75 results about "Operational optimization" patented technology

Provided is an intelligent operational optimization method in the municipal solid waste incineration process, which belongs to both the field of municipal solid waste treatment and the field of intelligent optimization. The method includes: constructing a sample data set by collecting historical data in the municipal solid waste incineration process; with optimization objectives of nitrogen oxide emissions and combustion efficiency, establishing an SORBF neural network-based indicator model to characterize the mapping relationship between operational variables and optimization objectives in the municipal solid waste incineration process; and setting the established indicator model as evaluation functions of a multi-objective optimization algorithm, and obtaining optimal setting values of the operational variables by the multi-objective particle swarm optimization algorithm, so as to improve combustion efficiency while reducing concentrations of nitrogen oxide emissions.

The invention discloses a wind-light-water-storage combined body spot market operation optimization scheduling strategy, and belongs to the technical field of electric power system operation and control. Aiming at the problems of fluctuation of wind-solar power generation and uncertainty of prediction errors, the invention proposes that a wind-solar group is combined with cascade hydropower and an energy storage pump station thereof, and a wind-solar-water storage combination body is constructed to participate in a multi-time-scale power market; by constructing an optimal scheduling model considering the source-price uncertainty, the comprehensive benefit maximization of the complex in the day-ahead market, the real-time market and the medium-and-long-term contract decomposition is realized; an information gap decision theory and a conditional value-at-risk method are adopted to ensure that the joint profit can still be ensured when the wind and light prediction error is expanded; meanwhile, profits are distributed fairly through a Shapley method, and the benefits of all subjects are improved; and the market competitiveness and executive force of wind and light power generation are effectively improved, the absorption and utilization of new energy are promoted, the flexibility and toughness of the system are high, and powerful support is provided for constructing a green, low-carbon and efficient modern energy system.

The invention relates to the field of intelligent power grids, and discloses an electric power marketization operation optimization method and system based on user-side resource aggregation, and the method comprises the following steps: a), recognizing and classifying user-side resources, specifically including distributed power generation, energy storage equipment and adjustable loads, forming a user-side resource database, and facilitating the subsequent management and scheduling; b) formulating transaction rules of market participants, and determining participation conditions, rights and obligations of household users, small commercial users and large industrial users to ensure fair participation of various users in the market; and c) automatically executing the transaction through the smart contract. Through user side resource aggregation, the method can effectively integrate distributed power generation, an energy storage system and an adjustable load, optimizes supply and demand balance of power, and enables a user to adjust a power utilization strategy in different demand time periods through a real-time monitoring and dynamic pricing mechanism, thereby reducing resource waste and improving the overall efficiency of a power system.

The invention discloses a low-carbon economic operation optimization method for an electricity-hydrogen-heat comprehensive energy system, and belongs to the crossing field of intelligent energy systems and artificial intelligence, and the method comprises the following steps: 1, building a low-carbon economic dual-target operation optimization problem model of the electricity-hydrogen-heat comprehensive energy system; 2, constructing a dual-objective operation optimization problem solving framework with a third-generation non-dominated sorting genetic algorithm as a core; 3, modeling a population evolution decision problem in the genetic algorithm into a Markov decision process, and constructing a deep reinforcement learning agent and population evolution environment interaction mechanism; 4, training the intelligent agent by adopting a double-depth Q network algorithm, and outputting a genetic action according to an environment state; 5, obtaining a new filial generation in combination with a selection strategy associated with the reference points and genetic actions output by the intelligent agent; the step 4 and the step 5 are iteratively evolved until the maximum iteration generation number is reached, and the operation cost can be reduced under the same carbon emission condition.

The invention provides an operation optimization method and system for an energy storage participation auxiliary service market, and the method comprises the steps: determining an energy storage performance aging factor, and defining an energy storage aging time function according to the energy storage performance aging factor; the energy storage performance aging factors comprise calendar aging and cycle aging; based on the energy storage aging time function, constructing an intra-day operation model taking energy storage expected life maximization and intra-day benefit maximization as objective functions; and solving the intra-day operation model by adopting a multi-agent algorithm of a non-adversarial game to obtain an optimal strategy of energy storage intra-day operation. According to the invention, the relation between the energy storage aging characteristic and the working characteristic is introduced to carry out the energy storage intra-day operation decision, the economic benefit can be truly evaluated, the energy efficiency of energy storage and the stability of scheduling are considered, and the operation maintenance level is greatly improved.

The invention discloses a multi-microgrid shared operation optimization method considering an electricity-carbon-green certificate transaction. The method comprises the following steps: establishing a multi-microgrid point-to-point energy transaction framework; establishing a multi-micro-grid system model of a novel combined heat and power generation unit equipped with coupling electricity-to-gas-carbon capture in the micro-grid; based on the multi-micro-grid system model, establishing a multi-micro-grid low-carbon operation model considering a stepped carbon-green certificate combined transaction mechanism; based on the Nash negotiation theory, the original problem of the multi-microgrid low-carbon operation model is decomposed into a multi-microgrid cost minimization sub-problem and an asymmetric bargaining-based income distribution sub-problem, and an adaptive alternating term multiplier algorithm is adopted to solve the multi-microgrid cost minimization sub-problem and the asymmetric bargaining-based income distribution sub-problem in a distributed manner; fair distribution is realized according to the energy contribution of each micro-grid while the benefit maximization of the multiple micro-grids is realized, and the carbon emission in the micro-grid operation process is effectively reduced while the economic benefit is ensured.

The invention relates to the technical field of gas station operation optimization, in particular to an intelligent gas station operation optimization system and method based on big data and artificial intelligence. The data acquisition module acquires and uploads multi-source data in real time, the data processing module cleans and standardizes the multi-source data to generate auditable tagged data, the AI analysis module performs demand prediction based on a demand prediction model, and performs grouping through a K-means algorithm to generate user portraits. The intelligent scheduling module carries out intelligent dynamic scheduling according to a demand prediction result, optimizes service efficiency and reduces client waiting time, the operation optimization module carries out precise marketing according to a client portrait and formulates a personalized preferential strategy, and the feedback suggestion collection module monitors operation data and client feedback in real time. And the result is returned to the AI analysis module to optimize the prediction model and the marketing strategy. According to the invention, the scheduling and prediction precision is improved, an operation decision basis is provided for managers, and the gas station operation efficiency and customer satisfaction are improved.

The invention discloses a method for planning a commodity display effect and electronic equipment. The problem that the commodity display effects of different stores are uneven can be solved. The method for planning the commodity display effect comprises the following steps: constructing an operation planning optimization model based on a preset commodity display demand; wherein the preset commodity display requirements comprise reasonable classification and partition, partition boundary orderliness and partition commodity orderliness, the operation planning optimization model comprises decision variables, auxiliary variables, parameters, constraint conditions and an objective function, and the constraint conditions comprise position constraints for displaying the commodities on a shelf and sequence constraints for placing the commodities on the shelf; taking the boundary deviation value as a target function, wherein the boundary deviation value is inversely proportional to the boundary alignment degree of the same block between any two layers of goods shelves; solving the operation planning optimization model to obtain a display scheme with the highest block boundary alignment degree; wherein the display scheme with the highest block boundary alignment degree is the optimal solution of the operation planning optimization model.

The invention discloses an infeasible boundary calculation method and system based on machine learning and operation planning optimization, and the method comprises the steps: obtaining the current operation boundary data of a to-be-dispatched power grid when a security constraint unit combination is infeasible in the operation process of the to-be-dispatched power grid; inputting the current operation boundary data into a pre-trained machine learning model to obtain a conflict constraint type output by the machine learning model; the machine learning model is obtained based on historical clearing data training, and the historical clearing data is historical data which is infeasible when security constraint unit combination occurs in power grid operation; calculating a minimum infeasible conflict set based on the conflict constraint type; and calculating an infeasible boundary adjustment amount based on the minimum infeasible conflict set and the current operation boundary data, the infeasible boundary adjustment amount being used for adjusting the scheduling scheme of the to-be-scheduled power grid, so that the infeasible boundary in the security constraint unit combination can be quickly and accurately positioned, and the operation boundary parameters in the scheduling scheme can be correspondingly adjusted.

The invention discloses a virtual power plant coupling multi-market operation optimization and risk evaluation method, and relates to the technical field of virtual power plants. Comprising the steps of 1, establishing a resource model in a virtual power plant; 2, establishing corresponding market models for different participated markets, and establishing an interface model of the virtual power plant and the markets; 3, designing a virtual power plant overall operation optimization model; 4, establishing a risk evaluation model, and considering various risks possibly faced by the virtual power plant participating in the market; 5, coupling the overall operation optimization model and the risk evaluation model to form a comprehensive model; 6, a real-time scheduling system is established, and market changes and resource states are monitored; and 7, performing system simulation verification by using actual data, adjusting model parameters, and optimizing an algorithm. According to the invention, the overall operation problem of the virtual power plant in a complex market environment is solved, including the technical problems in the aspects of resource scheduling, market transaction and risk management.

This invention discloses a privacy-preserving energy-data collaborative optimization method and system for Virtual Power Plants (VPPs), relating to the field of virtual power plant operation optimization technology. The method includes establishing an outer master-slave game model to obtain strategic parameters output by the model; combining these strategic parameters with an inner multi-timescale operation optimization model to obtain operational data; constructing a bidirectional coupling feedback mechanism and a Stackelberg equilibrium mechanism using the strategic parameters and operational data; and employing a distributed collaborative solution method based on the bidirectional coupling feedback mechanism and Stackelberg equilibrium mechanism to obtain the VPP privacy-preserving energy-data collaborative optimization results. This invention constructs a bidirectional closed-loop coupling framework, achieving deep integration and collaborative value-added of data element value and energy physical value.

The invention discloses a new energy-thermal power collaborative optimization scheduling method based on a control performance standard, and belongs to the technical field of power system scheduling. CPS1 and CPS2 standards are converted into prospective constraints in a scheduling model in real time, and are converted into constraint conditions of a unit dynamic optimization scheduling model; the scheduling model not only considers traditional unit physical limitation and economical efficiency, but also embeds two types of core requirements of network security constraint and control performance standard to guarantee safe and stable operation of a power grid in different dimensions into an optimization decision process, so that CPS standard reaching is actively guaranteed in the scheduling process, the frequency quality of the power grid is improved, and the scheduling efficiency is improved. The dynamic characteristics of the thermal power generating unit and the second-minute level fluctuation requirements of new energy are accurately matched through an ultra-short periodic monitoring architecture combining ultra-short rolling optimization and minute-level real-time scheduling and output decision variables of the thermal power generating unit output based on a model.

The invention discloses an electric-gas-heat integrated energy system operation reliability evaluation method and system, and the method comprises the steps: obtaining an integrated energy system operation state sequence through combining an integrated energy system supply and demand scene and a system element state scene at a moment t; based on the operation state sequence of the integrated energy system, solving an operation optimization model of the electricity-gas-heat integrated energy system by taking maximum recovery energy supply as an objective function, calculating an optimal operation strategy in a system state at a moment t, realizing cooperation and mutual assistance among electricity-gas-heat networks through network reconstruction and energy conversion, and simulating a response process of the system under interference; according to the state consequences of all moments in the period, the current operation reliability index is calculated according to the index definition, and an operation reliability index result is obtained; and integrating the operation reliability index results of all evaluation periods, and evaluating the mathematical expectation to evaluate the operation reliability of the electricity-gas-heat integrated energy system.

A time sequence operation simulation integrated energy system energy supply adequacy analysis method belongs to the technical field of energy, and comprises the following steps: considering the operation characteristic difference of each subsystem and the characteristics of multi-energy coupling equipment, and modeling a power grid, a gas network, a heat supply network and system coupling equipment; considering the constraint relationship among the subsystems, and establishing an electricity-gas-heat comprehensive energy system operation optimization model; and solving and analyzing the operation optimization model of the electricity-gas-heat integrated energy system based on an annual time sequence simulation method of a segmented time sequence and a solution-free rollback mechanism, obtaining operation data and outputting a result. The method plays a supporting role in characteristic analysis of the comprehensive energy system and energy coupling characteristic analysis of the comprehensive energy system under the long-term scale.

The invention relates to the field of unmanned aerial vehicle scheduling, and provides an intelligent unmanned aerial vehicle scheduling method based on operation optimization. According to the method, an optimization model is constructed by collecting information such as task positions, task quantities, priorities, time windows, unmanned aerial vehicle endurance, loads and speeds. The model takes maximization of task completion total priority and minimization of unmanned aerial vehicle total flight cost as targets, and multiple constraint conditions are considered. And solving the model by adopting optimization technologies such as a genetic algorithm and the like to obtain an optimal scheduling scheme. During task execution, environment change is monitored in real time, and the scheme is dynamically adjusted. The method is suitable for the fields of logistics distribution, agricultural plant protection, surveying and mapping, emergency rescue and the like, can efficiently dispatch the unmanned aerial vehicles, improves the resource utilization efficiency, reduces the cost, and has a wide application prospect.

The invention discloses an ethylene cracking furnace optimization control method based on a self-adaptive multi-objective evolutionary algorithm. Comprising the following steps: firstly, obtaining historical operation parameters of the ethylene cracking furnace and constructing a cracking furnace multi-objective optimization model; then, solving the cracking furnace multi-objective optimization model by using an adaptive multi-objective evolutionary algorithm until solving is completed, and obtaining a Pareto optimal solution set; and determining an optimal cracking furnace outlet pipe temperature control scheme according to the Pareto optimal solution set to realize control of the ethylene cracking furnace. The method provided by the invention can effectively balance the increase of the ethylene yield and the decrease of the propylene yield in the ethylene cracking furnace, and provides a new scheme for operation optimization.

The invention provides an express trunk transportation scheme planning method and device and a medium. The method comprises the steps of obtaining input data; based on the input data and a preset generation rule, constructing a candidate direct route set and a candidate transport route set between the provincial centers; based on the input data, the candidate direct route set and the candidate transport route set, establishing a multi-target integer programming model with the purpose of minimizing the weighted sum of the total processing capacity exceeding amount and the comprehensive cost, and solving the multi-target integer programming model by using an operation planning optimization solver, and outputting the inter-provincial center transportation route, the inter-provincial center dispatching trunk line and the handling capacity information of each inter-provincial center. According to the technical scheme, the overall time limit level of express delivery can be improved, and meanwhile the overall transportation and transfer cost is reduced.

The disclosure provides a method for obtaining a business global optimal solution, and relates to the technical field of data processing, in particular to the fields of big data, operational optimization, artificial intelligence and the like. The specific implementation scheme is: according to the description information of the target business, the target search space and the target function are determined; according to the target search space, an initial solution group is generated; based on the initial solution group, searching is performed in the target search space to obtain a reference solution group associated with the initial solution group; according to the initial solution group and the reference solution group, a new solution group is reconstructed; based on the new solution group, the operation of obtaining the reference solution group is executed until the optimal solution that makes the target function take the minimum value is obtained. Thus, by iterating the initial solution group, the reference solution group is obtained, and based on the initial solution group and the reference solution group, the new solution group is reconstructed, thereby increasing the dispersion of the solution group, and then the global optimal solution is solved based on the new solution group, thereby improving the reliability and accuracy of obtaining the global optimal solution.

A computer-implemented method for operational optimization of an industrial plant (500) comprising at least one hydrogen producer (510) and at least one hydrogen consumer (520), the industrial plant (500) being couplable to at least one source of electrical energy (600) is proposed. The method comprises receiving, at a computing system (100), values of operational parameters of the industrial plant (500), the operational parameters including at least hydrogen production by the at least one hydrogen producer (510), hydrogen consumption by the at least one hydrogen consumer (520), energy production of the industrial plant (500), energy consumption of the industrial plant (500), availability of electrical energy via the at least one source of electrical energy (600) and pricing of electrical energy; computing a forecasted time-series for values and / or boundaries of the operational parameters of the industrial plant (500); and determining, by means of an optimization module (124) of the computing system (100) based on the received values of the operational parameters and the computed forecasted time-series for values and / or boundaries of the operational parameters of the industrial plant (500), one or more optimum operational setpoints for optimizing operational control of the industrial plant (500) in terms of at least one of energy consumption and operation costs of the industrial plant (500), wherein the one or more optimum operational setpoints are determined based on at least one degree of freedom for the industrial plant (500).

The application discloses an enhanced method for operational optimization modeling, comprising: based on an operational optimization problem text file, constructing a structured problem representation containing constraint interdependence and original semantic information; according to the structured problem representation, constructing and solving a forward and reverse problem pair, performing logical consistency deduction, generating a forward modeling result and a reverse verification set; performing multi-dimensional verification on the forward modeling result to identify potential modeling defects, and generating a structured counterexample test set for the potential modeling defects; comprehensively testing the performance of the reverse verification set and the structured counterexample test set, evaluating the credibility of the forward modeling result, and generating a final optimization model and a credibility evaluation report. The application can improve the logical rigor and credibility of the automatically generated model, and provide quantitative reliability evaluation for the model.

The invention provides a compilation method and system of an expert extraction scheme based on operation planning optimization, and relates to the technical field of material professional operation planning optimization, and the method comprises the steps: carrying out the preprocessing of collected expert extraction multi-source data, and obtaining a standardized data set; based on the standardized data set, configuring parameters of the multi-target operation optimization model; solving the multi-target operation optimization model by using an optimization algorithm, generating an optimal solution data set, and generating an initial expert extraction scheme based on the optimal solution data set; and performing compliance verification on the initial expert extraction scheme, receiving a manual fine adjustment instruction based on a visual interface, automatically verifying the adjusted compliance and evaluation indexes by the system, and compiling a final expert extraction scheme. According to the invention, through multi-system automatic docking and model automatic solving, the compilation efficiency of an expert extraction scheme is improved; global optimization is realized based on a multi-target operation optimization model, the response period of demand change is shortened, and a high-quality expert extraction scheme is compiled.

The invention discloses a new energy agricultural machine energy distribution method based on virtual twins, and relates to the technical field of new energy agricultural machine intelligent control, and the method comprises the following steps: S1, collecting a multi-source operation data set of a new energy agricultural machine, and carrying out the preprocessing; s2, constructing a real-time twin overall model; s3, performing weighted fusion on the physical prior constraint and the long-term prediction result, and outputting predicted twinborn; s4, an optimization problem is constructed and solved, and a final control strategy is obtained. The invention designs a new energy agricultural machine energy distribution method based on virtual twins, which is used for realizing energy dynamic management of an agricultural machine operation process under a complex working condition, and can solve the problems of low energy utilization rate, inaccurate load change prediction, energy distribution lag, fast battery life attenuation and the like of a new energy agricultural machine. Scientific basis is provided for energy management and operation optimization of electric tractors, electric plant protection machines, electric harvesters, hybrid power agricultural machines and other equipment.

The invention relates to a natural gas transportation plan intelligent compilation method based on an operation planning optimization technology. The method comprises the following steps: initializing node information; configuring actual natural gas supply quantity and actual demand quantity on the current day, and estimating daily estimated supply quantity and demand quantity from the current day to the end of the month; configuring various limiting parameters and carrying out rationality test; when the test is passed, a constraint condition, a decision variable and a target function are selected to construct a mixed integer programming model; and the mixed integer programming model is solved, if a solution exists, an output result is obtained, and if no solution exists, the limiting parameters are reconfigured. The method has the beneficial effects that the optimal daily gas transmission scheme from the current day to the end of the current month is generated in real time under the condition that constraint conditions such as supply and demand balance constraint and path transmission capacity constraint are met, the planning efficiency can be effectively improved, and the calculation burden of business personnel and the operation cost of enterprises are reduced.

This invention discloses a cogeneration method integrating predictive maintenance and deep waste heat recovery, belonging to the field of cogeneration technology. The method first collects real-time system operation, status monitoring, and external prediction data, and then identifies key performance parameters of the equipment online based on a physical model and adaptive estimation algorithm, generating a real-time health index (HI) and its predicted future degradation trajectory. The core lies in constructing a multi-objective real-time optimization controller, whose objective function integrates short-term operational economic benefits and long-term virtual health costs based on HI predictions, with constraints dynamically correlated with HI. The controller uses a model predictive control framework for rolling solution, outputting optimal coordinated control commands for generator output and deep waste heat recovery components such as flue gas bypass valves and variable frequency pumps. This invention achieves a deep integration of predictive maintenance and operational optimization, significantly improving the overall energy efficiency and economy of the system under all operating conditions while ensuring long-term equipment reliability.

The present invention relates to a Radial Inflow Turbine system (9) in an Organic Rankine System (102) includes a stator (7) in a ring configuration and a rotor assembly (2) with aerodynamic blades to reduce drag and enhance energy transfer. It features a tachometer (3) for real-time shaft (5) speed feedback and a gearbox (6) driven by a stepper motor (4) for precise angle adjustments. The gearbox (6) connects with stator (7) for uniform vane angle adjustments. A hermetically sealed housing (8) maintains the working medium in a gaseous state and prevents leakage. The system integrates pressure sensors (10), temperature sensors (11), and gas flow meters (12) to monitor parameters, alongside control valves (13) and gear adjustments to regulate fluid velocity and vane angles. The present invention discloses a method (200) for real-time operational optimization of system (9) integrated within an ORC (102) comprising a plurality of steps.

Provided is an intelligent operational optimization method in the municipal solid waste incineration process, which belongs to both the field of municipal solid waste treatment and the field of intelligent optimization. The method includes: constructing a sample data set by collecting historical data in the municipal solid waste incineration process; with optimization objectives of nitrogen oxide emissions and combustion efficiency, establishing an SORBF neural network-based indicator model to characterize the mapping relationship between operational variables and optimization objectives in the municipal solid waste incineration process; and setting the established indicator model as evaluation functions of a multi-objective optimization algorithm, and obtaining optimal setting values of the operational variables by the multi-objective particle swarm optimization algorithm, so as to improve combustion efficiency while reducing concentrations of nitrogen oxide emissions.

We provide the system. [Solution] A data acquisition method for collecting information from local facilities, An analytical means for predicting the operational optimization of a specific piece of equipment based on the aforementioned information, A notification means for providing the user with the aforementioned prediction results, A monitoring system that monitors the user's activity history and alerts them when excessive habits are detected, A means of providing guidance to recommend the optimization of facility utilization, A system that includes this.

The invention relates to the technical field of electric power markets, in particular to a distributed power supply and energy storage joint operation optimization method and device, which can construct a target function of distributed power supply and energy storage joint operation. And taking the charging duration and power of each energy storage time period as decision variables. And deploying a CMA-ES algorithm, generating an initial solution by applying distributed power supply output sample data, and performing mutation iteration to obtain a global optimal solution. According to the technical scheme disclosed by the invention, the uncertainty of the output of the distributed power supply is considered, the energy storage charging and discharging duration and the charging and discharging power are taken as decision variables, the real joint distribution of the output of the distributed power supply does not need to be known, a global optimal solution can be given by applying the CMA-ES algorithm, the benefit of system operation is improved, the new energy consumption level is improved, and the method is suitable for popularization and application. And the light and wind abandoning problem is reduced.

The invention discloses an auxiliary service-oriented EVA bidirectional energy interaction multi-period regulation and control method and system, and relates to the technical field of intelligent power grid and electric vehicle aggregator operation optimization, and the method comprises the steps: calculating an energy and power feasible region boundary of an EV cluster in a scheduling period based on a frame in which an electric vehicle aggregator EVA participates in an auxiliary service; establishing a user willingness judgment model, and judging the willingness probability of EV users participating in regulation and control; determining a user participation proportion in combination with a charging type demand response strategy CBDR and a discharging type demand response strategy DBDR; constructing an optimization model which takes electric vehicle aggregator operation revenue maximization as a target function; obtaining an optimal charging and discharging scheduling scheme by solving the optimization model; according to the method, a composite strategy is adopted when the EV access scale is expanded, the adjustment capacity and the user acceptance are improved, and the stability of benefits is enhanced.

The invention belongs to the technical field of production management and operation optimization of ferrous metallurgy enterprises, and discloses an intelligent distribution system and method for steel and iron material production resources based on multi-objective optimization, and the system comprises the steps: obtaining steel and iron material production resource data in a steel and iron material production resource database, and carrying out the data integration, determining steel and iron material production resource integrated data; steel material production resource integration data is received, an intelligent distribution model is constructed, an optimization target is set, an optimization algorithm is determined, and an optimal intelligent distribution scheme is generated in combination with constraint conditions; visually outputting the optimal intelligent distribution scheme and issuing the optimal intelligent distribution scheme to a manufacturing execution system for execution; according to the invention, through the multi-objective optimized intelligent allocation model, the resource allocation efficiency and quality of iron and steel enterprises are effectively improved.