60 results about "Power generation scheduling" patented technology

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention belongs to the technical field of new energy power generation scheduling and energy management, and discloses a wind and light energy storage strategy optimization method based on a data acquisition and monitoring control system, and the method comprises the following specific steps: S1, multi-source data acquisition; s2, data preprocessing and feature extraction; s3, performing short-term prediction calculation; s4, energy storage scheduling optimization calculation; s5, generating and issuing a scheduling instruction; s6, scheduling execution monitoring and feedback acquisition; and S7, carrying out closed-loop deviation analysis and strategy correction. According to the invention, through multi-source data acquisition and preprocessing, key parameters such as wind speed, illumination and state of charge, historical power and load data are jointly analyzed, and a hybrid prediction method combining time sequence prediction and environment variable correction is adopted, so that higher-precision prediction of wind and light output and load trend is realized; this prediction not only captures the time continuity of the power variation.

The invention discloses a carbon emission prediction and emission reduction strategy optimization method and system, and belongs to the field of power data processing, and the method comprises the steps: firstly building an accurate carbon flow incidence relation through the power grid topology and power flow data through employing an upstream apportionment principle, and generating carbon flow intensity time series data of spatial and temporal distribution; and fusing operation data of the power system, constructing a multi-dimensional time sequence data set, and extracting spatial-temporal characteristics of the carbon emission intensity by using deep learning. A multi-time-scale nonlinear mapping relation network is established based on the characteristics, and accurate carbon emission intensity prediction from the minute level to the day level is achieved. And finally, by taking a prediction result as a constraint, constructing an optimization model with minimum total carbon emission and minimum operation cost as targets, and generating an optimal power generation scheduling and load adjustment strategy. According to the method, accurate tracing and multi-time scale prediction of carbon emission are realized, and the accuracy and efficiency of low-carbon optimization operation decision making of the power grid are effectively improved.

The invention belongs to the field of thermal power generation dispatching, and relates to a thermal power generating unit deep peak regulation method, device and equipment and a medium, and the method comprises the steps: processing multi-source data of a thermal power generating unit through a multi-source data processing model, and obtaining a predicted load; performing dual-objective optimization based on the predicted load to obtain a thermal power generating unit instruction; based on the boiler instruction, the heat storage instruction and the wind power frequency modulation standby load, main steam parameters and the actual air distribution ratio are obtained through cooperative combustion control; processing the steam turbine instruction and the main steam pressure to obtain steam turbine parameters; wind power rapid frequency modulation is combined, the boiler climbing time is shortened, the service life loss of equipment is reduced, wind-light-fire storage deep coupling is achieved, new energy fluctuation is stabilized, and the heat storage utilization rate is increased.

The present invention belongs to the field of power generation scheduling of power systems. Disclosed is a cascade pre-flood energy storage risk analysis and control method for a hydro-wind-solar complementary system. The method comprises: using pre-flood energy storage as a constraint to establish a dry-season drawdown model and a flood-season water storage scheduling rule, so as to determine a simulation operation criterion; constructing a set of dry-season power shortage, flood-season water abandonment, insufficient year-end energy storage and wind-solar power curtailment indexes, so as to quantify multi-stage and multi-source operation risks; and coupling Monte Carlo and fuzzy membership degrees to characterize multi-dimensional uncertainty scenarios and probabilities thereof, and using simulation analysis to determine quantitative relationships between pre-flood energy storage and hydro-wind-solar complementary benefits, risk probabilities and risk losses. Results of instance analysis of a hydro-wind-solar complementary system in a certain extremely-large drainage basin show that the present invention can comprehensively describe risks in terms of occurrence probabilities and losses, and a verification result shows that by means of accurate risk description and reasonable energy storage control, the system power generation is increased by 580 million kWh, and the risk loss is reduced by 42% on average, and therefore the present invention exhibits good practicability.

The invention provides an automatic power generation dispatching control method and device of a thermal power generating unit, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting multi-source observation data of an electric side and a hearth combustion side, and extracting a hearth negative pressure fluctuation amplitude, a flame centroid jitter amplitude and a sound pressure fluctuation amplitude to construct a sound-combustion coupling stability index; establishing a coal consumption cost function and an emission cost function in combination with historical unit operation data; dividing an acoustic-fuel coupling stability domain according to an acoustic-fuel coupling stability index, monitoring a rising trend, and taking an acoustic-fuel coupling risk as a scheduling constraint triggering condition; a scheduling optimization model is reconstructed in a risk dominant scene, active power output, a coal mill combination and air distribution parameters are used as decision variables, an optimal track is solved with physical limits, power grid side constraints and a stability domain as limiting conditions, and finally the optimal track is issued to a coordination control layer. According to the method, the output curve can be actively deviated in the acoustic-fuel coupling instability risk rising stage to avoid acoustic resonance, and therefore the dynamic dispatching capacity of the unit is guaranteed.

The invention relates to the technical field of photovoltaic power station management, in particular to a photovoltaic operation and maintenance management method and system based on edge computing, and the method comprises the steps: obtaining multi-source data through a sensor, and carrying out the preprocessing of the multi-source data through an edge computing node; fusing the preprocessed multi-source data by using an auto-encoder, mining depth correlation features, and obtaining a comprehensive feature data set; on the basis of the comprehensive feature data set and the real-time data, a future generating capacity predicted value is obtained through a dynamic self-adaptive model; comparing the future power generation prediction value with the actual power generation result, judging whether a fault point exists or not according to the comparison result, and positioning a potential fault point in combination with a knowledge graph and a historical record to obtain a fault diagnosis result; and generating an equipment maintenance plan and a power generation scheduling scheme by comprehensively considering the actual operation condition and resource configuration of the power station according to a future power generation prediction value and a fault diagnosis result. According to the scheme, the operation and maintenance efficiency of the power station is improved through edge calculation and multi-technology fusion.

The application relates to a cascade hydropower station power generation optimization scheduling method and device and computer equipment. The method comprises the following steps: according to historical wind power generation data, historical solar power generation data, historical water power generation data and power generation related parameters, estimated results of multiple resource parameters are calculated; according to the estimated results of the multiple resource parameters, an unbalanced risk constraint condition is constructed; the unbalanced risk constraint condition represents a constraint condition required to be met by monthly reservoir regulation of the cascade hydropower station in the case that wind power generation and solar power generation appear continuous shortages; under the constraints of the unbalanced risk constraint condition and system operation constraint conditions, a cascade hydropower station power generation optimization scheduling model is solved with the minimum operation cost as the target, and cascade hydropower station power generation scheduling results are obtained. The method can make up for the defect that the limited fluctuation ability of water power in cooperation with wind power and solar power in the month is ignored in the traditional annual reservoir power generation scheduling plan.

The present application discloses a low-carbon power generation scheduling method based on a graph attention network and a sequence model, which relates to the technical field of power generation scheduling. The method comprises: constructing a power generation scheduling bipartite graph based on carbon emission decision variables and power generation constraints, and using the carbon emission evaluation calculation formula and power generation constraints as sequence data; extracting the structural features of the power generation scheduling bipartite graph based on the graph attention network, and extracting the dependency features between the sequence data based on the long short-term memory network; transferring the structural features to the first self-supervised loss function of the graph attention network training to perform graph attention network learning, and transferring the dependency features to the second self-supervised loss function of the long short-term memory network training to perform long short-term memory network learning to obtain a target model; inputting test set data into the target model to obtain predicted data on the power generation of each generator set. This improves the generalization ability of the model when dealing with complex power generation scheduling constraint problems.

This invention belongs to the field of virtual power plant scheduling technology, specifically involving a joint scheduling method for multiple virtual power plants considering distributed energy optimization and control. The steps include: constructing a control model for photovoltaic power generation resources within the virtual power plant, a scheduling and control model for the series-connected battery energy storage system, and a short-term power generation scheduling model for multiple virtual power plants; jointly optimizing the intraday operation of multiple virtual power plants; setting the objective function of the short-term power generation scheduling model for multiple virtual power plants with the optimization objectives of minimizing the joint operation cost of multiple virtual power plants and minimizing the renewable energy curtailment rate, and solving it using a simulated annealing algorithm to obtain the optimal scheduling plan for multiple virtual power plants within a short-term scheduling cycle when connected to a distributed energy control strategy. This invention can improve the distributed renewable energy absorption capacity, reduce the operating losses of the energy storage system, and enhance the economy and operational flexibility of joint scheduling of multiple virtual power plants while satisfying the constraints of coordinated operation of multiple virtual power plants.

A deep reservoir ecological scheduling method, system and device based on a data-driven model of a coupled physical mechanism belong to the technical field of water resources management and environmental protection. First, a physical water temperature model is constructed based on measured data, diversified water temperature change scenarios are generated, and a deep learning model with physical mechanism constraints is constructed. Second, sensitivity analysis is carried out to identify key influencing factors driving water temperature change, a reservoir optimization scheduling model is constructed, and a deep learning model with physical mechanism constraints is coupled to derive a set of scheduling rules under the premise of meeting water temperature targets. Finally, multi-index optimization analysis is carried out. A deep reservoir ecological scheduling system and electronic device are also provided to implement the above deep reservoir ecological scheduling method. The present application can scientifically derive a set of power generation scheduling rules for deep reservoirs, filter out the optimal scheduling scheme with both ecological and economic benefits by introducing a multi-index optimization method, and achieve the overall balance of ecological demand and power generation benefit.

The invention discloses an integrated energy system low-carbon scheduling method and system considering P2G and CCS, and belongs to the technical field of power generation scheduling, and the method comprises the steps: building a mathematical model of integrated energy system equipment, building a CCP combined operation mode model, combining CHP, P2G and CCS, and converting electric energy into natural gas through P2G by a CHP unit at an electric load trough, and achieving thermoelectric decoupling; the method comprises the following steps: establishing an optimal scheduling model, and when the integrated energy system is optimized, establishing an objective function with the lowest total cost, including the unit operation cost of a CHP unit, a P2G and a CCS, the wind curtailment and light curtailment penalty cost and the carbon transaction cost in a CCP combined operation mode. According to the method, the output range of the CHP unit is expanded, the adjusting capacity of the CHP unit is enhanced, then the wind power absorption capacity of the system is improved, carbon emission of the system is effectively reduced, economic benefits are improved, and meanwhile the problem of high cost after CCS is added to a coal-fired unit can be effectively solved through the established CCP combined operation mode.

This invention discloses a reservoir power generation optimization scheduling method that takes into account the spawning protection of the four major Chinese carps, including: 1) establishing a mathematical model of an ecological scheduling mode that takes into account the spawning protection of the four major Chinese carps; 2) establishing a reservoir power generation optimization scheduling model that takes into account the spawning protection of the four major Chinese carps; 3) proposing a dual-state dynamic programming model (DPTS) that couples the optimal state of the scheduling process with the additional state of the scheduling mode; 4) using the proposed dual-state dynamic programming model (DPTS) to solve the reservoir power generation optimization scheduling model established in step 2) to obtain the optimal reservoir power generation scheduling process. This invention addresses the problem of coordinating the power generation scheduling process and the ecological scheduling mode, which cannot be solved by traditional mathematical programming methods or intelligent optimization methods. It proposes a dual-state dynamic programming model (DPTS) optimization modeling method, which can effectively solve the aforementioned mathematical model and obtain the optimal reservoir power generation scheduling process that coordinates the conflict between the ecological scheduling mode that takes into account the spawning protection of the four major Chinese carps and the power generation scheduling process.

The invention discloses a power generation scheduling method and device based on power generation enterprise comprehensive capability evaluation, and relates to the technical field of power generation scheduling, and the method comprises the steps: obtaining a combination weight of power generation enterprise comprehensive capability evaluation indexes according to historical power generation enterprise operation data and subjective and objective weights; correcting the combined weight based on a variable weight strategy; obtaining a comprehensive capability evaluation result of the power generation enterprise based on a cloud model according to the corrected combination weight and the current operation data of the power generation enterprise; and executing power generation scheduling strategy output according to a power generation enterprise comprehensive capability evaluation result. The method has the beneficial effects that the accuracy and adaptability of comprehensive capability evaluation of the power generation enterprise in different time states are improved, the output of the power generation scheduling strategy is executed according to a relatively accurate comprehensive capability evaluation result of the power generation enterprise, and the power generation reliability is improved while the power generation enterprise is excited to perform targeted optimization according to the comprehensive capability evaluation defect of the power generation enterprise.

The application discloses a carbon emission prediction and emission reduction strategy optimization method and system, and belongs to the field of electric power data processing. Firstly, accurate carbon flow correlation is established by using upstream allocation principle through power grid topology and power flow data, and time and space distribution carbon flow intensity time sequence data is generated. Then, power system operation data is fused, multi-dimensional time sequence data set is constructed, and deep learning is used to extract time and space features of carbon emission intensity. Based on these features, a multi-time scale nonlinear mapping relationship network is established to realize accurate carbon emission intensity prediction from minute level to day level. Finally, the prediction result is taken as a constraint to construct an optimization model with the minimum total carbon emission and the lowest operation cost as targets, and an optimal power generation dispatching and load adjustment strategy is generated. The application realizes accurate tracing and multi-time scale prediction of carbon emission, and effectively improves the precision and efficiency of low-carbon optimal operation decision of the power grid.

The application discloses an energy management system of an offshore wave-wind-light-storage integrated power generation platform, comprising an energy management server, wherein the energy management server is provided with a wave-wind-light-storage integrated power generation platform energy management prediction scheduling strategy, the wave-wind-light-storage integrated power generation platform energy management prediction scheduling strategy comprises a wave-wind-light-storage integrated power generation day-ahead optimization scheduling strategy and a wave-wind-light-storage integrated power generation day-in optimization scheduling strategy; the energy management server sends the wave-wind-light-storage integrated power generation platform energy management prediction scheduling strategy to a wind power generation system, a photovoltaic power generation system, a wave energy power generation system and a battery storage of the power generation platform through a communication management host to complete day-ahead-day-in prediction scheduling, realize steady-state power balance of the power generation platform and an accessed island reef, and thus realize high-stable high-quality power generation scheduling of the mobile power generation platform and guarantee reliable power supply of the offshore power generation platform and micro-grid loads such as an accessed island reef.

The invention discloses a reservoir group power generation optimization scheduling method based on a wide-area particle swarm algorithm, and belongs to the technical field of water conservancy and electric power, and the method comprises the steps: building a reservoir group power generation optimization scheduling model; a wide-area particle swarm algorithm is adopted to solve a model, specifically, algorithm parameters are set, a population is initialized, an algorithm search area is expanded, and a standard particle swarm algorithm position and speed updating formula is improved by introducing a multi-dimensional synchronous long Cauchy mutation operator, a self-adaptive nonlinear inertia weight and a space-time correlation constraint processing mechanism. And updating the population by adopting an improved position and speed updating formula to generate a next-generation population, and outputting an optimal solution as an optimal scheduling scheme of the reservoir group when the maximum number of iterations is judged to be reached. According to the method, the'time-space relevance 'characteristic of the reservoir group power generation scheduling problem is considered, a standard particle swarm algorithm search mechanism is improved and perfected, a more efficient and robust swarm intelligence algorithm is constructed, and the solving quality and efficiency of the reservoir group power generation scheduling problem are improved.

The invention discloses a combined scene generation method, system, equipment and medium considering wind and light correlation, and relates to the field of new energy power generation dispatching, and the technical scheme is characterized in that the method comprises the steps: obtaining wind and light output data, carrying out marginal probability modeling through multi-distribution and kernel density estimation, and screening an optimal probability model of each variable; based on the optimal marginal distribution, constructing a wind and light output joint probability model by adopting a Copula function, evaluating and screening an optimal Copula model by utilizing a maximum likelihood estimation and information criterion (AIC / BIC), and generating a joint output scene considering correlation; an improved density-variance dynamic time warping clustering (DV-DTW k-Medoids) method is proposed, and a typical daily joint output scene set is generated; a density peak fast search clustering (CFSFDP) algorithm is combined with a DTW distance to generate a clustering decision diagram to identify an extreme scene. According to the invention, the problem of insufficient scene generation typicality caused by the fact that dynamic time similarity and initial center sensitivity are not considered when the wind-solar combined scene is generated in the prior art is solved.

The application discloses a power generation scheduling method and related device, which can be used in the field of energy scheduling. In the method, firstly, current operation data of a power system is acquired; the current operation data comprises power grid side operation data and power plant side operation data; then, power plant side safety calculation and power grid side safety calculation are performed based on the current operation data to obtain function abnormality information; finally, based on the function abnormality information, a target scheduling scheme is determined through a preconfigured slide ruler group; the slide ruler indicates a numerical correlation relationship between characteristic parameter values; the characteristic parameters are extracted based on the operation data. Thus, the numerical correlation relationship between the characteristic parameter values is pre-stored in the slide ruler group, when a function abnormality occurs, the target scheduling scheme can be quickly obtained through the slide ruler group without re-modeling, the time from abnormality detection to scheduling scheme generation is greatly shortened, and the safety of the power system is improved.

The invention belongs to the field of multi-energy power system dispatching, and relates to a water-wind-light power source base power generation dispatching method considering alternating current and direct current peak regulation requirements. The method comprises the following steps: firstly, constructing an optimal scheduling model containing time and space double dimensions; in the aspect of time, a differentiated objective function is adopted in a dry period, the dry period minimizes the sum of squares of residual loads of an alternating current receiving end to smooth the loads, and the flood period minimizes the system abandoned electric quantity to guarantee clean energy consumption. In space, the model overall plans multiple types of actual constraints such as cascade hydraulic connection, upstream and downstream collaboration, water and electricity distribution proportion, direct-current stepped power and standby, and differential peak regulation driving is implemented on alternating-current and direct-current receiving ends. And through model solution, optimal distribution of base output among multiple receiving ends and cascade power station cooperative scheduling are realized. According to the method, the resource utilization efficiency and the power grid operation safety are remarkably improved, and a reliable method is provided for efficient scheduling of the alternating-current and direct-current hybrid delivery clean energy base.

A kind of probability evaluation method of water, wind and light complementary system sending power, comprising the following steps: step 1: based on the water, wind and light complementary system sending power under the typical wind light uncertain scene, the Cornish-Fisher series method is used to build the display mathematical expression of sending power quantile;Step 2: establish water, wind and light complementary system sending power probability evaluation model;Step 3: the multiple iteration solution of linear model is converted into highly nonlinear model to obtain the probability evaluation result of water, wind and light complementary system sending power.The method can maximize the complementary flexibility of water, wind and light system, to evaluate the upper and lower limit of each time section sending power of system with certain confidence, the obtained sending power boundary can be used for system to report power adjustable range to power grid, and as the reference basis for power grid to formulate day-ahead generation dispatching instruction, so that water, wind and light system can meet the execution of power generation plan of power grid with greater probability under the premise of considering wind light prediction error.

The invention discloses an energy management system for an offshore wave wind-light-storage integrated power generation platform, and the system comprises an energy management server which is preset with a wave wind-light-storage integrated power generation platform energy management prediction scheduling strategy. The energy management prediction scheduling strategy of the wave wind-light-storage integrated power generation platform comprises a wave wind-light-storage integrated power generation day-ahead optimization scheduling strategy and a wave wind-light-storage integrated power generation intra-day optimization scheduling strategy; the energy management server sends the energy management prediction scheduling strategy of the wave-wind-light-storage integrated power generation platform to a wind power generation system, a photovoltaic power generation system, a wave energy power generation system and a storage battery of the power generation platform for energy storage through a communication management host, so that day-ahead-intra-day prediction scheduling is completed; steady-state electric power and electric quantity balance of the power generation platform and the connected island reef is realized, so that high-stability and high-quality power generation scheduling of the mobile power generation platform is realized, and reliable power supply of the offshore power generation platform and micro-grid loads such as the island reef to be connected is ensured.

The invention provides a zero-carbon heat storage power generation scheduling method and device. The method comprises the steps that the heat energy state of a heat storage tank is updated based on illumination intensity, power grid loads and heat collection power; calculating a heat storage priority index and a power generation priority index by taking the illumination intensity, the power grid load and the heat energy state of the heat storage tank as input, and solving an optimal heat storage power generation scheduling strategy in a future rolling time window by taking the economical efficiency of power grid operation and the power output stability as optimization targets; and according to the optimal heat storage power generation scheduling strategy, controlling the photo-thermal system to execute at least one of the following operations: switching to a heat storage mode, switching to a power generation mode, and adjusting the output of a power generation unit. According to the technical scheme, collaborative optimization from solar energy capture and heat energy storage to electric energy output and dispatching is achieved, and the energy utilization efficiency and the overall operation performance of the zero-carbon heat storage system are improved.

The present application relates to photovoltaic power station management technical field, especially in kind of photovoltaic operation and maintenance management method and system based on edge computing, including: obtaining multi-source data through sensor, pre-processing multi-source data through edge computing node;Using auto-encoder to fuse the pre-processed multi-source data, mining deep correlation characteristics, obtaining comprehensive feature dataset;Based on comprehensive feature dataset and real-time data, through dynamic adaptive model, obtain future power generation prediction value;Compare future power generation prediction value with actual power generation result, according to the comparison result, judge whether there is fault point and combine knowledge graph and historical record to locate potential fault point, get fault diagnosis result;According to the future power generation prediction value and fault diagnosis result, comprehensively consider the actual operation of power station and resource allocation, generate equipment maintenance plan and power generation scheduling scheme.The scheme improves the operation and maintenance efficiency of power station through edge computing and multi-technology fusion.

The invention relates to an optimal scheduling system and method based on wind-solar-storage combined power generation, and belongs to the technical field of new energy power generation scheduling and energy optimization control. The system comprises a data acquisition module, a computing center and a central control module, and the modules cooperate to realize accurate prediction of wind and light output and load demand. The data acquisition module collects wind and light output, load demand and energy storage state data in real time and carries out data processing. End-to-end optimization of a wind and light output and scheduling strategy is realized in the computing center through a Physics-informed DDPG integrated intelligent decision-making model; the central control module dynamically distributes output of each unit according to an optimization result to realize collaborative balance of each unit; the wind and light storage execution module adopts a power generation and energy storage unit designed by a flexible material, the installation adaptability and the operation toughness of equipment are improved, a scheduling instruction is executed, and closed-loop feedback control is formed. Compared with the prior art, a reliable solution can be provided for supply and demand balance, low-carbon operation and large-scale application of the new energy power system.

The invention relates to a panoramic analysis and display method and device for power generation dispatching, equipment, a storage medium and a program product, and relates to the technical field of power generation dispatching. The method can improve the accuracy and efficiency of label distribution. The method comprises the steps of obtaining target operation data according to historical operation data of a power plant, and performing feature extraction on the target operation data to obtain a feature data set; performing data integration on the feature data set to obtain integrated data, and performing multi-dimensional feature representation construction on the integrated data to obtain multi-dimensional feature data; performing data fusion through a panoramic analysis technology according to the multi-dimensional feature data to obtain global feature representation; dividing the global feature data through a decision tree algorithm according to the category of the power plant to obtain target feature data, and clustering the target feature data to obtain standard power plant calling data; and performing label distribution on the standard power plant calling data according to a label distribution rule to obtain power plant label data, and performing panorama display.

The present application relates to the technical field of management platform, especially to a maintenance plan and power generation scheduling collaborative optimization system and method based on deep reinforcement learning. The method is: collecting power grid multi-source heterogeneous data, standardizing and extracting features of the multi-source heterogeneous data, and constructing a multi-dimensional state feature vector; constructing a deep reinforcement learning model; inputting the real-time generated multi-dimensional state feature vector into the trained deep reinforcement learning model to output a comprehensive decision vector; generating a maintenance work order and a power generation scheduling instruction based on the comprehensive decision vector, and issuing and executing; collecting power grid actual operation data after the instruction is issued and executed, calculating the evaluation results based on the preset evaluation index system, and adjusting the parameters of the deep reinforcement learning model according to the evaluation results to drive the continuous optimization of the model. The present application improves the collaboration of maintenance plan and power generation scheduling, improves the efficiency of power grid operation, guarantees the safety and stability of power supply, and reduces the operation and maintenance cost.

The invention provides a power generation side dispatching electric quantity optimization method and device considering multiple time scales, and relates to the technical field of electric power grids. According to the method, the unified multi-scale collaborative framework is constructed, power generation curve optimization of the medium-and-long-term stage and power generation curve optimization of the spot stage are organically combined, the problem that the two stages in traditional dispatching are disjointed is solved, the day-degree power generation curve is generated in the medium-and-long-term stage, refined rolling optimization is carried out in the spot stage, and the dispatching efficiency is improved. Therefore, the final power generation scheme not only meets medium and long-term macroscopic targets, such as the total carbon quota, but also can adapt to real-time operation conditions of the spot market, the deviation between the plan and actual execution is reduced, and the accuracy of power generation scheduling is improved. The problem that the power generation side power generation plan is inaccurately formulated is solved, and the accuracy of formulating the multi-time-scale power generation side power generation plan is improved.

The invention discloses a multi-energy fusion power generation scheduling method and system based on power gap prediction. The method comprises the steps that a power gap curve in a future preset time period is calculated based on collected environmental meteorological data and historical load data; extracting a power gap amplitude and a corresponding gap duration based on the power gap curve; the gap duration is compared with a preset time threshold value, a comparison result is obtained, and the time threshold value is used for defining the time limit of the short-time power gap; and according to a comparison result, determining a target response main body matched with the gap duration, and generating a dynamic operation strategy based on the target response main body. Compared with the prior art, the power gap prediction precision, the matching degree of the response strategy and the gap duration and the equipment operation economy of the multi-energy system can be remarkably improved, and the technical defects that in a traditional scheme, power gap prediction deviation is large, the response strategy and the gap duration are not matched, and equipment is frequently started and stopped, and losses are high are overcome.

The application discloses a photovoltaic power station power prediction method based on prior knowledge guided genetic programming, first, obtaining training data preprocessed by symbol normalization, learning photovoltaic power generation symbol rules through self-supervised pre-training of a Transformer model, and outputting a prior symbol vector of a target function; a double fitness collaborative optimization mechanism containing numerical fitting accuracy and physical mechanism similarity is designed, and the prior vector is used to strengthen and select individuals with high physical matching degree; a Pareto multi-objective optimization is used to balance the double-dimensional conflict, the prior knowledge is transferred to the whole evolution process of genetic programming, and finally the optimal explainable prediction model is extracted from the Pareto frontier solution set. The application solves the problems of traditional genetic programming evolution blindness, easy falling into local optimum and poor physical consistency of generated model, realizes explicit physical explainability of the model while ensuring prediction accuracy, and can be widely applied to power generation scheduling and operation optimization of photovoltaic power stations.