1170 results about "Power balancing" patented technology

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 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 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 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 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 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.

The invention belongs to the technical field of power supply recovery, and particularly relates to a rapid power supply recovery method and device based on source-load-storage cooperative power grid partition optimization, and the method comprises the steps: S1, respectively constructing an electrical coupling degree index, a source-load-storage power balance index, a grid structure index and a node scale index, and comprehensively forming a power grid partition comprehensive index; s2, taking the comprehensive index of the power grid partition as a fitness function, designing an energy entropy driven hybrid selection mechanism to improve a simulated annealing genetic algorithm, and solving the power grid partition by using the improved simulated annealing genetic algorithm; s3, on the basis of the power grid partitions, taking each partition as a unit, and establishing an optimal scheduling model taking the maximum recovery of the important load as a target; and S4, solving the optimal scheduling model by adopting a particle swarm algorithm to obtain a rapid power supply recovery scheme. Priority recovery of the key load can be realized, and rapid recovery capability and reliability of the power grid are enhanced.

The invention relates to the technical field of photovoltaic power generation, and discloses a photovoltaic energy storage cooperative power generation system and method.The photovoltaic energy storage cooperative power generation system comprises a photovoltaic assembly, an intelligent string structure, an energy storage system and an intelligent controller, and the photovoltaic assembly comprises an east assembly and a west assembly and is connected to the direct current end of the same inverter to balance the morning and evening power generation peak values. The group string structure adopts a fixed group string and a switchable group string to be connected in parallel, and the group strings are dynamically switched to be connected and disconnected through an intelligent switch to adapt to irradiation fluctuation. The energy storage system comprises an energy storage battery and a bidirectional converter, a direct current end is connected with the battery, an alternating current end is connected with a grid through an intelligent switch, and charging and discharging scheduling under a peak-valley electricity price strategy is supported. The intelligent controller integrates a power prediction module, an economic dispatching module and an equipment protection module, monitors photovoltaic output, an energy storage SOC and a power grid state in real time, and dynamically optimizes string switching, energy storage charging and discharging and inverter operation parameters. Through east-west assembly power balance, flexible string reconstruction and energy storage intelligent scheduling, power generation income maximization and equipment service life prolonging are realized.

The invention discloses an island energy system opportunity constraint weekly optimization scheduling method and system, and the method comprises the steps: converting the opportunity constraint of the actual output of wind and light into a deterministic inequality constraint based on the prediction error probability distribution characteristics of wind power and photovoltaic; according to the obtained wind and light output constraint, a dynamic operation model is established, and waste heat output of the hydrogen fuel cell is coupled with an absorption refrigerator; with the purpose of minimizing the total operation cost of the system, combining the obtained deterministic inequality constraint and the dynamic operation model, defining the power balance constraint of electricity / heat / cold / fresh water, the hydrogen storage tank capacity constraint and the equipment climbing rate constraint, and constructing an optimization model; and solving the optimization model by adopting mixed integer programming to obtain an optimization scheduling scheme. The requirements of electricity, cold, heat and fresh water of an ocean island energy system are met, and meanwhile energy storage and cross-period utilization during wind and light resource enrichment are achieved.

The invention belongs to the field of power distribution network operation control, and discloses a power distribution network global optimization regulation and control method, system and device and a medium, and the method comprises the steps: obtaining multi-dimensional indexes including regional modularity, regional reactive power balance degree and regional active power balance degree in a power distribution network; based on a comprehensive evaluation mechanism of a multi-dimensional index, an optimal power distribution network region division result is obtained by utilizing iterative calculation of a heuristic search algorithm; a power distribution network global optimization model with cost optimization as a target is constructed so as to realize control of the comprehensive cost of the power distribution network; on the basis of a distributed optimization algorithm, a global optimization problem of the power distribution network is decomposed into a cloud main problem and a side terminal problem, and global optimality of resource allocation among different power distribution network regions is realized by solving the cloud main problem; and the optimal control strategy in the power distribution network area is realized by using the control result of the comprehensive cost of the power distribution network and the solution of the edge terminal problem. And the coordination control of the global massive controllable resources of the power distribution network is realized.

The invention discloses a multi-time-scale coordination control method and system for a virtual power plant with new energy access, and aims to solve the challenge of the fluctuation of new energy on the stability of a power grid. The method comprises the following steps: in a day-ahead scheduling stage, constructing a baseline scheduling plan based on natural conditions and historical loads, and allocating energy storage node demands through a block chain; in the intra-day rolling optimization stage, the plan is dynamically adjusted through short-term prediction, and energy storage requirements are redistributed; in the construction stage, a digital twinborn technology is adopted to simulate dynamic interaction between new energy and bus nodes and energy storage nodes; in the real-time control stage, energy storage demand sharing is realized in combination with model prediction control and a block chain. The system comprises a baseline scheduling module, an intraday optimization module, a virtual power plant construction module and a real-time control module. According to the method, through multi-time scale cooperation, block chain distributed management and total element simulation, the power balance capability is remarkably improved, the adaptability to the uncertainty of natural conditions is enhanced, and the operation efficiency and transparency are improved.

The invention provides a wind storage frequency modulation control method and device based on virtual inertia support and a medium, and the method comprises the steps: carrying out the aggregation of wind turbine generators through employing an equivalent model, and obtaining the virtual inertia of a wind power plant and the inertia of an energy storage power station; detecting system frequency deviation by introducing frequency tracking negative feedback, and calculating required auxiliary power according to power grid frequency deviation by utilizing inertia response characteristics of wind energy storage; the wind turbine generator releases kinetic energy of a rotor of the wind turbine generator to provide inertia support required by the system; through the design of the energy storage controller, the energy storage system adapts to the operation condition of new energy, necessary inertia support is provided for the system, charging and discharging of the storage battery are controlled, real-time response is achieved, power balance is maintained, and then constant frequency control is achieved. According to the invention, an efficient and integrated control system is constructed, and the flexibility and response speed of the wind energy storage system to cope with power grid frequency fluctuation challenges are greatly improved.

The invention relates to the technical field of battery energy storage station heat management, and discloses a battery energy storage station heat management optimization method based on multi-target control, which comprises the following steps: constructing a mathematical model of an energy storage station heat management system, and defining a state variable and a control variable of the model; the mathematical model comprises a battery heat production model, a battery heat exchange model, an SOC dynamic model and an energy storage station power balance model; based on the mathematical model of the thermal management system of the energy storage station, establishing a multi-target comprehensive evaluation function and setting constraint conditions; according to the defined state variables and control variables, the multi-objective comprehensive evaluation function and set constraint conditions, learning iteration is carried out based on a Q learning algorithm, a Q value is obtained when a preset convergence condition is reached, and an optimal control variable distribution strategy is output; and performing thermal management optimization on the energy storage station based on the optimal control variable distribution strategy. According to the invention, the balance among the targets is realized based on the combination of the multi-target control strategy and the optimization algorithm, and guarantee is provided for the performance and safety of the battery energy storage station.

The present application discloses a microgrid control method and apparatus, a microgrid, and a storage medium. A power type energy storage device is connected within a microgrid, and the microgrid control method comprises: when it is determined that the microgrid is in an unplanned islanding mode, controlling the power type energy storage device to enter a power support mode, wherein the power type energy storage device charges and discharges the microgrid in the power support mode; when the power type energy storage device is in the power support mode, carrying out source-load power adjustment on the microgrid; and once the microgrid reaches a source-load power balance, controlling the power type energy storage device to exit the power support mode. According to the microgrid control method, during unplanned islanding of the microgrid, flexible source-load power adjustment can be carried out thereon within a relatively sufficient time frame, thereby ensuring the stable operation of the microgrid.