138 results about "Grid scheduling" patented technology

The invention relates to the technical field of virtual power plants, in particular to a multi-target scheduling optimization method and system for a virtual power plant to participate in a power market, and the method comprises the steps: carrying out the dynamic integration of distributed energy, an adjustable load and an energy storage unit in the virtual power plant, and constructing a resource model; establishing a distributed resource identity authentication and data storage mechanism; constructing a multi-objective optimization function, comprehensively considering a plurality of objectives such as resource utilization rate, carbon emission and load balance, and designing a dynamic weight adjustment mechanism; calculating a multi-objective optimization result, determining the optimal output and operation state of virtual power plant resources, and generating a virtual power plant optimization scheduling strategy; and cooperative optimization of internal resource scheduling and external power grid scheduling of the virtual power plant is realized by combining a game theory and a swarm intelligence algorithm. According to the method, the utilization efficiency of distributed resources can be improved, source-network load-storage coordination and new energy consumption are promoted, and the flexibility and comprehensive benefits of a power system are enhanced.

The invention provides a micro-grid dispatching method based on a hybrid genetic particle swarm optimization algorithm. The method comprises the following steps: constructing a micro-grid system comprising a photovoltaic cell PV, a fan WT, a diesel generator DE and a micro gas turbine MT; based on the micro-grid system, a multi-objective optimized micro-grid optimization scheduling model is constructed, an objective function of the optimization scheduling model is set to be cost for minimizing the micro-grid in a grid-connected mode, and the cost comprises operation cost and environment cost; and solving the optimal scheduling model of the micro-grid by adopting a hybrid genetic particle swarm optimization algorithm MOPSO, and outputting an optimization result meeting a scheduling strategy. According to the method, the operation cost is optimized, the microgrid dispatching strategy with the optimal economical efficiency can be formulated by accurately predicting the future load and the new energy output and fully utilizing adjustable and controllable means such as energy storage and the hybrid genetic particle swarm optimization algorithm, and the operation cost is reduced.

The invention provides a virtual power plant energy storage system co-scheduling and control method and system, and relates to the technical field of power management, and the method comprises the steps: obtaining power grid scheduling data, calculating the charge and discharge income, collecting the real-time parameters of a battery, analyzing the performance attenuation law through deep reinforcement learning, determining the initial working parameters, predicting the system state based on a rolling time domain, and obtaining the real-time parameters of the battery. An optimal scheduling scheme is calculated through mixed integer linear programming and model prediction control in combination with a power distribution strategy and is corrected in real time, operation is executed according to a time-phased scheduling instruction and is monitored in real time, and an emergency strategy is started when necessary, so that the economic benefit and the safety of an energy storage system are improved, and the service life of a battery is prolonged.

The invention is suitable for the technical field of short-term power load prediction and intelligent scheduling, and provides a short-term power load prediction method based on improved sparrow search algorithm (ISSA) optimization. The method comprises the following steps: constructing a multi-scene prediction task according to the time resolution and regional seasonal characteristics of a load; local features are extracted in combination with a convolutional neural network (CNN), time sequence dependence is modeled by a long short-term memory (LSTM) network, and an attention mechanism is introduced to strengthen key features; meanwhile, an ISSA is adopted to optimize a model network structure and hyper-parameters, the number of layers, the learning rate and the batch size of the CNN and the LSTM are adjusted in a self-adaptive mode, and the search efficiency and convergence performance of the ISSA are improved through Latin hypercube sampling, cosine annealing, dynamic spiral search and a Levy flight strategy. Simulation results show that the method can maintain high prediction precision under different time resolutions and regional and seasonal conditions, the generalization ability and cross-scene adaptability of the model are enhanced, and a stable and efficient load prediction scheme is provided for power grid dispatching optimization.

The invention discloses a block chain-based distributed power grid dispatching cooperation method and system, and relates to the technical field of block chains, and the method comprises the steps: calculating the total expected cost through an ADMM distributed optimization method, obtaining a consistency error through the total expected cost, obtaining a consensus priority based on an optimal strategy set and the consistency error, and carrying out the optimization of the consensus priority. Priority nodes are obtained based on the consensus priorities, and time sensitive weights are calculated through the consensus priorities and the priority nodes. A multi-scene generation method based on probability modeling is introduced, the depiction capability of photovoltaic power generation and load demand uncertainty is improved, a node strategy space is constructed, a power utility ratio and revenue function joint optimization is introduced, deep fusion of energy efficiency evaluation and strategy game is realized, an ADMM distributed optimization method and a block chain consensus priority mechanism are combined, and energy efficiency evaluation and strategy game optimization are realized. Centralization dependence is broken, and the dispatching intelligence level and the operation stability of the distributed power grid in a complex environment are improved.

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

The invention provides a new energy power plant assessment and compensation information transparent management method and system, and relates to the technical field of new energy power plant management, and the method comprises the steps: predicting a power generation capability reference value through a deep learning model, and constructing a power generation capability assessment model based on the reference value, and calculating a comprehensive assessment reference value in combination with the real-time power generation data and a preset assessment rule, performing grading assessment on the power plant, and generating a report. And a cause-and-effect analysis model is utilized to judge a power generation loss reason, if the power generation loss reason is caused by power grid dispatching limitation, compensation power data is calculated, and related data is written into the alliance block chain. And the intelligent contract automatically calculates a compensation amount, calculates an operation efficiency index based on historical data, generates a transparent management report, and distributes the transparent management report to users with different permissions through an encryption channel.

The invention relates to the technical field of intelligent scheduling of photovoltaic power stations, in particular to a management method for improving the power generation capacity of a photovoltaic power station, and the method comprises the steps: analyzing a power regulation and control policy text, meteorological early warning data and electricity price historical fluctuation data, and generating a three-dimensional dynamic constraint condition set; a basic generated output prediction sequence is generated by using an LSTM time sequence model, and a three-dimensional constraint generation policy is superposed to adapt to the schedulable capacity; screening adjacent reference power stations based on a digital twinborn model, and correcting to obtain meteorological correction schedulable capacity; starting in-station compensation when the generated output of the target power station is insufficient; if compensation is insufficient, screening the auxiliary power station and generating an incremental power generation instruction set; storing the instruction and the result by using the alliance chain; and cross-power-station cooperative training is realized through federal learning. The power generation capacity of the photovoltaic power station and the power grid dispatching efficiency can be effectively improved, and the problems of power generation loss and low cross-station cooperation efficiency caused by meteorological fluctuation are solved.

The invention relates to a photovoltaic power generation power prediction method, which comprises the steps of collecting and processing real-time multi-source meteorological data to obtain a meteorological feature set; according to historical photovoltaic power generation data, time sequence information is obtained, dynamic time features are extracted, and a time dimension feature set is obtained; splicing and fusing the meteorological feature set and the time dimension feature set to obtain a fused feature set; the fused feature set is input into a power prediction model, a photovoltaic power generation power prediction value is obtained, the power prediction model is constructed through a double attention mechanism and an LSTM network and is obtained through training of a training set, and the training set comprises historical multi-source meteorological data, photovoltaic power generation data and corresponding photovoltaic power generation power values. Through the multi-source data fusion and deep learning method, the accuracy and stability of photovoltaic power generation power prediction are effectively improved, and important support is provided for efficient operation of a photovoltaic power station and power grid dispatching.

The present invention relates to the technical field of steel industry load participation in power grid interaction, and provides a double-layer optimization scheduling method and system for steel industry load, the method comprising: constructing a continuous time interactive resource task network model based on double-layer optimization data information; constructing a resource task network model with production process constraints; constructing a complex order characteristic analysis model for steel industry load; setting dynamic maximum demand analysis model operation constraints for the complex order characteristic analysis model; constructing an upper-layer power grid scheduling optimization model that achieves optimal power grid economic cost, and the lower-layer steel industry load side full system model and the upper-layer power grid scheduling optimization model jointly form a double-layer optimization system; proposing a double-layer optimization algorithm for the double-layer optimization system, solving the double-layer optimization scheduling; and outputting double-layer optimization scheduling result information. The present invention significantly reduces the electricity cost of short-process steel industry loads, and allocates production orders in combination with steel order characteristics to achieve better peak shaving and valley filling effects.

The invention discloses a distributed power grid dispatching management and optimization method, and relates to the technical field of power grid automation, and the method comprises the steps: collecting multi-dimensional power grid data in a system, carrying out the data preprocessing of the multi-dimensional power grid data, carrying out the electrical load prediction of the initial multi-dimensional power grid data through an LSTM neural network, and carrying out the optimization of the electrical load. Performing energy storage SOC state real-time evaluation on the electrical load data based on a DTW dynamic time warping load identification model, performing state judgment on power grid equipment according to an evaluation result, constructing a multi-target optimization model by using mixed integer second-order cone programming, performing power grid constraint adjustment by using the multi-target optimization model, and performing power grid constraint adjustment on the basis of a power grid constraint result. And performing real-time power generation plan adjustment by using an improved particle swarm algorithm to obtain target electric power output of the power grid, and performing power grid energy storage control optimization according to the target electric power output. And the prediction accuracy of the new energy output and the load demand is obviously improved.

The invention discloses a micro-grid group operation optimization method and system based on a multi-target dragonfly algorithm, and the method comprises the steps: constructing a system model of a micro-grid cluster, and enabling the system model to comprise a plurality of micro-grids; taking the connection state of the plurality of micro-grids as a decision variable to represent the communication mode between the micro-grids; a dynamic grouping mechanism is introduced, and the connection relation of the multiple micro-grids is adjusted according to actual requirements; establishing a multi-objective optimization model, and designing operation constraint conditions; combining decision variables and constraint conditions, designing a structure-scheduling joint optimization strategy, and carrying out collaborative optimization on a micro-grid group structure and a scheduling strategy; and according to an optimization result, outputting a Pareto optimization scheduling solution set covering different operation preferences, and obtaining a low-carbon economic multi-energy-flow cooperative operation scheme of the micro-grid group. According to the method, the problems of local optimum and low convergence speed during multi-objective optimization in the micro-grid dispatching process can be solved.

The invention relates to the technical field of micro-grid scheduling, and discloses a time-sharing carbon flow-based micro-grid scheduling method, system and device and a storage medium, and the method comprises the steps: inputting power load data and characteristic data into a carbon flow calculation model, and calculating the carbon flow of a micro-grid; according to the energy consumption of the enterprise, calculating other energy carbon flow of the enterprise, and according to the power consumption period, adding the micro-grid carbon flow and the other energy carbon flow to obtain a time-sharing carbon flow data sequence; and performing carbon flow analysis on the time-sharing carbon flow data sequence, determining a scheduling period, constructing an optimal scheduling model of the micro-grid according to the scheduling period and the carbon flow calculation model, and solving the optimal scheduling model to obtain an optimal scheduling strategy. Optimized scheduling is carried out according to carbon emission conditions of different time periods, organic unification of operation of the micro-grid and operation of an enterprise is achieved under the low-carbon target, and carbon emission of the enterprise is indirectly reduced by improving the participation degree of new energy elements at the specific moment and optimizing the energy structure.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention provides a micro-grid intelligent scheduling and control method and system, and relates to the technical field of micro-grid scheduling, and the method comprises the steps: extracting battery state sensing data and timestamp sensing data; when the state belongs to the abnormal state, stopping the process, and when the state belongs to the healthy state, matching a scheduling constraint rule; performing local sample adjacency analysis to obtain production demand electric quantity; performing scheduling scheme configuration to obtain a first scheduling scheme; analyzing to obtain an economic cost evaluation value and an energy-saving parameter evaluation value; when at least one of the economic cost evaluation value greater than or equal to the economic cost evaluation threshold value and the energy-saving parameter evaluation value less than or equal to the energy-saving parameter evaluation threshold value is triggered, performing multi-constraint optimization to obtain a scheduling scheme optimization result; and micro-grid intelligent scheduling control is executed. The micro-grid scheduling method solves the technical problem that a micro-grid scheduling method in the prior art often adopts a control rule of a fixed mode and is lack of flexibility for rapidly changing market conditions and continuously changing energy supply and demand conditions.

The invention provides a multi-type micro-grid cross-layer collaborative scheduling method based on multi-agent reinforcement learning, and relates to the technical field of power system and micro-grid scheduling, and the method comprises the steps: firstly constructing a multi-type micro-grid layered collaborative architecture, a multi-agent reinforcement learning scheduling model, and a centralized coordination layer where global agents are deployed in a platform layer; and the local agents are deployed at each distributed micro-grid node of the platform layer, a cross-layer cooperative training mode is adopted to train the model, and finally, cooperative decision results of the global agents and the local agents are integrated to generate a global-local cooperative scheduling scheme. According to the method, global optimization and local flexibility can be considered, and the hierarchical multi-agent architecture adopts a multi-type micro-grid hierarchical collaborative architecture, so that the method can be suitable for multi-type micro-grid operation scheduling of a novel power system, and multi-type micro-grid collaborative scheduling considering global optimization and local flexibility is realized.

The invention provides a microgrid scheduling method, and the method comprises the steps: constructing a first optimization model which comprises a plurality of objective functions and a plurality of constraint conditions; converting the first optimization model into a second optimization model in a QUBO form; the second optimization model is solved through a mixed quantum approximate optimization algorithm to obtain a micro-grid scheduling strategy, the mixed quantum approximate optimization algorithm comprises a quantum approximate optimization algorithm and a classical algorithm, the quantum approximate optimization algorithm is used for solving the second optimization model through quantum evolution in the multi-round iteration process, and the classical algorithm is used for solving the second optimization model through quantum evolution in the multi-round iteration process. The classical algorithm is used for adjusting optimization parameters of the quantum approximate optimization algorithm based on candidate solutions generated by quantum evolution in each iteration process, parallel exploration of multiple potential solutions is achieved, and therefore the probability of finding an optimal solution is improved within feasible time.

The invention discloses a micro-grid scheduling strategy generation method and device, equipment, a medium and a product, and belongs to the technical field of micro-grid scheduling. The method comprises the following steps: constructing a micro-grid optimization scheduling physical model, wherein the micro-grid optimization scheduling physical model at least comprises a flexible load response model; based on the flexible load response model, establishing a multi-objective optimization function including a power supply reliability objective and an operation cost objective; system operation constraint conditions of the multi-objective optimization function are set, and the system operation constraint conditions at least comprise distributed power supply output constraints; and carrying out optimization solution on the multi-objective optimization function by adopting an optimization algorithm to obtain a micro-grid scheduling strategy, and carrying out cooperative control on various resources in the micro-grid based on the micro-grid scheduling strategy. According to the micro-grid scheduling strategy generated by the embodiment of the invention, the efficiency and robustness of micro-grid scheduling can be effectively improved.

The invention discloses a micro-grid intelligent scheduling method based on multi-energy collaborative optimization, and relates to the technical field of micro-grid scheduling, and the method comprises the steps: obtaining the subunit operation state information of an energy storage unit, and constructing a basic state parameter set for representing the current operation condition of the energy storage unit based on the subunit operation state information; the scheduling state of the energy storage unit in the current scheduling period is judged based on the basic state parameter set, a scheduling state identifier is generated, and the scheduling state identifier is a unique value in a single scheduling period and is used for limiting that the energy storage unit is only allowed to participate in one scheduling behavior type in the scheduling period. According to the method, the basic state parameter set is constructed and the state parameters representing the current operation conditions are extracted, so that the power capability, the energy boundary and the operation limitation of the energy storage unit in the current scheduling period can be accurately described, and a credible basis is provided for subsequent scheduling behavior identification and parameter selection.

The invention relates to the technical field of energy management, calculation and prediction, in particular to an energy storage micro-grid dynamic collaborative scheduling system based on photovoltaic prediction, a photovoltaic prediction system comprises a data acquisition module and a prediction model, and a micro-grid scheduling system comprises a load management module, a grid interaction module and a real-time response control module. The energy storage system comprises an energy storage battery pack, a charging and discharging control module and an SOC monitoring module. The photovoltaic prediction system is in communication connection with the micro-grid scheduling system, the micro-grid scheduling system is in communication connection with the energy storage system, photovoltaic and load prediction is fused, a multi-target optimization algorithm and a closed-loop feedback mechanism are introduced, and regulation and control perspectiveness, multi-target collaborative scheduling and whole-process system linkage are achieved. And the requirements of various types of micro-grid systems on controllability and stability of photovoltaic power generation are met.

The invention belongs to the technical field of power systems, and more specifically relates to a power grid intra-day scheduling optimization method considering photovoltaic output uncertainty in severe weather, and the method employs an A2C algorithm framework and a knowledge migration technology to solve and optimize a power grid scheduling problem of photovoltaic output uncertainty in severe weather. The method comprises the steps of firstly determining a sequential decision process of power grid intra-day scheduling and establishing a mathematical model, and then determining a power grid intra-day scheduling optimization target and constraint conditions; secondly, using a CGAN scene generation method to generate a training sample, and using an A2C algorithm framework to optimize a scheduling agent capable of adapting to various source load scenes; and finally, optimization of a specific intra-day scheduling agent is improved by using transfer learning, so that the problem that the accuracy of prediction data in a large-scale photovoltaic access power system causes great influence on stable operation of a power grid is solved, and the power grid is ensured to operate in a relatively economical manner on the premise of safety.

The invention provides a micro-grid multi-energy coordinated scheduling method and device, and the method comprises the steps: building a multi-energy coordinated scheduling optimization model containing time-varying constraints through obtaining the data of a photovoltaic power generation system, an energy storage system and a load system in a micro-grid, and carrying out the optimization solving through employing an improved particle swarm optimization algorithm, and designing a gridding vector current control strategy to realize fault ride-through control, executing multi-energy coordinated scheduling control, and realizing stable operation of the micro-grid through hierarchical execution. According to the method, the problem that a traditional micro-grid dispatching method is insufficient in stability and economical efficiency in the face of renewable energy source uncertainty and power grid faults is solved, and the reliability and economical efficiency of micro-grid operation are improved.

The invention discloses a multi-target cost optimization scheduling system for a wind-light-gas storage micro-grid, and relates to the technical field of micro-grid scheduling, and the system comprises an equipment health parameter collection module which collects the operation parameters of wind-light-gas storage equipment through a sensor, and transmits the operation parameters to a health state evaluation module after preprocessing; the module constructs a multi-dimensional model to calculate a health index and predict an attenuation trend; an aging loss cost quantification module constructs a dynamic model to calculate the cost; the multi-target optimization scheduling module sets multiple targets and searches an optimal strategy; the strategy execution and feedback module executes the instruction and returns data; according to the invention, by constructing a linkage mechanism of the equipment health parameter acquisition module and the health state evaluation module, real-time perception and quantitative analysis of the state of the wind-light-gas storage micro-grid equipment are realized, and a multi-dimensional stress-fatigue accumulation model is adopted to calculate the health index of the equipment and divide the health level. The problems that in a traditional method, health assessment is high in subjectivity, and stress accumulation influences cannot be quantified are solved.

The invention relates to the technical field of micro-grid scheduling and optimization, in particular to a source-grid load storage coordinated micro-grid double-layer economic optimization scheduling method and system, and the method comprises the steps: firstly collecting and sorting the power generation data of distributed resources and the power consumption peak condition; secondly, establishing a source network load-storage side model; then, based on the source network load-storage side model, establishing a double-layer collaborative economic optimization scheduling model of the micro-grid, covering a lower micro-grid layer and an upper power distribution network layer; then, the income of the power distribution network is increased and the cost of the micro-grid is reduced through an iterative updating algorithm; and finally, by effectively scheduling controllable resources, the operation cost is reduced, the peak regulation function is enhanced, the economic benefit of power exchange is improved, the waste of renewable energy sources is reduced, and the utilization rate of the renewable energy sources is improved. Optimized cooperation between the micro-grid and the power distribution network is achieved, and important support is provided for stable and economical operation of a modern electric power system.

The present invention discloses a grid dispatch decision method and a large model based on a graph neural network, which includes collecting an original grid data set, generating a fused feature tensor through adaptive distribution mapping and recursive decomposition; importing the fused feature tensor and the grid topology matrix, generating a dynamic graph embedding matrix through spectral decomposition and matrix manifold analysis; performing Markov jump process analysis and optimal transmission fusion on the dynamic graph embedding matrix to generate an integrated feature matrix; constructing a multi-objective Lyapunov function based on the integrated feature matrix, generating a robust decision strategy by solving the Hamilton-Jacobi-Bellman equation; and performing multi-dimensional performance monitoring and statistical manifold learning on the robust decision strategy to achieve strategy optimization. The present invention can effectively handle the uncertainty in grid operation and improve the accuracy and reliability of dispatch decisions.

The invention discloses an optical storage and charging micro-grid scheduling method and system, and the method comprises the steps: obtaining photovoltaic power generation data, energy storage state data, charging demand data and power grid operation data, carrying out the data cleaning and feature engineering processing, and obtaining a standardized feature vector; constructing a neural network search space by using an architecture embedding and transfer ranking method to obtain an optimal network structure for different prediction tasks; performing dependency modeling on the classification variables and the continuous variables by adopting a multi-head self-attention layer to obtain a mixed variable optimization model; and an improved NSGA-III algorithm is adopted to carry out multi-objective optimization solution, and a scheduling strategy of the optical storage and charging microgrid is obtained. According to the method, prediction precision is improved through framework automatic optimization, interaction among mixed variables is effectively processed, collaborative optimization of energy balance, power grid support and charging requirements is realized, and the method has good practicability and economic benefits.

The application provides a micro-grid scheduling control method and device based on policy optimization, equipment and medium, belonging to the power grid scheduling technical field, the method: collecting real-time operation data of new energy power generation equipment, power load equipment and energy storage system; receive user power generation and power consumption plan, combine real-time operation data and historical operation data, extract multi-resource dynamic response characteristics through LSTM model, output adjustment capacity evaluation matrix; receive power grid scheduling plan, based on the adjustment capacity evaluation matrix, match the optimal operation mode from the knowledge base, generate the scheduling instruction set; issue the scheduling instruction set to the target equipment and execute, collect the execution data in real time and calculate the operation deviation; when the operation deviation is over the threshold, the instruction weight is updated by using the online Q learning algorithm, and the optimization strategy is fed back to the knowledge base. The application realizes fine scheduling of micro-grid through real-time data collection and intelligent optimization algorithm, improves resource utilization and operation flexibility, and reduces operation cost.

The invention relates to the technical field of micro-grid dispatching, in particular to a micro-grid collaborative optimization dispatching method based on a layered bee foraging algorithm, which comprises the following steps of: constructing a dispatching model containing multiple targets of economy, environmental protection and stability, and designing a layered optimization algorithm consisting of a global exploration layer, a local development layer and a collaborative decision-making layer. The global exploration layer adopts a self-adaptive step length mechanism and chaotic mapping to initialize a food source, and the search breadth is improved; the local development layer performs neighborhood optimization through a self-adaptive crossover mutation operator to enhance the local search capability; a multi-subject benefit coordination mechanism is introduced into the collaborative decision-making layer, scheduling schemes with subject satisfaction degrees larger than 0.8 are screened from Pareto optimal solutions based on Nash equilibrium, fair and efficient unification is achieved, and the algorithm effectively balances the operation cost, the emission level and the system stability of the micro-grid through dynamic step length adjustment, chaos initialization and multi-target collaborative decision-making. And the scheduling precision and the convergence speed are improved.

The invention discloses an active power distribution network two-stage risk scheduling method and device, a medium and a product, and relates to the technical field of power distribution network scheduling, and the method comprises the steps: constructing a source-load typical scene set considering prediction error space-time correlation; constructing an active power distribution network two-stage risk scheduling model based on the source-load typical scene set; the active power distribution network two-stage risk scheduling model comprises a day-ahead scheduling stage model and an intra-day scheduling stage model; and converting the two-stage risk scheduling model of the active power distribution network from a mixed integer nonlinear programming problem to a mixed integer second-order cone programming problem through a second-order cone relaxation method, and solving the two-stage risk scheduling model of the active power distribution network to obtain a scheduling scheme of the active power distribution network. The method can effectively cooperate with the source-grid-load storage to flexibly adjust the resources so as to reduce the power grid dispatching operation risk caused by the source-load uncertainty.

The application provides an AC / DC hybrid micro-grid multi-objective scheduling method and system based on digital twinning, relates to the technical field of micro-grid scheduling, and comprises the following steps: establishing an AC / DC hybrid micro-grid digital twinning model and dynamically integrating grid related data; and establishing a comprehensive total cost model of a small AC / DC hybrid micro-grid system; the application combines photovoltaic power generation, photo-thermal power generation and wind power generation, and an energy storage device, establishes a device dynamic efficiency model based on the influence of local load, external grid environment and other factors on the efficiency of the AC / DC hybrid micro-grid equipment, obtains various twinning data such as local load and external grid environment through the digital twinning model, takes economic benefits and the health degree of the energy storage device as the target, proposes a multi-objective particle swarm optimization algorithm based on an improved file maintenance strategy and a fuzzy decision method, and optimizes the output of the energy storage device in real time, thereby improving the multi-energy complementation and conversion capacity of the system.