572 results about "Power market" patented technology

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

The invention belongs to the technical field of novel electric power system operation control and trust management, and particularly discloses a virtual power plant full-process trusted aggregation method and system based on a hierarchical trust chain. Differentiated hierarchical trust chain models of a data acquisition link, a scheduling control link and a market transaction link are constructed respectively; through real-time credibility evaluation and adaptive weight adjustment, credibility calculation of multi-agent collaborative decision is realized. According to the invention, a trust network formed by a plurality of efficient, safe and transparent virtual power plant trust chains is constructed, solid technical support is provided for fair competition, intelligent scheduling and reliable operation of a power market, and efficient interaction and stable operation of a power system are ensured.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

Provided is a day-ahead electricity price prediction method based on a parallel multi-dimensional attention mechanism, which method belongs to the technical field of day-ahead electricity price prediction in a power market. The maximum mutual information coefficient is used to select variables having a certain relevance with an electricity price, so as to assist in predicting future electricity price data; next, a decomposition algorithm is used to decompose an original electricity price signal; and then, electricity price sub-components obtained by means of decomposition and the electricity-price-related variables are inputted into an MBI-IPMDA-PBISA deep learning model, so as to predict a future electricity price. In this way, an MBI-IPMDA-PBISA deep learning model can fully learn a complex association relationship between electricity price sub-components and electricity-price-related variables and a future electricity price, the problem of error superimposition caused by separately predicting the electricity price sub-components and then superimposing same to obtain the future electricity price is also avoided, and the problem of the accuracy of existing attention mechanisms during prediction being low is solved, thereby improving the prediction accuracy.

The invention discloses an energy storage income maximization method based on a strategy gradient algorithm, and the method comprises the following steps: collecting energy storage equipment state parameters, power market real-time price data and load prediction data, and carrying out the preprocessing; feature extraction is carried out, charging and discharging strategy parameters are initialized, and an initial charging and discharging strategy is generated; according to the initial charging and discharging strategy, generating a charging and discharging action of a cost cycle, executing the charging and discharging operation, and generating charging and discharging feedback information; a reward signal is calculated, and the reward signal, the energy storage state feature vector and the charging and discharging action are used for updating charging and discharging strategy parameters; performing feedback optimization on future cycle income by using a time sequence dependency relationship; and performing lower-layer autonomous strategy optimization and upper-layer equipment coordination, and generating a globally optimal cooperative charging and discharging strategy. The strategy gradient and graph optimization method is fused, energy storage cooperative control is achieved, and the method has the advantages of being high in income, high in adaptability and stable in strategy.

The invention discloses a distributed adjustable resource layered dynamic aggregation method and system for a virtual power plant. The method comprises the steps of collecting operation data, environment data, user behavior data and market data of a distributed power supply, a flexible load and an energy storage facility, extracting core features, extracting feature parameters by adopting a quantification method, and constructing an adjustable resource feature database; constructing an operation capability index model, and obtaining a resource comprehensive operation capability value based on the index and the weight thereof; scheduling the resources in a classified, partitioned and graded manner; establishing a three-layer structure of a bottom-layer resource model, a middle-layer aggregation model and an upper-layer optimization model, and constructing a multi-objective optimization model; by executing a marketization transaction strategy, a power grid dispatching response strategy and an income distribution strategy, collaborative optimization operation of the virtual power plant, the power market and power grid dispatching is achieved, and finally multi-scene simulation verification and model parameter optimization are conducted. According to the scheme of the invention, efficient aggregation and collaborative optimization scheduling of adjustable resources are realized.

A microgrid island division method based on a cumulative foreground theory and a transfer learning thought comprises the following steps: step 1, aiming at a day-ahead decision-making scene, taking income maximization in a scheduling period as a core target, integrating short-term renewable energy output prediction and electricity price fluctuation data, and constructing a capacity optimization configuration model; 2, constructing a double-layer model, constructing a transaction decision model by the upper-layer model by taking energy storage power station revenue maximization as a target, and determining a power station quotation strategy; the lower layer model takes the sum of the electric energy cost and the frequency modulation service cost as the target to construct a combined clearing model of the electric energy and the frequency modulation market; and 3, solving the double-layer model constructed in the step 2. According to the method, energy storage can participate in an electric energy and frequency modulation market through strategic quotation under a reasonable market mechanism, and an energy storage power station is stimulated to optimize resource allocation, so that the frequency modulation pressure of a power system is reduced, the energy storage economic benefit is improved, and the stability and resource utilization efficiency of the power market are further improved.

The invention discloses a virtual power plant regulation capability test method and system based on a deep belief network, and the method comprises the steps: deploying an intelligent terminal system at an aggregation resource side in a virtual power plant, and collecting resource operation data in real time; dynamically loading market rule parameters of the target transaction variety through a rule configuration engine; the method comprises the following steps: preprocessing historical scheduling data of a virtual power plant, collecting resource-level data and virtual power plant-level data, cleaning abnormal data, and interpolating and filling missing values; calculating an adjustment capability index based on the preprocessed data; inputting the indexes and the rule parameters into a pre-trained deep belief network DBN model, and outputting a virtual power plant overall regulation capability predicted value and a marginal contribution weight of each aggregation resource; generating a virtual power plant admission judgment report and a resource optimization suggestion according to a comparison result of the predicted value and the market rule parameter; according to the invention, differential evaluation of the virtual power plant regulation capability indexes is realized, and the access test requirements under diversified power market rules are met.

The invention relates to a multi-expert non-marketization power prediction method and device and a storage medium, and is applied to the technical field of power market transactions, and the method comprises the steps: carrying out the adaptive recognition of a working condition through a multi-expert architecture, and accurately capturing the boundary change direction caused by the sudden weather change, load fluctuation and other factors, it is ensured that the prediction curve is consistent with the real adjustment direction of the scheduling side, and the problem that an existing simplified extrapolation method easily causes prediction direction errors is solved; according to the method, only the physical quantity which can be obtained by the transaction subject is used as input, so that the model has higher applicability and stability in an actual prediction scene. Modeling is carried out on a multivariable coupling relation through a KAN coupling mapping model, it is ensured that the predicted non-marketization boundary and variables such as new energy output and provincial dispatching loads meet the real supply and demand balance logic, the prediction result is closer to the actual market condition, and the physical rationality of the prediction result is ensured.

The invention provides a power transaction agent system based on AI auxiliary decision making, the system can provide intelligent power transaction service based on an intelligent contract, the system is responsible for processing massive, high-frequency and multi-dimensional data, providing deep insight and accurate prediction, liberating traders from heavy data work, and improving the working efficiency of the traders. The method focuses on strategy formulation and risk control of transactions. The system can provide questioning and answering services for power transactions for users, and can perform power transaction prediction and visualization in each mode based on collected power transaction big data, so that power market intelligent analysis, retrieval and other services of traders, analysts and decision makers can be deeply endowed, multiple functions are integrated, and the user experience is improved. The system assists traders, users, managers and the like, realizes intelligent service and lightweight service of power transaction, reduces transaction cost, and improves market circulation efficiency and benefits of power transaction.

The invention discloses an electric power economic dispatching model solving method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: constructing a safety constraint mathematical model based on an overload generator tripping type electric power system safety and stability control device, and determining an original feasible region of an overload generator tripping type condition section according to the safety constraint mathematical model; reconstructing the original feasible region through the introduced cut plane to obtain a reconstructed feasible region; solving is carried out based on power monitoring and data acquisition SCADA real-time acquisition data, market clearing data and a reconstructed feasible region to generate a new cut plane through iteration, so that on the premise of guaranteeing precision, power market intra-day real-time rolling clearing can be rapidly solved, and the efficiency is improved. The method effectively guarantees the speed and precision requirements of power market construction for safety constraint economic dispatching, ensures that the market clearing result has the actual performability of a power grid, and especially solves the problem that the real-time clearing of the power market is difficult due to overload generator tripping type condition sections.

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 new energy computing power collaborative optimization scheduling method based on global economic optimization, and belongs to the technical field of new energy-computing power collaborative optimization scheduling of a power system. The method comprises the following steps: constructing a mapping relation between a computing power task and power consumption, and constructing an arithmetic unit demand response model based on the mapping relation between the computing power task and the power consumption; constructing a new energy model based on the arithmetic unit demand response model; and solving the new energy model by taking global economic optimization as an optimization target in consideration of time-sharing differentiation characteristics of wind power, photovoltaic power, on-grid electricity price and energy consumption side electricity price so as to determine an optimal operation mode of energy storage and adjustable computing power load, and performing new energy scheduling in the optimal operation mode. According to the invention, the self flexible adjustment characteristic of the computing power center is deeply coordinated with the wind and light, energy storage and electricity markets.

The invention relates to the technical field of virtual power plants, in particular to a highway micro-grid virtual power plant optimal scheduling method considering carbon emission, and the method comprises an energy management module which is used for managing and controlling an energy storage module, a charging base and a service area infrastructure; the virtual operation platform is used for coordinating and dispatching electric energy in the power grid and the electric power market; the energy management module is provided with a peak valley selection module, a power supplement module, a power storage module and a direct power supply module. Power is supplied to the service area infrastructure and the charging base through the direct power supply module in the power consumption valley period, and power is supplied to the service area infrastructure through the power storage module in the power consumption peak period; through peak-valley time-sharing power supply, intelligent scheduling and multiple protection, charging and discharging of the power storage module are reduced in the low ebb period, stable charging is guaranteed in the peak period, meanwhile, real-time monitoring and protection are achieved, the service life is prolonged, and energy consumption is reduced.

The invention discloses an energy storage system economy evaluation method and system and a storage medium, and belongs to the technical field of energy storage systems. Comprising the following steps: acquiring operation data, power market transaction data and equipment state data of an energy storage system in real time through a multi-source data acquisition module; a dynamic feature engineering module is used for screening key influence factors, and a comprehensive evaluation index system fusing market features, technical features and environment features is constructed; constructing an economy prediction model based on an LSTM neural network, inputting the data of the comprehensive evaluation index system into the economy prediction model, and outputting a multi-time scale economy evaluation result, including short-term income prediction, medium-term probabilistic income interval and long-term attenuation correction evaluation; and a revenue-risk balanced charging and discharging strategy is generated through a reinforcement learning algorithm, a strategy effect is rehearsed in a digital twin environment, and high-risk operation is dynamically adjusted. And the precision and adaptability of economic evaluation of the energy storage system can be remarkably improved.

The invention discloses an energy storage excitation value distribution measurement method, system, equipment and medium, and relates to the technical field of power market and carbon transaction optimizing.The method comprises the steps that historical transaction data and system equipment parameter data of a power market and a carbon transaction market are collected, and a power-carbon coupling quotation model is constructed according to power-carbon market data; defining constraint conditions in the scheduling optimization process, and constructing a novel energy storage optimization scheduling model by adopting a double-layer optimization modeling method according to the constraint conditions; and a scheduling strategy is optimized through the novel energy storage optimization scheduling model, and the market value of the bidding subject is calculated by applying a VCG mechanism to the output of the power-carbon coupling quotation model. According to the method, bidding payment is optimized through a VCG mechanism, fair and reasonable market value distribution is ensured, a double-layer optimization scheduling model is adopted, and the earnings of energy storage equipment in an electric energy market and a frequency modulation market are improved.

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 discloses an electricity purchase strategy risk assessment method based on multiple market types, and belongs to the technical field of electricity market transaction risk management. Medium and long term, in-province spot goods and inter-province spot goods are brought into a unified risk assessment framework, and collaborative optimization of three markets is realized; three-dimensional risk measurement is introduced, risk identification is more comprehensive, and global optimum can be obtained between cost and risk based on combined risk measurement and scene optimization of CVaR; and through Hash calculation and block chain evidence storage, the integrity of the transaction data is protected, and the non-tampering and transparency of the transaction data are ensured. According to the electricity purchase strategy risk assessment method based on the multiple market types, quantitative risk assessment is provided for traders, the assessment provides more practical data support for transaction decisions, the traders are helped to better understand potential risks and opportunities, and therefore the transaction strategies of the traders are optimized. According to the method, the problem of risk dynamic balance under multi-market collaboration is solved, and auditable and traceable decision support is provided for power grid enterprises.

The invention provides a multi-target dynamic optimization optical storage and charging operation control method. The method comprises the following steps: step 1, a dynamic priority control mechanism; 2, a multi-time-scale optimization model is established; step 3, an energy storage life protection strategy; and step 4, a power market interaction module. According to the invention, through dynamic optimization of cooperative scheduling of photovoltaic power generation, energy storage charging and discharging and charging pile loads, multi-target cooperative optimization of economy, energy efficiency and power grid stability is realized, the economy is improved, the light abandoning rate is reduced, and the power grid supporting capability is improved.

The invention relates to the technical field of causal discovery, provides an electric power spot market load prediction method and system based on causal discovery, and solves the problems of low accuracy and poor timeliness of electric power transaction trend prediction in extreme weather. The method comprises the steps of obtaining power transaction related data and sound wave and vibration signals of a new energy power station, performing analysis to obtain micro-meteorological data and photovoltaic panel abnormal state data, and generating disaster monitoring data; power station simulation data and market clearing price data are combined, a causal discovery algorithm is utilized to analyze the causal relationship among weather, output and market variables, and a dynamic causal graph is constructed; and performing fusion enhancement on the causal graph and the meteorological raster data to generate semantic data, distributing weights by adopting a space-time attention mechanism, and finally predicting the influence of the new energy output loss on the power market load. According to the invention, the accuracy and timeliness of power transaction trend prediction in extreme weather are improved.

The invention provides a transaction risk early warning system and method for electric power marketization reform. Relates to the technical field of electric power systems and electric power marketization reform, and comprises a risk data acquisition module used for acquiring multi-source data in the whole electric power marketization transaction process in real time; the risk data fusion module is used for performing time sequence synchronization, format standardization and heterogeneous data fusion on the multi-source data to generate a unified risk feature data set; and the risk assessment module is used for calculating market multi-dimensional risk indexes in real time through the risk feature data set and a Gaussian mixture model. According to the trading risk early warning system and method for electric power marketization reform, the false alarm rate and the missing report rate are remarkably reduced, and full-chain and all-weather monitoring and intelligent warning of price risks, performance risks, credit risks and market manipulation risks are achieved.

The invention provides a virtual power plant two-stage joint optimization scheduling method and system for participation of a power market, and a related device, and the method comprises the following steps: 1, depicting the uncertainty characteristics of renewable energy sources, and obtaining a prediction error confidence interval; step 2, constructing a virtual power plant day-ahead scheduling model taking the maximum market income as a target; step 3, obtaining a day-ahead energy declaration curve and an energy storage operation strategy corresponding to the virtual power plant based on the obtained virtual power plant day-ahead scheduling model; 4, obtaining the actual output of the renewable energy in real time, and calculating the output deviation between the obtained actual output and the date energy declaration curve obtained in the step 3; 5, constructing a virtual power plant real-time scheduling model according to the obtained output deviation; 6, generating an interaction strategy between energy storage and a power grid according to the obtained real-time scheduling model of the virtual power plant; on the premise of guaranteeing the power supply stability of the system, the economic benefits of the virtual power plant participating in the power market transaction can be remarkably improved.

The invention relates to a block chain-based distributed energy storage equipment collaborative optimization system and method. The system comprises a data acquisition and preprocessing module, a data analysis module, a collaborative optimization module and a block chain interaction module. The data acquisition and preprocessing module acquires operation parameters, power market information and load data of the distributed energy storage equipment in real time and performs preprocessing; the data analysis module constructs a constraint model of the energy storage equipment participating in the auxiliary service and a full-cycle economy evaluation system, and calculates probabilistic benefits of the energy storage equipment participating in the auxiliary service in combination with a Monte Carlo method; the collaborative optimization module adopts an improved particle swarm optimization algorithm to solve an optimal scheduling strategy; and the block chain interaction module implements information interaction, and executes a scheduling strategy, income distribution and record evidence storage based on an intelligent contract. Meanwhile, the invention further discloses a method using the system. According to the invention, collaborative optimization scheduling, credible information interaction and reasonable income distribution of distributed energy storage equipment participating in auxiliary services are realized.

According to the frequency modulation and spot goods combined clearing method and device, the storage medium and the computer equipment provided by the invention, the time-phased prediction curve of the wind turbine generator is generated based on the historical meteorological data and the historical unit operation data, the equivalent full-load hours of water and electricity are determined according to the reservoir dispatching plan, and meanwhile, the energy storage parameters of the power system are obtained; the power grid topology constraint generated through the data can accurately represent the cooperative operation boundary of the new energy and the stored energy. Then, wind power energy storage operation constraints are constructed according to wind power output and energy storage real-time adjusting quantities of the wind power nodes, so that new energy fluctuation is effectively smoothed; a joint optimization model between frequency modulation and electric energy is constructed according to frequency modulation demand indexes and independent clearing varieties of the electricity market, so that the frequency modulation demand can be flexibly responded through a differential pricing mechanism; and finally, on the basis of the generated constraint, a commercial solver is adopted to solve the joint optimization model to obtain frequency modulation clearing data, so that the total operation cost is reduced, and the social welfare is improved.

The invention provides an automatic decision-making and real-time ordering execution system and method for electric power spot transaction, relates to the technical field of electric power market transaction, and solves the technical problems of slow transaction response and high operation risk caused by incapability of forming closed-loop automation due to separation of decision-making and execution links in the prior art. The system comprises a sensing layer, a decision algorithm layer and an execution layer, the sensing layer is used for acquiring multi-source data of an electric power spot market and preprocessing the multi-source data to obtain standard data; the decision algorithm layer is used for receiving standard data and carrying out analysis and prediction to obtain a prediction result; generating a transaction strategy by using an optimization algorithm based on the prediction result; the execution layer is used for receiving a transaction strategy; the transaction strategy is converted into a transaction instruction conforming to the transaction platform specification, and automatic ordering execution is carried out; and monitoring the execution state of the transaction instruction to obtain an instruction execution result. The method is used in the electric power spot transaction process.

The invention relates to the technical field of electric power energy storage control, and discloses a multi-dimensional state driven energy storage cold energy storage optimization control system and method, and the method comprises the steps: collecting the multi-dimensional state parameters of an energy storage system and the price of an electric power market, and judging a current operation time period mode; in the active cold storage mode, target cold storage capacity is determined according to the maximum allowable heat capacity buffer margin, and cold pre-storage is completed; in the cold capacity release mode, cold capacity-heat capacity collaborative multi-stage judgment is executed, the minimum critical mass flow rate meeting the heat exchange requirement is reversely solved, the minimum critical mass flow rate is mapped to a discrete control gear of the liquid cooling circulating pump to generate a final rotating speed instruction, and meanwhile closed-loop flow correction is executed; according to the method, the minimum critical mass flow rate is accurately solved, the rotating speed of the liquid cooling circulating pump is remarkably reduced while battery thermal inertia buffering is utilized, parasitic energy consumption is greatly reduced through the cubic relation between pumping power consumption and the rotating speed, and the problem that electric charge income is counteracted due to excessive pumping is solved.