1912 results about "Demand response" patented technology

The invention discloses a virtual power plant collaborative optimization operation method based on distributed energy, and the method achieves the real-time monitoring of the operation state of a distributed energy resource through the precise modeling of the distributed energy resource, and predicts the change of the power load demand. A multi-objective optimization model and an advanced optimization algorithm, such as a genetic algorithm or a particle swarm algorithm, are utilized, so that the virtual power plant can dynamically adjust energy supply and load demands, and economic benefit maximization, energy efficiency optimization and carbon emission minimization are realized. The method also considers the energy efficiency and economic benefits of the virtual power plant, and improves the comprehensive benefits of the system through comprehensive analysis and optimization. Through effective interaction with the power grid, the virtual power plant can provide peak regulation, frequency modulation and standby services, a demand response mechanism is implemented, and operation of the power grid is further optimized. The method has the beneficial effects of improving the energy utilization efficiency, enhancing the stability of a power grid, optimizing economic benefits, promoting environmental protection, improving the flexibility and adaptability of the system and supporting policies and market mechanisms.

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The present invention belongs to the technical field of building integrated energy. Disclosed is a virtual energy storage-considered double-layer optimization scheduling method for a building integrated energy system, the method comprising: constructing an energy hub-based low-carbon building integrated energy system containing wind-solar energy storage and energy conversion devices; comprehensively analyzing characteristics of loads of the system to improve the demand response capability thereof; further providing a double-layer optimization model containing an upper-layer energy operator pricing layer and a lower-layer building user optimization layer, building virtual energy storage and building user comfort indicators being considered in said model to improve the system scheduling flexibility so as to construct an overall user satisfaction indicator; and finally, solving the double-layer optimization model to optimize device contributes, demand responses and electricity purchasing and selling plans of the building integrated energy system, so as to obtain an optimal scheduling policy. The present invention can finely regulate and control various loads of the building integrated energy system, thus improving the energy utilization efficiency, alleviating the power supply pressure of the system, and achieving the purposes of energy conservation and emission reduction of buildings.

The invention relates to the technical field of energy scheduling, and discloses an intelligent optimization scheduling method of an informatization energy management system, which comprises the following steps of: data acquisition and fusion: acquiring real-time data of each node in an energy system through distributed Internet of Things equipment and a multi-mode sensor network, and fusing and processing the acquired data to obtain a data fusion result; a high-quality data set in a unified format is generated; and prediction modeling and demand response: based on user behavior analysis and a machine learning algorithm, establishing a demand response model, and constructing a multi-stage energy demand and supply prediction model including short-term, medium-term and long-term time dimensions. Through combination of a collaborative optimization algorithm and a distributed decision-making mechanism, a global optimal energy scheduling scheme is realized on the basis of local optimization of each distributed energy unit, high collaboration and optimal resource allocation among the energy units in a complex energy network are ensured, and the energy scheduling efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the whole system are improved.

The invention relates to the technical field of power-carbon collaborative comprehensive energy systems, in particular to a virtual power plant robust collaborative optimization method considering uncertainty demand response. According to the method, a multi-dimensional feature fusion demand response model is established, and the characteristics of four dimensions of time delay, adjusting speed, tracking power and response energy and the influence of the characteristics on demand response precision are identified and analyzed. And fitting a normal distribution function through historical data, estimating a confidence interval of uncertainty features, constructing a multi-dimensional coupling deviation model, and accurately depicting a demand response dynamic contour. A two-stage optimization strategy is adopted, the first stage is an instruction decomposition model, the second stage is an instruction tracking model, and the operation cost and the total penalty cost are minimized through a mixed integer linear programming and energy storage system deviation compensation strategy. According to the method, the uncertainty in demand response can be effectively managed, and the flexibility and economy of the virtual power plant are improved.

The invention provides a photovoltaic energy storage collaborative optimization scheduling prediction method and system based on reinforcement learning, and relates to the technical field of photovoltaic energy storage, and the method comprises the steps: constructing a multi-target evaluation system containing peak-valley difference minimization and demand response income, and combining a hierarchical strategy selection mechanism of adaptive weight to determine an optimal scheduling strategy, and parameter optimization and rolling prediction are carried out by using the dual Q network action value evaluation model, so that optimal scheduling of the energy storage system is realized. The economic benefit of the photovoltaic energy storage system can be effectively improved, the peak-valley difference of the power grid is reduced, and the stability and reliability of system operation are improved.

The invention discloses an industrial park demand response potential assessment method and system. The method comprises the following steps: collecting basic data of each user in an industrial park; load characteristic key indexes are determined, and a typical daily load curve of each user is extracted through clustering analysis; the production characteristics of the user equipment are analyzed, and an interactive response model capable of translating, transferable and reducing loads is established; a multi-target optimization model with the minimum park load peak-valley difference and the minimum operation cost as targets is constructed; and carrying out statistics on the variable quantity of each user load in an optimization result, and evaluating the peak clipping and valley filling potential of the industrial park in different time periods. The method effectively improves the accuracy of industrial park demand response potential evaluation, can accurately describe the power regulation characteristics and time translation characteristics of the flexible load resources of industrial users in different periods of time within the day, fits the actual power consumption behaviors of the users, can accurately obtain the upper and lower adjustable potentials of the industrial park, and improves the evaluation accuracy of the industrial park demand response potential. And a powerful basis is provided for formulating a reasonable load interaction strategy for the industrial park.

The invention discloses an electric power system climbing demand evaluation and optimization method considering load side resources, and the method comprises the steps: providing a calculation method for maximum upward and downward climbing demands in different time periods, including a calculation method for deterministic and uncertain climbing demands, in order to effectively cope with the fluctuation and uncertainty of loads or new energy; a climbing demand optimization model which takes minimization of the uncertainty climbing demand as an objective function and takes constraint conditions such as load adjusting quantity and demand response into consideration is provided; a gradeability optimization model which takes minimization of comprehensive consumables of the power system as an optimization target and considers constraints such as node balance and line power flow is provided; modeling is carried out on AMPL software, a solver GUROBI is called for solving, and an electric power system climbing demand evaluation and optimization scheme is obtained. The comprehensive operation consumables of the power system can be effectively reduced, and the net load peak load shifting effect on the power system is achieved, so that the operation reliability of the power system is improved, and the dispatching pressure of the power system is relieved.

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

The invention relates to the technical field of power system optimization, and discloses a regional building group source network load storage demand response optimization method. Comprising the following steps of multi-source heterogeneous data fusion collection and intelligent preprocessing, power utilization behavior spatial-temporal characteristic deep mining, multi-dimensional response potential dynamic evaluation modeling, multi-target layered optimization decision generation, personalized excitation strategy self-adaptive generation and closed-loop cooperative regulation execution and feedback. According to the method, user strategy updating is simulated through a replication dynamic equation of an evolutionary game, efficient search of excitation parameters is realized by combining a Bayesian optimization Gaussian process and an expectation improvement function, a user group strategy evolution rule can be dynamically captured, parameters such as electricity price discount and subsidy gradient are accurately optimized in a limited sampling range, and the method is suitable for large-scale popularization and application. A'behavior modeling-data optimization 'closed loop is formed, users are stimulated to participate in demand response, optimal configuration of power resources is realized, and the flexibility and economy of the system are improved.

The invention belongs to the technical field of energy storage resource regulation and control, and particularly relates to a distributed energy storage network resource regulation and control decision-making method, which comprises the following steps of: collecting and processing multi-level data; a two-way interaction mechanism with a user side is established, the electricity demand and the energy storage intention of the user are collected, and a demand response signal is sent to the user to encourage the user to adjust the electricity consumption behavior when the electricity is insufficient or the electricity price fluctuates; and based on the multilevel data and the user demand response result, constructing a dynamic energy routing model of the distributed energy storage network, and predicting future energy demand and supply conditions. According to the method, multi-level data can be comprehensively utilized, the accuracy of energy demand prediction is improved, the flexibility and the intelligent level of a regulation and control strategy are enhanced, a global optimization method is adopted for resource regulation and control, and the robustness and the reliability of the system are improved.

The invention provides an active power distribution network electric vehicle V2G optimization method and system considering user will, and belongs to the technical field of power distribution network operation scheduling, and the method comprises the steps: obtaining the real-time basic load, photovoltaic output and electric vehicle charging and discharging power data of power distribution network operation; calculating the real-time net load of the power distribution network based on the acquired data; based on the real-time net load, dividing the time-of-use electricity price by using a membership function; determining a demand response price upper limit; within the interval of the upper limit and the lower limit of the demand response price, the discharge electricity price of the electric vehicle user participating in the demand response is determined according to the time-of-use electricity price and the peak clipping compensation coefficient; whether the V2G condition is met or not is inspected according to whether the electric vehicle networking time preset by the user according to the travel demand is greater than the charging and discharging time or not; if yes, dispatchable electric vehicle discharge power is obtained; and constructing an active power distribution network electric vehicle V2G optimization scheduling model, and solving the optimization scheduling model to obtain the optimal values of the discharge electricity price, the actual scheduling discharge power and the photovoltaic consumption rate.

The invention discloses a power grid operation control method for realizing source-grid-load-storage cooperation, and relates to the technical field of power grid operation control, and the method comprises the steps: carrying out the ultra-short-term prediction of power grid operation data through LSTM, ARIMA, VAE, MDP and other models, covering the new energy output, power flow change, load demand and energy storage state, and carrying out the fusion of the day-ahead prediction and real-time prediction data, thereby achieving the real-time prediction of the power grid operation data. Optimizing a prediction result by using a weighting method; in the aspect of scheduling optimization, a scheduling optimization model is established according to prediction data, a day-ahead scheduling plan is calculated, the operation cost of a power grid is minimized, and meanwhile, source-side wind and light output, grid-side power flow regulation and control, load-side demand response and a storage-side charging and discharging strategy are optimized; scheduling optimization is introduced, so that the output of a conventional unit is reduced in a wind power high output period, and the new energy consumption rate is improved; in the real-time scheduling stage, dynamic optimization is carried out by adopting MPC in combination with a day-ahead scheduling plan and real-time prediction data; and when the wind power output is excessive, the energy storage equipment is controlled to absorb redundant electric energy.

The invention discloses a park integrated energy scheduling method and system based on a master-slave game model, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting basic operation parameter data in a park integrated energy system; constructing an upper-layer game model taking an operator as a dominant, setting constraints, and making an electric heating and cooling price strategy and a unit output plan by taking operator profit maximization as a target; constructing a lower-layer game model, and making a load energy consumption strategy by taking user energy consumption cost minimization as a target; and converting the upper-layer game model and the lower-layer game model into a mixed integer linear programming problem, and obtaining an electric heating cold price scheduling scheme and an output scheduling scheme of each unit by adopting an optimization solution algorithm. The building thermal inertia and demand response mechanism is introduced, the KKT condition, the linear relaxation technology and the strong dual theory are used for converting the complex problem into the MILP model easy to solve, optimization of operator and user benefits is achieved, and the calculation speed and the solving efficiency are remarkably improved.

The invention discloses an intelligent water supply management system and method based on wind-light-water multi-source data fusion, and relates to the technical field of electric digital data processing. The system comprises a time alignment deviation quantification module, a time alignment regulation and control module, a data fusion timeliness response quantification module and a timeliness response regulation and control module. According to the method, the time alignment deviation quantization result is obtained by quantizing the multi-source data time parameters, whether time alignment regulation is carried out or not is judged, if yes, the timeliness response parameters are obtained after regulation, and if not, the timeliness response parameters are directly obtained and quantized to obtain the data fusion timeliness response quantization result; and whether PLC scanning interval regulation and monitoring verification are carried out or not is judged based on the data fusion timeliness response quantification result, so that the timeliness of water supply management demand response is improved, and the problem that in the prior art, the water supply management execution response flexibility is poor due to the fact that the time difference of multi-source data processing is not fully considered is solved.

The invention discloses a multi-target demand response optimization method considering low carbon and user satisfaction, which belongs to the technical field of demand side management and optimization scheduling, and comprises the following steps: randomly generating an initial scheduling solution set containing a population 1 and a population 2, establishing a multi-target scheduling model simultaneously including power balance constraint, user comfort constraint and resource capacity constraint; an infeasible solution in the population 1 is processed through an adaptive penalty function, the population 2 is optimized through a feasibility priority strategy, and the population is evolved and updated in combination with crossover, variation, tournament selection and environment selection mechanisms; and evaluating a multi-target fitness value of a scheduling solution in each generation of iteration, screening a Pareto frontier solution set, continuously updating the Pareto frontier solution set until a termination condition is met, and selecting a compromise optimization scheduling scheme from the final Pareto solution set. According to the method, low carbon, cost and user satisfaction are integrated, a dual population optimization algorithm is adopted, resource coordinated scheduling is achieved, and system efficiency and environmental benefits are remarkably improved.

The invention discloses an optimal scheduling method for a multi-energy complementary virtual power plant, and the method comprises the following steps: building an objective function of an optimal scheduling model of the multi-energy complementary virtual power plant, the objective function comprising two optimization objectives of economic optimization and carbon emission optimization; establishing constraint conditions of the multi-energy complementary virtual power plant optimization scheduling model, wherein the constraint conditions comprise power supply system balance, gas supply system constraint, heat supply system constraint, heat supply network constraint and cold supply system constraint; based on the objective function and the constraint condition, a two-stage robust optimization model of the multi-energy complementary virtual power plant is constructed, and two types of adjustment resources of distributed energy storage and demand response load are introduced into the two-stage robust optimization model; and for the two-stage robust optimization model of the multi-energy complementary virtual power plant, an original problem is decomposed and iterative solution is carried out by using a column and constraint generation algorithm, and an optimal scheduling strategy for the multi-energy complementary virtual power plant is obtained. According to the invention, the power grid can be flexibly regulated and controlled by optimizing the scheduling strategy.

The invention relates to the technical field of participation of iron and steel industry loads in power grid interaction, and provides a network load game peak regulation optimization scheduling method and system for iron and steel industry loads, and the method comprises the steps: analyzing and obtaining an adjustable potential interval of key production equipment based on the specific load characteristics of the iron and steel industry loads; fine modeling is carried out on the adjustable potential of the iron and steel industry load, production factor constraint conditions are considered, and the adjustable capacity of different devices and multi-production-line combinations is obtained through analysis; and finally, analyzing behaviors of peak regulation strategy participants and formulators, and constructing a master-slave game optimization scheduling model by using the Stackelberg game and solving the model. According to the method, the enthusiasm of the short-process iron and steel industry load participating in power grid peak regulation can be reasonably mobilized, the load income of the iron and steel industry is increased while the peak-valley difference of the power grid load is effectively reduced, and theoretical guidance and reference can be provided for participation of the iron and steel industry load in demand response, interaction with the power grid, optimal scheduling and the like.

The invention relates to the technical field of industrial scheduling, and discloses an excitation-driven demand response-oriented flexible job shop energy-saving scheduling method, which comprises the following steps of: representing an energy-saving scheduling state of a flexible job shop based on an improved heterogeneous disjunction graph, and constructing a graph reinforcement learning model; training the graph reinforcement learning model by adopting a near-end strategy optimization algorithm; and outputting a corresponding scheduling scheme according to the production task and the day-ahead peak clipping invitation based on the trained graph reinforcement learning model. The method effectively coordinates and optimizes the three targets of the completion period, the total energy consumption and the incentive return, provides technical support for power grid operation personnel to design incentive policies, and helps to encourage more industrial loads to participate in power system operation.

The invention relates to a hydrogen-containing comprehensive energy system low-carbon economic dispatching method based on a near-end strategy optimization algorithm. The method comprises the following steps: constructing an equipment model, a carbon transaction mechanism model and a comprehensive demand response model of an electricity-heat-gas-hydrogen comprehensive energy system; based on the equipment model, the carbon transaction mechanism model and the comprehensive demand response model, determining an objective function and constraint conditions of low-carbon economic dispatching of the hydrogen-containing comprehensive energy system; a Markov decision process is used for describing the uncertainty problem of the hydrogen-containing integrated energy system, a state space, an action space and a reward function are determined, a near-end strategy optimization algorithm is adopted for solving, and output of each device is optimized and dispatched. Compared with the prior art, the new energy consumption can be effectively promoted, the economical efficiency and the low-carbon property of the system are improved, real-time scheduling is carried out according to random fluctuation of energy output, and there is no need to depend on accurate prediction or uncertainty modeling of source and load output.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention discloses a reinforcement learning-based mobile vehicle charging and storage dynamic scheduling method and system, and solves the problems of insufficient scheduling flexibility and low peak-valley electricity price utilization rate of a fixed charging facility of an existing parking lot. A dynamic environment model is constructed, the real-time SOC of the mobile charging and storage vehicle, the position topological relation and the charging demand space-time distribution are integrated, and a deep reinforcement learning algorithm is adopted to train an intelligent body to generate a multi-dimensional collaborative optimization strategy. According to the method, a charging / discharging time sequence, a task path and energy distribution are autonomously planned, a reward function mechanism fusing dynamic path cost and energy constraint is innovatively designed, a multi-vehicle asynchronous collaborative decision framework is established, and dual targets of charging demand response efficiency and operation cost optimization are achieved. According to the method, an MCSV hardware embedded system which supports an ROS2 communication protocol and has a real-time sensor data processing capability is deployed, so that effective transition from a theoretical strategy to actual application is realized.

The invention belongs to the technical field of building heat supply and intelligent power grid demand response control, and particularly relates to an intelligent power grid-oriented heat pump dynamic preheating and demand response control method, which comprises the following steps of: identifying building thermal characteristic parameters, introducing real-time environment variables such as external meteorological parameters and the like and thermal resistance and thermal capacity characteristics of a building, excessive or insufficient preheating caused by only relying on a timer is effectively avoided, and the balance of accurate heat supply and energy conservation is achieved; by adjusting the preheating starting time and the peak period temperature control strategy, the heat pump can dynamically respond according to the electricity price or the power grid demand signal, and the peak load is translated or reduced, so that the dual purposes of economic benefits, energy conservation and emission reduction are achieved; and formulating a peak period control strategy and formulating a non-peak period control strategy. According to the control method, actual thermal characteristics and external meteorological conditions of a building are taken as cores, load reduction in the peak period of a power grid and indoor thermal comfort are taken as double targets, and preheating time self-adaption, demand response flexibility and system operation reliability are achieved at the same time.

The invention discloses a demand response scheduling decision-making method, device and equipment based on electricity-carbon combination, and relates to the field of distributed energy response scheduling. The method comprises the following steps: acquiring user side information data; the information data comprises a power-carbon joint node marginal electricity price signal corresponding to an operator at the power distribution network end; the information data is determined based on a node marginal electricity price model of power distribution network side electricity-carbon combination; the node marginal electricity price model is determined by an operator at the power distribution network end by adopting a power distribution network branch flow algorithm; determining a user side demand response model; the user side demand response model is constructed based on the information data by taking the minimum power coefficient as a target; and performing mathematical transformation on the user side demand response model by adopting a Markov decision, and performing iterative optimization solution by adopting a deep reinforcement learning method to obtain a scheduling decision scheme so as to coordinate the user side load of the user side and the distributed energy power generation. The invention aims to realize coordinated adjustment and scheduling of the distributed energy and the user load.

The invention provides an intelligent power grid energy optimization management method and system, and relates to the technical field of data processing, and the method comprises the steps: 1, synchronously collecting photovoltaic output fluctuation data, user load demand data and power grid frequency state data, so as to obtain an original monitoring data set; step 2, performing spatial-temporal feature mapping on the original monitoring data set, extracting a multi-dimensional feature vector, determining a dominant feature shaft system based on covariance analysis, and calculating a dynamic coupling degree between the feature shaft systems to divide a regulation and control domain; time sequence key characteristic quantities are selected in a regulation and control domain, a load response state transition path is constructed, a user behavior correction coefficient is deduced according to the load response state transition path, dynamic load balance is achieved through a multi-period rolling strategy, and a load distribution scheme is generated. According to the invention, through real-time dynamic regulation and control of power supply output, an excitation electricity price signal is generated and user demand response is linked, so that intelligent power grid energy supply and demand balance optimization is realized.