31results about How to "Improve consumption rate" patented technology

The invention discloses a control method and device of an air conditioner, cloud computing equipment, electronic equipment and a medium, power is supplied to a load through a power supply system, the power supply system comprises a photovoltaic device and a battery device, the load comprises the air conditioner, and the control method of the air conditioner comprises the steps that target data of the power supply system is obtained, the target data of the power supply system comprises photovoltaic power data of a photovoltaic device and battery data of a battery device; based on target data of the power supply system, determining a target interval in which the power of the photovoltaic device is located from the plurality of power intervals obtained by division; and on the basis of the target interval, the on-off state of the air conditioner is controlled so as to change the number of the air conditioners in the on-state, and / or the air outlet direction of the air conditioner is controlled. According to the control method of the air conditioner, the number of the air conditioners in the start-up state and the air outlet direction of the air conditioners are adjusted in combination with the interval where the power of the photovoltaic device is located, when the photovoltaic power is sufficient, the photovoltaic power consumption rate can be increased, and the air conditioner brings better temperature experience to a user by consuming the photovoltaic power.

The invention relates to the technical field of energy system optimization control, and discloses an energy storage cooperation method, device and system of an energy system. The method comprises the following steps: acquiring energy state data of an energy system and corresponding system state data; performing data fusion on the energy state data and the corresponding system state data to obtain system situation data of the energy system; and performing energy scheduling on the energy system based on a preset scheduling mechanism according to the system situation data. According to the invention, the new energy consumption rate is improved, and the global collaborative optimization of the multi-energy system is realized.

The invention discloses a mining area integrated energy system optimization scheduling method coupling gravity energy storage and CCUS-P2G. The method comprises the following steps: constructing a mining area integrated energy system architecture containing gravity energy storage and CCUS-P2G coupling; a vertical shaft type gravity energy storage device model is established, and mutual conversion between electric energy and gravitational potential energy is achieved through vertical displacement of a weight; establishing a CCUS-P2G coupling model, and synthesizing methane from carbon dioxide captured by a CCUS device and hydrogen generated by an electrolytic bath in the two-stage P2G in a methane reactor; a mixing gas turbine model is established, methane generated according to the CCUS-P2G coupling model is mixed with gas in coal bed gas in the mining area, and gas meeting the combustion concentration requirement of a gas turbine is obtained; a system optimization scheduling model considering a stepped carbon transaction mechanism is constructed, and solving is carried out on the premise that system power balance constraints are met by taking the minimum total cost of the system as an objective function. The defects that in the prior art, the utilization rate of mining area associated energy is low, waste mine resources are wasted, and a single energy system cannot give consideration to low carbon and economical efficiency at the same time are overcome to a certain extent.

The invention belongs to the technical field of power dispatching and operation, and particularly provides a negative electricity price scene-oriented source-grid-load-storage collaborative interaction and marginal cost optimization system and method, and the method comprises the steps: monitoring the state of a power market through a data collection module, predicting the supply and demand conditions of the system, and judging that there is a negative electricity price risk; calculating real-time marginal cost of the schedulable resources, wherein the real-time marginal cost comprises power generation resource marginal cost, energy storage resource marginal cost and load regulation marginal cost; establishing an optimization model by taking the minimization of the total marginal cost of the system as a target, and solving to obtain an optimal scheduling scheme of each resource; converting the optimal scheduling scheme into a specific control instruction, issuing the specific control instruction to each resource main body of a source, a network, a load and a storage, and executing power generation adjustment, charging and discharging or load interruption operation; and monitoring the execution effect of each resource instruction and the actual clearing electricity price of the market in real time, calculating the execution deviation, and carrying out rolling correction in the next optimization period. According to the invention, early warning of the negative electricity price risk can be realized, and global optimization scheduling is carried out on the source network load storage resources.

The invention relates to a cluster division method of a building direct current interconnection power distribution system for photovoltaic consumption rate improvement. The cluster division method comprises the following steps: acquiring building group data; obtaining a spanning tree representing the interconnection condition in the cluster; calculating the interest-considered interconnection cost of the spanning tree, and generating a cluster division result, an interconnection condition and a maximum photovoltaic consumption rate when the fitness is optimal; and (3) changing the interest rates and the energy storage coefficients, returning to the step (2), generating a cluster division interconnection diagram under different interest rates and different energy storage coefficients, and analyzing the influence of the interest rates and the energy storage coefficients on the cluster division result and the maximum photovoltaic consumption rate when the fitness is optimal. According to the method, the advantage of iterative optimization of the particle swarm algorithm and the advantage of uniform cluster division of the K-means algorithm are combined, cluster division can be more uniform, the electricity purchase demand of the building for the power grid is reduced, the consumption rate of photovoltaic energy is improved to the maximum extent, and the load demand, namely the peak load, of the power utilization peak period of the building is reduced.

An electric power generation engine (2) of a series hybrid vehicle has a compressor (15) on an upstream side of a throttle valve (21) of an intake passage (12), and has a negative pressure generation valve (27) of a butterfly valve type on an upstream side of the compressor (15). In an EV mode in which a combustion operation of the engine (2) is stopped, energization to an electric actuator of the negative pressure generation valve (27) is stopped (time t6-t7). The negative pressure generation valve (27) is kept in a fully open state by a pressing force of a return spring. In order to suppress a collision against a fully open limiter, after a prescribed opening degree (θpre) before opening to a full opening, the negative pressure generation valve (27) is gently opened to a position of a full opening (θmax) limited by the fully open limiter (time t2-t6).

The invention discloses a source-network-load-storage integrated flexible regulation and control method for an industrial enterprise, and aims to solve the problems that production scheduling and energy regulation and control of an existing regulation and control system are disjointed, emergency response is slow, light abandoning loss is large and the like. The method comprises a flexible adjustment step and a quasi-flexible emergency adjustment step: the flexible adjustment step is based on a four-level time scale intelligent production scheduling plan with deducible and invertible functions, combines real-time operation data of each subsystem, cooperatively regulates and controls photovoltaic, energy storage, waste heat power generation and load subsystems, preferentially stores energy and charges when photovoltaic is over-generating, and carries out emergency adjustment when the photovoltaic is over-generating. After the stored energy is saturated, the output of the photovoltaic inverter is limited in proportion; the quasi-flexible emergency adjustment step is started when an emergency situation is monitored, a minimum branch removal combination is solved through a branch removal algorithm, an instruction is quickly issued by means of IO equipment connected with a specific optical fiber link, emergency logic can be reused to all subsystems, and output is gradually recovered according to a preset strategy after the emergency is finished. The method improves the new energy consumption rate, reduces the light abandoning loss and the power consumption cost, guarantees the system stability and the production continuity, is suitable for various high-energy-consumption industrial scenes, and is low in improvement cost.

The application discloses an off-grid type reverse control integrated energy scheduling method based on cold storage thermal inertia, defines a thermal state of charge SOT through a first-order thermal resistance and thermal capacity model, and quantifies the cold storage thermal inertia as virtual energy storage; based on a SOT and SOC cooperative criterion, redundant power is over-cooled and absorbed as cold storage when photovoltaic power is abundant, and cold is released by using thermal inertia when photovoltaic power is insufficient; the second-order change rate of photovoltaic power is used to predict cloud shadow sudden drop and reduce load in advance; and residual error analysis is used to diagnose refrigeration energy efficiency abnormalities in real time. The application breaks through the limitation of electric-thermal scheduling separation, suppresses battery impact and avoids photovoltaic power abandonment through virtual energy storage scheduling by replacing electricity with heat, and improves the service life of the energy storage system and the comprehensive energy utilization rate.

The invention discloses a control method and device of an air conditioner, cloud computing equipment, electronic equipment and a medium, power is supplied to a load through a power supply system, the power supply system comprises a photovoltaic device and a battery device, the load comprises the air conditioner, and the method comprises the steps that target data of the power supply system is obtained, the target data of the power supply system comprises photovoltaic power data of a photovoltaic device and battery data of a battery device; based on target data of the power supply system, determining a target interval in which the power of the photovoltaic device is located from the plurality of power intervals obtained by division; determining a current working mode of the air conditioner; and based on the target interval and the current working mode, at least one of the set temperature and the electric auxiliary heating function of the air conditioner is controlled. According to the control method of the air conditioner, the set temperature and the electric auxiliary heating function of the air conditioner are adjusted in combination with the interval where the power of the photovoltaic device is located, when the photovoltaic power is sufficient, the photovoltaic power consumption rate can be increased, and the air conditioner can bring better temperature experience to a user by consuming the photovoltaic power.

The application provides a large model multi-agent A2A virtual power grid dispatching and power selling system and method, which comprises a vertical field energy large model module, an MCP tool layer module, an A2A multi-agent negotiation layer module and an application service layer module; the vertical field energy large model module serves as a system core reasoning engine; the large model multi-agent A2A virtual power grid dispatching and power selling system and method; the large model multi-agent A2A virtual power grid dispatching and power selling system provided by the application improves system operation safety and reliability, constructs a continuous learning closed loop of execution, evaluation, optimization and updating, iteratively optimizes dispatching and power selling strategies through review and optimization of agents, strengthens system adaptive and autonomous evolution capabilities, guarantees long-term stable and efficient operation, and provides a new technical path for intelligent operation of a virtual power grid.

The invention belongs to the technical field of power system optimization scheduling, and particularly relates to a new energy-thermal power collaborative scheduling method considering a section dynamic power flow limit, which comprises the following steps of: constructing a multi-region new energy output space-time correlation model, describing space-time correlation by combining a Copula function, and generating a typical scene through clustering reduction; the method guarantees the comprehensiveness of scene coverage and balances the calculation efficiency, achieves the collaborative optimization of the economic cost and the environmental protection target through defining a core parameter and variable system and constructing a target function matrix, and achieves the real-time updating of the dynamic limit along with the fluctuation of the new energy output through building a multi-dimensional constraint system containing the section dynamic power flow constraint. And finally, a CPLEX solver is combined with an adaptive differential evolution algorithm to efficiently solve a mixed integer quadratic programming problem, so that scientific and reliable technical support is provided for optimal scheduling of a power system, and the economical efficiency, the safety, the environmental protection property and the new energy consumption efficiency of scheduling are comprehensively considered.

The application discloses an energy management strategy of a new energy direct-current micro-grid water production system, which adopts daily operation cost, daily water production and daily abandoned wind and light rate as optimization indexes of the whole system, uses an improved radar chart method to obtain a fitness function, and uses a genetic algorithm to solve an optimal solution of the fitness function under full consideration of specific requirements of water production equipment and influences of converter droop control on unit output. Since a deviation range of a direct-current bus voltage has certain requirements, droop coefficients of the converter droop control have certain limitations, and the droop coefficients influence unit output. The application fully considers influences of control modes of units of the direct-current micro-grid on unit output and special constraint conditions of water production equipment, and realizes optimal operation of water production, water production cost and abandoned wind and light rate.

This invention provides a flexible control method for a DC58.4V low-voltage DC power routing flexible control system. Specifically, after the system is powered on, the bus current and voltage are collected by a dedicated metering sampling sub-board, filtered, and processed by a metering chip before being transmitted to the main control CPU. The HPLC carrier main module couples signals through the bus and distributes them independently to each branch via a signal isolator. The HPLC slave module transmits equipment data back, achieving bidirectional communication and full-domain monitoring. The main control CPU generates instructions, which are used to achieve orderly load startup via a snap-on expansion interface. The flexible control execution module collects branch data and adjusts the conduction level of the MOSFETs via PWM to achieve flexible adjustment of photovoltaic and mains power, while simultaneously controlling the balanced charging and discharging of the battery pack and initiating auxiliary charging when energy storage and photovoltaic power are insufficient. This method achieves flexible energy management across the entire DC58.4V system, with stable and reliable communication.

The invention discloses a transmission and distribution cooperation-based mountainous area power grid source-grid load-storage progressive coordination planning method, system and device, and a medium, and the method comprises the following steps: generating an original operation scene through a sampling method according to historical load data and historical output data, and reducing the original operation scene into a typical operation scene; solving the source network load storage coordinated optimization model by adopting a decomposition algorithm, and obtaining an optimal investment decision of each planning stage through iteration; and performing load flow calculation on the typical operation scene based on the power grid frame parameters and the optimal investment decision, and calculating a new energy consumption rate. According to the invention, by constructing a full-voltage class coordinated optimization framework and a progressive rolling planning mechanism, the technical problems of bidirectional power flow, poor planning collaboration, insufficient uncertainty processing capability and the like caused by the access of high-proportion new energy to the mountain power grid are effectively solved.

The application relates to the field of energy supply equipment of expressway service areas, and discloses a photovoltaic energy storage and charging collaborative power supply device for an expressway service area, which comprises a base, a protection box and a support are arranged at the top of the base, a photovoltaic power supply assembly is arranged at the top of the support, the output end of the photovoltaic power supply assembly is electrically connected with an energy storage buffer unit and an intelligent charging and distribution control module respectively, the energy storage buffer unit and the intelligent charging and distribution control module are electrically connected, the intelligent charging and distribution control module and a load scheduling unit are electrically connected, a cleaning assembly is arranged at the top of the support, and a heat dissipation assembly is arranged in the protection box. Through the collaborative operation of the service area photovoltaic energy storage and charging system, the photovoltaic energy consumption rate is improved, the power supply pressure of the power grid at the peak is relieved, the power supply stability of the service area is enhanced, the energy utilization efficiency of the road is optimized, and the application requirement of the expressway photovoltaic energy storage and charging integrated application is met.

The present application relates to a zero-carbon park energy intelligent control method and system based on power prediction, comprising the following steps: obtaining park multi-source data, the multi-source data including historical power load data, weather forecast data, renewable energy power generation data and carbon emission monitoring data; based on the multi-source data, performing multi-time scale power prediction through a pre-constructed hybrid prediction model to generate ultra-short-term power prediction sequence, short-term power prediction sequence and medium and long-term power prediction sequence; according to the ultra-short-term power prediction sequence, the short-term power prediction sequence and the medium and long-term power prediction sequence, a park energy system optimization scheduling model considering carbon flow coupling characteristics is constructed; the optimization scheduling model is solved to generate a day-ahead scheduling plan and a real-time correction plan containing source-grid-load-storage multi-end cooperation; the present application can realize more intelligent zero-carbon park energy intelligent control.

The invention relates to a zero-carbon park energy intelligent control method and system based on power prediction, and the method comprises the following steps: obtaining park multi-source data which comprises historical power load data, weather forecast data, renewable energy power generation data and carbon emission monitoring data; based on the multi-source data, performing multi-time-scale power prediction through a pre-constructed hybrid prediction model, and generating an ultra-short-term power prediction sequence, a short-term power prediction sequence and a medium-and-long-term power prediction sequence; according to the ultra-short-term power prediction sequence, the short-term power prediction sequence and the medium-and-long-term power prediction sequence, constructing a park energy system optimization scheduling model considering the carbon flow coupling characteristic; solving the optimization scheduling model, and generating a day-ahead scheduling plan and a real-time correction plan containing source-network-load-storage multi-terminal cooperation; according to the invention, more intelligent zero-carbon park energy intelligent control can be realized.

A micro-grid multi-machine transmission intelligent frequency conversion integrated device for an oil production well site belongs to the technical field of oil field well site energy supply and equipment transmission control, and is characterized by comprising an integrated cabinet body, a photovoltaic energy storage system, a multi-machine transmission system and a well site intelligent control system are arranged in the integrated cabinet body; the photovoltaic energy storage system is respectively communicated with the multi-machine transmission system and the well site intelligent control system; and the multi-machine transmission system is communicated with the well site intelligent control system. All core systems are integrated through the container type cabinet body, the layout is integrated and simplified, the occupied area is reduced by 60%, the installation and debugging period is shortened by 30%, and the construction cost is reduced; the photovoltaic direct current is directly connected to the direct current bus for direct driving of the pumping unit, and the absorption rate is increased to 90% or above; the matched energy storage battery is used for storing redundant electric energy, so that the photovoltaic consumption rate can be effectively improved, near-zero-carbon operation is realized, and near-zero-carbon well site construction requirements are met; continuous production during failure is ensured through power frequency loop backup, failure loss is reduced, and multiple safety guarantees are achieved.

The invention discloses a building-oriented energy cell bidirectional direct current interconnection micro-grid system. The system comprises a solar photovoltaic power generation device, a modular wall-mounted energy storage battery unit, various direct current loads, a bidirectional direct current-direct current converter, a system interaction port, a cooperative control unit and a communication interface. The photovoltaic module provides direct-current electric energy, the energy storage unit is used for peak clipping and valley filling and standby power supply, the direct-current load realizes terminal consumption, the bidirectional converter is used for electric energy complementation between cells, and the system interaction port can be connected with commercial power for rectification and other distributed energy sources to improve the energy supply stability. The cooperative control unit is responsible for real-time detection, power allocation and operation strategy execution. And by introducing a model prediction control algorithm and a bidirectional direct current interconnection architecture, the problems that a traditional building energy system has many energy conversion links and a scheduling strategy is rigid are solved, so that the self-consumption rate of renewable energy sources is improved, and the operation cost of the system is reduced.

This application provides a comprehensive energy optimization scheduling system and method based on big data analysis. It acquires historical and real-time monitoring data from multiple energy sources; identifies pattern similarity characteristics between renewable energy output curves and load demand curves from historical operating data; and divides the energy optimization scheduling cycle into multiple non-uniform scheduling periods based on these pattern similarity characteristics. It determines the predicted values ​​of renewable energy output and load demand, as well as the associated power deviation confidence intervals, for each non-uniform scheduling period. When the real-time power prediction deviation in a future non-uniform scheduling period exceeds the corresponding power deviation confidence interval, it reallocates power resources for the future non-uniform scheduling period, generating power adjustment instructions for global energy optimization scheduling; and performs comprehensive energy optimization scheduling based on big data analysis using these power adjustment instructions. The technical solution provided in this application can achieve optimized scheduling of comprehensive energy resources under the influence of power imbalance.

The application relates to the field of power system automation, and specifically discloses a micro-grid hierarchical rolling collaborative optimization scheduling method under an extreme weather environment on a plateau. The method realizes accurate perception and prediction of weather mutations by constructing a multi-source weather monitoring network and an attention mechanism neural network system; realizes global optimization and local response collaboration by adopting a hierarchical scheduling architecture at the system level, the micro-grid level and the device level; establishes a multi-objective optimization function considering economic, reliable and resilient targets, and introduces robust constraints such as a plateau attenuation coefficient and temperature compensation; uses an improved multi-agent deep deterministic policy gradient algorithm for rolling solution, and triggers an emergency mechanism when a mutation is identified; and realizes model adaptive iteration based on operation data through federated learning. The application can significantly improve the accuracy of weather mutation identification and the real-time performance of scheduling, enhance the resilience and power supply guarantee rate of the micro-grid on the plateau, and ensure the safe operation of key loads under extreme environments.

The invention discloses a control method and device of an air conditioner, cloud computing equipment, electronic equipment and a medium, power is supplied to a load through a power supply system, the power supply system comprises a photovoltaic device and a battery device, the load comprises the air conditioner, and the control method of the air conditioner comprises the steps that target data of the power supply system is obtained, the target data of the power supply system comprises photovoltaic power data of a photovoltaic device and battery data of a battery device; based on target data of the power supply system, determining a target interval in which the power of the photovoltaic device is located from the plurality of power intervals obtained by division; the current working mode and the current environment temperature of the air conditioner are determined; and controlling the rotating speed of a compressor of the air conditioner based on the target interval, the current working mode and the current environment temperature. According to the method, the rotating speed of the compressor of the air conditioner is adjusted by combining the power surplus degree of the photovoltaic device and the current environment temperature, the photovoltaic consumption rate is increased when photovoltaic power is sufficient, and better temperature experience can be brought to a user.

The utility model relates to a heat dissipation mechanism, in particular to a heat dissipation mechanism of a high-pressure diesel generating set, which comprises a circulation box, and a liquid inlet pipe, a first liquid outlet pipe and a second liquid outlet pipe are fixedly mounted on the circulation box. A ball seat is further included. A first pipeline and a second pipeline are fixedly mounted on the ball seat; a ball tube is rotationally and hermetically connected in the ball seat, and a channel is formed in the ball tube; a liquid pump; the heat dissipation piece comprises a heat dissipation pipeline; the LED lamp further comprises a switching piece, and the heat dissipation piece acts to drive the bulb tube to rotate so that the channel can be communicated with the second pipeline. In the initial stage of starting, the heat dissipation piece does not work, at the moment, heat generated by the diesel generator can rapidly increase the temperature of cooling liquid in the circulation box to the optimal working temperature, and therefore the situation that power output is delayed after the unit is started due to the fact that the temperature of the cooling liquid rises slowly in the initial stage of starting is avoided; when the temperature of the cooling liquid reaches the optimal working temperature, the heat dissipation piece maintains the temperature of the cooling liquid, and performance reduction and service life shortening of the diesel generator caused by over-high temperature of the cooling liquid are avoided.

This invention provides a method and system for coordinated optimization scheduling of power distribution networks, grids, loads, and storage systems, including thermal storage industrial loads. The method includes: acquiring historical prediction error data of new energy sources and loads; establishing a mathematical model of a gas turbine equipped with a flexible carbon capture device, and performing convex relaxation processing on the non-convex problem of the model when the flue gas split ratio of the carbon capture device is adjustable; constructing a mathematical model of the demand response of thermal storage industrial loads, considering daily regulation capacity, daily regulation frequency, and daily output constraints, and performing piecewise linearization processing on the nonlinear constraints of the daily production tasks of thermal storage industrial loads; obtaining a set of typical prediction error scenarios based on the acquired historical prediction error data of new energy sources and loads; and constructing a coordinated optimization scheduling model of power distribution networks, grids, loads, and storage systems, including the demand response of thermal storage industrial loads. This invention leverages flexible resources across multiple stages of the power distribution network, improving the absorption rate of new energy sources and the economic efficiency of system operation, and reducing carbon emissions, thereby contributing to the achievement of dual-carbon goals and sustainable social development.

This invention provides a method and system for optimizing multi-source coordinated charging and discharging strategies for energy storage systems. The method includes: predicting electricity prices based on acquired multi-source historical data to obtain predicted electricity prices; outputting an optimized load curve with the objective of minimizing the weighted sum of maximum demand power and electricity cost within a scheduling cycle; using the optimized load curve as a consumption benchmark, determining the existence of curtailment risk and identifying a dynamic operating range when the predicted photovoltaic power generation exceeds the sum of the consumption benchmark and the current rechargeable capacity of the energy storage system; constructing a scheduling curve model based on the predicted electricity prices to maximize net revenue; constructing constraints for the scheduling curve model based on the optimized load curve and the dynamic operating range; and solving the scheduling curve model based on the constraints to obtain a charging and discharging scheduling curve and control the operation of the energy storage system. This invention improves energy utilization efficiency and reduces electricity costs.

The application discloses a virtual grid division method for improving source-grid-load-storage multi-element cooperation, and the method comprises the following steps: initializing a virtual grid division of a power grid network topology structure, forming a corresponding grid adjacency matrix for different virtual grid divisions; using a generalized node search algorithm to traverse node information of the virtual grid division, and obtaining division parameters of the virtual grid division; based on the division parameters of the current virtual grid division, solving a pre-constructed grid division optimization model, calculating a grid division target function adaptive value and a grid division result, wherein the target function in the grid division optimization model considers virtual grid communication cost, virtual grid power supply rate, virtual grid power interaction and virtual grid node control dimension; when it is determined that the target function adaptive value corresponding to the current virtual grid division is smaller than the target function adaptive value corresponding to the last virtual grid division, saving the current virtual grid division result.

The invention discloses a park micro-grid regulation and control system and method based on a fusion terminal. The system comprises a micro-grid management and control system and the fusion terminal. The method comprises the steps that a fusion terminal collects and preprocesses multi-source data; the micro-grid management and control system receives the real-time operation state data uploaded by the fusion terminal, constructs a multi-target optimization model for optimization, and constructs a full-dimensional constraint condition; the micro-grid management and control system executes multi-target optimization scheduling to generate a regulation and control strategy; and the fusion terminal issues and executes the regulation and control instruction, and carries out closed-loop feedback adjustment at the same time. Multi-protocol data fusion is realized through the fusion terminal, a'source network load storage and charging 'island is broken, and global + real-time regulation and control are realized through cloud-edge collaboration; through multi-objective optimization scheduling, the operation cost is effectively reduced, the network loss is reduced, the new energy consumption rate is improved, the capability of coping with distributed energy fluctuation of the micro-grid is enhanced, and the operation reliability of the system is improved.

The application relates to the technical field of data center energy management and power dispatching, and discloses a double-layer dispatching method, system, equipment and medium for data center load and energy storage collaborative optimization. The method comprises the following steps: collecting data center operation data and preprocessing, constructing a prediction model to obtain load and electricity price prediction data; formulating a day-ahead dispatching scheme based on the prediction data, solving a first optimization model, and generating a first dispatching instruction; executing the first dispatching instruction and monitoring the actual deviation, and if the actual deviation exceeds a threshold value, starting a second optimization model for real-time correction to obtain a second dispatching instruction; performing safety inspection on the charging and discharging power of the energy storage, and feeding back the safety state to the optimization model to form a closed-loop control; and based on the safety inspection result, starting and stopping the dispatching server and allocating the calculation tasks. The double-layer dispatching architecture combining day-ahead optimization and real-time correction is adopted, the electric-computing-storage collaborative optimization is realized, the operation cost is reduced, the renewable energy consumption capacity is improved, and the real-time response and safety operation level of the system are enhanced.

The invention belongs to the technical field of electric energy transaction, and discloses a multi-park spare capacity collaborative transaction method and system based on a master-slave game. The method comprises the following steps: S1, establishing an electric energy transaction framework of a virtual operator and park cluster master-slave game, and issuing an initial electric energy purchase price and a spare capacity purchase price to each park by the virtual operator; s2, constructing a lower-layer park optimization model for each park, and solving an optimal reserve capacity supply plan and an optimal electric energy transaction plan of each park and a power grid while meeting a coupling constraint condition; s3, the virtual operator calculates own income and cost, constructs an income maximization model, and constructs a corresponding model constraint condition; s4, the virtual operator adopts an optimization algorithm to update the electric energy purchase price and the reserve capacity purchase price based on a revenue maximization target, and a final collaborative transaction scheme is obtained; according to the invention, global optimal cooperative configuration of electric energy and standby capacity among multiple parks is realized through guidance of price signals.

The application discloses a power storage system for a rural power grid and a method thereof, and belongs to the technical field of power storage systems.The power storage system comprises the following steps: step 1, collecting operation parameters of energy storage batteries of the power storage system for the rural power grid and load power of the rural power distribution network and transmitting the operation parameters and the load power to a master station control unit; step 2, performing risk judgment on the energy storage batteries according to the operation parameters by the master station control unit; step 3, generating a control instruction according to a result of the risk judgment by the master station control unit; and step 4, performing a control charging and discharging action according to the control instruction by the master station control unit. The power storage system overcomes the overcharging and overdischarging defects that are prone to occurring in the existing power storage method for the rural power grid, realizes accurate control on a power storage process, prolongs the service life of the energy storage batteries, improves the safety, stability and economy of system operation, and adapts to the demand of in-situ access and consumption of new energy of the rural power grid.