1126 results about "Integrated energy system" patented technology

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

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 relates to a source-load multi-subject hierarchical collaborative optimization method based on a fused niche. According to the scheme, multi-subject modeling, a master-slave game mechanism, niche evolution optimization and an interlayer feedback coordination strategy are combined. Firstly, a unified mathematical model of various power supplies and loads in a source-load system is constructed, and optimization targets and constraints of the unified mathematical model are defined. Secondly, introducing a master-slave game (Stackelberg) mechanism, and simulating a dynamic game behavior of source first-onset and load response; thirdly, a niche evolution algorithm is adopted to improve the search diversity and the global optimal solution obtaining capability; and finally, through an interlayer feedback mechanism, realizing mutual guidance and correction of a game result and an evolution process, and outputting a global consistent scheduling scheme. According to the method, the optimization precision, the operation coordination and the scheduling robustness of the source-load system in a complex and changeable environment can be effectively improved, and the method is suitable for multi-source and multi-load collaborative optimization scenes such as a micro-grid and an integrated energy system.

The invention discloses a hydrogen energy park planning method and system based on a heuristic algorithm, and relates to the technical field of smart energy management, and the method comprises the steps: constructing a mathematical model of a park comprehensive energy system which is composed of a renewable energy power generation unit, an energy conversion device, a carbon capture and gas power device and a multi-type energy storage unit; based on the mathematical model, with the minimum annual comprehensive cost of the park comprehensive energy system as a target, establishing a cost target function, setting constraint conditions, and formulating an energy management strategy; and according to an energy management strategy, solving according to an improved taboo horse racing optimization algorithm THRO, and planning the hydrogen energy park according to a solving result. According to the invention, not only is the interior of the exterior of the environment realized, but also the maximization of economic benefits is realized.

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 low-carbon economic operation optimization method for an electricity-hydrogen-heat comprehensive energy system, and belongs to the crossing field of intelligent energy systems and artificial intelligence, and the method comprises the following steps: 1, building a low-carbon economic dual-target operation optimization problem model of the electricity-hydrogen-heat comprehensive energy system; 2, constructing a dual-objective operation optimization problem solving framework with a third-generation non-dominated sorting genetic algorithm as a core; 3, modeling a population evolution decision problem in the genetic algorithm into a Markov decision process, and constructing a deep reinforcement learning agent and population evolution environment interaction mechanism; 4, training the intelligent agent by adopting a double-depth Q network algorithm, and outputting a genetic action according to an environment state; 5, obtaining a new filial generation in combination with a selection strategy associated with the reference points and genetic actions output by the intelligent agent; the step 4 and the step 5 are iteratively evolved until the maximum iteration generation number is reached, and the operation cost can be reduced under the same carbon emission condition.

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.

The invention discloses a hydrogen-containing integrated energy system robust optimization scheduling method considering a dynamic green certificate-carbon emission collaborative transaction mechanism. The method comprises the following steps: establishing the dynamic green certificate-carbon emission collaborative transaction mechanism considering reward and punishment characteristics; establishing a hydrogen-containing comprehensive energy system equipment model according to a hydrogen-containing comprehensive energy system architecture; determining a target function and constraint conditions according to the dynamic green certificate-carbon emission collaborative transaction mechanism considering the reward and punishment characteristics and the hydrogen-containing integrated energy system equipment model so as to establish a hydrogen-containing integrated energy system robust optimization scheduling model considering the dynamic green certificate-carbon emission collaborative transaction mechanism; and solving the hydrogen-containing integrated energy system robust optimization scheduling model considering the dynamic green certificate-carbon emission collaborative transaction mechanism based on a robust linear optimization theory. According to the method provided by the invention, the running flexibility of the hydrogen-containing comprehensive energy system is improved, and meanwhile, the running economy and the carbon emission level of the system are improved.

A power distribution network-multi-integrated energy system distributed optimization scheduling method considering interval uncertainty and multi-agent game comprises the following steps: representing fluctuation characteristics of a distributed power supply and a load by adopting an interval number, and establishing a multi-agent game model with robustness; a dynamic electricity price transaction mechanism based on Nash bargaining is constructed, the income requirements of all subjects in the DN-MIES system are met, and system benefit balance is achieved; the MVA-ATC algorithm is proposed based on the interval information of the source load uncertainty to carry out distributed iterative solution, and the main body privacy is guaranteed while the solution efficiency is improved. For the problem of benefit distribution after the MIES alliance is accessed to the power distribution network, the invention provides a DN-MIES mixed game distributed optimization method considering multi-subject benefits, so that benefit distribution of the power distribution network accessed to multiple IESs is realized, and benefit balance during cooperative operation of the power distribution network and the multiple IESs is effectively guaranteed; meanwhile, an MVA-ATC distributed solving algorithm is provided by utilizing interval uncertainty and an ATC algorithm, so that the solving efficiency of the model is improved, and meanwhile, the fluctuation of the source load is stabilized.

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 relates to the technical field of power system load prediction, in particular to a multi-modal Bayesian optimization short-term power load dynamic prediction method, which specifically comprises the following steps: collecting multi-source data, and preprocessing the data; decomposing a load trend module, a season module, a special event module and other component modules by using a Neuralprophet model; constructing a CNN-LSTM model, extracting meteorological-load spatial features, and modeling time sequence dependence; fusing multi-model prediction results based on a belief function theory BFT framework; and combining Bayesian optimization to dynamically adjust fusion weight and quality interval parameters, and minimizing a prediction error. According to the method, high-precision and high-robustness load prediction is realized by fusing deep spatial-temporal feature modeling, a probability decomposition framework and dynamic parameter optimization. The method is suitable for real-time scheduling and optimization management of a micro-grid, a power distribution network and an integrated energy system, and helps an electric power company to optimize resource configuration, reduce cost and improve power supply reliability.

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 discloses an industrial park comprehensive energy system low-carbon planning method considering a production process, and the method comprises the following steps: 1, obtaining the production line condition of an industrial park, and constructing a production line model; 2, determining to-be-planned equipment and parameters, and constructing a lower-layer planning operation model considering short-term uncertainty; 3, determining medium and long term uncertainty parameters, and constructing a medium and long term uncertainty set; step 4, determining a multi-objective optimization model; 5, searching a Pareto leading edge by using multi-target reinforcement learning based on a decomposition technology; and 6, selecting a proper Pareto frontier solution, and substituting the Pareto frontier solution into the lower-layer planning operation model to obtain a corresponding planning result. The invention aims to realize low-carbon transformation of an integrated energy system (IRIES) in an industrial area so as to cope with carbon reduction, emission reduction and challenges in a carbon dense area.

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 low-carbon scheduling method, system, equipment and medium for an integrated energy system, and belongs to the technical field of low-carbon scheduling, and the method comprises the steps: obtaining power network data, thermal network data and natural gas network data of the integrated energy system, establishing a multi-energy-flow carbon flow dynamic model according to the power network data, the thermal network data and the natural gas network data; calculating the time-varying node carbon intensity of each node based on the multi-energy-flow carbon flow dynamic model; calculating carbon flow betweenness centrality according to the time-varying node carbon intensity and a network topology structure, and identifying a carbon flow propagation key node through the carbon flow betweenness centrality; and establishing a low-carbon optimal scheduling model based on the time-varying node carbon intensity, and determining a low-carbon scheduling scheme of the integrated energy system in combination with the low-carbon optimal scheduling model and the carbon flow propagation key node. The method has the beneficial effects that the carbon emission of the comprehensive energy system can be effectively reduced, and meanwhile, the increase amplitude of the operation cost is reduced.

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.