54 results about "Peak demand" patented technology

A method of operating a dryer appliance includes a step of initiating a dryer cycle. The method also includes a step of receiving a demand-side management signal defining a current state of an associated energy supplying utility. The current state of the associated energy supplying utility being indicative of a peak demand period or an off-peak demand period. The method also includes a step of analyzing operating conditions of the dryer appliance following receiving the demand-side management signal. The method further includes a step of directing a suspension sequence following analyzing operating conditions of the dryer appliance.

The systems and methods provide techniques for a distributed energy resource management system (DERMS). The system can optimize energy distribution by leveraging Distributed Energy Resources (DERs) (such as thermostats for HVAC systems, electric vehicle (EV) chargers, and home battery storage) by shifting energy among devices and home. During peak demand, the VPP can minimize the cost to the company, reduce demand on the grid, and ultimately reduce long-term consumer costs. The system can utilize home sensors to determine a state of occupancy (such as a home is occupied, unoccupied, or the residents are on vacation). The system can consider a desired comfort level. The various sources of data (e.g., energy data, cost data, and home sensor data) can be used to manage energy distribution in a VPP and generate demand response events or other instructions for energy usage based on real-time occupancy data.

A building energy system includes equipment configured to consume, store, or discharge one or more energy resources purchased from a utility supplier. At least one of the energy resources is subject to a demand charge. The system further includes a controller configured to determine an optimal allocation of the energy resources across the equipment over a demand charge period. The controller includes a stochastic optimizer configured to obtain representative loads and rates for the building or campus for each of a plurality of scenarios, generate a first objective function comprising a cost of purchasing the energy resources over a portion of the demand charge period, and perform a first optimization to determine a peak demand target for the optimal allocation of the energy resources. The peak demand target minimizes a risk attribute of the first objective function over the plurality of the scenarios.

A grid management platform is disclosed for predicting and mitigating peak electricity demand events using probabilistic modeling and cost-optimized control. The system includes a likelihood model that evaluates the probability of each remaining day within an evaluation interval being the peak load day. This is achieved through a Monte Carlo simulation engine that generates multiple stochastic realizations of future load trajectories based on historical data, forecast data, and forecast error profiles derived from back testing. The system computes likelihood scores and classifies days using optimized bin thresholds that minimize operational cost, considering demand charges, available distributed energy resources, and forecast uncertainty. Based on the predicted likelihoods, the platform generates actionable insights and transmits control signals to grid-connected assets such as batteries, HVAC systems, and electric vehicle chargers to shift or reduce load during predicted peak periods.

The invention discloses an air liquefaction and energy comprehensive utilization system, which relates to the technical field of air liquefaction and utilization, and comprises an air compression unit, an air liquefaction unit, a liquid air energy storage unit, an energy release power generation unit, a heat storage unit, a cold storage unit and an optimization control unit, the air compression unit sucks and compresses ambient air, and then cools the air. Through close cooperation and efficient operation of all the units, the system achieves efficient compression, liquefaction, storage and energy release of air, the utilization efficiency of energy is greatly improved, the storage and energy release process of liquid air provides flexible energy dispatching capacity for the system, stable power output can be provided at the demand peak, and meanwhile, the system has the advantages that the energy utilization efficiency is improved. Due to the arrangement of the heat storage unit and the cold storage unit, the energy comprehensive utilization efficiency of the system is further improved, waste heat in the compression process and the cooling capacity in the liquefied air gasification process are recycled and reused, energy waste is reduced, and the operation cost of the system is reduced.

The invention discloses a user-side shared energy storage optimal configuration method considering an evolutionary game, and the method comprises the steps: obtaining typical power load curves of peak-facing type, peak-flattening type and peak-avoiding type users; according to the typical electrical load curve, constructing an industrial user evolution strategy set, and establishing a payment income model corresponding to a shared energy storage joint co-construction strategy and a self-construction energy storage strategy; building a user participation shared energy storage evolution game model, modeling the strategy evolution process of each user into a replicator dynamic equation, and realizing discretization processing; and solving evolutionary game equilibrium based on an evolutionary iterative algorithm, optimizing user-side shared energy storage configuration in combination with an improved grey wolf algorithm, determining a planning and operation scheduling strategy of each user, and obtaining the optimal investment construction capacity of shared energy storage. According to the shared energy storage optimal configuration method, the influence of individual behavior differences and finite rationality factors is considered, the determined optimal shared energy storage capacity can optimize the investment cost on the basis of reducing a large amount of peak demand electric charge of a user, and resource waste is avoided.

The invention discloses a charging station site selection planning method and device fusing user behavior characteristics, electronic equipment and a storage medium, and belongs to the field of charging station planning, and the method comprises the steps: obtaining electric vehicle user time sequence behavior characteristics of each grid in a to-be-decided region, extracting typical behavior characteristics through clustering, and carrying out the clustering of the typical behavior characteristics; and inputting the typical behavior characteristics into a charging demand prediction model, and generating time distribution and space distribution corresponding to each cluster. And based on the prediction result, constructing a multi-objective optimization function model aiming at maximizing the demand coverage rate, minimizing the total cost and the power grid load peak-valley difference, and setting high-demand area coverage, peak demand satisfaction and key class cluster guarantee constraint conditions. And solving the model under the constraints, generating an optimal solution set, and carrying out charging station site selection according to the optimal solution set. By implementing the method, the problem that the demand prediction precision is limited due to the lack of deep mining and modeling of the time sequence behavior characteristics of the electric vehicle user in the prior art can be solved.

The invention relates to the technical field of heat supply, and discloses an intelligent prediction method and system for the water supply temperature of a central heating secondary network, and the method comprises the steps: obtaining the real-time operation data of a pipe network and the external environment data, and generating the real-time heat load distribution; performing time sequence analysis in combination with historical data to obtain a demand fluctuation mode, and fusing the demand fluctuation mode with the thermal load distribution; when the demand peak value after fusion exceeds a threshold value, integrating the environmental data and predicting the future water supply temperature through a model; and calculating the flow deviation, if the deviation exceeds the limit, optimizing the distribution scheme, and finally generating a dynamic control instruction associated with the demand peak time point. According to the method, through multi-model cooperation and closed-loop feedback, accurate prediction and dynamic response of the thermal load are achieved, and the energy efficiency and the regulation and control precision of the heat supply system are remarkably improved.

An HVAC system is configured to regulate a temperature of a space. The HVAC system includes a single-stage compressor configured to compress a refrigerant used to cool air provided to the space and a controller communicatively coupled to the single-stage compressor. The controller determines that a demand response time period is starting at a start time. After determining that the demand response time period is starting at the start time, an operation schedule is determined indicating alternating portions of the demand response period during which the single-stage compressor is to be turned off and turned on. At or after the start time of the demand response time period, the controller begins operating the single-stage compressor according to the determined operation schedule.

The present disclosure provides a method for predicting coincident peak events in electricity demand. The method includes receiving historical electrical usage data and forecasted demand data for a given timeframe, training a simulation engine based on the historical data, generating a plurality of load scenarios for a target period of time using the trained simulation engine, and computing a probability that the target period of time will include a new record high peak in electricity demand based on the generated scenarios. The method enables accurate prediction of peak demand events without relying on weather or economic data, allowing utilities and consumers to better manage electricity usage and reduce costs associated with peak demand periods.

1. The name of this design product: Energy storage cabinet. 2. The use of this design product: A device for storing electrical energy, capable of storing electrical energy when the power supply is sufficient and releasing electrical energy during peak demand or when the power is insufficient. 3. The design key points of this design product: Lies in the shape. 4. The picture or photo that best shows the design key points: Perspective view.

Aspects of the invention relate to phase change materials which may be used for the cooling of air. In particular, the compositions are phase change materials comprising dimethyl sulfoxide, and a salt selected from the group consisting of: sodium nitrate, potassium nitrate, and lithium bromide. The phase change materials are designed for the efficient storage and release of thermal energy, by using them, heat and cold can be conserved during low-demand, night-time hours, taking advantage of lower energy rates. This stored energy is then released during peak demand times, reducing the strain on power grids and aligning with energy-saving strategies that capitalize on off-peak pricing.

The present application relates to a kind of energy storage scheduling method and system for peak demand reduction and peak valley arbitrage.The method comprises the following steps: setting parameters and initializing;at each time t in the demand control time period, the real-time discharge power is calculated according to the current remaining available power of the energy storage system and the remaining time to the end of demand control;at the end of each demand metering period, the grid-side demand and the load-side demand of the period are calculated, and the grid-side maximum demand and the load-side maximum demand are updated in real time;the real-time load power is continuously monitored and obtained, if the real-time load power is greater than the grid-side maximum demand, the energy storage system is controlled to discharge according to the calculated real-time discharge power to reduce demand;otherwise, control the energy storage system to execute standby;whether to enable peak valley arbitrage mode is judged based on the dynamic discharge power and the realized demand reduction amount;if enabled, the energy storage system continuously discharges according to the dynamic discharge power;if not enabled, the energy storage system remains in demand lock control mode.

The present application belongs to the technical field of energy storage transformer charging and discharging monitoring, and discloses an integrated energy storage transformer meeting peak electricity demand. The present application can precisely compensate the energy loss of the whole process by respectively analyzing the equipment loss and power transmission loss and then correcting the energy storage demand, thereby avoiding capacity configuration deviation. The energy storage demand is more in line with the actual operation scenario, the response capability of the system to peak load is improved, and the equipment life management and power grid compatibility are optimized. The present application generates the charging power of the energy storage equipment based on the preset power grid power limit threshold and the corrected energy storage demand, so as to dynamically match the actual carrying capacity of the power grid. The charging overload safety risk can be avoided by threshold limitation, and the power grid capacity can be maximized within the allowable range to improve the charging efficiency. This mode balances the safety of the power grid and the efficiency of the energy storage, and optimizes the cooperative operation mechanism of the energy storage equipment and the power grid.

The invention provides an electric vehicle wireless charging control scheduling method and system under random traffic flow, and relates to the field of dynamic wireless charging control, and the method comprises the steps: obtaining the static structure information and dynamic state information of a to-be-controlled dynamic wireless charging system, in the dynamic wireless charging system, a centralized power supply supplies power to a plurality of electrified road sections; according to the static structure information, time-varying wireless transmission power caused by dynamic vehicle and charging pile displacement is considered, peak demand minimization or profit maximization is taken as a target, a dynamic wireless charging system is modeled, and a dynamic wireless charging model is obtained; based on the dynamic state information, the dynamic wireless charging model is solved in a layered optimization mode, and the optimal charging rate of each electric vehicle in the dynamic wireless charging system is obtained; according to the invention, the peak power can be obviously reduced, and the wireless charging operation income is improved.

The application provides a multi-time scale power supply guarantee analysis method and system, which comprises the following steps: collecting load parameters in a system to be studied; performing load characteristic analysis and new energy characteristic analysis based on the load parameters to obtain preliminary typical day simulation analysis results; performing typical day simulation calculation based on the preliminary typical day simulation analysis results to obtain an intraday power supply guarantee result, and ending the power supply guarantee analysis if the system has no power shortage; if there is a power shortage, performing the next step of analysis; if there is a power shortage, performing monthly power balance calculation to measure seasonal power shortage, and formulating a seasonal power supply guarantee scheme; after the seasonal power supply guarantee scheme is adopted to ensure seasonal power balance, performing continuous multi-day power balance calculation to measure continuous multi-day peak shaving demand, formulating a continuous multi-day power supply guarantee scheme according to the size of the extreme weather power shortage, and performing typical day simulation analysis again if there is no power shortage. The method can quantitatively analyze the peak demand of different time scales in the system during the power supply guarantee period, and provide support for power source planning.

This invention relates to the field of automated control technology for thermal power generation, and discloses a peak-shaving optimization control system for thermal power units. The system includes a data acquisition and processing module, a multi-objective optimization module, and a peak-shaving execution module. This invention connects to the power plant's distributed control system and plant-level monitoring information system to collect real-time unit operation data and user demand data. The collected data is preprocessed, and the peak electricity demand for the day is predicted based on the user demand data. The theoretical power generation is calculated using an objective function model based on the predicted peak demand and unit operation data. Peak-shaving is then performed based on the theoretical power generation; if the theoretical power generation exceeds the current actual power generation, peak-shaving is executed. The objective function model comprehensively analyzes multiple aspects, including economic cost, power generation revenue, and user demand, achieving coordinated optimization of multiple objectives during peak-shaving and improving the intelligence and economy of peak-shaving control.

A work vehicle may include a first power supply located on a base portion of the vehicle and a second power supply located on a remote portion of the vehicle, such as a boom platform. The second power supply is electrically coupled to one or more devices positioned on the remote portion and is configured to provide electrical power during periods of peak demand. The second power supply may be recharged by the first power supply via an electrically conductive connector during periods of reduced demand.

1. The name of the design product: lithium battery energy storage integrated cabinet. 2. The use of the design product: mainly used in photovoltaic, mains, diesel power supply system, which can store excess energy and release it during peak demand period. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

A modular electric vehicle (EV) charging station may be integrated with Battery Energy Storage System (BESS) capabilities. The charging station may provide a modular architecture, allowing for scalable deployment in various locations, from urban environments to remote areas. The modular design may enable easy expansion or upgrading of the station's charging capacity and power output. Integrating BESS within the charging station may ensure a reliable and efficient energy supply, even in areas with unstable grid connections or in areas experiencing catastrophic weather events resulting in lost power. The BESS may store energy during off-peak hours or from renewable sources, such as solar, and deliver that energy during peak demand periods, thereby reducing energy costs and alleviating strain on the local power grid. The system may be equipped with advanced energy management software that optimizes charging times, balances loads, and enhances overall efficiency for ground and / or aerial electric vehicles (EVs).

The application discloses a kind of wisdom operation management systems of geothermal energy and air energy in severe cold area, and it is related to operation management technical field.The wisdom operation management system of geothermal energy and air energy in severe cold area, including load prediction module, peak demand identification module, air energy supplement module, dispatching scheme determination module and collaborative operation determination module, realize heat load accurate prediction by multidimensional data, peak demand is intelligently identified and air energy supplement proportion is reasonably determined, to generate low-consumption efficient energy dispatching scheme with geothermal priority principle, dynamically locate deviation source and adjust collaborative operation mode in combination with real-time feedback, finally realize the energy-saving, precision and stabilization of severe cold area heating operation, solve the problem that heat load is difficult to accurately predict in severe cold area heating, and energy scheduling lacks optimization.

Deep reinforcement learning agents for demand response in home energy management systems are provided via training an agent via power availability data, electricity use data for an electrical load in a household, and an effect on a length of service of a power supply device to optimize a reward function that rewards: reduced electricity usage at peak demand times for the power grid according to the power availability data, increased user satisfaction with activation of the electrical load, and increased length of service for electrical devices used to delivery electricity to the electrical load; deploying the agent to the household; and activating electrical devices that are part of the electrical load for the household according to a schedule generated to optimize the reward function for delivery of power from at least one of the power grid and the power supply device to the household.

“Purely electrical” solutions to power oscillations involve expensive storage techniques (e.g., batteries and / or capacitors) or wasted energy in “dummy loads” (e.g., resistive banks and / or heaters). Some power delivery systems may incorporate flow batteries as part of the energy storage and delivery solution, particularly using piped electrolyte to distribute power directly to storage racks. For longer duration fluctuations in power consumption, flow batteries may store power during off-peak demand periods and release power during peak demand periods. However, flow batteries typically do not react fast enough to compensate for rapid fluctuations in power consumption. The presently disclosed technology utilizes the pipework of electrolyte distribution systems in place for the flow battery as a distributed electrolytic capacitor. This form of “fast” energy storage is ideally suited to complement “slow” chemical energy storage of a flow battery and is thus capable of acting as a power-smoothing solution and a UPS supplement or replacement.

The invention discloses a Mars nuclear energy-compressed air energy storage and magnesium combustion pressurization integrated power system based on an open Brayton cycle, belongs to the field of space energy engineering, and aims to provide a multi-level comprehensive energy solution from stable base load to extreme peak power for a Mars base. The system sucks in Mars atmosphere, and after the Mars atmosphere is compressed by the gas compressor and heated by the nuclear reactor, the turbine generator set is driven to generate stable base load electric power; in the electric power valley period, the mars atmosphere is compressed and stored in the gas storage container through surplus electric power of the base load power source; in the peak period of power demand, the stored high-pressure gas is released to drive the energy storage turbine generator set to carry out peak regulation; when extreme peak power is needed, the stored high-pressure gas is guided into the combustion chamber before entering the energy storage turbine, and then the high-pressure gas is instantly heated to ultrahigh temperature by injecting combusted metal magnesium, so that the peak power far beyond the conventional mode is exploded. The method is suitable for efficient utilization of local resources of Mars and scenes with wide-range power requirements.

The invention discloses a port liquefied natural gas loading and unloading demand structured prediction method, and relates to the technical field of energy supply and scheduling. Comprising the following steps of structured demand prediction and capacity constraint correction, onshore supply capacity analysis and prediction, total loading and unloading demand gap calculation, peak consumption demand quantification, peak onshore supply peak regulation prediction and loading and unloading peak demand final determination. According to the method, the regional natural gas consumption demand is subjected to multi-stage structured decomposition according to industries and terminal users, and the onshore pipeline gas supply capacity and the peak regulation characteristic are synchronously coupled, so that refined and mechanism prediction of the port LNG loading and unloading demand is realized, the total loading and unloading demand meeting the annual total consumption can be accurately measured and calculated, and the total loading and unloading demand can be accurately calculated. And moreover, by introducing a high-month high-day coefficient and a pipeline peak coefficient, the loading and unloading peak demand necessary for seasonal and time-period gas consumption peak values can be independently quantified and guaranteed, and a scientific and comprehensive decision basis is provided for planning design and capacity configuration of the LNG receiving station.

1. Name of the Design Product: Lithium Battery Energy Storage System (H5). 2. Use of the Design Product: Mainly used in photovoltaic systems to store excess energy and release it during peak demand periods. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective view.

The invention relates to the technical field of alternating current charging, and discloses a control method and device for a multi-power alternating current charging pile, and the method comprises the steps: obtaining the instant load information of each interface and the load information of a power grid; calculating and sorting power headroom, and generating pre-adjustment power flow configuration; performing feasibility judgment and distribution optimization in combination with the power grid margin and the peak demand to obtain a final distribution proportion; the data are written into a power module after equipment state detection, and user experience and load data are synchronously collected; and generating a power fine tuning instruction according to the feedback and the power grid state, and outputting a linkage power scheduling instruction after secondary convergence and continuity curve optimization. The method can solve the problem of low utilization efficiency of power headroom in the prior art.

A heating system having a first heating appliance powered by electricity provided via: a transmission line from an electric power grid that provides electricity to a plurality of buildings including the building; a second heating appliance powered via a fuel; and a controller operationally coupled to the first heating appliance and the second heating appliance, and configured to receive control signals over a telecommunications network, wherein the control signals are associated with reducing consumption of electricity via the electric power grid, wherein, responsive to receiving the control signals, the controller is configured to switch between powering the first heating appliance via the electric power grid and the second heating appliance with the fuel.

An HVAC system is configured to regulate a temperature of a space. The HVAC system includes means for compressing a refrigerant used to cool air provided to the space and a controller communicatively coupled to the means for compressing. The controller determines that a demand response time period is starting at a start time. After determining that the demand response time period is starting at the start time, an operation schedule is determined indicating alternating portions of the demand response period during which the means for compressing is to be turned off and turned on. At or after the start time of the demand response time period, the controller begins operating the means for compressing according to the determined operation schedule.

"Purely electrical" solutions to power oscillations involve expensive storage techniques (e.g., batteries and / or capacitors) or wasted energy in "dummy loads" (e.g., resistive banks and / or heaters). Some power delivery systems may incorporate flow batteries as part of the energy storage and delivery solution, particularly using piped electrolyte to distribute power directly to storage racks. For longer duration fluctuations in power consumption, flow batteries may store power during off-peak demand periods and release power during peak demand periods. However, flow batteries typically do not react fast enough to compensate for rapid fluctuations in power consumption. The presently disclosed technology utilizes the pipework of electrolyte distribution systems in place for the flow battery as a distributed electrolytic capacitor. This form of "fast" energy storage is ideally suited to complement "slow" chemical energy storage of a flow battery and is thus capable of acting as a power-smoothing solution and a UPS supplement or replacement.