2366 results about "Power flow" patented technology

The invention relates to the technical field of power dispatching, in particular to a digital twin energy management method and system for source-network-load-storage cooperative dispatching, and the method comprises the steps: collecting source-network-load-storage multi-dimensional space-time operation data, and extracting space-time coupling features through a graph convolution-long and short-term memory network; establishing a simulation model of a digital twin environment, simulating an uncertain operation condition by using a Monte Carlo scene generator, and processing a power flow constraint by using a second-order cone relaxation technology; training an energy storage scheduling agent in a digital twin environment, and learning an energy storage charging and discharging strategy through a near-end strategy optimization algorithm; designing a source-network-load-storage hierarchical collaborative optimization framework, optimizing power output and load distribution by using an improved particle swarm optimization algorithm on the upper layer, and solving power flow distribution by using an alternating direction multiplier method on the lower layer; and establishing a self-adaptive feedback correction mechanism, and dynamically adjusting a cooperative scheduling strategy. According to the invention, intelligent collaborative scheduling of source network load storage is realized, and the operation efficiency and stability of a power system are improved.

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.

A right-hand circularly-polarized patch antenna comprising a ground plane and a patch connected to each other with one or more wires for which the wire shape and location of the end points are selected such that they do not cause an antenna mismatch, and the electrical current carried in the wires produces an extra electromagnetic field subtracted from the patch field in the nadir direction.

An offshore wind power plant composite ring current collection system topology planning method based on an N-1 safety criterion comprises the following steps that S1, wind power plant basic data including wind turbine and transformer substation node coordinates, wind turbine rated power, cable electrical parameters and the like are obtained; s2, generating a candidate cable set and a cable cross avoidance constraint pair set based on the basic data; s3, a scene set containing a normal scene and all single cable fault scenes is constructed, an optimization model with the minimum total cost of the whole life cycle as the target is established, and cable construction / operation variables and direct current power flow power balance, node degrees and cable cross avoidance constraints are integrated; s4, solving the model to obtain an initial topology scheme; and S5, carrying out full fault set N-1 security verification on the initial scheme, and bringing a violation scene into a fault set to resolve until the scheme meets an N-1 criterion. According to the method, through non-predefined topological optimization and planning, reliability and economical efficiency are taken into consideration, calculation complexity is reduced, and the method can be expanded to a cross-substation scene.

The invention discloses an electric energy meter error real-time monitoring and intelligent calibration system and method, and relates to the technical field of electric energy meter calibration, and the system comprises a target power data calculation module which is used for collecting voltage and current signals in real time, and carrying out the calculation to obtain target power data; the reference power data calculation module is used for performing real-time calculation to obtain reference power data; the error vector acquisition module is used for acquiring an error vector; the error prediction result generation module is used for carrying out modeling analysis on the error vector and generating an error prediction result; and the target electric energy value output module is used for outputting the target electric energy value. The technical problem that the metering accuracy is insufficient due to the fact that the electric energy meter lacks an effective error analysis and prediction means and a dynamic calibration mechanism in a bidirectional power flow scene in the prior art is solved, and the purposes of real-time monitoring, error analysis and prediction and dynamic calibration of the forward and reverse power data of the electric energy meter are achieved. And the metering accuracy of the electric energy meter in a bidirectional power flow scene is improved.

The invention relates to an active splitting and isolated island operation method based on load importance under a disaster condition, and belongs to the technical field of power systems and automation thereof. On the basis of disaster types and power supply area characteristics, constructing a load importance dynamic grading system, optimizing the weight through an analytic hierarchy process-index weight optimization collaborative algorithm, and combining with real-time updating to generate a grading result; the method comprises the following steps: deploying wide-area and local monitoring devices to collect power grid data, setting a safety threshold, triggering a splitting decision when the safety threshold exceeds the limit and the trend deteriorates, calculating an instability rate by using a multi-parameter collaborative instability pre-judgment algorithm, and starting pre-splitting preparation; and the main control center calls a target splitting section optimization algorithm to determine a section in combination with the grading result and the power grid data, generates a splitting instruction after verifying the stability of the island through load flow calculation, and performs classified stability control on the split island. The method can realize accurate load grading, early instability pre-judgment, guarantee of important load power supply such as medical treatment and the like, reduces catastrophe loss, and is suitable for power grid management and control under various disasters.

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

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 relates to a power distribution network voltage partition control method and system based on a hierarchical K-means clustering algorithm, and belongs to the technical field of power distribution system voltage partition optimization. According to the technical scheme, power distribution network node parameters are collected in a self-adaptive partition mode, a node sensitivity coefficient and an eigenvector of an electrical distance are constructed, and then a K-means algorithm is used for fine region division; selecting a dominant node: solving a Jacobi matrix through load flow calculation, extracting a voltage sensitivity coefficient, and selecting a node with the maximum sensitivity as a dominant node in each partition; multi-objective optimization: constructing an optimization model with minimum network loss, minimum voltage deviation and highest voltage stability as objectives; and partition cooperative control: accessing wind power and photovoltaic power to the dominant node, adjusting reactive power output in real time according to an optimization result, and realizing partition autonomy and global cooperation. According to the invention, voltage fluctuation and out-of-limit are inhibited, system network loss is reduced, control efficiency and economy are improved, and the method is suitable for complex topology and high permeability scenes.

The invention belongs to the technical field of power dispatching control, discloses an intelligent dispatching accident plan optimization method based on deep learning, and aims to solve the problems that traditional power accident dispatching depends on artificial experience, the plan generation efficiency is low, and the current situation of wide application of distributed photovoltaic and energy storage is difficult to adapt. According to the core technical path, on the basis of massive historical dispatching accident data, accident processing key features are automatically mined through a deep learning model, distributed photovoltaic output and energy storage charging and discharging features are fused, and a multi-source feature learning framework considering power flow balance in the accident state is constructed; and generating a dynamically optimized structured plan. By applying the method, the generation efficiency and accuracy of the accident plan can be remarkably improved, the accident handling speed is increased, the power failure duration and the economic loss are effectively reduced, meanwhile, the power flow stability of the power system after distributed energy access is guaranteed, and finally the overall reliability and the safe operation level of the power system are improved.

The invention discloses a power grid maintenance plan reliability post-evaluation method based on Monte Carlo simulation and data driving, and belongs to the technical field of power system operation and reliability analysis. The method comprises the following steps: firstly, collecting multi-source data such as historical load, renewable energy output, equipment operation state and maintenance record of a power grid, constructing a time sequence database through cleaning, time alignment and feature extraction, and establishing a load and renewable energy probability model; constructing a maintenance plan model containing a state variable, a constraint condition and a peak clipping weight mechanism, and establishing a continuous time Markov chain state model for the key equipment to generate an availability sequence; generating a large-scale random operation scene set through a Monte Carlo method based on multiple models, and carrying out supply-demand balance and power flow analysis on each scene; multi-dimensional indexes of reliability, economy and safety are calculated and subjected to weighted fusion, a comprehensive post-evaluation report is generated after results are counted, and finally the maintenance plan is optimized according to the report. According to the method, the uncertainty of the power system can be comprehensively considered, multi-dimensional quantitative evaluation and closed-loop optimization of the maintenance plan are realized, intelligent support is provided for power grid maintenance decision making, and the method is suitable for a power transmission network, a power distribution network and a micro-grid.

The invention discloses a power distribution equipment health assessment method and system based on multi-source data, and the method comprises the steps: mapping each modal feature into a comparable measure, constructing a learnable cost containing power flow and heat consistency, outputting a modal weight through scene gating, and forming a fusion representation of physical consistency; driving a neural differential equation by fusing the stress force obtained through representation decoding, adopting monotone weight parameterization and introducing equipment-level damage budget, and obtaining damage and health indexes which are irreversible along with time; under the constraint of physical baseline life, combining a working condition input time-scene gating danger rate model, performing causal consistency correction through virtual intervention, and outputting an interval failure probability and residual life; and calibrating a dynamic threshold value in the working condition cluster, and triggering routing inspection, sampling and load shedding based on the risk sensitivity and the topological linkage risk priority. According to the method, multi-source physical consistent fusion, individualized health modeling and dynamic closed-loop optimization can be realized, and the accuracy and interpretability of health assessment of the power distribution equipment are improved.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention discloses a wind power generation energy storage load intelligent prediction and power distribution management method, and relates to the technical field of new energy power generation, and the method comprises the steps: generating a clock synchronization signal when a phase gradient quantity exceeds a stable threshold value, correcting the node voltage phase deviation of a power distribution network according to the clock synchronization signal, and outputting a whole network synchronization voltage waveform; inputting an energy storage charging and discharging control instruction into the power flow optimization model, and generating a voltage suppression control vector through a dynamic power deviation compensation algorithm; and extracting stability parameters in the wind power operation data, carrying out weight distribution and state matching on the energy storage charging and discharging control instruction and the voltage suppression control vector, and outputting a cooperative control instruction. According to the method, heterogeneous data such as wind speed, power and voltage are fused into multi-dimensional sequence parameters through a space-time dynamic coupling method, the phase gradient quantity is generated through phase field gradient extraction, a quantitative correlation model of wind speed fluctuation and power grid response is established, and the load prediction accuracy is improved.

The invention relates to the field of power systems and intelligent power grids, in particular to an intelligent analysis system and method for the influence of multi-scene new energy power generation on a distribution network, and the system comprises a data collection module, a multi-scene modeling module, an influence analysis module, an intelligent evaluation module and an optimization suggestion generation module. And the data acquisition module, the multi-scene modeling module, the influence analysis module, the intelligent evaluation module and the optimization suggestion generation module are in communication connection with an external data bus. The hybrid digital twinborn model is constructed through the multi-scene modeling module, four typical scenes of new energy output fluctuation, extreme weather, AC / DC hybrid access and power grid topology change are dynamically simulated, and dynamic configuration of scene parameters (such as new energy permeability and load growth rate) is supported. According to the model, through fusion modeling (such as a power flow equation, an LSTM time sequence and a graph neural network GNN) driven by a physical mechanism and data, the running state of the distribution network under the multi-scene coupling effect can be reflected more truly.

The invention provides a new energy power grid prospective scheduling method and device, and relates to the technical field of electric power system intraday economic scheduling. The method comprises the following steps: firstly, constructing an opportunity constraint optimal power flow model of prospective scheduling, analyzing the influence of new energy uncertainty on opportunity constraint, establishing a constrained Markov decision process of prospective scheduling, and then utilizing a risk evaluator network fitting risk function probability distribution and an actuator network considering extreme scene performance to determine the opportunity constraint optimal power flow model of prospective scheduling. The processing capability of the intelligent agent on a prospective scheduling scene containing a new energy extreme climbing event is enhanced; and finally, the training of the intelligent agent is accelerated by utilizing an imitation learning technology in a power grid prospective scheduling off-line simulation environment. According to the method, the solving speed and the strategy robustness and safety of the double-layer robust optimization model of the look-ahead scheduling can be considered.

Provided in the embodiments of the present disclosure are an optimal control method and system for a distribution network with a high penetration of distributed generators, and an electronic device, a computer-readable storage medium and a computer program product. The optimal control method is applied to a centralized control platform. The method comprises: on the basis of an association relationship between power outputs of distributed generators at various levels, performing multi-level forecasting on historical power generation data of each distributed generator, so as to obtain a day-ahead forecasted output value of each distributed generator; for each distributed generator, determining a day-ahead output adjustment value on the basis of historical power flow data and an optimal power flow optimization model; and sending to the corresponding distributed generator the output adjustment value and forecasted output value of each distributed generator as centralized control instructions, so that the distributed generator controls an output value during each control period within the day ahead on the basis of an acquired real-time output measurement value, the acquired output adjustment value and the acquired forecasted output value.

The invention relates to the field of low-carbon electric power technology and electric power system optimization scheduling, in particular to an electric power system non-iterative low-carbon scheduling method and system based on carbon emission flow calculation and power flow decomposition, and the method comprises the steps: obtaining various types of input data in an electric power system, constructing an electric power carbon emission flow model based on power flow direction decomposition, a node carbon emission factor real-time calculation method and a carbon flow balance model suitable for non-iterative optimization solution are obtained; and finally, establishing a low-carbon dispatching optimization model of the power system, carrying out calculation and solution, and finally obtaining output results such as a unit output plan, a system power flow result and a node carbon emission factor. By establishing an electric power carbon emission flow model, synchronous calculation of a power flow equation and a carbon flow equation is achieved, the problem that traditional carbon flow calculation depends on a power flow calculation result is solved, load node carbon emission constraint is embedded in an optimized dispatching model, and a new technical path is provided for an electric power system to achieve refined low-carbon dispatching.

The invention relates to the technical field of data processing, and discloses a power sample data acquisition and classification method and system. The method comprises the following steps: synchronously acquiring active power, reactive power fluctuation and voltage harmonic data through a multi-point terminal, and constructing six types of characteristic index vector groups; a power load genetic optimization algorithm is used to optimize a classification threshold under power flow constraint; based on the optimal threshold value, clustering analysis is carried out through an EFC-KMeans algorithm in combination with impedance matrix characteristics; and inputting the clustering center into a multi-head power topology attention mechanism modeling node coupling relationship to realize power sample data classification and identification. The physical constraint conditions of the power system are effectively fused in the power sample data acquisition and classification process, so that the physical feasibility and engineering practicability of the classification result are improved.

The invention discloses a power grid transient stability margin prediction method based on steady-state power flow and anticipated faults, and the method comprises the steps: carrying out the sample generation based on different operation conditions of a power system, an AC power flow, operation modes under different time sequences, and faults under N-1 and N-2 scenes; labeling the generated sample according to two transient stability margin evaluation indexes of power angle stability and voltage stability; the labeled sample data and the anticipated fault data after the embedded coding are combined to serve as input features to be input into a deep learning model and trained, and a trained deep learning model is obtained; and predicting the transient stability margin of the power grid according to the trained deep learning model, and judging the stable state of the power system. The transient stability evaluation sample generation method is provided, the steady-state power flow and the expected fault before the fault are considered, the transient stability margin prediction of the power grid is realized by using the deep learning model, and the transient stability margin prediction speed, precision and reliability are improved.

The invention discloses a distribution-micro collaborative operation simulation optimization method, which belongs to the technical field of simulation optimization, and comprises the steps of preprocessing grid-connected point voltage data, tie line power data and communication time delay data, constructing a power distribution network power flow physical network following a Kirchhoff's law, outputting a source load power prediction curve by using a long short-term memory network algorithm, and calculating the distribution-micro collaborative operation according to the source load power prediction curve. And a distribution-micro collaborative simulation optimization model is obtained based on residual error rolling correction tie line impedance parameters, a delay penalty term is set in a target function in combination with the preprocessed communication delay data, a power regulation instruction is obtained by using a particle swarm optimization algorithm, and a dynamic simulation video stream is generated. According to the invention, through rolling correction of the tie line impedance parameters and setting of the delay penalty term positively correlated with the time delay, the problem of control failure caused by physical deviation caused by model parameter solidification and communication time delay accumulation is solved, and the defects of voltage deviation calculation distortion and inaccurate network loss evaluation are eliminated. And the simulation precision and the operation stability of distribution-micro cooperation are improved.

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 cooperative parameter optimizing and setting method and system for a network construction type reactive power compensation device, terminal equipment and a storage medium, and belongs to the field of power system control. The method comprises the steps of obtaining operation condition data and a power flow initial value of a power grid region where a reactive power compensation device to be set is located, constructing a discretization state space model, calculating a damping ratio according to a calculated system characteristic root, and determining a dominant oscillation mode; respectively changing the installed capacity, configuration points and control parameters of the reactive power compensation device to be set in a preset step length, and generating a characteristic root moving track; the installed capacity, the configuration points and the control parameters are continuously optimized based on the feature root moving trajectory until the preset stabilization capability index is larger than 0 and the optimized feature root real part is smaller than 0, the optimal installed capacity, the optimal configuration points and the optimal control parameters of the to-be-set reactive power compensation device are obtained, the to-be-set reactive power compensation device is adjusted, and the to-be-set reactive power compensation device is adjusted. According to the invention, the problem of lack of cross-band parameter setting in the prior art is solved.

The invention discloses a new energy power grid probability harmonic load flow calculation method based on improved semi-invariant and Gram-Charlier series expansion. The new energy power grid probability harmonic load flow calculation method comprises the following steps: establishing a random variable probability model, a wind-light output model and a wind-light joint distribution model; secondly, considering the output correlation between wind and light fields, converting the wind and light combined distribution model based on a Copula function into independent variables by utilizing Rosenblatt conversion for calculation, then calculating each-order semi-invariant of load, photovoltaic output and wind power output according to the expected values of node voltage and branch power flow and a sensitivity matrix, and finally calculating the output correlation between the wind and light fields according to each order semi-invariant of the load, the photovoltaic output and the wind power output. The method comprises the following steps of: obtaining a state variable, further solving each-order semi-invariant form of the state variable and the branch power flow, and finally, obtaining a probability density function and a probability distribution function of harmonic voltage of each node by combining Gram-Charlier series expansion.

The invention discloses a power distribution network reconstruction decision risk assessment method and system based on state deduction and rule reasoning, and a medium, and the method comprises the steps: carrying out the static power flow and voltage checking of an established reconstruction decision, and determining that the established reconstruction decision is a high risk if the line power flow and / or node voltage are / is out of limit; otherwise, constructing model input based on the node characteristics at the current moment and in a certain historical time period before the current moment and the time sequence data of the topological structure of the power distribution network, inputting the model input to a trained space-time diagram deduction model, and deducing to obtain time sequence data of each node voltage and each line power flow in a future preset time period; evaluating an expected risk of the established reconstruction decision by using three indexes of a line overload risk, a voltage out-of-limit risk and a load loss risk; and reasoning the risk level of the established reconstruction decision by using the constructed rule reasoning model. According to the method, the risk deduction result is ensured to conform to the physical characteristics of the power grid, and the interpretability and transparency of the model can be remarkably improved.

The invention relates to the technical field of electric power system planning, in particular to a carbon emission evaluation method, system and device for multi-type adjusting power supply and new energy collaborative planning and a storage medium. The method comprises the following steps: acquiring power system equipment parameters, constructing a hierarchical planning platform architecture, and establishing a parameterized model library through data collection and governance; based on the parameterized model library, various adjusting power supply operation characteristic parameters are extracted, a collaborative optimization objective function is constructed in combination with new energy output characteristics, and a preliminary collaborative planning scheme is generated; formulating an active support strategy according to a power supply configuration result, determining a scheduling plan and generating an energy management and control instruction sequence, constructing typical and extreme weather scenes for collaborative operation simulation, and obtaining power supply output and power flow distribution; the node carbon emission intensity and the total system carbon emission amount are calculated through tidal current tracking in combination with the carbon emission factors, and carbon emission space-time distribution is generated; and comprehensively evaluating the collaborative planning scheme through a multi-dimensional evaluation index, and generating a visual decision support report.

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 invention relates to the technical field of power distribution network electric energy quality optimization, and discloses a distributed energy access power distribution network electric energy quality optimization method and system, and the method comprises the steps: collecting distributed energy output and power grid node voltage data in real time, and quantifying the system discordance state through calculating the volatility and dispersion; performing multi-unit preliminary time sequence coordination based on the state, and determining an output time sequence; node estimated voltage is obtained through load flow calculation, the harmonic level is evaluated, and an amplitude optimization process is triggered; constructing an optimization model by taking harmonic suppression and voltage stabilization as targets, fitting a nonlinear relationship between output and voltage / harmonic, generating a constraint set, and solving to obtain a final output amplitude sequence; and integrating the time sequence and the amplitude sequence, and generating a cooperative control instruction. According to the method, voltage fluctuation and harmonic pollution are effectively suppressed, and the electric energy quality and the operation stability of the power distribution network under high-proportion distributed energy access are improved.

The invention discloses a power distribution network carbon emission metering and responsibility distribution method and device and a medium, and relates to the technical field of smart power grids. The method comprises the following steps: collecting multi-source carbon emission data in real time, and preprocessing the multi-source carbon emission data to obtain standard carbon emission data; inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain a dynamic carbon factor of the thermal power generating unit, and combining the dynamic carbon factor of the thermal power generating unit with a carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; performing load flow calculation on the multi-source carbon emission data to obtain a node active flux matrix and a branch load flow distribution matrix, and combining the dynamic carbon intensity vector, the node active flux matrix and the branch load flow distribution matrix to obtain the total carbon potential of each node on the load side; and based on the total carbon potential and the real-time load distribution matrix, calculating the carbon flow rate distributed to each node on the load side so as to obtain the carbon emission responsibility of each node on the load side.