41 results about "Energy system optimization" patented technology

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 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 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 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 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.

The invention provides a multi-integrated energy system game playing method based on two-stage random robustness, and belongs to the field of integrated energy system optimization operation. The method comprises the following steps: constructing a plurality of integrated energy system models based on renewable energy output and power price of a power distribution system in different integrated energy systems; constructing a plurality of multi-energy load aggregator models based on energy prices formulated by different integrated energy systems; constructing an N-S game model based on the plurality of integrated energy system models and the plurality of multi-energy load aggregator models by using a game theory; and solving the N-S game model by adopting a KKT condition in combination with an ADMM algorithm and C & CG-AIS to obtain the optimal interaction power among all the integrated energy systems and the pricing of the energy price. The method can stimulate the demand response potential of the user, reduce the dependence of the integrated energy system on an external energy network, and improve the overall benefits and individual benefits of multiple market subjects.

The invention discloses a random robust optimization method considering multiple uncertainties, and belongs to the field of optimal scheduling of an integrated energy system. The method is used for obtaining the power output of each device in the integrated energy system, and comprises the following steps: firstly, establishing a first-stage optimization model by taking the planning cost of the integrated energy system in a minimum basic scene as a target; establishing a second-stage optimization model by taking minimization of the power interaction cost of the integrated energy system and the regional power grid in the worst electricity price scene as a target; secondly, establishing a third-stage optimization model by taking the operation cost of the minimum integrated energy system as a target; and finally, constructing a three-stage random robust model, and solving the three-stage random robust model by using a column sum constraint generation algorithm to obtain the power output of each device in the integrated energy system. According to the method, the relation between the economical efficiency and the robustness of the system can be well balanced, and the worst scene better conforms to engineering practice.

This invention discloses a method and system for optimized scheduling of integrated energy systems based on a large language model in the field of integrated energy system optimization scheduling and intelligent control. The method includes: partitioning the integrated energy system according to power supply sources and energy conversion equipment; constructing an integrated energy system model, an energy trading matrix, and a carbon emission rights trading matrix; and calculating the total net carbon emissions and total green electricity certificates of the integrated energy system; constructing the state space, action space, and reward function of a deep reinforcement learning model; training the deep reinforcement learning model based on historical optimized scheduling data of the integrated energy system and the large language model; and generating optimized scheduling actions for the integrated energy system using the trained deep reinforcement learning model based on real-time operating data of the integrated energy system. This invention can improve the coordinated optimization capability of integrated energy systems and enhance the adaptability and flexibility of scheduling strategies.

The application discloses a kind of low-carbon scheduling methods of integrated energy system considering energy-carbon coupling, which is based on carbon emission flow theory, combined with the strong fitting ability of neural network, proposes the method of carbon flow constraint learning, converts the complex mapping relationship between power flow and carbon flow into mixed integer linear constraint, realizes the effective embedding of carbon flow constraint in optimization model. At the same time, in order to reduce the structural complexity of neural network, the sparse training strategy is introduced, the model parameter size is effectively compressed, and the ReLU activation function is linearized by the improved big-M method, the feasible region is gradually tightened by introducing the cut plane constraint, so as to significantly improve the solving efficiency of optimization model. Finally, the carbon flow constraint model is embedded in the integrated energy system optimization scheduling problem, the carbon emission reduction consciousness of load side is stimulated, the demand response behavior of load side based on its own carbon signal is guided, low-carbon energy adjustment is promoted, and low-carbon scheduling under energy-carbon cooperation is realized, to reduce the overall carbon emission level of the system.

The application provides a mine integrated energy system operation optimization method and system considering equipment importance, and belongs to the technical field of mine energy system optimization. The method comprises the following steps: S1, establishing a mine integrated energy system operation optimization model, including determining a to-be-optimized equipment type, collecting system operation parameters, determining an optimization target and a constraint condition; S2, solving the mine integrated energy system operation optimization model by using a large-scale constraint multi-objective evolution algorithm considering equipment importance to obtain a Pareto optimal solution set; S3, selecting a compromise solution from the Pareto optimal solution set as a final scheduling scheme by using a fuzzy decision method, and issuing the scheduling scheme to corresponding equipment through multiple communication protocols to realize automatic operation of the mine integrated energy system. The application realizes efficient scheduling optimization of the mine integrated energy system by establishing an optimization model, analyzing equipment variable importance and rapidly seeking optimization in a large-scale decision space through an evolution optimization algorithm.

The invention discloses an integrated energy system optimization method based on OCC-P2G-dynamic hydrogen doping coupling, and relates to the technical field of energy system optimization. The method comprises the following steps: firstly, constructing an oxygen-enriched combustion trapping system model, a two-stage electricity-to-gas system model and a fuel gas dynamic hydrogen doping mathematical model to realize cyclic utilization of oxygen, carbon dioxide, hydrogen and reaction heat in the system; secondly, a stepped carbon transaction mechanism is introduced, and a nonlinear piecewise function model between the carbon transaction cost and the net carbon emission is constructed; and finally, with the goal of minimizing the total operation cost of the system including the carbon transaction cost, establishing an optimal scheduling model of the integrated energy system and solving the optimal scheduling model. Through the synergistic effect of physical coupling and stepped economic excitation, the renewable energy consumption capacity, the overall energy efficiency of the system, the operation economy and the scheduling flexibility are effectively improved, and the energy structure is driven to be deeply transformed to low carbon.

The invention provides a regional integrated energy system adjustable capacity optimization method and system, and belongs to the technical field of regional integrated energy system optimization. The method comprises the following steps: determining upper and lower limits of adjustable capacity of equipment to determine total upward and downward adjustment capacity of the system; according to the upward and downward adjustment total capacity of the system, the total transmission loss and the available transmission capacity of the line, solving the upward and downward adjustable capacity of the region; upward and downward capacity prediction errors, wind and light output errors and load fluctuation errors are determined, and probability feature vectors of up-regulation and down-regulation directions are obtained through Bayesian reasoning modeling; calculating the attention weight of the historical window through a time sequence attention mechanism; and determining a dynamic safety margin to dynamically correct the upward and downward adjustable capacities of the current region to obtain the final upward and downward adjustable capacities of the region. According to the method, the precision of the regional integrated energy system capacity and the system operation reliability are improved, and the regional adjustable capacity is more suitable for the actual network state.

The present application relates to the field of energy system optimization control, and specifically discloses a kind of energy management and optimization method of SOFC-GT combined heat and power system, adopts 15 minute time scale's intraday real-time rolling optimization method, in combination with prediction model, rolling optimization and feedback correction, real-time adjustment combined heat and power system equipment output, suppress control deviation;First, prediction model is established using historical data and random error;In control time domain, the system operation plan is optimized, and rolling optimization is realized;According to real-time data, the prediction error is corrected, and feedback correction is completed;Real-time simulation model is constructed, and the effect of energy management strategy is verified.The present application uses the above method, can reduce the influence of day-ahead prediction error, improve system scheduling accuracy, reduce performance loss, and give consideration to economy and environmental protection;Real-time simulation verification shows that the system stability and economy are good, and the energy utilization efficiency and operation reliability of SOFC-GT combined heat and power system are significantly improved.

The invention provides an integrated energy system multi-dimensional resource adequacy assessment method, device and equipment and a medium, and relates to the technical field of integrated energy system optimization. The method comprises the following steps: acquiring a time sequence power curve of a target transaction resource and uncertainty data; performing morphological decomposition on the time sequence power curve, and constructing a plurality of adequacy evaluation indexes; calculating a dynamic objective weight and a basic subjective weight of each adequacy evaluation index; fusing the dynamic objective weight and the basic subjective weight through an attention mechanism to obtain an intermediate weight of each adequacy evaluation index; determining cloud model parameters according to the uncertainty data, and optimizing the intermediate weight through the cloud model to obtain the final weight of each adequacy evaluation index; and based on the final weight of each adequacy evaluation index, fusing each adequacy evaluation index to obtain a dynamic adequacy evaluation value of the target transaction resource. According to the invention, the resource adequacy of the energy system can be integrated more accurately.

The invention provides a comprehensive energy system optimization method considering user satisfaction based on an MATD3 algorithm. The method comprises the following steps: 1) constructing a multi-agent architecture; 2) deploying a global coordination module; 3) defining a dual-objective function and initializing parameters; and 4) multi-agent double-target collaborative decision and training: realizing collaborative optimization of the total operation cost of the system and the user satisfaction through multi-agent collaboration and double-target fusion. According to the comprehensive energy system optimization method considering the user satisfaction based on the MATD3 algorithm, the inherent defects of single-agent scheduling are overcome through equipment-level multi-agent collaboration and algorithm improvement.

The invention discloses a low-carbon operation decision generation method and system for an integrated energy system, and belongs to the technical field of energy system optimization operation. Therefore, the upper-layer strategy generation model can continuously optimize the generated operation strategy in different scenes, economical efficiency and operation stability are considered, operation risks caused by fluctuation of electricity price, output and the like are solved, the situation that the upper-layer decision is averagely optimal but extremely unbalanced due to neglect of uncertainty is avoided, and the method is suitable for large-scale popularization and application. The problem that the actual benefit of the optimized operation strategy is not good due to the fact that the expected optimal effect is difficult to achieve under the condition of actual scene fluctuation change in the generation of the existing integrated energy system optimized operation strategy is solved.

The invention discloses a coupling energy system optimization scheduling method and device based on carbon capture, and belongs to the field of power system scheduling operation. The method comprises the following steps: acquiring operation data of a coupled energy system comprising an oxygen-enriched coal-fired power generation subsystem and a green hydrogen methanol synthesis subsystem; respectively establishing a first operation model of an oxygen-enriched coal-fired power generation subsystem and a second operation model of a green hydrogen methanol synthesis subsystem according to the operation data; inputting the historical renewable energy power generation data into a preset generative adversarial network, and generating a time sequence scene set and a scene probability; and according to constraint conditions in the first operation model and the second operation model, establishing an optimization model by taking the minimum average free carbon concentration and the minimum scheduling total cost as objective functions, randomly inputting scene data in the time sequence scene set into the optimization model, and outputting to obtain an optimal scheduling scheme of the coupled energy system. According to the invention, the operation cost of coal-fired power generation can be effectively reduced, and the operation stability of the whole system is improved.

The invention discloses an optimal scheduling method and device for a multi-agent integrated energy system, electronic equipment and a storage medium, and belongs to the field of optimal scheduling of the integrated energy system, and the method comprises the steps: obtaining an optimal scheduling model of the multi-agent integrated energy system; wherein the optimal scheduling model takes operation parameters of each main body as decision variables; randomly generating a plurality of candidate scheduling instructions to form an initial population; wherein each candidate scheduling instruction is a group of random assignments to the decision variable; the multi-target group search operation is repeatedly executed until the preset execution times are reached, and a scheduling instruction set is obtained; and according to the scheduling instruction set, performing optimization scheduling on the multi-agent integrated energy system. Therefore, by implementing the method, the problems of fixed role proportion and single search strategy of the existing algorithm in the prior art can be solved.

The invention relates to an integrated energy system optimization scheduling method considering source load uncertainty, and the method comprises the following steps: obtaining a wind and light output time sequence data set of each time period, and simulating a wind and light output scene based on the data set; reducing the simulated wind and light output scene to obtain a wind and light output typical scene; a linear carbon transaction mechanism is constructed based on the stepped carbon transaction mechanism, and an optimal scheduling model of the integrated energy system is constructed based on the linear carbon transaction mechanism and power balance constraints considering cold, heat and electric load demand responses; and inputting the load time sequence data and the wind and light output typical scene into the optimization scheduling model to obtain an optimization scheduling result. Compared with the prior art, the method has the advantages that the contradiction between the wind and light output volatility and the system operation stability is solved, and the model solving efficiency is improved.

The invention discloses an optimal scheduling method of a multi-integrated energy system in a shared energy storage mode considering a three-layer mixed game. The method comprises the following steps: 1, constructing a three-layer mixed game system consisting of an upper-layer shared energy storage system, a middle-layer integrated energy system and a lower-layer user; 2, establishing an upper-layer shared energy storage optimization model which comprises an objective function and constraint conditions of shared energy storage; 3, establishing a middle-layer integrated energy system optimization model which comprises an objective function and a constraint condition of an integrated energy system; 4, establishing a user optimization model of a lower layer, wherein the user optimization model comprises a target function and a constraint condition of a user; and 5, carrying out optimization solution on the three-layer mixed game model to obtain optimal scheduling when the operation energy consumption of each layer of model is minimized. According to the method, under the condition of considering the mixed game and the shared energy storage mode, optimal scheduling of each layer of model can be considered, so that the energy utilization efficiency of each layer of main body is improved.

The invention relates to the technical field of optimal scheduling of an integrated energy system, and discloses a fully-distributed combined heat and power optimal scheduling method, device and equipment and a medium. In order to solve the problems of linear convergence, low calculation efficiency, dependence on a coordination center and the like of a traditional distributed optimization algorithm in electric heating system scheduling, on the basis of reserving an original electric heating coupling model core architecture, a distributed collaborative interior point conjugate gradient method is introduced as an optimization algorithm, and fully-distributed scheduling with efficient convergence is achieved. According to the method, an electric heating comprehensive energy system optimization model is converted into a convex optimization problem, a correction equation is decoupled, a coefficient matrix is ensured to be symmetrical and positive definite, a distributed conjugate gradient method is adopted for solving, and collaborative optimization of an electric power system and a thermodynamic system can be achieved without a coordination center. Simulation verification shows that the calculation efficiency is improved by more than 90% on the premise of ensuring that the optimization precision is consistent with a centralized algorithm, the privacy of each energy main body can be protected, and the method is suitable for real-time scheduling of electric heating comprehensive systems of different scales.

This invention discloses a collaborative optimization scheduling method, device, electronic equipment, and storage medium for a wind-wave-hydrogen multi-energy system, belonging to the field of multi-energy system optimization scheduling technology. The method includes: acquiring a wind speed sample matrix, predicted load data, and real-time operating data; extracting daily wind speed samples to construct seasonal and model centroids, forming a seasonal-model combination, and iteratively clustering based on a hierarchical weighted objective function to obtain typical wind speed scenarios and joint probabilities; solving a day-ahead stochastic optimization scheduling model with the objective of minimizing the expected total cost to obtain a day-ahead baseline plan; combining real-time operating data with input to a near-end policy optimization agent, outputting incremental control commands, superimposing them to form actual control commands, and controlling system operation. By implementing this invention, the problems of poor scheduling flexibility, difficulty in achieving optimal overall economic efficiency, and lack of robustness in day-ahead scheduling plans due to distorted characterization of uncertain scenarios in existing wind-wave-hydrogen multi-energy systems can be solved.

The invention discloses an electric heating interconnection system distribution collaborative scheduling method, system and device based on risk quantification and a storage medium, and belongs to the technical field of comprehensive energy system optimization scheduling. The method comprises the steps of obtaining electric heating interconnection system data; constructing a moment fuzzy set based on wind power historical data, and introducing distribution robust opportunity constraints to construct a centralized risk scheduling model; the distribution robust opportunity constraint is converted into a convex optimization problem through second-order cone programming to reconstruct a centralized risk scheduling model; decomposing the centralized risk scheduling model into a power system optimization problem and a thermodynamic system optimization problem by adopting an adaptive penalty coefficient alternating direction multiplier method, and iteratively solving the two optimization problems to obtain a distributed scheduling intermediate result; and calculating a risk index based on the distributed scheduling intermediate result, and if the risk index is not greater than a preset threshold, taking the distributed scheduling intermediate result as a final scheduling strategy. According to the method, the balance risk controllability and the scheduling economy are ensured, and the optimal output strategy of the unit is output.

The invention relates to the technical field of comprehensive energy system evaluation, in particular to a comprehensive evaluation method for an optimal configuration scheme of a comprehensive energy system, which comprises the following steps of: constructing a comprehensive evaluation index system of the comprehensive energy system; calculating the combined weight of the evaluation indexes based on an IAHP-entropy weight method; carrying out comprehensive evaluation and scheme sorting based on a combined weight-TOPSIS method; according to the method, the system covering multi-dimensional evaluation indexes is constructed, the problem that a traditional evaluation method is single in view angle is solved, and comprehensive evaluation of the comprehensive energy system configuration scheme is achieved; a combined weighting mode of an IAHP-entropy weight method is adopted, expert experience and data objective information are effectively fused, and index weight distribution conforms to engineering practice and data rules; through deep coupling of the combination weight and the TOPSIS method, accurate quantification of the distance between each configuration scheme and the ideal solution is realized, the scheme sorting result is more accurate and visual, and the scientificity and reliability of decision making are greatly improved.

The application discloses a port comprehensive energy system optimization method based on quantum calculation, and steps are as follows: 1) a binary quadratic unconstrained optimization model of a port comprehensive energy system objective function is established; 2) a binary quadratic unconstrained optimization model penalty term of a port comprehensive energy system equation constraint is established; 3) a binary quadratic unconstrained optimization model penalty term of a port comprehensive energy system inequality constraint is established; 4) the binary quadratic unconstrained optimization model penalty term of the inequality constraint established in the step (4) is combined with the objective function established in the step (2) and the equation constraint established in the step (3) to obtain a binary quadratic unconstrained optimization model of the port comprehensive energy system; and 5) a quantum computer is used to solve the binary quadratic unconstrained optimization model of the port comprehensive energy system to obtain a port comprehensive energy system scheduling scheme.

The invention discloses an optimal scheduling method, system and device for an electricity-hydrogen-ammonia-alcohol comprehensive energy system and a medium, and the method comprises the steps: obtaining all device parameters of the electricity-hydrogen-ammonia-alcohol comprehensive energy system, and building an electricity-hydrogen-ammonia-alcohol comprehensive energy system optimal scheduling model with economic optimality as a target; reconstructing the established optimal scheduling model of the electro-hydrogen ammonia-alcohol comprehensive energy system into a constrained Markov decision process, and optimizing the constrained Markov decision process by using a Lagrange multiplier; and adopting a diffusion actor-commentator algorithm considering information entropy, and combining with a Gaussian mixture model to train and solve an electric hydrogen ammonia alcohol comprehensive energy system optimization scheduling model corresponding to the constrained Markov decision process to obtain an optimal scheduling scheme. According to the method, the electro-hydrogen ammonia-alcohol comprehensive energy system can realize economic optimal scheduling on the premise of meeting multi-equipment safety constraints of thermal power generating units, wind and light, hydrogen production and hydrogen storage and the like, the scheduling solving efficiency is improved, new energy consumption is promoted, and stable and efficient operation of the system is guaranteed.