782 results about "Demand response" patented technology

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.

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 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.

Provided are a method and apparatus for optimizing an energy storage system, and a device and a storage medium. The method comprises: on the basis of an energy storage configuration to be solved, unit energy storage configuration cost, demand response cost to be acquired, net load fluctuation value to be acquired, cost discount rate and energy storage service life of an energy storage system to be optimized, constructing an energy storage configuration objective function; on the basis of a total demand response duration, demand response load, system output value, electricity price to be solved and unit clean energy curtailment cost of the energy storage system to be optimized, constructing a demand response objective function; on the basis of the energy storage configuration objective function, energy storage configuration constraints, the demand response objective function and demand response constraints, jointly solving the energy storage configuration to be solved and the electricity price to be solved to obtain a target energy storage configuration and a target electricity price, so as to realize the optimization of the energy storage system to be optimized. Thus, the operating costs are reduced and clean energy integration is also maximized.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention relates to the technical field of power dispatching management and control, and discloses a power distribution network power dispatching method based on a virtual power plant AI large model and demand response, and the method comprises the steps: collecting the operation data of a power distribution network, and constructing a feature vector; an AI large model is adopted to calculate and predict load output, and joint uncertainty information is output; calculating a system power unbalance amount, and generating a scheduling strategy; issuing a scheduling instruction corresponding to the scheduling strategy and executing the scheduling instruction; comprehensive performance evaluation indexes are calculated, and whether a performance reduction reason diagnosis mechanism is started or not is judged; according to the method, the AI large model is adopted, the load power, the photovoltaic output and the wind power output are predicted at the same time through a multi-task learning strategy, and the correlation among multiple variables is fully utilized; by constructing a multi-objective optimization model, comprehensively considering economy, safety and reliability and adopting an improved particle swarm optimization algorithm for solving, coordinated optimization configuration of demand response resources is realized, power grid fluctuation is effectively reduced, and power supply reliability is improved.

The invention discloses a distribution line load prediction and optimal scheduling method and system, and relates to the technical field of intelligent scheduling of power systems, and the method comprises the steps: generating a time-aligned multi-source fusion input data set; constructing a mixed time sequence load prediction model, and introducing a weighted quantile loss function in a model training process; constructing a joint probability distribution model of the renewable energy output and demand response participation rate, and sampling joint probability distribution; constructing a rolling time domain power distribution network optimization scheduling model; a two-layer mixed strategy is adopted to deal with uncertainty, an optimization problem is decomposed into a plurality of sub-problems, and an alternating direction multiplier method with adaptive penalty parameters is used for distributed solution. According to the method, a multi-objective optimization scheduling model is established in a rolling time domain, and dynamic closed-loop optimization is realized; and by introducing a two-layer hybrid solving strategy and an ADMM distributed algorithm with an adaptive penalty parameter, the calculation efficiency and expandability are remarkably improved while the global consistency is ensured.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

The invention relates to the technical field of charging station regulation and control, and discloses a charging station regulation and control method and system considering ordered charging and demand response. The method comprises the steps of processing uncertainty of vehicle arrival time and user departure time through a demand prediction model, constructing a scheduling optimization model to maximize charging station income, and comprehensively considering charging income, V2G discharge income, electricity purchase cost and user waiting penalty. After a power grid demand response signal is received, two-stage decoupling optimization is carried out, the first stage is to optimize a charging pile shutdown strategy, and the second stage is to optimize a vehicle scheduling scheme. The time uncertainty is adaptively adjusted through an affine decision rule, a mixed integer linear programming solver is utilized to obtain an optimal regulation and control instruction of the charging station, and robust scheduling execution is realized. According to the method, the problem that the charging station cannot realize multi-target collaborative optimization scheduling under double challenges of demand prediction uncertainty and power grid demand response is solved, and the robustness and the economic benefit of a charging station scheduling scheme are improved.

The invention provides a data center calculation, electricity and heat collaborative optimization scheduling method and system, and relates to the technical field of comprehensive energy scheduling. According to the scheduling method and system, a collaborative scheduling model of computing power, electric power and thermal power is constructed, and a user side thermal demand response mechanism is introduced, so that mismatching of waste heat supply of a data center and user thermal demand in time and space is dynamically relieved, and the waste heat utilization rate and the overall energy efficiency of the system are remarkably improved; by establishing a joint optimization framework, a complex coupling relationship among computing power, electric power and heating power is accurately described and coordinated, so that the comprehensive operation cost of the data center is minimized on the premise of ensuring the service quality of the workload of the data center; and a large language model-assisted deep learning algorithm is further adopted to solve the scheduling model so as to cope with multiple challenges of workload fluctuation, electricity price change and heat demand uncertainty, and a self-adaptive, intelligent and interpretable scheduling decision is realized.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention relates to a multi-time-scale toughness scheduling strategy of a multi-energy complementary system under an extreme high temperature condition in optimal scheduling of a power system. In order to solve the problems of load rising caused by high temperature, derating of a source network and difficulty in guaranteeing power supply safety by traditional economic dispatching, a source-network-load-storage temperature effect model of photovoltaic, thermal power, a power transmission transformer and a load is constructed, and a day-ahead, day-intraday and real-time three-layer collaborative optimization framework is embedded; weighted unsupplied electric quantity is used as a toughness index, node vulnerability and load grade weight are combined, dictionary order optimization is adopted before the day, prediction deviation is corrected in a rolling mode within the day, toughness-oriented model prediction control and rolling supply stop window constraint are adopted in real time, and conventional unit, energy storage and layered demand response are cooperatively scheduled. Therefore, load loss is reduced and new energy is abandoned in an extreme high-temperature scene, and the power supply guarantee capability of key nodes and important loads and the overall toughness and economical efficiency of the system are improved.

The invention relates to a generalized energy storage and micro-grid collaborative low-carbon operation method based on a dynamic electricity price strategy. The method comprises the following steps: S1, constructing a micro-grid operator unified scheduling model framework and initializing an electricity price signal; s2, constructing a demand response model driven by user satisfaction, and obtaining a user load curve; s3, generating an uncertainty random disturbance scene set based on a wind and light power prediction probability density function; s4, establishing a generalized energy storage model, and respectively constructing a virtual energy storage model of entity energy storage and EV aggregation in the multi-microgrid; step S5, constructing a low-carbon optimal scheduling model for the flexibility resource collaboration of the microgrid group; s6, solving the multi-microgrid scheduling optimization model in a wind-solar disturbance scene, and extracting a tail scene mean value as a risk constraint target of scheduling optimization based on CVaR; and S7, the micro-grid operator corrects the direction of the electricity price signal based on a CVaR result, and iteratively updates in combination with a differential evolution algorithm until the target value tends to be stable.

The invention provides a virtual power plant load prediction and demand response optimization method and system, and relates to the technical field of power systems, and the method comprises the steps: carrying out the multi-scale time sequence decomposition of historical data, obtaining a hierarchical feature set, recognizing a load fluctuation transmission link through cross-equipment correlation analysis, calibrating a load prediction time reference based on response delay time, and carrying out the optimization of demand response. The adjustable capacity and response time delay of energy equipment are calculated, the equipment is divided into a plurality of virtual aggregation units, a collaborative scheduling rule is established, and a distributed demand response instruction is generated through Nash equilibrium optimization. According to the invention, the load prediction precision and demand response capability of the virtual power plant are improved.

The invention relates to a high-robustness V2G-VPP multi-time scale closed-loop feedback cooperative scheduling method, and the method comprises the following steps: obtaining V2G-VPP system multi-source data in real time, and carrying out the data cleaning and feature extraction processing; a day-ahead-day-real-time three-level scheduling architecture is constructed, uncertainty of key parameters in a system is quantitatively described by adopting a multi-dimensional uncertainty modeling method, and in day-ahead robustness scheduling, a day-ahead scheduling plan is made by taking the minimum total cost as a target; performing intra-day robustness scheduling on the basis of the day-ahead scheduling plan, and performing further correction according to the SOC state and the charging and discharging cost to obtain an intra-day correction scheduling plan; and performing real-time scheduling on the basis of the intra-day correction scheduling plan to form a real-time scheduling instruction, forming a feedback signal according to an execution result, adjusting demand response excitation, and forming closed-loop feedback. Compared with the prior art, the robustness of the virtual power plant to an extreme operation scene can be remarkably improved.

The invention discloses a power grid energy storage demand response multi-agent reinforcement learning dynamic optimization method, which belongs to the technical field of power system scheduling and optimization control, and comprises the following steps of: constructing a joint optimization model based on a Markov decision process, representing an energy storage charge state, demand response regulation potential and renewable energy output fluctuation, defining a state space, and establishing a dynamic optimization model; establishing a joint action space; constructing a dual-network structure; an improved hierarchical time memory network is introduced, spatial-temporal features are extracted through a multi-level memory unit and a spatial-temporal attention mechanism, and the spatial-temporal features are used for strategy optimization and value evaluation; optimization learning is carried out by adopting multi-agent interaction and centralized training decentralized execution, and strategy updating is carried out by combining an experience playback pool and target network soft updating; and outputting a power grid dynamic scheduling strategy, realizing energy storage and demand response joint optimization, and improving system stability and adaptive capacity under a high-proportion renewable energy access condition.

The invention discloses an island micro-grid multi-objective optimization scheduling method based on a pond goose optimization algorithm, and the method comprises the steps: dividing a power supply into a basic load power supply and a frequency modulation power supply through building a micro-grid model which comprises a renewable energy output model, a demand response model and a constraint condition, a basic load power supply scheduling scheme is optimized by adopting a gooseneck optimization algorithm to minimize the average operation cost of a sample, and a frequency response strategy of a frequency modulation power supply is optimized by utilizing a long-short term memory network to predict power distribution based on historical power shortage data, so that multi-target optimization scheduling is realized. And the operation efficiency, the stability and the renewable energy consumption capability of the island micro-grid are improved.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention discloses a park energy double-layer optimization scheduling method considering electric vehicles and demand response, and the method comprises the following steps: constructing a park integrated energy system which comprises an energy supply side, an energy conversion side and a demand side; a double-layer optimization model is established, the upper layer is a multi-target optimization model with the target of minimizing the total cost of the system, minimizing the carbon emission and maximizing the renewable energy consumption rate, and the lower layer is a single-target optimization model with the target of minimizing the charging cost of the electric vehicle user and the load fluctuation of the power grid; based on the Pareto theory, designing a multi-objective litsea coreana optimization algorithm to solve an upper-layer multi-objective model; introducing chaos initialization, adaptive step length adjustment and a Gaussian disturbance strategy improved litsea coreana optimization algorithm to solve a lower-layer single-target model; and through a vehicle network interaction and demand response cooperation mechanism, real-time scheduling is executed, a day-ahead plan is fed back and corrected, and finally an optimal park energy optimization scheduling strategy is solved.

The invention discloses an optimization system considering differentiated demand response of electric vehicles, the electric vehicles participating in demand response are divided into an agile contract-signing EV and a stable contract-signing EV, the agile contract-signing EV aims to maximize self-income, minimize mileage guarantee and load curve variance weighted difference, and based on an automatic demand response result of the agile contract-signing EV, the agile contract-signing EV performs automatic demand response of the agile contract-signing EV. And the stable contract signing EV performs output power optimization and aggregation by taking minimization of aggregation demand response cost as a target, performs autonomous demand response decision making based on deep reinforcement learning and autonomously participates in power grid demand response. The deep reinforcement learning converts a demand response scheduling problem into a Markov decision process, and the objective of optimizing an objective function of agile contract signing EV and an objective function of stable contract signing EV is achieved through a reward function. Compared with the prior art, the method has the advantages that the differentiated demand response of the electric vehicle is optimized through deep reinforcement learning, so that peak load shifting is effectively carried out on the power grid, and the demand response cost is reduced.

The invention provides a multi-load characteristic-oriented high-power radio frequency power supply parameter matching method and system, and relates to the technical field of containerized load demand response, and the method comprises the steps: configuring P load demand response containers for P load devices connected to a high-power radio frequency power supply; setting a load updating window according to historical load switching response characteristics; synchronously executing real-time electric energy demand dynamic analysis, and outputting P real-time load parameter packets; generalizing the P real-time load parameter packets according to the P load fluctuation characteristics to obtain P fault-tolerant parameter intervals, and merging to obtain a load demand aggregation interval; and after the dynamic output capability range of the high-power radio frequency power supply is locally called, multi-load power distribution control is executed based on a matching result. The technical problem that the output stability of the radio frequency power supply is reduced due to the fact that the adjustment requirements of a plurality of loads are processed mainly through a simple power distribution model and mutual influence and fluctuation characteristics among load equipment cannot be fully considered in the prior art is solved.

The invention relates to a quick-freezing production line control technology, in particular to a multi-temperature-zone energy consumption dynamic balance scheduling method for a quick-freezing production line. The method comprises the following steps: acquiring real-time operation data of each temperature zone of the quick-freezing production line; establishing a digital twinborn model for each product unit based on the real-time operation data; collecting schedulable flexibility data of a refrigerating unit, a fan and a cold storage device; constructing a virtual cold energy aggregation model; determining a batch scheduling and strategy network based on a reinforcement learning strategy, performing rolling optimization on the batch scheduling and strategy network, and outputting a power regulation sequence and a batch start-stop action sequence; and issuing the power regulation sequence and the batch start-stop action sequence to the refrigerating unit, the fan and the cold storage device, performing prediction and optimization, and updating the digital twin model and the strategy network. Therefore, core temperature control and freezing margin dynamic scheduling of multiple temperature zones of the quick-freezing production line are realized, and the energy efficiency level and the demand response capability of the system are improved.

The invention relates to the technical field of energy scheduling, in particular to a flexible resource electricity price optimization method considering a master-slave game under a spot market condition, which comprises the following steps: constructing a double-layer electricity price optimization model taking a flexible resource operator as a leader and taking an energy equipment operator EEA and a load aggregator LA as followers based on a master-slave game framework; and solving an optimal operation strategy of the double-layer electricity price optimization model. Based on a master-slave game framework, a two-level dynamic decision model with a flexible resource operator as a leader and an energy equipment operator EEA and a load aggregator LA as followers is constructed, power generation side output optimization and load side demand response are guided through an electricity price signal, and resource collaborative configuration and overall revenue maximization are achieved. And designing an energy storage compensation mechanism and a flexible load excitation strategy, and introducing a whale optimization algorithm to solve a multi-period game equilibrium problem, so as to improve the economy and operation flexibility of flexible resources in a spot market.

The invention relates to the technical field of power distribution network toughness demand response, in particular to a power distribution network toughness improvement optimization method, system and device based on response prediction and a storage medium. Training an integrated decision tree model based on historical data to predict a response intention value of the cooling and heating load user, and determining a temperature regulation boundary and a response frequency upper limit according to the response intention value; generating an initial scene set through source load uncertainty sampling, extracting a typical scene by adopting a transportation distance scene reduction method, and introducing an overall offset constraint and a single-point extreme value constraint to perform two-dimensional limitation on scene probability distribution; a scene probability combination enabling the load recovery value to be minimum is searched in a probability distribution domain, and a scheduling scheme enabling the key load recovery value to be maximum is solved under the condition that the power flow constraint, the cold and heat power balance constraint and the temperature regulation boundary constraint are met; and carrying out probability weighting on the load recovery values of different scenes to obtain a toughness evaluation value, and carrying out sensitivity analysis.

The invention discloses a building air conditioner energy consumption-electric charge-comfort level intelligent control method based on MMOE and ParetoMTL, and the method comprises the following steps: (1) collecting the cooling load data of a large commercial building air conditioner, carrying out the abnormal value detection, and completing the normalization preprocessing; (2) constructing an environment-price-behavior multi-source time sequence data set and performing grading standardization; (3) dividing a training set, a verification set and a test set according to building grouping; (4) completing feature fusion and realizing task decoupling under cooperative constraint by using an MMOE mechanism; (5) jointly training a three-branch expert network, an MLP gating network and a PCGrad shared parameter optimization model; (6) generating a multi-task prediction and Pareto solution set on the test set; and (7) evaluating prediction performance and trade-off efficiency by using a plurality of indexes. According to the method, multi-target conflict, physical consistency and Pareto solution set missing difficulty can be effectively solved, and a new scheme is provided for demand response HVAC intelligent control.

The invention provides a virtual power plant demand side response and load management method and system, and relates to the technical field of power system scheduling, and the method comprises the steps: obtaining a demand side response request and a historical response track of each distributed energy source; performing time-frequency domain decomposition on the historical response trajectory, extracting a transient component and a steady component, constructing a dynamic response model containing the current output power and the load deviation cumulant, and calculating load deviation distribution through Kalman filtering; a Markov decision process is constructed based on load deviation distribution, an optimal power distribution strategy is solved by adopting a value iteration algorithm, and a response execution scheme is generated; collecting a power change sequence after execution of each distributed energy source, converting the power change sequence into a load recovery demand vector, constructing a recovery capacity constraint cone based on the current available adjustment margin of the power grid, and solving an optimal projection point; and performing hierarchical division according to the optimal projection point to obtain a staged recovery sequence. According to the method, the accuracy of virtual power plant demand response and the coordination of load recovery are improved, and the power grid regulation pressure is reduced.

The invention relates to the technical field of power system resource coordination control, and provides a novel power system flexible resource coordination control method and system, and the method comprises the steps: enabling an energy storage system, a demand response system and a thermal power generating unit to serve as resources, collecting new energy output data, load data and resource state data, setting a short time window, a medium time window and a long time window, calculating system net load fluctuation and fluctuation components, and determining an initial control instruction of resources; calculating a response adaptation degree and an effective response capability; and calculating an adjustment weight, setting a target resource, calculating overflow power, calculating a demand response control instruction, realizing overflow distribution until power balance, and realizing resource coordination control. According to the invention, the coordination effect of different types of flexible resources can be improved.

The invention discloses a multi-modal information fusion load prediction method based on a cross attention mechanism. The method is suitable for an integrated energy station energy efficiency optimization scene. The method comprises the following steps: firstly, collecting and preprocessing multi-source time sequence data such as meteorological texts, satellite cloud pictures and temperature and humidity, and extracting high-dimensional semantic representations through texts, images and a time sequence encoder respectively; secondly, setting a modal significance pre-gating mechanism, and adaptively adjusting the importance of each modal feature; a time lag compensation cross attention module is introduced to realize time alignment and semantic association among multiple modes; and finally, realizing unified representation of weather, space and time information through a multi-modal fusion network, and outputting a cooling load prediction result. According to the method, the dynamic change rule of the cooling load of the comprehensive energy station can be accurately described under the complex meteorological condition, the prediction precision and stability are remarkably improved, and technical support is provided for cold machine starting and stopping, energy storage scheduling and demand response optimization of the comprehensive energy station.

The invention relates to the technical field of park comprehensive energy system low-carbon operation, in particular to a park comprehensive energy system low-carbon demand response method and system, and the method comprises the following steps: carrying out the low-carbon demand response of a load side and an energy storage side based on a power side CCS-P2G coupling operation mechanism; constructing a source-load-storage collaborative carbon reduction park integrated energy system low-carbon operation framework; aiming at carbon emission characteristics of park equipment and energy storage, establishing a park integrated energy system extended carbon emission flow model; calculating a dynamic carbon emission factor based on an extended carbon emission flow model; and the dynamic carbon emission factor is used as a guide to excite a user to change a power utilization mode and change charging and discharging behaviors through energy storage so as to carry out low-carbon demand response. Compared with the prior art, the method has the advantages that the low-carbon potential of resources on the load side and the energy storage side is effectively excavated, and the carbon emission of PIES is effectively reduced; the extended carbon emission flow model provided by the invention can overcome the difficulty of carbon emission flow analysis caused by diversified equipment in PIES.