538 results about "Electricity price" patented technology

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 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 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.

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.

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 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 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 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.

The invention discloses an energy storage system control method and system based on time-of-use electricity price and multi-modal optimization, and the method comprises the steps: simulating supply and demand fluctuation in a complex scene through a pre-constructed energy storage system control strategy model based on deep reinforcement learning, dynamically optimizing a charging and discharging plan of energy storage equipment, and generating an energy storage resource distribution efficiency optimization scheme; generating a real-time electricity price adjustment scheme by adopting a game theory price adjustment algorithm based on the energy storage resource distribution efficiency optimization scheme, the real-time demand of power grid supply and demand balance, the user electricity consumption behavior change and the power grid operation state dynamic state; generating a user behavior response data set through a pre-constructed user behavior response model based on the real-time electricity price adjustment scheme in combination with user economic benefit protection requirements; and calculating a fluctuation ratio of a power grid load change trend by combining the user behavior response data set with real-time monitoring data through a power grid supply and demand balance dynamic evaluation algorithm, and determining a final energy storage scheduling scheme. The purposes of real-time monitoring and collaborative optimization of energy storage system control are achieved.

The invention provides a carbon powder production line energy consumption optimization method and system, and is applied to the technical field of energy consumption optimization in the production and manufacturing field, and the method comprises the steps: obtaining a production order, decomposing the production order into subtasks and time-of-use electricity price information, dividing a time slot, calculating the energy cost, constructing a mathematical optimization model according to the energy cost, and solving the mathematical optimization model. And a group of optimal decision variable values are found, so that the total energy cost is minimum, and all production constraints are met. Through the steps, the method integrates the production task, the equipment capability, the dynamic energy price and the production constraint into an optimization framework, and finds the production scheduling scheme with the lowest energy cost through the solving model, so that the technical problem of reducing the energy consumption cost while meeting the production requirement is solved, and the method has the advantages of reducing the energy consumption and improving the production efficiency. And the production plan is optimized.

The invention discloses an electricity price execution and bill risk combined preposed intelligent early warning method, which relates to the technical field of electric power charging risk control, and comprises the following steps of: constructing rate digital twinning, managing atomic change according to a version directed acyclic graph, generating an explanation path of a trigger condition-calculation expression-bill line item, and mapping with a metering object, a customer object and a bill object; executability and traceability are realized. Based on layered samples and boundary samples, new and old rules are played back on double tracks of a sandbox, the bill difference, the uncertainty and the exposure degree are obtained, and differential hotspots are identified; generating a version binding scene pre-bill after a semantic structure, a coverage boundary, a performance budget and compliance consistent access control are passed, providing an alternative scene and collecting a receipt; gray level monitoring is carried out, rollback and secondary account checking are carried out according to the TariffID subgraph when triggering is carried out, and evidence fingerprints are formed; according to the scheme, aperture consistency and boundary coverage are improved, and accurate rollback and auditing verification are supported.

The invention is suitable for the field of electric power system operation and market clearing, and provides an electric power spot market annual security constraint unit combination rapid clearing method and system, and the method comprises the following steps: receiving input data; constructing an annual SCUC optimization problem based on the input data; the annual SCUC optimization problem is solved by adopting a two-stage calculation process, in the first stage, an initial solution is calculated by simplifying network topology and reducing time resolution, and in the second stage, the annual optimization problem is decomposed into a plurality of subtasks for parallel rolling calculation; according to the method, annual market and system data are received, an annual SCUC problem is constructed, a special solving method is applied, the optimal or near-optimal power generation plan and market clearing price in the annual period are determined in an actual time frame, more effective long-term planning is facilitated, and the method is suitable for popularization and application. The market efficiency is improved, and reliable operation of a power system is supported.

The invention relates to a charging and discharging management method and device, computer equipment and a readable storage medium. The method comprises the steps of obtaining multi-source data of a target regional power grid in a preset historical time period, preprocessing the multi-source data to obtain a feature vector, and performing feature extraction processing on the feature vector through a pre-trained deep learning model to obtain an electricity price fluctuation prediction result of the power grid in a specified time period, based on the electricity price fluctuation prediction result and the energy storage system parameters of the power grid, an optimization algorithm is adopted to generate a charging and discharging strategy of the energy storage system, and charging and discharging management is carried out on the energy storage system according to the charging and discharging strategy; the method can systematically analyze the long-term trend and periodic change rule of the electricity price fluctuation, makes up for the defect that the traditional method cannot fully reflect the electricity price fluctuation, can accurately capture the characteristics of the electricity price fluctuation, and provides effective support for the construction planning of a new energy power station, the capacity configuration of an energy storage system and the formulation of a charging and discharging strategy.

The invention relates to the technical field of energy-saving air conditioners, in particular to a control method and device of a refrigerating system and the refrigerating system.The control method comprises the following steps that a time-of-use electricity price table of the next 24 hours is obtained, the upper limit and the lower limit of an indoor comfortable temperature interval set by a user are read, quantified building thermodynamic parameters are called, and a set of electricity price weights and indoor temperature constraints is established. According to the method, the time-of-use electricity price in the next 24 hours and the upper and lower limits of the indoor comfortable temperature interval set by the user are introduced in the refrigeration operation process of the compressor, the electricity price factor is combined with the thermodynamic dynamic response constraint, an economical optimization path of compressor power output is constructed, start-stop control logic purely oriented by demands is avoided, and the energy consumption is reduced. And on the basis of power regulation and control, the temperature difference between the coil temperature of the outdoor heat exchanger and the environment temperature is collected, the motor current of an outdoor fan is monitored at the same time, dynamic frost judgment logic is established, a static threshold judgment mode is replaced, and the compressor has the real-time sensing capacity in the operation process.

The invention provides a node electricity price scene generation method and device based on space-time combination and residual diffusion, and relates to the technical field of electricity price scene generation. A historical node electricity price sequence data set is formed, then a space-time joint neural network model is input, space-time features of node electricity price data are extracted, and after a predicted node electricity price sample fusing time sequence dynamic and space topology information and a condition information matrix are generated, a residual error denoising diffusion model is input; the power price probability distribution is subjected to refined modeling through forward noise adding and reverse denoising processes, a node power price scene covering multi-dimensional uncertainty is generated, the space-time relevance and uncertainty of the node power price can be captured, the generated node power price scene is more real and closer to reality, and the node power price is more accurate. And a reliable decision basis is provided for power market participants to optimize a scheduling strategy.

The invention discloses an optimal control method for metering a cold and heat storage air conditioning system in a laboratory, and relates to the technical field of optimal control, and the method comprises the following steps: predicting hourly cold load and heat load in a future preset period, and generating hourly load prediction data; obtaining time-of-use electricity price information, and generating electricity price time period division data; formulating an energy storage strategy based on hourly load prediction data and electricity price time division data; the method specifically comprises the steps that in the valley electricity price period, when it is predicted that a load demand exists in the future peak electricity price period, a cold and heat source module is controlled to operate, energy is stored in a cold and heat storage module till the rated capacity is reached or the valley period is ended, and the temperature and humidity in a laboratory are maintained within a first set range through a tail end processing module in the energy storage period; and formulating an energy release and conventional operation strategy based on hourly load prediction data, electricity price time division data and the real-time energy storage state of the cold storage and heat storage module. The method has the effect of improving the optimization control efficiency.

The invention discloses a multi-energy complementary optimal configuration method and system for a new energy base, and belongs to the technical field of power system planning. The method comprises the following steps: acquiring energy base system operation parameters, power generation equipment parameters, delivery channel capacity and power market electricity price data; constructing an optimal configuration model taking a receiving end time-of-use electricity price signal as input and aiming at stabilizing new energy output fluctuation and meeting delivery channel constraint; solving the model to obtain a thermal power, wind power, photovoltaic and energy storage capacity configuration scheme and a time sequence output plan; and determining a final optimal configuration scheme by analyzing the operation stability and delivery power volatility of the configuration scheme in different pricing modes. According to the method, the technical problem that the influence of the receiving end electricity price market is not fully considered during optimal configuration of the existing new energy base is solved, an optimal configuration scheme which meets the technical constraint and adapts to the market environment can be generated, and the operation economy and stability of the new energy base are remarkably improved.

The invention relates to the technical field of power supply and demand prediction, and discloses a power supply and demand prediction method suitable for a complex market environment. The method comprises the following steps: collecting multi-dimensional real-time data of a power market, wherein the multi-dimensional real-time data covers a user-side load fluctuation sequence and a market electricity price fluctuation signal; constructing a power supply and demand dynamic prediction model based on the data, and outputting a theoretical supply and demand prediction value; comparing the theoretical predicted value with the actual measurement electric power data, carrying out multi-dimensional difference analysis containing load sequence deviation response delay, and generating a market-level difference coefficient matrix; inputting the matrix into a regional energy network analysis model, generating a supply and demand abnormal propagation path probability distribution diagram and positioning a potential market imbalance region in combination with power grid node transmission capability and economic main body distribution information; and a prediction adjustment strategy is adaptively configured according to the probability distribution diagram, high-frequency data monitoring is started for a high-probability imbalance area, policy disturbance testing is applied to an associated market subject, and stable operation of the power market is supported.

The invention relates to the technical field of data processing, and particularly provides a virtual power plant scheduling method, system and device considering electricity price, and a storage medium, and the method comprises the steps: collecting multi-source monitoring data at regular intervals, the multi-source monitoring data comprising electricity price data, load data, meteorological data and power data; constructing a space-time tensor according to the acquisition time and the monitoring position of the multi-source monitoring data; inputting the space-time tensor into a prediction model to obtain probability distribution of load demand, distributed power supply output and market electricity price in a set time span; and solving an optimal scheduling scheme according to the probability distribution, the established minimum economic cost objective function and the minimum load fluctuation objective function. According to the method, the space-time tensor is constructed by fusing the multi-source data such as the electricity price, the load and the weather, the prediction precision is remarkably improved, and the problem of scheduling deviation caused by dependence of single data in a traditional method is solved.

The invention discloses a monthly load prediction method fusing time-of-use electricity price elements and long and short term time sequence characteristics, and relates to the field of load prediction, and the method comprises the steps: building a monthly load prediction model through employing a knowledge migration algorithm based on a dual-channel time sequence neural network and a long and short term time sequence routing module; the dual-channel time sequence neural network comprises a multi-scale partitioning module, a Mamba module and a local window converter; key characteristic parameters of monthly loads are obtained; and inputting the key characteristic parameters into a monthly load prediction model, and predicting the monthly load. According to the invention, a scientific and reliable quantitative basis can be provided for power demand prediction and system planning decision.

The present invention provides a method, device, system and electronic equipment for managing photovoltaic storage and charging energy, which are applied to the technical field of photovoltaic storage and charging energy management. The method dynamically adjusts the photovoltaic output, charging power and energy storage output at the next moment according to the electricity price period by obtaining the current electricity purchase price, electricity selling price and load power. The energy storage is discharged at the maximum during high-price periods, charged at the maximum during low-price periods, and the power is balanced during medium-price periods to ensure operation within the preset load limit, and the confirmed parameters are sent to the device side to achieve efficient energy management. The present invention comprehensively considers the real-time dynamic changes in the purchase price and the selling price of electricity, and adjusts the photovoltaic output, charging efficiency and energy storage output in combination with the electricity price period, thereby optimizing energy use, improving energy utilization and reducing overall energy costs.

The invention relates to the technical field of carbon neutralization, and discloses a critical electricity price calculation method based on green electricity energizing tempering coupling methanol preparation, and the method comprises the steps: building an ironmaking byproduct gas database and defining green hydrogen preparation parameters based on different ironmaking processes, and building a gas purification cost model based on the ironmaking byproduct gas database and the green hydrogen preparation parameters; constructing a ton methanol complete cost model of a tempering coupling methanol preparation path X; a reference process HCTM is selected, C (X) is made to be equal to C (HCTM), and an analytic expression of the critical electricity price is solved; the actual electricity price is compared with the critical electricity price, if the actual electricity price is compared with the critical electricity price, it is judged that the path X has economic advantages relative to the HCTM under the current year T and the carbon tax condition, and if not, it is judged that the path X does not have economic advantages relative to the HCTM. The investment risk of a green methanol project can be remarkably reduced.

The present invention discloses a method and system for dynamic scheduling of multiple energy storage cabinets, comprising: obtaining a registration request from each energy storage cabinet, assigning a unique identifier to each energy storage cabinet, and then establishing a UDP communication connection with each energy storage cabinet; obtaining operating status data periodically sent by each energy storage cabinet; calculating real-time load demand based on a power grid electricity price signal, generating a scheduling instruction based on the operating status data and the real-time load demand, and sending the scheduling instruction to the corresponding energy storage cabinet via a communication connection. The present invention uses UDP to establish a connection with each energy storage cabinet, realizing the application of the UDP protocol in the master-slave architecture of the energy storage system, significantly reducing communication delays, meeting the requirements of dynamic scheduling in seconds, enabling the energy storage system master control terminal to obtain the operating status data of each energy storage cabinet in real time, and quickly generating corresponding scheduling instructions based on the real-time load demand and electricity price signal, thereby realizing efficient coordinated control and dynamic energy distribution of multiple energy storage cabinets and improving the response speed and scheduling flexibility of the energy storage system.

The invention relates to the technical field of power system demand side management, in particular to a demand response optimization method for power marketing business. A multi-scene dynamic reference library is constructed and a feature matching priority is set by collecting historical power consumption data of a user and associating external features. And based on a weighted matching result of the current scene and the reference library, determining an optimal scene and calculating a single / multi-dimensional deviation index, and in combination with a threshold value, judging a graded abnormal user and triggering a response mark. And for abnormal users, establishing a standby load resource pool scheduling model, and optimizing and compensating load deviation by integrating energy storage, adjustable load and other resources. Meanwhile, a dynamic credit rating is generated in combination with the user historical fulfillment rate and the load prediction accuracy, motivation such as electricity price discount and priority scheduling is implemented for high-credit users, punishment measures such as electricity price floating and permission limitation are taken for low-credit users, and a closed-loop optimized demand response management system is formed. Accurate optimization of demand response and user behavior regulation and control in power marketing are realized.

The invention provides an electricity market bidding strategy optimization method, system and device and a medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: acquiring public data of a power market, performing structured processing, extracting key information, and generating power market data; establishing a data optimization prediction model, and predicting the electricity price, the power demand load, the supply load, the enterprise power consumption cost and the power consumption at the future moment based on the power market data; receiving and configuring risk preference parameters input by a user, and generating an enterprise feature configuration file; sequentially calculating a market supply and demand influence factor, a real-time transaction price deviation degree, a reference income fluctuation value and a risk constraint term, and generating a final premium proportion based on a calculation result; on the basis of the final premium proportion, determining a bidding price at a future moment, constructing an objective function to optimize the bidding cost, solving an optimal premium proportion sequence and generating a bidding plan; and monitoring the income fluctuation rate in real time, triggering a feedback mechanism, and updating data.

The invention belongs to the technical field of energy storage power station operation management, and provides an industrial and commercial energy storage power station operation method in an electric power spot market, and the method comprises the steps: collecting data: collecting the time-sharing time period and time-sharing electricity price of the electricity price of a user side, and the day-ahead clearing time period and electricity price of the electric power spot market in real time; formulating a charging / discharging strategy: formulating a peak-valley arbitrage mode based on the time-sharing period and the time-sharing electricity price of the user side; according to a peak-valley arbitrage mode, 24 hours of the whole day are divided into a charging time period, a discharging time period and a standing time period, and charging / discharging power and charging / discharging electric quantity of the charging / discharging time period and the standing time period are determined; and adjusting a charging / discharging strategy: adjusting the charging / discharging strategy according to the current clearing electricity price of the spot transaction market. According to the invention, under the condition of guaranteeing the peak-valley arbitrage of the user side, the hour-level time period of charging and discharging of the energy storage power station is divided into the minute-level time period according to the daily clearing result of spot transaction, and the charging and discharging power of the minute-level time period is adjusted, so that the settlement electric charge is reduced.

The invention discloses a node marginal electricity price calculation method considering carbon cost and reliable operation cost, and belongs to the technical field of electricity markets. The node marginal electricity price calculation method considering the carbon cost and the reliable operation cost comprises the steps of establishing an electricity-carbon coupling market clearing model under the condition of normal operation of an electric power system and a line disconnection emergency by taking the minimum power generation cost and the minimum carbon transaction cost as a target, and solving the electricity-carbon coupling market clearing model, obtaining an electricity-carbon coupling market clearing result; according to an electricity-carbon coupling market clearing result, node marginal electricity price considering carbon emission cost and system reliable operation cost is calculated under the normal operation condition of a power system and under the line disconnection emergency event; carbon cost and reliability cost are brought into node marginal electricity price calculation, and a clear economic signal is provided for participants of the electricity market; and the electric power market is promoted to develop towards a more efficient, environment-friendly and sustainable direction.

The invention discloses an operation control method and device of an energy storage system based on demand defense. The method comprises the following steps: predicting and obtaining a load change curve, a photovoltaic output change curve, an electricity price change curve and a charge state change curve in a future preset time period based on a collected multi-source data set of a user side; predicting the electricity purchase cost of the user based on the electricity price change curve, the load change curve, the photovoltaic output change curve and the charge state change curve; predicting demand penalty cost based on the load change curve and the photovoltaic output change curve; constructing a target function at least based on the user electricity purchase cost and the demand penalty cost; and determining a day-ahead charging and discharging strategy based on the target function and a predetermined constraint condition, and controlling the operation of the target energy storage system based on the day-ahead charging and discharging strategy. According to the invention, the operation of the energy storage system can be reasonably and accurately controlled.

The invention belongs to the technical field of virtual power plants, and relates to a power spot market oriented virtual power plant resource dynamic aggregation method. The method comprises the following steps: extracting a distributed resource feature vector and mapping the distributed resource feature vector into a response type label; determining a price fluctuation period according to a spot price sequence, calculating a time sequence matching degree between each resource and a market rhythm by combining the feature vector, and constructing a comprehensive distance by fusing a clustering weight and a response type label; dividing the resource cluster into dynamic aggregation units by taking the variance of the minimum comprehensive distance as a target; a resource aggregation strategy is generated based on the aggregation unit, a control instruction is issued, and clustering weights and aggregation parameters are iteratively updated according to the clearing deviation rate fed back by market clearing. According to the method, the technical problems that the market dynamics is ignored, the clustering model is easy to merge heterogeneous resources and the closed-loop optimization is lacked in the existing static division are solved, and the effects of improving the market response capability of the virtual power plant, the control consistency of the aggregation unit and the adaptive optimization of continuous transactions are achieved.

The invention belongs to the field of electric power facility protection, and relates to an electric power facility external damage follow-up method and system, and the system comprises a follow-up process management module, an electric quantity loss calculation module, a block chain data analysis module, an electricity price loss calculation module, a simulation evaluation module and a typical case library module. The follow-up process management module manages whole-process information processing and follow-up work of the external breaking case according to a standardized process; the electric quantity loss calculation module analyzes the electric quantity loss of the external damage accident through a neural network algorithm and performs loss accounting; the block chain data analysis module carries out real-time evidence storage on external damage event data by utilizing a block chain technology; the electricity price loss calculation module calculates the electricity price loss of the user according to the electric quantity loss analysis result; the simulation evaluation module simulates an external damage accident scene and generates a standardized loss report; and the typical case library module is used for storing and managing typical external breaking cases. According to the invention, high efficiency, accuracy and credibility of external damage follow-up of electric power facilities can be ensured.