961 results about "Electricity price" patented technology

The invention relates to the technical field of power system optimization scheduling, and discloses a multi-load equipment energy consumption management method and device and computer equipment, and the method comprises the steps: obtaining electricity price data, and the energy consumption data, the operation state and the load demand of electric equipment, based on the electricity price data, the energy consumption data, the operation state and the load demand, the priorities of the electric equipment are classified; adopting a preset multi-task learning model to predict the load demand of the electric equipment in a future preset time period; generating a multi-target optimization model based on the priority, the load demand and the electricity price data of the electric equipment, and generating a dynamic power distribution scheme of the electric equipment based on the multi-target optimization model; and obtaining the load demand and the external environment of the electric equipment in real time, and adjusting the dynamic power distribution scheme of the electric equipment based on the load demand and the external environment. According to the method, the energy consumption optimization and the operation efficiency are always kept balanced, and the problem that the energy consumption optimization and the operation efficiency are not balanced is solved.

The invention discloses a regional energy internet hierarchical control method considering flexible loads, which comprises the following steps of: constructing a global layer, dividing a regional energy internet into a plurality of electrically strongly coupled sub-regions by adopting a dynamic partitioning algorithm, aggregating equivalent adjustable capacities of the flexible loads in the sub-regions, and performing rolling optimization in an hour-level time scale, an improved Benders decomposition algorithm is adopted to accelerate solving and generate a partition-level power regulation instruction; constructing a regional layer, designing a thermodynamic-economic hybrid constraint model according to flexible load physical characteristics and user behavior elasticity, introducing a user comfort recovery time constraint and elastic electricity price response function, and decomposing an adjustment instruction issued by the global layer to each flexible load cluster in a minute-level time scale by adopting a parallel ADMM optimization algorithm; and constructing a load layer, constructing a millisecond-level adjustable load priority switching mechanism based on FPGA hardware acceleration and finite-state machine logic, calculating a load adjustable power boundary in real time, and dynamically adjusting a response queue in combination with system frequency deviation.

The invention provides a power system optimization control method and device based on power market transaction. The method comprises the following steps: acquiring real-time electricity price fluctuation data, load demand prediction data and dynamic topology connection data between power grid nodes; generating a simplified network structure adaptive to a transaction scene based on the data, and adjusting an equivalent impedance parameter of a key transmission path in real time; a node voltage amplitude and a phase angle are cooperatively regulated and controlled through a multi-stage regulation mechanism of the solid-state transformer, and a power distribution scheme is formed; monitoring operation data of the solid-state transformer in real time by utilizing an edge computing node, and generating a local control instruction by combining the current equivalent impedance parameter; and carrying out space-time mapping matching on the local instruction and the electricity price fluctuation data, generating a combined control strategy through collaborative priority ranking, and dynamically adjusting a power distribution scheme. According to the technical scheme provided by the invention, collaborative optimization of economical operation and physical transmission performance of the power system is realized.

The invention relates to the field of energy management, and discloses an industrial park load prediction method based on artificial intelligence, and the method comprises the steps: collecting historical load, production plan, weather and holiday and festival information through multi-source data; through data preprocessing, a time sequence feature extraction module fusing an attention mechanism and LSTM and a deep learning model integrating a sudden change adaptation module are constructed, and high-precision load prediction is realized. Wherein the abrupt change adaptation module dynamically adjusts model parameters to cope with load abrupt change through sliding window statistical feature monitoring, incremental learning and GAN simulation abrupt change scenes; and a real-time feedback mechanism further optimizes the prediction result, and generates a power dispatching suggestion in combination with a dynamic electricity price strategy. According to the method, the problems of large prediction deviation and poor adaptability of a traditional model in a load sudden change scene are solved, and the efficiency and stability of industrial park energy management are remarkably improved.

The embodiment of the invention relates to the field of data processing, in particular to a multi-market transaction prediction method and system for a virtual power plant. The method comprises the steps of obtaining first predicted output data based on historical electricity prices, user load data and time-of-use electricity prices before a transaction, obtaining a first preset total energy demand through correction of a load fuzzy response model, and generating a day-ahead market bidding strategy and a first bidding electricity price; and on the same day of the transaction, acquiring second predicted output data based on the real-time data, receiving the first bidding result, correcting the first bidding result to obtain a second preset total energy demand, and generating a real-time market bidding strategy and a second bidding electricity price. And after the second bidding result is received, dynamically adjusting the real-time preset total energy demand, optimizing the power scheduling and charging and discharging strategy of the energy storage system, and balancing the energy reserve after the transaction. According to the method, the bounded rationality problem of the controllable load in the demand response process is solved, the user demand response result and the DER are effectively combined, and a solution is provided for the VPP to participate in the bidding plan of multiple types of power markets.

The invention relates to the technical field of power system operation and control, in particular to a medium-and-long-term marketization operation bidding strategy for a wind-light-water-storage combination. Aiming at the problems of volatility, large prediction error, limited profit space and the like of wind and light power generation in the medium-and-long-term power market, the invention proposes that wind and light power generation and cascade hydropower are combined to participate in medium-and-long-term market bidding transaction together; wind and light power generation is encouraged to fully participate in the market by introducing a carbon transaction and green certificate transaction mechanism, and a double-layer optimization model is constructed by considering the influence of current market electricity price fluctuation; the upper layer is a combination quantity report and quotation decision-making model, and the lower layer is a market clearing model so as to realize combination market expected total revenue maximization and social welfare maximization; the strategy significantly improves the medium and long term contract competitiveness and profit space of the wind and light new energy, promotes the consumption of the new energy, and has good environmental benefits and economic benefits.

The invention discloses a source-load interaction method based on an electricity utilization information acquisition system, and relates to the field of power distribution network operation and optimization. The method comprises the following four steps: S1, power consumption data acquisition: dynamically acquiring user power consumption, distributed energy output and power grid state data through intelligent equipment, and ensuring real-time accuracy; s2, load characteristic analysis: constructing a quantitative model to analyze characteristics such as a user load elastic coefficient and adjustable potential, establishing a supply and demand balance evaluation system in combination with distributed energy fluctuation, and determining peak and valley time periods and gaps; s3, generating an interactive strategy, and generating and issuing dynamic strategies such as electricity price excitation, load regulation and energy scheduling based on an analysis result; and S4, executing and feeding back the strategy, monitoring the execution effect in real time, and optimizing the strategy through a closed loop mechanism. According to the method, the problems of staticization, single mode and low efficiency of a traditional method are solved, the power grid supply and demand balance capability, the renewable energy consumption rate and the strategy accuracy are improved, and low-carbon and intelligent operation of the power distribution network is supported.

The invention relates to the technical field of charging pile management and power dispatching, and particularly discloses a charging pile operation management method and system. The method comprises the following steps: acquiring charging request data and power grid load data, and generating and cleaning a task vector data set; predicting a charging demand based on the data set and generating electricity price gradient information; a multi-objective optimization scheduling function is constructed and solved, and a task allocation scheduling scheme is generated; executing power control according to the scheduling scheme and collecting charging state data; executing dynamic contract charging and incentive generation according to the state data; and finally, performing Hash packaging and privacy processing on an execution result, and performing uplink to generate a credible record. According to the method, the resource allocation efficiency and the charging incentive intelligence level in a multi-user multi-period concurrent charging scene can be improved, and efficient cooperation and credible execution of the whole charging scheduling and settlement process are realized.

The invention provides a virtual power plant power transaction method and device, and the method is applied to a server, and the method comprises the steps: executing a resource aggregation operation for an aggregable resource in a target region; executing a resource evaluation operation on the aggregable resources of each resource type to determine a corresponding evaluation result; constructing an optimal scheduling model corresponding to the virtual power plant based on the evaluation result; obtaining current electricity price curve data and power grid load prediction data of the electricity market in real time; substituting the current electricity price curve data into the first sub-objective function, and substituting the power grid load prediction data into the second sub-objective function so as to update the model parameters of the optimization scheduling model; and solving the updated optimal scheduling model by adopting an optimization algorithm to determine a power transaction strategy. Thus, the optimization scheduling model is established through resource aggregation and evaluation to determine the power transaction strategy, the efficiency and the income of the power transaction of the virtual power plant are improved, and the accuracy and the timeliness of strategy formulation are ensured by a real-time data acquisition and model parameter updating mechanism.

The embodiment of the invention relates to the field of data processing, in particular to an adaptive robust bidding method and system for a virtual power plant. The method comprises the steps that a load fuzzy response mechanism model is adopted, a load transfer parameter is generated according to time-of-use electricity price obtained in real time, and a bidding basis is obtained based on the load transfer parameter; obtaining historical transaction data of the first transaction day, and predicting first prediction bidding strategy data in the second transaction day based on the historical transaction data through the day-ahead market bidding strategy model; performing real-time bidding strategy calculation through a real-time market bidding strategy model based on the real-time transaction data of the day of the second transaction day to obtain second prediction bidding strategy data; and dynamically adjusting the real-time electricity price transaction information in each time period based on the second prediction bidding strategy data so as to realize a self-adaptive robust electric power bidding strategy. And through a load fuzzy response mechanism and an adaptive robust optimization model, bidding risks are reduced, and the economic benefits of the virtual power plant and the stability of the power market are improved.

The invention discloses a distributed energy scheduling management method and system for a virtual power plant, and relates to the technical field of distributed energy management, and the method comprises the steps: collecting electricity price information and operation information, carrying out the cleaning and preliminary processing of data, guaranteeing the data quality, and carrying out the data management; a machine learning algorithm is utilized to predict load information, electricity price and related energy price in a virtual power plant range, a hybrid optimization model is constructed and solved, optimal power generation, power utilization and energy storage operation plans for different power transaction markets are generated, efficient configuration of resources is realized, and the optimal power generation, power utilization and energy storage operation plans are obtained based on operation information data and prediction result data. According to the method, the operation information data and the prediction result data are acquired, the distributed energy units are recombined, an optimal resource combination for different market transactions is formed, a scheduling strategy for optimally configuring the resource combination is executed, and a real-time scheduling optimization strategy is generated according to the latest operation state information. And dynamic optimal configuration of the distributed energy unit is realized.

The invention relates to a virtual power plant adjustable capacity boundary construction method considering conditional value-at-risk, and the method comprises the steps: firstly constructing a virtual power plant electric energy dispatching model based on time-of-use electricity price guidance by taking the minimum electricity purchasing cost of a virtual power plant spot market as an objective function; constructing a virtual power plant regulation capacity boundary depicting model by taking maximum output and minimum output of the virtual power plant in the regulation time period as objective functions; the maximum output and the minimum output of the virtual power plant in the adjustment period respectively correspond to the upper limit and the lower limit of the adjustment capacity boundary of the virtual power plant; solving the virtual power plant electric energy scheduling model to obtain a scheduling strategy of the virtual power plant in the electric energy market; and finally, according to a scheduling strategy of the virtual power plant in the electric energy market, performing global optimization on the virtual power plant adjustment space capacity description model to obtain an adjustable capacity boundary of the virtual power plant in an adjustment time period. The invention is beneficial to improving the flexibility of the power system, promoting the green and low-carbon development of the power industry and optimizing the energy structure.

The invention relates to the technical field of electric power spot transaction prediction, and discloses a multi-dimensional load electricity price prediction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining multi-scale historical data, the multi-scale historical data comprises multi-scale time sequence data, multi-dimensional meteorological data, historical load data and historical clearing price data; performing correlation analysis on the multi-scale historical data, and extracting strong correlation coupling data from the multi-scale historical data to obtain multi-dimensional feature data; sequentially carrying out weighted fusion according to the data features of the multi-dimensional feature data to obtain fused data; and taking the fusion data as input data of an Informer network, and predicting the load electricity price to obtain a load value and an electricity value. The problem that the spot transaction price is difficult to accurately predict is solved.

The embodiment of the invention discloses a multi-virtual power plant energy optimization method and device based on energy-carbon sharing. The method comprises the steps of establishing a master-slave game architecture of a power distribution system operator and each virtual power plant based on a transaction electricity price and a transaction carbon price of the power distribution system operator, and a transaction electricity quantity, a transaction carbon quota and equipment output of each virtual power plant; a dynamic pricing model for maximizing the income of a power distribution system operator is constructed based on a master-slave game architecture, an energy management model for minimizing the operation cost of the virtual power plant is constructed based on the master-slave game architecture, and a master-slave game double-layer optimization model for energy-carbon sharing of the multiple virtual power plants is determined based on the dynamic pricing model and the energy management model; and equivalently converting the master-slave game double-layer optimization model into a single-layer optimization model based on a Carlo-demander-Kuhn-Tuck condition, and solving the single-layer optimization model to obtain a target energy management strategy of each virtual power plant, thereby giving consideration to economic benefits and carbon emission reduction.

The invention relates to a load adjustable capability evaluation method and device based on sequence decomposition and a storage medium, which are applied to the technical field of power grid load adjustment. Decomposing the load sequence into a temperature sensitive load and an electricity price sensitive load; secondly, on the basis of component load sequence shape features, considering temperature sensitive load feature factors such as meteorological factors such as environment temperature and the like and electricity price sensitive load feature factors such as time-of-use electricity price and the like, adopting an XGBoost time sequence algorithm prediction model, respectively constructing component load short-term prediction models, and performing component load short-term prediction; and finally, based on the short-term prediction value of the component load, obtaining the adjustable capability range of the temperature-sensitive load at different environment temperatures, and obtaining the adjustable capability range of the electricity price-sensitive load at different moments. Therefore, the accuracy of the evaluation result is improved.

The invention discloses a day-ahead optimal scheduling method for a virtual power plant containing multiple types of loads. The day-ahead optimal scheduling method comprises the following steps: step 1, investigating and analyzing the power plant; step 2, collecting load data; 3, constructing a model; step 4, optimizing a scheduling strategy; step 5, plan transmission response; according to the method, the time-of-use electricity price of the user side is associated with different types of users, so that the electricity utilization cost of the users is reduced while the benefits of the virtual power plant are improved; establishing a virtual power plant day-ahead optimization scheduling model containing multiple types of loads and designing time-of-use electricity price pricing strategies of three different types of users to obtain an optimal scheduling strategy based on the time-of-use electricity price; and transmitting the peak-valley flat price data to different types of users for load response, distributing a next-day output plan to each power generation resource for actual power generation, calling an internal power generation side and a user side of the virtual power plant while considering carbon emission permit transaction, realizing optimal configuration of internal resources of the virtual power plant, and promoting low-carbon economic operation of the virtual power plant.

The invention relates to a large-scale new energy base scheduling method based on uncertainty of an electric power spot market, and the method comprises the steps: collecting historical data of spot market node marginal electricity price and wind-solar power generation power, and generating a day-ahead market decision scene set comprising the spot market electricity price and new energy unit output through a Monte Carlo sampling method; establishing a two-stage stochastic optimization model for the large new energy base to participate in the day-ahead market by considering the uncertainty of the spot market, and establishing a model for participating in the real-time market; calculating a day-ahead market bidding strategy, a thermal power generating unit start-stop plan and a wind turbine generator maintenance plan in combination with the uncertain day-ahead market decision scene set; and according to high-precision short-period price prediction and renewable energy power prediction values, a real-time market bidding strategy and a charging and discharging strategy of energy storage equipment are calculated, and profit maximization in a large-scale new energy base market environment is realized. The problem of low economic benefit during energy storage scheduling of a large-scale new energy base in an electric power spot market is solved.

The invention provides a regional intelligent power utilization method and system based on source-grid-load-storage cooperation and dynamic electricity price. The method comprises the following steps: collecting photovoltaic output, energy storage SOC, load demand and time-of-use electricity price data in real time; determining a corresponding dynamic control mode in combination with the economic target model, the environmental protection target model and the constraint conditions; when the running dynamic control mode is a light storage and charging cooperative arbitrage mode, dynamically determining an energy storage charging and discharging strategy; when the running dynamic control mode is a virtual power plant aggregation mode, forming scheduling capacity by aggregating distributed resources, and responding to a power grid peak regulation instruction; when the running dynamic control mode is a carbon-electricity joint optimization mode, a carbon trading platform is connected, green certificate binding sales is carried out, scheduling capacity is formed through dynamic integration of energy storage charging and discharging strategies and aggregation of distributed resources, the carbon trading platform is connected, green certificate binding sales is carried out, economic benefits are effectively improved, carbon emission is reduced, and the service life of the system is prolonged. And the double-target optimization of the electric cost and the carbon emission is realized.

The invention provides an electrical load data privacy protection method and system, a storage medium and electronic equipment, and is applied to the technical field of electrical data processing. Actual electrical load data of a user and noise data of an energy storage device are collected in real time, and charging and discharging behaviors of the energy storage device are controlled through electricity price linkage; the optimization noise is superposed to the actual power utilization load data to generate target power utilization load data meeting the power grid transmission limitation, the energy storage charging and discharging noise is utilized to cover the real power utilization mode of the user, the energy storage charging and discharging behavior is controlled through electricity price linkage to guarantee the economic benefit, and the dynamic balance of the privacy intensity and the energy storage economical efficiency is achieved.

The invention relates to the technical field of intelligent power grids, in particular to a collaborative optimization system and method for distributed power resources, and the system comprises a data collection module, a time-space correlation analysis module, a user response module, a data privacy protection module, a time-of-use electricity price optimization module and a scheduling control module. The space-time correlation analysis module performs interval prediction of distributed photovoltaic power by using a Bayesian-long short-term memory network model, the user response module designs a hierarchical response strategy according to user categories, and the data privacy protection module protects power utilization data through a differential privacy mechanism. The time-of-use electricity price optimization module dynamically adjusts the electricity price according to the peak-valley load difference, and the scheduling control module achieves the cooperative regulation and control of distributed resource integration, local-level power grid coordination and provincial-level power grid optimization. The technical problems of an existing power system in the aspects of efficiently integrating distributed resources, improving scheduling flexibility and optimizing an electricity price strategy are solved.

The invention provides a power marketing archive intelligent management method, which comprises the steps of receiving archive data through a power data acquisition interface, matching an electricity price feature code in an archive based on a real-time electricity price policy identifier, and injecting a version calibration mark into unmatched data; constructing an electricity consumption abnormity prediction model based on the user historical load mode and the environmental factors; when a query request is responded, dynamic penalty parameters are generated based on the user behavior portrait and the rule base; a quantum key distribution path is generated based on a power grid topological structure, and cascade encryption is implemented along physical connection nodes; dynamically associating user operation authority with a power grid operation risk signal, and automatically disabling core operation in a high-risk state; and analyzing archive data in a security isolation environment to drive line loss optimization, electric charge tracing and fault pre-judgment service flow in real time, and outputting feedback archive update based on the service. According to the invention, the safety, reliability and operation efficiency of power marketing archive management are improved.

The invention relates to the technical field of power systems, provides an electricity price and energy storage joint optimization method and system in an electricity market environment, and aims to solve the problem that in the prior art, deep cooperation between electricity price and energy storage and a market mechanism is difficult to achieve. The method comprises the steps of obtaining actual and predicted load data of a power grid, and generating load prediction error distribution of a power market; according to load prediction error distribution, actual and predicted load data of the power grid and available rotation kinetic energy of the composite flywheel array, active power compensation is carried out on power shortage of the power grid; acquiring a real-time electricity price signal of each transaction period in the power grid, and generating a dynamic charging and discharging threshold value; generating a dynamic charging and discharging strategy of the energy storage equipment based on the dynamic charging and discharging threshold value; and based on the compensated power and a dynamic charging and discharging strategy, suppressing the electricity price fluctuation of the power vacancy generation node. According to the invention, suppression of instantaneous electricity price suddenly rising and market fluctuation closed-loop control are realized, and the problems of physical response delay and market strategy separation are synchronously solved.

The invention relates to the technical field of smart power grids, and discloses a smart power grid real-time electricity price adjustment method and system, and the method comprises the steps: collecting user electricity consumption data in real time through a sensor, and carrying out the preprocessing; clustering the power utilization data of the users, dividing the users into different groups, and designing a personalized electricity price strategy; and dynamically adjusting the electricity price by adopting a preset rule according to the real-time load state of the power grid. The electricity utilization data of the users are collected in real time, personalized clustering is carried out, the electricity price is dynamically adjusted in combination with the load state of the power grid, and a more flexible and accurate electricity price strategy is achieved. The method not only can improve the supply and demand balance of the power grid, but also encourages the user to adjust the power consumption behavior in the peak period through a differential pricing strategy, supports the demands of renewable energy sources and electric vehicle users, facilitates the optimization of the use of power grid resources, and improves the overall efficiency and sustainability of a power system.

The invention relates to the technical field of power system optimization scheduling, and provides a multi-time-scale new energy-stored energy joint output optimization method and system based on spot price, so as to solve the problems of poor system operation stability and low new energy regulation and control precision in the prior art. The method comprises the following steps: detecting a temperature change rate of a composite phase change material in an energy charging and discharging process to determine a heat storage capability state of an energy storage unit, and generating a day-ahead joint scheduling plan considering operation cost and a new energy consumption rate in a coordinated optimization framework by combining day-ahead electricity price data; constructing an elastic matrix based on historical electricity consumption data, correcting the elastic matrix by using latest market information and a system state, and determining a load adjustment amount in combination with an intraday electricity price; and based on the real-time electricity price and the load adjustment amount, a model prediction control algorithm is adopted to calculate real-time output adjustment instructions of the new energy unit and the energy storage unit in a rolling manner, and the operation states of the two units are synchronously adjusted to realize collaborative optimization. The system operation stability and the new energy regulation and control precision are improved.

The invention discloses an energy storage system multi-scene optimization scheduling method and device, equipment, a medium and a product, and relates to the field of model optimization, and the method comprises the steps: obtaining an initial decision variable and a constraint term which are used for training a scheduling model; obtaining a prediction data set; based on the predicted photovoltaic output in the prediction data set, the first predicted electricity price set, the second predicted electricity price set, the initial decision variable and the constraint term, generating the predicted total income of the electric energy market and the auxiliary service market; on the basis of the predicted total income, updating the initial decision variable so as to optimize the scheduling model, and obtaining an optimized target scheduling model; based on the optimal decision variable of the target scheduling model, adjusting a charging and discharging control strategy of the energy storage system; according to the predicted electricity price and total income, the charging and discharging strategy of the energy storage system is dynamically adjusted, so that the energy storage system can participate in an electric energy market and an auxiliary service market at the same time, and the economic benefit of the whole electric power market is maximized.

The invention provides a control method and equipment of a cold storage system and the cold storage system. The control method comprises the steps that the accumulated cold storage capacity of the cold storage tank in the previous day is obtained; acquiring 24-hour hourly electricity price per day; obtaining a cooling load demand hour-by-hour value in the next 24 hours in the previous day through a prediction technology; and according to the accumulated cold storage capacity of the previous day, the 24-hour hourly electricity price and the cooling load demand hourly value of the next 24 hours, cold release control is carried out on the cold storage tank with the goal of the lowest all-day operation electricity price. The dynamic cold release potential based on different electricity prices can be brought into full play, and the operation cost is saved to the maximum extent.

The invention relates to a day-ahead electric power spot market clearing method based on quantum approximate optimization, and the method comprises the steps: building a day-ahead electric power spot market clearing model, and abbreviating the day-ahead electric power spot market clearing model; the abbreviated day-ahead electric power spot market clearing model is decoupled into a discrete variable model and a continuous variable model; the discrete variable model is quantized through a quantum approximation optimization algorithm, the unit start-stop state and the bid winning electric quantity of the day-ahead electric power spot market are obtained through continuous iterative optimization, and the node electricity price is obtained through solving partial derivative of a Lagrange multiplier objective function constructed in the solving process; and carrying out security check on the current clearing result of the electric power spot market, and carrying out market settlement after the requirement is met. The real-time performance and convergence of electric power spot market transaction are improved, the workload of workers in the electric power market is reduced, and a supporting effect is achieved for guaranteeing safe, stable, economical and efficient operation of the electric power spot market.

The invention discloses an electricity-carbon collaborative industrial park energy optimization scheduling method and system. The method comprises the following steps: establishing an industrial park production full-link electricity consumption model and a transferable load demand response model; establishing an electricity-carbon coupling price model based on the time-of-use electricity price and the floating carbon price; an industrial park comprehensive energy double-layer optimization model with electricity-carbon coupling price transmission is constructed, an upper-layer model is an energy consumption price optimization decision model with the minimum operation cost of an industrial park comprehensive energy system as the target, and a lower-layer model is a user energy consumption optimization model with the optimal energy consumption utility as the target; and solving the industrial park comprehensive energy double-layer optimization model by adopting an improved particle swarm to obtain an optimal electricity-carbon coupling price and optimal power of each link of industrial production, and carrying out electricity-carbon collaborative industrial park energy optimization scheduling. The low-carbon target is achieved, meanwhile, the energy utilization efficiency is improved, and effective conduction of the carbon value is promoted.

A power distribution network-multi-integrated energy system distributed optimization scheduling method considering interval uncertainty and multi-agent game comprises the following steps: representing fluctuation characteristics of a distributed power supply and a load by adopting an interval number, and establishing a multi-agent game model with robustness; a dynamic electricity price transaction mechanism based on Nash bargaining is constructed, the income requirements of all subjects in the DN-MIES system are met, and system benefit balance is achieved; the MVA-ATC algorithm is proposed based on the interval information of the source load uncertainty to carry out distributed iterative solution, and the main body privacy is guaranteed while the solution efficiency is improved. For the problem of benefit distribution after the MIES alliance is accessed to the power distribution network, the invention provides a DN-MIES mixed game distributed optimization method considering multi-subject benefits, so that benefit distribution of the power distribution network accessed to multiple IESs is realized, and benefit balance during cooperative operation of the power distribution network and the multiple IESs is effectively guaranteed; meanwhile, an MVA-ATC distributed solving algorithm is provided by utilizing interval uncertainty and an ATC algorithm, so that the solving efficiency of the model is improved, and meanwhile, the fluctuation of the source load is stabilized.

An optimized scheduling method for guiding the orderly charging of electric vehicles includes the following steps: S1. Establish an unordered charging load prediction model for electric vehicles based on the Monte Carlo method; S2. Introduce a carbon trading mechanism into the unordered charging load prediction model of electric vehicles in step S1 to calculate the carbon emissions and carbon quotas of power generation enterprises; S3. Introduce a reward coefficient ξ1 and a carbon trading cost growth rate τ1 into the conventional stepped carbon price to establish a reward and punishment stepped carbon price model; S4. Establish a dynamic time-of-use electricity price model based on the reward and punishment stepped carbon price; S5. Establish an electricity price regulation model for electric vehicles; S6. Establish a multi-objective optimization function for the orderly charging of electric vehicles; S7. Adopt a multi-objective non-dominated genetic NSGA-II solution algorithm; The present invention guides electric vehicle users to perform orderly charging behaviors by using a regulation method based on the reward and punishment stepped carbon price and the dynamic time-of-use electricity price, thereby effectively achieving the effect of peak shaving and valley filling of the power grid and reducing the fluctuation of the power grid.