122 results about "Economic scheduling" patented technology

The present invention discloses a data-driven three-stage scheduling method for electricity, heat and gas networks based on wind electricity indeterminacy, including the following steps: S1, initializing; S2, establishing a deterministic electricity-heat-gas coordination optimized scheduling model; S3, establishing a data-driven distributed robust scheduling optimization model under mixed norms; S4, solving a master economic scheduling problem; S5, verifying convergence of a wind electricity indeterminacy subproblem: if the subproblem converges, going to step S6, otherwise going to step S4 and adding a constraint to the master economic scheduling problem by using a CCG algorithm; and S6, checking the convergence of a gas network operation constraint subproblem: if the gas network operation constraint subproblem converges, ending the calculation to obtain an optimal solution, otherwise, going to step S4 and adding a Benders cut set constraint to the master economic scheduling problem.

The invention discloses a robust environment economic scheduling method considering multi-microgrid energy interaction. The method comprises the following steps: in the background of supporting the rapid development of an active distribution network by microgrids, the energy interaction among multiple microgrids is thoroughly considered; a renewable energy generation model and a cost model in themicrogrid are built; an environment economic scheduling model considering multi-microgrid energy interaction is built; the uncertainty of renewable energy and load is considered, a robust environmenteconomic scheduling model considering multi-microgrid energy interaction is built, a Latin hypercube method is adopted for sampling, and the robust environment economic scheduling model is converted to a robust certainty model; and a multi-objective chemotaxis algorithm is adopted to solve the above robust certainty model, and the Pareto optimal solution is found out. The uncertainty of renewableenergy and load prediction is thoroughly considered, the energy interaction among multiple microgrids is considered, the calculation result is closer to the actual situation, the rationality is strong, and reliable basis is provided for the economic operation of the power system.

The invention discloses an integrated energy system optimization scheduling method considering a carbon trading mechanism and a flexible load. The technical solution of the invention comprises a stepof modeling a coupling link of an integrated energy system based on an energy center model and outputting electric energy, heat energy and natural gas to supply load demand, wherein input energies areelectric energy and natural gas, a step of introducing the carbon trading mechanism into an integrated energy system scheduling model, comprehensively considering the carbon emissions of an entire energy supply chain, and establishing an over-discharge penalty mechanism, a step of applying the flexible load to the low-carbon economic operation of the integrated energy system, and a step of introducing a power system network constraint, a natural gas system network constraint and an energy center internal constraint on the above basis and establishing an integrated energy system low-carbon economic scheduling model with the goal of carbon trading cost, flexible load scheduling cost and system energy cost. According to the method, the consumption of renewable energy can be promoted, the system energy consumption and carbon emission are reduced, and the comprehensive benefits of the system are significantly improved.

The invention relates to a micro-grid economic and optimal operation and scheduling method based on an improved quantum genetic algorithm. The micro-grid is in a grid-connected mode operation state and comprises multiple micro sources and loads, wherein the loads comprise electric loads and thermal loads; and the micro sources comprise a micro turbine, a wind turbine, a photovoltaic cell, a fuel cell, a storage battery and an electric vehicle. The method comprises the following steps: S1, state information of each load and each micro source in the micro-grid is acquired; S2, with minimum of operation cost and pollutant treatment cost as a target, a multi-target economic scheduling model is built; S3, the improved quantum genetic algorithm is adopted for carrying out optimal solution on the multi-target economic scheduling model, and the optimal active power of each micro source is acquired; and S4, according to the optimal active power of each micro source, active power output by each micro source is controlled. Compared with the prior art, the micro-grid formed by distributed power sources operates in a more economic, flexible and environment-friendly mode, and power generation advantages of the distributed power sources can be taken.

The invention discloses a multi-target dynamic scheduling method for a wind storage hydrothermal power system based on multi-source complementation, and the method comprises the following steps: 1, building a multi-target dynamic economical scheduling model: 1.1, building four optimization targets: take the minimum thermal power coal consumption, the minimum thermal power output adjustment volume, the maximum thermal power generation and the minimum load fluctuation after the thermal power is inputted to a power grid; 1.2, determining the constraint conditions of the four optimization targets; 2, carrying out the weighting and conversion of the four optimization targets, and obtaining a target function F of the multi-target dynamic economical scheduling model; 3, combining with the target function F, and solving the multi-target dynamic economical scheduling model through employing the improved plant growth simulation algorithm. According to the invention, the method gives full consideration to the economical performance and stability of thermal power operation, the utilization rate of the wind power and hydropower and the adjustment effect of a pumped storage power station, and guarantees the economical performance and stability of operation of the thermal power generating unit while promoting the consumption of the wind power.

The invention provides an electro-thermal gas interconnected energy system scheduling method, which aims at realizing dynamic environmental protection and economic flexibility, and establishes a plurality of equipment models with continuous adjustment capability and higher flexibility potential on the basis of acquiring data and information of an electro-thermal gas interconnected energy system. Secondly, a unified steady-state power flow model considering an electric power, thermal power and natural gas sub-heterogeneous energy system is proposed; a dynamic environment-friendly economic flexibility scheduling model is provided on the basis of considering a dynamic environment-friendly scheduling target, a dynamic economic scheduling target and an operation constraint condition; and finally, a multi-objective fuzzy optimization method is adopted to solve the collaborative flexibility scheduling, and the scheduling result information of the electric heating gas interconnected energy system is output. Effectiveness and reasonability of collaborative flexible scheduling of the electric heating gas interconnected energy system are verified, and important basis and theoretical basis areprovided for multi-objective scheduling and system flexibility analysis of the electric heating gas interconnected energy system.

The invention discloses a microgrid energy management control method, and relates to the field of power system microgrid optimized control. The method is based on main network power market environment accessed by a microgrid, network constitution of the microgrid and features of energy storage equipment, and an energy management optimized control model of a target microgrid is built. A prediction value of a distributed power supply and the load in the microgrid in a short-term or ultra-short-term energy management control cycle serves as a state variable, a charging state of an energy storage component serves as a decision variable, and solving of energy management optimized control is carried out. Modeling is carried out according to a cost function of each stage, market environment factors accessed by the microgrid are isolated, charging and discharging efficiency of the energy storage equipment and energy storage depreciation and operation cost are fully taken into consideration, and the method has wide adaptability. According to the technical scheme, dynamic economic scheduling is adopted, optimal operation cost under different state situations of each stage can be obtained in the solving process, and analysis, comparison and decision making of operators are facilitated while economical efficiency of microgrid energy management optimized control is improved.

The invention discloses a heating-power system joint economic scheduling method by using heating network energy storage features. Influences of heating network energy storage on a thermoelectric unitare analyzed; constraints when the heating network keeps safe and stable operation after the thermoelectric unit changes a heating supply extraction flow are determined; according to specific reasonsfor wind curtailment generation by a power system containing a high proportion of thermoelectric units, a heating-power system two-stage scheduling policy considering the heating network energy storage is made; the system safety risk cost caused by wind power uncertainty is calculated; and a heating-power system joint economic scheduling model in consideration of heating network heat storage and wind power uncertainty cost and with the minimum wind curtailment amount as a target is built; and a fast particle swarm optimization algorithm is adopted to solve the model. The method of the invention can improve the peaking capability of the thermoelectric unit, the two-stage thermoelectric unit scheduling policy is adopted according to the specific wind curtailment reasons of the system in theheating supply stage, and system wind curtailment is thus reduced; and during the thermoelectric unit scheduling process, the wind power uncertainty is quantified as the risk cost and is added to a scheduling objective function, and the system operation cost and the risk can be coordinated.

The invention relates to a multi-target operation scheduling method for a micro-grid with an electric vehicle hybrid energy storage system. The micro-grid runs in a grid-connection mode and comprises a plurality of micro-sources and loads, wherein the loads include an electric load and a heat load; and the micro-sources include a micro-gas turbine, a fan, a photovoltaic cell, a fuel cell, a storage battery and an electric vehicle. The method comprises the following steps of S1: obtaining state information of each load and each micro-source in the micro-grid; S2: establishing a multi-target economic scheduling model with minimum running and pollutant control costs; S3: performing optimization solving on the multi-target economic scheduling model by adopting an NSGA-II multi-target optimization algorithm to obtain optimal active power of each micro-source; and S4: according to the optimal active power of each micro-source, controlling active power output of each micro-source. Compared with the prior art, the method has the advantages that the micro-grid consisting of a distributed power supply can run more economically, flexibly and environmentally, and the power generation advantage of the distributed power supply can be brought into full play.

The invention discloses an optimization scheduling method for a comprehensive energy system of a smart park on basis of multiple objectives. The optimization scheduling method comprises the followingsteps: determining and acquiring basic operation parameters of the comprehensive energy system; formulating evaluation indexes of multi-objective operation of the smart park; establishing an optimization scheduling model of a day-ahead economic scheduling stage of the comprehensive energy system; and solving the established multi-objective optimization scheduling model by adopting a mixed linear integer programming solver. According to the invention, through construction of a target function with minimized operation cost in a scheduling stage, on the basis of meeting power balance and safety constraints, evaluation indexes and constraint conditions are established from the perspectives of energy consumption, environmental protection and reliability; starting, stopping and output states ofenergy storage, flexible load and combined heat and power supply controllable units in the system are optimized; and multi-objective optimization scheduling of the comprehensive energy system of the smart park is realized. Thus, economic and environment-friendly operation of the system can be achieved; various energy supply reliability of the system can be guaranteed; and safe, stable, efficient and economical operation of the comprehensive energy system is guaranteed.

The invention discloses a power distribution network double-layer optimization scheduling method and device, and the method comprises the following steps: obtaining the basic information of a load, wind power, photovoltaic and energy storage equipment in a load aggregator LA, and obtaining the basic information of a topological structure, a power supply and a load of a power distribution network;inputting the basic information of the load, wind power, photovoltaic and energy storage equipment in the load aggregator LA and the basic information of the topological structure, the power supply and the load of the power distribution network into a pre-established power distribution network double-layer optimization scheduling model to obtain an optimization scheduling result; wherein the powerdistribution network double-layer optimization scheduling model comprises an upper-layer LA economic scheduling model and a lower-layer power distribution network optimization model; by establishingthe power distribution network double-layer optimization scheduling model considering participation of the LA, the LA obtains economic benefits and plays a positive role in safe and economic operationof the power distribution network at the same time.

A predication control based multi-time-lag intelligent power grid economic scheduling method relates to intelligent power grid scheduling. The method includes 1, constructing a network structure typological graph and obtaining an adjacent matrix, a degree matrix and a Laplacian matrix; 2, constructing a mathematic model for intelligent power grid economic scheduling and acquiring system parameters; 3, designing a power generation device output state predicator; 4, giving initial values of the power generation devices; 5, setting a communication mode so as to enable each power generation device to communicate with an adjacent device only, operating a distributive control algorithm and adjusting the state of each node; 6, judging whether the incremental cost state of all the nodes achieves consistence or not, if the consistence is achieved, turning to step 7, and if the consistence is not achieved, returning to step 5; 7, obtaining the optimal solution of the intelligent power grid economic scheduling problem.

The invention discloses an intelligent power grid deep learning scheduling method considering schedulable electric vehicle fast/slow charging and discharging forms, belongs to the field of regional intelligent power grid operation, and aims to perform power supply power optimal distribution by taking total operation cost as an objective function in a day-ahead scheduling stage. In the intra-day pre-scheduling stage, load fluctuation and a day-ahead scheduling plan are simulated to serve as input samples of the deep learning network, prediction data generated through simulation are input into aregional intelligent power grid model in the intra-day pre-scheduling stage, and controllable unit scheduling data in the scheduling plan in the model training stage serve as output samples of the deep learning network. An intra-day scheduling model of the regional smart grid is trained based on the deep learning network through the input sample and the output sample to acquire a predicted valueof the load at the next scheduling moment through ultra-short-term prediction. The predicted value and the day-ahead scheduling plan are inputted into an intra-day scheduling model of the regional smart power grid to obtain an intra-day scheduling value of the controllable unit. The method solves the problems that errors exist in prediction of distributed power supplies, electric vehicles and loads of a regional smart power grid, and intra-day economic dispatching of the regional smart power grid is difficult to achieve.

The invention discloses a photovoltaic interval prediction method and system based on self-coding and an extreme learning machine. A dual-output extreme learning machine model is used for constructinga photovoltaic interval prediction model, dual outputs are the upper limit and the lower limit of a photovoltaic interval respectively, and due to lack of actual values of the photovoltaic interval,a traditional extreme learning machine training algorithm fails accordingly. According to the method, a heuristic algorithm is adopted to optimize a training extreme learning machine, a correspondinginitialization algorithm is provided for improving the calculation result and efficiency of the heuristic algorithm, and the method comprises: carrying out prediction interval initialization based onlinear regression interval estimation; carrying out inputting weight matrix initialization of an extreme learning machine based on self-coding; and carrying out optimization training of a particle swarm-based double-output extreme learning machine. The photovoltaic prediction interval obtained through the model replaces a traditional photovoltaic point prediction value, and more sufficient information can be provided for day-ahead robust scheduling and intra-day economic scheduling of a power system.

The invention discloses a multi-time-scale rolling scheduling method and system for dynamic economic scheduling, and the scheduling method comprises the following steps: constructing a new energy power generation aggregation party with complementary power generation characteristics based on a power generation composition structure of a power system; establishing a load matching coefficient, and carrying out smooth optimization on a load curve; making a day-ahead plan for the output of the power generator according to an optimized load maximum matching principle, and solving the real-time output of the new energy aggregator under the condition that the load matching coefficient is minimum; performing output adjustment on the power generation end according to the intra-day scheduling model;and performing output fine adjustment on the power generation end according to the real-time scheduling model. According to the multi-time-scale rolling scheduling method, the method comprises the steps of: performing aggregation scheduling on new energy with complementary characteristics; and considering the power supply and demand matching degree, optimizing the load, and stabilizing the outputfluctuation of the conventional energy unit. The new energy consumption rate is effectively increased, the phenomena of wind curtailment and light curtailment are reduced, and meanwhile, the scheduling burden and the regulation cost of a power grid on a conventional energy unit are reduced.

The invention discloses an optimal scheduling method and system for an electricity-gas-heat comprehensive energy system. The method comprises the following steps: constructing an electricity-gas-heatlow-carbon economic scheduling model based on a stepped carbon transaction mechanism; constructing an objective function and constraint conditions of a low-carbon optimization model based on the electricity-gas-heat low-carbon economic dispatching model; and solving the low-carbon optimization model to obtain low-carbon optimization parameters, performing low-carbon scheduling on the comprehensiveenergy system according to the low-carbon optimization parameters, and therefore, proving the reasonability of the stepped carbon transaction mechanism for low-carbon scheduling.

The invention relates to the technical field of power system scheduling, and specifically relates to an economic scheduling method for a power system with an optical storage and charging park power grid. The economic scheduling method comprises the steps of first, establishing an optical storage and charging integrated controller model, an energy storage device model and an electric automobile state model for optical storage and charging of a park; second, accessing the park as a load node to a regional power grid; third, establishing an objective function with social benefits being optical on the whole; fourth, establishing constraint conditions; fifth, carrying out day-ahead economic optimization by taking the [delta]T as a time interval to obtain a dispatching result. The method has the substantial effects that the day-ahead economic optimization scheduling according to the load and photovoltaic prediction data, the comprehensive social benefit maximization of park users and the power grid is realized through reasonably planning the charging and discharging behaviors of the energy storage device and the electric automobile, the economic potential of various energy factors is explored, and the method has the great advantages and popularization and application prospects compared with the traditional method that only new energy is accessed without control.

The invention discloses a two-stage scheduling model optimization method for an integrated energy system. The method comprises the following steps: S1, constructing a day-ahead economic scheduling model according to the operation cost of system equipment; S2, correcting the operation state of each micro-unit based on the day-ahead economic scheduling model, and constructing a real-time optimization scheduling model; S3, based on the real-time optimization scheduling model, improving a population variation mode by mapping a chaotic crisscross algorithm so as to ensure population diversity and all information of excellent individuals; and S4, constructing a two-stage scheduling optimization solving process based on a CCSO-BOS algorithm. According to the method, a day-ahead scheduling optimization model and a real-time scheduling optimization model of the comprehensive energy system are established on the basis of load prediction and renewable energy processing prediction data. The proposed multi-objective nonlinear optimization problem can be solved, so that renewable energy can be consumed and utilized to the maximum extent. The operation cost of the system is effectively reduced.

The invention discloses a method for determining optimal trading capacity between large regional grids. The method comprises the steps of firstly, checking the safety of an ultrahigh voltage tie line in a large regional grid and grid ultrahigh voltage tie line synthesizing model, and selecting the minimum of maximum transmission capacities of the tie line under different constraints as a transmission capacity limit of the tie line under a safe constraint; secondly, concluding an economic indicator in an interregional power trading, and establishing a synthetic economic indicator of large regional grid absorption external power; and finally, establishing a proper neighborhood based on optimal tidal current distribution according to the tidal current of a single-target optimization system and the transmission capacity limit of the tie line, and solving a synthetic economic indicator extreme value in the distribution domain, wherein the tie line transmission capacity corresponding to the extreme value is the desired optimal trading capacity. According to the method, the optimal trading capacity between large regional grids at any time period can be analyzed only by updating the large regional grid as well as a transmission channel integrating model and a real-time power price parameter thereof, so as to provide a reference for trading planning and economic scheduling.

The invention discloses a hydropower station plant inside economic scheduling method considering an anomaly restriction operation zone, comprising steps of scattering the water head variable zone in the scheduling period into a plurality of continuous subintervals, introducing water head subinterval indication variables for replacement to realize the water head dynamic changing, on the basis of that, determining a representative water head in each water head subinterval to determine the characteristic of machine set dynamics, and realizing the fact that the dynamic characteristics of the machine set are changed along with the water head, for each water head subinterval, performing pre-processing on a machine set limitation operation zone, generating a rectangle machine set operation zone, introducing an indication variable of the operation zone for identification, and realizing the fact that the machine set limitation operation zone changes along the dynamic of the water head. A segmentation linear method is adopted to describe the water level-reservoir capacity relation and the tail water level-discharge non-linear relation. The invention utilizes the whole plant optimum dynamic characteristics and adopts the principle of determining water by electricity to perform initial estimation on the power generation water head dynamic characteristics of the dynamic machine set and the limitation operation zone, and solves the problem through a branch and bound algorithm.

The invention discloses a power system scheduling method and device considering wind power probability distribution uncertainty. The method comprises the following steps of: constructing a wind power probability distribution uncertainty set; based on the wind power probability distribution uncertainty set, defining operation risk cost; based on the operation scheduling cost and the operation risk cost of an power system, taking the pursuit of the lowest total operation cost of the power system as a target, constructing a robust economic scheduling model considering the wind power probability distribution uncertainty; determining an optimization constraint condition of the robust economic scheduling model; solving the robust economic scheduling model considering the wind power probability distribution uncertainty to obtain an optimal decision variable; and realizing the scheduling of the power system based on the optimal decision variable. Automatic optimization of the system operation risk level and the wind power admissible range under the condition of uncertain wind power probability distribution is realized. For a formed complex nonlinear optimization model, the original model is converted into a linear programming problem of iterative solution on the basis of the characteristics of the wind power probability distribution uncertainty set and the characteristics of actual operation of the system.