25results about How to "Maximize energy efficiency" patented technology

The invention discloses an air conditioner energy-saving operation optimization scheduling method and system. The method comprises the following steps that position coordinate data of all air conditioner units in a factory workshop are obtained; according to the position coordinates and the relative positions of the air conditioner units, the air conditioner units are divided into a basic air conditioner set and adjacent air conditioner sets; according to the spatial distribution relation of the air conditioning units, an influence relation graph between the air conditioning units is constructed; based on the heat generated in the workshop, the thermal load condition of each area is determined; based on the heat analysis result, the operation parameters of each air conditioning unit are set in combination with the mutual influence relation between the air conditioning units; scheduling optimization is conducted on the operation state of the air conditioning unit based on the operation parameters of the air conditioning unit; according to the air conditioner energy-saving operation optimization scheduling method and system, the problems of low energy efficiency, energy waste, unreasonable scheduling, lack of flexibility and the like of an air conditioner system are solved. Through intelligent optimization scheduling, the operation efficiency of the air conditioning system can be effectively improved, energy consumption is reduced, and the purposes of energy conservation and emission reduction are achieved.

The invention discloses a non-user air conditioner regulation and control modeling method considering power grid interaction and user comfort, and relates to the field of air conditioner regulation and control, and the method comprises the steps: obtaining indoor and outdoor state data of an air conditioner through a monitoring sensor, carrying out the preprocessing of the state data, and constructing an air conditioner power prediction model in combination with a neural network technology; obtaining an air conditioner load demand based on the air conditioner power prediction model, and combining the air conditioner load demand with the power grid demand response signal to design a demand response mechanism; and based on the demand response mechanism, an optimal control strategy model with the user comfort level as the target is established, a simulated annealing algorithm is adopted for conducting partition solving on the optimal control strategy model, and an air conditioner regulation and control result is determined according to the solving result. According to the invention, through real-time data analysis and a prediction model, system operation parameters are dynamically adjusted, so that energy efficiency maximization and cost minimization are realized, and adaptability and response capability of the system to power grid demand changes are enhanced.

The invention relates to an Internet of Things equipment deployment and control management method, in particular to a building Internet of Things equipment deployment and control management method. Comprising the following steps: deploying various sensors including an infrared sensor, a camera and an air quality sensor in a building, and collecting personnel flow and environment change data in real time; through data analysis and scene classification, the data are converted into scenes including office peaks, night modes and emergency evacuation of the building; training a scene recognition model by using a random forest classifier; a time sequence prediction model is adopted to predict scene change in a future short period, and a corresponding equipment control instruction is generated; the method comprises the following steps: establishing an incidence matrix of use frequency, spatial position and function interaction between devices by using sensor data, and identifying a device cooperation mode; dynamically generating an equipment deployment scheme through a graph theory algorithm based on the incidence matrix; issuing the generated deployment scheme to each equipment control node; and performing fine adjustment according to real-time data feedback.

The invention provides a cooling tower and refrigerating machine collaborative optimization method based on a whale algorithm and a control system. The method comprises the steps of collecting operation data of a cooling tower and a refrigerating machine to form an initial state set; initializing an optimization parameter space, and designing a target function of the cooling tower based on the thermophysical constraint model; designing an energy efficiency optimization objective function of the refrigerating machine based on a thermophysical constraint model, and screening out a refrigerant flow and compressor power combination of the refrigerating machine meeting conditions; data are monitored in real time, a collaborative dynamic optimization objective function of the cooling tower and the refrigerating machine is designed according to the real-time data, and the goal of the collaborative dynamic optimization objective function is to maximize the comprehensive energy efficiency ratio of the system. According to the method, a two-stage collaborative optimization framework based on thermophysical constraints is constructed, so that the problems of target conflicts, insufficient physical constraint adaptability, insufficient dynamic adjustment capability and the like in the collaborative optimization problem of the cooling tower and the refrigerating machine are solved.

The invention relates to a variable-frequency water pump control method for a dual-combined heat supply pump unit and a heat pump system. Comprising the steps that S1-S2, a lift-flow performance curve and a flow-power performance curve corresponding to each rotating speed of the variable frequency water pump are obtained; s3, according to the number of the started user end devices, the water flow needed by the two-combined heat supply pump unit is determined; s4, the rotating speed of the variable frequency water pump is adjusted, the current power is calculated, and the real-time flow at the current rotating speed is obtained according to the flow-power characteristic curve; s5, the rotating speed of the variable-frequency water pump is adjusted to enable the real-time flow to meet the required water flow, and the real-time lift of the variable-frequency water pump is detected in real time; s6, the real-time flow, the rotating speed of the variable frequency water pump and the real-time lift are substituted into the lift-flow performance curve, and whether the real-time lift meets the theoretical lift or not is checked; s7, if yes, executing according to the current rotating speed; and if not, the steps S4 to S6 are executed, and the rotating speed of the variable-frequency water pump is adjusted again until the real-time flow and the real-time lift both meet the requirements. The stability and the energy efficiency of the heat pump unit are effectively improved.

The invention relates to an annealing kiln waste heat recycling method and a related device. The method comprises the following steps that the production batch condition and the production schedule of each annealing kiln are obtained; determining waste heat gas standard emission information of a production batch corresponding to the annealing kiln; acquiring current environment air temperature and current environment air pressure information corresponding to the annealing kiln to adjust waste heat gas emission information of the annealing kiln so as to obtain waste heat gas adjustment emission information; based on the current time and the environment temperature, the plan demand condition of waste heat is obtained; and controlling the opening degree of a non-return air valve of each gas buffer chamber and the power of a variable-frequency traction fan corresponding to a waste heat utilization project based on the waste heat gas adjustment emission information and the plan demand condition of the waste heat. Real-time adjustment can be achieved according to the actual working condition of the annealing kiln, so that the waste heat utilization efficiency is improved to the maximum extent, and the production quality of glass is guaranteed.

The invention discloses a multi-tube type energy-saving radiator capable of adjusting flow and radiating area, which comprises a mounting frame, an upper transverse tube, a lower transverse tube, a vertical tube and an inner adjusting tube, the upper transverse tube and the lower transverse tube are arranged on the mounting frame, the vertical tube is communicated with the upper transverse tube and the lower transverse tube, and the inner adjusting tube is rotatably connected with the upper transverse tube and is provided with an adjusting hole. The vertical pipe can be conducted in different areas, and the cross sectional area of the conducted vertical pipe can be adjusted. The inner adjusting pipe is arranged in the upper transverse pipe, a heating medium flows into the vertical pipe after passing through the adjusting hole and the upper communicating hole after passing through the inner adjusting pipe, and the effective communicating area of the adjusting hole and the upper communicating hole can be adjusted by rotating the inner adjusting pipe; in conclusion, flow control in the heat dissipation area is achieved, meanwhile, partitioned heat dissipation of the heat dissipation area is achieved, adjustment can be conducted according to actual requirements, the energy efficiency is maximized, and the energy-saving effect is achieved.

The invention discloses a low-power-consumption RSU deployment method based on a genetic algorithm, which is used in the technical field of Internet of Vehicles, and is used for reducing power consumption by considering the vehicle distribution condition, adaptively adjusting the transmitting power of RSUs and switching between an active mode and a sleep mode, and specifically comprises the following steps of: firstly, determining a communication service area of the RSUs according to the vehicle distribution; modeling an RSU deployment problem into an RSU communication service area combination problem; then, constructing a digital sequence as a genotype individual of a solution, and evaluating the advantages and disadvantages of the RSU communication service area combination through a fitness function; through crossing, variation and iteration processes in the genetic algorithm, the synergistic effect between RSUs is optimized, and the overall energy efficiency is maximized; according to the method, the stability of a communication link between the RSU and the vehicle can be guaranteed, meanwhile, the total power consumption of the RSU in the system is remarkably reduced, and the effectiveness of the method in the aspect of reducing the total power consumption of the RSU is verified through a simulation experiment.

This utility model discloses a thermal management system suitable for containerized energy storage systems, comprising an air conditioning refrigerant circuit and a battery thermal management circuit, both of which include plate heat exchangers. Through this structure, the integration of the air conditioning refrigerant circuit and the battery thermal management circuit reduces system complexity and cost, while also reducing space requirements and maintenance costs. The battery thermal management system utilizes the plate heat exchanger to form a heat exchange with the air conditioning refrigerant circuit, effectively dissipating the heat absorbed from the energy storage battery module through the air conditioning system. The air conditioning refrigerant circuit dynamically adjusts the refrigerant distribution to regulate the heat exchange distribution between the air conditioning refrigerant circuit and the battery thermal management circuit. The battery thermal management circuit includes a phase change thermal storage unit connected in parallel with the plate heat exchanger, releasing stored heat / cold energy during charging and discharging intervals, reducing the frequent start-stop of the compressor and PTC heater.

The application relates to a heat management system, a control method and a controller of the heat management system, the heat management system comprising an expansion valve, the heat management system being used for heat management of a cabin, the method comprising the following steps: determining a current heat exchange capacity of the heat management system; determining a required heat exchange capacity of the cabin in a working mode and a preset capacity interval in which the required heat exchange capacity is located, obtaining a control strategy of the heat management system, wherein the control strategies associated with different preset capacity intervals are different; and adjusting the opening degree of the expansion valve of the heat management system according to the control strategy, so that the current heat exchange capacity approximates to the required heat exchange capacity. According to the application, the heat management system can be controlled in intervals, and the optimal efficiency in the intervals under various working conditions is considered, so that the energy efficiency of the heat management system is maximized.

The embodiment of the invention discloses an optimal scheduling method and device for a power system, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical operation data and power consumption data of the power system and current environment data at the current moment; inputting the historical operation data, the power consumption data and the environment data into a pre-established optimization scheduling model, and obtaining a net present value of the power system according to an upper layer model of the optimization scheduling model; and inputting the net present value into a lower-layer model of the optimal scheduling model, obtaining an optimal scheduling strategy based on the lower-layer model and the net present value, and controlling the operation of the power system according to the optimal scheduling strategy. According to the method, the maximum energy efficiency is obtained while it is ensured that the photovoltaic power generation capacity meets the load requirement, the power system can more effectively configure resources, and the energy utilization efficiency is improved.

A hybrid drive mode independent water loop semiconductor chip heating system comprises a water inlet tank, a main water tank and an auxiliary water tank, the water inlet tank is connected with a water inlet, the main water tank is provided with a main water outlet, the auxiliary water tank is provided with an auxiliary water outlet, and the main water outlet and the auxiliary water outlet are jointly connected with an adjusting valve. The water outlet side of the adjusting valve is communicated with the main water outlet; the water inlet tank is communicated with at least one wide water channel and at least one narrow water channel, the wide water channel is communicated with the main water tank, and the narrow water channel is communicated with the auxiliary water tank; the outer wall of the wide water channel is tightly attached to the heat dissipation metal thin plate. The narrow water channel is connected with the cold end of the semiconductor chip, and the hot end of the semiconductor chip is connected with the radiating metal sheet. The semiconductor chip is combined for heating, the characteristics of different driving modes are fully considered, the heating mode is flexible and controllable, the energy utilization efficiency is maximized, the practicability and applicability of a warm air system are improved, and comfortable, energy-saving and efficient heating experience is brought to users.

This invention proposes a rapid braking control strategy for multiple units in a ramp-type gravity energy storage system. By constructing an energy efficiency evaluation function, the optimal delay time for multi-stage braking that can balance small-power impacts and rapid shutdown braking is determined. This invention is based on the braking delay time Δt, the number of grid-connected units N, and the total braking time T of the entire unit in the ramp-type gravity energy storage system. brake Braking peak power P max By identifying the relationship between parameters and the speed curve of a single unit during braking, the delay time t of a single unit can be determined. brake By setting weight factors for the braking time and peak braking power of the entire unit and the number of units in the system, and fitting the braking time curve and peak braking power curve of the entire unit using the basic interpolation method, a multi-level braking energy efficiency evaluation function model for the sloped gravity energy storage system is constructed, and the optimal delay time setting scheme under different weight factors is studied.

The invention is applicable to the technical field of vehicle control, and provides an amphibious vehicle control method based on stability and economy collaborative optimization, which comprises the following steps: solving additional torque required for maintaining the stability of a vehicle attitude through real-time working condition identification and adaptive model prediction control, and calculating a stable power demand in combination with multi-target optimization distribution; superposing the stability power and the longitudinal propulsion power to obtain the total power demand of the system; on the basis of the total power demand, a hybrid power system energy management model is constructed, and global optimization distribution of engine and battery power is realized by using a neural network-based adaptive equivalent factor iteration updating strategy; and converting the optimized power instruction into torque and distributing the torque to a corresponding driving unit. By deeply coupling the full-scene stability constraint to the energy management closed loop, the problem that stability and economical efficiency optimization are disjointed is solved, and the energy economical efficiency of the whole vehicle is improved on the premise that all-working-condition driving safety and attitude stability of the amphibious vehicle are guaranteed.

The invention provides a base station energy efficiency evaluation optimization method, which comprises the following steps of: obtaining the economic total energy consumption cost of a coding multicast system based on the placement energy consumption of the coding multicast system in a content placement stage, the delivery energy consumption of the coding multicast system in a content delivery stage and real-time electricity price; taking the ratio of the user file request load to the total cost of actual economics energy consumption as the economics energy efficiency of the coding multicast system, and taking the economics energy efficiency as an energy efficiency evaluation index of the coding multicast system; and when the delivery delay of the user request is satisfied, the content placement transmission rate and the content delivery transmission rate with the economic energy efficiency maximization are realized, and the coding multicast system is optimized. According to the method, a network peak-valley period differential pricing strategy is considered, an economics energy consumption cost model is established, a base station energy efficiency index based on economics is provided, a base station economics energy efficiency optimization mechanism based on communication delay sensitivity is provided, and the base station economics energy efficiency is maximized under the constraint of communication delay, so that the service satisfaction degree of a user is guaranteed.

The invention provides a photovoltaic module adaptive monitoring and early fault early warning method and system. The method comprises the following steps: constructing a three-layer intelligent monitoring network architecture; the photovoltaic module intelligent monitoring node PV-SN executes a multi-mode cooperative sensing and self-adaptive sampling strategy, collects multi-dimensional state parameters of the photovoltaic module, and calculates a local health index LHI (t); the ECN executes edge side data fusion and diagnosis, and operates a lightweight anomaly detection algorithm to identify suspicious components; the cloud digital twinborn platform CDTP runs a high-precision fault diagnosis model, performs component performance degradation prediction and fault propagation simulation by using a digital twinborn model, and generates a predictive maintenance decision; and based on the suspicious component and the predictive maintenance decision, optimizing sensing parameters and network communication strategies of intelligent monitoring nodes PV-SN of each photovoltaic component to form a closed-loop intelligent optimization system. The technical problems that the monitoring dimension is single, the communication mode is limited, the diagnosis capability is insufficient, and the system intelligence degree is low are solved.

The invention discloses an energy system predictive control method based on a dynamic Bayesian network model, and relates to the technical field of energy system predictive control, a dynamic Bayesian network is constructed based on physical information of an energy system, and the dynamic Bayesian network is used for determining the state quantity node, the control quantity node and the environment parameter node of the current time step according to the state quantity node, the control quantity node and the environment parameter node of the current time step. Predicting a state quantity node and posterior probability distribution of a next time step; performing predictive control on the energy system based on a dynamic Bayesian network, and gradually predicting a state quantity node and posterior probability distribution of each time step in future N time steps by using the dynamic Bayesian network according to a state quantity node, a control quantity node and an environment parameter node of the current time step; and setting a target equation and a constraint condition, performing optimization solution on a decision variable, namely a control quantity node, in combination with prediction results of future N time steps, and finally outputting a control quantity node of the next time step so as to obtain a system control quantity of the next time step. According to the invention, uncertainty quantification and optimization control of the energy system are realized.

An ammonia complete combustion system according to an embodiment of the present application can include an ammonia complete combustor configured as a primary combustor or a secondary combustor to heat ammonia supplied to the inside of a combustion chamber to a preset temperature, an ammonia water spray nozzle to spray ammonia water into the inside of the combustion chamber heated by the ammonia complete combustor and to vaporize the ammonia water, a combustion cavity to accommodate combustion gas and ammonia vaporized from the ammonia water spray nozzle for maintaining an internal temperature and creating a stable combustion environment, an ammonia oxidation catalyst to completely combust trace amounts of ammonia generated from the combustion cavity and to completely combust ammonia including combustion gas generated in the combustion process before being discharged to the atmosphere, and a combustion air heating preheater to recover heat of combustion gas discharged from the ammonia oxidation catalyst to preheat combustion air required for the ammonia complete combustor.

This application discloses a downlink power control method and an apparatus. A network management node determines a low energy consumption area, where the low energy consumption area is an area that is within signal coverage of a network node and whose communication distance from the network node is greater than a first preset distance; and generates a power control policy, where the power control policy is used to control the network node to reduce energy consumption for the low energy consumption area, and the network node executes the power control policy to save energy. In embodiments of this application, energy saving is performed based on loads of some areas in a cell, to maximize an energy efficiency gain.

The invention relates to the technical field of wireless communication, in particular to an energy efficiency maximization method for a reconfigurable intelligent surface-assisted movable antenna system. The method comprises the following steps of: constructing an energy efficiency maximization objective function with double overhead, and taking an antenna position, a reconfigurable intelligent surface reflection coefficient matrix and a base station transmitting beam forming matrix as joint optimization variables; taking a three-variable initial value as a starting point, combining channel state information iteration from a base station to a reconfigurable intelligent surface and from the reconfigurable intelligent surface to a user, in the first round, fixing an initial antenna position, optimizing a transmitting beam forming matrix and a reflection coefficient matrix, then optimizing the antenna position, and carrying out subsequent cyclic updating; and when the energy efficiency converges or reaches the maximum number of iterations, outputting an optimal parameter and controlling the antenna to move to a target position and then transmitting data, and realizing system energy efficiency maximization in a real scene by explicitly incorporating double overhead and cooperatively optimizing a core variable.

The invention discloses a gear-shifting and energy-saving control method, device and system for a plug-in hybrid electric vehicle. The method comprises the following steps: acquiring vehicle driving data; c-means clustering analysis is carried out on the vehicle driving data; according to the vehicle driving data after clustering analysis, a BP neural network is constructed, and the BP neural network is utilized to carry out on-line optimization on a gear shifting sequence; determining a reference curve of the SOC, and comparing the reference curve with the current SOC to iteratively update the equivalent factor; and gear shifting and power distribution are optimized through real-time updating of the equivalent fuel oil minimum strategy. By the adoption of the technical scheme, the plug-in hybrid electric vehicle adapts to different working conditions, gear shifting and power distribution are optimized, the problem of mixed integers in a control algorithm is avoided, and better fuel economy is shown under hybrid drive.

The utility model relates to air dehumidification field, concretely is a kind of energy-saving dehumidification system based on phase change PVT technology, and dehumidification system includes rotary dehumidifier, and the processing section of rotary dehumidifier extracts external space and is injected target dehumidification space after dehumidification, and the regeneration section of rotary dehumidifier extracts external air and restores the dehumidification capacity of rotary dehumidifier after heating;Its characterized in that, along gas conveying direction, front regeneration fan, heat exchanger, electric auxiliary heater, regeneration section and rear regeneration fan are sequentially arranged, and heat exchanger is connected with the heat storage tank in heat supply system and exchanges heat;Heat supply system includes photovoltaic panel and the solar energy evaporator of setting with the back of photovoltaic panel, and solar energy evaporator, compressor, condenser are sequentially communicated to form refrigerant circuit, and the cooling water in condenser is communicated with heat storage tank to store heat energy.The utility model keeps production environment humidity control, and greatly reduces the energy consumption of dehumidification system.

The invention belongs to the field of integrated circuits, and particularly relates to a 9T-SRAM unit, a floating point memory computing circuit and a CIM chip. In the 9T-SRAM unit, P1, P2 and N1-N4 form a latch structure with inverted storage nodes Q and QB; the grid electrode of the N6 is connected with the Q; the source electrode of the N6 is connected with the VSS; the drain electrode of the N6 is connected with the source electrode of the N5; the grid electrode of the N5 is used as an input port INPUT of multiplication; the grid electrode of the P3 is connected with a switch signal line KEY; drain electrodes of the N5 and the P3 are connected and serve as an output port OUT of a multiplication result. The floating point in-memory calculation circuit comprises a memory calculation array, a finger tail precision mapper, a sensitive amplifier array and a shift adder. The storage array is formed by arranging a 9T-SRAM unit array; and the finger tail precision mapper is used for converting the input index difference value into a multi-bit precision mask for controlling the starting state of each column of the storage and calculation array, and carrying out bit-by-bit adjustment on the precision mask in a subsequent period. According to the invention, the problems of high power consumption and large area commonly existing in the existing floating point memory calculation can be solved.

The invention discloses a predictive control method for an energy system based on a dynamic Bayesian network model, and relates to the technical field of predictive control for energy systems. A dynamic Bayesian network is constructed based on the physical information of the energy system, and the dynamic Bayesian network is used to predict the state quantity nodes and posterior probability distribution of the next time step according to the state quantity nodes, control quantity nodes and environmental parameter nodes of the current time step; the energy system is predictively controlled based on the dynamic Bayesian network, and the state quantity nodes and posterior probability distribution of each time step in the next N time steps are gradually predicted by using the dynamic Bayesian network according to the state quantity nodes, control quantity nodes and environmental parameter nodes of the current time step; the target equation and constraint conditions are set, and the decision variables, i.e., the control quantity nodes, are optimized and solved in combination with the prediction results of the next N time steps, and the control quantity nodes of the next time step are finally output, so as to obtain the system control quantity of the next time step. The invention realizes the uncertainty quantification and optimization control of the energy system.

The invention relates to the technical field of power grid coordinated planning, in particular to a flexible power distribution network multi-energy coordinated planning method. Firstly, multi-energy information and demand information including various energy supply node and load node data are collected. Then, a real-time planning model and a prediction planning model are established, and decision variables, objective functions and constraint conditions of all the models are determined; and then obtaining current information, and performing energy matching based on the real-time planning model, including real-time energy scheduling, result evaluation and preparatory energy scheduling. And finally, judging a real-time energy scheduling result, if the real-time energy scheduling result is not satisfied, determining an energy shortage amount, preparing to input and then executing a prediction planning model, and distributing energy based on a greedy algorithm to realize efficient energy management and supply and demand balance. The method comprehensively considers the multi-energy characteristics and the supply-demand relationship, dynamically plans and dispatches, improves the energy utilization efficiency, and enhances the stability and adaptability of the power distribution network.