1633 results about "Integrated energy system" patented technology

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

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.

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

The present disclosure discloses an optimization control method for an integrated energy system based on a physical-informed neural network, which comprises the following steps: S1, constructing an a solar-electricity-heat-gas integrated energy system optimization control model; S2, generating a node connection relation matrix based on the network topology structure of the integrated energy system; S3, constructing a deep graph neural network model with physical-informed fusion; S4, constructing a loss function of the deep graph neural network model with physical-informed fusion; and S5, training a physical-informed neural network model according to the historical operation data to be used for system optimization control. The present disclosure can effectively deal with the influence of uncertainty of renewable energy and unexpected situations on the energy system, thereby ensuring the safe and stable operation of the integrated energy system.

The invention provides a computing-electric-thermal coupled data center integrated energy system collaborative optimization method, and relates to the field of data center energy-saving management and control, and the method comprises the steps: firstly obtaining real-time data collected by a data center; secondly, a data center load model is constructed based on the relation among real-time data quantification calculation power, electric power and heating power; constructing a collaborative optimization model according to the data center load model, and solving the collaborative optimization model to obtain an optimization result; and finally, adjusting calculation task distribution, a power supply strategy and cooling system operation parameters according to an optimization result to form a calculation-electricity-heat coupled data center integrated energy system collaborative optimization strategy. According to the calculation-electricity-heat coupled data center comprehensive energy system collaborative optimization method, collaborative optimization is carried out on the data center energy system through multi-energy complementation, and energy conservation and efficiency improvement of the data center are achieved.

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.

Provided in the present application is an energy optimization method for a multi integrated energy system (MIES) including shared energy storage. A unified shared energy storage power station is established by the MIES, thereby saving on the initial investment costs of subsystems respectively establishing energy storage apparatuses; and shared energy storage is more conducive to electric energy transmission inside the system, thereby saving operation costs. In the present application, the optimization method for an MIES including shared energy storage is provided, i.e., a two-stage three-layer optimization model is established: the upper layer is a shared energy storage power station capacity configuration model, and the lower layer is an MIES optimization scheduling model. In the present application, an uncertain fuzzy set is constructed on the basis of a Wasserstein distance and moment information, such that first-order moment information is merged into a Wasserstein distance fuzzy set, thereby making the establishment of the fuzzy set more accurate. By means of the present application, an MIES including shared energy storage can be constructed, thereby saving on the initial investment costs of subsystems respectively establishing energy storage apparatuses; and shared energy storage is more conducive to electric energy transmission inside the system, thereby saving operation costs.

The invention discloses a multi-park cross-space-time comprehensive energy consumption multi-objective optimization method and system based on deep learning, and the method comprises the steps: carrying out the feature extraction of data in the operation of a multi-park energy system through an extended long-short term memory network model, and constructing a multi-park multivariate energy efficiency evaluation index model according to the extracted multi-dimensional energy consumption features; evaluating the correlation degree of the operation process of the multi-park integrated energy system, and constructing an energy complementary relation matrix; according to the multivariate energy efficiency evaluation index model and the energy complementary relation matrix, constructing a multi-park system multi-target optimization model; solving the optimal solution of the optimization model under the operation constraint through a Transform-based multi-target particle swarm optimization algorithm, and obtaining an energy operation scheduling strategy of the integrated energy system of the plurality of parks; user interaction feedback is adopted in optimization, so that the result better meets the actual demand and target of the user; according to the method, cross-space-time comprehensive energy multi-target optimization of multiple parks can be realized, and the comprehensive energy utilization efficiency of the parks is improved.

The invention discloses an integrated energy system real-time regulation and control method and system considering spot market electricity price, and belongs to the technical field of integrated energy system operation regulation and control optimization. The method comprises the following steps: solving output power, electrochemical energy storage charging power, electrochemical energy storage discharging power and to-be-adjusted load capacity of a CCHP unit at a moment t according to a dynamic electricity price of an electric power spot market at a moment t-1, and solving power generation cost, power transmission blocking cost and marginal cost of loss of the unit; and summing the auxiliary service cost constructed according to the auxiliary service demand fluctuation quantity at the moment t to obtain the dynamic electricity price of the electric power spot market at the moment t, constructing a real-time optimization regulation and control model of the integrated energy system, and solving the model in combination with a model prediction control theory and a machine learning algorithm to obtain an optimal regulation and control strategy of the integrated energy system. The economical efficiency, the reliability, the timeliness and the accuracy of regulation and control of the comprehensive energy system in the electric power spot market are improved, and the response will of a user is improved.

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 provides a wind and light storage micro-grid dispatching method based on an ant colony algorithm, and relates to the technical field of micro-grid dispatching, and the method comprises the following steps: S1, building a wind and light storage micro-grid system, and building a model of each device in a micro-grid; s2, establishing a comprehensive cost model, and forming a target function considering both the economic cost and the environmental cost of the system; s3, various constraint conditions of the system are established; s4, establishing a mathematical model of microgrid optimization scheduling based on S1-S3, and obtaining various basic parameters; s5, initializing related parameters and a pheromone matrix of the ant colony algorithm; according to the method, the comprehensive energy system micro-grid model is constructed, the economic cost and the environmental cost are taken as indexes, the output condition of each device in the system is reasonably configured, so that the cost is reasonably dispatched and reduced, the micro-grid is taken as a small system capable of realizing power generation and distribution, the utilization rate of clean energy can be increased, and the energy utilization rate is increased. The method plays an important role in energy conservation and environmental protection.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

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

The invention relates to the technical field of park optimization scheduling, in particular to a park low-carbon optimization scheduling method and system, and the method comprises the steps: constructing a typical low-carbon park comprehensive energy system framework; based on a typical low-carbon park comprehensive energy system framework and a carbon emission flow theory, constructing a dynamic carbon emission factor model; quantifying the economic cost of the carbon emission based on the carbon tax, fusing the dynamic carbon emission factor model, and generating an economic-environment two-dimensional dynamic carbon cost signal; the time-of-use electricity price signal and the dynamic carbon cost signal are fused, and an electricity-carbon joint demand response mechanism is established; and establishing a source-load collaborative park optimal scheduling model based on carbon responsibility allocation and power-carbon joint demand response, and performing optimal scheduling on park low carbon. According to the invention, the problem of economic and environmental cost separation caused by a single electricity price signal is effectively solved, economic-environmental two-dimensional dynamic excitation is realized, and source-load collaborative optimization and user carbon reduction responsibility accurate allocation are promoted.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention discloses an integrated energy system low-carbon scheduling method considering energy-carbon coupling, and the method is based on a carbon emission flow theory, is combined with the strong fitting capability of a neural network, proposes a carbon flow constraint learning method, converts a complex mapping relation between power flow and carbon flow into mixed integer linear constraint, and achieves the low-carbon scheduling of an integrated energy system. And effective embedding of the carbon flow constraint in the optimization model is realized. Meanwhile, in order to reduce the structural complexity of the neural network, a sparse training strategy is introduced, the model parameter scale is effectively compressed, a ReLU activation function is linearized through an improved large-M method, and a cut plane constraint is introduced to gradually tighten a feasible region, so that the solving efficiency of an optimization model is remarkably improved. And finally, embedding the carbon flow constraint model into the optimal scheduling problem of the integrated energy system, exciting the carbon emission reduction consciousness of the load side, and promoting the load side to perform low-carbon energy consumption adjustment by guiding the demand response behavior of the load side based on the carbon signal of the load side, thereby realizing low-carbon scheduling under energy-carbon coordination and reducing the overall carbon emission level of the system.

The invention discloses a regional integrated energy system two-stage optimization method considering # imgabs0 # efficiency and stepped carbon transaction. The method comprises the following steps: constructing a regional integrated energy system model based on an energy concentrator model; establishing a multi-objective optimization model considering the efficiency of the equipment # imgabs1 #, and incorporating the multi-objective optimization model into a stepped carbon transaction mechanism; constructing an RIES two-stage optimization model which comprises an upper-layer capacity configuration model and a lower-layer scheduling model; solving the established RIES two-stage optimization model, solving a Pareto leading edge of equipment capacity configuration by an upper-layer capacity configuration model by adopting an NSGA-III algorithm, and generating a plurality of groups of candidate schemes; the lower-layer scheduling model obtains an optimal scheduling strategy corresponding to each candidate scheme through a solver; and objective weighting is carried out based on an entropy weight method to determine index weight coefficients such as economy, carbon emission intensity and # imgabs2 efficiency, a TOPSIS method is adopted to carry out full-dimension quantitative evaluation on a Pareto leading-edge solution set, and an optimal capacity configuration scheme and optimal scheduling data of the system are selected. According to the method, the system energy efficiency can be improved, the carbon emission intensity is reduced, and the equipment configuration redundancy is optimized.

The invention relates to an electricity-hydrogen comprehensive energy system planning method considering multi-time scale uncertainty. The method comprises the following steps: establishing an electricity-hydrogen comprehensive energy system model; based on the electricity-hydrogen comprehensive energy system model, considering the uncertainty of wind and light output and load requirements, and constructing a planning mathematical model of the electricity-hydrogen comprehensive energy system by using a multi-time scale uncertainty modeling method fusing opportunity constraint and stochastic optimization; based on the electricity-hydrogen comprehensive energy system model, establishing an electricity market model as a lower-layer optimization model of a planning mathematical model; and solving the planning mathematical model by adopting a two-stage optimization method to obtain a planning scheme. Compared with the prior art, a multi-time-scale planning mathematical model of the electricity-hydrogen comprehensive energy system is established, and planning of the electricity-hydrogen comprehensive energy system is achieved.

The invention, which relates to the technical field of energy system regulation and control, provides a seasonal multi-scene-driven adaptive regulation and control system for a park integrated energy system, comprising a seasonal feature intelligent extraction module, a multi-scene plan library construction module, a three-party non-cooperative game model module and an adaptive execution module. The seasonal feature intelligent extraction module is used for decomposing historical load data, extracting seasonal feature parameters and generating an energy supply priority sequence; the multi-scene pre-arranged plan library construction module is used for constructing a regulation and control pre-arranged plan for coping with electricity price fluctuation, equipment faults, extreme weather and production scheduling, and realizing multi-target dynamic balance; the three-party non-cooperative game model module is used for defining strategy spaces and revenue functions of an energy supply side, an energy consumption side and an energy storage side; according to the method, through dynamic operation of the seasonal feature vector and the load incidence matrix, accurate adjustment of the energy supply priority is achieved, the refrigeration power supply response time in summer is shortened, and the waste heat recovery efficiency in winter is improved.

The invention provides an island energy system optimization scheduling method considering ammonia-to-electricity waste heat power generation, and the method employs an independent island integrated energy system containing an ammonia-to-electricity system to execute a weekly-day-intra-day multi-stage operation optimization scheduling strategy, and the independent island integrated energy system is a system considering the ammonia supply demand. Wind energy and solar energy are used as energy input, energy is supplied to seawater desalination equipment and an electricity-to-ammonia system, and the electric load requirements of island residents are met; meanwhile, seawater desalination equipment is used as a power demand response resource to increase the water-energy coupling relationship of the system model, and cross-time storage and utilization of fresh water resources are realized through a reservoir, so that an ammonia-hydrogen power ship which is used for meeting daily travel demands of island residents is used as an ammonia load; an electricity-to-ammonia system and an ammonia storage tank are adopted to realize effective consumption of island wind and light resources; according to the invention, the long-period energy supply reliability of the island can be improved, and the problem of uncertainty of wind energy and light energy resources of the island can be effectively solved.

The invention relates to the technical field of comprehensive energy systems, and particularly discloses a multi-source cooperative fault recovery control method for an electricity-gas comprehensive energy system, which aims at minimizing the sum of weighted power-loss load capacity, weighted gas-loss load capacity and network loss. A remote controllable switch of a power distribution network, opening and closing of a pipeline valve in a natural gas network and distributed energy output of an electricity-gas integrated energy system are used as control variables, corresponding constraint conditions are constructed for the power distribution network, the natural gas network and electrical coupling equipment, and a multi-source cooperative fault recovery problem is constructed. And an optimal recovery strategy is obtained by solving the multi-source collaborative fault recovery problem. According to the method, the characteristics of flexibility and coordination of the power-gas internet and the pipeline storage characteristic of the gas network are fully utilized, the power and gas load recovery state and the output of each distributed energy source are optimized in time, the system source load fluctuation can be effectively resisted, continuous supply of key loads is guaranteed, and the new energy utilization rate is increased.

An electric-thermal integrated energy control method is provided. The method comprises predicting renewable energy and multivariate loads in an integrated energy system based on a pretrained SA-PSO-BP neural network; constructing an objective function of the integrated energy system, and adding power network constraints and heat network constraints for optimal scheduling; and obtaining an optimal solution of the objective function by means of a SA-PSO algorithm based on prediction results of the renewable energy and the multivariate loads, and controlling the integrated energy system according to the optimal solution of the objective function; wherein, a training process of the SA-PSO-BP neural network comprises: training a BP neural network by means of a feature training set, and iterating and updating weights and thresholds in the BP neural network in the training process by means of the SA-PSO algorithm to obtain the SA-PSO-BP neural network.

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 belongs to the field of energy system optimization scheduling, and particularly discloses a prediction driving-based integrated energy system uncertainty optimization scheduling method, which comprises the steps of establishing an integrated energy system, and constructing an optimization scheduling model of the integrated energy system; collecting meteorological energy consumption data in the research area based on a fixed time period, and preprocessing the meteorological energy consumption data to obtain preprocessed data; the meteorological energy consumption data comprises wind speed, illumination radiation intensity, temperature and power load data; the preprocessed data are input into a prediction model to obtain a prediction result of wind and light output and load, a prediction value interval is determined according to the prediction result to measure prediction uncertainty, and the prediction model is obtained by training historical data samples; and performing coupling optimization according to the objective function of the optimal scheduling model and the predicted value interval to obtain an optimal scheduling result of the system uncertainty. According to the invention, operation risks caused by prediction errors can be effectively reduced.

The invention discloses a comprehensive energy low-carbon optimization scheduling method and system based on a master-slave game. The method comprises the following steps: constructing a hydrogen-containing comprehensive energy system model, and inputting data of an external energy supply side, energy conversion and energy storage equipment and a load side to obtain initialized system operation parameters; building a user side model based on the model, generating an initial electricity / heat load demand, calculating a user alternative energy consumption rate and dynamically adjusting an electricity / heat energy conversion ratio in combination with a price difference of real-time electricity and heat prices and user energy consumption behavior characteristics, and forming a new electricity / heat load distribution scheme; the integrated energy operator generates an electricity / heat price signal and formulates a dual excitation strategy, the user aggregator adjusts the user energy load and response strategy according to the signal and the strategy and feeds back the strategy to the integrated energy operator, and the user aggregator and the integrated energy operator adjust bidirectional feedback iteration to solve Stackelberg balance through the electricity / heat price signal and the load to obtain an optimal scheduling scheme. According to the invention, economic low-carbon operation and benefit win-win between subjects can be realized.

The invention relates to the technical field of energy-carbon scheduling of a comprehensive energy system, and provides a multi-time-scale energy-carbon optimization method for a comprehensive energy system with light, storage and charging integration, which comprises the following steps: establishing a light, storage and charging integration system model and an electric heating interconnection comprehensive energy system model, and constructing a coupling relation model of the comprehensive energy system with light, storage and charging integration; then, further setting day-ahead and intra-day multi-time scale optimization scheduling strategies, and setting a coupling connection condition of intra-day rolling energy carbon optimization scheduling and day-ahead energy economy optimization scheduling; and finally, through day-ahead and intra-day optimization scheduling calculation, outputting energy-carbon optimization scheduling result data information of the integrated energy system. According to the method, the light-storage-charging integrated electric heating comprehensive energy system fine-grained model and the day-ahead and intra-day optimization scheduling strategy are constructed, and reference and guidance can be provided for multi-time-scale operation optimization regulation and control management, energy conservation, emission reduction and cost reduction analysis, source load storage energy economy and carbon emission tracking analysis and the like of the comprehensive energy system.

The invention discloses an integrated energy system micro-grid group optimization method and system and related equipment. The method comprises the following steps: collecting observation data of an operator agent and a micro-grid agent; on the basis of the collected observation data of the operator intelligent agent and each micro-grid intelligent agent, alternately training an action network and an evaluation network of a double-layer intelligent agent, and repeatedly iterating to obtain a stably converged operator intelligent agent and a plurality of micro-grid intelligent agents; on the basis of the one-master multi-slave double-layer game model, a micro-grid group autonomous collaborative optimization model considering the multi-agent game is constructed; based on the state space, the action space, the environment and the reward function and the micro-grid group autonomous collaborative optimization model considering the multi-agent game, a micro-grid group autonomous collaborative optimization model based on deep reinforcement learning under the multi-agent environment is constructed; and solving the micro-grid group autonomous collaborative optimization model based on multi-agent deep reinforcement learning to obtain a micro-grid group optimization scheme. The method is beneficial to protecting the privacy of each micro-grid.

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.