31 results about "Economic scheduling" patented technology

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-micro-grid power distribution system two-stage robust collaborative optimization method and device, and belongs to the technical field of multi-micro-grid power distribution system optimization scheduling, and the method comprises the steps: inputting the obtained basic operation data and uncertainty characterization parameters of a multi-micro-grid power distribution system into a pre-constructed multi-micro-grid economic scheduling model, solving the model by adopting a column and constraint generation algorithm to obtain a day-ahead scheduling plan of the multi-microgrid power distribution system in the worst scene considering the space-time correlation; inputting the day-ahead scheduling plan, the obtained basic operation data of the multi-microgrid power distribution system and the tie line power coupling constraint between the power distribution network and the multiple microgrids into a pre-constructed double-layer collaborative optimization model, and solving the model in a distributed manner by adopting an alternating direction multiplier method, an intra-day optimization scheduling scheme meeting the requirement of good cooperative operation of the power distribution network and the multi-micro network friends is obtained; according to the method, the optimization result in the worst scene meets the robustness requirement, and meanwhile, the economical efficiency of the system is guaranteed.

The invention relates to the technical field of power systems and energy management, discloses an industrial and commercial energy storage system energy resource scheduling method and system, and aims to solve the problems that in the prior art, economy optimization and battery health management are separated, degradation model simplification causes life damage, and the adaptability to load and electricity price uncertainty is insufficient. Specifically, multi-source heterogeneous state data are fused; constructing and calibrating a battery digital twinborn model coupled with electrochemistry, a thermal field and an aging mechanism; and executing a three-layer cooperative strategy of long-term degradation track planning, medium-term random scheduling optimization and short-term model prediction control, enabling the battery degradation cost to be generated in an economic scheduling target, and achieving high-robustness real-time control through rolling optimization. The system comprises a data acquisition and fusion unit, a digital twinning module, a probability prediction module, a hierarchical scheduling engine and an instruction execution unit. According to the invention, the unification of full-life-cycle value maximization and operation reliability is realized.

The application discloses a power distribution network scheduling method and system, and a computer program product, and belongs to the technical field of smart grids, and comprises the following steps: dividing a power distribution network into multiple power distribution network levels according to voltage grades, collecting data of each power distribution network level, setting a minimum total cost of virtual power plant output as a target function, and constructing an optimization model; starting from a lower power distribution network level, sequentially and independently calculating power flow of each power distribution network level, equivalently taking first-end injected power of the lower power distribution network level as corresponding node load of an upper level, outputting power flow operation conditions of each power distribution network level, establishing a power flow checking constraint model, judging whether the safety operation checking constraint is met, if yes, solving the optimization model by using an optimization algorithm to obtain the minimum total cost and corresponding output values of an output unit group, and if not, reassigning data to perform a new round of optimization. The application considers inverse-level dynamic coupling modeling of grid connection of new power generation units, and provides an integrated solution for dynamic safety and economic scheduling of the power distribution network.

The application provides a kind of multi-microgrid collaborative economic dispatching method based on blockchain, comprising: based on microgrid group multi-group architecture model, construct microgrid group dispatching architecture model based on blockchain;Based on objective function submodel, constraint condition submodel and incremental cost submodel, an economic dispatching model is constructed;Wherein, economic dispatching model includes: microgrid layer economic dispatching model and microgrid group layer economic dispatching model;Based on microgrid group dispatching architecture model, intra-group scheduling is carried out on microgrid layer economic dispatching model, and inter-group scheduling is carried out on microgrid group layer economic dispatching model, to obtain multi-microgrid collaborative economic dispatching result.The application constructs microgrid group system distributed dispatching architecture, designs system economic dispatching model with comprehensive economic and environmental protection targets, and proposes a kind of intra-group optimization and inter-group optimization economic dispatching method based on blockchain.In the case of ensuring good expansibility, the economic efficiency and fault tolerance of microgrid group are improved.

The invention relates to the technical field of power dispatching, and provides a digital twin power energy dispatching system based on distributed control, and the system comprises a data collection layer which is used for collecting the operation data of a plurality of energy nodes distributed in a target power grid in real time and the environment data of the environment where each energy node is located; the digital twinborn layer is used for updating a pre-constructed digital twinborn model based on each operation data and each environment data so as to enable the updated digital twinborn model to be mapped with each energy node in real time, and performing analogue simulation and state prediction of power grid operation based on the digital twinborn model to generate simulation prediction data; and the distributed scheduling layer is used for calculating a scheduling instruction set by adopting a distributed optimization algorithm based on the simulation prediction data so as to perform power energy scheduling on the target power grid based on the scheduling instruction set. Safe and economic dispatching of the power grid is achieved, and the intelligent level and the operation economical efficiency of the power grid are remarkably improved.

The invention provides a time sequence coupling constraint embedded power resource scheduling method and system and a storage medium, and relates to the technical field of energy management, and the method comprises the steps: obtaining a classical security constraint economic scheduling model; recursive calculation is carried out on the classic security constraint economic dispatching model; wherein in the forward propagation process of the recursive calculation process of the neural network model, parameters of the neural network at all time points are kept consistent, and meanwhile, the output of the calculation unit of the current time step serves as the input of the calculation unit corresponding to the next time step so as to connect all the calculation units; embedding the constraint condition into an output end of a set neural network model, and constructing a gradient return information highway; and training a set neural network model by using the power training data, and adjusting neural network parameters to obtain a power scheduling model so as to output a power resource scheduling scheme. Through the coupling calculation unit, the parameter sharing and the recursive structure, the calculation efficiency is remarkably improved, and the power resource scheduling scheme can be quickly generated.

The application discloses a micro-grid economic dispatching method and device, relates to the technical field of micro-grid operation control, and solves the technical problem that the stability of the whole micro-grid operation is reduced due to the fact that the existing micro-grid economic dispatching method usually adopts fixed time intervals to make scheduling plans; the method comprises the following steps: generating a corresponding predicted power curve according to predicted environment data; generating a scheduling time period based on the predicted power curve; constructing a target function in an economic dispatching model corresponding to the micro-grid based on the scheduling time period; constructing a plurality of constraint conditions in the economic dispatching model corresponding to the micro-grid based on operation constraint data; the constraint conditions comprise power constraint conditions and basic constraint conditions; solving the target function in the economic dispatching model to obtain an optimal scheduling scheme; and scheduling power resources in the micro-grid based on the scheduling scheme; so that the economic dispatching is more targeted, and the stability of the whole micro-grid system is improved.

The application provides a multi-micro-grid system optimization method, device and storage medium, relates to the new energy power supply field, and the method comprises the following steps: determining the topological structure of the multi-micro-grid system in a shared energy storage mode, establishing an economic scheduling model based on the optimization scheduling process of the multi-micro-grid system, determining a source-load uncertainty model and a constraint condition, establishing a fuzzy opportunity constraint based on the source-load uncertainty model, constructing a game model to solve the economic scheduling model under the fuzzy opportunity constraint, and obtaining the optimization result of the multi-micro-grid system; and the device and the storage medium are used for realizing the method. The application has the beneficial effects that the source-load uncertainty in the multi-micro-grid system is considered, the scheduling precision is improved, and the application has important theoretical and application values for solving the multi-micro-grid system optimization problem.

The present application belongs to the field of optical storage system scheduling, and particularly relates to a kind of economic scheduling method of optical storage system fusing uncertainty information, to improve the intelligent level of photovoltaic energy storage system under uncertainty environment scheduling.Method includes obtaining photovoltaic array power generation and meteorological data through data acquisition module, after pretreatment, it is divided into training set and test set.Use training set to train time series convolution network, through time series convolution network, photovoltaic power day-ahead point prediction is carried out, and multiple confidence interval prediction results are generated based on prediction error distribution.Compression sensing and sparse coding method are used to convert interval prediction results into sparse connection weight matrix, complete reserve pool network initialization and carry out spectral radius normalization, based on the initialized reserve pool network, strategy network and value network embedded in proximal policy optimization algorithm are constructed.In the preset photovoltaic power station simulation environment, strategy network and value network are iteratively trained, and the day-ahead scheduling strategy that meets economic optimization is output.

The invention discloses an industrial park multi-energy dynamic complementary scheduling method and system based on multi-source data, and belongs to the technical field of energy scheduling. The method comprises the following steps: an acquisition module, a first determination module, a second determination module, a constraint determination module and a scheduling scheme determination module. According to the method, the power change rate is predicted in real time and quantified into the dynamic stability margin, then the dynamic stability margin is used as the rigid constraint to be embedded into the economic dispatching optimization model, the standby capacity is reserved in advance in the dispatching decision-making stage to prevent risks, the balance of economy and safety is converted into economic optimization within the safety boundary, and the economic optimization efficiency is improved. According to the method, the safety requirement is improved from a soft target to a hard constraint, active beforehand defense of an electric energy quality problem is realized, economic optimal scheduling is realized while the system stability is guaranteed, and the problem that voltage sag and frequency fluctuation are difficult to effectively suppress in renewable energy fluctuation processing in the prior art is solved.

The application discloses a comprehensive energy system Pareto optimal scheduling method based on multi-target weight variability. The method uses Markov decision to describe the low-carbon economic scheduling problem of the comprehensive energy system, builds an Actor-Critic network, including an Actor network and two Critic networks which are respectively focused on operation cost and carbon emission cost. The state of the environment of the comprehensive energy system is input, the optimal action is output through the Actor network, the reward vector and the next time system state are obtained after execution, and the five-tuple training sample is stored in the experience storage area. The advantage function of the two Critic networks is weighted and fused by using K sets of weights, the advantage function values of the networks under different weights are obtained, the network parameters are updated through the proximal policy optimization algorithm, and the optimal scheduling strategy is obtained. After completing the scheduled rounds of proximal policy optimization, the adaptive update of the advantage function weight is triggered, so that the intelligent agent learns the Pareto optimal decision.

The invention relates to the technical field of energy management, and discloses a source-network-load-storage multi-energy complementary collaborative optimization scheduling method and system, and the method comprises the steps: deploying collection points at a source end, a network end, a load end and a storage end, and collecting operation parameters in real time; carrying out denoising, abnormity correction, missing value interpolation and normalization processing on the data; extracting data key feature indexes; and formulating a scheduling strategy on the basis, including deciding energy storage charging and discharging according to a source-load power matching condition in a peak-valley period so as to realize economic scheduling of peak clipping and valley filling, and introducing a power supply side risk real-time evaluation and switching mechanism based on cosine similarity so as to realize economic scheduling of peak clipping and valley filling. And a power transmission line risk coefficient calculation and line switching protection mechanism based on a multi-parameter weighted fusion model. The system correspondingly comprises a data acquisition module, a data processing module, a data analysis module and an evaluation scheduling module which jointly realize safe, economical and optimized operation of the energy system.

The invention discloses a wind power generation prediction and scheduling method and system, and the method comprises the steps: firstly constructing a probability wind power generation prediction model based on a self-adaptive federal learning framework which integrates self-adaptive clustering and an elastic weight consolidation mechanism, generating a calibration prediction interval of quantization uncertainty, and carrying out the prediction of the prediction of the probability wind power generation; the data heterogeneity is overcome and the prediction precision is improved on the premise of protecting the data privacy of each power generation field. And then, taking generated power uncertainty information provided by the prediction interval as key input, embedding the generated power uncertainty information into a load side distributed optimization model based on an alternating direction multiplier method, converting the optimization problem into a standard form, and constructing an augmented Lagrange function to carry out distributed iterative solution. Therefore, decentralized economic dispatching of each load agent is realized, and abnormal load behaviors are identified in real time by monitoring the deviation between the state of each agent and the global consensus in the process. According to the method, a robust, efficient and safe intelligent power grid operation scheme capable of coping with high wind power permeability challenges is formed.

The invention relates to a quotient gradient system assisted day-ahead scheduling plan making method considering frequency safety. The method comprises the following steps: collecting and processing power grid data; constructing an objective function and a constraint set of a day-ahead economic dispatching optimization model according to the processed power grid data; and calculating the constructed previous economic dispatching optimization model based on a quotient gradient system auxiliary interior point method to obtain a day-ahead dispatching plan. According to the invention, the scheduling result generated by model optimization solution can consider both economic efficiency and safety margin, and the applicability of actual power grid production planning is effectively improved. Besides, by adopting a mode of combining quotient gradient system integration and interior point method accurate solution, reliable convergence of the algorithm is ensured on the premise that the problem is feasible, and an accurate diagnosis result is provided for an operator for reference when the problem is not feasible.

The application provides a new energy power grid forward scheduling method and device, and relates to the technical field of power system daily economic scheduling. The application firstly constructs an opportunity constraint optimal power flow model of forward scheduling, analyzes the influence of new energy uncertainty on the opportunity constraint, and establishes a constraint Markov decision process of forward scheduling, then uses a risk evaluator network fitting the probability distribution of a risk function and an actor network considering the performance of extreme scenarios to enhance the processing capacity of the agent for the forward scheduling scene of extreme ramping events containing new energy, and finally uses the imitation learning technology to accelerate the training of the agent in the offline simulation environment of the power grid forward scheduling. The application can balance the solution speed, strategy robustness and safety of the double-layer robust optimization model of forward scheduling.

The application provides a multi-scale multi-group cognitive intelligent scheduling and control method for a comprehensive energy system, which comprises a multi-time scale fusion control framework and two modules, the first module is a control module based on adaptive noise complete ensemble empirical mode decomposition and time series prediction, and the second module is a scheduling module based on adaptive noise complete ensemble empirical mode decomposition and bidirectional long short-term memory network prediction and fusing an artificial behavior cognitive knowledge matrix. The method takes a frequency deviation sequence of the comprehensive system, a regional control deviation sequence, user behavior and climate factors as inputs, and outputs an automatic generation control unit control instruction updated every 4 seconds and a non-automatic generation control unit scheduling instruction updated every 5 minutes and 60 minutes through the control module and the scheduling module respectively. The method can solve the problem of incoordination between generation control and economic scheduling in the comprehensive energy system, improve the control precision and reduce the frequency deviation.

The invention provides a time-of-use electricity price-oriented distributed energy storage cooperative group scheduling and group control method and system, and the method comprises the steps: carrying out the clustering based on the current attenuation degree index, SOC adjustment rate index and communication delay index of each distributed energy storage unit in a transformer area, and the electrical distance between each distributed energy storage unit and other nodes, and obtaining a plurality of virtual energy storage clusters; calculating a performance index of each virtual energy storage cluster based on the performance parameter of the energy storage unit in each virtual energy storage cluster; inputting the performance index of each virtual energy storage cluster into an economic scheduling model to obtain a scheduling instruction of each virtual energy storage cluster; the scheduling instruction of each virtual energy storage cluster is decomposed to each energy storage unit and executed; wherein the economical scheduling model is constructed by taking minimization of the total electricity purchase cost of the transformer area as a target; the method can flexibly adapt to time-of-use electricity price policies of various places with the purpose of minimizing the total electricity purchase cost of the transformer area as the target, naturally guides energy storage off-peak charging and peak discharging, and assists power grid balance.

The present application relates to the technical field of optimization scheduling, and more particularly to an optimization scheduling method and system considering global-local balance responsibility risk, the method steps comprising: analyzing the unbalance mechanism of micro-scheduling balance area; quantifying the unbalanced operation risk considering grid scheduling-actual output; establishing a risk coordination control model considering global balance responsibility and an economic scheduling model; and proposing an optimization scheduling model considering global-local balance responsibility risk. The present application solves the problem that the existing risk quantification evaluation only considers a single dimension, and lacks unified modeling and scheduling decision-making from the perspective of global-local balance responsibility of the system. The present application achieves the purpose of encouraging the subject to actively assume the system imbalance and obtaining the optimal risk and benefit level.

The invention provides a flexible zero-carbon community energy optimization method for a wide-area gathering building group, and the method comprises the steps: designing a basic structure and an operation mode of a multi-energy system, and constructing a wide-area gathering flexible community energy intelligent platform; then, a seasonal-day-real-time multi-time scale operation strategy is formulated for enabling the intelligent platform; according to the seasonal strategy, active heat energy management oriented to soil heat balance is implemented, and system waste heat is fully utilized to guarantee operation sustainability and ecological benefits; according to the daily adaptive operation strategy, the soil heat balance requirement and the building energy consumption flexibility are combined, and daily economic dispatching is achieved; the real-time scheduling uses gravity energy storage to quickly stabilize supply and demand power fluctuation so as to assist the energy system to climb. And finally, constructing a system optimization model to optimize a key planning and scheduling parameter algorithm. According to the method, efficient, stable and zero-carbon operation of the community energy system is achieved, and meanwhile, the self-adaptive capacity of the system for dealing with source-load fluctuation is improved.

The invention provides an adaptive sliding mode controller dynamic load attack positioning method and system, and the method comprises the following steps: 1, defining a residual evaluation function, selecting a threshold value of the residual evaluation function, and recognizing an attack event through the residual evaluation function; step 2, positioning a node where the attack is located by using an estimated value of the observer; step 3, performing state estimation and attack reconstruction on the attacked cyber-physical power system model CPPS by using a sliding-mode observer; 4, designing a robust sliding-mode observer, and carrying out safety state estimation and attack reconstruction on the power grid system; and 5, introducing an unknown bounded nonlinear term, designing an intermediate observer, and carrying out safety state estimation and attack reconstruction on the power grid system. According to the technical scheme, the observer data can provide important data support for attack defense, and a subsequent attack defense strategy based on the most economic dispatch is provided on the basis of security state estimation realized by the observer.

A multi-microgrid low-carbon and economic dispatching optimization method based on P2G-CCS and shared energy storage comprises the steps that a'shared energy storage operator (ESO)-multi-microgrid 'dual game framework is constructed, and the dual game framework comprises an upper-layer master-slave game and a lower-layer cooperative game; executing an upper-layer master-slave game and a lower-layer cooperative game; and solving an equilibrium solution of the first-stage master-slave game by adopting a bisection method and a column generation and constraint generation algorithm (Camp; CG), and outputting a multi-microgrid low-carbon economic dispatching scheme in combination with an income distribution result of the second-stage cooperative game. The method aims at solving the problems that an existing multi-micro-grid scheduling optimization technology is fragmented in technology integration, lacks low-carbon guidance in game income distribution and is disjointed in uncertainty control and low-carbon scheduling, and traditional energy storage is high in investment cost, low in equipment utilization rate and poor in multi-main-body collaboration.

This invention discloses a multi-energy system scheduling-guided learning method based on prediction-optimization-closed loop. The method first constructs and pre-trains a renewable energy output prediction model based on a bidirectional long short-term memory network. Second, it uses a clustering algorithm to extract typical operating scenarios of the multi-energy system under different seasons and weather conditions. Then, it builds a two-stage economic scheduling optimization model and, for each time period in each typical scenario, constructs a mapping relationship between the prediction error rate and the additional cost caused by the error, forming an error rate-additional cost curve, defined as the decision loss function. Finally, it constructs a hybrid total loss function including prediction loss and decision loss, and uses a backpropagation algorithm to fine-tune the parameters of the pre-trained prediction model. The economic loss of scheduling decisions is used as a guiding signal to feed back into the training process of the prediction model, achieving closed-loop collaborative optimization of prediction and scheduling.

This invention discloses a method and system for allocating reserve resources in a power system. The method comprises: acquiring real-time operating data and predicted data of the power system at the current scheduling moment; inputting the real-time operating data and predicted data into a preset economic dispatch model considering unit reserves, solving for a reserve capacity allocation plan, and calculating the reserve stress score of each grid node under the current operating state based on the reserve capacity allocation plan; constructing a comprehensive similarity matrix by combining the reserve stress scores and the topological connectivity of the power system, and using an improved spectral clustering algorithm to cluster the comprehensive similarity matrix to obtain reserve resource-scarce regions and reserve resource-sufficient regions; solving a cross-regional reserve resource dispatch optimization model to obtain a capacity dispatch strategy for resource allocation. Therefore, this invention can accurately allocate reserve resources from resource-sufficient regions to resource-scarce regions.

This invention discloses a low-carbon economic scheduling method for virtual computing power centers based on Lyapunov optimization, belonging to the field of collaborative optimization of power and computing resources. The method first integrates heterogeneous computing power nodes through a distributed computing power resource aggregation management platform, transforming them into equivalent CPU computing power to form a computing power cluster. It then combines energy storage systems with grid power purchase and sale to construct a comprehensive energy management system, establishing a net benefit maximization optimization objective and related constraints. Next, it introduces task backlog and virtual carbon deficit queues to handle coupled constraints. Finally, based on Lyapunov drift-reward theory, the long-term stochastic optimization problem is decoupled into two real-time solvable subproblems: computing power allocation and energy storage-carbon arbitrage, generating optimal scheduling instructions. This invention requires no predictive information, achieving low-carbon economic scheduling of computing power resources while meeting long-term carbon constraints and service quality requirements, thus improving the utilization efficiency and overall net benefit of computing power resources.

The invention provides an electric-thermal-hydrogen system optimization scheduling method considering a flexibility constraint, and relates to the field of integrated energy system scheduling. The method comprises the following steps: establishing hydrogen energy multi-mode utilization equipment; quantifying the flexibility adjusting capability of hydrogen energy equipment, a supply side and a demand side; a double-layer optimization model is constructed, the upper layer is an improved stepped carbon transaction model adopting a differential evolution algorithm to optimize parameters, and the lower layer is an economic dispatching model considering flexibility constraints; and an optimal scheduling scheme is obtained through iterative solution. According to the method, the low-carbon scheduling is guided by optimizing the carbon transaction parameters, and the scheduling scheme is ensured to meet the flexible supply and demand balance of each subsystem of electricity, heat, gas and hydrogen by using the rigid constraint, so that the collaborative optimization of economy, low-carbon property and operation flexibility is realized on the premise of ensuring the safe and stable operation of the system.

The invention provides a power distribution network optimal scheduling method based on improved convex internal approximation and feasible solution recovery cooperation, and belongs to the technical field of power grid scheduling optimization. By constructing a double-layer optimization architecture of'dynamic boundary improved convex internal approximation-sensitivity guided feasible solution recovery 'and combining an alternating current power flow comprehensive verification mechanism, collaborative optimization of power distribution network security constraint and economic dispatching in a high-proportion renewable energy access scene is realized. The method comprises the following steps: constructing a dynamic current boundary and stability criterion based on a local operating point, and generating an initial feasible solution meeting alternating current power flow constraint; performing active / reactive power gradient adjustment on the initial solution by using a branch power flow and unit output sensitivity matrix; step length constraint and unit ramp rate constraint are embedded to ensure adjustment safety; and a multi-objective function is constructed to realize global optimization, so that the network loss is reduced and the new energy consumption rate is improved while the voltage safety and the power flow constraint are strictly guaranteed.

The application discloses a multi-region collaborative heating scheduling method based on multi-agent adjustment cost consistency, and comprises the following steps: a mechanism modeling and data identification method is used to establish a digital twin model of a multi-region collaborative heating system; one region in the multi-region collaborative heating system is responsible for tracking a total heating scheduling instruction, and each region exchanges information with adjacent regions; the multi-region collaborative heating system is set as a corresponding agent, and a consistency collaborative communication topology graph of a multi-agent system is established; a low-carbon economic scheduling model of the multi-region collaborative heating system is established; when total load demand changes result in heating output power changes, a multi-agent consistency algorithm is used to solve the low-carbon economic scheduling model of the multi-region collaborative heating system, and optimal incremental cost and heating output power are obtained; the multi-agent consistency algorithm is used to realize optimal power distribution, and low-carbon economic operation of the multi-region collaborative heating system is ensured.

The application discloses a hydrogen-containing comprehensive energy system optimal operation method considering seasonal scheduling, and specifically comprises the following steps: step 1, a comprehensive energy system integrating a carbon capture power plant and hydrogen energy multi-element utilization is built, and an NBRO-Kmeans++ algorithm is used to select representative typical scenes from annual data; step 2, on the basis of step 1, a scientific carbon emission quota allocation model is established by integrating multiple principles and multiple indexes through a distance between optimal and poor solutions method combined with grey correlation analysis, and carbon emission characteristics in different seasons are analyzed; step 3, on the basis of step 2, when the PIES is subjected to low-carbon economic scheduling, optimal scheduling is carried out with the minimum total operation cost as the target. The application can realize minimization of the operation cost and carbon emission of the comprehensive energy system.