2722 results about "Integrated energy system" patented technology

The present invention provides a hydrogen integrated energy dispatching method and system integrating green certificate and carbon emission trading, comprising: building a day-ahead price type demand response model and an alternative demand response model on the basis of the composition and operation characteristics of a hydrogen integrated energy system; building a stepped carbon emission trading model and a stepped green certificate trading model; building an integrated mechanism of green certificate trading and carbon emission trading; and building an optimized dispatching model of the hydrogen integrated energy system on the basis of the energy purchase cost, the green certificate trading income, and the carbon emission trading cost. By building a mechanism integrating stepped green certificate trading and stepped carbon emission trading, a dispatching strategy method of the hydrogen integrated energy system under the mechanism is further provided, which reduces the carbon emission level and improves the operation economy.

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 discloses an integrated energy system economic dispatching model method based on deep reinforcement learning, and the method comprises the steps: constructing a park equipment environment model, and carrying out the mathematical description of a park integrated energy system low-carbon economic dispatching problem through taking economy and carbon emission as targets; secondly, the scheduling problem is expressed as a Markov decision process framework, and a state space, an action space and a reward mechanism of the system are reasonably designed; then, a near-end strategy optimization algorithm is adopted to carry out low-carbon economic dispatching decision making, and an action noise dynamic updating strategy is provided to gradually improve the exploration capability and learning capability of a network model; according to the method, the action space, the state space, the reward mechanism, the neural network structure and the like of the intelligent agent are designed in detail, the minimization of the system operation cost is considered on the basis, and a deep reinforcement learning method is adopted for scheduling simulation. The method provided by the invention can perform optimal scheduling according to the load demand of the system, and has good scheduling performance and self-learning ability.

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 invention discloses an integrated energy system short-term multivariate load prediction method fusing a deep learning combination model, and the method comprises the steps: obtaining the multivariate load historical data and meteorological data of an integrated energy system, and obtaining a correlation factor which is greatly affected by the multivariate load; dividing the multi-element load historical data into two parts, namely a training set and a test set, and performing maximum and minimum normalization processing; extracting multi-level spatial features of the multi-element load historical data by using a series-parallel superposed multi-scale convolutional neural network; load historical data time sequence features are extracted by using a bidirectional gating cycle unit neural network, and a multi-head attention mechanism is introduced to extract important information of a load prediction task, so that a multi-scale convolution bidirectional gating cycle unit neural network fused with the attention mechanism is designed; and a self-adaptive multi-strategy grey wolf optimization algorithm is utilized to realize optimization parameter search of the bidirectional gating circulation unit neural network, an evaluation index is adopted to carry out error analysis on a prediction result, an optimal parameter is obtained, and accurate load prediction is realized.

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

The invention discloses an energy equipment carbon emission scheduling optimization method in an integrated energy system, and the method comprises the steps: determining an energy equipment carbon emission factor, constructing a carbon emission cost function, employing Monte Carlo simulation to generate a carbon transaction price fluctuation scene, and carrying out the carbon transaction price fluctuation scene. The method comprises the following steps: calculating a carbon transaction risk index of equipment in each scene according to a cost function, determining start-stop time by economic benefit optimization, constructing a comprehensive energy system topology network structure, updating a carbon flow path set according to the start-stop time, calculating a comprehensive carbon emission coefficient, establishing an energy flow and carbon flow coupling model between a high emission path and a low emission path, and constructing a comprehensive energy system topology network structure. A scheduling strategy feasible space is obtained through a cooperation strategy, and an optimal cooperation scheduling operation strategy is obtained through optimization of a particle swarm algorithm by taking the minimum carbon emission as a target. The method can effectively reduce the carbon emission / optimize the start and stop of the equipment, improves the economical efficiency and environmental protection property of system operation, achieves the dual goals of energy conservation and emission reduction and benefit improvement, and has good interpretability at the same time.

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

The 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 discloses an integrated energy system model prediction control method based on deep reinforcement learning, which is used in the field of energy prediction control, and comprises the following steps: constructing a state prediction model according to the state, output increment and disturbance quantity of each unit in integrated energy equipment in a park; predicting the output state of each device of the system at each moment in the prediction time domain within the day, establishing a rolling optimization objective function based on a prediction value and a day-ahead plan value, and calculating an intra-day scheduling optimal control quantity; and constructing an intelligent agent, and learning weight parameters in the rolling optimization objective function by using a double-delay depth deterministic strategy gradient algorithm to realize optimization of model prediction control of the integrated energy system. According to the invention, through learning of the neural network on different scenes, the optimization efficiency and quality of the algorithm on the integrated energy system are improved.

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 distributionally robust optimization method and apparatus for an electric-hydrogen-thermal integrated energy system, relating to the field of power system optimization operation. The method comprises: establishing a multi-agent optimization scheduling model of an electric-hydrogen-thermal integrated energy system; using a data driving-based distributionally robust optimization model to describe the source-load bilateral uncertainty of a power grid system agent optimization scheduling model, establishing a three-layer two-stage power balance model of a power grid system, and using a column and constraint generation algorithm for solving; and using an alternating direction method of multipliers to alternately iterate the three-layer two-stage power balance model of the power grid system, a hydrogen network system agent optimization scheduling model, and a heat network system agent optimization scheduling model, to realize distributed parallel solving of the multi-agent optimization scheduling model of the electric-hydrogen-thermal integrated energy system. Compared with the prior art, each agent scheduling scheme is solved by means of interactive iteration in the present method, thereby protecting the information privacy of each agent while improving the operation economy and reliability of an interconnected power grid.

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.

An integrated energy system includes a nuclear thermal plant situated on a nuclear site. The nuclear thermal plant produces thermal energy that is transported to a thermal energy storage system located outside the nuclear site. The thermal storage system is thermally coupled to a power generation system which is also remote to the nuclear site. By this arrangement, the nuclear thermal plant is isolated and decoupled from the power generation system. The nuclear thermal plant may supply thermal energy upwards of 800° C. or more to be stored at the thermal energy storage system until needed such as for industrial heat, power generation, or other uses. The thermal storage system is source agnostic, and one or more additional thermal energy generators, such as additional nuclear reactors, solar thermal plants, or other thermal energy generators can be coupled to a common thermal storage system and power generation system.

The invention provides an optimized scheduling method of an electric hydrogen ammonia comprehensive energy system from a full life cycle perspective. The method comprises the following steps: S1, building an electric hydrogen ammonia comprehensive energy system model; s2, performing full-life-cycle carbon emission evaluation on the electro-hydrogen-ammonia comprehensive energy system, and accurately acquiring all carbon emission data of the system in the full cycle; s3, introducing the carbon emission coefficient obtained in S2 into a carbon transaction model, and building a reward and punishment step carbon transaction mechanism model; s4, by taking the minimum total operation cost as an objective function, setting constraint conditions, and constructing an optimized dispatching model of the electro-hydrogen-ammonia integrated energy system; s5, modeling is carried out on the characteristics and probability distribution of the historical data, a distribution rule is described, and scene generation and reduction are carried out; and S6, solving the scheduling model to obtain an optimal scheduling result. The carbon emission of the system is analyzed from the perspective of the full life cycle, the energy utilization efficiency and the application proportion of renewable energy sources can be effectively improved, and low-carbon and sustainable development of the energy system is promoted.

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 belongs to the technical field of integrated energy system planning optimization, and provides an integrated energy system planning optimization method and system based on deep reinforcement learning in order to solve the problems of difficult solution, long time consumption and the like in integrated energy system planning. Determining an objective function and constraint conditions of the integrated energy system, and forming an initial dynamic programming optimization problem; converting an initial dynamic programming optimization problem into a Markov decision process; performing off-line learning on historical operation data of the integrated energy system by using a DDPG algorithm and a prediction network; and applying the optimization parameters of offline learning to an Online-DDPG algorithm, and solving an initial dynamic planning optimization problem to obtain an optimal planning scheme of the integrated energy system. The problem of solving the model caused by curse of dimensionality and information loss is effectively solved, and meanwhile, the anti-interference capability to load and renewable energy fluctuation is improved.

The invention provides an integrated energy system dynamic scheduling method based on multi-agent deep reinforcement learning, and the method comprises the steps: constructing an agent for the scheduling of an integrated energy system based on a Markov decision interaction architecture, and the agent comprises a state space, an action space and a reward function; dividing the integrated energy system into multiple environments with different characteristics in different time periods by using K-means clustering; capturing environmental characteristics of the integrated energy system in a scheduling period by using a prediction model, and matching an intelligent agent of a corresponding environment; setting an economical depth deterministic strategy gradient model for each environment to carry out interactive learning and decision making, and forming a distributed learning-distributed execution architecture; and carrying out dynamic scheduling on the integrated energy system by utilizing the distributed learning-distributed execution architecture. Compared with other deep reinforcement learning methods and traditional optimization methods, the method provided by the invention can obtain a more economic and stable scheduling result.

Described herein are techniques that may be performed in an Integrated Energy System (IES). The IES may include a power plant, an emission source, and a chemical processing plant. The IES may include one or more sub-plants configured to receive Carbon Dioxide from an emission source, convert a first portion of the Carbon Dioxide into Carbon Monoxide, receive Sodium Hydroxide, combine the Carbon Monoxide and the Sodium Hydroxide to produce Hydrogen gas, and combine, using a reaction chamber, a second portion of the Carbon Dioxide, the Carbon Monoxide, and the Hydrogen to produce Methanol.

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

The invention discloses an 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 provides a collaborative optimization scheduling method and system for an integrated energy system comprising an electric heavy truck. The method comprises the following steps: randomly generating key driving characteristics of the electric heavy truck; by analyzing the key features, the charging demand, the schedulable time period and the charging power range of the electric heavy truck are evaluated so as to construct an adjustable potential model of the electric heavy truck; based on the adjustable potential model and the current running state of the electric heavy truck, a daily charging load curve is generated by establishing a load prediction model of the electric heavy truck; establishing a power output model of photovoltaic power generation and a charging and discharging model of an energy storage system; an upper-layer optimization model of electric heavy truck charging scheduling based on IGDT and a lower-layer optimization model of energy storage system and power grid combined scheduling are established to generate a double-layer optimization model; and solving the double-layer optimization model by adopting an iterative solving method so as to generate a collaborative optimization scheduling strategy of the integrated energy system. According to the invention, the energy supplement problem of the new energy heavy truck is effectively solved, and the peak load shifting capability of the power grid is improved.

The invention belongs to the technical field of island integrated energy systems, and particularly discloses a short-time scale low-carbon optimal scheduling method and system for an island integrated energy system, and the method comprises the steps: constructing an electricity-hydrogen-heat multi-energy coupled island integrated energy system model; constructing a real-time stepped carbon transaction model based on the interval to which the carbon emission quota participating in the carbon emission transaction belongs by using an island integrated energy system model; on the basis of a real-time stepped carbon transaction model, deviation penalty of new energy prediction is considered, one day is divided into n scheduling cycles, n is an integer larger than 1, a target function is constructed with the highest total income of a system as an optimization target, the optimization target is solved, and an optimized scheduling result is obtained. According to the invention, wind energy, solar energy, wave energy, stored energy, a photo-thermal power station and other energy sources are co-scheduled with electricity, hydrogen and water loads to form a dynamically balanced energy system; and high consumption and low carbon emission are kept as far as possible.

The invention belongs to the technical field of comprehensive energy system optimization, and discloses a comprehensive energy system optimization method and system based on power-to-gas and carbon capture equipment. The comprehensive energy system comprising two-stage power-to-gas equipment, the carbon capture equipment, electric energy storage equipment and the like is constructed, and the coupling relation and adjusting characteristics between load energy conversion are combined; a low-carbon economic dispatching model is constructed, objective functions are established by considering power purchase cost, gas purchase cost and the like, a constraint set is established by considering thermoelectric power balance, system capacity, carbon capture and the like in a comprehensive energy system, and a multi-objective evolutionary algorithm is adopted to carry out optimization solution on a plurality of objective functions. According to the technical scheme, the total operation cost and the carbon emission of the system are comprehensively considered, the low-carbon and environment-friendly characteristics of hydrogen energy are fully played, the renewable energy consumption of the system is improved, the multi-target intelligent algorithm is adopted for solving, economic benefits are considered, the method is environmentally friendly, and the total operation cost and the CO2 emission of the system are reduced.

The invention provides a scheduling control method for an industrial integrated energy system. The scheduling control method comprises the steps that S1, the electricity purchase price and the electricity purchase quantity of the integrated energy system are acquired; s2, acquiring a gas purchase price and a gas purchase amount of the integrated energy system; s3, acquiring a hydrogen purchase price and a hydrogen purchase amount of the comprehensive energy system; s4, the wind curtailment punishment price and the wind curtailment power of the integrated energy system are obtained; s5, calculating the carbon transaction cost of the integrated energy system; s6, calculating a translational power variable quantity and a replaceable demand response power variable quantity of the integrated energy system; and S7, establishing a scheduling control model of the integrated energy system, and calculating the total operation cost minimization of the system according to the data. By comprehensively and systematically analyzing the carbon emission process of the comprehensive energy industry park, deeply discussing the source and the flow direction of carbon emission and accurately estimating the capacity of IES by adopting a comprehensive carbon emission measurement method, the energy utilization efficiency is improved to the maximum extent, and meanwhile, the carbon emission is reduced to the maximum extent.

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 discloses a multi-region integrated energy system optimization scheduling method, system and device and a medium, and the method comprises the steps: dividing a power distribution network into a plurality of regions, setting an intelligent agent for an integrated energy system of each region, enabling each intelligent agent to update a target network through a reinforcement learning algorithm, obtaining a target updated network; quantifying importance weights of parameters in the target update networks, selecting a plurality of elite agents from the agents, and performing global aggregation on the parameters in the target update networks of the elite agents according to the importance weights to obtain aggregation parameters; and sharing the aggregation parameters among all intelligent agents through a soft update method, so that the intelligent agents after sharing the parameters solve an objective function of the power distribution network, an optimal scheduling scheme for the multi-region integrated energy system is obtained, the power distribution network is controlled to execute, and the accuracy of an integrated energy optimal scheduling result is improved.

The invention discloses an integrated energy system participation power-carbon market operation strategy considering a carbon market clearing mechanism, and aims to solve the problems that a current carbon market transaction mechanism is not clear and how to realize balanced income maximization of an integrated energy system in a power-carbon market. By establishing a double-layer optimization game model in which an integrated energy system participates in joint clearing of a power market and a carbon trading market, an upper layer constructs a bidding model by taking system operation cost minimization as a target, and quantifies a CCER voucher-carbon quota offset mechanism; the lower layer constructs a market clearing model by taking social welfare maximization as a target; according to the technical scheme, the method comprises the steps that an IES structure, an internal optimization scheduling model, a carbon quota distribution and CCER mechanism, an electric power and carbon market bidding model, a market clearing model and a double-layer optimization model are established; by adjusting the electricity-carbon collaborative transaction strategy, the effects of reducing the system operation cost, reducing the carbon emission and improving the social welfare are achieved, and an optimization strategy is provided for effective operation of the comprehensive energy system in the electricity-carbon market.

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.