88 results about "Energy system optimization" patented technology

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 optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

The invention relates to a comprehensive energy system optimization scheduling method based on an improved particle swarm algorithm, and belongs to the technical field of comprehensive energy system optimization scheduling. The method comprises the steps of constructing an operation model of an integrated energy system with electrical cold and heat as main energy flows, constructing double-target integrated energy system optimization scheduling with the lowest operation cost and the optimal power supply reliability, and solving a multi-target problem through an improved particle swarm algorithm on the premise of meeting power balance constraints and equipment operation constraints. Self-adaptive variation and variable inertia factors are introduced on the basis of a traditional particle swarm algorithm, the self-adaptive variation refers to a variation thought in a genetic algorithm, and variation operation expands a population search space which is continuously reduced in iteration, so that particles can jump out of the position of an optimal value which is searched previously, search is carried out in a larger space, and the search efficiency is improved. The population diversity is maintained, and the possibility of searching the optimal value by the algorithm is improved. And the comprehensive energy system can give full play to economy and reliability under constraint conditions.

The invention relates to the technical field of comprehensive energy optimal configuration, in particular to a comprehensive energy system optimal configuration method and system considering medium and long term characteristics of hydrogen energy storage, and the method comprises the steps: constructing a multi-energy coupling mathematical model of a rural comprehensive energy system; a typical day containing multi-dimensional data is generated by a typical day generation method based on spectrum joint clustering, a hydrogen energy storage long-period operation model is constructed, and the hydrogen storage amount at any moment in the whole-year operation period is decomposed into dynamic superposition of the initial hydrogen storage amount and a typical day scene; constructing a double-layer optimization configuration model comprising capacity configuration and scheduling operation, wherein the double-layer optimization configuration model comprises an upper-layer capacity configuration model and a lower-layer scheduling operation model; and solving the double-layer optimal configuration model by adopting a Benders decomposition method, splitting the double-layer optimal configuration model into a main problem and a sub-problem, and carrying out iterative approximation to an optimal solution by utilizing a plane cutting method. The system capacity configuration is optimized, the solving efficiency is improved, and the consumption capability of renewable energy sources and the overall flexibility of the system are effectively improved.

The invention relates to the technical field of energy system optimization, in particular to a multi-source heterogeneous energy data fusion method and system based on a neural network, and the method specifically comprises the steps: collecting and preprocessing structured, semi-structured and unstructured multi-source heterogeneous energy data, carrying out the feature convergence of the energy data based on neural routing driving, forming a comprehensive feature vector, and carrying out the fusion of the multi-source heterogeneous energy data. The method comprises the following steps: firstly obtaining a comprehensive feature vector, then obtaining a collaborative feature vector generated by collaborative feature learning developed by each subsystem in a distributed system, and finally fusing the collaborative feature vector and the comprehensive feature vector based on a deep fusion network to form a centralized and distributed fusion architecture, thereby realizing accurate fusion of centralized and distributed multi-source heterogeneous energy data. The problems that existing energy data sources are wide, formats are diversified, fusion difficulty is increased dramatically, compatibility and collaboration dilemma is caused remarkably by different service system architecture differences, and an existing fusion algorithm is difficult to consider precision and efficiency at the same time can be solved.

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

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

The invention relates to an integrated energy system optimal scheduling method and system based on a carbon emission factor and wind power prediction, and relates to the technical field of energy system scheduling, and the method comprises the steps: building a carbon emission metering model based on a dynamic carbon emission factor through metering the direct carbon emission and indirect carbon emission of an integrated energy system; meteorological features are extracted based on the improved wind power hybrid prediction model, and a wind power prediction interval is generated in combination with kernel density estimation; taking the sum of the energy purchase cost, the carbon transaction cost and the wind curtailment cost to be minimized as a target function, and constructing an economic dispatching model; and on the basis of the economic dispatching model and the target function, solving an optimal dispatching scheme through the dynamic carbon emission metering interval and the wind power prediction interval under the condition that a set constraint condition set is met. According to the method, the technical problems of large carbon emission accounting error and low wind power prediction precision in the optimal scheduling process of the existing integrated energy system are solved.

The invention relates to the technical field of integrated energy systems, and provides an integrated energy system optimization method and system in multiple uncertain environments, and the method comprises the steps: obtaining the state of an integrated energy system, and obtaining a multi-energy-flow equipment action through an actor network; wherein MPC actors are embedded into a double-delay depth deterministic strategy gradient architecture, an actor network and transfer tuples of the MPC actors are respectively stored in an agent experience playback pool and an expert experience playback pool, a priority is given to each sample according to a time sequence difference error of the samples in the two experience playback pools, and a mixing ratio is determined according to a training time step; in combination with the mixing ratio and the priority, the sampling probability of samples is calculated, and then the degree of dependence of updating of the comment network on an expert experience playback pool in the initial training stage is controlled to be high, and the degree of dependence on an agent experience playback pool in the later training stage is controlled to be high. And the performance is better in a multi-uncertainty coupled IES environment.

The invention discloses an electricity-to-ammonia-carbon capture coupled comprehensive energy system optimization scheduling method based on dynamic pricing, and belongs to the technical field of comprehensive energy system optimization scheduling. Constructing a comprehensive energy system double-layer optimization model comprising an electrolytic hydrogen production module, an ammonia synthesis module and a carbon capture module; a real-time power supply price and a real-time heat supply price are generated through a dynamic pricing mechanism, and a double-layer interactive optimization framework of an upper-layer comprehensive energy operator and a lower-layer load aggregator is established in combination with electric load and heat load demand response characteristics; and based on the integrated energy system double-layer optimization model, calling a solver to carry out iterative optimization on the output of the energy supply unit, the operation state of the energy storage equipment and the dynamic energy price in the double-layer interaction optimization framework, and obtaining a joint optimal scheduling scheme. According to the method, the problems of traditional time-of-use electricity price stiffness and low-carbon excitation deficiency are solved.

The invention discloses an optimal scheduling method for a regional integrated energy system, and relates to the technical field of energy optimization. Comprising the following steps: step 1, multi-source data hierarchical acquisition and preprocessing; step 2, multi-time scale uncertainty scene modeling is carried out; step 3, constructing a multi-objective optimization model; 4, intelligent algorithm solving and dynamic scheduling are carried out; 5, carrying out constraint processing and resource integration; and 6, carrying out closed-loop correction and strategy optimization. According to the regional integrated energy system optimization scheduling method, a dynamic relaxation factor mu (t) is introduced to adjust equipment climbing constraint in real time, and a virtual energy storage equivalent model is combined to integrate a transferable load, so that when the load of the system suddenly increases, the equipment power adjustment fluctuation is controlled within 10% of rated power, frequent out-of-limit of energy storage is avoided, and meanwhile, the optimal scheduling of the regional integrated energy system is realized. The grading pricing mechanism excitation system of the stepped carbon transaction model preferentially consumes new energy, and the carbon emission is greatly reduced compared with that of a traditional fixed carbon price model.

The invention discloses a multi-role large model assisted hydrogen-containing building energy system operation optimization method, and belongs to the technical field of building energy system optimization control, and the method comprises the steps: firstly, building a hydrogen-containing building multi-energy system operation cost minimization problem in an off-grid operation mode; secondly, re-modeling the problem into a security Markov decision process, and defining a system state space, an action space and a composite reward function; then, solving a safety Markov decision process of modeling based on a multi-role large language model assisted near-end strategy optimization algorithm, and obtaining an intelligent agent operation strategy related to the hydrogen-containing building multi-energy system; finally, the intelligent agent makes an online decision based on the obtained optimization strategy, the decision acts on the actual hydrogen-containing building multi-energy system, the system operation cost can be effectively reduced, and the energy supply reliability is improved.

The invention provides an integrated energy system optimization scheduling method and system based on partial decision information, and relates to the technical field of integrated energy systems, and the method comprises the steps: obtaining the topological structure information of a target integrated energy system; based on topological structure information of the target integrated energy system, independent models of the three main bodies are constructed respectively; the independent models of the three subjects are utilized to construct a double-layer scheduling model, the double-layer scheduling model comprises an outer-layer model constructed based on interaction among the three subjects and an inner-layer model constructed based on interaction among comprehensive energy suppliers, and the inner-layer model is constructed by utilizing a distributed game framework under part of decision information; solving the double-layer scheduling model to obtain a game equilibrium point, and realizing collaborative optimization operation of the integrated energy system; according to the invention, under the condition of partial decision information, new energy consumption is effectively ensured, economic benefits are improved, and reasonable income distribution of all interest subjects is realized.

The invention discloses a multi-energy system optimization method and system based on hydrogen energy storage, and belongs to the field of multi-energy system optimization, and the method comprises the steps: obtaining the historical wind speed, wind direction and solar irradiance data of an anemometer tower and a photovoltaic power station, training a time sequence analysis model through the historical wind speed, wind direction and solar irradiance data, and obtaining a time sequence analysis model; wind energy power and solar energy power prediction curves in a future time period are obtained; the method comprises the following steps: acquiring pressure and temperature data of a hydrogen storage tank, inputting the pressure and temperature data into a pre-established leakage rate dynamic compensation algorithm to obtain a real-time hydrogen loss amount, and if the hydrogen loss amount is greater than a preset threshold value, reducing the operation pressure or temperature of the hydrogen storage tank; and according to the wind and light power prediction curve, the hydrogen production equipment operation data, the hydrogen storage tank operation parameters, the hydrogen conversion equipment control signal, the constraint model and the income cost model as input of a linear programming algorithm, an equipment capacity configuration scheme for maximizing the system economic benefits is obtained.

A method for optimizing dispatching of a fully distributed integrated energy system with preset times is provided. [Solution] The optimization dispatch method establishes an integrated energy system operation optimization model and constructs an objective function as a dispatch problem for the integrated energy system with the goal of minimizing the total cost of the integrated energy system. Safe operation constraints are set in the objective function, including a power balance constraint, a power upper / lower limit constraint, and an operation ramp rate constraint. The method also assumes that the communication topology of the integrated energy system's source side, load side, storage side, and station side is an undirected connected graph. A fully distributed optimization algorithm based on TBG with a preset time is used to solve the objective function, so that the integrated energy system operation optimization model reaches a convergence state within a preset time, and an optimal output strategy for the integrated energy system is obtained.

The invention belongs to the technical field of comprehensive energy system optimization and intelligent control, and particularly relates to a comprehensive energy system demand response self-adaptive control method for multi-energy coordinated regulation, which comprises the following steps: identifying multi-energy response resources; performing multi-energy demand response collaborative decomposition; and closed-loop adaptive adjustment and excitation correction: in the instruction execution process, monitoring the deviation between the actual response and the expected response of each energy source in real time, dynamically generating a compensation control instruction based on the deviation and carrying out online correction on excitation parameters so as to realize the closed-loop adaptive control of the response. According to the method, the sensitivity and prediction precision of the system to load change can be remarkably improved, and the flexible regulation and control capability and response stability of the integrated energy system are enhanced from the source.

The invention provides an integrated energy system optimization scheduling method and device, and relates to the technical field of integrated energy system scheduling. Firstly, a comprehensive energy system architecture considering carbon-electricity-storage coupling is constructed, participants of a double-layer game are explained, an objective function of an upper-layer leader and lower-layer participants is constructed, the earnings of the upper-layer leader are maximized, and the cost of the lower-layer participants is minimized; then corresponding mathematical models are established for wind and light prediction, an energy storage side and a load side, and constraint conditions are set; combining the carbon market with the electricity market according to the stepped carbon transaction, so that the carbon cost is considered in the operation process of the system; and finally, verifying the existence of a unique equilibrium solution of the master-slave game, converting a target function of a lower-layer model to an upper layer according to a KKT condition, and solving the overall model by using CPLEX to obtain an optimal scheduling scheme of the system. The method has both calculation efficiency and solution precision, greatly reduces the model complexity, and ensures that the system operation cost is minimized and the carbon emission is obviously reduced.

The invention relates to a comprehensive energy system optimization scheduling method considering indirect carbon emission uncertainty. The method comprises the following steps: acquiring basic data; quantitative modeling is carried out on the uncertainty factors, and an uncertainty set used for describing the fluctuation range of the uncertainty factors is constructed; establishing a carbon emission and transaction cost calculation model of the system, firstly accounting the free carbon emission quota of the system, secondly calculating the actual net carbon emission of the system, and finally calculating the carbon transaction cost required to be paid or obtained by the system; constructing a stochastic optimization scheduling model, taking a minimum actual total net carbon emission amount of the system as a target function, and setting constraint conditions including a system total operation cost constraint, a power balance constraint and an equipment operation constraint; and converting the stochastic optimization scheduling model into a mixed integer linear programming problem for solving to obtain an optimal output plan and an energy scheduling scheme of each device, which enable the actual total net carbon emission of the system to be minimum. According to the method, the net carbon emission of the system can be minimized in multiple uncertain environments.

The invention relates to a multi-agent optimal compromise scheduling method for a multi-agent hydrogen-containing comprehensive energy system. A multi-agent reinforcement learning model composed of a hydrogen-containing comprehensive energy service provider agent, an electrical load aggregator agent, a thermal load aggregator agent and a hydrogen load aggregator agent is constructed for a multi-agent hydrogen-containing comprehensive energy system optimization scheduling model. Solving is carried out through a multi-agent near-end strategy optimization algorithm and an ideal solution approaching sorting method; according to the multi-agent optimal compromise reinforcement learning method, the state space cooperation is enhanced by fusing the action information of the adjacent agents, a multi-scheme evaluation mechanism is constructed based on the approximate ideal solution sorting method, the action combination of the optimal compromise solution is screened, the multi-agent optimal compromise reinforcement learning algorithm is formed, and the cooperation and solving efficiency between the agents is improved.

The invention discloses a rural comprehensive energy system optimal configuration method based on multi-energy complementation. The method comprises the following steps: S1, collecting multi-dimensional rural energy data; s2, mathematical modeling of the energy equipment; s3, performing matching analysis on the rural multi-energy demand and the equipment; s4, constructing a multi-objective optimization constraint system; s5, constructing and solving an optimal configuration model of the integrated energy system; according to the method, layered modeling is carried out on the performance parameters of the energy conversion type equipment and the cold and heat supply type equipment, so that the suitability and accuracy of rural comprehensive energy system equipment modeling are effectively improved; a multi-agent system is adopted to carry out matching analysis of rural multi-energy demands and equipment, and it is ensured that multi-energy complementary potential evaluation can accurately adapt to multi-element demands of different rural scenes; an optimal configuration model is solved by constructing a target function fused with a rural power grid vulnerability index, so that an optimal configuration result can realize collaborative optimization and can adapt to the characteristics of a rural power grid.

The application discloses a two-stage distribution robust planning method of a comprehensive energy system considering flexibility, relates to the technical field of comprehensive energy system optimization planning and intelligent decision-making, and comprises the following steps: constructing a comprehensive energy system equipment and multi-network coupling model; establishing a random source and load model of photovoltaic available output, wind power available output, and electric load, heat load and gas load; extracting covariant information corresponding to a target scene, and constructing a conditional experience distribution and a Wasserstein conditional distribution fuzzy set based on historical error samples; establishing a two-stage distribution robust optimization model of one-stage capacity configuration and two-stage operation scheduling, which takes the minimum of annualized investment cost and expected operation cost under the worst distribution as a target and contains flexibility margin constraints; and obtaining an optimal capacity configuration scheme of the comprehensive energy system through a model reconstruction and decomposition algorithm. Compared with the prior art, the application can improve the flexibility, robustness and economy of the planning result of the comprehensive energy system under uncertain operation conditions.

The invention discloses an event-triggered energy system optimization method based on a time-event dual-scale framework, and belongs to the technical field of information, and the method comprises the steps: firstly constructing an optimization problem of the operation of an industrial energy system; secondly, reinforcement learning elements are constructed to build a simulation environment; thirdly, state representation with explicit time distribution characteristics is obtained based on a neural Kalman model; a near-end strategy optimization method is adopted to construct a reinforcement learning framework, and the reinforcement learning framework, a simulation environment and a neural Kalman model jointly construct a time-driven optimization model based on hidden state representation and train the time-driven optimization model; and finally, constructing an event-driven optimization model, constructing an event-driven optimization model based on state transition matrix link through combination of time-varying state matrix link and a neural Kalman model, training the event-driven optimization model, and finally realizing event-triggered optimization scheduling through time-event dual-scale cooperative calculation. According to the method, an energy scheduling strategy with higher operation economy can be obtained, and the actual industrial scheduling requirement is met.

The multi-time-scale asynchronous optimization scheduling method for the ammonia-containing integrated energy system based on the layering TD3 comprises the following steps: step 1, analyzing an electricity-to-ammonia operation mechanism, and establishing an electricity-to-ammonia and fuel gas ammonia doping mathematical model; 2, constructing a day-ahead scheduling model of the ammonia-containing integrated energy system, and designing an intra-day asynchronous optimization scheduling strategy by considering energy difference characteristics; 3, establishing an intra-day upper and lower layered Markov decision process, and proposing an intra-day rolling asynchronous optimization scheduling strategy generation method based on a layered TD3; and step 4, carrying out optimization scheduling on the ammonia-containing integrated energy system. According to the ammonia-containing integrated energy system optimization scheduling method provided by the invention, relatively low system operation cost and carbon emission can be realized in a short time, and meanwhile, compared with a traditional synchronous scheduling method, the training speed can be remarkably improved, and the training time can be shortened.

The invention discloses an incremental learning-based optimization control method and system for a modified energy system, and the method comprises the steps: obtaining the field collection data of an original energy system, and carrying out the processing through a multiple data processing method, and obtaining an original energy system data set; training a pre-selected data model by using the normalized original energy system data set, and selecting a system energy consumption and temperature model by comparing fitting effects of different types of data models; for the transformed new energy system, adopting an incremental learning algorithm to form a new energy system energy consumption and temperature model which can be simultaneously suitable for old knowledge and new knowledge; the optimal operation mode, the operation state and the optimal system control parameters under different working conditions are solved by utilizing the energy consumption and temperature model of the new energy system according to different optimization objectives and matching with corresponding optimization algorithms, so that energy conservation is realized. According to the method, the new energy system model is rapidly formed by forward migration of old energy system knowledge, and the problem of disastrous forgetting is avoided.

The invention belongs to the technical field of energy system optimization, and particularly relates to a comprehensive energy park operation optimization method and device, equipment and a medium. The method comprises the steps of performing economic dispatching based on day-ahead prediction data to obtain a unit output plan; traditional dynamic carbon emission factors are calculated and improved, the abandoned energy of renewable energy sources is considered during improvement, and the equivalent carbon emission benchmark is transmitted to a heat energy and cold energy network through multi-energy coupling equipment; determining a carbon intensity partition threshold based on the improved carbon emission factor and executing partition; generating a carbon reward and punishment price signal; the adjustable load is optimized through a user side objective function, and a responded load curve is obtained; and finally, economic dispatching is executed again to obtain a final output plan. The technical problems that traditional carbon emission factor calculation is inaccurate, the carbon transaction price suddenly changes, and carbon signal transmission is incomplete are solved, and the low-carbon operation effect of the comprehensive energy park is improved.

A kind of comprehensive energy system optimization method and system giving consideration to cost and new energy consumption, comprising: comprehensively considering the characteristics of multi-energy equipment and spot market electricity price mechanism, constructing regional comprehensive energy system model;Based on the regional comprehensive energy system model, a multi-objective optimization scheduling model containing day-ahead, intra-day, real-time multi-time scale and minimizing system total cost and maximizing new energy consumption is established;The day-ahead and intra-day multi-objective optimization scheduling model is solved;Based on the day-ahead and intra-day optimization results, the real-time multi-objective optimization scheduling model is solved by designing a distributed intelligent agent architecture and using a distributed model predictive control algorithm;According to the solving result, the output plan of each unit, the charging and discharging plan of energy storage and the system energy purchase plan are determined to realize optimal scheduling.The present application comprehensively considers operation cost and new energy consumption, and realizes economic and low-carbon operation of regional comprehensive energy system.

The invention discloses a rural integrated energy system optimization scheduling method considering multi-energy coupling and microclimate. The method comprises the following steps: S1, constructing an electricity-gas-heat-hydrogen multi-energy flow coupling system model; s2, microclimate modeling and scene extraction are carried out, and a dynamic correction model of meteorological variables on photovoltaic and wind power output, user load behaviors and electric heat pump energy efficiency under microclimate is established; extracting a typical scene set according to the collected historical microclimate data; s3, establishing a unified time sequence constraint model for various heterogeneous flexible resources, and constructing a'operation area polyhedron 'of the heterogeneous flexible resources in a multi-dimensional space; s4, constructing a day-ahead-real-time two-stage random optimization scheduling model which comprises a day-ahead decision-making stage and a real-time scheduling stage; and S5, constructing a cloud edge collaborative hierarchical distributed optimization framework, solving a day-ahead-real-time two-stage random optimization scheduling model, and finally outputting a multi-time scale scheduling decision covering a day-ahead plan and real-time adjustment.

The invention relates to the field of integrated energy system optimization, and particularly discloses a time-frequency combined feature extraction and clustering acceleration optimization model training method, which comprises the following steps: S1, acquiring mixed integer programming (MIP) equation data; s2, performing data enhancement, and extracting and preprocessing the MIP data; s3, time domain features are extracted, and low-dimensional MIP time domain features are extracted from variables and constraint node matrixes of the MIP bipartite graph through a time domain auto-encoder; s4, extracting a frequency domain feature, and calculating a frequency spectrum energy entropy of each node as the frequency domain feature; s5, inputting the time domain and frequency domain features into a fusion network to obtain final time-frequency combined MIP low-dimensional features; s6, training a feature extraction model to enable the obtained feature space to better serve a downstream clustering task; and S7, clustering the fusion features, and extracting typical examples. According to the method, the characteristics of each MIP can be identified more accurately, typical instances can be extracted for subsequent training of the MIP solver model, a large number of redundant MIP problems are eliminated, and the solving accuracy is improved.

The invention discloses a cross-border integrated energy system electric heating collaborative transaction optimization operation method based on a block chain technology, and relates to the technical field of integrated energy system optimization operation. The method comprises the following steps: firstly, constructing a multi-energy coupled cross-border comprehensive energy system model containing electricity, heat, hydrogen, natural gas and the like, and introducing diversified utilization mechanisms such as electricity-to-gas conversion, hydrogen energy storage, fuel gas hydrogen doping and the like; secondly, an energy transaction exchange rate prediction model is established by using LSTM, and the financial risk in the cross-border settlement process is reduced; then based on an asymmetric Nash negotiation theory, constructing a double-layer optimization model composed of an alliance cost minimization sub-problem and a profit distribution sub-problem, and adopting an improved dynamic penalty factor ADMM algorithm to realize distributed solution of the multinational integrated energy system; finally, an alliance chain adopting a PoA consensus mechanism is introduced, and safe storage, credible verification and anonymous interaction of the electric heating transaction data are achieved. The economical efficiency, the safety and the low-carbon operation level of the cross-border energy system can be remarkably improved.

An incremental learning-based optimization control method and system for modified energy systems includes: acquiring on-site data from the original energy system and processing it using multiple data processing methods to obtain an original energy system dataset; training a pre-selected data model using the normalized original energy system dataset, and selecting a system energy consumption and temperature model by comparing the fitting effects of different types of data models; for the modified new energy system, using an incremental learning algorithm to form a new energy system energy consumption and temperature model applicable to both old and new knowledge; and using the new energy system energy consumption and temperature model, according to different optimization objectives, combining appropriate optimization algorithms to solve for the optimal operating mode, operating state, and optimal system control parameters under different operating conditions to achieve energy saving. This invention utilizes the forward transfer of knowledge from the old energy system to quickly form a new energy system model, avoiding the problem of catastrophic forgetting.