2978results about "Ac networks with different sources same frequency" patented technology

The invention discloses a virtual power plant capacity configuration and regulation operation optimization method, which comprises the following steps: constructing an aggregation model and physically consistent digital twinning, establishing a linearized power distribution network model containing voltage and power flow constraints, and depicting resource efficiency and time delay characteristics; forming a time-varying uncertainty set through quantile calibration and set drift constraint; establishing a capacity-operation joint double-layer optimization model, realizing capacity configuration under the constraint of the whole life cycle cost, and obtaining a rolling scheduling strategy through distributed robust optimization; before issuing, control shielding and formalized constraint are adopted to ensure the safety of the power grid; a multi-variety collaborative quotation is generated on the market side through opportunity constraint and risk measurement; hierarchical adaptive re-optimization is realized based on a trigger criterion, and a twin model is continuously calibrated by using a hardware-in-the-loop experiment; model updating is realized by adopting federated learning and differential privacy; executing degradation control under an abnormal condition, and performing smooth rollback after recovery; and finally, the capacity and operation parameters are evaluated and corrected through performance and service life linkage.

The invention provides a large-scale ultrafast charging facility vehicle network interaction-oriented adaptive regulation and control system, and belongs to the technical field of charging pile load scheduling. During working, a multi-source data acquisition and vehicle network state sensing module acquires and fuses multiple types of data, and a vehicle network interaction strategy optimization module generates a strategy according to the data; the power dynamic distribution module distributes power according to a strategy, and the energy storage coordinated control module optimizes energy storage charging and discharging. The real-time response and feedback module quickly responds to a power grid instruction and corrects a strategy, and the security and protocol adaptation module guarantees security and docking. During use, after the terminal is started, fusion data is automatically collected, a regulation and control strategy is generated and executed, instructions are responded and corrected in real time, and safety and cross-platform adaptation are guaranteed in the whole process.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

The invention discloses a multi-energy-storage thermal power generating unit load prediction and optimal scheduling method and system, and relates to the technical field of multi-energy-storage thermal power generating units, and the method comprises the steps: collecting the operation parameters and external environment parameters of a thermal power generating unit in real time, and constructing a real-time state parameter matrix; constructing a load prediction model based on the historical state parameter matrix, importing the real-time state parameter matrix into the load prediction model, outputting a load trend prediction curve, and triggering an early warning signal through a secondary discrimination mechanism; identifying a load disturbance value based on the load trend prediction curve, obtaining a load disturbance sequence, and decoupling the load disturbance sequence into a plurality of components; inputting the plurality of vectors into a preset decision network, dynamically correcting a constraint condition built in the decision network in combination with the early warning signal, introducing an improved dragonfly algorithm for optimization iteration, and generating an optimization scheduling instruction; according to the method, the adaptability of optimal scheduling and high-precision prediction of the load trend are improved.

The invention discloses a variable frequency driving device energy recovery control system based on direct power supply of a high-voltage power grid, and relates to the technical field of electrical transmission and energy recovery control. Updating the power grid impedance model in real time by using a complex impedance calculation method; load operation state parameters are dynamically adjusted in combination with motor end output power, rotating speed change and torque calculation; according to the method, regenerative energy is accurately calculated, a feedback path is adjusted according to voltage fluctuation and frequency deviation of a power grid, a feedback current fluctuation ratio and a power factor are monitored, a feedback path conduction proportion is optimized in real time, when the current is unstable or the power factor is abnormal, a suboptimal path is automatically switched, and continuous and stable operation of a system is guaranteed; according to the invention, the energy feedback efficiency and the power grid compatibility of the high-voltage power grid variable-frequency driving device are effectively improved, and the method is suitable for energy feedback application of a high-power motor driving system.

The invention relates to the technical field of power management, in particular to a multi-source data fusion photovoltaic power scheduling control system, which comprises a sensing extraction module, a disturbance identification module, an offset trend analysis module, a path switching module and a frequency band suppression module. According to the method, a disturbance pre-judgment interval is identified by constructing a time derivative sequence and combining illumination and wind speed trend change in a continuous period, a disturbance time period is accurately discriminated, the disturbance interval is confirmed and disturbance characteristics are extracted through conjoint analysis of photovoltaic and energy storage current change trend and grid-connected point fluctuation amplitude, and the disturbance accuracy is improved. An offset trend curve is formed by calculating photovoltaic and energy storage current change differences, the energy supply offset degree is judged in real time to trigger scheduling conditions, dynamic suppression adjustment is performed on regulation and control signal parameters according to spectrum energy distribution, the accuracy and timeliness of disturbance recognition, energy supply offset judgment and frequency band regulation and control are improved from multiple levels, and the energy storage and energy supply efficiency is improved. And more stable and efficient optical storage combined scheduling control is realized.

The invention relates to the technical field of power systems, and discloses a management and control system for effectively managing and controlling distributed power supply loads based on power grid automation equipment. The system comprises an acquisition module for acquiring dynamic load parameters of a distributed power supply and topological information of a target power grid; the division module extracts load sudden change characteristics and divides the power grid into coordinated regulation and control partitions in combination with topological information; the injection module injects a preset disturbance signal to a time delay sensitive area node in the partition to obtain voltage offset distribution; the positioning module is used for positioning electrical weak nodes according to the oscillation attenuation rate of the electrical nodes and topological connection distribution; a verification module performs gridding ratio verification on the weak nodes and the coordination regulation and control partition to obtain a dynamic boundary constraint condition; an output module reconstructs a distributed power supply power output priority instruction based on the condition. According to the invention, the problems of lagging response and insufficient precision of power grid management and control under large-scale access of the distributed power supply are solved.

The invention discloses a power distribution-micro cooperative fault recovery method considering network dynamic reconstruction, belongs to the technical field of power distribution networks, and completes power distribution-micro cooperative fault recovery through a power distribution-micro cooperative fault recovery model. The method has the following beneficial effects: the optimization model taking the electricity purchase cost, the voltage deviation, the network loss, the load reduction and the microgrid operation cost as the target is constructed, and the power flow constraint, the safe operation constraint and the network reconstruction constraint of the power distribution network are comprehensively considered. For privacy protection requirements of distributed energy in the micro-grid, a k-order approximation method is adopted to establish a micro-grid operation feasible region model, and operation characteristics of the micro-grid are represented through power and energy boundary constraints. In order to further realize distribution-microgrid collaborative optimization, a non-iterative solution method based on equivalent projection is provided, an original problem is decomposed into distribution network layer optimization and microgrid layer sub-problems, internal variables are eliminated through an equivalent projection technology, and collaborative optimization is realized on the premise of protecting microgrid privacy.

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

The invention provides a power grid operation analysis method based on a cloud platform, and relates to the technical field of power grid operation analysis, and the method comprises the steps: configuring a semantic event template library, configuring multi-source power grid operation data access and time reference synchronization, configuring an event triggering rule and an event judgment mechanism, and extracting an event fragment data set, and configuring a semantic abstract vector, executing semantic compression, generating a unique analysis task number, registering a task metadata directory, and setting a task priority and priority queue arrangement. By configuring the semantic event template library and realizing the access and time reference synchronization of the multi-source power grid operation data, the unity and timeliness of the event fragment data set can be ensured in a mass data environment, the problem of analysis errors caused by non-uniform data formats and non-uniform time sequences in the prior art is solved, and the analysis accuracy is improved. Therefore, the reliability of power grid operation state identification and analysis results is improved.

Embodiments of the present application provide a flexible resource partitioning method and system based on power flow distribution. The method comprises: using a flexible resource aggregation model partitioning method to perform topological aggregation on flexible resources to obtain aggregation units; using the aggregation units as the basic unit for aggregating the adjustable capacity of the flexible resources, using an adaptive robust optimization algorithm to construct an aggregated resource adjustable capacity calculation model considering network constraints, and performing aggregation calculation on a power adjustment range that can be provided by all the flexible resources within an aggregation area for power dispatch; and solving the aggregated resource adjustable capacity calculation model considering the network constraints, so that feasible upper and lower power bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed among the flexible resources, thereby implementing flexible resource partition aggregation in continuous time periods.

The invention relates to the technical field of power system dispatching automation control, and discloses an AGC load distribution control method and system based on a multi-region greedy hydropower station, and the method comprises the steps: generating dynamic boundary thresholds of a high-efficiency region, a medium-efficiency region and a low-efficiency region based on a real-time performance curve and a dispatching set value of a hydroelectric generating set; judging whether to start a cross-interval distribution strategy or not by utilizing the generated dynamic boundary threshold value; when the number of the units participating in distribution is lower than a set scale, an enumeration algorithm is automatically switched to carry out global optimization, and generation of an optimal solution under a small-scale unit combination is ensured; calculating a deviation value between the actual total load and a target value based on the obtained cross-interval distribution strategy; and preferentially adjusting the output of the high-efficiency area unit through a dynamic compensation mechanism. The system comprises an operation interval dynamic calibration module, a region selection and load pre-distribution module and a deviation compensation and adaptive adjustment module. The power grid frequency adjusting precision and the overall operation efficiency of the hydroelectric system are remarkably improved.

The invention relates to the technical field of power distribution network bidirectional power flow coordination control, and discloses a power distribution network bidirectional power flow coordination control method based on V2G aggregation, and the method comprises the following steps: S1, constructing a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharge; s2, aggregating the logic energy storage units into virtual power plant nodes which can be dispatched by a power grid through an information communication technology (ICT); and S3, bidirectional power flow sensing and real-time state estimation: utilizing an intelligent electric meter, a sensor and a topology identification algorithm. According to the method, a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharging is constructed, the logic energy storage unit is aggregated into a virtual power plant node capable of being dispatched by a power grid through an information communication technology (ICT), and specific charging and discharging actions are decomposed and executed, so that dispersed V2G resources can be effectively integrated, and the V2G resources can be effectively integrated. And collaborative optimization with operation states such as voltage and power flow of the power distribution network is carried out.

The ELM-based frequency safety constraint linearization optimization scheduling method comprises the following steps: establishing a fire-wind-light multi-energy complementary frequency dynamic response model, and deducing a time domain analytical expression of a frequency safety index by taking a frequency maximum change rate and a frequency maximum deviation as the frequency safety index of a system; considering a frequency lowest point security constraint and a frequency maximum change rate security constraint, and establishing a frequency security constraint-containing optimization scheduling model; a maximum bearable disturbance power linearization mapping method based on ELM is provided, nonlinear frequency security constraints are converted into a series of linear constraint sets, and linearization processing of the frequency security constraints is achieved; and piecewise linearization is carried out on quadratic terms in the established objective function containing the frequency security constraint optimization scheduling model, the overall model is converted into a mixed integer linear model, and a Gurobi solver is used to solve the linearly processed model. The scheduling method can ensure that the system frequency is safe and stable, so that the established optimal scheduling model gives consideration to both economy and frequency safety.

The invention discloses an urban rail transit energy management method based on multi-source fusion, and the method comprises the steps: employing a support vector machine algorithm to analyze a correlation mode between train intensive operation and passenger flow surge according to an obtained energy demand fluctuation index, and determining a potential energy consumption peak value position; the determined adjustment parameters are obtained, a power supply system control instruction is updated in combination with real-time train track information, and dynamic power supply load configuration is obtained; whether the obtained dynamic power supply load configuration is matched with the current passenger flow surge data or not is judged, if yes, a mode switching signal is sent to an equipment controller, and energy use feedback data after execution is obtained; according to the obtained energy use feedback data, evaluating the response accuracy of the system integration effect to demand fluctuation by adopting a gradient boosting decision tree algorithm, and determining further trajectory optimization suggestions; and updating a train operation scheduling model through the determined trajectory optimization suggestion to obtain an integrated multi-source information linkage mechanism.

The invention relates to the technical field of virtual power plant resource configuration, and discloses a virtual power plant resource dynamic coordination configuration method and system, and the method comprises the following steps: collecting the operation data of various resources in a virtual power plant and the external power grid environment data in real time, and carrying out the dynamic coordination configuration of the resources based on the operation data; extracting operation characteristic parameters reflecting response speed differences of various resources; clustering analysis is carried out on the multiple resources to form resource clusters with different response speed levels, and the collaborative deployment demand intensity of the power grid in the current time period is evaluated; predicting a power grid load balance state in a future target time period, and determining the callable response capability of each resource cluster in the target time period; and carrying out multi-target optimization calculation meeting the stability constraint of the power grid, and generating a dynamic coordination configuration scheme of various resources in the target time period. According to the method, through the synergistic effect of multi-level technical characteristics, the matching problem between resource characteristic differences and dynamic requirements is fundamentally solved.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention provides a robust optimization method and system for a flexible power distribution network, computer equipment and a storage medium, and belongs to the field of power grid planning, and the method comprises the steps: obtaining a network topology structure and line electrical parameters of a to-be-optimized flexible power distribution network, and constructing a line model corresponding to the to-be-optimized flexible power distribution network according to the network topology structure and the line electrical parameters; acquiring historical operation data of each node in the line model; performing clustering analysis on the historical operation data of each node based on a fuzzy C-means clustering algorithm to obtain a typical scene of the to-be-optimized flexible power distribution network; constructing a polyhedral uncertainty set according to the typical model; taking the limited extreme scene as input, and minimizing the operation cost as an optimization target to construct a main problem; and taking a decision scheme for solving the main problem and the polyhedral uncertainty set as input, taking a scene with the maximum search operation cost as a target to construct a sub-problem, and iterating to obtain a robust optimization result of the to-be-optimized flexible power distribution network. According to the method, reliable optimization of the flexible power distribution network in a real uncertain environment is realized.

The invention relates to a generalized energy storage and micro-grid collaborative low-carbon operation method based on a dynamic electricity price strategy. The method comprises the following steps: S1, constructing a micro-grid operator unified scheduling model framework and initializing an electricity price signal; s2, constructing a demand response model driven by user satisfaction, and obtaining a user load curve; s3, generating an uncertainty random disturbance scene set based on a wind and light power prediction probability density function; s4, establishing a generalized energy storage model, and respectively constructing a virtual energy storage model of entity energy storage and EV aggregation in the multi-microgrid; step S5, constructing a low-carbon optimal scheduling model for the flexibility resource collaboration of the microgrid group; s6, solving the multi-microgrid scheduling optimization model in a wind-solar disturbance scene, and extracting a tail scene mean value as a risk constraint target of scheduling optimization based on CVaR; and S7, the micro-grid operator corrects the direction of the electricity price signal based on a CVaR result, and iteratively updates in combination with a differential evolution algorithm until the target value tends to be stable.

The invention discloses a distribution network multi-source roof photovoltaic cluster collaborative aggregation regulation and control method, and belongs to the technical field of distribution network large-scale roof photovoltaic aggregation upgrading consumption, and the method comprises the following steps: collecting a power distribution network topological structure and line impedance parameters in real time, calculating the electrical distance modularity between nodes, and calculating the electrical distance modularity between the nodes; taking the reactive voltage sensitivity as a clustering core index, dividing the power distribution network into a plurality of photovoltaic clusters, selecting a node with the highest comprehensive sensitivity as a dominant node for each cluster, and establishing an inter-cluster reactive voltage sensitivity relation matrix; multi-stage collaborative optimization regulation and control are carried out, day-ahead, intra-day and real-time three-stage optimization models are constructed, and a cluster-stage optimization model is established by taking the minimum voltage deviation as a target; and carrying out multi-agent cooperative voltage regulation and strategy verification and updating. According to the method, the power grid topology is collected, node impedance is calculated, clustering is carried out through reactive sensitivity, a target function is solved through day-ahead optimization, intra-day voltage out-of-limit correction is carried out, margins are distributed in real time, and the technical problem of slow voltage regulation response is solved.

The invention relates to the technical field of power dispatching, in particular to an energy storage combined frequency modulation control method and system in a source-network-load-storage integrated regional power grid, and the method comprises the steps: dynamically calculating the adjustable capacity of each resource, dynamically aggregating the total adjustable capacity of the system, sensing the multi-dimensional frequency situation and calculating the adjusting power, and selecting a frequency modulation mode and generating a power distribution strategy. And generating a splitting and isolated network frequency modulation stability control strategy and performing system grid-connected resynchronous operation. The method is suitable for guiding power supply, energy storage and load all-resource collaborative frequency modulation strategy generation in a regional power grid. For consistency of frequency deviation, frequency change rate and active power flow direction, multi-dimensional frequency situation awareness is constructed, power distribution strategies in different modes are generated based on a total adjustable capacity interval of a system, and a frequency stability control and grid-connected resynchronization strategy after splitting is given when the frequency and the adjustable interval exceed limits. Accurate matching of all-resource collaborative frequency modulation and strategies in a regional power grid is realized, and the frequency modulation response speed of the system is improved.

The invention discloses a multipath load control acquisition device and system and a load control acquisition method, the device adopts an architecture in which a master device and a slave device cooperate, the master device comprises a master controller, a master communication module, a user display interface and a master interface unit, and the slave device comprises a slave interface unit, a slave controller, an acquisition unit and a slave communication module. Flexible marshalling management is achieved by distributing electronic identity numbers to the breakers, load parameters and the states of the breakers are synchronously collected through the collection unit, a two-way data transmission channel is constructed by means of the master-slave communication module, a control signal is generated based on preset logic and real-time data through the master controller, and the state of the breakers is controlled. And the slave controller issues an instruction to drive the circuit breaker electric operation mechanism to realize millisecond-level rapid switching-on and switching-off operation. According to the invention, the problems of insufficient number of loops, lack of state monitoring, slow response and the like of an existing load control system are solved, and accurate, rapid and intelligent management of multi-path loads on a client side is realized.

The invention relates to the technical field of source network load storage cooperative control, in particular to an area source network load storage cooperative optimization control system based on an intelligent fusion terminal, and the system comprises an acquisition module which acquires energy characteristic parameters and cooperative response characteristic parameters of a target area in a historical period; analyzing an energy characteristic characterization value based on the energy characteristic parameter through an analysis module, and calculating a collaborative response characteristic characterization value based on the collaborative response characteristic parameter; determining a corresponding control strategy based on the energy feature representation value through a control module; through a verification module, whether the stability of the transformer area source network load storage meets the standard is judged based on the collaborative response characteristic representation value; and through a feedback module, based on a difference value between the energy feature representation value and a predetermined energy feature representation threshold, determining an adjustment amplitude of the cooperative response feature representation threshold, and performing collection and verification again. According to the invention, through the verification module and the feedback module, the response rate of cooperative control of each link of source network load storage is improved.

The invention discloses a power distribution network two-stage robust fault recovery method considering photovoltaic fault ride-through capability. The method comprises the following steps: establishing a power distribution network fault recovery model based on fault propagation path analysis modeling; based on the power distribution network fault recovery model, establishing a power distribution network node power characteristic model considering the photovoltaic fault ride-through capability; on the basis of the power distribution network fault recovery model and the power distribution network node power characteristic model, establishing a power distribution network two-stage robust fault recovery model in a source load uncertainty scene based on robust optimization; the method is based on Camp; and solving the two-stage robust fault recovery model of the power distribution network through a CG algorithm to realize two-stage robust fault recovery of the power distribution network. The method is based on a robust optimization thought and utilizes Camp; the CG algorithm evaluates the fault recovery capability of the power distribution network in a source load fluctuation scene so as to improve the capability of coping with an extreme scene in the fault recovery of the power distribution network, and theoretical support is provided for the improvement of the elasticity of the power distribution network of a novel power system.

The invention discloses a power distribution network source load space-time distribution collaborative prediction system based on a deep learning model, and particularly relates to the technical field of power system operation and power distribution automation. The system accesses and aligns multi-source data under a unified time base, constructs graph priori carrying version weight in combination with operation topology, and outputs multi-step prediction of each node. Dynamic updating is introduced, wherein switching, reconstruction and power flow information is obtained through SCADA / power distribution automation, and graph priori is updated by event driving according to planned / instantaneous dual modes; performing time matching of the operation and the work order / instruction and historical plan probability weighting to obtain a planning coefficient, and setting a hysteresis threshold for discrimination; events such as tripping and protection action are directly judged to be instantaneous; and the undefined but continuously existing persons are conservatively classified according to the threshold duration. And the state duration threshold is self-adaptive along with the planning coefficient and the topological disturbance intensity. The system is matched with consistency auditing and gray release, and stable operation is supported.

The invention discloses a power grid energy storage demand response multi-agent reinforcement learning dynamic optimization method, which belongs to the technical field of power system scheduling and optimization control, and comprises the following steps of: constructing a joint optimization model based on a Markov decision process, representing an energy storage charge state, demand response regulation potential and renewable energy output fluctuation, defining a state space, and establishing a dynamic optimization model; establishing a joint action space; constructing a dual-network structure; an improved hierarchical time memory network is introduced, spatial-temporal features are extracted through a multi-level memory unit and a spatial-temporal attention mechanism, and the spatial-temporal features are used for strategy optimization and value evaluation; optimization learning is carried out by adopting multi-agent interaction and centralized training decentralized execution, and strategy updating is carried out by combining an experience playback pool and target network soft updating; and outputting a power grid dynamic scheduling strategy, realizing energy storage and demand response joint optimization, and improving system stability and adaptive capacity under a high-proportion renewable energy access condition.

The invention discloses a ring main unit intelligent regulation and control method based on deep reinforcement learning, and the method comprises the following steps: building an electrical topological graph model through collecting the real-time operation data of a ring main unit and a power distribution network, extracting the characteristics of nodes and branches, and generating an initial state vector; a comparison strategy embedding layer is arranged in front of the strategy network, and state-action representation is optimized through comparison learning; constructing a reinforcement learning decision structure by adopting a trust region strategy optimization algorithm, and outputting a ring main unit control action sequence; before execution, an out-of-limit action is judged and corrected by a control barrier function convoy layer; and closing and opening control is completed according to the corrected action, rewards are recorded, and incremental learning and parameter fine adjustment are performed by using an experience playback buffer area. According to the invention, by fusing the graph neural network and deep reinforcement learning and introducing a comparison strategy embedding layer and a control barrier function escorting layer, safe, adaptive and efficient intelligent regulation and control of the ring main unit in a complex power distribution network are realized.

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 relates to the technical field of power system dispatching, in particular to a distributed storage system for source-network-load-storage collaborative dispatching, which comprises a distributed data acquisition module, a regional collaborative sensing module, a distributed energy storage evaluation module, a dispatching instruction generation module and a dispatching instruction distribution module, wherein the distributed data acquisition module is used for acquiring real-time operation data of source, network, load and storage nodes in parallel; the regional collaborative sensing module is used for calculating a real-time power deviation value; the distributed energy storage evaluation module is used for generating an energy storage adjustment instruction set; and the scheduling instruction generation module is used for generating a final refined scheduling instruction. According to the invention, through constructing a cooperative scheduling mechanism of the source network load storage full chain, accurate identification of power deviation and optimal distribution of energy storage resources are realized, and scheduling response efficiency and operation stability of the system are significantly improved.