65 results about "Grid optimization" patented technology

The invention discloses a micro-grid optimization scheduling method based on an improved Loueer fox algorithm, relates to the technical field of micro-grid operation, and realizes collaborative optimization scheduling of a distributed power supply, an energy storage device, a load and the like in a micro-grid system by simulating various perception and behavior strategies of the Loueer fox. Aiming at the demand of distributed power supply system micro-grid optimization scheduling, the micro-grid system operation economy is taken as a main target, a function corresponding to a micro-power supply scheduling scheme is constructed, improvement is carried out based on an RFO algorithm, and a quantum fractal annealing disturbance strategy, a hyperbolic tangent self-adaptive olfactory disturbance strategy and a Riemannian metric accumulation disturbance strategy are introduced, so that the optimal scheduling of the micro-power supply system is realized. Through the fusion of the triple innovation strategies, the defects of poor population diversity, easy premature convergence, insufficient balance capability, low precision and the like when the original RFO algorithm and the traditional intelligent algorithm are used for solving the complex optimization problem are solved, and the efficient solution of the micro-grid optimization scheduling scheme is realized.

The invention discloses a multi-target collaborative optimization scheduling control method and system for an optical storage and charging micro-grid, and aims to solve the multi-target conflict problem of economy, environmental protection and stability in the operation of the micro-grid by constructing a micro-grid model containing photovoltaic, energy storage, load and a power grid. A multi-objective function set for economic cost minimization, carbon emission minimization and supply and demand balance optimization is constructed, and an adaptive multi-population genetic algorithm and a Pareto optimization algorithm are innovatively proposed to collaboratively iteratively solve a Pareto optimal solution set. The search efficiency is improved through an adaptive variation rate mechanism and a multi-population strategy, and the problem of multi-target conflict is effectively solved. The method is suitable for optimal scheduling of the independent micro-grid containing photovoltaic power generation, an energy storage system and a charging load, and the energy utilization efficiency and the system stability are improved.

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 provides a wind and light storage micro-grid optimization scheduling method and system containing small hydropower storage, and the method comprises the steps: obtaining historical operation data, and carrying out the prediction according to the historical operation data, and obtaining a full-dimension prediction data set; according to the full-dimension prediction data set, constructing a multi-objective optimization model with the goal of minimizing residual load, minimizing power abandoning cost, minimizing cascade water abandoning amount, maximizing green energy collaborative utilization efficiency and minimizing time weighted voltage-power comprehensive error; and solving the multi-objective optimization model by adopting a hybrid optimization algorithm fused by a Hanniac optimization algorithm and an improved sparrow search algorithm to obtain a final output. According to the invention, efficient consumption of green energy can be realized, the electricity abandoning phenomenon is reduced, carbon emission is reduced, and the stability, economical efficiency and environmental protection property of a power system are improved.

The application discloses a two-dimensional coherent signal DOA estimation method based on double parallel relatively prime array movement, which comprises the following steps: a double parallel array comprises a first subarray and a second subarray, phase compensation is performed on the output of the first subarray and the output of the second subarray to obtain first subarray compensation output and second subarray compensation output; a first vector is obtained based on the first subarray compensation output and the second subarray compensation output, and a second vector is obtained based on the first subarray compensation output; a sparse off-grid optimization function is constructed; accurate angle estimation of an incident signal and an X axis is obtained based on the sparse off-grid optimization function; angle estimation of the incident signal and a Y axis is obtained based on the first vector and the second vector; and angle estimation value of the two-dimensional coherent signal is obtained based on the accurate angle estimation of the incident signal and the X axis and the angle estimation of the incident signal and the Y axis. The application uses the double parallel relatively prime array to perform angle estimation of the two-dimensional coherent signal, and makes up for the deficiency of the relatively prime array in angle estimation of a coherent source.

The application discloses a kind of wind light storage micro-grid optimization operation method and system.The method adopts three-layer architecture of top optimization, middle layer cooperation and bottom execution, and forms the intelligent closed loop of " prediction-planning-coordination-execution-feedback ".Top is based on LSTM prediction and traffic jam algorithm to carry out global power flow congestion identification and dredging, and utilizes improved particle swarm algorithm in feasible interval to carry out multi-objective fine optimization, and generates optimal operation instruction;Middle layer dynamically adjusts optimization target weight according to operation scene, and decomposes instruction into multiple device cooperation strategy;Bottom execution customized control algorithm, realize device accurate regulation and millisecond level fast protection.The application effectively solves the problems of micro-grid multi-objective cooperative optimization, prediction control disconnection and operation risk foresight protection, and significantly improves the voltage stability, economy, dynamic response speed and safety and reliability of system operation.

The invention discloses a micro-grid optimization scheduling method containing interface solar steam power generation and wave energy power generation. The method comprises the following steps: establishing an interface solar steam power generation device discretization model and a wave energy power generation fitting model; with electrical load, hydrogen demand and water demand as the core, electric energy storage and hydrogen storage are considered in a combined manner, and a micro-grid optimization model containing interface solar steam power generation and wave power generation is established; establishing constraint conditions of the micro-grid optimization model; performing simulation calculation in different scenes; and testing the system stability under extreme working conditions. According to the scheduling method, the dual dilemma of energy shortage and fresh water shortage in the island region can be solved, the dependence on traditional energy can be reduced, the energy transportation cost and the environmental risk are reduced, and the energy utilization efficiency and the environmental friendliness are improved.

The present application relates to the technical field of power system grid optimization control, in particular to a data-driven grid voltage and reactive power control optimization method and system, which comprises constructing a voltage and reactive power control mathematical model of a three-phase unbalanced grid, taking the minimum node voltage deviation rate, the lowest grid line loss and the optimal three-phase unbalance degree of bus voltage as the optimization goal; using Latin hypercube sampling to cover the photovoltaic-load typical scene of the whole operation condition, combining with the historical operation data of the grid to generate sample data, training the highway neural network combined with the improved NSGA-II algorithm, and obtaining the globally optimal grid voltage and reactive power control strategy through population iteration solution. The present application adopts a data-driven method, replaces the traditional physical model with a proxy model, effectively adapts the uncertainty of new energy access, shortens the single time period decision time to seconds, stabilizes the node voltage and three-phase unbalance degree within the safety limit, and at the same time realizes the minimization of the whole grid loss.

The invention provides a V2G residential area micro-grid optimization method and system considering battery aging cost, and belongs to the field of smart grid energy scheduling crossing, and the method comprises the steps: collecting micro-grid multi-source operation data of a preset power utilization area; obtaining residential area load prediction data and photovoltaic power generation power prediction data based on the multi-source operation data and a pre-constructed load and power generation prediction model; constructing a state space of a reinforcement learning environment; inputting the state space into a pre-trained deep reinforcement learning decision model, and generating a real-time optimization control instruction of the micro-grid; and executing the real-time optimization control instruction, and controlling charging and discharging behaviors in the micro-grid, including adjusting the charging and discharging power of the electric vehicle, the charging and discharging power of the energy storage system and the refrigeration power of the cooling system. According to the method, multi-objective optimization is carried out by considering the battery aging cost and utilizing deep reinforcement learning, so that the economy and the stability of microgrid operation are improved, and the service life of the battery is prolonged.

The invention discloses an island micro-grid optimization scheduling method and system considering a frequency response whole process, and belongs to the field of island micro-grid optimization scheduling, and the method comprises the steps: generating a photovoltaic output typical day scene through K-means clustering, and constructing an island micro-grid basic random optimization scheduling model; dividing the frequency response of the island micro-grid after disturbance into three parts; constructing an island microgrid disturbance power and mechanical inertia expression based on frequency response characteristics under generator N-1 off-grid disturbance; and aiming at the three parts of frequency response, linear frequency stability constraints are respectively constructed by coordinating multiple frequency modulation resources, and the linear frequency stability constraints are integrated into a basic random optimization scheduling model of the island micro-grid, so that the island micro-grid optimization scheduling method considering the whole process of frequency response is formed. According to the method, the stable frequency under the N-1 event is guaranteed, the source-load-storage response potential is mined, the operation cost and the dependence on the generator are reduced, and safe and efficient operation of the island microgrid is supported.

The application discloses a kind of based on grid fitting's brick laying pose calculation method, device and brick laying robot, the method includes: the first corner point coordinate information of multiple laid bricks of target brick laying area is determined;According to the preset brick grid optimization algorithm, and the first corner point coordinate information of multiple laid bricks, the corresponding brick grid relationship model of the target brick laying area is calculated;According to the brick grid relationship model, and the brick information of target to be laid brick, the second corner point coordinate information of the target to be laid brick is determined;According to the second corner point coordinate information of the target to be laid brick, the laying pose information of the target to be laid brick is determined;The laying pose information is used to indicate that the target to be laid brick is laid by brick laying equipment.It can be seen that the application can accurately calculate the coordinate information of to-be-laid brick according to the corner point coordinates of laid brick, to improve the accuracy and efficiency of subsequent brick laying.

The invention belongs to the technical field of micro-grid optimization, and particularly relates to a multi-objective optimization method of a micro-grid system and an energy management system. Comprising the following steps: establishing a multi-target optimization model of the micro-grid system according to the power generation cost, the equipment cost and the environmental pollution cost of each system; and according to the prediction data of the photovoltaic power generation system and the wind power generation system and the interaction data of the micro-grid system and the main grid system, solving the multi-objective optimization model through a PSO algorithm of adaptive inertia weight, and obtaining the optimal solutions of the output power of the storage battery system and the generated power of the diesel engine power generation system. According to the invention, the particle swarm optimization algorithm is introduced as a multi-objective optimization method, and the optimal solution is searched by optimizing the position and speed of particles, so that the multi-objective optimization operation of the micro-grid is realized, and the power generation cost, the operation cost and the environmental pollution cost are reduced. According to the invention, on the premise of satisfying stable operation of the micro-grid, the energy is effectively managed and optimized.

The invention relates to a configuration optimization method of a grid-connected oil field microgrid considering new energy. The method comprises the following steps: firstly, constructing a grid-connected oil field micro-grid model containing new energy; then, establishing an oil field micro-grid wind-light-storage fusion optimization scheduling objective function based on the model; then, determining a micro-grid optimization scheduling related constraint condition; and finally, carrying out optimization solution on the target function by adopting a grey wolf algorithm and combining with a micro-grid optimization scheduling related constraint condition, and determining the optimization configuration of the grid-connected oil field micro-grid. That is to say, the established grid-connected oil field micro-grid model can reflect seasonal and temporal characteristics of local natural resources, and accords with general real laws. The output complementary characteristic of photovoltaic power generation and wind power generation can be effectively utilized, and green production and development of an oil field and absorption and utilization of new energy are promoted; the power supply capability of energy storage equipment configured in the oil field micro-grid is remarkably improved, and the stability of system energy supply is enhanced.

The invention provides a power distribution network power supply grid division method and system considering resource cluster clustering, and belongs to the technical field of power systems. The method comprises the steps of inputting annual load data and electric vehicle behavior data into a photovoltaic power supply output model, an energy storage charging and discharging model and an electric vehicle charging pile model, and generating time sequence data; clustering the time sequence data by adopting an improved K-means algorithm, determining an optimal clustering number through an elbow method, and determining an output curve of a typical scene according to the optimal clustering number; dynamically adjusting load imbalance degree weights and system active power loss weights in different scenes based on an output curve of a typical scene, and constructing a power distribution network power supply grid optimization model; and solving the power distribution network power supply grid optimization model to obtain an optimal grid division scheme. According to the method, the precision of power distribution network grid planning and the adaptive capability and scientificity of typical scene extraction are improved, and the complexity of power distribution network grid planning is reduced.

The invention discloses a wind and light storage micro-grid capacity optimization configuration method and system, and relates to the technical field of new energy micro-grid energy management and optimization configuration, and the method comprises the steps: building a wind and light combined power data set through employing the historical output data of a wind power and photovoltaic system; performing power signal decomposition and power distribution based on the wind-solar combined power data set to obtain a power distribution result; establishing an energy storage capacity optimization model according to the power distribution result; and solving the energy storage capacity optimization model by adopting an improved whale optimization algorithm to obtain an optimal configuration result of the wind and light storage micro-grid. According to the method, the response speed and the energy utilization efficiency of an energy storage system can be improved under the multi-source fluctuation power condition, the system operation cost is reduced, the stability and the economical efficiency of micro-grid operation are enhanced, and a new technical approach is provided for optimal scheduling and planning design of a wind-light-storage integrated system.

The application discloses a kind of wind turbine unit optimization layout method based on three levels dynamic variation rate, comprising the following steps: step one: obtaining target wind farm information, including (1) the annual average wind resource information of target wind farm;(2) unit specification parameter;(3) wind farm planning area boundary;Step two: combined with fan wake wind speed and turbulence model, calculate annual total average power generation and evaluate full life cycle degree electric cost;Step three: the wind farm planning area is gridded, with the optimization target of flat standardization energy cost, carries out unit grid location preliminary optimization using the improved genetic optimization algorithm based on three levels dynamic variation rate, obtains several grid optimization schemes by integration theory;Step four: according to the preliminary optimization result of grid location, constructs the grid boundary of each candidate unit, carries out fine coordinate position optimization in corresponding grid using adaptive weight particle swarm optimization algorithm, and obtains the final unit layout scheme.

This invention discloses an exploration grid optimization method and system. The method includes: reading N original boundary sampling coordinates of a target mining area, constructing a mining area constraint boundary of the target mining area, obtaining the current grid spacing pairs of the mining area constraint boundary, dividing the mining area constraint boundary into grids with the current grid spacing pairs as the step size to obtain J candidate exploration grids, extracting the grid coordinates of the J candidate exploration grids, calculating the parameter variation intensity of the current grid spacing pairs based on the grid coordinates of the J candidate exploration grids, iteratively updating the current grid spacing pairs with a preset step size, and simultaneously calculating the updated parameter variation intensity. If the updated parameter variation intensity is lower than a set threshold, the updated current grid spacing pairs are determined as the optimal grid spacing pairs. This invention can truly reflect the degree of control of the current grid density on the ore body variation, avoiding over-densification or over-sparse grids caused by a lack of quantitative standards.

The invention discloses an optimized operation method of an off-grid direct-current micro-grid. The off-grid direct-current micro-grid comprises a photovoltaic power generation system, a battery energy storage system, a fuel cell and an electrolytic cell which are connected through a direct-current bus. The optimized operation method comprises the following steps: constructing a system mathematical model of an off-grid direct current micro-grid, and defining a power balance relationship between a direct current load and each energy component; the system mathematical model comprises mathematical models of a photovoltaic power generation system, a battery energy storage system, a fuel cell, an electrolytic cell and a direct current load; based on the system mathematical model, establishing a linear optimization model by taking minimization of hydrogen fuel consumption or maximization of electrolytic cell hydrogen energy production as a target; the constraint condition of the linear optimization model comprises a power balance relation; and solving the linear optimization model by adopting a linear programming algorithm so as to dynamically distribute power output of each energy component, and controlling the operation of the micro-grid based on a solving result. The method can solve the problems that in the prior art, an off-grid direct-current micro-grid is high in operation cost, and an optimization algorithm is complex in calculation and difficult to apply in real time.

The invention discloses a micro-grid multi-target interval optimization scheduling method based on reinforcement learning, and relates to the technical field of micro-grid optimization scheduling. According to the method, firstly, network loss and a branch stability coefficient are brought into an optimization target, and safe and stable operation is achieved while economical efficiency is guaranteed; an interval optimization method is adopted to process the uncertainty problem of renewable energy sources, and the problem that a probability distribution function depends on a common method, but the probability distribution function is difficult to predict accurately due to the influence of factors such as weather and the like is solved. Secondly, an independent safety evaluation network is introduced into a standard reinforcement learning algorithm, the defect that a proper penalty value is difficult to design when constraint violation behaviors are penalized in a reward function is overcome, and meanwhile, an adaptive PID (Proportion Integration Differentiation) is adopted to dynamically update a Lagrange operator lambda, so that the oscillation phenomenon is effectively inhibited. And finally, an event triggering mechanism is introduced into a security reinforcement learning framework, and actions are updated only when the constraint cost exceeds a threshold value, so that the training effect is improved, and the calculation burden is reduced.

The invention belongs to the technical field of micro-grid optimization, and particularly discloses a continuous time hierarchical aggregation and cooperative scheduling method for micro-grid active support. According to the application, hierarchical aggregation of the decentralized aggregation unit-microgrid unit-coordination layer is adopted, and the superior only uses the equivalent flexible set / active support power supply model to perform optimal scheduling, so that the information interaction and unified modeling scale are significantly reduced, and the solving efficiency and implementability of collaborative scheduling are improved; through continuous time processing, dynamic changes of variables such as power, voltage and the like in each scheduling time period are brought into model expression, so that middle border crossing and plan deviation caused by discrete constant approximation are reduced, and the flexibility and the performability of a scheduling instruction are improved; through performing consistency verification and iterative correction on the active support power supply model of the micro-grid, an effective instruction range satisfying internal operation constraints of the micro-grid is formed, a solved boundary scheduling plan can be decomposed into output instructions of distributed resources in the micro-grid, and the support operation reliability of the micro-grid is improved.

The invention provides a real-time simulation test platform and method for a cloud side-end collaborative microgrid. The real-time simulation test platform comprises a cloud platform, a real-time simulation machine and at least one edge controller, the real-time simulation machine is configured to run a real-time digital twin model of the micro-grid system and simulate real-time dynamic characteristics of the micro-grid system; the real-time simulation machine is connected with the edge controller through the multi-protocol communication interface subsystem, and the multi-protocol communication interface subsystem comprises a protocol interface model library and a protocol switching module; the cloud platform is in communication connection with the edge controller and the real-time simulation machine and is used for deploying and training a prediction model. According to the test platform, a safe, efficient and comprehensive research and development and verification environment is provided for a micro-grid optimization scheduling solution through the flexible system construction capability, the closed-loop test verification capability, the intelligent cloud edge cooperation capability and the automatic test evaluation capability, and the trial and error cost of the AI algorithm applied in a real system is reduced; and closed-loop verification of the cloud edge collaborative intelligent decision process is realized.

This invention belongs to the field of power system stability control technology and discloses a method for assessing the frequency support strength of a power grid based on inertial response and primary frequency regulation coordination. It constructs typical operating modes and a set of anticipated faults based on power grid planning data, obtains the frequency response trajectory through time-domain simulation, and extracts frequency change rate, minimum frequency, and steady-state frequency deviation characteristics. Based on power grid safety and stability guidelines, it establishes safety boundary values ​​for the frequency change rate and minimum frequency adapted to the current scenario. It constructs a comprehensive evaluation function including inertia, frequency regulation speed, and capacity strength, and introduces configurable dynamic weighting coefficients to calculate the comprehensive frequency strength evaluation index S. Based on the dual criteria of boundary non-exceeding limits and the comprehensive frequency strength index S≥1, it determines system safety and identifies weak links in scenarios that do not meet the requirements. This invention can effectively quantify the frequency support capability of the power grid under different planning schemes, providing a basis for grid optimization and resource allocation in high-proportion renewable energy power grids.

The invention relates to the technical field of micro-grid scheduling, in particular to a bee foraging algorithm micro-grid optimal scheduling method considering energy storage constraint, which constructs a micro-grid scheduling model with the goal of minimum comprehensive operation cost, and covers distributed power generation cost, energy storage loss, power grid interaction and power shortage penalty. Energy storage charging and discharging power, capacity and SOC dynamic constraints are strictly embedded, an energy storage constraint adaptation factor alpha is introduced, a bee foraging algorithm is improved, and a mapping relation between a honey source and an energy storage state is established; the fitness value is subjected to weighted calculation by the objective function and the constraint satisfaction degree; the neighborhood search step length of employed bees is dynamically adjusted along with alpha, and reconnaissance bees generate a new nectar source based on a constraint boundary; by combining a dynamic crossover mutation operator, local optimum is effectively avoided, an optimal scheduling scheme including photovoltaic, wind power and diesel engine output and an energy storage charging and discharging strategy is finally output, and the method is suitable for intelligent scheduling of a high-proportion renewable energy microgrid.

The application provides a micro-grid light storage capacity optimization configuration method and system, relates to the micro-grid optimization technical field, and the method comprises the following steps: setting segmented capacity configuration parameters according to the load characteristic parameters and energy access conditions of a micro-grid; setting a first compensation correction value and a second compensation correction value based on the impedance matching characteristics of the micro-grid and the charging and discharging efficiency parameters of the energy storage unit; combining the capacity optimization configuration parameters obtained by optimization and inversely analyzing the segmented capacity configuration parameters; collecting real-time monitoring data in real time through an industrial Ethernet, and dynamically refreshing and rolling optimizing the capacity optimization configuration parameter combination according to the preset power balance threshold and the actual operation deviation of the light storage collaborative configuration execution scheme. The application solves the technical problem that the capacity redundancy or deficiency is caused due to the static fixation of the micro-grid light storage capacity configuration in the prior art, and improves the configuration precision of the micro-grid light storage capacity.

A system and method for dynamic energy grid management with centralized intelligence and distributed edge execution is disclosed. An energy grid optimization computer continuously receives operational data from edge units distributed throughout an energy distribution network. The computer monitors the data to detect trigger events such as renewable generation fluctuations or predicted grid congestion, performs system-wide analysis to identify affected edge units, calculates updated parameters accounting for variable fault current contributions from renewable sources, generates lightweight configuration packages, and deploys them to edge units through secure communication networks. Edge units receive configurations, perform validation, execute atomic rolling updates without interrupting operations, continuously sample measurements, autonomously execute protection and control algorithms, coordinate actions using GPS / PTP synchronized timing, and stream filtered operational data back to the central platform. The continuous configuration update methodology enables dynamic adaptation to variable renewable generation and changing grid conditions while maintaining millisecond-level autonomous protection response.

The application discloses a kind of microgrid optimization scheduling method and system of distributed energy, belong to electric energy distribution and control technical field, method includes: constructing and physical grid synchronous update dynamic mirror model and extracting safe operation boundary;Risk early warning signal containing risk type and predicted occurrence time is generated by parallel deduction;Call distributed conditional reflex rule library to generate preventive control instruction set;Distributed subject strategy model is driven in virtual simulation environment and collaborative evolution is generated operation plan;The data of electric carbon flow bound is recorded using distributed account book synchronization;Based on execution effect, update adaptability score and feedback optimization rule base.The application adopts the scheduling mechanism that physical mirror deduction and biological heuristic conditional reflex are combined, can realize the advance perception of microgrid risk and millisecond level self-healing control, improve the physical stability, economic benefit and carbon emission tracking transparency of microgrid in extreme fluctuation scenario.

This invention discloses a method and system for optimizing the scheduling of mobile energy storage and charging systems within an operational grid, relating to the field of energy internet scheduling technology. The method includes: dividing the target operational area into a target operational grid set and monitoring multi-source information in real time; performing predictive analysis to obtain the target load curve and carbon emission analysis to obtain the target carbon emission curve; performing scheduling optimization analysis on the target grid to form a target scheduling decision; and executing operational scheduling for the target mobile energy storage and charging devices within the target grid based on the target scheduling decision. This invention solves the technical problems of extensive energy storage scheduling and insufficient precision in regional load control in existing technologies, leading to difficulties in coordinating low-carbon development and scheduling optimization. It achieves refined scheduling and low-carbon coordinated management of the target operational area grid, improving the scheduling adaptability and load balancing efficiency of mobile energy storage and charging systems.

The application provides a multi-set meal game pricing method for coordinated operation of micro-grid alliances and shared energy storage, and belongs to the technical field of power energy storage. Firstly, a top shared energy storage operator model considering constraints such as power interaction with micro-grids and distribution networks is established with the maximum operation income of the shared energy storage system of the shared energy storage operator as a target. Secondly, a lower micro-grid optimization operation model considering constraints such as power balance is established with the minimum operation cost of each micro-grid alliance as a target. Then, based on the above model, a master-slave game basic framework and model are established with the shared energy storage operator as a leader and multiple wind farms as followers according to the actual shared energy storage system pricing field. Finally, the above model is solved to obtain a pricing scheme. The application can propose different energy storage set meals according to the power consumption characteristics of different micro-grid alliances, thereby effectively improving the economy of the operation of the micro-grid alliance, being also beneficial to improving the energy storage utilization rate and realizing mutual benefits of both parties.

This invention discloses a wind power forecast-driven grid energy management method, system, and equipment, belonging to the field of grid optimization and dispatching technology. The method includes: acquiring power forecast distribution sequences and confidence interval data of wind farms over multiple forecast periods; constructing an uncertain energy evolution tensor containing temporal gradients, spatial diffusion, and probabilistic discrete components; mapping this tensor to the grid node topology to generate a risk seepage topology map; identifying key channels and nodes and constructing a risk seepage channel set; subsequently calculating a partitioned elasticity threshold to generate an elastic risk envelope region; and finally, combining stored grid energy to perform hierarchical energy regulation and allocation, establishing a grid energy management strategy. This invention solves the technical problem that the intermittency and uncertainty of wind power easily lead to energy dispatching risks in distribution circuit systems, achieving accurate risk identification and dynamic control within the grid, and combining grid energy storage to complete differentiated energy regulation and allocation.

The invention discloses a source network load hydrogen storage rural micro-grid optimal configuration method, which comprises the following steps: S1, basic data acquisition and scene definition: determining core data required by optimal configuration, and ensuring that a model fits rural reality; s2, source end optimization configuration: focusing'space-time complementation + capacity matching '; s3, load end optimization management: enabling the load to adapt to the output of the source end, and improving the absorption capability; s4, optimal configuration of a storage end: constructing an electrochemical energy storage and hydrogen energy storage collaborative system, and covering the requirements of short-term fluctuation stabilization and long-term energy storage; s5, interactive optimization with the public power grid: defining the interactive logic of'complementation but not dependence ', and reducing the impact on the power grid and the power purchase cost; s6, mathematical modeling and algorithm solving: landing an optimization target through a refined model, and outputting an optimal configuration scheme; the system has the advantages of high new energy consumption rate, high power supply reliability, good overall stability of the power grid, more reasonable energy structure and high overall utilization efficiency of energy.