1510 results about "Power balancing" patented technology

The invention relates to an optical storage cooperative control optimization method, electronic equipment and a medium. The method comprises the steps of collecting historical energy consumption and photovoltaic data of a park, constructing a photovoltaic electronic model based on the data, performing day-ahead prediction by adopting CNN-BiGRU, and generating a load power and photovoltaic generation power sequence; calculating the electricity consumption and the photovoltaic generating capacity of each time period; setting the capacity of an energy storage module, and calculating the energy storage charging and discharging capacity based on an SOC formula; according to the electricity consumption, the generating capacity and the energy storage capacity, determining a peak section discharge demand; photovoltaic fluctuation constraint is combined, and energy storage charging and discharging power is adjusted to stabilize fluctuation; optimizing load power utilization, photovoltaic power generation and energy storage power scheduling based on the power balance relation; and constructing a revenue maximization objective function, establishing a light storage collaborative optimization model in combination with constraints, and solving an optimal operation scheme of a prediction day. According to the method, photovoltaic power generation errors are reduced, power output is balanced, power grid requirements are met, the photovoltaic consumption rate is improved, healthy operation of the system is guaranteed, and operation benefits are improved.

The invention discloses an emergency demand response resource optimal allocation method under a hierarchical aggregation architecture, which comprises the following steps of: by constructing the hierarchical aggregation architecture, dividing flexible resources into a plurality of regions according to geographic positions and communication characteristics, and enabling a region coordination center to be responsible for local power allocation and load scheduling in each region; a power grid dispatching center is responsible for coordinated management of global demand targets, dispatching complexity is remarkably reduced, real-time performance is improved, an optimization model adopts a multi-target optimization method, power response cost, response reliability and time delay penalty are integrated, and through power balance, capability constraint and time constraint, accuracy and economical efficiency of an allocation scheme are ensured, and the allocation efficiency is improved. According to the method, rapid adjustment of resource state abnormity, response deviation or node faults is realized through dynamic adjustment and a distributed consensus algorithm, the surplus capacity in the region is preferentially utilized to compensate the insufficient demand, and redundant power support is scheduled through an inter-region coordination mechanism when necessary.

A distributionally robust optimization method and apparatus for an electric-hydrogen-thermal integrated energy system, relating to the field of power system optimization operation. The method comprises: establishing a multi-agent optimization scheduling model of an electric-hydrogen-thermal integrated energy system; using a data driving-based distributionally robust optimization model to describe the source-load bilateral uncertainty of a power grid system agent optimization scheduling model, establishing a three-layer two-stage power balance model of a power grid system, and using a column and constraint generation algorithm for solving; and using an alternating direction method of multipliers to alternately iterate the three-layer two-stage power balance model of the power grid system, a hydrogen network system agent optimization scheduling model, and a heat network system agent optimization scheduling model, to realize distributed parallel solving of the multi-agent optimization scheduling model of the electric-hydrogen-thermal integrated energy system. Compared with the prior art, each agent scheduling scheme is solved by means of interactive iteration in the present method, thereby protecting the information privacy of each agent while improving the operation economy and reliability of an interconnected power grid.

The invention discloses an optimal scheduling method and system for a wind-light-hydrogen storage micro-grid, and relates to the field of wind-light-hydrogen storage micro-grids, and the method comprises the steps: obtaining multi-source time sequence data, and carrying out the combined denoising and dynamic time alignment to generate a standardized input sequence; adopting a quantile regression model fused with a space-time attention mechanism to output a power prediction interval of wind and light output and load demand in a future time period; constructing a layered multi-objective optimization model; an improved adaptive particle swarm optimization algorithm is adopted to solve the layered multi-objective optimization model, and an equipment scheduling instruction set is generated; and dynamic correction is carried out, and micro-grid instruction distribution is carried out according to the equipment response priority. According to the method, multi-target conflicts such as power balance, equipment loss and energy efficiency are effectively balanced through multi-source data efficient preprocessing, space-time joint prediction interval generation and hierarchical multi-target optimization, and efficient and stable operation of the wind-light-hydrogen storage micro-grid is achieved.

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 power balance control method and system based on a photovoltaic sodium ion battery energy storage system, and relates to the technical field of intelligent power balance. The battery pack state parameters comprise a battery capacity recession rate, a single battery environment temperature gradient and a voltage balance degree at a single battery environment temperature; calculating the thermal management efficiency of the battery pack based on the battery pack state parameters; when the thermal management efficiency of the battery pack is lower than a preset standard threshold value, calculating the ionic conductivity and the rate value of a single battery in the battery pack; and when the ionic conductivity and the rate value of the single batteries in the battery pack are both lower than the preset values, switching the temperature-lost single batteries into a charging mode. By quantifying the thermal management efficiency, the problem that the sodium ion battery cannot be accurately controlled to be stably located at the proper temperature and voltage by the existing battery power balance control technical means is solved.

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 discloses a collaborative optimization scheduling method and system considering energy storage frequency modulation reserve. The method comprises the following steps: acquiring basic data of a power system; based on the basic data, a collaborative optimization scheduling model considering energy storage frequency modulation reserve is established, and the collaborative optimization scheduling model takes the minimum sum of the coal consumption cost, the unit start-stop cost and the wind curtailment cost of the thermal power generating unit as a scheduling target under multiple constraint conditions; the multiple constraint conditions comprise a lithium battery power constraint, a charging and discharging state constraint, an SOC constraint, a frequency modulation response constraint, a frequency safety constraint, a frequency modulation standby constraint, a flywheel energy recovery constraint, a thermal power generating unit operation constraint and a power balance constraint; solving the collaborative optimization scheduling model to obtain an optimal scheduling scheme of the power system; by comprehensively utilizing the advantages of a traditional unit, flywheel energy storage and lithium battery energy storage, the safety and operation economy of the system during frequency disturbance are improved.

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 provides a smart power grid dispatching optimization method based on adaptive evolution control, which comprises the following steps: performing power network graph modeling and standardization processing on a power network to generate a standardized graph structure; constructing a Markov decision framework of power grid dispatching based on the standardized graph structure; performing interactive training under a Markov decision framework to generate a preliminary scheduling strategy meeting power balance constraints; taking the preliminary scheduling strategy as an initial population, and generating a global optimization scheduling scheme through fitness function evaluation and multi-mode crossover mutation operation; and deploying a global optimization scheduling scheme to an actual power grid, and dynamically updating strategy network parameters based on node loads, power generation output and frequency data acquired in real time. According to the method, the power grid dispatching strategy can be dynamically adjusted in real time, consumption and dispatching of new energy are optimized, and the adaptive capacity of the power grid to different loads and power generation fluctuations is improved.

The invention discloses an electric power system active safety margin assessment method considering new energy output uncertainty. The method comprises the following steps: S1, constructing a first model according to an active power balance relation of an electric power system, phase angle difference constraints at two ends of a transmission line and generator active output constraints; s2, obtaining an active static security domain boundary according to the first model in combination with a maximum volume optimization method; dividing the operation state of the power system into a risk state and a safety state according to the new energy output uncertainty; s3, evaluating the static active safety margin of the system in a risk state according to the active static safety domain boundary in combination with an interval mathematical theory; and evaluating the static active safety margin of the system in a safe state according to the active static safety domain boundary in combination with a safe distance optimization model based on a Lagrange multiplier method. The invention also provides a power system active safety margin evaluation system considering the new energy output uncertainty.

The invention belongs to the technical field of comprehensive energy system optimization, and discloses a comprehensive energy system optimization method and system based on power-to-gas and carbon capture equipment. The comprehensive energy system comprising two-stage power-to-gas equipment, the carbon capture equipment, electric energy storage equipment and the like is constructed, and the coupling relation and adjusting characteristics between load energy conversion are combined; a low-carbon economic dispatching model is constructed, objective functions are established by considering power purchase cost, gas purchase cost and the like, a constraint set is established by considering thermoelectric power balance, system capacity, carbon capture and the like in a comprehensive energy system, and a multi-objective evolutionary algorithm is adopted to carry out optimization solution on a plurality of objective functions. According to the technical scheme, the total operation cost and the carbon emission of the system are comprehensively considered, the low-carbon and environment-friendly characteristics of hydrogen energy are fully played, the renewable energy consumption of the system is improved, the multi-target intelligent algorithm is adopted for solving, economic benefits are considered, the method is environmentally friendly, and the total operation cost and the CO2 emission of the system are reduced.

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 discloses an island micro-grid operation scheduling method and device. According to the scheme, the method comprises the steps that power generation characteristic modeling is conducted on a distributed power source and an energy storage system of a diesel storage island micro-grid; constructing a function taking the minimum daily total operation cost as a target, wherein the function covers the operation cost of a diesel generator and an energy storage system and the pollutant treatment cost; establishing a power impact type load mathematical model including power characteristics, system dynamic frequency response characteristics, micro-grid dynamic frequency safety constraints and diesel generator frequency control constraints; establishing a micro-grid operation constraint model which has constraints such as power balance, Distflow power flow, energy storage, charging, discharging and capacity, diesel generator output and climbing rate; and solving the model by using a particle swarm optimization algorithm, generating a diesel generator output, energy storage system charging and discharging and frequency modulation reserve capacity distribution strategy, and obtaining an island microgrid optimal operation scheduling decision.

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 an energy storage equipment power balance dynamic load automatic regulation and control method and system, and the method comprises the steps: building a charging power optimization model based on an electricity price policy divided in peak and valley time periods, combining historical electricity demand data, and obtaining the optimal charging power in each time period; the method comprises the following steps: constructing a discharge power dynamic adjustment model through operation state parameters acquired by a real-time environment parameter acquisition device in combination with real-time electricity demand data, and acquiring optimal discharge power in each time period; parameters of the two models are optimized, and the load state based on the optimal charging and discharging power is balanced; based on the historical regulation and control data, the overload condition under the abnormal factors is predicted; and on the basis of a power imbalance condition, preferentially ensuring power supply of a key load according to a power distribution priority and executing energy storage equipment state self-checking. The method has the advantages that the charging power and the discharging power are optimized in combination with the electricity price and the real-time requirement, so that the load state is balanced, the energy cost is reduced, and the equipment overload risk is avoided.

The invention provides a photovoltaic output prediction method and a distributed optical storage transformer area active / reactive support capability optimization method, and belongs to the technical field of power system automation. Comprising the following steps: constructing a prediction model based on meteorological information, extracting multi-scale features of meteorological data, reducing dimensions through kernel principal component analysis, predicting photovoltaic power through a long-short-term memory network, quantifying the uncertainty of the photovoltaic power, and forming a prediction interval. Based on a photovoltaic power prediction result, a robust nonlinear programming model is established, the sum of absolute values of active / reactive power transmitted to a main power grid by a light storage area is maximized as a target, and linear and nonlinear constraints such as energy storage operation, power balance, equipment capacity and power factors are considered at the same time. The robustness under the photovoltaic output uncertainty is ensured through a budget uncertainty set; the model is solved by adopting a sequential quadratic programming algorithm. According to the method, the photovoltaic power prediction precision is effectively improved, and organic combination of high-precision prediction and robust nonlinear optimization is realized.

The invention provides an AC / DC hybrid microgrid control method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring AC side bus frequency information and DC side bus voltage fluctuation information in an AC / DC hybrid microgrid; and based on the frequency information and the voltage fluctuation information, determining the working mode of the AC / DC hybrid microgrid, thereby controlling the operation and working state of each sub-grid in the AC / DC hybrid microgrid according to a power autonomous mode or a power mutual aid mode. And the working mode of the hybrid energy storage system is determined based on the energy storage charge states of the super capacitor and the storage battery. According to the alternating-current and direct-current hybrid microgrid control scheme provided by the invention, the hybrid energy storage system composed of the super capacitor and the storage battery is provided, stable transmission and fluctuation stabilization of power are realized through coordinated control of the interconnection converter and the energy storage subnet, power fluctuation working conditions of multiple scenes can be adapted, source-load-storage power balance is effectively realized, and the stability of the system is improved. And the stability and reliability of the micro-grid are improved.

The invention discloses a multi-mode switching control method suitable for a hydrogen-electricity combined energy storage system, and the method comprises the steps: carrying out the coupling of a plurality of control units through a DC bus, and obtaining a system architecture; according to the operation mode of the system, coordinating the power output of the energy storage unit by using a dynamic collaborative optimization method based on a virtual synchronous machine technology and a variable droop coefficient, and introducing a dynamic energy distribution model and a dynamic energy distribution constraint to obtain a power balance framework of the energy storage unit; performing collaborative optimization and joint optimization on a plurality of energy storage units in the power balance framework by using the optimization model based on the cooperative game and the dynamic power regulation model to obtain a joint dynamic balance mechanism; and iteratively optimizing and improving the combined dynamic balance mechanism by using a distributed dynamic optimization algorithm to obtain the hydrogen-electricity combined energy storage system. The invention relates to a hydrogen-electricity combined energy storage control technology, and solves the technical problems that an existing hydrogen-electricity combined energy storage system is difficult to coordinate and insufficient in dynamic response in the multi-mode switching process.

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 relates to a sodium electric energy storage system dynamic partition power control method based on string type cluster level management, which comprises the following steps of: firstly, realizing physical isolation of battery clusters through a string type framework, configuring an independent power conversion module and a cluster level controller for each cluster, acquiring voltage, current, SOC (state of charge) and SOH (state of health) of each cluster in real time, and on the basis of the data, in a discharging or discharging process, determining the state of charge of each cluster; a dynamic partition strategy and automatic adjustment of power distribution are realized according to inter-cluster SOC, SOH, voltage range, temperature range, power limit and other differences, and an energy storage system is dynamically divided into a high-energy high-health area, a high-energy medium-health area, a medium-energy high-health area, a medium-energy medium-health area, a low-energy or sub-health area and a fault early warning area in a discharging and charging mode; and rapid balance of electric quantity among clusters is realized through the converter module, so that power distribution is ensured to meet power grid requirements, overcharge and overdischarge of batteries are avoided, and the service life of a battery system is prolonged.

The invention discloses an electricity-hydrogen coupling port comprehensive energy system flexible scheduling method considering port-ship two-way interaction. The method comprises the following steps: 1) establishing an objective function of an electricity-hydrogen coupling port comprehensive energy system flexible scheduling model; 2) establishing constraint conditions of a logistics system in the electricity-hydrogen coupling port comprehensive energy system, comprising an electric energy ship scheduling constraint considering port-ship bidirectional interaction, a hydrogen energy ship scheduling constraint considering port-ship bidirectional interaction, an electrically-driven storage yard crane scheduling constraint, an electrically-driven wharf crane scheduling constraint, an electric energy container truck scheduling constraint, a hydrogen energy container truck scheduling constraint, a logistics system logic constraint and a logistics system electricity-hydrogen power balance constraint. 3) establishing constraint conditions of an energy system in the electricity-hydrogen coupling port comprehensive energy system, wherein the constraint conditions comprise an energy system gas turbine constraint and an energy system electricity-hydrogen power balance constraint; and 4) solving the flexible scheduling model of the integrated energy system of the electricity-hydrogen coupling port, and obtaining a logistics system and energy system scheduling scheme.

The invention relates to the technical field of power control strategy analysis, and discloses a carbon electricity energy storage power control strategy analysis method and system considering multi-source-load cooperation. The method comprises the following steps: performing power balance analysis processing on a source-load power cooperation characteristic matrix under carbon emission constraint to obtain a power scheduling initial solution of the carbon electricity energy storage system; performing source-load collaborative optimization processing on the power scheduling initial solution to obtain a test power optimization sequence; performing energy storage capacity distribution processing on the power optimization sequence to obtain a charging and discharging power instruction set; and performing power correction processing on the charging and discharging power instruction set under the carbon emission cost constraint to obtain an energy storage scheduling strategy, and performing multi-target collaborative optimization processing to obtain a carbon electricity energy storage power control strategy. According to the method, under the condition of considering the carbon emission constraint, accurate extraction and multi-target collaborative optimization of the source load power characteristics are realized, so that the power control performance of the carbon electricity energy storage system is improved.

The invention relates to the technical field of energy, in particular to a capacity configuration method of an off-grid hydrogen-electricity hybrid energy storage system. The capacity configuration method of the system comprises the following steps: 1, establishing a mathematical function of the off-grid hydrogen-electricity hybrid energy storage system; 2, establishing a target optimization function by taking the lowest total cost of the system as an economical efficiency target and taking the maximum self-power generation rate and the minimum power abandoning rate as operation targets of the hydrogen-electricity coupling direct-current micro-grid system; 3, establishing a capacity configuration constraint function by taking system power balance, thermodynamic balance, grid-connected interaction power balance, equipment capacity upper limit and equipment power upper and lower limits as constraint conditions; and 4, establishing a capacity configuration optimization model of the system based on the mathematical function, the target optimization function and the capacity configuration constraint function of the off-grid hydrogen-electricity hybrid energy storage system, and solving through an NSGA-II algorithm to obtain an optimal capacity configuration scheme. The economical efficiency, stability and efficiency of system operation are considered at the same time, and reasonable capacity configuration of the system is achieved.

The invention relates to the technical field of micro-grid capacity configuration, in particular to a micro-grid capacity configuration optimization method and system. According to the method, a total cost objective function is constructed by taking the minimization of the total cost of all main bodies in the wind-solar storage micro-grid as a target, a wind-abandoning and light-abandoning objective function is constructed by taking the minimization of the penalty cost of wind-abandoning and light-abandoning as a target, and an ACG wind-solar frequency modulation objective function is constructed by taking the maximization of ACG wind-solar frequency modulation benefits as a target. Setting a power balance constraint and a wind curtailment and light curtailment constraint as constraint conditions, and establishing a micro-grid capacity non-cooperative game model; and based on the micro-grid capacity non-cooperative game model and the constraint conditions thereof, solving the micro-grid capacity non-cooperative game model by adopting an improved particle swarm algorithm to obtain a capacity target configuration result. According to the method, the power output of each main body is optimized by considering the wind curtailment and light curtailment factors, the Nash equilibrium of the system is achieved, the system income is maximized, and the overall performance and economical efficiency of the micro-grid system are effectively improved.

The invention relates to the technical field of power systems, and discloses a new energy power control method and device based on distributed power supply access, and the method comprises the steps: constructing a unified topology model comprising distributed source loads, energy storage equipment and charging piles based on an access mode of a distributed power supply; acquiring operation data of the distributed power supply through the unified topology model, and performing data preprocessing on the operation data to generate a standardized operation data set; monitoring the real-time operation state and operation index of the power grid in real time based on the standardized operation data set, dynamically generating a corresponding power control instruction, and issuing the power control instruction to the distributed power supply to execute a power regulation task; and generating a power balance instruction after the occurrence of the power grid abnormal event is detected, decomposing the power balance instruction based on the unified topology model and the standardized operation data set, and executing a power balance task. According to the invention, real-time monitoring and dynamic power adjustment of the distributed power supply can be realized, and the power grid stability and the power balance capability are improved.

The invention relates to the field of locomotive power battery electric quantity control, and particularly discloses a locomotive power battery electric quantity hysteresis equalization control method, which comprises the following steps that: a traction force exerting mode is reset into an axle load transfer mode, and the initial value of an electric quantity equalization compensation coefficient K is a rack axle load transfer coefficient corresponding to zero speed; judging whether the current mode is a power battery power supply mode; if yes, the TCU receives the electric quantity of a front power battery and the electric quantity of a rear power battery in real time according to the current locomotive working mode, calculates the electric quantity average value difference value, monitors the auxiliary load power and the traction motor power and calculates the power difference value; entering a hysteresis control logic process; calculating a dynamic traction force compensation coefficient, ensuring that the traction force compensation amount is matched with the actual load characteristic of the system, correcting the traction characteristic curve in real time, and dynamically compensating the electric quantity difference of the battery; and performing traction distribution control according to the locomotive speed and outputting corresponding traction.

The invention discloses a distributed resource autonomous regulation and control method based on multi-agent learning, and relates to the field of intelligent power grids and resource scheduling. According to the method, a multi-agent reinforcement learning (MARL) framework is adopted, distributed resources are modeled into a plurality of agents, each agent makes an independent decision according to local information, and autonomous regulation and control of the resources are achieved. The system optimizes a global target and improves the economy, power balance and robustness of the system through a cooperation and competition mechanism between intelligent agents. In order to improve the adaptability of the system, a dynamic learning mechanism based on communication is provided, and the response capability of the system to real-time requirements and environmental changes is enhanced. Compared with a traditional centralized regulation and control method, the method has the advantages that dependence on central control can be reduced, regulation and control efficiency can be improved, resource allocation can be optimized, and the method has higher dynamic learning and adaptive capacity, shows higher regulation performance in a complex environment and has better economical efficiency, stability and robustness.

The invention relates to the technical field of power systems, and particularly discloses a multi-energy coupling system optimal scheduling method considering grid-connected safety, and the method comprises the steps: constructing a multi-energy coupling system architecture model; the multi-energy coupling system architecture model comprises a renewable energy power generation unit, a traditional energy power generation unit, an energy storage unit, an energy conversion unit and a combined cooling heating and power generation unit; constructing a distributed energy storage optimization operation model based on the multi-energy coupling system architecture model; the constraint conditions comprise charging and discharging constraint, charge state constraint and peak clipping constraint of battery energy storage; carrying out output constraint on the energy conversion equipment; electric vehicle charging station operation constraint; new energy power generation constraint; power balance constraint; public network line transmission power constraint; constraining transmission power of an internal distribution network line; and solving the distributed energy storage optimization operation model by adopting an optimization algorithm to obtain an optimization scheduling scheme. The method has the advantage that scientific and reliable technical support is provided for efficient operation of the multi-energy coupling system.