141 results about "Energy system optimization" patented technology

The invention provides an integrated energy system optimal scheduling method based on multiple time scales, which comprises the following steps of: constructing a radix uncertainty set, constructing a day-ahead robust optimization model based on the radix uncertainty set, inputting day-ahead one-hour-level power prediction data of an integrated energy system into the day-ahead robust optimization model, and performing optimal scheduling on the day-ahead robust optimization model. Obtaining a day-ahead scheduling scheme of the integrated energy system; generating an intra-day random scene set, constructing an intra-day stochastic optimization model based on the intra-day stochastic scene set, acquiring intra-day power prediction data of the integrated energy system by taking 15 minutes as a period, inputting the intra-day stochastic optimization model to obtain an intra-day optimization scheduling scheme, and repeating the steps until optimization scheduling of all periods in the day is completed; and generating a real-time random scene set, constructing a real-time random optimization model based on the real-time random scene set, and inputting the real-time 5-minute-level power prediction data of the integrated energy system into the real-time random optimization model to obtain a real-time scheduling scheme of the integrated energy system.

The invention discloses a multi-energy system optimization scheduling method based on a carbon flow theory and deep reinforcement learning. The method comprises the following steps: firstly, carrying out mathematical description on a multi-energy system dynamic optimization problem based on a carbon emission flow theory, and establishing an objective function and constraint conditions of multi-energy system optimization scheduling; then, the scheduling decision problem is expressed as a Markov decision process framework, a reinforcement learning environment is created, and a state space, an action space and a reward function of the multi-energy system are defined; meanwhile, in the depth deterministic strategy gradient algorithm, the training efficiency is improved by introducing an experience replay mechanism based on proportion priority, the overfitting risk in the network training process is reduced by adopting an L2 regularization measure, and the generalization ability of the network in the optimization scheduling decision of the multi-energy system is improved. The method can respond to random fluctuation of a source side and a load side in real time, outputs a coordinated scheduling strategy, ensures low-carbon operation of a multi-energy system, and overcomes the defect that a traditional energy scheduling system cannot quickly adapt to changes.

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 provides a comprehensive energy system optimization scheduling method and device considering diversified application of wind, light and green power, electronic equipment and a storage medium, and relates to the technical field of new energy technology and power system integration optimization. According to the method, a comprehensive energy system mathematical model integrating wind, light, green electricity, a hydrogen and ammonia production process, thermal power ammonia-doped combustion and thermal power oxygen-enriched combustion is constructed; combining the comprehensive energy system mathematical model to construct a comprehensive energy system scheduling model considering cost effectiveness, new energy utilization rate and environmental protection; and selecting a target solution algorithm from the plurality of solution algorithms, constructing an operation scene, and performing simulation solution on the integrated energy system scheduling model to obtain an integrated energy system optimization scheduling result. According to the embodiment of the invention, wind-solar-green-electricity hydrogen production ammonia synthesis and thermal-electricity ammonia-doped combustion and oxygen-enriched combustion technologies are effectively integrated, an ecological system for efficient cyclic utilization of energy and resources is created, and green transformation and industrial upgrading of an energy structure are promoted.

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

The invention discloses an integrated energy system optimization method based on hierarchical uncertainty, and the method comprises the following steps: taking an error between historical power generation data and historical prediction power generation data of an integrated energy system as a historical prediction error, and constructing a prediction error fuzzy set according to the historical prediction error, constructing a distribution robust optimization model based on the prediction error fuzzy set; obtaining the day-ahead one-hour power prediction data of the integrated energy system, and inputting the day-ahead one-hour power prediction data into the distributed robust optimization model to obtain a day-ahead scheduling scheme of the integrated energy system; the method comprises the following steps: constructing an intra-day random scene set, constructing a random optimization model based on the intra-day random scene set, acquiring intra-day power prediction data based on a day-ahead scheduling scheme by taking 15 minutes as a period, inputting the intra-day power prediction data into the random optimization model to obtain an intra-day scheduling scheme, and repeating the steps until optimization scheduling of all periods in the day is completed. And obtaining a final scheduling scheme of the integrated energy 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 belongs to the technical field of energy system optimization, and provides a combined cooling heating and power comprehensive energy system multi-target operation optimization method, which comprises the following steps: by combining chaotic mapping and a cuckoo algorithm, introducing a dynamic adaptive step factor and a dynamic discovery probability, and introducing disturbance reverse learning, constructing a multi-target optimization scheduling model; by improving the cuckoo optimization algorithm, performing population initialization by utilizing Logistic chaotic mapping, enhancing the randomness and global search ability of the algorithm, introducing a dynamic adaptive step factor and a dynamic discovery probability, increasing the diversity and flexibility of bird nest initialization, improving the global search ability, and introducing disturbance reverse learning, the algorithm has the advantages of improving the robustness of the algorithm, and improving the robustness of the algorithm. The algorithm performance is fully improved, and falling into a local optimal solution is avoided; compared with a common micro-grid, the improved comprehensive energy power grid has the advantages that the economic benefit can be effectively improved, pollutant emission is reduced, and the total cost of the whole system is reduced to a certain extent.

The present invention provides a method for optimizing a microgrid energy system based on real-time electricity price incentives, and belongs to the field of electronic information technology. It includes establishing a microgrid energy system and acquiring system data; determining a state set, an action set, a reward function, and a real-time electricity price incentive mechanism; and using the real-time electricity price incentive mechanism and the Dueling Double DQN algorithm to perform offline learning to obtain the optimal strategy, so that the system can achieve maximum economic benefits in any state. The optimization method of the present invention uses real-time buying and selling electricity prices to incentivize exploration of strategies, an online evaluation network to obtain the action corresponding to the optimal action value in the current state, and a target evaluation network to calculate the action value of the action to obtain a target estimated value, which effectively avoids the "overestimation" problem of the algorithm, improves the algorithm estimation accuracy, and thus achieves maximum economic benefits of the microgrid energy system.

The invention discloses an industrial park integrated energy system optimization method and system, and belongs to the technical field of building and energy system design optimizing.By determining constraint conditions of energy balance constraint, equipment capacity constraint and equipment operation constraint, load data in an industrial park are used as initial conditions, design parameters and meteorological data are input, and the optimal energy balance constraint, equipment capacity constraint and equipment operation constraint are determined; taking the hydrogen doping ratio as a decision variable, constructing a comprehensive energy system optimization model and determining a target function; solving the objective function by taking minimization of annual total cost, carbon emission and energy consumption of the integrated energy system as an objective, and outputting a Pareto solution set; and performing multi-target sorting on the Pareto solution set, screening out an optimal solution corresponding to the decision variable, and obtaining a real-time power condition of each device corresponding to the optimal solution. According to the comprehensive energy system optimization model constructed by the method, the use proportion of hydrogen energy can be adjusted under different operation conditions, so that the optimal energy efficiency and environmental protection effect are achieved.

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 relates to the field of comprehensive energy, and discloses a low-carbon comprehensive energy system optimization planning method considering clean energy, which comprises the steps of determining an optimization planning target, analyzing energy requirements of a system, and determining constraint conditions in physical, technical, economic and environmental aspects; all kinds of energy resources available in the system are evaluated, the potential and availability of all kinds of renewable energy resources are evaluated based on geographical and climate conditions, the supply modes of all kinds of energy resources are analyzed and determined in combination with energy demand, and a multi-energy collaborative supply scheme is provided; establishing a mathematical model of the multi-energy system, simulating the operation condition of the system, evaluating the contribution of each energy form and the influence of the contribution on the stability of the system, and analyzing the influence of different parameter changes on the operation of the system; a comprehensive objective function is constructed according to requirements and objectives of the system, and constraint conditions of system operation are set according to equipment performance, energy efficiency and environmental protection laws and regulations. The method has the advantages of optimizing multi-energy collaborative supply and ensuring stable operation of the system.

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 relates to an electric heating integrated energy system optimization planning method and device considering seasonal heat storage and a medium, and the method comprises the following steps: constructing a seasonal heat storage model based on a heat charging and discharging state and an internal thermal power state, the internal thermal power state comprising a weekly state and an intra-period state, and being obtained through the prediction of a deep neural network; based on the seasonal heat storage model, an electric heating integrated energy system coordinated planning model is built, and the electric heating integrated energy system coordinated planning model takes the sum of minimum annual investment cost and operation cost as a target function and builds constraint conditions based on investment constraints and operation constraints; and carrying out hybrid linear transformation on the electric heating integrated energy system coordinated planning model by adopting a large m method, and solving to obtain an optimal planning scheme of the electric heating integrated energy system. Compared with the prior art, the method has the advantages that the electric heating comprehensive energy system can be accurately modeled, the nonlinear optimization problem of a traditional physical modeling method is solved, and scientific and efficient energy planning is achieved.

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 discloses an energy system optimization scheduling method and system based on data center load transfer, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: collecting data through a sensor, and carrying out the preprocessing; constructing a comprehensive energy system model based on the data center based on the preprocessed data; constructing a target function and a constraint condition of a calculation task transfer model; and an optimization problem of the target function is converted into a mixed integer linear programming problem, and the problem is solved by using a branch and bound method. According to the method, the comprehensive energy system model and the calculation task transfer model are constructed, so that efficient transfer of data center loads and optimal scheduling of energy are realized, the energy utilization efficiency is remarkably improved, the operation cost is reduced, and the environmental influence is reduced.

The invention discloses an integrated energy system optimization scheduling method based on source-load interactive excitation low-carbon transformation, and belongs to the technical field of power systems. According to the method, an IES source-network-load full-chain carbon tracking method is constructed based on the CEF theory, the carbon emission of the whole link of the IES is accurately metered, and meanwhile, the carbon emission responsibility of the source side is allocated to the load side, so that a spatial dynamic carbon emission factor is obtained to serve as a standard for measuring the carbon reduction of the user side of the CCER market. And secondly, a CET-CCER combined market operation mode is constructed based on the carbon reduction value of the CCER market, and a carbon emission factor is utilized to drive the energy consumption behavior of the user side to be low-carbon, so that the smooth development and implementation of the LCDR are orderly guided. And finally, a source-load interactive excitation low-carbon transformation mode is formed, interactive coupling between the source load and the load is realized, and the carbon efficiency level of the system is improved from two ends of the source load. According to the comprehensive energy system optimization scheduling method based on the source-load interactive excitation low-carbon transformation, the carbon cost and the overall carbon emission in different wind power permeability scenes are effectively reduced.

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.