190 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 distributed photovoltaic power generation grid-connected anti-countercurrent control system and control method. The system comprises a load prediction and optimization assembly, a countercurrent detection and response assembly and a control and optimization scheduling assembly. The method comprises the steps of receiving input of various data sources such as power grid dispatching data, user power utilization habits and weather forecast, predicting load requirements and power grid capacity in a period of time in the future in real time, and providing a decision basis for a control and dispatching component; receiving the load demand and the power grid capacity predicted by the load prediction and optimization component, and real-time power grid state data; when it is detected that countercurrent in the power grid is about to occur, an instruction for adjusting photovoltaic output is sent to the control and optimization scheduling assembly; receiving the predicted load demand and power grid capacity, and an instruction for adjusting photovoltaic output; and optimizing the scheduling strategy of photovoltaic power generation, dynamically adjusting the output of photovoltaic power generation, and sending a control signal to photovoltaic power generation equipment. According to the invention, power grid faults and loss caused by countercurrent can be reduced.

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 micro-grid robust optimization scheduling method considering a segmented multi-interval uncertainty set, and the method comprises the steps: carrying out the segmentation fitting of the source load prediction power and error data of wind, light and loads based on a Gaussian mixture model, and combining with a box-type interval uncertainty set to build a multi-interval uncertainty set of power segmentation segments; the uncertainties are used for describing wind, light and load uncertainties; constructing a microgrid robust optimization scheduling model considering the segmented multi-interval uncertainty set and the carbon emission by taking the minimization of the operating cost and the carbon emission cost of the microgrid as a target; and solving the micro-grid robust optimization scheduling model by adopting a column sum constraint generation algorithm. According to the method, the problems of uncertainty modeling and carbon emission of the micro-grid can be solved, the conservative property of a robust optimization scheduling strategy is reduced, and the economical efficiency of micro-grid scheduling is improved.

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 hydrogen-containing energy storage micro-grid dispatching optimization method in combination with ARIMA-LSTM and DDPG. Comprising the following steps: firstly, acquiring historical renewable energy productivity data, historical power load data, historical electricity price and historical energy storage equipment state of the hydrogen-containing energy storage microgrid; secondly, historical renewable energy productivity and power load data are predicted through an ARIMA-LSTM model, a complete state space is constructed in combination with the renewable energy productivity, the electricity price, the energy storage state and the like of the current time slot and input into an Actor and Critic network of the DDPG, a DDPG network model is trained, and a reward function model in the DDPG network model aims at optimizing the annual operation cost; the constraints comprise equipment operation constraints, renewable energy source output constraints, power imbalance and the like; and finally, a micro-grid scheduling strategy responding to external environment change is obtained, dynamic scheduling optimization of each device is realized, and adaptability and robustness of the system are improved.

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 discloses a regional drought-resistant water source dynamic configuration platform and method, and relates to the technical field of water resource management, and the method comprises the steps: obtaining the spatial and temporal distribution characteristics of a target regional water resource, superposing the drought influence, and generating a planning grid; a gray correlation coefficient is introduced, the gray correlation coefficient is calculated and determined with the drought level as a reference sequence and the influence factors as a comparison sequence, and the number of the comparison sequence is at least one; a game decision module is constructed, according to the game decision principle, a first equilibrium game is carried out through inter-grid scheduling competition, and a second equilibrium game is carried out through inter-water structure competition; in combination with the planning grid and the grey correlation coefficient, a water resource dynamic configuration decision is made based on a game decision module, and a water resource configuration strategy is determined; and performing terminal visualization on the water resource allocation strategy, and guiding water resource allocation management of the target area. The technical problem that in the prior art, the configuration efficiency is low during water source dynamic configuration is solved, and the technical effect of improving the water resource configuration efficiency and accuracy is achieved.

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 a micro-grid scheduling method based on a source-grid-load-storage multi-agent cooperative game, the main body of micro-grid scheduling comprises a photovoltaic system, a combined cooling heating and power supply system, an electricity storage system, a heat storage system, a cold storage system and a user, and the method comprises the following steps: associating strategy sets of different main bodies through the cooperative game, and constructing a power distribution network source-grid-load-storage cooperative planning model with finite rationality; modeling by taking the highest comprehensive income of each main body of the micro-grid as a target, and determining respective target functions and constraint conditions; initial populations of different game subjects are randomly generated, and the game is evolved until an evolved stable state is reached; and obtaining a micro-grid dispatching scheme. Compared with the prior art, the micro-grid energy management strategy based on the cooperative game theory is adopted, the optimal fitness solution conforming to the real-time output power of the power generation device in the micro-grid can be obtained, and the operation cost of the micro-grid can be effectively controlled.

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 power quality adjustment resource capability dynamic evaluation method and system based on adaptive federated learning and voltage out-of-limit, electronic equipment and a computer storage medium, and the method comprises the steps: building a power quality adjustment capability evaluation model of adjustable resources, and monitoring the voltage and current changes of the adjustable equipment in real time, providing an evaluation index of the adjustable resource electric energy quality adjustment capability according to the active power output, the reactive power output, the voltage and the current change condition of the adjustable resource, and evaluating the active / reactive power adjustment capability of the adjustable resource according to the evaluation index; the original data, the curve and the evaluation result are analyzed and trained by using adaptive federated learning and a convolutional neural network, and the trained model can dynamically and quickly analyze the active and reactive adjustment performance of the adjustable resources. According to the invention, the problem of active and reactive power regulation operation capability evaluation of the power grid adjustable device is solved, and auxiliary tools and convenient means are provided for power grid dispatching operation.

The invention relates to the technical field of power dispatching, and provides a multi-data center microgrid dispatching method considering carbon quota and data load interaction in order to solve the problem of multi-data center microgrid distributed and two-stage robust collaborative optimization comprehensively considering carbon quota and data load interaction in an uncertain environment. The method comprises the following steps: step 1, establishing a multi-data center microgrid collaborative operation scene containing carbon quota and data load interaction; step 2, establishing a data center microgrid DCM two-stage robust optimization scheduling model; 3, performing linearization conversion on nonlinear constraints contained in constraint conditions in the scheduling model by adopting a piecewise linearization method; 4, performing equivalent conversion on the ellipsoid uncertainty set by adopting a binary variable expansion method; and 5, on the basis of comprehensively considering the participation of the DCM in carbon quota and data load interaction, performing profit distribution by adopting an asymmetric Nash negotiation model. The method is mainly applied to power dispatching occasions.

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 an automatic intelligent operation and maintenance method and system for power grid dispatching based on a multi-modal large model, the multi-modal large model composed of a language processing model, an image recognition model and an AIGC operation and maintenance model is constructed, input information of different information sources is processed, different operation and maintenance scheme texts are obtained, and the operation and maintenance scheme texts are stored in the multi-modal large model. And searching a historical operation and maintenance scheme based on a keyword of the input information, carrying out three-party confirmation, and when the matching degree is lower than a threshold value, considering that an output result is unreliable. Through innovation points of multi-modal information input, dual operation and maintenance scheme output, matching degree judgment and the like, the problem that the generative AI is easy to express mistakenly is solved, so that the operation and maintenance system can judge and prompt the reliability of the result when outputting the result, frequent recheck of operation and maintenance personnel is avoided, the operation and maintenance reliability and efficiency are greatly improved, and the operation and maintenance cost is reduced. Comprehensive understanding and efficient processing of operation and maintenance scenes are realized, and powerful technical guarantee is provided for safe and stable operation of a power system.

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 discloses a power grid dispatching model construction method, a power grid task generation method and a related device, the power grid dispatching model construction method comprises the following steps: obtaining an equipment node relation and dispatching data of a power grid, the dispatching data comprising equipment parameters, line parameters and actual task planning data; an adjacency matrix is constructed based on the equipment node relation, the adjacency matrix comprises information of nodes and edges, the nodes are power grid equipment, the edges are the connection relation between the power grid equipment, and a graph neural network model suitable for power grid task generation is constructed; and training the graph neural network model based on the adjacency matrix and the scheduling data to obtain a power grid scheduling model. The power grid dispatching model obtained through training can perform dispatching planning based on the connection relation between the power grid devices, the device parameters and the line parameters to obtain dispatching task planning data, and the actual task planning data can serve as the inspection standard of the model quality, so that the automatically generated task planning data can meet the actual requirements of the power grid. And the feasibility is realized.

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 relates to the technical field of micro-grid control and adjustment, in particular to a source network load storage artificial intelligence scheduling method, system and equipment, and the method comprises the steps: training and updating a neural network prediction model through employing real-time new energy power generation data and real-time electrical load data; determining a power generation power curve of new energy power generation and a power utilization load curve of user power utilization by using the neural network prediction model updated in real time; based on the generated power curve and the electrical load curve, comprehensive operation cost information of the micro-grid in the next power distribution period is determined; and determining target scheduling planning information of the micro-grid based on the comprehensive operation cost information. According to the source network load storage artificial intelligence scheduling method, model training is performed by using real-time data, and a future prediction curve is continuously updated, so that prediction errors are significantly reduced, and the accuracy and efficiency of micro-grid scheduling are improved.

The invention provides a power regulation method based on buffer pool compressed air energy storage and an energy storage system, and belongs to the field of power grid dispatching. A comprehensive energy management and power control system receives a real-time power dispatching instruction sent by a power grid dispatching center; the energy storage capacity of the energy storage system and the allowable power change range of the energy storage system are fed back to a power grid dispatching center, so that the power grid dispatching center can conveniently plan and dispatch the compressed air energy storage system, and meanwhile, the comprehensive energy management and power control system receives a signal of a sensor of an air storage chamber of the compressed air energy storage system; and calculating the energy storage capacity of the compressed air energy storage system and the fluctuation power adjustment available power consumption and the frequency modulation available power consumption corresponding to the water level of the buffer pool. The comprehensive energy management and power control system sends a peak regulation power instruction to the compressed air energy storage power control system, and sends a power instruction and a frequency modulation function enable signal to the water pump power control system and the water turbine power control system. The method can provide frequency modulation response for the power grid and stabilize new energy output fluctuation.

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.