2153 results about "Renewable energy" patented technology

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses a stability analysis method for a hybrid grid-connected system of a grid-following converter, and the method comprises the steps: constructing precise impedance models of a grid-following type (GFL) converter and a grid-constructing type (GFM) converter respectively based on a phase-locked loop (PLL) mechanism and a virtual synchronous generator (VSG) control strategy, and the details are shown in an abstract figure 1; deducing the total equivalent impedance of the system at a point of common coupling (PCC) according to a circuit equivalence principle; performing stability analysis and sensitivity evaluation on different converter combination proportions, topological structures and key control parameters of the hybrid grid-connected system based on an impedance ratio criterion; and verifying the accuracy of the analysis result through frequency domain simulation. The method can be widely applied to a new energy power system, solves the problem of system stability evaluation and optimization under the high-proportion renewable energy access background, and has the characteristics of high adaptability, accurate model and comprehensive analysis.

The invention relates to the technical field of micro-grids, and discloses a micro-grid real-time load balancing scheduling method and system based on deep reinforcement learning, and the system comprises a multi-source sensing module, an intelligent decision module, a safety protection module, an execution control module, a digital twin module and an energy efficiency evaluation module. When real-time load balancing scheduling of the micro-grid is carried out, a multi-time-scale scheduling strategy is generated in real time by dynamically coordinating economical efficiency, environmental protection and power supply reliability targets through a deep reinforcement learning agent, so that the problems of high operation cost, standard exceeding of carbon emission and voltage instability caused by a single optimization target in a traditional method are further solved, and the real-time load balancing scheduling of the micro-grid is realized. According to the method, the multi-dimensional collaborative optimization of the micro-grid in a complex operation environment is ensured, and meanwhile, a cloud global optimization module generates a long-cycle strategy, so that the problems of response delay and insufficient expandability of a centralized control architecture are further solved, and the real-time power balance capability in a high-permeability renewable energy source scene is improved.

The invention provides a resource collaborative scheduling system and method for a virtual power plant. The method comprises the following steps: determining space-time probability distribution of wind and light output in the virtual power plant through historical meteorological data and historical illumination data; determining space-time load distribution of an electric vehicle cluster in the virtual power plant, and constructing a source-load interaction scene set under multiple space-time scales in the virtual power plant by fusing the space-time probability distribution and the space-time load distribution; determining a multi-objective optimization function of the virtual power plant according to the price demand signal of the electric energy service market and the source-load interaction scene set; and performing optimization solution on the multi-objective optimization function to obtain a collaborative scheduling plan of the virtual power plant, decomposing the collaborative scheduling plan into a control instruction sequence, and issuing the control instruction sequence to a local controller of each distributed resource. According to the scheme of the invention, a multi-target optimization system considering the operation benefit and the renewable energy power abandonment rate can be constructed through the source-load interaction scene under multiple spatial-temporal scales, so that the closed-loop management and control of the resource scheduling of the virtual power plant can be realized.

The invention discloses a water electrolysis hydrogen production intelligent control system and method based on artificial intelligence. The system and method are suitable for a large-scale water electrolysis hydrogen production scene under the power supply condition of fluctuating renewable energy sources such as wind power and photovoltaic. The system comprises a data acquisition layer, an edge calculation layer, an intelligent control layer and an execution layer. Dynamic modeling of the running state of the hydrogen production system is achieved through multi-parameter real-time monitoring and feature extraction. The intelligent control layer fuses an LSTM prediction module and a reinforcement learning controller, the LSTM prediction module is used for predicting future renewable energy input and hydrogen demand trends, and the reinforcement learning controller calculates an optimal current density set value based on a prediction result so as to maximize hydrogen production efficiency per unit energy consumption. And meanwhile, a digital twinning technology and a safety protection mechanism are combined, thermoelectric dual regulation and control of the electrolysis process are achieved, and the control response speed and the system stability are improved. According to the invention, energy consumption can be effectively reduced, the service life of the stack is prolonged, and the operation efficiency and reliability of the hydrogen production system under complex load are improved.

The invention relates to the technical field of renewable energy hydrogen production and intelligent control, discloses a multi-target prediction control method and system based on an ALK-PEM hybrid electrolytic cell array, and solves the problems that in the prior art, wind and light power generation fluctuation cannot be deeply adapted, and hydrogen production economy, response speed and long-term reliability of equipment are difficult to consider. According to the scheme, the method comprises the following steps: obtaining wind-solar power generation power prediction data, and operating parameters and health state indexes of each ALK and PEM in a hybrid array to form a time sequence data set; a hybrid array prediction model based on data driving is constructed, a multi-target cost function is constructed by taking maximization of wind and light absorption, stable hydrogen production and minimization of equipment life loss as targets, rolling optimization is performed on the model, and an optimal power set point sequence of each electrolytic cell in a future preset time domain is solved; and in combination with the operating characteristic difference of the ALK and the PEM, a differential control instruction is generated and executed, and meanwhile, dynamic grouping management is performed on the hybrid array according to the wind-solar power prediction data and the health state of the electrolytic cell.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention discloses a micro-grid load distribution control method, device and equipment based on entropy increase disturbance, and the method comprises the steps: analyzing the operation state of a micro-grid through chaos measurement, and constructing an entropy increase disturbance excitation mechanism to actively excite the vitality of a system; after random disturbance is applied, inherent frequency characteristics of each power supply are extracted, and a frequency synchronization coordination mechanism is established to realize multi-source coordination; adopting counter-example search to actively identify distribution scheme vulnerabilities, and generating an enhanced distribution strategy to improve robustness; evaluating the load bearing capacity of each power supply, constructing a load conduction topology and analyzing pressure distribution; each power supply bearing limit parameter is obtained through boundary active detection, and the equipment potential is fully excavated; and a final control instruction is generated based on a multi-layer time sequence synchronization mechanism, and multi-power-supply coordinated output is realized. A chaos theory is introduced into micro-grid control, local optimum is avoided through active disturbance, a multi-layer coordination mechanism is adopted to adapt to new energy volatility, and a self-adaptive load distribution control scheme is provided for a micro-grid with high-proportion renewable energy access.

The invention discloses a distribution line load prediction and optimal scheduling method and system, and relates to the technical field of intelligent scheduling of power systems, and the method comprises the steps: generating a time-aligned multi-source fusion input data set; constructing a mixed time sequence load prediction model, and introducing a weighted quantile loss function in a model training process; constructing a joint probability distribution model of the renewable energy output and demand response participation rate, and sampling joint probability distribution; constructing a rolling time domain power distribution network optimization scheduling model; a two-layer mixed strategy is adopted to deal with uncertainty, an optimization problem is decomposed into a plurality of sub-problems, and an alternating direction multiplier method with adaptive penalty parameters is used for distributed solution. According to the method, a multi-objective optimization scheduling model is established in a rolling time domain, and dynamic closed-loop optimization is realized; and by introducing a two-layer hybrid solving strategy and an ADMM distributed algorithm with an adaptive penalty parameter, the calculation efficiency and expandability are remarkably improved while the global consistency is ensured.

The invention provides an uncertain scene-oriented micro-grid and shared energy storage collaborative robust optimization method. The method comprises the following steps of S1, constructing a micro-grid group and shared energy storage collaborative scheduling optimization operation framework; s2, constructing a wind and light uncertain scene set by adopting a scene generation technology; s3, constructing a load uncertainty scene set based on data-driven K-means clustering; s4, the activation probability of the load disturbance boundary is stably estimated based on the Wasserstein distance; s5, constructing a microgrid group and shared energy storage two-stage robust scheduling optimization model; step S6: carrying out dual transformation and Camp; solving the two-stage robust optimization model through a CG algorithm; step S7, designing an improved Shapley value method income allocation mechanism based on the network topology sensitive model; through load scene construction of data driving and clustering analysis, wind and light scene generation and clustering reduction technologies and in combination with collaborative optimization scheduling of the micro-grid group and shared energy storage, the renewable energy utilization efficiency can be improved, the intraday operation cost can be reduced, and collaborative development of energy storage and new energy can be assisted.

The invention belongs to the technical field of comprehensive utilization of ocean renewable energy sources, and particularly relates to a pilot frequency coupling-based wind wave energy multi-degree-of-freedom broadband control method, which comprises the following steps of: performing frequency domain identification on wind wave environment characteristics, and constructing a wind wave pilot frequency energy coupling model; based on the motion response characteristics of the multi-degree-of-freedom floating platform, designing a pilot frequency energy regulation and conversion mechanism; a full-working-condition dynamic optimization control strategy is adopted, and cooperative operation and energy flow self-adaptive distribution of the wind energy conversion unit and the wave energy conversion unit are achieved. Aiming at the significant difference and relevance of wind energy and wave energy in frequency characteristics, time scale and spatial distribution, a pilot frequency wind wave coupling dynamic model is established, and broadband capture of low-frequency wind-induced and high-frequency wave-induced coupling energy is realized under complex sea conditions through multi-degree-of-freedom motion decoupling and state observation, so that the wind-induced and high-frequency wave-induced coupling energy is obtained. The energy utilization rate, the attitude stability and the structural safety of the system are effectively improved, and good robustness and engineering adaptability are achieved.

The invention discloses a power distribution network dynamic partitioning method, system and device considering new energy output fluctuation and a medium, and relates to the technical field of power distribution network partitioning, and the method comprises the steps: building a measurement matrix based on a power transmission relation quantification means in a new energy output stable state, and selecting a main power supply node according to the node correlation reflected by the measurement matrix, load nodes are classified according to the electrical correlation degree and the spatial proximity principle to form a preliminary partition framework, new energy output time sequence prediction information is introduced, a partition quality evaluation function is constructed, a partition optimization model is established, the preliminary partition framework is dynamically adjusted and corrected, and new energy output is obtained; and solving the partition optimization model by adopting an optimization solving method, and outputting a dynamic partition scheme adapted to new energy fluctuation. According to the method, comprehensive optimization of the power distribution network dynamic partitioning method considering the new energy output fluctuation is realized, and the operation economy, safety and reliability of the power distribution network in a high-proportion new energy access environment are remarkably improved.

The invention provides a water-wind-light integrated system optimization scheduling method considering source load uncertainty. Based on historical output data of wind power, photovoltaic and hydropower, constructing a three-dimensional joint probability density function, and generating multiple groups of typical scheduling scenes; establishing a water-wind-light integrated dispatching optimization model; solving the scheduling optimization model by adopting an improved NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; and carrying out source load sensitivity analysis based on the optimal solution set, identifying the influence weight of the load fluctuation rate and the wind-solar prediction error on the system operation, and dynamically adjusting the hydropower output strategy according to the influence weight so as to improve the stability and adaptability of the system under the extreme load change. The method can effectively cope with the dual uncertainty of wind power, photovoltaic output and load demand, and improves the scheduling feasibility and renewable energy utilization rate of the water-wind-light integrated system.

The invention relates to the technical field of power electronics and power system control, and discloses an intelligent control method and system for a multi-network converter, and the method comprises the steps: collecting the frequency deviation, the power change rate and the voltage amplitude of a power grid in real time, carrying out the filtering and feature extraction, and analyzing the coupling relation, thereby obtaining the parameter coupling strength. And if the intensity exceeds the threshold value, an inertia adjusting signal is generated through a fuzzy logic controller. And calculating a deviation correction value by combining the state evaluation response performance of the converter, and generating an optimization instruction by fusing the frequency deviation. And solving a voltage stability threshold based on the instruction, updating a coupling index, and issuing and executing after verification. And finally, updating the fuzzy rule base by adopting neural network iterative learning according to feedback data, and forming an inertia support frame with self-learning capability. According to the method, the inertia response speed and the operation stability of the multi-network converter system under high-proportion new energy access can be effectively improved, and adaptive cooperative control is realized.

Provided is a method of operating a hybrid power plant, including energy generating units including a wind park and at least one unit of another type of renewable energy source and including an energy storage system, the method including: creating a power production schedule based on an actual forecast of power production, an actual forecast of energy price and actual hybrid power plant status, the power production schedule including at least scheduled power for points in time in the future; and controlling, during the points in time in the future the energy generating units and the energy storage system based on the power production schedule, the hybrid power plant status at the points in time in the future, in particular energy storage system status, such as to dispatch power according to the power production schedule, but complying with any grid operator reference at the points in time in the future.

The invention belongs to the technical field of ocean renewable energy power generation, and particularly relates to a floating type storm power generation system control method based on knowledge distillation and a partitioning strategy. Constructing a multi-teacher model system; knowledge distillation and student model training; designing a partition control strategy; designing a target function; designing a weight adaptive mechanism; defining constraint conditions; performing upper layer optimization solution; executing a lower-layer control instruction; and dynamically feeding back a correction mechanism. The method covers all operation and survival working conditions of the combined power generation system, on one hand, the modeling difficulty and complexity of a control model are reduced, the real-time performance and reliability of prediction and control are improved, on the other hand, the zoning control strategy is proposed in combination with the stormy wave condition from the angle of engineering application of the combined system, and the control efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the floating type wind and wave combined power generation system under complex sea conditions are enhanced.

The invention relates to the technical field of stability analysis of a new energy grid-connected system, in particular to a new energy real-time short-circuit ratio margin evaluation method, system equipment and a medium, and the method comprises the steps: building an equivalent model based on power grid parameters, calculating equivalent capacity, and carrying out online correction in combination with multi-source data; calculating a new energy grid-connected short-circuit ratio according to the equivalent capacity ratio, calculating a short-circuit ratio index based on a constraint equation, refreshing a real-time index through measurement data of local voltage and current, and outputting a real-time short-circuit ratio; calculating the short-circuit ratio margin of the new energy station based on the short-circuit ratio difference value, quantitatively evaluating the stability, judging a stable state according to a positive value and a negative value of the margin, quantifying an instability risk level according to an absolute value of the margin, and generating a graded early warning signal and a control instruction; through dynamic fusion of multi-source data correction capacity, local measurement of millisecond-level refreshing indexes and margin positive and negative values, a control instruction is triggered, stable monitoring and active defense are realized, and the anti-disturbance capability and the safety level of a high-proportion new energy power grid are remarkably improved.

The invention discloses a pumped storage power station trans-provincial capacity configuration method, device and equipment and a medium. The method comprises the following steps: collecting and arranging a grid structure, system total adjustable capacity, load and new energy prediction data of a regional power grid in a research level year; the regional power grid pumped storage capacity trans-provincial mutual aid research is carried out by taking reduction of the overall system operation cost and the new energy power abandoning rate of the regional power grid as the target, and a pumped storage power station trans-provincial capacity configuration model is established. Optimal configuration analysis is carried out by combining the newly-added total capacity of the pumped storage power station in regional power grid planning in a research level year, and an optimal configuration scheme of the newly-added pumped storage capacity among provinces is obtained. The regional power grid trans-provincial power transmission channel and the unified scheduling mechanism are utilized, optimal allocation of resources on the regional level is achieved through pumped storage trans-provincial mutual aid, the operation economical efficiency of the whole regional power grid can be improved, and the problem that pumped storage resources of all provinces are distributed unevenly is effectively solved.

Disclosed are an ultra-short-term wind power forecasting method and system, relating to the technical field of artificial intelligence. The method comprises: obtaining an original dataset of a wind farm, processing the original dataset, and performing training on the basis of processed original data; decomposing wind speed data in the trained original data, calculating each decomposition component, and constructing a feature matrix on the basis of the calculation results; and introducing a residual attention mechanism to reconstruct the feature matrix, using the reconstructed result to establish a network model, performing secondary training, and forecasting ultra-short-term wind power. The present invention improves the accuracy and reliability of ultra-short-term wind power forecasting and achieves significant advances in algorithm optimization, thereby providing effective support for the stable power supply of renewable energy sources such as wind farms and for power grid operation.

The invention relates to a new energy micro-grid multi-objective optimization control method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time output data of renewable energy sources of the micro-grid, operation parameters of energy storage equipment and load demand records, and performing time sequence analysis to obtain an energy output variation rule and a load demand curve; constructing a multi-objective function model containing economy-stability double objectives and a coupling correction term based on the law and the curve, generating an initial balance scheme through linear programming, and iteratively determining resource collaborative allocation parameters through a particle swarm optimization algorithm; the distribution parameters and the energy storage available capacity are input into the agent model, a real-time optimization decision instruction is generated, and an economic evaluation value is calculated in combination with running log data; and if the evaluation value does not reach the power grid stability threshold value, recalculating a balance scheme through a multi-objective function model, and generating a standardized equipment instruction set. According to the method, collaborative optimization of economy and stability of the micro-grid is realized, and stable operation of the micro-grid in different scenes is guaranteed.

The invention relates to a mixed scale optimization regulation and control method and device for rural energy and a storage medium, and belongs to the technical field of new energy. According to the method, layered optimization is carried out by establishing a monthly time scale optimization scheduling model, a day-ahead time scale optimization scheduling model and an intra-day time scale optimization scheduling model which are connected with one another, and long-term economic planning and medium-short-term operation scheduling are organically combined, so that multi-target collaboration and multi-energy complementation are realized; specifically, through monthly optimization, an optimal purchase and inventory strategy can be formulated according to seasonal price fluctuation of biomass, the fuel cost is remarkably reduced from the source, and the overall economical efficiency of the system is improved; through a day-ahead gas storage plan and intra-day real-time scheduling, the fluctuation and intermittency of wind energy and solar energy power generation can be effectively stabilized by utilizing the stability and controllability of biomass power generation, and the power load demand can be stably met under various working conditions, so that the stability and reliability of power supply are enhanced, and the energy consumption is reduced. And on-site efficient consumption of rural renewable energy sources is promoted.

The invention discloses a hybrid energy storage optimization configuration method based on a multi-time scale regulation and control demand, and the method comprises the steps: collecting the operation data of a power system, and obtaining the power fluctuation and the frequency deviation of the power system under a short time scale through a preset high-proportion renewable energy power system model; on the basis of power fluctuation and frequency deviation, the regulation and control requirements of the power system are divided according to different time scales, different energy storage modules are correspondingly configured, and a multi-level collaborative energy storage regulation and control structure matched with the time scales is formed; based on the power system model and the mathematical model of each energy storage module, constructing a hierarchical nested optimization model oriented to multiple time scales; and selecting a typical scene from the operation data by adopting a time sequence clustering method, and obtaining the optimal capacity and power configuration of each energy storage module based on a hierarchical nested optimization model, and obtaining a cross-time-scale collaborative operation strategy. The global optimal configuration is realized by establishing a multi-time-scale collaborative hierarchical nested optimization architecture.

A tool for management of irrigation schedules is provided. The tool can include a controller that can communicate with an irrigation system to build and implement automatic irrigation schedules using sensed data about the environment, historical data, and manually input data. These schedules can be optimized for capital cost, lifetime cost, farm revenue, water use, and energy efficiency of the irrigation system. The schedule can be adjusted based on sensed data and / or manual inputs to ensure optimal use of resources. In some embodiments, the controller can communicate the schedule to a user via electronic communication to implement irrigation. In such embodiments, the user can interface with the controller to confirm directions to implement irrigation and receive additional directions.

The invention provides a low-carbon modular artificial wetland system and a carbon sink collaborative purification method thereof, and relates to the technical field of artificial wetlands, the low-carbon modular artificial wetland system comprises a modular box body, and a water inlet layer, a carbon sink layer and a purification layer which are sequentially arranged in the modular box body from top to bottom; the water inlet layer is used for filtering suspended matters through coarse sand; the carbon sequestration layer is used for adsorbing carbon dioxide through biochar and carbon sequestration microorganisms, and meanwhile, the carbon sequestration capacity is enhanced through the plant-microorganism synergistic effect; the purification layer is used for purifying a water body through an iron-based phosphorus removal material and a zeolite denitrification filler. The technical problems of low-carbon material selection, carbon sink potential excavation, renewable energy source coupling and greenhouse gas emission inhibition of an existing constructed wetland system are solved, the technical effects of efficient sewage purification and carbon emission reduction are achieved, and a carbon emission reduction closed loop of the whole life cycle is achieved.

The invention specifically relates to a photovoltaic building design and control system based on a multi-modal neural network, and relates to the technical field of building energy saving, renewable energy utilization and artificial intelligence application. Comprising a multi-modal data acquisition and fusion module, a multi-modal neural network modeling and optimization design module, a real-time control strategy generation and execution module and a digital twin platform and continuous learning module. According to the method, global optimization is designed, powerful nonlinear fitting and feature fusion capabilities of the multi-modal neural network are utilized, multi-dimensional complex factors such as climate, buildings, users and a power grid are comprehensively considered, the optimal BIPV integration scheme which is high in power generation efficiency, small in influence on building performance and good in economical efficiency is rapidly generated, and the design efficiency and the scheme quality are remarkably improved.

The invention relates to a multi-time-scale toughness scheduling strategy of a multi-energy complementary system under an extreme high temperature condition in optimal scheduling of a power system. In order to solve the problems of load rising caused by high temperature, derating of a source network and difficulty in guaranteeing power supply safety by traditional economic dispatching, a source-network-load-storage temperature effect model of photovoltaic, thermal power, a power transmission transformer and a load is constructed, and a day-ahead, day-intraday and real-time three-layer collaborative optimization framework is embedded; weighted unsupplied electric quantity is used as a toughness index, node vulnerability and load grade weight are combined, dictionary order optimization is adopted before the day, prediction deviation is corrected in a rolling mode within the day, toughness-oriented model prediction control and rolling supply stop window constraint are adopted in real time, and conventional unit, energy storage and layered demand response are cooperatively scheduled. Therefore, load loss is reduced and new energy is abandoned in an extreme high-temperature scene, and the power supply guarantee capability of key nodes and important loads and the overall toughness and economical efficiency of the system are improved.

The invention provides a power distribution network multi-source cooperative scheduling method and system based on big data, relates to the field of power distribution network scheduling, and solves the technical problem that an optimization strategy is not flexible enough in the prior art. The method comprises the following steps: acquiring multi-source real-time data of a power distribution network through an Internet of Things terminal and an intelligent electric meter; based on the multi-source real-time data, predicting a behavior mode of the electric vehicle and an output value of renewable energy by adopting a machine learning algorithm, and estimating a charging state of the electric vehicle by using a probability SOC prediction model; the behavior pattern is used for representing a travel rule of the user; generating a multi-time-scale scheduling strategy based on the behavior pattern, the output value of the renewable energy source and the charging state by adopting a collaborative optimization algorithm; and testing the scheduling strategy based on a simulation environment constructed by a digital twinning technology, and issuing a control signal based on a test result.

The invention relates to a probability distribution determination method and device for marginal carbon emission factors and computer equipment. The method comprises the following steps: sampling according to first probability distribution of renewable energy output and second probability distribution of a power system load to obtain a parameter sample of the sampling; matching the parameter sample with each critical domain in the critical domain list; in response to a result that the parameter sample is not matched with the critical domain, solving by utilizing a multi-period economic dispatching model to obtain a marginal carbon emission factor corresponding to the parameter sample, the constraint conditions of the multi-period economic dispatching model comprise the power balance constraint of each period and the inequality constraint formed by the line power flow constraint, the unit output constraint and the unit climbing constraint, and the constraint conditions are generated by utilizing the network topology information and the unit configuration information; and generating probability distribution of the marginal carbon emission factors by using the marginal carbon emission factors obtained by each sampling. By adopting the method, the calculation efficiency and accuracy can be improved.

The invention relates to the technical field of renewable energy source prediction, and provides a method and system for driving a CNN-LSTM neural network to predict photovoltaic power generation based on an eagle optimization algorithm, and the method comprises the steps: collecting historical meteorological factor data and photovoltaic power generation power data, and screening out meteorological factors having a significant influence on the photovoltaic power generation power; building a CNN (convolutional neural network)-RCBAM-LSTM (radio channel Performing global exploration and local mining through an eagle optimization algorithm, and dynamically optimizing hyper-parameters of the CNN-RCBAM-LSTM model; the meteorological factor data are input into the CNN-RCBAM-LSTM model; sequentially carrying out one-dimensional convolution preliminary feature extraction, feature re-calibration based on an RCBAM attention mechanism, and carrying out multiple stacking to obtain a preprocessed feature matrix; in the feature re-calibration process, related coefficients are obtained through an eagle optimization algorithm; outputting a preliminary photovoltaic power generation prediction result; iteratively updating the position of the eagle to obtain a final optimization result; and inputting real-time meteorological factor data into the trained CNN-RCBAM-LSTM model to obtain a final photovoltaic power generation prediction result, so as to solve the problem of insufficient estimation precision of photovoltaic power generation.