3109results about "Power oscillations reduction/prevention" patented technology

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

The invention relates to the technical field of energy storage systems, in particular to a source network load storage coordination control method for multi-configuration network type energy storage coordination operation. According to the technical scheme, the source-network-load-storage coordinated control method for multi-construction-network-type energy storage coordinated operation comprises the following steps: generating a new energy output and load change trend through a dynamic prediction model based on historical power data, real-time load requirements and meteorological information; optimization is carried out to obtain a power generation plan containing a clean energy consumption target and economical efficiency constraints; decomposing the power generation plan into a photovoltaic power adjusting instruction, an energy storage charging and discharging instruction and a load control instruction according to the power grid safety priority, wherein the emergency control instruction is executed prior to the economic dispatching instruction; and controlling parallel operation of a plurality of pieces of networking type energy storage equipment, and realizing multi-machine power distribution and circulating current suppression through a virtual synchronous control technology. And through layered optimization scheduling and multi-machine cooperative control, the new energy consumption capability is remarkably improved, and the phenomenon of abandoning light and wind is reduced.

The invention discloses a V2G-based dynamic discharge power optimization control method, and belongs to the field of smart power grid and electric vehicle cooperative control, and the method comprises the following steps: collecting the battery state information of an electric vehicle and the load demand data of a power grid in real time; determining a demand response priority through a multi-objective optimization algorithm based on the battery state information of the electric vehicle and the load demand data of the power grid; calculating a power distribution strategy scheme based on the response priority; when the power distribution strategy scheme does not meet the battery health constraint, a safe discharge power range is adjusted based on a battery state evaluation result; generating a correction parameter based on the adjusted discharge power range and the environment variable; and monitoring the operation state of bidirectional energy flow in real time, and when the operation state of bidirectional energy flow does not achieve the response effect, adaptively adjusting the power distribution strategy based on the correction parameter and updating the execution parameter.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention belongs to the field of data processing, and particularly relates to a dynamic cooperative control system and method for a gas turbine and a micro-grid, and the method comprises the steps: constructing a micro-grid real-time monitoring module, continuously collecting distributed energy real-time output, controllable load demands, bus voltage frequency and equipment state parameters, and carrying out the filtering and noise reduction through a preprocessing unit, thereby guaranteeing the data precision; calculating a real-time power difference value based on the preprocessed data, calling an adaptive neural fuzzy inference system, taking the power difference value, the bus voltage deviation and the frequency deviation as input, and judging whether the power difference value, the bus voltage deviation and the frequency deviation exceed a preset threshold value by means of a fuzzy rule base and a neural network model; if the threshold values are not exceeded, the current states of the gas turbine and the energy storage system are maintained; if any one exceeds the threshold value, a dynamic cooperative control instruction is triggered, precise cooperative control of the gas turbine and the micro-grid is achieved, and the operation stability, the operation efficiency and the reliability of the micro-grid are improved.

The invention discloses a virtual synchronous generator grid-connected current harmonic suppression method and system, and relates to the technical field of power systems, and the method comprises the steps: S1, carrying out the digital sampling of intermittent high-frequency disturbance through a wind power plant grid-connected current signal in a power grid through an analog-to-digital converter, and obtaining an original digital current sequence; s2, according to the original digital current sequence, analyzing a wind load frequency component by adopting a fast Fourier transform algorithm, and determining amplitude and phase characteristics of a high-frequency harmonic component; s3, if the amplitude of the high-frequency harmonic component exceeds a preset threshold value, extracting an intermittent disturbance harmonic sequence from the frequency component to obtain a to-be-suppressed harmonic subset; according to the virtual synchronous generator grid-connected current harmonic suppression method and system, the stability of a remote wind power plant power grid is remarkably improved, continuous monitoring and real-time suppression of intermittent disturbance are achieved, and high efficiency and reliability of power grid operation are guaranteed.

The invention discloses a game reinforcement learning-based reactive power dispatching method for an electric vehicle to participate in a power distribution network, and the method comprises the steps: collecting node voltage, line current, electric vehicle charging and discharging states and topological information, and constructing graph attention embedding; generating an initial reactive power regulation action in the multi-agent game reinforcement learning framework, and shaping and updating a strategy gradient through double rewards; the improved particle swarm is initialized according to the updating strategy gradient, and a self-adaptive inertia coefficient is set; iteratively searching according to the node voltage sensitivity, the line reactive margin and the expected convergence step number to obtain an optimized particle swarm; the particle speed is mapped to a main strategy network through online cooperative training, the inertia coefficient is synchronously adjusted, a cooperative optimization strategy is output, a reactive power dispatching instruction is generated, and the algorithm is updated in a closed-loop mode according to real-time feedback. According to the invention, rapid and cooperative reactive dynamic scheduling of an electric vehicle group is realized, and the voltage stability and the electric energy quality are remarkably improved.

The invention discloses a fault ride-through hierarchical control method for a network-constructed converter, and the method comprises the steps: introducing a virtual resistor formed by fault current negative feedback into the voltage and current inner loop control of the network-constructed converter based on VSG control through a current inner loop dynamic amplitude limiting control layer after the converter enters a fault ride-through state, the size of the virtual resistor is adjusted in real time along with the amplitude of the fault current so as to carry out dynamic current limiting control on the virtual resistor; and the power outer loop dynamic compensation control layer dynamically adjusts an active power instruction value and a reactive power instruction value of the network construction type converter based on VSG control during the fault ride-through period according to the voltage drop degree of the power grid after the converter enters the fault ride-through state. And respectively performing active-frequency control and reactive-voltage control based on the active power instruction value and the reactive power instruction value. In addition, the method also comprises a state coupling and parameter self-adaption module. According to the method, the GFM converter can have better dynamic performance in the fault recovery stage.

Disclosed are a second-level optimal control method and apparatus for joint frequency regulation of heterogeneous load resources, and an electronic device, a storage medium, a computer program product and a computer program. The method comprises: establishing a standardized operation model for heterogeneous load resources; on the basis of parameters of the standardized operation model for the resources and historical frequency regulation signal data of a frequency regulation market, predicting frequency regulation signal distribution and price distribution over a future time period; solving an optimal operation model involving the joint participation of multiple heterogeneous resources in energy and frequency regulation markets, in order to determine a baseline energy output and a frequency regulation capacity; constructing an optimal decomposition problem of load resources and performing variable substitution, and calculating the marginal cost and adjustable range of power adjustment for each resource; and in response to receiving a frequency regulation signal sent by a power grid, starting dispatching from the resource having the lowest marginal regulation cost, and updating the power of each load-side resource until power adjustment requirements are met.

Disclosed in the present invention are a method and system for full-range participation of energy storage charging and discharging in power grid frequency regulation. The method comprises: collecting operating power and frequency data of a power grid, so as to construct a full-range droop coefficient; setting constraints for an upper limit value and a lower limit value of a frequency dead band; obtaining an actual output power instruction value by means of a constraint function constructed on the basis of a theoretical power instruction value and a charging / generated power rated value, and controlling the power grid to operate on the basis of the actual output power instruction value; setting a hysteresis range on a power instruction of an energy storage system, and when a power demand of the power grid exceeds an upper limit threshold value of the hysteresis range, switching the energy storage system from a charging mode to a discharging mode; and when the power demand of the power grid is lower than a lower limit threshold value of the hysteresis range, switching the energy storage system from the discharging mode to the charging mode. In the present invention, when the frequency of the power grid is increased, generated power is reduced until power is absorbed from the power grid, and when the frequency of the power grid is reduced, the absorption of power from the power grid is reduced until electrical power is delivered to the power grid, thus achieving full-range participation of energy storage in power grid frequency regulation control, and satisfying regulation requirements of power grids.

The invention discloses an online prediction method for a transient frequency track of a power grid under coexistence of wind power low voltage ride through and off-grid, and belongs to the technical field of operation and control of a power system. A multi-source heterogeneous data fusion monitoring system is constructed, power grid and wind turbine generator data are collected, faults are recognized through an improved algorithm, and feature vectors are output; building an energy flow model based on a fault result, calculating a trajectory divergence index by using technologies such as phase-space reconstruction, estimating power vacancy, and obtaining a power unbalance sequence; designing a prediction algorithm by using the sequence, predicting a frequency trajectory in combination with an improved K-nearest neighbor algorithm and a trajectory feature library, and introducing a confidence coefficient to evaluate a correction error; and finally, establishing a three-level control response system, and implementing multi-time scale cooperative control according to a prediction result. The method can accurately predict the frequency trajectory, effectively deal with the wind power fault, improve the stability of the power grid and the wind power consumption capability, and provide powerful guarantee for the safe and stable operation of the power grid.

The invention discloses a virtual power plant intelligent aggregation optimization control method for multi-type flexible resources, and the method comprises the steps: constructing a dynamic characteristic model of distributed resources, wherein the dynamic characteristic model comprises a photovoltaic output probability prediction model, an energy storage SOC-life coupling model, an electric vehicle behavior chain model, an adjustable load constraint model, and an industrial interruptible load model; an edge agent node calculates an adjustable potential interval of a resource cluster in real time and uploads the adjustable potential interval to a cloud end, a global optimization target is solved on the cloud end based on an improved sparrow search algorithm (ISSA), after a scheduling instruction is generated, model parameters are corrected in a rolling mode according to actual output deviation calculated in real time, and a scheduling result is obtained. And triggering a resource fault emergency strategy for prediction deviation and resource fault problems occurring in the operation process of the virtual power plant. Through an edge-cloud collaborative architecture and a multi-stage optimization strategy, accurate modeling, optimization aggregation and intelligent scheduling of distributed resources are realized.

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

The invention relates to the technical field of smart power grids, and discloses a source-grid load-storage cooperation method, which comprises the following steps: carrying out data fusion on real-time acquired source-side distributed power supply output fluctuation data, grid-side topology constraint parameters, load-side time-sharing load demand data and storage-side charge state data to obtain a multi-dimensional system state set; performing short-medium-long term multi-scale coupling prediction on the multi-dimensional system state set to obtain a source load dynamic balance prediction curve; constructing a multi-target collaborative optimization function according to the power fluctuation interval, and generating a target adjustment instruction set based on the multi-target collaborative optimization function; updating the target running state based on the adjustment instruction set; based on the deviation degree of the updated operation state and the prediction state, correcting a weight parameter of the multi-target collaborative optimization function, and based on the corrected multi-target collaborative optimization function, performing source-network-load-storage collaborative regulation and control on the target power platform; according to the invention, the efficiency of source-network load-storage collaboration can be improved.

The invention discloses a multi-scene cooperative control method, device and equipment for a network construction type energy storage converter, and a storage medium, relates to the technical field of converter control, and discloses the multi-scene cooperative control method for the network construction type energy storage converter, and the method comprises the steps: obtaining the state data of a power grid, determining grid-connected point power, power grid frequency, power grid voltage and power grid harmonic frequency spectrum according to the power grid state data; establishing a multi-objective optimization function, an energy storage system charge state constraint and a converter output current constraint based on the grid-connected point power, the power grid frequency, the power grid voltage and the power grid harmonic spectrum; and performing multi-objective optimization solution based on the multi-objective optimization function, the charge state constraint of the energy storage system and the output current constraint of the converter to obtain a fundamental current reference value and a harmonic current limit value so as to control the output current of the grid-forming type energy storage converter. According to the scheme of the invention, the capacity conflict between network construction type control and harmonic suppression can be solved.

The invention discloses a branch topological structure automatic switching control method and system based on load balancing, and relates to the technical field of automatic switching control, and the method comprises the following steps: obtaining working condition data, and constructing a joint map; extracting a power conduction path from the trunk node to each load node based on the joint map to form a high-load segment set; aiming at the high-load transmission segment, constructing a multi-target switching feasibility set, and generating a coupling relation mapping graph; based on the coupling relation mapping map, constructing a path search model, and solving an optimal topology switching path by adopting an enhanced iterative algorithm; and generating a topology switching sequence according to the optimal topology switching path, and implementing power transfer regulation and control on a high-load transmission section. According to the method, the joint atlas is constructed, the optimal topology switching path is generated, local overload is effectively relieved, power fluctuation is weakened, and the overall load balancing level and the active regulation and control capability of the system are improved.

The invention provides a split type direct current charging multi-split group charging and group control control system, and belongs to the technical field of split type direct current charging piles. The split type direct current charging system comprising a main control cabinet, a power distribution unit, a charging pile terminal and other hardware architectures is constructed; a voltage monitoring unit, a current sensor module, a harmonic filter and other multi-dimensional sensor networks are integrated to acquire power grid state data in real time, and a sliding time window method is used to carry out segmented analysis on the data to extract power grid characteristic parameters; establishing a multi-objective optimization model comprising a power balance equation and a power grid stability equation to calculate an optimal power distribution matrix, adopting a neural network to realize intelligent distribution and dynamic balance adjustment of the power of each charging pile terminal, and starting an emergency power redistribution mechanism to ensure stable operation of the system when the power grid is detected to be abnormal. The technical problem of unbalanced power distribution of the multi-pile charging system caused by power grid voltage fluctuation and harmonic pollution is solved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention relates to the field of power systems, in particular to a multi-energy complementary power supply network cooperative control method and system. The method comprises the following steps: collecting dynamic characteristic data of wind power, photovoltaic power, energy storage and load through a sensor group, and generating a multi-energy state characteristic matrix with a timestamp; and matching the feature matrix with a dynamic response feature library, and outputting a dynamic feature parameter set. Based on the set, a multi-objective optimization model is constructed, second-level frequency deviation suppression, minute-level tie line power stabilization and hour-level economic dispatching are covered, and a cooperative control parameter set is generated. And generating a thermal power generating unit compensation amount, an energy storage charging and discharging sequence and an interruptible load priority list. After execution, power grid deviation data are collected, a dynamic deviation value is calculated, and when a threshold value is exceeded, feature library parameters are corrected and loop optimization is carried out. According to the invention, the problem of insufficient cooperative control of the multi-energy system in the prior art is solved, and the stability and economy of the power grid are improved.

The invention relates to the field of optical storage, and particularly discloses an optical storage system cooperative control method and device, a terminal and a medium, and the method comprises the steps: collecting multi-dimensional data in real time, including photovoltaic array data, user side load data, power grid side information data, meteorological data and weather forecast information; generating a photovoltaic power generation power prediction curve, a user load demand prediction curve and a power grid electricity price prediction curve by using the multi-dimensional data through a machine learning model; and taking the current state of the optical storage system and each prediction curve as input parameters, carrying out optimization problem solving based on a multi-objective optimization function and constraint conditions through a model prediction control algorithm, generating an optimal control sequence in a period of time in the future, and controlling corresponding equipment through the optimal control sequence. According to the method, the response speed to uncertainties such as illumination abrupt change and load fluctuation is increased, source-storage-load-network coordination is realized, the sub-optimal problem of independent control of each unit is avoided, the operation cost is reduced, and renewable energy consumption is improved.

The invention relates to the technical field of distributed energy storage and intelligent power grid control, in particular to an intelligent distributed energy storage cluster collaborative management system. Comprising a state acquisition module used for acquiring local power grid state parameters of an edge computing node in real time; the power grid state parameters comprise power grid frequency, power grid voltage and a heartbeat communication state with the cloud management platform; the risk calculation module is used for calculating quantified island risk indexes; the mode judgment module is used for determining a current operation mode decision; the mode switching module is used for switching the system from a vertical cooperative mode to a horizontal autonomous mode when the island risk index is greater than an island switching threshold value; and when the island risk index is smaller than or equal to the island switching threshold value, the system is maintained to operate in the vertical cooperation mode. According to the system, misjudgment and missed judgment of a traditional single threshold value mode are overcome, it is ensured that the mode switching module can be switched to the horizontal autonomous mode from the vertical cooperative mode in time under the real risk, and decision making is accurate and reliable.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention relates to the technical field of power grids, and discloses a port green power virtual energy storage aggregation regulation and control method, which comprises the following steps: acquiring an aggregation scene and resource data; generating a power hard constraint mapping and pile division execution reference by calculating a multi-band energy index and identifying a system power transfer characteristic; a local rapid control configuration set is generated by solving a virtual impedance feasible region and combining power hard constraint allocation compensation budget; and fusing a sub-pile execution reference (slow channel) generated based on the power hard constraint with a fast channel execution track obtained after local online execution compensation, performing priority arbitration under the constraint of the power hard constraint, and outputting a final execution power track. According to the invention, a double-layer regulation and control framework of slow channel planning and fast channel compensation is constructed, and through a core link of power hard constraint mapping, coordinated operation of fast and slow channels and safety and stability of the whole system are ensured, and fast and reliable dynamic support capability can be provided for a power grid.

The invention provides a shaft power generation and energy storage hybrid power efficiency optimization system for a container ship under multiple working conditions, which is applied to the field of ship energy management, and comprises a working condition strategy module, a fluctuation suppression module and a power distribution module, the working condition strategy module is electrically connected with ship radar navigation equipment and a ship identification system receiver, and the fluctuation suppression module is electrically connected with the ship identification system receiver. The power distribution module is electrically connected with a ship power grid load detector, a main engine rotating speed sensor and an energy storage charge state sensor; according to the invention, by cooperatively regulating and controlling the output power of the main engine shaft power generation equipment, the auxiliary power generation equipment and the composite energy storage equipment, a high-efficiency power calling mechanism for a ship power grid is constructed, so that under the complex ship working condition information, the safety redundancy and reliability of power supply are improved, the utilization efficiency of electric energy is improved, and the energy consumption is reduced. And the fuel consumption is reduced, so that the comprehensive target of energy conservation and emission reduction is achieved.

The invention relates to the technical field of power generation, power transformation or power distribution, and discloses a control method for preventing overload of an energy storage system transformer, which comprises the following steps: monitoring the three-phase total power and the power of each phase of a power grid side ammeter in real time, and calculating the three-phase unbalance degree based on the three-phase total power when the three-phase unbalance phenomenon occurs; when the three-phase unbalance degree is greater than a preset unbalance degree threshold value, dynamically adjusting the charging power of the energy storage converter according to a starting threshold value, a charging limiting threshold value and a charging forbidding threshold value by adopting a multi-threshold value dynamic adjustment mechanism; the phase with the maximum deviation between the power of each phase and the average power of the three phases is calculated as an overload phase, load reduction is performed on the overload phase based on a load increasing and decreasing strategy, load increasing is performed on a non-overload phase, and the adjusted three-phase power is obtained; a power distribution instruction is calculated in real time in combination with a dynamic response mechanism, and the three-phase unbalance degree does not exceed a safety threshold value in cooperation with total power control. The problems of easy overload, three-phase imbalance and slow response of a transformer in the prior art are solved, and the purposes of accurate load control, high system stability and high response speed are achieved.

The invention provides a fault ride-through control method and a fault ride-through control system for a grid-forming converter, which are characterized in that the current of the grid-forming converter under an asymmetric fault is controlled by adopting a negative-sequence current reference generation strategy aiming at an inner ring of a controller, and negative-sequence reactive current is injected into a power grid; adopting a partial sequence current amplitude limiting strategy to control the maximum phase current amplitude of the injected asymmetric current not to exceed an amplitude limiting value; controlling the boundary of an active reference value by adopting an active reference value amplitude limiting strategy for an active synchronization ring; a synchronous damping link is adopted to enhance damping in power angle swing; for a reactive voltage loop, a reactive voltage variable coefficient control strategy is adopted to control the output of the maximum reactive current; and an internal potential locking strategy is adopted to suppress overvoltage. According to the invention, negative-sequence reactive power is provided, and overcurrent of each phase is avoided; the power angle characteristic is improved, and the transient stability is improved; stable transient voltage support is provided in a current output capability range; and after the fault is recovered, the voltage of the converter grid-connected point is smoothly recovered.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

The invention discloses a micro-grid dispatching optimization method and system based on multi-modal information, and relates to the technical field of micro-grid dispatching optimization systems. The method comprises the following steps: performing space-time alignment on power data, energy storage charge state data and power grid operation data to generate a space-time associated multi-modal state matrix; solving the multi-target dynamic optimization model according to the multi-modal state matrix to obtain a hierarchical control instruction and an energy storage charging and discharging plan; decomposing the hierarchical control instruction to obtain and execute a substation-level global optimization task, a feeder-level power mutual aid task and a court-level in-situ balance task of a prediction time domain; correcting the energy storage charging and discharging plan in a short period, or updating the load priority list in a long period to obtain an execution result; and updating the data acquisition rule according to the execution result, and restarting data acquisition according to the updated data acquisition rule. According to the invention, the operation performance of the micro-grid can be improved.

The invention relates to the technical field of intelligent control of a power system, in particular to a photovoltaic power generation-energy storage-power grid intelligent scheduling method and system based on multi-mode energy management, and the method comprises the steps: 1, collecting and fusing multi-source operation data in real time; 2, establishing a multi-modal feature fusion model; 3, according to the photovoltaic fluctuation amplitude and the frequency deviation component, the energy storage adjusting capacity is formed; 4, constructing a differential control strategy for each mode, and achieving the dynamic matching of the virtual inertia and the power grid demand in the power grid supporting mode; and step 5, executing multi-time scale rolling optimization control, triggering strategy reconstruction according to mode switching, and maintaining power continuity among different modes through a transition process. According to different modal requirements, differentiated control strategies are adopted, so that the service life loss of the energy storage system can be reduced and the service cycle of the energy storage equipment can be prolonged while the energy storage system is ensured to quickly respond to the power grid requirement.