930 results about "Integrated energy system" patented technology

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention relates to a low-carbon park comprehensive energy system multi-agent collaborative carbon responsibility allocation method, which comprises the steps of collecting multi-source data of a park, and performing multi-energy flow optimization scheduling by taking the minimum total operation cost of a comprehensive energy system as a target; constructing a dynamic carbon flow distribution matrix by utilizing a bidirectional carbon flow dynamic correction model considering carbon flow transfer caused by an energy storage charging and discharging process; a load carbon responsibility decoupling distribution model is adopted to decouple the total load into a plurality of sub-load categories, and different carbon responsibility distribution coefficients are given to each category of sub-load basis; the method comprises the following steps: performing multi-agent collaborative optimization by taking each benefit party in a park as a game participant, taking a Nash bargaining as a collaborative optimization framework, taking maximization of utility gains of all participants as an objective function, and taking a Shapley value as a carbon responsibility distribution basis, and obtaining a carbon responsibility distribution scheme of each agent in combination with a user carbon responsibility distribution coefficient. Compared with the prior art, the method ensures fairness, efficiency and operability of a carbon responsibility allocation scheme.

The invention discloses a multi-objective optimization method and system for a regional integrated energy system, and the method comprises the steps: constructing a multi-source input sequence sample; inputting a multi-source input sequence sample into the wind power and photovoltaic prediction model for processing, obtaining a current wind power and photovoltaic output optimization prediction result, calculating the current wind power and photovoltaic output optimization prediction result and a really constructed sample, obtaining a combined loss function value to train the model, obtaining the trained wind power and photovoltaic prediction model, processing the sample collected in real time, and obtaining a wind power and photovoltaic output prediction result. Outputting current wind power and photovoltaic output optimal values; and constructing an online optimization model of the integrated energy system, performing online optimization solution on the constructed model by an accelerated particle swarm optimization algorithm to obtain a control strategy of the energy system, issuing the control strategy to the energy equipment, and performing multi-target optimization scheduling. According to the method, the wind power and photovoltaic prediction model is combined with the accelerated particle swarm optimization algorithm, prediction errors are fully considered, and the stability and flexibility of the regional integrated energy system are enhanced.

The invention relates to the technical field of energy prediction, in particular to an energy short-term load prediction method and system based on SE-Block improved Transform. The method comprises the steps of performing reversible normalization preprocessing based on acquired multi-element load sequence data; carrying out feature extraction and fusion on the preprocessed data by utilizing improved cross-scale interaction Patching, wherein the feature extraction and fusion comprise multi-scale feature extraction, cross-scale interaction alignment, residual error correction and dynamic fusion; and performing feature screening on the fused features based on a channel attention mechanism, wherein the feature screening comprises feature response based on improved SE-Block and non-linear interaction of context vectors. Aiming at the non-stationarity of the actual load caused by the influence of meteorological conditions and user behaviors, the model accurately depicts the fluctuation details of the load curve by automatically eliminating the noise interference among multiple variables, and the robustness of the model in the multi-element load prediction of the integrated energy system is reflected.

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 the technical field of hydrogen load capacity configuration, in particular to a multi-scene hydrogen load comprehensive energy system double-layer capacity optimal configuration method, which comprises the following steps of: firstly, constructing a multi-scene model containing strong, weak, discontinuous and non-hydrogen loads based on the time sequence characteristics of hydrogen loads in an industrial park; then a comprehensive energy system mathematical model covering wind and light power generation, hydrogen energy conversion, multi-type energy storage and cogeneration units is established; then, constructing a double-layer capacity optimization configuration model of which the upper layer takes the whole life cycle cost minimization and the lower layer takes the operation cost minimization and the system stability optimization as targets; and finally, solving a Pareto leading edge by adopting a non-dominated sorting genetic algorithm with an elitist strategy, making a decision by utilizing an approximate ideal solution sorting method, and screening out a capacity configuration scheme with optimal comprehensive performance. The problem that a traditional configuration scheme is difficult to adapt to multi-scene hydrogen load fluctuation is effectively solved, and the economical efficiency, the stability and the renewable energy consumption capacity of the system are remarkably improved.

A multi-energy coupling aggregator feasible region rapid identification method relates to the technical field of comprehensive energy system operation optimization, and comprises the following steps: firstly, establishing an optimization operation model which aims at minimizing the total operation cost and covers electricity-gas-heat multi-energy coupling and equipment operation constraints; secondly, on the basis of the model, defining an interactive energy feasible region (IEFR) and a flexible climbing feasible region (FRFR) to be identified; then, a max-min robust optimization model used for depicting a feasible region boundary is constructed, and the max-min robust optimization model is converted into a mixed integer second-order cone programming MISOCP model capable of being efficiently solved through a strong dual theory and a KKT condition; and finally, carrying out iterative solution by adopting a polyhedral projection algorithm based on a dichotomy and a plane cutting mechanism, and rapidly obtaining an accurate boundary of the IEFR and the FRFR.

The invention relates to an integrated energy system optimization method considering a carbon emission reward and punishment mechanism and shared electrochemical energy storage, and the method comprises the following steps: carrying out the modeling of an integrated energy system which comprises energy supply, energy conversion, load and shared electrochemical energy storage; constructing a peak-flat-valley period stepped carbon emission mechanism considering the carbon emission difference of different energies and a dynamic carbon price factor, and determining a corresponding carbon transaction cost / reward according to the total carbon emission of the integrated energy system in a scheduling period; based on a capacity leasing and power service mechanism, modeling is carried out on the cost of the shared electrochemical energy storage mode; an optimization scheduling problem is constructed by taking system operation cost minimization and clean energy utilization rate improvement as targets; and solving the optimization scheduling problem to obtain an optimal output scheme and an energy storage charging and discharging strategy of each energy device in the integrated energy system, thereby realizing optimization of the integrated energy system.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention provides a cross-day two-stage random scheduling method for an industrial park integrated energy system, and the method comprises the steps: fitting the output fluctuation characteristics of new energy power generation equipment in a cross-day time scale based on historical data, and constructing an uncertainty scene set containing a new energy prediction error; establishing a cross-day two-stage stochastic programming model; according to the two-stage model, an optimal scheduling scheme is solved so as to minimize the overall operation cost, and the operation cost comprises the demand electric charge, the electricity purchase electric charge, the new energy power abandoning cost, the unit start-stop cost and the standby penalty cost; and on the basis of tie line power constraint and dynamic response characteristics of the multi-energy coupling equipment, feasibility verification and rolling optimization adjustment are performed on the scheduling scheme. According to the method, the new energy consumption capability of the industrial park integrated energy system under the cross-day time scale can be effectively improved, the total operation cost is reduced, and collaborative scheduling of demand cost optimization and spot market participation is realized.

The invention discloses an electrical integrated energy system toughness planning method considering an extreme event tail risk, and relates to the technical field of integrated energy system optimization, and the method comprises the steps: firstly, considering the massive load shedding risk caused by a small-probability extreme event, and constructing a pre-disaster defense-post-disaster emergency response electrical integrated energy system toughness planning frame; secondly, comprehensively considering toughness planning cost, emergency scheduling cost and tail risk loss in a small-probability extreme scene, and establishing an objective function of a planning model; then, for a disaster stage, establishing disaster prevention resource deployment related constraints of the toughness planning model; then, considering operation constraints of the post-disaster power distribution system and the natural gas system, and establishing post-disaster emergency response related constraints; and finally, performing linearization processing on the established planning model, and calling a commercial solver for solving.

The invention discloses a comprehensive energy optimization scheduling method considering combustion of gas turbines with different hydrogen doping ratios, and belongs to the technical field of gas turbines, and the method comprises the steps: constructing a source-network-load-storage integrated combined cooling heating and power comprehensive energy system which covers an energy production layer, an energy conversion layer, an energy storage layer and an energy consumption layer; wherein in the energy consumption layer, electric load is supplied by gas turbine power generation, wind and light direct supply and energy storage discharge in a combined mode, heat load is borne by gas turbine waste heat, a gas-fired boiler and a heat storage system together, the coverage rate of a sensitive area is increased through the ergodicity of a chaotic system, population aggregation is avoided through initial value sensitivity, and it is ensured that all optimization schemes cover a key decision interval; meanwhile, in the algorithm evolution process, through a self-adaptive crossover variation mechanism, early enhancement of sensitive area exploration to capture efficiency inflection points and later fine development and positioning of an optimal solution, the problem of key solution omission of a traditional algorithm is thoroughly solved, and a more accurate hydrogen doping ratio reference is provided for system operation.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

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 multi-time-scale park integrated energy system distribution robust optimization scheduling method, which comprises the following steps of: constructing an electric heating collaborative system model taking a combined heat and power generation unit as a core, and introducing a carbon transaction mechanism; constructing a confidence set in combination with a 1-norm and an infinity-norm, and respectively making a robust start-stop plan and a flexible operation strategy in day-ahead and intra-day two-stage scheduling; a column and constraint generation algorithm is adopted to decompose the constructed day-ahead and intra-day two-stage model into a main problem and a sub-problem for repeated iterative solution, and an optimal scheduling scheme with both economical efficiency and robustness is obtained. According to the method, the uncertainty of new energy prediction is fully considered, and the coping of the park to the randomness of the new energy can be better played through processing of different time scales.

The invention discloses a waste heat recovery strategy analysis method and related equipment, and the method comprises the steps: firstly obtaining the flow and inlet temperature of each waste heat medium, and calculating the theoretical waste heat recovery power hour by hour in combination with environmental parameters and discharge temperature limitation; an energy conversion model is established based on the power and the equipment performance parameters, and a cascade framework is constructed according to the waste heat grade; and finally, by taking system energy efficiency maximization as a target, establishing an optimization model containing multiple types of constraints for solution, and obtaining an optimal equipment configuration and operation strategy. According to the cascade structure, efficient matching of waste heat and equipment is achieved, and recycling waste caused by mismatching is avoided; variable heat utilization requirements can be dynamically responded through hourly calculation and load constraint, and accurate space-time matching of waste heat and the requirements is achieved; the multi-constraint model enhances the multi-target optimization capability and makes up for the optimization deficiency in the prior art; the system planning process solves the problem of systematicness shortage of an existing method, the waste heat potential is effectively excavated, and the efficiency of the waste heat utilization comprehensive energy system is improved.

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 a hydrogen energy park scheduling method based on an improved cloud drift optimization algorithm, and relates to the technical field of intelligent development and scheduling of a comprehensive energy system, and the method comprises the following steps: building a target function with the minimum economic cost and carbon processing cost of the park comprehensive energy system as a target; and after wind and light power supply constraints, energy storage equipment constraints, power balance constraints, carbon sequestration capacity constraints, carbon capture constraints, gas turbine output and climbing constraints, gas boiler constraints, fuel cell constraints and power-to-gas constraints are set, an improved cloud drift optimization algorithm is used for solving, and the hydrogen energy park is scheduled. According to the invention, the bottleneck of insufficient regulation capability of a single energy network is broken.

The invention provides a comprehensive energy system multi-element load prediction method based on double-layer decomposition and reconstruction and TECNFormer, and the method comprises the steps: obtaining standardized input data, meteorological factors and time characteristics, which are obtained through the preprocessing of a historical multi-energy load sequence of a comprehensive energy system, wherein the historical multi-energy load sequence comprises an electric load sequence, a cold load sequence and a heat load sequence; performing double-layer modal decomposition on the standardized input data, and classifying the standardized input data into different types of dynamic components; respectively inputting the dynamic components into a time sequence enhanced convolution module, and extracting multi-scale local features by combining multiple heterogeneous differential convolution operators with causal convolution; taking a TECNFormer composed of a time sequence enhanced convolution module and an improved long sequence prediction network as a unified shared feature extraction layer, combining the improved long sequence prediction network with a bidirectional long-short-term memory network and a sparse attention mechanism to capture long-range dependence and local details, and obtaining joint modeling features; on the basis of a hard shared network architecture, a multi-task branch is arranged at an output end, and electric, cold and heat load prediction results are synchronously output.

The invention relates to a distributed integrated energy system instruction data set automatic generation method and system based on agent cooperation. The method comprises the following steps of: S1, purifying a seed data set in the field of the distributed integrated energy system by using an information metrology method; s2, on the basis of the purified seed data set, constructing an intelligent agent to generate an instruction data set in parallel; and S3, performing quality evaluation and closed-loop optimization on the instruction data set. According to the method, firstly, core questions and answers are purified from high-influence literatures and laws and regulations through information metrology and a knowledge graph to serve as seeds, and data accuracy is ensured; parallel expansion, screening and duplicate removal are carried out by a generator and a calibrator; and finally, a feedback closed loop is finely adjusted, cue words and verification rules are continuously optimized, and high-quality and wide-coverage question and answer pairs are quickly produced, so that the diversity and the accuracy are ensured, the labor cost is saved to the greatest extent, and the application effect of a large language model in the field of a distributed comprehensive energy system is improved.

The invention discloses a multi-stage coordinated scheduling method and system for a hydrogen-electricity coupling comprehensive energy system, and the method comprises the steps: solving an initial scheduling scheme according to a day-ahead scheduling model of the hydrogen-electricity coupling comprehensive energy system, the initial scheduling scheme comprises the unit output of a thermal power generation unit in the hydrogen-electricity coupling comprehensive energy system, the renewable energy grid-connected proportion of a renewable energy power station and the electricity and hydrogen purchasing amount of a hydrogen production center; monitoring the safety confidence of the hydrogen-electricity coupled comprehensive energy system within specified time after the hydrogen-electricity coupled comprehensive energy system executes the initial scheduling scheme, if the safety confidence is greater than a preset threshold value, ending and exiting, and otherwise, skipping to the next step; and an intra-day correction network based on an MAPPO algorithm is adopted to correct the day-ahead scheduling model, and a corrected scheduling scheme is issued to the hydrogen-electricity coupling integrated energy system for execution. According to the method, the economical efficiency, the safety and the calculation efficiency performance of the hydrogen-electricity coupling system in a complex and uncertain environment can be guaranteed.

The invention provides a direct-current bus type electric vehicle charging system with a source-load-storage cooperative management capability, and belongs to the technical field of electric vehicle charging. Comprising an interface connected with a medium and low voltage power distribution network, a bidirectional AC / DC converter, a common DC bus, a plurality of bidirectional DC / DC converters and a plurality of unidirectional DC / DC converters, the medium and low voltage power distribution network is connected with the AC side of the bidirectional AC / DC converter through a transformer, and the bidirectional AC / DC converter realizes conversion between AC and DC; the direct current side of the bidirectional AC / DC converter is connected to each bidirectional DC / DC converter and each unidirectional DC / DC converter through a common direct current bus; the system takes a direct current bus as a core, has the technical advantages of bidirectional energy flow, modular expansion, multi-scene flexible deployment and the like, and is suitable for various typical application scenes such as intelligent buildings, residential parks, industrial factory areas, micro-grids, power grid energy storage nodes and the like; the defect that an existing electric vehicle charging system cannot be cooperatively managed with a comprehensive energy system can be effectively overcome.

The invention belongs to the technical field of energy system optimization, and particularly provides a multi-objective collaborative optimization method and system for an integrated energy system. Comprising the steps of generating a multi-dimensional uncertainty time sequence scene of the integrated energy system; establishing a high-fidelity dynamic behavior model library; defining decision variables, and constructing a multi-target two-stage stochastic programming optimization model by taking the economy, environmental protection and toughness of the comprehensive energy system as a three-dimensional optimization target; solving the multi-objective two-stage stochastic programming optimization model by adopting a self-adaptive multi-objective evolution algorithm assisted by an agent model; and according to a solving result, realizing a mixed multi-attribute decision based on a fuzzy analytic hierarchy process and a multi-criterion compromise solution sorting method. According to the method, the park-level IES global optimal, high-robustness and high-efficiency planning design is realized from uncertainty modeling, equipment characteristic description and large-scale solution to multi-attribute decision full-chain innovation.

The invention discloses an end-to-end scheduling method and system for a multi-source weather-driven urban integrated energy system. The method comprises the following steps: constructing an integrated energy system model; constructing a source load prediction model based on multi-source numerical weather forecast data in combination with historical photovoltaic output data and power load and thermal load data; the method comprises the following steps: establishing a comprehensive energy system optimization scheduling model with minimization of system operation cost as an optimization target, designing a differentiable optimization layer, and reversely transmitting the gradient of the optimization target in the scheduling model to a source load prediction model parameter to the source load prediction model through a back propagation algorithm by the differentiable optimization layer, the parameters of the driving source load prediction model are updated, and end-to-end linkage optimization from prediction to scheduling is achieved; and periodically obtaining updated multi-source numerical weather forecast data and source load data, readjusting prediction model parameters, and executing optimization solution of the integrated energy system optimization scheduling model. According to the invention, cooperative training and iterative optimization of the prediction model and the scheduling decision are realized.

The invention discloses an electrical integrated energy system toughness index evaluation method, which comprises the following steps: firstly, considering disaster-causing uncertainty of extreme disasters, and establishing a typical fault scene set of an electrical integrated energy system; secondly, considering dynamic and static characteristics and coupling constraints of an electric and gas heterogeneous energy flow system, constructing a numerical simulation model of the electrical integrated energy system under a fault condition, and simulating a cascade fault and energy supply recovery process of the system under a typical fault scene set; and finally, based on a simulation result, drawing a multi-stage toughness curve of the system, and constructing a multi-dimensional comprehensive evaluation index system of the electricity-gas dependency under a toughness perspective. The invention further discloses an electrical integrated energy system toughness index evaluation system, full-dimension quantitative analysis of the multi-energy dependency relationship under the fault working condition can be achieved, and weak links caused by multi-energy flow strong coupling can be effectively screened out.

The invention belongs to the field of power system evaluation, and discloses an integrated energy system multi-criterion energy efficiency evaluation method based on scene adaptive dynamic weighting, which comprises the following steps: acquiring an energy efficiency evaluation index model; actual operation data of the integrated energy system are collected and extracted to serve as initial index data; inputting the initial index data into the energy efficiency evaluation index model to obtain energy efficiency evaluation index data; and calculating the energy efficiency evaluation index data through a method of combining a grey correlation method and an approximate ideal solution sorting method to obtain a comprehensive evaluation index. And the comprehensiveness of comprehensive energy efficiency index evaluation is improved.

Disclosed is an integrated energy system optimized dispatching method based on a variable time constant gradient algorithm. A Markov decision making process model is established based on an economic dispatching characteristic of an integrated energy system first, and a target optimization function is established. Then, a neural network is established and trained by applying a double-delay depth deterministic strategy gradient algorithm, effective experience is determined before updating a target network, and a variable time constant is set according to a reward value of a current round and a reward value of the last round of soft update. Finally, a trained intelligent agent is used for intra-day dispatching of the integrated energy system, so as to realize optimal economic cost operation of the integrated energy system.