6369 results about "Power storage" patented technology

The invention provides an automatic waste heat recovery method which comprises the steps that waste heat pressure parameters, component media and flow characteristic data are obtained, waste heat data are classified, characteristic vectors of different types of waste heat are obtained, and the characteristic vectors of different types of preheating are stored in a waste heat characteristic database; analyzing and predicting randomness and intermittency rules of waste heat generation and a waste heat concentration area, and dynamically adjusting a cold absorption strategy or a heat absorption strategy; corresponding cold absorption or heat absorption operation parameters are called from the waste heat characteristic database, and cold absorption or heat absorption equipment is controlled to set a self-adaptive adjustment operation state according to the parameters; the cold and heat storage energy in the recovery process is monitored in real time, and the energy loss value of waste heat recovery is analyzed; and constructing a photovoltaic power storage energy optimization scheduling model, solving to obtain an energy distribution strategy, dynamically adjusting a use strategy of cold and heat storage energy, optimizing a distribution proportion and a time sequence of cold and heat energy, and realizing collaborative optimization control of photovoltaic power storage and waste heat recovery.

The invention provides a source network load electricity storage quantity distribution method based on multiple targets of prediction and reinforcement learning. The method comprises the following steps: firstly, by collecting and preprocessing multi-modal data, constructing a GRU prediction model based on an attention mechanism so as to improve the prediction precision of power demand and supply; then, designing a multi-objective optimization particle swarm resource allocation model based on reinforcement learning, and optimizing a resource allocation strategy by adaptively adjusting particle swarm parameters and introducing a reinforcement learning component; and finally, a resource allocation scheme is corrected and adjusted through a dynamic adjustment mechanism, and cost minimization and system operation efficiency maximization are realized while system constraints are ensured to be satisfied. According to the method, the response speed and the scheduling precision of the power system can be improved, the stability and the economical efficiency of power supply are ensured, the resource configuration is optimized, the operation cost is reduced, the adjustment capability of the load side can be improved, and medium-and-long-term time-phased transaction can be better carried out.

The invention discloses a multi-type virtual power plant participation market regulation capacity optimization distribution method and system, and the method comprises the steps: constructing a power supply type, load type and energy storage type virtual power plant participation market operation characteristic model, and then combining a value-at-risk assessment CvaR model to obtain a value-at-risk assessment model; a refined virtual power plant capacity distribution model is established by taking maximum pumped storage income and minimum risk when the virtual power plant participates in capacity distribution of main and auxiliary markets as targets, constraint conditions are set for modeling, and finally, the virtual power plant is combined with an electric energy market demand, a peak regulation market demand and a frequency modulation market demand for joint optimization. And continuously detecting and optimizing through the virtual power plant capacity distribution model. According to the method, on one hand, execution of a power generation plan is guaranteed along with wind and light output, on the other hand, a reasonable capacity distribution mechanism in a multi-element market is designed according to different mechanisms of the current power market, services such as peak regulation, frequency modulation and standby are provided, and the optimal configuration problem of a virtual power plant in the power market is solved.

The invention belongs to the technical field of energy storage power stations, and particularly relates to an energy storage power station electric quantity optimization method based on graph calculation, which comprises the following steps: S1, predicting future power generation capacity and load demand of an energy storage power station based on graph calculation: acquiring historical power generation data and load data of a distributed power station in a grid-connected area of the energy storage power station, and external influence data, constructing a time-space diagram neural network model, and obtaining prediction results of the power generation data and the load data in each hour period of the next 24 hours; s2, calculating the power and load value of each node in the power grid through power flow analysis; and according to the predicted future power generation capacity and the load demand, the electric quantity needing to be provided or consumed by the energy storage power station is calculated. Optimization of energy of the power storage station is achieved, and the cost of the power storage station is reduced.

An integrated building block is provided and belongs to the field of toy products, which includes a battery pack formed by splicing an upper shell and a lower shell, wherein outer surfaces of an upper end and a lower end of the battery pack are respectively provided with plug-in convex points and plug-in concave points with matched sizes; a cavity is formed in the battery pack, and a power storage module and an expansion board are provided in the cavity; the power storage module is provided with a power supply interface on an outer surface of the battery pack by the expansion board, and the power supply interface is used to supply power to a corresponding electric module after being butted with other electric modules. The invention is improved in ease of use and overall aesthetics by integrating the battery pack, power storage module and expansion board into one structure.

The invention relates to the technical field of information, in particular to a control method and system for an intelligent photovoltaic charging pile in the technical field of automobile charging piles, and the method comprises the steps: obtaining the charging demands and residual electric quantity information of a plurality of electric automobiles, dynamically adjusting the charging power of each automobile through combining with an electric power distribution algorithm, and guaranteeing the reasonable distribution of electric power resources; acquiring real-time load data through a power grid load monitoring system, predicting a future power grid load change trend in combination with a historical load curve, and judging whether a charging strategy needs to be adjusted or not; according to historical data and real-time data, cooperative working parameters of photovoltaic power generation and storage battery power storage are optimized by adopting a machine learning algorithm, and the overall efficiency of the system is improved; a cooperative working mode of photovoltaic power generation, storage battery power storage and power grid power supply is dynamically adjusted through an intelligent optimization algorithm; if the illumination fluctuation is large, a standby power supply is started to supplement power supply, it is ensured that the charging process is not affected, meanwhile, the energy storage efficiency is optimized, and power resource waste is reduced.

To provide an operation method of a battery for performing appropriate operation in a use mode of charging / discharging each of a plurality of power storage facilities in an SOC range with a relatively small SOC width while appropriately reducing progress of deterioration of the power storage facilities.SOLUTION: In an operation method of a power storage system, a plurality of SOC ranges shifted from one another are set, and one of the plurality of SOC ranges to be an option is selected as an operation SOC range for each of a plurality of groups so as to be in a state where the SOC ranges selected are different among the groups. In the operation method, a power storage facility belonging to each of the groups is operated in the SOC range selected as the operation SOC range, and the SOC range to be selected as the operation SOC range is sequentially switched among the plurality of SOC ranges for each of the groups so as to be in a state in where the SOC ranges selected are different among the groups.SELECTED DRAWING: Figure 5

Disclosed is an integrated thermal and electrical system with self-regulating capabilities to provide enhanced cooling capacity and auxiliary power. The system includes two cooling loops, a primary cooling loop whose cooling capacity is fixed and a secondary cooling loop that supplements the primary cooling loop when the cooling capacity of the primary cooling loop is insufficient. The two cooling loops may use a phase change fluid whose vapor pressure is monitored to control the cooling capacity of the secondary cooling loop to respond to fluctuating thermal load. The system includes two types of energy sources such as a photovoltaic system and a power storage. The photovoltaic system may power the secondary cooling loop to control the fluid flow rate or the airflow rate through the secondary cooling loop based on the vapor pressure. The photovoltaic system may charge the power storage when not powering the secondary cooling loop.

The invention relates to the technical field of power system control, in particular to a novel power system control method with participation of new energy. The method comprises the following steps: acquiring historical and predicted environmental data, power consumption data and power supply quantity data; determining a stability coefficient according to the numerical fluctuation of the power supply quantity data and the correlation of the environmental data; determining a predicted power supply accuracy index in combination with the numerical fluctuation of the predicted power supply quantity data; determining a historical power consumption influence coefficient according to the numerical value change of the historical power consumption data; determining a power storage reliability index at the current moment by combining the numerical fluctuation of the predicted power consumption data in the prediction time period; and performing new energy access control at the current moment according to the power storage reliability index and the predicted power supply accuracy index. The stability of power consumption and power supply can be effectively combined, so that stable control of the whole power system is ensured, and the stability of power planning of the power system is improved.

A structural assembly for an electric vehicle includes a vehicle frame, a battery structure, a pair of mounting structures, and a plurality of fasteners. The vehicle frame includes opposed longitudinal rails. The battery structure is disposed between the opposed longitudinal rails and includes a battery housing configured to house power storage units. Each mounting structure is secured to a respective side of the battery housing and supports a corresponding longitudinal rail. The plurality of fasteners extend through a corresponding mounting structure of the pair of mounting structures and a corresponding longitudinal rail of the opposed longitudinal rails to secure the vehicle frame and the battery structure to each other. An outer periphery of the corresponding mounting structure is substantially flush with an outer periphery of the corresponding longitudinal rail.

The invention belongs to the technical field of power supply, and discloses a source network load storage power supply-based energy control method and system and a storage medium, and the method comprises the steps: determining a first photovoltaic power generation amount and a first power consumption demand amount of a prediction day at a set moment based on first historical data and prediction data, calculating a difference value between a first available power consumption amount and the first power consumption demand amount, and when the difference value is greater than a first preset value, establishing a cost objective function based on the capacity of the energy storage device, the difference value, the operation loss and maintenance loss of the energy storage device, and the electricity price cost of each time period in the prediction day, and charging the energy storage device based on the charging time and the charging electric quantity obtained by solving the cost objective function. And periodically determining a second photovoltaic power generation amount and a second power consumption demand amount in each set time period, and carrying out charging and discharging control on the energy storage device according to a source-grid-load-storage cooperative power supply mechanism. According to the method, the source network load storage power supply system can be comprehensively optimized through accurate prediction and control.

A control method for an energy management and control system, an apparatus, a device and a storage medium. The method comprises: acquiring weather data of a target date (110); on the basis of the target date, the weather data and a prediction model, generating first predicted electric power data and second predicted electric power data, the first predicted electric power data being data of electric power which can be converted by a renewable energy module on the target date, and the second predicted electric power data being data of electric power required by a load device to work on the target date (120); and, on the basis of the first predicted electric power data and the second predicted electric power data, generating a charging policy of the target date, the charging policy comprising: a first charging policy for charging an energy storage device on the basis of the renewable energy module, or a second charging policy for charging the energy storage device on the basis of the renewable energy module and another power supply module (130). The method achieves accurate control on power storage of energy storage devices, thereby improving the utilization efficiency of photovoltaic storage systems.

The invention discloses a hybrid pumped storage power station seasonal regulation characteristic-considered water-wind-light-storage complementary scheduling method, which relates to the technical field of energy scheduling, and comprises the following steps: additionally building a pump station between built conventional cascade hydropower stations to build a hybrid pumped storage power station which has pumped storage and runoff power generation functions; the hybrid pumped storage power station and the cascade water-wind-light power station are co-constructed to form a water-wind-light-storage complementary system; constructing a water-wind-light-storage complementary long and short term nested scheduling model considering seasonal regulation characteristics of the hybrid pumped storage power station, and taking maximum power generation benefits as a model objective function; based on a dynamic programming successive approximation algorithm, a pumping power generation working condition conversion model is introduced to construct a model solving method; solving the scheduling model through a model solving method, and obtaining a water-wind-light-storage complementary power generation plan of the seasonal regulation characteristics of the hybrid pumped storage power station; and while the short-term peak shifting benefit and the new energy consumption level are improved, the long-term water storage and energy storage effects of abundant storage and dry supplement of the hybrid pumped storage power station are brought into full play.

The invention relates to the technical field of intelligent power grids, in particular to a virtual power plant multi-level collaborative optimization control method based on digital twinning. According to the invention, data of the power generation end, the power storage end, the power transmission end and the load end are collected and processed in real time, a digital twin model is established, and accurate modeling and real-time monitoring can be carried out on each device of the virtual power plant. Through real-time optimization scheduling and cooperative control, energy loss can be minimized, supply and demand can be balanced, the energy use efficiency is improved to the maximum extent, and the cost is reduced; according to the method, the genetic algorithm and the particle swarm optimization algorithm are combined, scheduling optimization is carried out based on a multi-level collaborative optimization model, a virtual power plant can rapidly respond according to changes of real-time environment and load requirements, fossil energy reduction and clean energy increase are preferentially considered in the power generation equipment scheduling process, and greenhouse gas emission reduction is facilitated.

The invention belongs to the technical field of emergency power supply of power supply vehicles, and particularly discloses and provides an emergency power supply system of a large mobile storage and charging power supply vehicle, which comprises a vehicle-mounted energy storage unit, a power conversion unit, a PWM (Pulse Width Modulation) signal generation unit, a load monitoring unit and a control unit. The control unit determines a grid-connected mode or an off-grid mode by collecting the on-off state of a power grid and the total power of a load and calculating the power change rate. During grid connection, a voltage instantaneous value is monitored, and a power supply process is executed after locking cooperative control is triggered. Load impact components are identified and verified in the off-grid process, loads are cut off in a graded mode and cyclic verification is conducted if the load impact components do not pass or power exceeds the limit, and after the load impact components pass, time sequence graded loading control is executed according to the adjustment stage number determined by the load type and the proportion. According to the invention, flexible selection between grid connection and grid disconnection can be realized according to actual working conditions, the condition of single power supply mode is changed, the adaptive capacity to different scenes is improved, the real uninterruptible power supply requirement is met, and the impact load is precisely controlled.

The application discloses a shared energy storage system optimization method and system for comprehensive energy microgrids, which take into account a shared energy storage-multi-microgrid system model invested by a third party. The comprehensive energy microgrids are regional energy systems integrating multiple energy sources and adjustable loads, including CHP, heat storage, power storage and distributed renewable energy. Shared energy storage is invested by a third part, serve as a power price publisher, and is limited by power price boundaries. The method takes into account a carbon emission and trading model to reduce carbon emission. Costs of the microgrids include a power / gas buying cost, a demand response cost, a carbon trading cost, and an operating cost of CHP units. Finally, a minimum cost solution is resolved by a MILP algorithm. The invention realizes microgrid scheduling and improves the economic efficiency and environmental friendliness of the comprehensive energy microgrids.

The invention discloses a charging pile operation management method, system and platform based on a cloud platform, and relates to the field of charging pile management, and the method comprises the steps: generating a user portrait of each charging station; according to the electric energy redundancy of the target area and the user portraits of the charging stations, electric energy consumption of the charging stations at future time nodes is predicted, and whether the charging stations are connected to a power grid energy storage device or not is judged; for one charging station, if the charging station is connected to the power grid energy storage device, determining an allocation strategy between a target charging pile in the charging station and the power grid energy storage device according to the equipment information; generating a power demand prediction result of the target charging pile at the future time node according to the user portrait of each charging station, the electric energy redundancy of the target region and the distribution strategy; and obtaining a regulation and control strategy of the target charging pile in the charging station at the future time node according to the power demand prediction result. The charging experience feeling of a user can be effectively improved.

An image capturing apparatus includes an image sensor, a power supply circuit, and a power storage device. A charging unit charges the power storage device using power from the power supply circuit. A control unit performs control such that in a first operation state of the image capturing apparatus, power is supplied from the power supply circuit to the image sensor without power being supplied from the power storage device to the image sensor. The control unit further performs control such that in a second operation state of the image capturing apparatus in which a maximum power consumption of the image sensor is higher than in the first operation state, power is supplied from the power supply circuit and the power storage device to the image sensor.

The invention discloses a port shore power direct-current system topological structure with a super capacitor. The port shore power direct-current system topological structure comprises a port power grid, a wind power unit, a photovoltaic power generation unit, a direct-current bus, a super capacitor bank, a ship power storage unit and a shore bridge feedback load. The port power grid, the wind power unit, the photovoltaic power generation unit and the super capacitor bank are respectively connected to the direct current bus through a first bidirectional AC / DC converter, a unidirectional AC / DC converter, a non-isolated DC / DC converter and an isolated DC / DC converter; the DC bus is connected to the ship power storage unit and the shore bridge feedback load through the bidirectional DC / DC converter and the second bidirectional AC / DC converter. The invention further discloses a seaport integrated energy system optimal scheduling method considering the ship demand response under the multi-time scale. According to the method, firstly, an electricity-heat-hydrogen coupling system model is established, and then optimization scheduling is carried out through day-ahead and intra-day optimization scheduling models. The ship docking power utilization efficiency can be effectively improved, the loss is reduced, the on-site absorption of port new energy is promoted, and the stability and reliability of the system are improved.

To provide a technique that reduces the diffusion angle of an electrolyte diffused from a nozzle hole of a liquid injection nozzle.SOLUTION: A manufacturing method disclosed herein includes injecting an electrolyte into an assembly. For the injection, a liquid injection device including a liquid injection nozzle 81 having a nozzle tube portion 81A and a nozzle hole 81h on a side surface of the nozzle tube portion 81A is used. The nozzle tube portion 81A includes a main body 811 with an open tip, and a cap 812 inserted into the opening of the main body 811. The nozzle tube portion 81A includes the nozzle hole 81h on the side surface at the tip side of the main body 811. The cap 812 is open at a first end 8121 located inside the main body 811, and closed at a second end 8122 opposite to the first end 8121. The cap 812 is chamfered at an inner edge of the first end 8121. The cap 812 has a through-hole 812h on a side surface on the second end 8122 side. The nozzle hole 81h and the through-hole 812h overlap with each other.SELECTED DRAWING: Figure 5

A modular, low profiled, scalable, self-powered integrated structure luminaire is coupled electromechanically to a vertical structure that supports a power storage device.

A ground fault detection apparatus is used for an on-board system. The on-board system includes a power source unit, a power storage unit, a conductive path, and a disconnection unit. The conductive path includes a first conductive path, a second conductive path, and a third conductive path. The disconnection unit includes a first disconnection unit and a second disconnection unit. A second load is connected to one path out of the second conductive path and the third conductive path, and a third load is connected to the other path. The ground fault detection apparatus includes a detection unit (a current detection unit and a control circuit) configured to detect a ground fault on at least one of the second conductive path and the third conductive path.

Provided is a power storage device comprising: an electrode body that includes electrodes; a current collector that extends from the electrodes; an electrode terminal that is electrically connected to the electrode body via the current collector; an exterior film that is wrapped around the electrode body in such a manner as to have an opening; and a lid body that is disposed in the opening, wherein the lid body covers at least a part of the current collector.

The invention relates to the technical field of energy conservation, environmental protection and building energy management, and discloses a low-carbon high-speed service area system integrating waste heat recovery, photovoltaic power generation and intelligent energy storage, which comprises a waste heat recovery module, a photovoltaic power generation module, an energy storage module, an intelligent control module and a carbon emission reduction statistics and feedback module, all the modules work cooperatively to form an integrated energy-saving and carbon-reducing system, when the low-carbon high-speed service area system integrating waste heat recovery, photovoltaic power generation and intelligent energy storage is used, a stable heat source can be provided for domestic hot water through the waste heat recovery module, power self-supply of a service area is achieved through the photovoltaic power generation module, and energy conservation and emission reduction are achieved. The energy storage module stabilizes power supply during power generation fluctuation, the intelligent control module guarantees efficient cooperative work of all the modules, the autonomous learning module automatically distributes heat storage and power storage resources according to historical data and demand prediction, and the system stably operates under peak or fluctuation conditions. And the carbon emission reduction statistical module provides data support for low-carbon benefits of the system.

The present disclosure relates to a battery system supporting a plurality of operation modes according to the SoC. To this end, the battery system is characterized by comprising: one or more batteries supporting multiple operation modes for power storage or supply according to the state of charge (SoC); and a controller which determines an operation mode of the batteries among the plurality of operation modes according to the power supply state of a grid and the SoC of the batteries, wherein the batteries include a battery supporting an uninterrupted power supply (UPS) mode at an SoC at or below a first standard.

The invention discloses a multi-element energy storage collaborative distributed energy system operation regulation and control method, and the method comprises the steps: considering an optimal scheduling strategy of a power-energy type electricity storage, heat storage, cold storage and other technologies for cooperatively regulating and controlling a multi-energy load curve, and constructing a generalized technical model of energy equipment and system operation constraints; designing an energy system operation data preprocessing mechanism and a weight determination model under a given historical window; an energy system scheduling sequence of bipartite dynamic regression coordinated electric heating unit operation is provided, and an economic operation planning model is constructed; the model is applied to a historical window, optimal parameters of key regulation and control nodes are searched and determined through a heuristic algorithm so as to guide future time window scheduling and complete scheduling closed loop, and hyper-parameters of the method, such as historical and future window vision fields, time nodes and weights, are determined based on multiple scheduling closed loop behaviors so as to complete method closed loop. According to the method, a commercial solver is not needed, the economic operation rule of the distributed system is directly mined from historical operation, the method is used for determining the on-off and output plan of each device of the system in a future time sequence, and engineering application is facilitated.

To provide a power conversion device and program that can reduce the pulsation of a current flowing through a power storage unit.SOLUTION: The power conversion device includes a motor 10, an inverter 20, a battery inter-switch 50, a bypass switch 60, a connection path 73, and a motor ECU 100. The motor ECU 100 performs switching control of a target switch, which is at least one of upper and lower arm switches SUH to SWL of the inverter 20 to make a current to flow through the connection path 73. When the motor ECU 100 performs switching control of two or more phase target switches, it performs phase shift processing to shift the switching timing of each of the two or more phase target switches to an on state.SELECTED DRAWING: Figure 1

A hybrid power system, comprising at least two power storage components, each power storage component comprising a power source having a corresponding source profile and a source charger electrically connected to the power source, and a power path controller operatively connected to the at least two power storage components, comprises a processing circuit configured to receive an input signal and selectively switch between the source profiles correspond to the power sources of the at least two power storage components based on the received input signal, thereby configuring at least one power storage component of the at least two power storage components to provide stored power to an external load.

The invention provides a regional fracturing staged water pressure monitoring device which comprises a shell arranged in a packing pipe column of a rock stratum drill hole. The acquisition module is arranged in the shell, and the acquisition end of the acquisition module extends out of the shell; the monitoring module is arranged in the shell, the first communication end of the monitoring module is connected with the communication end of the collecting module, and the monitoring module is used for controlling the collecting module to collect the pressure, larger than the preset pressure value, in the drill hole according to the preset frequency value; and the power storage module is arranged in the shell, and the power supply end of the power storage module is connected with the power supply end of the monitoring module. According to the regional fracturing subsection water pressure monitoring device, direct and continuous monitoring of the pressure in the drill hole packing tubular column can be achieved, so that the error problem caused by indirect measurement is avoided, and then the whole-process data such as the rock stratum fracture pressure and the rock stratum expansion pressure of each fracturing subsection in a drill hole can be accurately and comprehensively measured.

The invention discloses a knowledge graph retrieval intelligent power storage system based on a feedforward neural network, and relates to the field of power equipment accessory management. The method comprises the following steps: constructing a power equipment resource classification model and a meteorological disaster risk periodic evaluation model in a coverage storage range; obtaining static data and dynamic data of different power devices; predicting the replacement demand level of the equipment through the feedforward neural network model; a model prediction evaluation result is used as a data source of the knowledge graph warehousing system, and terminal real-time fault prediction evaluation query, periodic equipment purchase guidance and backlog warning are realized. According to the invention, the storage demand can be intelligently predicted according to the actual state of the power equipment, the accuracy and response speed of storage management are improved, and an efficient spare part support scheme is provided for power network maintenance and equipment storage.