162 results about "Energy allocation" patented technology

An energy distribution may include a server and one or more databases. The system may communicate with an energy provider to receive energy provider data, at least one information collector to receive information collector data such as individualized energy usage data, customer preferences, and customer or location characteristics, and the one or more databases for receiving data for optimization. The system may calculate a cost of service or avoided cost using at least one of the individualized energy usage data and a system generation cost at a nearest bus. The system may also forecast individualized demand by end-use, individualized demand by location, energy prices, or energy costs. The system may optimize energy distribution, energy use, cost of service, or avoided cost using the forecasted individualized demand by end-use, the forecasted individualized demand by location, the forecasted energy prices, and the forecasted energy costs.

The present invention provides for multiple energy pooling stations to combine renewable energy, utility energy and back-up power services in the form of Green and Black Energy with energy storage to create a multi-income stream. An energy pooling station is an advanced part of an evolving “energy network” in which multiple energy pooling stations are communicating with each other to share energy credit, bank energy, and distribute the energy to customers.

Systems that allow energy allocation for energy storage cooperation are disclosed, with specific methods outlined concerning allocating energy stored by an energy storage device between a utility provider, a customer, and / or a third party. The methods may include allocating a portion of stored energy for a utility provider's benefit which may be gained as a result of a part of the allocated energy being dispatched into a utility network and allocating another portion of energy stored in the energy storage device for the benefit of the customer. Additional embodiments disclose the dispatching of the energy stored, signals used to bring about a dispatch, the nature of the relationship between the utility provider, the customer, and the energy stored, methods for making energy available to a utility provider without a prearranged reservation of the energy, penalties for misallocating energy, apparatus for allocating energy and causing dispatch, and more.

A system and method are provided for an energy distribution system having at least one energy storage system and at least one renewable energy resource. The method includes determining distribution optimal power flow optimization models of components of the distribution system. The components at least include the at least one energy storage system and the at least one renewable energy resource. The method further includes generating a composite model of the distribution system by integrating therein the distribution optimal power flow optimization models. The method also includes optimally scheduling, using a processor-based scheduling optimizer, an operation of resources in the distribution system using at least one of a fixed-window iterative optimization technique and a rolling stochastic optimization technique applied to the composite model.

The invention relates to an energy distribution method based on off-road condition prediction, belonging to the technical field of electric transmission, which solves the problem of energy managementof hybrid tracked vehicles in the prior art. The invention relates to an energy distribution method based on off-road condition prediction, comprising the following steps: according to vehicle historical driving information, establishing an off-road environment condition identification model of tracked vehicle based on SVM; Detecting current vehicle driving information in real time, inputting themodel, and predicting current vehicle driving condition; Predicting the vehicle speed and acceleration at the next time, and calculating the vehicle power demand at the next time according to the predicted current vehicle driving condition and the predicted values of the vehicle speed and acceleration at the next time; Based on the next time power demand, the model predictive control strategy is adopted, and utilizing the dynamic programming as an optimization algorithm to obtain the next time optimal energy distribution mode. The energy distribution of the tracked vehicle under complex off-road conditions is realized, and the power performance and fuel economy of the tracked vehicle are improved.

Systems and methods for energy distribution for one or more grid-scale Energy Storage Systems (ESSs), including generating one or more time series models to provide forecasted pricing data for one or more markets, determining battery life and degradation costs for one or more batteries in or more ESSs to provide battery life and degradation costs, optimizing bids for the one or more markets to generate optimal bids based on at least one of the forecasted pricing data or the battery life and degradation costs, and distributing energy to or from the one or more ESSs based on the optimal bids generated.

The invention discloses an energy consumption balancing method in a wireless ultraviolet light cooperative unmanned aerial vehicle formation network. An ultraviolet light non-line-of-sight single scatter communication model is established; an energy attenuation formula of channels in an ultraviolet NLOS(c) class communication mode is determined; and on the basis, energy consumption analysis is carried out on unmanned aerial vehicle formation in flight through adoption of a wireless ultraviolet light communication energy consumption model. Through circulation of three phases of cluster head selection, cluster establishment and stable data transmission, clustering management is carried out on the unmanned aerial vehicle formation, so a purpose of balancing network energy consumption is achieved. According to the method provided by the invention, through combination of the advantages of wireless ultraviolet light scatter communication, the energy consumption balancing is carried out on the unmanned aerial vehicle formation based on a clustering topology management mechanism, energy allocation of the unmanned aerial vehicle formation can be effectively optimized, and survival time of the unmanned aerial vehicle formation is prolonged.

The invention discloses a occupant restraint system optimization method based on energy analysis. The occupant restraint system optimization method based on energy analysis comprises the following steps: firstly, establishing a frontal collision model of a A-class vehicle, and verifying the effectiveness by the method of simulation and experiment; then, analyzing the energy absorption condition of each restraint subsystem combined with the energy theory and based on the energy transfer mode of the occupant, and obtaining the restraint system energy distribution curve; and then finding out the relationship between the energy distribution of the restraint system and the occupant weighted injury criterion (WIC); finally, analyzing the parameters of each restraint subsystem, and constructing the relationship between the parameters and the energy distribution of the restraint system, so as to adjust the relevant parameters and realize the performance optimization for occupant protection of the restraint system. The invention has great significance for improving the protection performance of the occupant restraint system and shortening the optimization period.

The invention discloses an energy management strategy for an airplane adaptive power and thermal management system (APTMS), and belongs to the technical field of airplane integrated heat / energy. According to the invention, firstly, an APTMS energy optimization rule is obtained through off-line simulation by means of an instantaneous optimization energy management strategy and by combining a plurality of working conditions, then energy management rules are classified by means of fuzzy C-means clustering, and a part of the rules are extracted to serve as training samples of a nerve network. A BP nerve network controller obtained through training controls energy distribution of the system in dependence on the real-time working condition of the APTMS in order to achieve energy optimization management. The energy management strategy for the airplane adaptive power and thermal management system (APTMS) can guarantee fuel economy of the APTMS, and the energy management real-time performance is obviously improved.

The invention discloses a flexible job shop scheduling system based on a Petri network and an improved genetic algorithm. The flexible job shop scheduling system is a system for minimizing completion time and power consumption according to peak-valley electricity price and indirect energy consumption, and comprises a job time selection module and a machine task assigning module, wherein the job time selection module is used for obtaining a migration activation time sequence FS and a migration processing sequence TS' by establishing an energy time Petri network model and a time selection simulation algorithm TSSA; the machine task assigning module is used for simulating by combination of improved genetic algorithm and the Petri network, finding out an optimal migration processing sequence TS, and obtaining a satisfactory solution of flexible job shop scheduling TI-FJSP. By adopting the flexible job shop scheduling system disclosed by the invention, making and implementation of a production plan can be effectively optimized, and a production mode with the lowest cost is provided for a company according to the peak-valley electricity price, so that the production cost of the company can be lowered, the utilization rate of energy can be increased, energy allocation can be optimized, resources can be saved, the environment can be protected, the economic benefits of the company can be optimized, and the industrial competitiveness of the company can be improved.

The present invention relates to an energy allocation system for operating at least an energy storage device to substantially balance the energy consumed by the energy storage device with the energy supplied thereto, comprising the steps of: determining a power flexibility of the energy storage device by generating an upper time-varying power function; predicting energy consumption data per discrete time-slots over a given time-window for the device; predicting a demand for energy by the device in a given planning time-period; generating a time-varying upper energy function for the demand prediction that defines an uppermost limit for the supply of energy to the device; generating a time-varying lower energy function for the demand prediction that defines a lowermost limit for the supply of energy to the device, and supplying energy to the device in a range defined by the limits specified by the upper energy function and the lower energy function.

The invention discloses a method of cryogenic separation for light hydrocarbons on the basis of LNG (liquefied natural gas) cold energy. The method includes: simulating to calculate cold energy allocation provided by a three-unit compression refrigerating system according to the traditional process of cryogenic separation for light hydrocarbons; determining according to the calculated cold energy and the actual conditions to use the cold energy provided by LNG to replace completely or partially the cold energy provided by the three-unit compression refrigerating system, thus determining usage of the LNG to obtain operation conditions of process flow of the cold energy that can be provided by the LNG in the traditional process of cryogenic separation for light hydrocarbons; according to operation conditions of a heat exchanger network using the LNG as a cold source and the process flow as a cold trap and according to Aspen Plus and Aspen Pinch simulation, synthetizing a heat exchanger network map, and determining the sequence of cold energy provision using the LNG as process flow; in the traditional process of cryogenic separation for light hydrocarbons, replacing completely or partially the three-unit compression refrigerating system with the LNG, and providing the cold energy to the process flow according to the sequence so as to complete the cryogenic separation of light hydrocarbons. The method has the advantages that compression refrigeration energy consumption in the process of light hydrocarbon separation can be saved and LNG gasification cost can be reduced.

A method, data processing system, and computer program product for managing energy. A processor unit identifies a plurality of groups of virtual machines in a computer system. The processor unit allocates the energy in the computer system to the plurality of groups of virtual machines based on a policy.

The invention discloses a wireless sensor network dynamic energy allocation method based on a thermoelectric cell. The method includes: building a wireless sensor network node model based on the thermoelectric cell; recording and storing power acquired by the thermoelectric cell and signal to noise ratio of wireless channels; acquiring water injection lines vi of time division slots; acquiring water injection lines of time division slots which satisfy power limitation on the time dimension, and determining allocated power of each time slot according the water injection lines. The thermoelectric cell is used to power wireless sensor network nodes, power generated by the thermoelectric cell is allocated in an optimized manner to achieve maximum throughput of information transmission. By the method, allocation of power acquired by the thermoelectric cell in the wireless sensor network nodes is achieved, and energy utilization efficiency is increased. The method is high in portability and large in application space.

The invention provides an energy distribution device and an energy distribution method of compound energy sources of an urban electric bus, relates to the configuration and the energy management method of compound energy sources of electric cars, and solves the problem of poor power characteristics caused by the fact that the existing electric vehicles adopt aluminum air batteries. According to the invention, by formulating the corresponding energy distribution method, energy of the compound energy sources is reasonably allocated, the energy of the aluminum air batteries is saved, and the aluminum air batteries do not need to be replaced repeatedly, so that the requirement of users for the driving range of electric vehicles is met, and the requirement for power of the urban electric bus most of the time is also met. The energy distribution device and the energy distribution method of the compound energy sources of the urban electric bus are suitable for distribution of compound energy sources of urban electric buses.

The invention relates to a micro-grid energy optimization and evaluation method based on a full energy flow model. The method includes following steps: (1) building the full energy flow model: including energy and energy flow analysis; energy conversion link analysis, energy distribution link analysis, energy storage link analysis, and energy utilization link and energy management link analysis; (2) building a full energy flow network model; (3) performing energy optimization management based on the full energy flow network model including energy static optimization management and energy dynamic optimization management; (4) establishing an index system of the full energy flow model; and (5) performing comprehensive evaluation of the full energy flow model. According to the method, processes of generation, conversion, transmission, storage, and utilization of different types of energies in a micro-grid are described via the manner of digital abstract representation, the index system is established, the micro-grid energy optimization method is proposed, unified quantification of different types of energies is realized via digital representation of energy flow, and comprehensive analysis and evaluation of energy efficiency utilization of overall and partial links are performed.

Operating a vehicle includes receiving, by a central controller, positional data related to the vehicle and environmental data related to a current route of the vehicle. The central controller calculates a desired energy allocation based on the positional data and the environmental data, and transmits the desired energy allocation to the vehicle for use in controlling engine function.

The invention relates to a multi-energy source storage system for rail transit and an energy allocation strategy. The multi-energy source storage system for the rail transit comprises a lithium ion battery pack module, a supercapacitor bank module, a DC / DC converter module, a battery management system and a control unit. The energy allocation strategy of the multi-energy source storage system for the rail transit comprises the following steps of carrying out charging / discharging test on a single lithium ion battery and a single supercapacitor by a charging / discharging device to obtain a mathematical model of the lithium ion battery pack module and the supercapacitor bank module; making obtained mathematical model parameters in a data table based on residual electrical quantity through linearization, and storing the data table in the control unit; calculating the minimum value of total loss of the lithium battery pack module and the supercapacitor bank module under different allocation results in real time by looking for the table, and taking the minimum value as a basis to obtain two energy allocation strategies of an energy storage component. With the multi-energy source storage system and the energy allocation strategy, the loss of the energy storage component is possibly reduced on the premise that the power utilization working condition of rail vehicle is met, and the comprehensive efficiency of the system is improved.

The invention discloses a video SoftCast method based on residual distributed compressed sensing, and belongs to the technical fields of video encoding-decoding and wireless transmission. The method comprises the steps of encoding at a transmitting end, channel transmission and decoding at a receiving end. At the transmitting end, a video sequence is read and blocked, and video blocks are observed in order to obtain observed values of frames; in an image group, the observed values of reference frames are subtracted from the observed values of non-reference frames respectively in order to obtain residual observed values; the residual observed values and the observed values of the reference frames are averagely allocated to a plurality of data packets; and meanwhile, energy allocation is performed on the observed value in the packets, so that energy distortions are minimized. During the channel transmission, the data packets are subjected to simple constellation mapping, and input into an orthogonal frequency division multiplexing (OFDM) channel to be transmitted to a decoding end. At the decoding end, users obtain different quantities of observed values according to acceptance capabilities of the users. An initial solution is obtained according to the observed values and an observation matrix; Wiener filtering is performed; multi-direction motion estimation and motion compensation are performed with correlation among videos; an SPL algorithm is used for updating; and repeated iteration is performed till the end of decoding. The different users recover different video qualities by decoding according to actually-accepted data packets.

The invention discloses a metasurface rectification antenna array for space electromagnetic wave detection and energy collection, and belongs to the technical field of a space energy collection rectification antenna array. On the basis of a multi-mode metasurface structure, a full-wave rectification topological structure is employed, and electromagnetic wave energy in various modes in a space is collected by reasonably arranging rectification antenna array layout. Compared with a traditional rectification antenna, the low-power radio frequency energy can be efficiently collected by the metasurface antenna array, and the metasurface rectification antenna array has greater development advantages; moreover, the full-wave rectification circuit array can be used for efficiently converting the low-power radio-frequency energy to DC energy, and the radio frequency energy in different modes is classified by an energy allocation circuit and then is supplied to different loads; and the electromagnetic wave energy of different frequencies or polarization in the space is detected by judging whether a DC power output exists in the load or not, the radio frequency energy is classified and collected, and the metasurface rectification antenna array can be widely applied to space electromagnetic wave frequency, polarization detection and space wireless energy collection.

The invention relates to a micro-gird community distributed energy distribution method based on a demand side response. The method includes following steps: (1) establishing an Operator optimization model; (2) providing a distributed optimization algorithm based on the demand side response; and (3) introducing a peak-to-average ratio PAR to adjust a load side to satisfy the requirement of a largepower grid. According to the demand side response technology, a distributed control policy is employed, micro-grids perform parallel operation, the leakage of privacy information is effectively avoided, the problem of single-point fault is avoided, the load requirement of the whole micro-grid community can be controlled in a coordinated manner through the setting of the PAR, and through a particleswarm intelligent optimization algorithm, the optimal operation cost of the micro-grid is guaranteed, and the power consumption cost of users is reduced.

Systems and methods are provided that integrate a modular load management solution within a general control architecture. One or more load modules deployed within a controller act as interlocks to energy consuming field devices or machines fed by an energy distribution system within a facility. One or more feeder modules deployed within the controller collect energy supply data from an energy source or feeder on the energy distribution system. The load and feeder modules publish their energy demand and supply data to a virtual energy bus, which facilitates exchange of energy data between the load and feeder modules. The load modules, feeder modules, and virtual energy bus generate multiple views of the facility's overall energy supply and demand status based on the energy data shared between the components, and perform automated load management for the energy distribution system based on the collected data.

The invention discloses an energy management control strategy of a multi-mode hybrid electric vehicle based on energy efficiency maximization, which is divided into two layers. The inner layer is an efficiency normalization maximization strategy, which is based on the efficiency analysis method of the global normalization efficiency factor. It is used to calculate the optimal energy distribution law, and can deal with the energy efficiency optimization problems of the hybrid mode and the pure electric mode at the same time. The outer layer is a dynamic programming, which is used to deal with the mode switching problem of multi-mode hybrid electric vehicle, and to solve the optimal mode switching commands, which can make the optimal mode switching control commands on the premise of avoidingfrequent shifting and shifting energy loss and improving the smoothness of shifting. The outer layer and the inner layer coordinate with each other to form a near-optimal energy management control strategy NEMS +, which is suitable for multi-mode hybrid electric vehicle. This method not only realizes the optimization of energy management control strategy, but also guarantees the smoothness of mode switching and the reasonable mode switching frequency.

The invention provides a user-side IoT intelligent optical storage device energy dynamic allocation method, comprising the steps that user historical power consumption data are collected and uploaded to a server, and the server predicts a ultrashort-term power load based on the user historical power consumption data to obtain a user hourly power consumption; the server obtains the device information of an intelligent optical storage device through the Internet of Things and the weather information through a weather forecast API, and predicts the hourly photovoltaic generated power of the intelligent optical storage device by a photovoltaic output model algorithm or photovoltaic simulation software; the server automatically analyzes and dynamically adjusts an energy allocation strategy hourly according to the predicted hourly user power consumption and the hourly photovoltaic generated power of the intelligent optical storage device; the intelligent optical storage device acquires and updates the energy allocation strategy from the server every hour, and executes the energy allocation for the next hour according to the energy allocation strategy. The method can automatically analyze and dynamically adjust the energy allocation strategy, and can resist the instability of photovoltaic power generation and the change of the user's power consumption habit.

Operating a vehicle includes receiving, by a central controller, positional data related to the vehicle and environmental data related to a current route of the vehicle. The central controller calculates a desired energy allocation based on the positional data and the environmental data, and transmits the desired energy allocation to the vehicle for use in controlling engine function.

A building lighting control system includes a central building server, distributed zone controllers, and light sensors and control units in each zone. Using occupant lighting preferences, occupancy state, and light levels, each zone controller computes a utility curve which represents the relationship between energy use and service level in the zone. The building server zone receives utility curves from all the zones and allocates energy units to the zones based on the utility curves using a utility-based trading algorithm in order to optimize service levels with minimal energy. Each zone controller then distributes energy to lights in its zone based on energy units allocated to the zone by the building server and based also on influence matrices representing the influences of the lights in the zone upon the sensors in the zone. The building server may also compute and output long-term operational information of the building.

The invention provides a composite energy storage system energy optimization and allocation method of an electric automobile and belongs to the field of composite energy storage system energy allocation. The problem that an existing composite energy storage system energy allocation method is long in operating time and poor in allocation effect. Firstly, a convex optimization method is adopted to generate a comparison table of the total output powder of an electric automobile after optimization and the output power of a super-capacitor bank; secondly, a two-dimensional array is established according to the comparison table, a whole automobile controller calculates the total toque required by the electric automobile in action operation according to the acquired speed of the electric automobile, front and rear motor rotation speeds and the opening degree of an accelerator pedal and calculates the real-time required powder Pd of the electric automobile according to the required total torque and the speed of the electric automobile; finally, the real-time output powder Psc of the super-capacitor bank is looked up from the two-dimensional array according to the real-time required powder Pd; according to the real-time required powder Pd, a two-way DC / DC converter is controlled so as to complete the energy allocation of the electric automobile. The composite energy storage system energy optimization and allocation method is used for energy allocation.

The invention discloses a hybrid bus online self-learning energy management method which comprises the following steps of firstly controlling the torque allocation of an engine and a motor according to the initial energy management strategy set when delivery; and along with the running of a bus on a fixed route, obtaining an operating value function corresponding to the initial strategy, starting from the operating value function, and automatically learning an energy management strategy applicable to a bus running road condition online by virtue of the reciprocating running of the bus on a road. By fully utilizing the characteristic that a hybrid bus runs on the same route in a reciprocating manner, a self-learning method is adopted to obtain the energy management strategy applicable to the bus running road condition. The method has the characteristics of reasonable energy allocation, high fuel economy, less exhaust emission, good robustness, energy conservation and environmental friendliness.

The invention relates to a micro-grid modeling approach, in particular to a micro-grid versatile flow modeling method based on different types of energy. The approach comprises the steps of (1) constructing a versatile flow model, including energy and energy flow analysis, energy conversion link analysis, energy distribution link analysis, energy storage link analysis, energy utilization link analysis and energy management link analysis; and (2) constructing a versatile flow network model. The approach systematically describes the energy characteristics of energy flow in the processes of generation, conversion, transmission, storage and utilization through a unified quantitative versatile flow digital model. Meanwhile, the theory can also be used flexibly for energy unified quantitative analysis of a single device through lumped constraints. In practical applications, the invention can freely and flexibly select each links in the energy network as required, takes factors under different situations and constraints into full consideration, systematically establishes a corresponding versatile flow model, and achieves unified quantitative analysis of different types of energy in a micro-grid.

The invention, which belongs to the field of electrical theory and the new technical field, relates to a method for coordinated distribution control of energy of a hybrid energy storage system. According to the method, four inputs including a charging and discharging state and a charge state of a lithium battery energy storage unit, a charge state of a supercapacitor, and a power command being a current power demand and two outputs including charging and discharging reference powers of a lithium battery and a super capacitor energy storage unit are provided. An input variable is fuzzified, a membership function is determined, and the four inputs are converted into linguistic variables; various charge state combinations of the four inputs are analyzed, energy allocation priority levels aredetermined in different states, and then a corresponding fuzzy rule is made; the needed output is expressed by using the linguistic variables, so that the charge and discharge reference power value isexpressed; and then de fuzzification is carried out on the output variables to obtain an actual reference power. Therefore, the depth of charge and discharge is reduced, so that the service life losscost is reduced and the performance of the hybrid energy storage system is improved.