediction models; and a target prediction unit that predicts electricity prices in a prediction target period, on the basis of the selected prediction model.

The invention provides a power transaction decision-making method, device and equipment and a storage medium, and the method comprises the steps: obtaining time sequence operation data of a power market; inputting the time sequence operation data into a pre-trained transaction decision model, and obtaining predicted transaction data output by the transaction decision model; wherein the transaction decision-making model comprises a price prediction sub-model and a transaction decision-making sub-model, the price prediction sub-model obtains the capability of predicting and obtaining price prediction data based on time sequence operation data through pre-training, and the transaction decision-making sub-model obtains the capability of generating a transaction decision based on the price prediction data through pre-training; the activation function of the transaction decision model is composed of a plurality of nonlinear activation functions, and the transaction decision model is obtained by training with the prediction error of the power price and the prediction error of the decision income as loss functions. According to the technical scheme, support can be provided for transaction decision-making of the electricity market.

Provided are an information processing apparatus, an information processing method, and a program that improve the prediction accuracy of electricity prices while achieving consistency among a plurality of markets. 【Solution means】An information processing apparatus 1 includes an acquisition unit 10 that acquires information regarding operating power sources by applying a power demand prediction to a power source operation model, and a prediction unit 20 that predicts the electricity price in a first system by applying the information regarding the operating power sources to a first individual model, and predicts the electricity price in a second system different from the first system by applying the information regarding the operating power sources to a second individual model.

The invention relates to the technical field of electricity price prediction, and discloses an electricity price prediction method based on a random forest algorithm. Comprising the steps of generating a first feature data set based on collected whole network load prediction data, new energy load prediction data, tie line plan data, necessary stop unit data, necessary start unit data, holiday sign data, total maintenance capacity data and historical day-ahead electricity price data; constructing a power price preliminary prediction model group based on the first feature data set; correcting the electric power price preliminary prediction model group in combination with thermal power bidding space data to generate an electric power price final prediction model; and generating an electricity price prediction curve of the to-be-predicted day in combination with the obtained feature data of the to-be-predicted day and the final electricity price prediction model. According to the method, prediction is carried out by constructing a plurality of decision trees and synthesizing the results of the decision trees, so that the model can better adapt to different data distributions, and the prediction accuracy is improved.

The invention provides a dynamic optimization control method for a building chilled water storage central air-conditioning system, which comprises the following steps of: predicting a historical cold load of the building air-conditioning system through a pre-trained cold load prediction model, and calculating a cold load prediction error rate and uncertainty distribution; performing power consumption prediction by using the power consumption prediction model; sampling the cold load prediction error rate in the next 24 hours by using a Monte Carlo method; and in combination with the initial predicted cooling load and the dynamic time-of-use electricity price, with the goal of minimizing the daily electricity consumption cost, an optimal control signal of the hourly cold charging or discharging capacity of the chilled water storage device in the next 24 hours is obtained through a particle swarm algorithm. According to the method, quantitative modeling is carried out on random distribution of the error rate of cold load prediction, and the control signal of the cold charging or discharging capacity of the chilled water storage device is optimized through the Monte Carlo method and the particle swarm optimization, so that the optimal control robustness under the condition that the cold load prediction is uncertain is improved; and the operation cost of the chilled water storage central air-conditioning system under the dynamic electricity price is saved.

The invention relates to the technical field of artificial intelligence, and provides a thermal power generating unit operation optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source input data which at least comprises thermal power generating unit operation data and power market data; performing coal consumption prediction based on the operation data of the thermal power generating unit to obtain a first prediction parameter representing the coal consumption; determining a second prediction parameter representing the power price signal based on the power market data and the first prediction parameter; based on the first prediction parameter, the second prediction parameter and the multi-source input data, output and declaration parameters used for guiding the thermal power generating unit to optimize the operation state are generated. According to the method, the technical path of multi-source data fusion and coupling of internal coal consumption and external price signals for step-by-step prediction and comprehensive optimization is adopted, accurate prediction and dynamic optimization of the operation parameters of the thermal power generating unit are achieved, and the method has the advantages of being high in prediction precision and flexible in operation control.

The invention belongs to the technical field of electric power price prediction, and particularly relates to a multivariable fusion electric power price medium and short term prediction method based on Transform, which comprises the following steps of: collecting a data set; obtaining electric power price data and related external source variables of the current day, wherein the external source variables comprise an electric power load prediction value and a wind power generation prediction value; and data preprocessing. And preprocessing the collected data, wherein the preprocessing comprises missing value filling and data cleaning. And sequence data embedding is carried out, and each power price sequence is sequentially embedded into a time token and a variable token. And performing multi-step prediction, and outputting a prediction result. According to the method, the time sequence characteristics of the power price data and the exogenous variable characteristics influencing the price are integrated into the model, so that the model can extract the time sequence characteristics and the variable characteristics of the power price sequence at the same time, and the prediction precision of the power price is improved.

The invention relates to the technical field of time sequence prediction, integrated deep learning and electricity market analysis, and discloses an electricity price prediction method and related equipment based on dynamic integrated self-adaptation, and the method comprises the steps: obtaining multivariate electricity price data, and carrying out the preprocessing of the multivariate electricity price data, and obtaining a training data set; constructing a dynamic integrated adaptive electricity price prediction model, training a plurality of base learner models by using a training data set, evaluating the performance of the plurality of base learner models by using a verification set, dynamically adjusting the weight of a model combination, and obtaining a final weight; inputting the test data into the plurality of base learner models, and combining the final weight to obtain a final prediction value; according to the method, the nonlinear statistical characteristics in the time sequence can be effectively captured by constructing the dynamic integrated adaptive price prediction model, the dynamic nature and non-stationarity of data can be better coped with, and accurate prediction of the short-term power price is realized.

The invention discloses an electric vehicle active power distribution network operation method driven by a multi-scene dynamic pricing mechanism, and the method comprises the steps: carrying out the double-layer optimization of the scheduling between an active power distribution network operator and an electric vehicle aggregator, and building a double-layer-four-stage random robust model based on the double-layer optimization, and carrying out iterative solution on the double-layer-four-stage model through a CCC-AIS coupled dichotomy, and outputting a strategy according to a condition for terminating iteration of the double-layer-four-stage model, and outputting the strategy as an operation method. According to the multi-scene dynamic electricity price mechanism provided by the invention, the EV can be effectively guided to be charged in order according to the charging and discharging characteristics of different scenes, and actual requirements are better met. Compared with a traditional fixed TOU electricity price and disordered charging mode, the new energy consumption level is improved, the load fluctuation amplitude of the ADN is effectively reduced, and the economical efficiency of the ADN is further improved. The model combines the advantages of RO and SO, and successfully balances the robustness and economy of the system.

The invention is suitable for the technical field of random clearing, and provides a power transaction method and system based on a random clearing mode, and the method comprises the steps: obtaining historical power supply and demand data, carbon emission data and supply and demand data monitored in real time of a preset region, and carrying out the power supply and demand analysis of the obtained data; determining a random distribution model of the power price of the preset region based on the power supply-demand analysis result; obtaining power related data of power generation enterprises and power utilization enterprises in a preset region, and determining a range distribution model of power distribution according to the obtained data; determining a random clearing model according to the random distribution model of the power price and the range distribution model of the power distribution, and carrying out clearing calculation and optimization based on the random clearing model; the transaction result is determined according to the clearing calculation and optimization, and the electric charge settlement is performed according to the bid-winning electricity price and the actual electricity consumption in the transaction result, so that the accuracy of the electric charge settlement based on the random clearing condition is effectively improved.

This application relates to a control method, apparatus, and television device. The method includes: obtaining the current electricity price period based on peak-valley tiered electricity price data for the user's region, and obtaining the user's household electricity consumption data; determining whether preset energy-saving conditions are met based on the current electricity price period and household electricity consumption data; when the preset energy-saving conditions are met, generating an adjustment strategy based on the preset energy-saving conditions and the television device's equipment parameter data; and controlling the television device to execute the adjustment strategy, so that the television device can reduce power consumption while maintaining continuous playback of media content. Using this application, intelligent energy-saving control linked to peak-valley tiered electricity pricing can be achieved, balancing energy saving of the television device with the viewing experience.

The invention discloses a central cold and hot water intelligent collaborative centralized control energy efficiency management system which comprises a central cold and hot water system, a central air conditioning system, an AI cloud energy efficiency cloud platform, a central CPU processor, a 5G communication module, an efficient host, an intelligent human-computer interface, a cold water output permanent magnet variable frequency water pump and a hot water output permanent magnet variable frequency water pump. The central cold and hot water system comprises a cold water heat preservation water tank and a hot water heat preservation water tank, the cold water heat preservation water tank is provided with a cold water unit and a cold water output pipeline, and the hot water heat preservation water tank is provided with a hot water unit and a hot water output pipeline. The efficient host is used for executing the electricity price strategy, responding to electricity price fluctuation and controlling the water chilling unit and the hot water unit. The intelligent cooperative centralized control energy efficiency management can be realized, the output power of the cold and hot water sources can be reversely adjusted based on the tail end load prediction, the cold and hot source output is matched with the tail end demand, the peak-valley arbitrage can be realized by combining the electricity price prediction curve and the temperature energy storage technology, and the operation cost is reduced.

The invention discloses an electric vehicle distributed charging optimization method and system based on a carbon emission sensitive dynamic electricity price, and the method comprises the steps: setting an initial iteration strategy based on a controllable electric vehicle load model, and averagely distributing the needed charging amount to all charging time periods; defining the size of an algorithm convergence criterion according to the required convergence precision and the number of the electric vehicles; constructing a dynamic electricity price containing coupling of a load item and a carbon emission item, and calculating an EVn cost function according to the dynamic electricity price; starting a kth round of iteration, traversing all EVn in each round of iteration, and counting k from 0; other electric vehicle charging power vectors except the EVn are calculated; after each iteration, checking whether a convergence criterion is met or not; if yes, the obtained load curve is an approximate solution of Nash equilibrium, and if not, the charging strategy is updated repeatedly; and after the load curve under the dynamic electricity price mechanism is obtained, the load curve under the time-sharing static electricity price with the same electricity price level is obtained through calculation. The method is suitable for electric vehicle group charging management in a community scene.

An automatic control method, system, equipment, and medium for a series refiner unit are disclosed, relating to the field of automatic control. The method includes the following steps: acquiring pulp demand information; configuring the operating mode of the series refiner unit based on the pulp demand information; determining the operating parameters of the pulping process based on the refiner unit's operating mode and acquiring time-of-use electricity price information, the operating parameters including at least the energy consumption per unit time of the pulper and the energy consumption per unit time of the refiner unit; establishing an operating energy consumption model for the pulping process based on the operating parameters and time-of-use electricity price information; optimizing the operating energy consumption model to minimize energy consumption; and planning the start-up and shutdown states of the series refiner unit and pulper in subsequent operating time units of the pulping process based on the optimization results. By adopting the technical solution provided in this application, the equipment involved in the pulping process can be better controlled, thereby utilizing the time-of-use electricity pricing system to reduce the total energy cost of the pulping process.

The invention discloses an electric power spot price prediction method, medium and system, and the method comprises the steps: carrying out the reduction of an original electric power spot market operation scene, and obtaining a reduced electric power spot market operation scene; a power price prediction model formed by sequentially connecting a long short-term memory network LSTM and an improved XGBoost model is constructed, the collected historical power market spot price is screened, and the screened historical power market spot price is used for training the power price prediction model; and inputting the acquired evaluation indexes of the reduced electric power spot market operation scene into the trained electric power price prediction model, and outputting a predicted electric power spot price. According to the invention, scene reduction and electricity price prediction are combined, electric power spot price prediction with both accuracy and high efficiency is realized, and the accuracy and speed of electric power spot market price prediction are improved.

This invention relates to a method for coordinating and optimizing energy storage and electricity pricing based on flexible resource synergy. This method can effectively address the increasingly severe power balance pressure on distribution networks by optimizing electricity prices and mobilizing the adjustment potential of flexible resources within the distribution network. The main steps include: acquiring photovoltaic and wind power load data and initial time-of-use electricity prices for a given day in the distribution network; calculating the energy storage operating power at each moment using a particle swarm optimization algorithm; calculating the energy storage configuration and operating costs; calculating the maximum revenue for the distribution network operator and using this as the objective function to optimize the time-of-use electricity price using a moth-flame algorithm; and outputting the optimal energy storage configuration capacity and the optimal electricity price. Compared with existing technologies, this invention achieves peak shaving and valley filling by adjusting user electricity consumption patterns through electricity pricing, and utilizes new energy sources for low-storage, high-generation generation, effectively promoting the local consumption of new energy, improving the balance capacity of the distribution network, and providing theoretical support for the safe and low-carbon operation of the distribution network.

The invention belongs to the technical field of energy optimization scheduling, and provides a comprehensive energy planning method and system considering the regulation capacity of a regulation and storage pool of a sewage plant, and the method comprises the steps: designing an economic cost function of a comprehensive energy system; the economic cost of the comprehensive energy system comprises investment cost, cost of purchasing electricity from a power distribution network, cost of purchasing natural gas and operation and maintenance cost of the comprehensive energy system; based on an integrated energy system economic cost function, establishing an integrated energy planning model; and solving the integrated energy planning model to obtain a design scheme of the integrated energy system. Under the condition of considering capacity limitation of the regulation and storage pool and natural gas and electric power price, the starting time and power of the water pump of the regulation and storage pool are optimized, and the water pump is started to pump the to-be-treated sewage into the regulation and storage pool as far as possible in the power margin period of the power distribution network, so that optimal configuration of energy of a sewage plant and reduction of energy consumption cost are realized.

A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply / demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.

The invention discloses a distributed optimal scheduling method based on a power grid friendly rural comprehensive energy system, which relates to the field of power grids and comprises the steps of establishing a power grid rural energy sensing network, summarizing collected rural comprehensive energy information, scheduling rural comprehensive energy and predicting the demand quantity of the rural comprehensive energy. Real-time scheduling is carried out on rural comprehensive energy, an electric power price optimization algorithm is established, a user participation incentive mechanism is established, self-scheduling and load management of users are promoted through price levers and reward measures, and analysis and summarization are carried out on rural comprehensive energy system optimization scheduling. According to the distributed optimization scheduling method based on the power grid friendly rural comprehensive energy system, efficient scheduling of rural energy is achieved by establishing a power grid rural energy sensing network, collecting and analyzing rural comprehensive energy use information and combining economical, energy-saving, sustainable development and people-benefiting constraint conditions.

The application discloses an extreme value dynamic correction day-ahead electricity price probability prediction method for tail risk, and the method comprises the following steps: an electricity price extreme value extraction method based on a quantized sliding window algorithm is used to extract candidate extreme value points from an electricity price time sequence, and target extreme value points corresponding to sharp peak electricity prices and high plateau electricity prices are screened; a generalized extreme value distribution is used for statistical modeling of the target extreme value points to determine generalized extreme value distribution parameters, which are used for describing tail risk; a deep learning model is constructed, Box-Cox transformation and Huber loss function are optimized through a joint training mode, a predicted mean value and a predicted standard deviation of the electricity price are outputted, and a predicted interval of day-ahead electricity price is constructed; a dynamic calibration mechanism is introduced, the predicted mean value is corrected and the predicted interval is dynamically adjusted based on the generalized extreme value distribution parameters, so that the reliability of the predicted interval when an extreme price event occurs is improved. The application significantly improves the accuracy and reliability of extreme fluctuation prediction of the electricity price.

The present invention discloses a method for pricing electric vehicle charging in a park considering carbon emissions, including the following steps: obtaining required data; constructing a model for the cost of electric vehicle electricity consumption according to the objective function of carbon emissions and photovoltaic power generation and power supply in the park; establishing a model for making electric vehicle charging plans according to charging needs; S3, formulating a time-of-use electricity price pricing model according to the comprehensive benefits of both the park and the user; formulating a corresponding charging plan according to the power situation when the electric vehicle is connected to the park; generating a park time-of-use electricity price according to the required data, calculating the carbon emissions and economic benefits generated by the charging of the electric vehicle, and constructing an evaluation target by using the constructed model; using the evaluation target as fitness, using a genetic algorithm, iterating, and finding the optimal solution; obtaining the optimal time-of-use electricity price in the park, and executing the charging plan. The present invention can formulate an electric vehicle charging pricing plan for the park, and reduce the carbon emissions generated by the charging of electric vehicles in the park.

This invention relates to the field of electric vehicle charging and discharging technology, and more particularly to a method, system, and computer-readable storage medium for extrapolating a vehicle-to-grid (V2G) interaction supply-demand coordination model. The method first collects data and determines model input parameters based on local policies, electricity pricing mechanisms, and technological development trends. Then, it constructs a user utility function, eliminates differences in indicator dimensions through normalization, and calculates the probability of user selection for each charging mode, thus constructing a demand-side mode selection model. Based on user selection probabilities and facility operation indicators, it establishes a model for calculating facility expansion scale, and uses a dual-objective optimization framework to balance maximizing user satisfaction with minimizing investment costs to determine the annual number of new facilities. The number of new facilities is fed back to the user model to update facility accessibility parameters until the supply and demand status meets preset convergence conditions. This invention significantly improves the scientific nature and prediction accuracy of V2G interaction planning through dynamic closed-loop modeling of supply and demand coordination and a dual-objective optimization strategy.

The invention discloses a new energy distribution and storage control system and method based on artificial intelligence, and belongs to the technical field of new energy distribution and storage control. The method comprises the following steps; acquiring power consumption demand and environment data and structure data of the new energy power station; wherein the electricity demand quantity is an electricity power time change curve within a preset time; inputting the environment data and the structure data into a constructed new energy power generation prediction model to obtain a power generation time change curve of the new energy power station within a preset time; constructing an electric power price prediction model to predict the electric power price, and obtaining an electric power price time change sequence within a preset time; inputting the power generation time change curve, the power price time change curve and the power consumption demand into a constructed energy storage system control model to obtain an energy storage control strategy in a preset time; and controlling the energy storage system to perform charging and discharging operation according to the energy storage control strategy.

The present invention relates to a two - layer optimization method and device for a rural virtual power plant electricity seller system, belonging to the field of power electronics technology, and solves the problem of how to perform two - layer optimization of VPP - ER, which is an urgent problem to be solved in the transformation of the current new power system. The method includes: establishing multiple unit models based on rural VPP and rural load demands; establishing a collaborative trading mathematical model based on electricity - carbon - green certificate collaborative trading, including a green certificate trading cost model; constructing an upper - layer electricity - carbon - green certificate market collaborative trading optimization model based on the collaborative trading mathematical model, considering the benefits and risk costs of the VPP - ER system participating in the electricity - carbon - green certificate market collaborative trading, and combining the day - ahead electricity price, real - time electricity price, carbon price and green certificate price to allocate the electricity purchase volume of the day - ahead market and the virtual power plant to maximize the weighted benefit; constructing a lower - layer virtual power plant scheduling optimization model based on multiple unit models to minimize the scheduling cost. It can minimize the trading risk and scheduling cost and maximize the comprehensive benefit.