720 results about "Power balancing" patented technology

The invention relates to the field of power distribution networks, in particular to a power distribution network voltage collaborative autonomous method and system based on dynamic partition. The method comprises the following steps: acquiring electrical measurement data and network topology parameters of distributed nodes of a power distribution network, and generating a characteristic state set representing the operation state of a system; performing dynamic subarea division based on node voltage coupling strength and power balance constraint to obtain a dynamic subarea set with an autonomous boundary; each partition control main body independently solves a voltage regulation objective function of the partition according to an autonomous boundary, and generates a partition autonomous control strategy; and boundary interactive iterative coordination is carried out between adjacent partitions, and a global optimal voltage cooperative control instruction is generated and executed. According to the method, the problems that partition division is not matched with the operation state, and partition collaboration is insufficient are solved, unification of partition autonomy and global optimization is achieved, and the real-time performance and accuracy of voltage regulation and control of the power distribution network are remarkably improved.

The invention discloses a virtual power plant global optimization scheduling method, system and device based on cloud edge collaboration, and a storage medium, and belongs to the technical field of power system scheduling. The method comprises the following steps: based on a cloud edge coordinated regulation and control framework comprising a cloud layer, an edge layer and an end side layer, taking minimization of the total operation cost of a system as a target, comprehensively considering a power balance constraint, a main network interaction constraint, a distribution network transmission constraint, a distributed resource operation constraint, an energy storage equipment constraint and a renewable energy consumption constraint; establishing a global optimization scheduling model; edge collaborative optimization is realized by adopting an alternating direction multiplier method, a global coupling problem is decomposed into local optimization sub-problems and a cloud coordination problem of each region, and aggregation power information is sent to the cloud after the local optimization sub-problems are solved in parallel in each region; and the cloud performs global coordination optimization to generate an optimal scheduling strategy, and issues a scheduling instruction to the edge layer to control the actual operation of the distributed power supply, the energy storage equipment and the controllable load, thereby realizing the collaborative optimization scheduling of the virtual power plant. The problems that in virtual power plant large-scale distributed resource coordination optimization, calculation complexity is high, communication burden is heavy, and real-time performance and global optimality are difficult to consider at the same time are effectively solved.

The invention relates to the technical field of electric power intelligent management, and discloses a cooperative scheduling method and system for a virtual power plant, and the method comprises the following steps: S1, collecting the real-time data of each distributed power supply, each load and an energy storage system in the virtual power plant, and carrying out the ultra-short-term prediction, and obtaining a prediction parameter; s2, dynamically calculating the dynamic operation boundary of the energy storage system based on the real-time state of the energy storage system; s3, on the day before the current operation day, generating a pre-scheduling plan through collaborative decision making of a multi-target fuzzy satisfaction function; s4, in the current running day, taking the pre-scheduling plan as a reference, updating boundaries and prediction parameters in a rolling manner, and generating a real-time scheduling instruction through model prediction; and S5, monitoring the deviation between the actual output of each resource and the real-time scheduling instruction in real time, and when the deviation exceeds a threshold value, starting a collaborative deviation compensation mechanism to carry out power balance. According to the invention, fine cooperative scheduling of different types of distributed resources can be realized in a complex environment with high uncertainty.

The invention relates to the technical field of power dispatching, in particular to a power distribution network photovoltaic energy storage collaborative optimization dispatching decision-making system based on big data and artificial intelligence. Comprising a source network load storage full-dimension data acquisition unit; a multi-source heterogeneous data fusion processing unit; the dynamic multi-target intelligent optimization decision-making unit is used for constructing a power distribution network photovoltaic energy storage collaborative scheduling strategy through an improved non-dominated sorting multi-target grey wolf optimization algorithm with adaptive weight adjustment; and a closed-loop control execution unit. According to the invention, photovoltaic, energy storage, load and power grid operation data in the power distribution network are comprehensively captured through the source-network-load-storage full-dimension data acquisition unit, and spatial correlation mapping of topological nodes of the power distribution network in a multi-source heterogeneous data fusion processing process is combined; and constraint conditions such as power balance and node voltage during dynamic multi-objective optimization decision making are included, so that neglect on topology and operation constraints of the power distribution network is effectively made up.

A wind-photovoltaic ratio system optimization configuration method for a new energy hydrogen production park, the method comprising the steps of: S1) calculating an output time series of local wind power and photovoltaic power generation, and analyzing wind power and photovoltaic output characteristics and a wind-photovoltaic correlation; S2) establishing equivalent device models of a hydrogen energy storage unit and an electrochemical energy storage unit to form an electric bus power balance and hydrogen bus power balance relationship, and taking the minimization of a full life cycle cost as an objective function and taking into consideration constraint conditions such as the power balance, device operation characteristics and a land restriction to establish a system planning optimization configuration model for a new energy hydrogen production park, which model takes a wind-photovoltaic ratio into consideration; S3) integrating the objective function and the constraint conditions to obtain a system planning and optimization configuration scheme for the new energy hydrogen production park and the total system configuration cost; and S4) taking into consideration an electricity price factor and an energy storage operation characteristic constraint, performing annual hydrogen production timing operation simulation, and analyzing a park electricity purchase demand and a new energy consumption capacity under the present planning optimization configuration scheme.

The invention relates to the technical field of micro-grids, and discloses a micro-grid real-time load balancing scheduling method and system based on deep reinforcement learning, and the system comprises a multi-source sensing module, an intelligent decision module, a safety protection module, an execution control module, a digital twin module and an energy efficiency evaluation module. When real-time load balancing scheduling of the micro-grid is carried out, a multi-time-scale scheduling strategy is generated in real time by dynamically coordinating economical efficiency, environmental protection and power supply reliability targets through a deep reinforcement learning agent, so that the problems of high operation cost, standard exceeding of carbon emission and voltage instability caused by a single optimization target in a traditional method are further solved, and the real-time load balancing scheduling of the micro-grid is realized. According to the method, the multi-dimensional collaborative optimization of the micro-grid in a complex operation environment is ensured, and meanwhile, a cloud global optimization module generates a long-cycle strategy, so that the problems of response delay and insufficient expandability of a centralized control architecture are further solved, and the real-time power balance capability in a high-permeability renewable energy source scene is improved.

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.

The invention discloses a construction method and a modulation method of a three-port multi-frequency MMC topology based on bridge arm multiplexing, and relates to the technical field of power electronics. The construction method comprises the steps that sub-modules of a traditional double-bridge-arm MMC are integrated and optimized into a three-bridge-arm structure through a current conversion branch, independent energy interaction of three ports is achieved through an auxiliary bridge arm, multi-frequency operation modes of power frequency, frequency division, direct current and the like are supported at the same time, and therefore the utilization rate of the bridge arm sub-modules is increased, and the energy utilization rate of the MMC is increased. And the overall size and the manufacturing cost of the converter are reduced. Based on three-port multi-frequency coordination control and power balance requirements, a modulation method of the topological structure is provided, a multi-frequency coordination modulation strategy is adopted, frequency offset is performed through an auxiliary bridge arm to reduce cross interference of port frequency, and output waveform quality is improved through multi-objective optimized capacitor voltage balance control. And meanwhile, direct-current side faults are effectively eliminated by means of the auxiliary bridge arm.

The invention discloses a computing power resource dynamic scheduling system integrating environmental perception and power self-balancing, and relates to the technical field of computing power resource scheduling. According to the system, multi-dimensional data inside and outside a specified computing power facility operated by a server cluster are collected in real time through a data collection and environment perception fusion module, and dynamic / static fusion processing is carried out on the multi-dimensional data and current environment perception parameters, so that comprehensive perception of the environment and the operation state is realized; then, resource configuration is optimized by combining a combined scheduling and self-power balance control module with a fusion processing result; finally, a hierarchical scheduling scheme is generated through a hierarchical scheduling and multi-scene adaptation verification module, and whether the generated hierarchical scheduling scheme adapts to the current scene or not is judged by combining progressive derating judgment and error-tolerant rate verification, so that dynamic linkage regulation and control of the load, the environment and the power are realized, the problem of unbalanced power distribution in computing power scheduling is effectively solved, and the service life of the computing power scheduling system is prolonged. And the energy efficiency ratio and the environment adaptive capability of the server cluster are improved.

The invention relates to the technical field of distributed micro-grids, and discloses a distributed micro-grid grid-connected protection and power balance intelligent regulation and control method, which comprises the following steps of: firstly, acquiring multi-source operation data of a micro-grid, extracting characteristics by using an intelligent analysis model to obtain an operation characteristic sequence, and calculating a grid correlation coefficient; dimensionality reduction is carried out on correlation matrixes of correlation coefficients of different regional power grids, key feature vectors are extracted, and regulation and control weights of the regional power grids are obtained through Sigmoid function conversion. And dynamically adjusting the reference regulation and control points of the operation feature sequence according to the weights, generating regulation and control parameters and updating regulation and control records. In addition, a multi-dimensional power grid regulation and control space is constructed to monitor abnormity, and a joint optimization model is established to solve an optimal regulation and control scheme. According to the method, the operation state can be accurately analyzed, and the grid-connected protection capability and the power balance regulation and control level of the distributed micro-grid are effectively improved.

The invention provides a photovoltaic consumption-oriented electric vehicle battery swap station collaborative planning method, and the method comprises the steps: obtaining planning input data which comprises power distribution network operation parameters, user charging demand data, electric vehicle battery swap station battery data, electricity price subsidy data and photovoltaic power generation data; planning constraint conditions are determined, wherein the planning constraint conditions comprise an electric vehicle battery swap station operation constraint, a node power balance constraint, a transformer substation output constraint, a line power flow limit value constraint, a node voltage constraint and a photovoltaic consumption constraint; determining a target function, wherein the target function is used for cooperatively optimizing the construction position and energy storage configuration of the battery swap station, a charging subsidy gear trigger strategy and a photovoltaic consumption level; and according to the planning input data, the planning constraint condition and the objective function, establishing and solving a mixed integer linear programming model to obtain an electric vehicle battery swap station collaborative programming scheme. According to the obtained electric vehicle battery swap station collaborative planning scheme, the consumption level of local photovoltaic electric energy is effectively improved.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention belongs to the technical field of power grid systems, and particularly relates to a cooperative scheduling method for a water-electricity-heat combined supply negative carbon building light-storage direct-flexible energy microgrid group. Comprising the following steps: constructing a negative carbon building light storage direct-flexible energy microgrid group mathematical model; wherein the mathematical model comprises a generator constraint model, an external power grid and micro-grid group exchange power constraint model, a power balance constraint model, a peak-valley difference constraint model, a storage battery constraint model, a phase change heat storage system operation constraint model and a water supply system operation constraint model; constructing a target function of the scheduling model by taking the minimum total operation cost of the negative carbon building light storage direct-flexible energy microgrid group in a scheduling period as a target; wherein the total operation cost comprises electric power procurement cost, carbon emission cost, energy storage cost, fuel cost and peak-valley difference punishment cost. Through linkage of the electricity storage system, the heat storage system and the water storage system, the utilization rate of renewable energy sources and the flexibility of the system are maximized.

The invention relates to the technical field of bidirectional PCS, and discloses a bidirectional PCS converter multi-target power balance optimization method and system based on model predictive control. According to the method, real-time operation parameters are analyzed by using a first-order lag initial model, prediction of power disturbance in a plurality of control periods in the future is realized, power balance optimization is converted from passive response to active pre-judgment, and a linear time-varying prediction model is constructed, so that the real-time performance of power balance optimization is improved. According to the method, the evolution trajectory of the direct current bus voltage in a period of time in the future is predicted, the power balance optimization behavior is continuous and prospective through a rolling optimization mechanism of prediction, evaluation and preferential selection, and meanwhile, the three targets of ensuring voltage stability, reducing switching loss and suppressing current harmonic content are fused into power balance optimization, so that the power balance optimization efficiency is improved. The problems of control hysteresis and target singleness existing in a traditional control method are solved, and quick and accurate power balance control of the bidirectional PCS is realized.

A space-time learning and lightweight direct-current optimal power flow projection scheduling method driven by a reactance perception graph kernel comprises the steps that the reactance perception graph kernel based on a susceptance matrix is constructed, a power grid node reactance perception spatial dependency relationship is extracted, and multi-period node feature tensors are organized; a space-time learning device comprising a graph convolution encoder and a time sequence encoder is adopted to carry out space-time joint modeling on the node features, and the cross-time evolution law of the node marginal electricity price and the voltage phase angle is learned; mechanism consistent physical regular terms of load flow safety and climbing consistency are introduced in training, and physical constraint influences are gradually enhanced by means of weight scheduling; and converting the phase angle trajectory into an equivalent unit output initial value through physical mapping, constructing a lightweight quadratic programming projection model with the minimum deviation initial value, and generating a feasible day-ahead scheduling plan under the constraint of meeting power balance and the like. According to the method, the whole link from prediction to scheduling is opened, the speed and the reliability are both considered, and an engineering available path is provided for day-ahead multi-period clearing and large-scale scene analysis.

The invention discloses a day-ahead market scheduling optimization method for power balance characteristics in an embedded scheduling time period, and the method comprises the following steps: 1), constructing an actual output expression and a net load prediction power expression of a generator set based on an asymmetric affine strategy; 2) based on the opportunity constraint optimal power flow, constructing a day-ahead market economic dispatching model of power balance characteristics and uncertainty in an embedded dispatching time period; 3) analyzing and converting the day-ahead market economic dispatching model with the power balance characteristics and uncertainty in the embedded dispatching time period, and constructing a day-ahead market safety constraint economic dispatching model with the power balance characteristics in the embedded interval; and 4) solving the day-ahead market security constraint economic dispatching model embedded with the power balance characteristics in the interval to obtain a node marginal price, and outputting a market dispatching scheme with the minimum node marginal price. According to the method, on the basis of considering the uncertainty of the net load prediction error, the power transmission risk cost caused by the uncertainty can be effectively internally reduced.

The invention relates to the technical field of fuel cells of hydrogen energy unmanned aerial vehicles, in particular to a power balance method and system of a fuel cell system for a hydrogen energy unmanned aerial vehicle, and the method comprises the steps: calculating the power balance of the fuel cell system for the hydrogen energy unmanned aerial vehicle based on the real-time state of a lithium battery, the current flight working condition of the unmanned aerial vehicle and the efficiency characteristic of the fuel cell; dynamically setting a target charging current of the lithium battery; obtaining a deviation value based on the actual charging current of the lithium battery and the target charging current; the PID controller generates a control signal, adjusts the reaction rate of the fuel cell, and changes the output current of the fuel cell, so that the actual charging current of the lithium battery works with the target charging current as the reference; power balance between the fuel cell and the lithium battery can be effectively achieved, the stability, reliability and system efficiency of the unmanned aerial vehicle power system are improved, meanwhile, the fault handling capacity is achieved, and normal flight of the unmanned aerial vehicle under various conditions is guaranteed.

The invention discloses a micro-grid off-grid operation distributed control method based on consistency cooperation, belongs to the field of new energy micro-grid control, and aims to solve the problems of high single-point fault risk and single control target of existing centralized control. The method is operated in a three-layer architecture formed by a distribution network master station control layer, a micro-grid coordination control layer and an equipment distributed coordination control layer, a distributed communication network is constructed through edge agents, and weight coefficients and security constraints are configured; the edge agent receives a master station total power target, collects and interacts equipment data, calculates and limits amplitude to obtain a final power instruction based on a consistency updating law fusing global power difference distribution, energy storage SOC balance and system loss minimization, and then decomposes the final power instruction into equipment-level instructions to be issued and executed. According to the method, a single-point fault is eliminated, multi-target collaborative optimization is realized, control is simplified, expansibility is high, and power balance and stable and efficient operation when the micro-grid is off-grid are effectively guaranteed.

The invention relates to the related technical field of power control, in particular to a photovoltaic energy storage collaborative power balance compensation method, system and equipment, and the method comprises the steps: connecting a photovoltaic power station, and determining an amplitude characteristic parameter and a fluctuation characteristic parameter associated with a photovoltaic energy storage operation state; and configuring a power deviation feature vector and an energy storage response feature vector, outputting a quantitative evaluation decision unit vector, and if a preset threshold condition is met, triggering an energy storage adjustment instruction and linking the photovoltaic inverter to execute power correction operation. The technical problems that fluctuation data are processed only according to analysis or fixed filtering parameters of a single photovoltaic power signal, the characterization of the photovoltaic energy storage operation state is one-sided, and over-compensation or under-compensation is prone to occurring are solved, and a multi-mode characteristic extraction method is achieved through a photovoltaic and energy storage cross-equipment multi-mode characteristic extraction mechanism. The amplitude and fluctuation characteristic parameters are synchronously obtained based on the inverter and converter signals, the two-way quantity weight is dynamically coordinated, the power sudden change area is focused, the power compensation precision is improved, and the technical effects of guaranteeing stable operation of a power grid are achieved.

The invention discloses a multi-element energy storage system anti-disturbance cooperative control method and system and a medium, and the method comprises the steps: obtaining the real-time operation data of a multi-element energy storage system which comprises an energy conversion and storage device, the energy conversion and storage device comprises an electric energy-heat energy conversion device, an electric energy-hydrogen energy conversion device, an electric energy storage device, a heat energy storage device, a hydrogen energy storage device and a superconducting magnetic energy storage device; constructing a multi-energy complementary comprehensive energy system model containing a minimum total operation cost objective function based on the real-time operation data; solving the multi-energy complementary comprehensive energy system model, and generating an anti-disturbance cooperative control strategy; and the charging and discharging instruction is controlled to be sent to the multi-element energy storage system based on the anti-disturbance cooperative control strategy to control cooperative operation of the multi-element energy storage system, so that real-time power balance of the multi-element energy storage system is realized, new energy output fluctuation and user load sudden change can be quickly responded, and the operation stability and anti-risk capability of the multi-element energy storage system are improved.

A power distribution network voltage cooperative control method considering a cluster division-optical storage all-in-one machine comprises the following steps: step 1, combining an electrical modularity index, a balance index and a membership index to construct a cluster comprehensive division index; 2, taking the comprehensive division index as a convergence condition for dividing a cluster region, and solving an optimal cluster division result of the power distribution network by utilizing an improved spectral clustering algorithm; 3, with the minimum power distribution network node voltage deviation punishment cost, the minimum network loss cost and the minimum power generation loss cost of the optical storage all-in-one machine as the target, a power distribution network voltage optimization model containing the optical storage all-in-one machine is constructed; and 4, based on the optimal cluster division result, solving the power distribution network voltage optimization model of the optical storage all-in-one machine through a cluster voltage optimization control strategy, optimizing active power, reactive power and voltage of the power distribution network, and improving power flow distribution of the whole network. The voltage regulation task sharing and power balance are realized, the voltage regulation effect is more reasonable, and the voltage out-of-limit problem under the random fluctuation of the source load power can be effectively solved.

The invention discloses a competitive evolution multi-task optimization method, system and equipment, and aims to solve the problem that an existing method is difficult to carry out collaborative optimization on fuel cost and gas emission under the condition that power balance and unit capacity constraint conditions are met. The method comprises the following steps: constructing main and auxiliary double-population parallel search; state indexes representing population convergence, diversity and feasibility are calculated in real time; based on the current state, a cooperation mode and evolution operator combined action is adaptively selected through a deep reinforcement learning agent; rewards are calculated according to improvement of the population on cost, emission and constraint satisfaction after action execution; and training the intelligent agent by using the state, the action and the reward, and iteratively optimizing the decision strategy until the Pareto optimal scheduling scheme meeting the constraint is output. According to the method, adaptive intelligent guidance of the evolutionary process is realized, and the optimization quality, the convergence speed and the algorithm robustness of the economic emission scheduling scheme of the power system are remarkably improved.

The invention relates to a household energy storage anti-reflux control system and method. The system comprises a grid-connected point electric energy acquisition module, an acquisition coordination unit and a plurality of energy storage devices. The acquisition coordination unit receives a grid-connected point electric energy acquisition signal and an equipment operation state signal, generates and issues a coordination control data set signal, receives a power adjustment feedback signal and updates a data set. The method sequentially coordinates energy storage equipment according to a control cycle, judges countercurrent or a gap based on a threshold value, generates a discharge power adjusting quantity and / or a charge and discharge mode switching instruction, and writes back a data set to realize countercurrent suppression and power balance.

The invention discloses an industrial park comprehensive energy management method and system based on a hierarchical system, and belongs to the technical field of energy management and automatic control. The method comprises the following steps: integrating external data, and predicting load and renewable energy power generation power by using a machine learning algorithm; making a start-stop and output plan of each energy device in the park through an MILP model; and based on short-term prediction and an intra-day scheduling plan, establishing an MPC model, solving a real-time control instruction of each energy device, and controlling operation of various energy production, conversion and storage devices. According to the method, an algorithm combining reinforcement learning and model prediction control is creatively applied, the prediction model and the cost function of MPC are adaptively adjusted on line through reinforcement learning, the equipment power is dynamically adjusted in combination with short-term rolling prediction, uncertainty is effectively dealt with, more accurate second-level power balance is achieved, and the reliability of the system is improved. And the adaptability and the operation economy of the system to high-proportion renewable energy sources are effectively improved through hierarchical collaborative optimization.

The invention relates to the technical field of micro-grids, and discloses a control method of a distributed power supply in a plant micro-grid, and the method comprises the steps: S1, constructing a multi-source collaborative micro-grid system; s2, executing real-time power dynamic allocation; s3, working condition power balance control; s4, active regulation of bidirectional power flow and the like. Based on a real-time AGC instruction and plant load data, a hierarchical coordination mechanism is adopted, charging and discharging power is dynamically adjusted by means of stored energy to track the AGC instruction, and constant power output of wind power is maintained in combination with a wind power prediction model; when a thermal power generating unit is subjected to deep peak regulation, shutdown or fault, the system absorbs excess wind power through energy storage charging, synergistically improves the hydrogen production load power, guarantees dynamic matching of the load and power generation, and actively adjusts energy storage charging and discharging and hydrogen production loads by monitoring the power flow directions of a thermal power generating unit section and a desulfurization section in real time according to voltage fluctuation caused by bidirectional power flow. And the voltage deviation is dynamically compensated, and the system voltage stability is greatly improved.

The invention relates to the technical field of electric vehicle charging and renewable energy source integration. The invention discloses an electric vehicle photovoltaic charging station multi-dimensional target collaborative optimization scheduling method fused with an improved particle swarm intelligent optimization mechanism. The method is characterized by comprising the following steps: 1) constructing a multi-objective optimization model: taking electricity purchase cost minimization and storage battery cycle electric quantity minimization as objective functions, and taking a charging time constraint, an energy storage constraint, a power balance constraint and an equipment capacity constraint as constraint conditions; the method comprises the steps of (1) selecting a Pareto optimal solution set, (2) improving the design of a particle swarm algorithm, (3) outputting a result: outputting a Pareto optimal solution set after iteration is ended, and (4) selecting a decision: selecting an optimal scheme from the solution set through a fuzzy membership function or a weight preference method (for example, the optimal scheme focuses on reducing the electricity purchase cost when the optimal scheme is equal to 0.6). The method achieves the minimization of the electricity purchase cost and the minimization of the cycle electric quantity of the storage battery, prolongs the energy storage life and reduces the operation cost.

The invention relates to the technical field of alternating-current and direct-current power distribution networks, in particular to an alternating-current and direct-current power distribution network SOP partition interconnection locating and sizing method and a related device, and the method comprises the steps: obtaining GIS multi-source data and power distribution network data of a target region; dividing a power supply area according to the GIS multi-source data and the power distribution network data to obtain a to-be-interconnected partition pair set; generating a high-quality interconnected partition pair set by using the to-be-interconnected partition pair set; constructing a target optimization model by taking the minimum total operation cost as an optimization target and taking a power balance constraint, a node voltage constraint, a branch capacity constraint, an SOP operation constraint, an SOP reactive power constraint and an SOP capacity constraint as constraint conditions; and inputting the high-quality interconnection partition pair set into the target optimization model, cooperatively solving the target optimization model by adopting a double-layer optimization algorithm to obtain the optimal position and the optimal capacity of the SOP, and solving the technical problem that the existing SOP locating and sizing method is difficult to meet the targets of engineering feasibility and optimal operation benefit at the same time.

The invention discloses a power and voltage dual regulation energy storage optimization configuration method and device, and the method comprises the steps: obtaining a to-be-optimized power distribution network parameter, and giving the voltage time sequence sensitivity and power time sequence sensitivity of each power distribution network node based on a voltage sensitivity time sequence model and a power sensitivity time sequence model; fusing the voltage time sequence sensitivity and the power time sequence sensitivity to obtain a comprehensive sensitivity index, analyzing and judging the comprehensive sensitivity index, and screening each power distribution network node by combining a preset number of equipment access nodes to obtain a target access node; and constructing an energy storage cost objective function, a power flow constraint, a system power balance constraint and an energy storage capacity constraint, analyzing the energy storage configuration of the target access node by adopting a disturbance optimization strategy, and giving an energy storage optimization configuration result. Through considering the change of the power distribution network from two aspects of power and voltage, the cooperation capability in a multi-period scene is quantified, the energy storage configuration of the power distribution network is optimized, and the stable operation of the power distribution network is ensured.

The invention discloses a performance simulation calculation method for an aviation hybrid electric power system. The method comprises the following steps: firstly, constructing a sub-component calculation model covering an aerodynamic thermal component, a power generation subsystem, a power conversion and transmission subsystem, a battery subsystem, a motor and an aerodynamic system; establishing a reverse logic calculation framework driven by a load side demand, and realizing hierarchical reverse parameter solution from a load end to a power source end; and establishing a power balance equation at a bus end, carrying out iterative solution by adopting a Newton-Rafson algorithm, and realizing dynamic distribution and coordination control of multi-source energy in combination with an energy constraint module. According to the method, through a reverse solution and bus end balance strategy, the simulation calculation efficiency and convergence of the complex aviation hybrid electric power system are remarkably improved, and meanwhile, the adaptability and expandability of the model to different topological architectures and task profiles are enhanced; and an efficient and high-credibility calculation tool is provided for design, performance evaluation and energy management strategy optimization of the hybrid electric propulsion system of the aircraft.