72 results about "Electrovalency" patented technology

The invention discloses a distributed photovoltaic power distribution network shared energy storage optimization method, and relates to the technical field of energy storage system optimization, and the method comprises the following steps: inputting the current electricity price of an electricity market into a preset electricity price prediction model, and obtaining a current rolling cycle electricity price prediction sequence, constructing a rolling optimization model based on the current rolling cycle electricity price prediction sequence, the energy storage system battery real-time charge state and the energy storage system operation constraint condition, taking the maximum energy storage system net income as an objective function, taking the battery life attenuation cost as a correction parameter, and obtaining an optimal charge and discharge power sequence, and controlling the charging and discharging actions of the energy storage system, monitoring an electricity price prediction deviation index in real time, optimizing and calibrating an electricity price prediction model, updating an optimal charging and discharging power sequence, adjusting the charging and discharging actions when the electricity price prediction deviation index is greater than a preset deviation threshold value, and repeating the steps at the beginning of a next rolling period, so that the benefits of the energy storage system are improved, and the battery loss is reduced.

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

The invention is suitable for the technical field of electrochemical energy storage, and provides a multi-source cooperative control method and device, computer equipment and a computer program product, and the method comprises the steps: obtaining basic parameters of a power grid, a hybrid energy storage system and a new energy power generation system; obtaining prediction parameters of the electric power energy system based on the historical working conditions and the prediction model; according to the preset initial investment amount, the cycle life of the hybrid energy storage system and the basic parameters and working conditions of the electric power energy system, calculating the unit energy charging and discharging cost of the hybrid energy storage system; based on the unit energy charging and discharging cost, the time step length, the power grid billing time-of-use electricity price and the prediction parameters of the hybrid energy storage system and the planned charging and discharging power of the hybrid energy storage system, an objective function with the optimal economical efficiency as an objective is constructed, the objective function is solved, and the optimal planned charging and discharging power of the hybrid energy storage system in each time period is obtained. According to the method, multi-dimensional data is combined, an objective function with the optimal economical efficiency is constructed and solved, and a hybrid energy storage charging and discharging strategy is automatically obtained.

The invention relates to the technical field of refrigeration energy efficiency monitoring of refrigeration houses, in particular to a dynamic monitoring system and method for refrigeration energy efficiency of a refrigeration house. A multi-objective optimization scheme analysis module in the system is used for analyzing the refrigeration energy efficiency of the refrigeration house based on the running state of each temperature zone of the current refrigeration house, the heat state of stored goods and an external electricity price signal; and by taking the minimization of the total operation energy consumption of the refrigeration house and the minimization of the temperature fluctuation of each temperature zone as optimization objectives, constructing a multi-objective optimization function, and generating at least one candidate energy-saving operation scheme in a future time window of a preset duration. According to the method, through multi-dimensional data perception, a digital twinborn model for specific characteristics of goods and refrigeration house scenes is established; a multi-objective optimization function with the lowest total energy consumption, the lowest temperature difference fluctuation of the temperature zone and the highest energy efficiency ratio of the system as the core is constructed; and a plurality of candidate energy-saving schemes are dynamically generated by using the function, and an optimal scheme is screened out through a normalized comprehensive evaluation system, so that clear energy efficiency data and optimization suggestions are provided for managers, and invalid energy consumption is avoided.

The invention discloses a multi-time-scale optical storage charging and discharging integrated energy management method and device, a medium and a product, and relates to the field of power scheduling, and the method comprises the steps: determining the earnings of an optical storage charging and discharging multifunctional integrated energy management system according to the day-ahead operation data; constructing an upper-layer control model; according to the upper layer control model, determining the load scheduling power of the zero-carbon park; power deviation is determined according to the real-time operation parameters and zero-carbon park load dispatching power, and a lower-layer control model is constructed; determining adjustment power according to the power deviation and a lower-layer control model; the load scheduling of the zero-carbon park is adjusted; and in combination with the real-time electricity price, the power utilization load, the charging pile load and the energy storage charging and discharging power of the zero-carbon park are adjusted and optimized in real time on the intra-day scale. According to the method, the time sequence coupling characteristics of the energy storage and the charging pile can be considered, so that the method better adapts to the time uncertainty of the user side load and the energy storage.

The invention discloses an intelligent soft switching and multilayer electro-hydrogen hybrid energy storage two-stage space-time decoupling configuration method and system. The method comprises the following steps: inputting basic data and initializing network basic parameters; in the first stage of constructing the two-stage MEH-SOP model, the power / capacity of each device of the MEH is optimally configured on the basis of annual 8760h time sequence operation simulation considering time-of-use electricity price; constructing a second stage of the two-stage MEH-SOP model, extracting typical scenes of MEH full-year high-power charging / discharging and power distribution network full-year voltage serious out-of-limit, and optimally configuring SOP multi-port capacity; and solving the constructed two-stage MEH-SOP model, and outputting an optimal configuration result. According to the invention, a multi-level energy storage system covering from the day to the week is constructed, and the multi-port SOP direct current side is connected in a modularized manner, so that the structural flexibility and the operation flexibility of the power distribution network are remarkably enhanced.

A distributed photovoltaic energy storage system configuration method based on depth feature clustering and robust double-layer optimization comprises the steps of performing normalization processing on multi-source historical data such as photovoltaic output, load, electricity price and weather, and generating a typical operation scene capable of representing uncertainty by adopting a depth embedded feature extraction and clustering algorithm; constructing a double-layer optimization configuration model: in the upper-layer planning optimization model, taking annual comprehensive cost, carbon emission cost, node voltage deviation and renewable energy consumption rate as multi-objective functions, and determining the access position, capacity and rated power of photovoltaic and energy storage; in the lower-layer operation optimization model, the charging and discharging strategy and reactive power output of the energy storage system are optimized with the daily operation cost, the voltage deviation, the electric energy quality index and the auxiliary service income as optimization targets; and an improved NSGA-II algorithm based on an elitist strategy is adopted to realize closed-loop optimization of planning and operation. According to the method, economy, low-carbon property and electric energy quality can be considered while uncertainty is considered, and the comprehensive performance of operation of the power distribution network is improved.

The invention discloses a multi-time scale coupled source-storage collaborative economic dispatching method and system, relates to the field of power system dispatching, and solves the problem of low dispatching economy of a power system dispatching scheme. According to the day-ahead scheduling optimization in the embodiment of the invention, the hidden cost of deep peak regulation and high-frequency charging and discharging can be quantified through the deep peak regulation coal consumption function of the thermal power generating unit and the charging and discharging depth-service life attenuation mapping table of the energy storage equipment; the intra-day rolling optimization dynamically adjusts the energy storage behavior based on the real-time electricity price, so that the scheduling plan can be matched with the market fluctuation, and the energy storage income is improved; by combining the two points, the operation characteristics of the power system under the complex working condition can be reflected more accurately, and the scheduling economy of the finally obtained optimal scheduling scheme is improved.

The invention discloses a new energy electric power spot transaction auxiliary decision-making system, and relates to the technical field of auxiliary decision-making. The system accurately measures and calculates initial charging and discharging power in each time period through an initial power determination module, optimizes power according to an electricity price time period and a wind power processing constraint by means of a power optimization decision module, and generates compliance declaration electric quantity based on a power-electric quantity conversion relation, a minimum unit and fluctuation limitation through a compliance declaration adaptation module. Transaction failure caused by non-compliance is avoided; a feedback regulation and control module carries out deviation judgment and correction on the optimized power and the compliance declaration electric quantity, it is guaranteed that transaction data is accurately matched with actual operation, and finally the transaction decision-making efficiency, the energy storage operation stability and the overall economic and environmental protection benefits are improved; the problems that in new energy electric power spot transaction, it is difficult to determine the energy storage initial power by combining the real-time cycle index and the charging and discharging depth, power optimization does not adapt to electricity price and wind power constraints, declaration electric quantity is not compliant, and the power and the electric quantity are prone to deviation are solved.

The invention relates to the technical field of new energy hydrogen production and chemical coupling application, and discloses a natural gas hydrogen-doped pipeline conveying system which comprises an energy supply layer, a chemical production layer, a digital integrated platform and a dispatching control module. The energy supply layer integrates photovoltaic power, wind power, nuclear power and a power grid standby power supply through a direct-current micro-grid, is matched with a sodium ion flow battery energy storage system, and supplies energy to the water electrolysis hydrogen production system of the non-noble metal catalyst and porous electrode structure. In the chemical production layer, pipeline steel or double-layer alloy steel is selected as a hydrogen-doped pipeline according to the proportion, a hydrogen concentration monitoring and sound wave leakage detection system is arranged, the hydrogen-doped safety upper limit of high pressure of 10% and low pressure of 30% is set, and a chemical hydrogen using system is connected through a gas distribution valve set and adjusted by an intelligent module. The digital platform is based on monitoring of the Internet of Things, hydrogen doping is optimized by means of a fuzzy PID algorithm and digital twinning, and risks are early warned. The scheduling module combines wind and light prediction and real-time electricity price, optimizes the start and stop of the electrolytic cell, the hydrogen doping ratio and the conveying pressure, and achieves efficient and stable conveying.

The invention provides an optical storage park micro-grid regulation and control method and device, electronic equipment and a storage medium, and belongs to the technical field of energy management, the method is applied to an optical storage park micro-grid system, the optical storage park micro-grid system comprises an energy storage system and a micro-grid controller, the energy storage system is in communication connection with the micro-grid controller, and the energy storage system is connected with a power grid in parallel. The method is executed by a micro-grid controller, and comprises the following steps: acquiring the electrical load power and the power grid incoming line power of the optical storage park; and if the power of the electrical load is greater than a preset power grid incoming line power threshold value and the current time period belongs to an electricity price flat peak time period, determining a first regulation and control threshold value, and when the power grid incoming line power is greater than the first regulation and control threshold value, controlling the energy storage system to discharge to the electrical load. The light storage park micro-grid regulation and control method provided by the invention can improve the refinement degree of light storage park micro-grid regulation and control.

The invention belongs to the technical field of energy storage joint optimization, and provides a multi-region heterogeneous energy storage joint optimization method based on model predictive control, which adopts slow time domain rolling optimization on an upper layer to realize joint scheduling of economy and service life. Fast time domain distributed power distribution and intra-group consistency control are adopted at the lower layer to realize fast frequency response and SOC balance; the upper layer cost function merges the operation loss cost, the time-of-use electricity price income and the degradation cost of unified monetization into the same target, and takes the energy storage unit as the dynamic adjustment basis of the degradation unit price to protect the aging unit; and the lower layer dynamically activates the power type and energy type energy storage units based on a disturbance severity index, formulates a charging and discharging group division and switching strategy, and adopts a distributed consistency algorithm to realize power distribution of the minimum communication overhead in the group so as to balance the SOC, so that the instantaneous frequency performance, the long-term economy and the equipment life are considered under the realizable engineering condition.

The invention discloses a visual monitoring and shooting and intelligent identification external damage early warning method and system, and relates to the field of power transmission line safety monitoring. The method comprises the following steps: acquiring external damage accident information; reversely tracing from the power grid topology database based on the accident occurrence position to determine an affected user list; constructing a causal inference model, and carrying out anti-factual estimation on the power consumption of the affected user when no external damage is assumed to occur in the accident influence time period based on the power consumption characteristic data of the affected user in the historical accident-free time period; acquiring actual electricity consumption of a user; determining an electric quantity loss causal effect value according to a difference value between the actual electricity consumption and the anti-factual electricity consumption; and according to the effect value and user electricity price information obtained from the marketing system, generating an external damage follow-up loss list containing the electricity loss cost. According to the method, accurate accounting of the electric quantity loss is realized through a causal inference technology, and a full-link closed-loop system from pre-event early warning to post-event follow-up is constructed in combination with power grid topology analysis and multi-system data fusion.

The invention belongs to the technical field of charging piles, and discloses a charging pile power control method and device, equipment, a storage medium and a product. In the current electricity price period, the to-be-charged amount corresponding to the load is determined according to the predicted photovoltaic surplus power, then the planned power of the charging pile in the current electricity price period is determined according to the to-be-charged amount, and then the current photovoltaic surplus power is compared with the planned power of the charging pile. And the expected output power of the charging pile in the current electricity price period is determined according to the comparison result. According to the invention, the to-be-charged amount of the load in the current electricity price period can be accurately obtained, then the current photovoltaic surplus power is compared with the planned power of the charging pile, whether the current photovoltaic surplus amount is sufficient to charge the load is judged, and the expected output power of the charging pile in the current electricity price period is determined according to the comparison result. The expected output power of the charging pile can be controlled based on the photovoltaic surplus condition and the current electricity price period, and therefore the economical efficiency of the charging task is improved.

The invention discloses an A / B / O three-position composite doped LLTO solid electrolyte ceramic material and a preparation method thereof, aiming at A-position Li < + > ions of a Li < 0.33 > La < 0.56 > TiO < 3 > ceramic material, high-valence ions M with high electronegativity are adopted to carry out A-position partial substitution, aiming at B-position Ti < 4 + > ions, low-valence ions Sb < 3 + > with high electronegativity are adopted to carry out B-position partial substitution, aiming at O-position O < 2-> ions, o-site partial substitution is performed by adopting high-electronegativity low-valence ions F <->, so that a stable and high-conductivity cubic phase crystal form of a high-electronegativity element is introduced, a lithium ion channel is widened, the electronic insulation performance of the material is improved, an electricity price compensation mechanism is triggered by utilizing non-equivalent substitution, more cation vacancy defects are generated in unit cells, and the performance of the material is improved. The oxygen vacancy concentration at the grain boundary is reduced by utilizing a lattice oxygen transition mechanism, and positive charge enrichment at the grain boundary is inhibited; and by utilizing the electricity price difference between O < 2-> and F <->, cation vacancies with certain concentration are ensured to exist in unit cells, certain cation vacancies are formed at the grain boundary, and the grain boundary conductivity of the material is improved, so that a novel environment-friendly solid electrolyte material with excellent comprehensive performance is formed. The preparation method is low in sintering temperature, low in preparation cost, simple in process and easy to operate, influence factors are easy to control, and the preparation method is suitable for large-scale industrial production and beneficial to popularization and application.

The application discloses a power-on dynamic adjustment method and related device, comprising: S1, judging whether the local E2PROM module saves a daily time period table when powered on, if yes, executing S3, otherwise executing S2; S2, obtaining a second set of rate period table switching time from a base table, and converting the rate period table according to the daily time period table of the second set of rate period table switching time; S3, analyzing whether to update the daily time period table of the module according to the total number of rate parameter programming, the rate period table, and the rate period table content saved by the communication module; S4, when receiving a change rate period parameter, setting the total number of rate parameter table programming according to the encryption mode type of the change rate period parameter; saving the received high-temperature rate period data, and when receiving a high-temperature electricity rate period table switching command, controlling the electric energy meter to work according to the high-temperature rate period data. The problems that the prior art does not support the flexible rate period function such as weather electricity price are solved.

The present application relates to the technical field of integrated energy system application, and relates to a station-network low-carbon interaction optimization method considering internal dynamic carbon potential of GCES, comprising: based on the correlation analysis of carbon-energy coupling relationship between each device in GCES, defining that the internal GCES is a complete electric-thermal coupling network; using Newton-Raphson method to iteratively solve the constructed power-hydropower-thermal equation, obtaining the power flow calculation result of the electric-thermal coupling network; comprehensively considering the influence of energy storage device on the internal node dynamic carbon potential of GCES, calculating the node dynamic carbon potential of the electric-thermal coupling network based on the power flow calculation result of the electric-thermal coupling network; taking the output of the device in GCES as the optimization variable, considering the influence of time-of-use electricity price and the node dynamic carbon potential of the electric-thermal coupling network, constructing a station-network low-carbon interaction optimization model to optimize the operation strategy of GCES. Compared with the prior art, the present application has the advantages of considering the influence of dynamic carbon potential on carbon emission, realizing the lowest carbon emission and the lowest economic cost, etc.

The invention discloses a water energy accumulator refrigeration system, and relates to the technical field of air conditioning and refrigeration, the cold discharge power, the secondary pump frequency and / or pressure difference setting and the branch valve opening degree are linked in a prediction time domain, so that excessive supply at the near end is avoided, supply loss at the far end is avoided, indoor temperature fluctuation and abnormal reduction of the return water temperature are obviously inhibited, and the energy efficiency is improved. The tank layering is stabilized and the heat exchange efficiency is improved; energy efficiency loss caused by over-discharging and over-charging is avoided through online estimation and physical feasible interval constraint of the SOC; under the constraint of time-of-use electricity price and demand, the optimization target gives consideration to comfort, energy consumption cost and actuator smoothness, and overall planning of energy efficiency and equipment life is realized; timestamp synchronization, degradation control and an early termination mechanism in a communication disturbance scene ensure real-time performance and continuity, and penalty parameter self-adaption and iteration step upper limit control enhance robust convergence under the condition that time delay and computing resources are limited.

This application relates to a method, apparatus, and electronic equipment for assessing the demand response potential of a zero-carbon hydrogen metallurgical system. The method includes: constructing an operating model of a hydrogen-based shaft furnace considering the metallization rate of direct reduced iron; establishing an objective function based on the production baseline load of the zero-carbon hydrogen metallurgical system; constructing a demand response potential assessment model for the zero-carbon hydrogen metallurgical system based on the objective function, constraints of the electrolyzer in the zero-carbon hydrogen metallurgical system, and constraints of the operating model of the hydrogen-based shaft furnace; determining the demand response potential assessment index for the zero-carbon hydrogen metallurgical system; and assessing the demand response potential of the zero-carbon hydrogen metallurgical system under real-time electricity pricing based on the assessment model and the assessment index. This solves the problems in related technologies, such as the lack of assessment of the flexibility of the zero-carbon hydrogen metallurgical system and the limited utilization of flexibility resources, leading to severe wind and solar power curtailment, grid backflow, and voltage exceedances, resulting in high production costs.

The invention discloses a demand cost-saving user side energy storage fine configuration method, which relates to the field of energy storage configuration, and comprises the steps of selecting key evaluation indexes and classifying users based on electrical load characteristics of the users, a distributed power supply access condition and an electricity price structure; according to a user classification result, demand capacity and arbitrage capacity are calculated respectively, and differentiated energy storage capacity configuration is carried out for users; on the basis of the typical daily load curve of the user, the optimal energy storage configuration capacity and operation strategy are determined through an optimization algorithm with the aim of improving the overall economy of the system, and a final recommended configuration scheme is formed; and calculating the total cost according to the initial investment, the operation and maintenance cost, the equipment replacement cost and the charging cost, comprehensively considering the demand electric charge reduction income and the peak-valley arbitrage income, and carrying out full-life-cycle economic evaluation by taking the net present value and the internal return rate as core evaluation indexes. According to the method, the comprehensive economic benefits of the energy storage system are maximized through differential capacity division and a collaborative optimization strategy.

The present application belongs to the technical field of low-carbon power system, and relates to a source-load collaborative day-ahead low-carbon dispatching method based on node carbon potential. First, a node carbon potential calculation model is established, and a power tracing model of unit output to load node is constructed. Second, the regional average carbon emission factor is taken as a limit to identify the carbon potential exceeding node, and the power tracing model is used to calculate the sensitivity of unit output to the carbon potential of each node, so as to construct a model for source side to share the responsibility of high carbon potential load node exceeding carbon potential. Third, carbon emission modeling is carried out on independent energy storage, reducible load and transferable load, and a step-by-step carbon trading cost is introduced to establish a load side low-carbon demand response model driven by node carbon potential and time-of-use electricity price double signals. Finally, a day-ahead economic dispatching optimization model based on source-load bidirectional perception is constructed and solved. Through iterative calculation, the optimal source-load day-ahead dispatching plan with the minimum system total operation cost as the target is obtained.

This invention relates to the field of energy storage system control technology, and discloses a collaborative control method and system for energy storage cooling capacity and peak-valley arbitrage. The method includes: collecting multi-dimensional operational data of the energy storage system and electricity market transaction data; generating a thermoelectric prediction tensor using a temporal convolutional network; analyzing the thermoelectric prediction tensor to calculate the cooling urgency weight of the battery cluster and constructing a heat demand Fibonacci stack; extracting the optimal cooling task and generating an arbitrage strategy skip list based on peak-valley electricity price constraints; traversing the skip list to obtain a control instruction set and driving independent heat exchangers and intelligent refrigerant boxes to perform refined temperature control operations. This invention solves the problems of lag in traditional heat dissipation control and difficulty in coordinating peak-valley electricity price arbitrage with temperature control through thermoelectric prediction, dynamic sorting of heat demand, and matching of cooling capacity supply and demand. It achieves stable battery temperature maintenance, maximized peak-valley arbitrage profits, and fully silent operation of the energy storage system.

The invention relates to the field of new energy storage, and provides an energy storage charging and discharging strategy generation method and device, a medium and a program product, and the method comprises the steps: obtaining a first charging and discharging strategy which accords with a time constraint and a predicted power quantile constraint, and obtaining a first charging and discharging income at a first moment according to a first charging and discharging amount and a first real-time node electricity price; when the charging and discharging operation is executed, the charging and discharging state is switched according to the historical charging and discharging operation and the time constraint, and the first reference power at the first moment meeting the predicted power quantile constraint is obtained and serves as the actual power; according to the first reference power and the first day-ahead declaration power, the first day-ahead spot income, the first real-time spot income and the first deviation assessment charging and discharging electric quantity are obtained; and updating the total income and the first deviation assessment charging and discharging electric quantity to a second state dictionary at a second moment, and determining a second charging and discharging strategy meeting a preset constraint from the second state dictionary. According to the scheme, the situation of performance deterioration occurring in execution of the optimal solution of the target function due to power errors can be reduced, and the operation effect of the energy storage system is improved.

The invention discloses a user side energy storage power station operation decision-making method based on hierarchical reinforcement learning. The method comprises the following steps: constructing a prediction model and a hierarchical reinforcement learning framework; performing day-ahead electricity price and load prediction by using the prediction model, designing a Markov decision process, defining an upper reinforcement learning model, designing a mapping method, and obtaining day-ahead declaration amount; performing real-time electricity price and load prediction by using the prediction model, designing a Markov decision process, defining a middle-level reinforcement learning model, designing a mapping method, and obtaining a charging and discharging plan, an output upper limit and an SOC target; combining the frequency, the day-ahead declaration amount, the output upper limit and the SOC target, and utilizing a lower-layer optimization model to decide charging and discharging output; the real electricity price and the load data are utilized to calculate and update operation incomes and rewards; dividing the electricity price data and the load data into a prediction training set, a reinforcement learning training set and a test set, training a prediction model, training a layered reinforcement learning framework, and testing and verifying an operation effect by using the test set.

An electric power system double-layer optimization scheduling method considering electricity-carbon joint demand response belongs to the technical field of electric power system optimization scheduling, and comprises the following steps: firstly, providing a CNN-BiGRU-Attention hybrid neural network, and carrying out wind and light generation power prediction; secondly, establishing an intelligent building cluster carbon emission model comprising an electric vehicle, a reducible load, a transferable load and stored energy, and realizing fine quantification of intelligent building cluster carbon emission; then, a power system double-layer optimization model considering the electricity-carbon joint demand response is established, an upper-layer power grid operator takes the minimum operation cost as the target, a lower-layer intelligent building cluster takes the minimum total cost as the target, loop iteration solution is carried out between the upper layer and the lower layer through node carbon potential, electricity price and electricity demand, and the power system double-layer optimization model is established; a more reasonable scheduling scheme considering both economical efficiency and low-carbon property is provided for the day-ahead optimization scheduling of the power system; an example result verifies that the proposed method can provide a reference for the optimization scheduling work of the power system.

A kind of energy storage system intelligent charge-discharge optimization method and system based on electricity price and capacity prediction.Electricity price prediction model, battery capacity attenuation prediction model, prediction-driven charge-discharge optimization module, control execution module and self-learning feedback module are included in the method and system.The method and system obtain multi-source operation data through data acquisition module and preprocess, predict future electricity price sequence using LSTM time series deep learning model, and predict battery state of health (SOH) by fusing "electrochemical mechanism model + LSTM correction model".The charge-discharge optimization module constructs multi-objective function based on the above prediction results, combines SOC, power, temperature and other constraint conditions, and generates optimal charge-discharge strategy using model predictive control.The method and system aim to solve the technical problems that existing energy storage charge-discharge strategy cannot perceive electricity price changes, does not consider battery capacity attenuation, relies on empirical rules and lacks closed-loop optimization.

The invention discloses a method and system for participating in electric power spot transaction through centralized energy storage, and relates to the technical field of electric power spot transaction energy storage optimization, and the method comprises the steps: employing an LSTM network and a random forest model, and carrying out the real-time prediction of an electric power demand value and a real-time prediction of an electricity price fluctuation value; based on the Actor-Critic architecture, the historical power demand predicted value and the electricity price fluctuation predicted value, constructing a power spot transaction charging and discharging decision model, and outputting a real-time charging and discharging strategy; based on the real-time power demand prediction value and the real-time electricity price fluctuation prediction value, multi-scene simulation is carried out through Monte Carlo simulation, earnings and risks of a multi-scene real-time charging and discharging strategy are calculated, and a simulation score report is output; and through Monte Carlo simulation, charge and discharge strategy operation conditions in different scenes are simulated, income and risk indexes are calculated, and the robustness and stability of the charge and discharge strategy are comprehensively evaluated.

The invention is suitable for the technical field of photo-thermal power generation and the technical field of power distribution, and provides a power distribution system and method based on an electric heater, and the power distribution system based on the electric heater comprises an electric heating module of a photo-thermal power station, a monitoring unit connected with the electric heating module, and a controller connected with the monitoring unit. The power adjusting unit is connected with the controller; the electric heating module is formed by connecting multiple stages of electric heaters in series; the distribution module is used for processing the electricity price and each optimal distribution scheme in each time period by adopting a cost function to obtain the electricity cost corresponding to each optimal distribution scheme, and selecting the optimal distribution scheme with the minimum electricity cost as a target distribution scheme; and the power adjusting unit is used for obtaining the target power of each stage of electric heater from the target distribution scheme and adjusting the current power of each stage of electric heater to the target power of each stage of electric heater. The reliability of the target power of the electric heaters at all levels can be improved.

The invention belongs to the technical field of power systems, and particularly relates to an IPSO-based day-ahead scheduling optimization method for a light storage and charging integrated charging station, which divides day-ahead scheduling optimization into two stages: a first stage, constructing a first-stage day-ahead dynamic electricity price optimization model based on a user demand price elastic matrix, and solving by using a gurobi solver; the EV charging cost is effectively reduced, the economic benefit of a user is improved, meanwhile, the load peak-valley difference is further reduced, and the power utilization reliability is improved; in the second stage, the dynamic service price of the station in the first stage and the adjusted charging demand curve are combined, the purposes of maximizing the economic benefits of the station, improving the utilization rate of renewable energy sources and improving the charging experience of users are achieved, the physical characteristics of charging station equipment and the electrochemical characteristics of energy storage are considered, and a station operation control model is constructed; physical constraint conditions are coupled to establish a day-ahead multi-objective optimization model; and solving the model based on an improved particle swarm optimization (IPSO), and finally obtaining an optimal charging station energy scheduling management strategy.

The invention discloses a hybrid energy storage optimal configuration method and device considering economic wind curtailment, and relates to the technical field of electric power system planning, and the method comprises the steps: carrying out the wind curtailment scene simulation of an original wind power curve through employing a segmented test method, calculating the annual wind curtailment loss cost and the wind curtailment rate of each wind curtailment scene based on time-of-use electricity price data, and calculating the wind curtailment rate of each wind curtailment scene; simulating until the wind curtailment rate reaches the specified upper limit of the power grid; and traversing a power distribution scheme for each power curve after wind curtailment, determining power components of the supercapacitor and the storage battery through empirical mode decomposition, and further calculating rated power and rated capacity of the supercapacitor and the storage battery. And constructing a system operation constraint based on the rated power, the rated capacity and the upper limit of the wind curtailment rate, calculating the annual comprehensive cost of each wind curtailment scene and the power distribution scheme under the condition that the constraint condition is met, and selecting the scheme with the minimum cost as a final optimization configuration scheme. According to the method, economic balance among the wind curtailment loss cost, the energy storage cost and the operation and maintenance cost is realized, and excessive configuration of energy storage can be effectively avoided.