737 results about "Electricity price" patented technology

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 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 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 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.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

The invention provides a self-adaptive electricity selling side spot auxiliary decision making method based on multi-model collaboration and risk dynamic grading, and is suitable for the technical field of electricity market transaction decision making. According to the method, historical and real-time market data are collected in real time, and the electricity price difference is predicted by using multiple prediction models. The mean square error of each model is calculated, the weight of the model is dynamically adjusted, and the prediction results of each model are integrated through a Stacking method, so that the prediction precision and robustness are remarkably improved. Based on the comprehensive prediction result and the real-time market index, the market risk is dynamically evaluated, and the arbitrage strategies are respectively formulated according to different risk levels: the aggressive arbitrage strategy is adopted under the low-risk condition, and the conservative strategy is adopted under the high-risk condition. According to the invention, through real-time transaction execution and closed-loop feedback, model parameters and strategy thresholds are continuously optimized, adaptive adjustment of spot transaction decisions is realized, and the profitability and risk control level of an electricity selling side are effectively improved.

The invention relates to the technical field of energy storage control, and discloses a control method and device of an energy storage system, equipment and a storage medium. The method comprises the steps of determining an electricity price peak-valley period in a preset time period according to electricity price data in the preset time period; acquiring an initial charging and discharging strategy of the energy storage system according to pre-acquired historical original load data and electricity price peak and valley periods; and controlling the energy storage system according to a preset power constraint model and the initial charging and discharging strategy. According to the method, the peak and valley periods of the electricity price are considered, and the change of the original load and the limitation of the power constraint are also fully considered, so that the charging and discharging cost of the energy storage system can be reduced, the condition that the power supply can meet the requirements of the original load and does not exceed the preset power constraint condition is ensured, and the electricity utilization safety is ensured.

The invention belongs to the technical field of transformer control, and particularly relates to a new energy transformer control method considering hybrid energy storage access, and the method comprises the steps: building a new energy transformer grid-connected architecture which comprises a distributed power supply, an electrochemical energy storage and super-capacitor hybrid energy storage system and a load; establishing a new energy transformer node power matching model according to source network load storage input and output characteristics; a hybrid energy storage control model is established based on electrochemical energy storage and super capacitor charging and discharging characteristics; optimizing an input and output model of the hybrid energy storage system according to a time-of-use electricity price policy; constructing a multi-objective optimization function with the minimum fluctuation, the lowest loss and the maximum operation benefit; and carrying out normalization weighting on the minimum fluctuation, the lowest loss and the maximum operation income to solve a comprehensive optimization target, and dynamically adjusting the input power of the transformer. The method aims at optimizing cooperative work of an electrochemical energy storage system and a super-capacitor energy storage system, optimizing a new energy transformer adjusting strategy and achieving reliability and economics of system operation.

The invention relates to an optimal control method and system for an energy storage system to participate in a medium-and-long-term and day-ahead electricity market, and a medium. The method comprises the steps of collecting historical load and historical spot transaction price of a user and local medium-and-long-term transaction time-of-use electricity price data; predicting the hourly load of the user in the future day and the local day-ahead power spot transaction price; constructing an energy storage model based on the energy storage physical property parameters; maximized energy storage system income is set as an optimization target, and constraint conditions are set according to battery capacity, efficiency, transformer capacity and the like; a genetic algorithm is adopted to obtain an energy storage system charging and discharging strategy, including energy storage charging and discharging power at each moment and a market participation mode, for 24 hours in the next day. The method can comprehensively consider the medium and long term transaction time-of-use electricity price, the day-ahead market price and the user load characteristics, and maximizes the economic benefit while meeting the electricity demand of the user.

The invention relates to a power plant unit maintenance optimization method considering multiple uncertain factors of a power market, and belongs to the technical field of power system operation management. The method comprises the following steps: firstly, obtaining spot market electricity price and new energy output data in each time period of a historical year in a power plant; a random scene set considering spot market electricity price and new energy output is established based on historical data, time-varying features are described through data segmentation, state space division and a non-uniform Markov model, and the random scene set is generated by using a Monte Carlo method; and finally, taking maximization of the total expected income of the power plant as an objective function, constructing an optimization model in combination with preset constraint conditions of the unit, inputting the random scene set into the optimization model for solving, and outputting an optimized unit operation and maintenance strategy which comprises maintenance plans of wind power, photovoltaic and thermal power units and corresponding output plans. Multiple uncertain factors in the power market can be effectively handled, and the operation and maintenance benefits of a power plant are improved.

The invention relates to a fused salt heat storage technology in a spinning process, in particular to a fused salt heat storage system based on electricity price adjustment and a control method.The fused salt heat storage system comprises a fused salt pipeline, a steam pipeline and a nylon pipeline, and the fused salt pipeline is provided with a fused salt storage tank, an electric heater, a first circulating pump, a heat exchanger and a second circulating pump; a third circulating pump is arranged on the steam pipeline; a screw extruder, a spinning machine and a steam sleeve are arranged on the nylon pipeline; the electric power demand in the peak period is remarkably reduced, the pressure of an electric power system in the peak period is relieved, the social benefit is remarkable, fused salt heat storage and heating are combined together, and the working efficiency is improved.

The invention discloses a transaction decision-making method and system in a market joint clearing environment, and relates to the technical field of electric power spot market transaction, and the method comprises the steps: S1, obtaining a predicted value of a node marginal electricity price, and constructing a quotation optimization model of each power generation main body; s2, designing an agent, and training the agent by taking the quotation optimization model as a reward function; s3, generating quotation data based on the trained agents, and constructing a joint clearing model of all power generation subjects; s4, calculating the actual value of the node marginal electricity price based on the joint clearing model, and calculating the deviation between the actual value and the predicted value; s5, judging whether the deviation exceeds a preset threshold value or not, if so, taking the actual value as a predicted value, and returning to the step S1 for closed-loop learning; and if not, taking the quotation data generated by each agent as a final quotation strategy. According to the invention, global optimization and intelligent collaboration of power generation subject transaction decisions can be realized in a complex market environment.

The invention provides a cold-hot electro-optical storage, charging and discharging combined intelligent scheduling method, which comprises a data acquisition module, a load prediction module, an optimization scheduling module, an equipment control module, a price regulation and control module and a monitoring feedback module. The data acquisition module acquires cold, heat and electric load data, photovoltaic power generation prediction data, meteorological data, electricity price data and user behavior data, preprocesses the data and constructs a training data set. The load prediction module predicts a load demand by using a hybrid model, and updates model parameters through online learning. And the optimization scheduling module is used for constructing a multi-target optimization model by taking the benefit maximization as a target, and solving an optimal scheduling strategy by utilizing a hybrid algorithm. And the equipment control module formulates an equipment start-stop plan and introduces virtual power plant aggregation resources. The price regulation and control module dynamically adjusts the charging and discharging price. And the monitoring feedback module monitors the system state in real time and dynamically adjusts the model. According to the invention, the energy efficiency and the system income are improved, the carbon emission is reduced, and the user comfort is improved.

The invention discloses an electric vehicle travel scene ordered charging method under the guidance of dynamic electricity price, and the method comprises the steps: carrying out the charging load space-time distribution prediction through an EV charging load space-time distribution prediction model, and obtaining the load space-time distribution characteristics of each charging station; generating an optimized electricity price strategy through a master-slave game model, and making a dynamic electricity price scheme by an electric vehicle aggregator in combination with a station charging load condition and a charging responsivity function; and simulating user travel behaviors according to the road network traffic flow data, and making a user charging plan based on the corrected electricity price information. According to the method, an ordered charging regulation and control strategy based on dynamic electricity price response is provided, a dynamic electricity price model based on charging load real-time change is constructed, meanwhile, a double-layer game model of a load aggregator and a user is established in combination with user travel space-time distribution characteristics, and an ordered charging optimization strategy giving consideration to multi-party benefits is formed; and finally, dual goals of power grid operation optimization and user economic benefits are realized.

The invention discloses a demand side aggregation load cooperative control method and system, and the method comprises the steps: obtaining user side electricity price, market electricity price fluctuation and user baseline load prediction data, and carrying out the analysis to obtain a demand response target; selecting corresponding price type and excitation type demand response modes based on the demand response target in combination with user characteristics, and constructing a corresponding demand side aggregation load model; wherein a price-type demand side resource aggregation load model is constructed based on a price-type demand response mode; based on the excitation type demand response mode, constructing an excitation type demand side aggregation load model; according to the demand response mode and the demand side aggregation load model, a regulation and control strategy of the aggregation load is determined, and the regulation and control strategy comprises a regulation type strategy, a control type strategy and a mixed type strategy; and classifying the regulation and control strategies according to an applicable scene to obtain a regulation and control strategy classification result. According to the method, the new energy consumption capability can be improved, the system operation cost is reduced, and the system operation economy is improved.

The invention discloses an optimal scheduling method considering participation of a multi-element load polymer in demand response, and aims to improve the safety and economy of a power system. The method comprises the following steps: firstly, constructing a mathematical model of a load, representing an adjustable domain of the load, and clustering the load by using a K-means algorithm; and then, carrying out internal approximation on adjustable domains of various loads through scaling and translation factors, optimizing parameters by adopting a linear programming method, and finishing aggregation processing of the loads. And finally, in combination with power supply parameters, a load aggregation domain, market demand prediction and the like, an MILP optimization model with the minimum operation cost is established, power dispatching is performed, a dynamic excitation electricity price mechanism is introduced, the price is adjusted according to the adjustment capability of the load, and the load is excited to actively participate in demand response. According to the scheme, by accurately describing the load adjustable domain and flexibly guiding the load to participate in the demand response, the scheduling of the power system is effectively optimized, the consumption level of renewable energy sources is improved, the operation cost of the system is reduced, and the method has a relatively strong engineering application prospect.

The invention provides an intelligent control method for a pump set, which belongs to the technical field of pump set control and comprises the following steps of: acquiring operation data and electricity price time period data of the pump set in real time; inputting the preprocessed data into a pre-trained liquid level prediction model to obtain liquid level prediction data; inputting the preprocessed data, the liquid level prediction data and the electricity price time period data into a pre-trained strategy optimization model to obtain a control instruction; the operation state of the pump set is controlled through the control instruction. Compared with the prior art, the control method has the advantages that the electricity price time period data is combined, the operation strategy of the pump set is dynamically adjusted by responding to the time-phased electricity price data, the electricity charge expenditure is reduced, and the problem of poor economical efficiency in the prior art is solved; and meanwhile, the control method is combined with liquid level prediction data to predict future working conditions, so that control parameters are adjusted, and the effects of avoiding liquid level abnormity and prolonging the service life of equipment are achieved. The system has the same beneficial effects.