104 results about "Power generation scheduling" patented technology

The invention, which relates to the technical field of intelligent power dispatching, discloses an optimal dispatching method for a water-wind-light complementary power generation system based on wind-light prediction, and the method comprises the steps: collecting historical meteorological data of a target power generation field, carrying out meteorological clustering, and generating a plurality of weather scene categories; collecting real-time wind and light meteorological data and carrying out periodic prediction to obtain a target weather scene change sequence; generating an initial power supply strategy based on the sequence; analyzing the incidence relation between the wind and light environment indexes and the power generation modules, extracting key influence factors and generating a multi-dimensional power generation evaluation network; and optimizing the initial power supply strategy by using the evaluation network to form a power supply optimization strategy, and implementing dynamic power generation scheduling. The technical problems of low power generation efficiency and low reliability caused by difficulty in realizing flexible power generation proportion adjustment of an existing water-wind-light complementary power generation system are solved, and the technical effects of dynamically optimizing and dispatching the power supply proportion of the water-wind-light complementary power generation system and improving the power generation efficiency and the reliability of the system are achieved.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction model construction method based on signal decomposition and parameter optimization, and the method specifically comprises the following steps: S1, original wind power data processing: employing a self-adaptive noise complete set empirical mode decomposition algorithm (CEEMDAN) to decompose the original wind power data; according to the method, a hybrid model fusing a bidirectional gating cycle unit (BiGRU), a bidirectional time convolution network (BiTCN) and a multi-head attention mechanism (MHA) is constructed, an improved parameter optimization algorithm is designed, the capacity of the model for capturing wind power short-term fluctuation characteristics is enhanced, the parameter optimization efficiency is improved, the local optimum problem is effectively avoided, and the method is suitable for the wind power short-term fluctuation characteristic capturing capability. According to the method, the limitation in traditional feature extraction is effectively improved, high-precision and high-efficiency ultra-short-term wind power prediction is realized, a reliable basis is provided for optimizing a power generation scheduling strategy for a power system, and the method can be popularized and applied to multivariate time sequence prediction scenes such as wind speed prediction and photovoltaic power generation prediction.

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 provides a power plant deep peak regulation and power generation optimization method and system based on steam extraction energy storage, and relates to the technical field of power grids, the method comprises the steps that high-temperature and high-pressure steam is generated through a steam turbine, the steam enters a steam extraction system, and the steam extraction system is used for extracting part of steam exhausted by the steam turbine when the load of the power grid is low; the extracted steam is conveyed into the energy storage device to be stored; based on historical power grid load data and real-time load prediction data, dynamic optimization adjustment of generated power is carried out by combining characteristics of a generator set of a power plant, and deep peak regulation operation of the generator set in a low-load operation state is realized by controlling a power generation dispatching control module of the power plant. The output power of each unit is adjusted through an optimization algorithm; when the load of the power grid is low, part of steam of the steam turbine is stored in the energy storage device through the steam extraction system, and when the load of the power grid is peak, the stored steam flows back to the steam turbine again to be reused.

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 hybrid pumped storage scheduling method for reducing the operation water consumption rate of cascade hydropower stations, which comprises the following steps: establishing a cascade water-light-storage complementary power generation scheduling model, and constructing a target function with the minimum total water consumption of the cascade hydropower stations based on the cascade hydropower scheduling model; determining an objective function constraint condition according to the cascade hydropower dispatching model; establishing a solving model on the basis of an Actor-Critic algorithm; time sequence data in the historical data set is used as environment state parameters to be input into the deep reinforcement learning algorithm model, and an optimal day-ahead scheduling strategy is solved through an iterative training network model; and calculating the water consumption rate by using the obtained optimal scheduling strategy result, and evaluating the result. By introducing an Actor-Critic algorithm in deep reinforcement learning, an optimization decision strategy in a complex environment is realized, the economic operation efficiency and the power generation benefit of the system are effectively improved, and the water consumption rate of cascade hydropower is remarkably reduced.

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 discloses a thermal power generation scheduling and load distribution method and system based on an optimization algorithm, and the method comprises the steps: collecting unit operation data, load demand data and environmental protection constraint conditions of a thermal power generation system in real time, and carrying out the preprocessing of the data; a multi-objective optimization model is constructed based on the collected data, optimization objectives comprise fuel cost minimization, pollutant discharge minimization, load demand deviation minimization and start-stop cost minimization, and constraint conditions such as power balance, pollutant discharge and start-stop intervals are considered; an intelligent optimization algorithm is adopted to solve the optimization model, and an optimal scheduling scheme meeting constraint conditions is generated; and verifying the feasibility of the optimization scheme, if the constraint condition is satisfied, dynamically adjusting the generation power and the start-stop state of the unit, and carrying out dynamic optimization according to the real-time load demand and the operation state. According to the invention, the power generation efficiency can be obviously improved, the operation cost is reduced, the pollutant discharge is reduced, and the equipment start-stop frequency is optimized.

The invention discloses an intelligent optimization control method for garbage disposal power generation and electronic equipment, and relates to the technical field related to garbage disposal, and the method comprises the steps: deploying a garbage sensor network on garbage disposal power generation equipment, and collecting and obtaining a multi-source garbage sensing data flow; performing digital twinborn simulation based on the garbage treatment power generation equipment characteristic parameter flow and the garbage historical power generation data set; constructing a garbage power generation optimization objective function and embedding a garbage treatment power generation digital twinborn body; and performing power generation control solution on the multi-source garbage sensing data flow by using the garbage power generation control digital twin, determining a target power generation control parameter, and performing power generation dynamic scheduling and feedback closed-loop control on the garbage treatment power generation equipment based on the target power generation control parameter. The technical problems of difficult dynamic optimization, insufficient multi-source data fusion capability and poor control strategy real-time performance in the waste incineration power generation process in the prior art are solved, and the technical effects of improving the waste power generation efficiency and optimizing the equipment operation stability are achieved.

The invention belongs to the technical field of solar power generation, and particularly relates to a solar photo-thermal power generation and supply system, which comprises a self-adaptive condensation tracking module for outputting stable high-temperature heat flow through multi-stage reflector array cooperative tracking and focus decentralized control; the layered cascade heat storage module receives the high-temperature heat flow and stores the high-temperature heat flow to different areas according to temperature grades; the multi-source cooperative power generation module receives heat energy and cooperatively generates power through multiple systems; the intelligent load prediction scheduling module generates a load prediction result and formulates a power generation scheduling plan; the system self-optimization evolution module monitors operation parameters, parameter self-adaption adjustment and strategy improvement are achieved, the solar energy utilization efficiency is remarkably improved, and the direct solar energy utilization rate is increased to 25% from 20%.

According to the new energy power generation scheduling method and device, the storage medium and the computer equipment, in the power generation control process, the power generation time sequence data is firstly determined, the wind speed probability model and the illumination probability model are obtained, and the probability models are constructed based on the historical data and updated regularly; the wind speed probability model and the illumination probability model are sampled respectively to obtain a wind speed prediction sample and an illumination prediction sample which are closer to reality, and the prediction precision of the generating capacity can be improved based on prediction of the wind generating capacity and the photovoltaic generating capacity. Meanwhile, the adjustment demand quantity of the power system can be calculated according to the power generation time sequence data, so that the charging and discharging behaviors of the energy storage equipment in the power system can be accurately scheduled according to the predicted wind power generation capacity, photovoltaic power generation capacity and adjustment demand quantity, and the power supply and demand of the power system can be better balanced; and the wind power and photovoltaic power generation conditions can be predicted in advance through accurate prediction of the power generation amount, and rapid response to the power change of the power system is realized.

According to the source-load-storage coupled multi-energy micro-grid double-layer optimization method and system for the oil and gas well field, an upper-layer model, namely a load optimization layer model with the purpose of maximizing the green power consumption rate, is constructed respectively, the working system and production parameters of production equipment are optimized, and the wind and light consumption level is enhanced while the production requirement is guaranteed; a lower-layer model, namely a power generation dispatching layer model taking the lowest daily operation cost of the multi-energy micro-grid as a target, is constructed, the energy storage peak regulation capability is fully exerted, and the economic benefit of system operation is improved; meanwhile, due to the fact that the coupling relation exists between the upper layer and the lower layer of the double-layer model, the double-layer nonlinear problem is converted into single-layer mixed integer linear programming through the KKT condition and Big-M linearization, the solving time is greatly shortened, and the method is suitable for a real-time scheduling scene. And the green electricity consumption rate and the well group daily fluid production capacity are respectively improved by 9.34% and 9.61%. And the double-target balance of economy and environmental protection is realized.

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 invention discloses a power generation scheduling method of a power system and a related device, which can be used in the field of power generation scheduling, and the method comprises the steps: firstly, obtaining the production element data of each power plant in the power system; then, on the basis of the production element data, through a pre-established production element model, the initial cost per kilowatt-hour of each power plant is determined; then, based on the production element data, stability parameters of all the power plants are determined respectively; and finally, according to the initial cost per kilowatt-hour and the stability parameter of each power plant, obtaining a power generation capacity scheduling scheme through a pre-trained scheduling model. Therefore, for a plurality of power plants in the power system, production element data can be comprehensively considered, a low-cost power generation scheduling scheme capable of meeting the stability requirement can be obtained from the two dimensions of the initial kilowatt-hour cost and the stability, and the power generation cost is reduced.

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 invention discloses a cloud intelligent interconnection platform and method for a large hydropower station, and relates to the technical field of cloud interconnection, and the platform comprises a data collection layer, a transmission layer, a cloud decision-making layer and a feedback control layer. The data acquisition layer is provided with a sensor for acquiring key operation data of the hydropower station in real time; the transmission layer adopts the Internet of Things technology, and transmits the key operation data from the data acquisition layer to the cloud decision-making layer through edge computing equipment and a network; the cloud decision-making layer processes the key operation data based on cloud computing resources, performs data analysis by applying an AI algorithm, and generates a power generation scheduling strategy; and the feedback control layer adjusts the working state of the field equipment according to the power generation scheduling strategy generated by the cloud decision-making layer. According to the large hydropower station cloud intelligent interconnection method provided by the invention, real-time data acquisition and intelligent analysis are realized, so that the timeliness and accuracy of a scheduling decision are improved, and stable operation of the hydropower station under changing conditions is ensured.

The invention provides a method for developing renewable energy sources of a closed mine. The method comprises the following steps: calculating a wind-light capacity coefficient of a closed mine unit; evaluating the wind-solar power generation potential of the closed mine unit; closing a wind-solar power generation grid-connected line of the mine unit based on the grid division design; designing a power generation dispatching layer; and constructing a power system model containing a shut-down mine unit to minimize the construction operation cost.

The present invention relates to the field of intelligent monitoring technology, and specifically, to an intelligent remote-controlled multi-station collaborative monitoring system and method for hydropower stations. It includes: a collection and transmission unit, which is used to collect data from hydropower stations in real time; a monitoring management unit is used to receive and process data from each hydropower station transmitted by the collection and transmission unit; an optimization scheduling unit establishes a power generation scheduling optimization model, a reservoir scheduling optimization model and an environmental impact minimization model, generates an optimization scheduling plan based on the solution of the model, converts the optimization scheduling plan into a control instruction and sends the instruction to a remote control unit; the remote control unit is used to send the received control instruction to each hydropower station. The intelligent remote-controlled multi-station collaborative monitoring system and method for hydropower stations can more accurately predict the power generation capacity under different temperature conditions, formulate a more reasonable power generation plan, and can more accurately predict the water level change of the reservoir to ensure that the water level is within a safe range.

The present invention belongs to the technical field of solar power generation, and specifically relates to a solar thermal power generation and supply system. The system includes an adaptive focusing and tracking module, which outputs a stable high-temperature heat flux through collaborative tracking and focus dispersion control of a multi-stage reflector array; a tiered cascade heat storage module receives the high-temperature heat flux and stores it in different areas according to temperature levels; a multi-source collaborative power generation module receives thermal energy and generates electricity through collaborative power generation through multiple systems; an intelligent load forecasting and scheduling module generates load forecast results and formulates a power generation scheduling plan; and a system self-optimizing evolution module monitors operating parameters, realizes adaptive parameter adjustment and strategy improvement, and significantly improves the efficiency of solar energy utilization, increasing the direct utilization rate of solar energy from 20% to 25%.

The invention discloses a power generation scheduling optimization system and method based on carbon-electricity linkage. The system comprises a data processing module, and a data acquisition module and a unit scheduling module which are connected with the data processing module; relates to the technical field of power generation scheduling optimization, and solves the technical problem of low actual power generation income of a power generation enterprise in a power generation scheduling scheme in the prior art. According to the method, the operating cost of the generator set is calculated by comprehensively considering the actual output, the carbon price, the electricity-carbon price conductivity, the market power generation demand and the power generation capacity of a power plant of the generator set, a clearing electricity price target function of a power generation enterprise is constructed, the clearing electricity price target function is solved based on a plurality of constraint conditions, and the clearing electricity price of each generator set is obtained; the unit power generation income is further calculated, and a plurality of generator sets are scheduled based on the market power generation demand, the power plant power generation capacity and the unit power generation income, so that the actual power generation income of a power generation enterprise is improved.

The present invention discloses a method for optimizing and dispatching a water-wind-solar complementary power generation system based on wind-solar forecasting, which relates to the field of intelligent power dispatching technology. The method comprises: collecting historical meteorological data of a target power plant for meteorological clustering to generate multiple weather scene categories; collecting real-time wind-solar meteorological data and performing periodic forecasting to obtain a target weather scene change sequence; generating an initial power supply strategy based on the sequence; analyzing the correlation between wind-solar environmental indicators and power generation modules, extracting key influencing factors and generating a multi-dimensional power generation evaluation network; optimizing the initial power supply strategy using the evaluation network, forming a power supply optimization strategy, and implementing dynamic power generation scheduling. This method solves the technical problem that the existing water-wind-solar complementary power generation system is difficult to achieve flexible power generation ratio adjustment, resulting in low power generation efficiency and low reliability, and achieves the technical effect of dynamically optimizing the power supply ratio of the water-wind-solar complementary power generation system and improving the power generation efficiency and reliability of the system.

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.

The invention discloses a power generation scheduling optimization method and system based on power generation capacity prediction, and solves the problems of low power generation strategy formulation accuracy and low demand change response capability in the prior art, and the method comprises the steps: constructing a power generation capacity prediction model, defining key influence factors, and constructing a power generation capacity prediction model; dynamically adjusting the weight of each influence factor in different periods of power generation; calculating and analyzing the variation trend of the power generation cost along with time, analyzing and predicting the future power generation cost trend by using a time sequence, and establishing a cost saving strategy; combining the assessment indexes with a compensation mechanism, establishing an excitation model, and adjusting excitation model parameters according to the influence of equipment defects on the power generation capacity; and formulating a combined power generation scheduling strategy, and dynamically adjusting and optimizing the power generation scheduling strategy in combination with different operation scenes according to the execution effect. And by enhancing an excitation mechanism, the flexibility and response speed of a power system are improved, and safe and stable operation of a power grid is ensured.

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.