18 results about "Peak demand" patented technology

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 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.

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 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 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 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.

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.

"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 relates to the technical field of industrial data processing, and discloses an urban centralized heat supply monitoring method, which comprises the following steps: acquiring multi-source data including meteorological data, user behavior logs and historical heat supply records in the same period, and obtaining standardized fusion data through noise filtering, time sequence synchronous alignment and feature weight adjustment; a dynamic demand model is trained and optimized, hour-level heat supply load prediction is generated, deviation is calibrated in combination with historical data, and a demand peak value is detected; then heat supply resources are distributed according to regional heat load attributes, parameters such as heat supply amount, temperature and pipe network pressure are calculated, a structured scheduling scheme is formed, and optimization model parameters and filtering parameters of a data processing link are fed back; according to the method, accurate monitoring and dynamic scheduling are realized, and the heat supply stability and the resource utilization rate are improved.

The invention discloses a power cooperative control method and system of a flexible interconnection system, a medium and equipment, and relates to the field of flexible interconnection systems. According to the method, a flexible interconnection system composed of a boosting transformer, an AC / DC bidirectional converter and an energy storage battery is utilized to store excess power of an electric energy surplus side into a matched battery, and the stored electric energy is timely released at an electric energy vacancy side, so that dynamic power balance and energy optimization scheduling between transformer substations on two sides are realized; meanwhile, an SOC balancing strategy is considered in the power distribution link of the multiple converters, and a charging and discharging power instruction is issued according to an adjustable balancing speed. The method can effectively stabilize the power fluctuation of the tie line, reduces the peak demand of an enterprise, improves the available capacity and service life of a battery, and finally achieves the economic operation of a power grid and the minimization of the power utilization cost.

The invention relates to the technical field of industrial data mining, in particular to a transformer load rate prediction method based on data mining, which comprises the following steps of: acquiring real-time voltage, current and active power readings according to sensing signals of intelligent electric meters at a low-voltage side of a transformer and at each user side in a power supply area; synchronously obtaining state values indicated by the transformer winding temperature and the oil conservator oil level, and generating a time sequence electrical state parameter set. According to the method, the expected pressure of a transformer insulation system and a conductive part under a specific working condition can be restored more truly, a matched demand response dynamic pricing strategy can be generated based on an overload risk prediction value, and a price lever is utilized to guide a high-sensitivity user to actively shift peaks; the capacity of existing equipment is utilized to the maximum extent while the operation safety of a power grid is guaranteed, and the economic cost caused by blind capacity expansion and the thermal aging risk caused by long-term overload operation of the equipment are reduced.

The invention discloses a method and a system for quickly generating a direct-current delivery curve of a new energy base, and belongs to the technical field of pre-evaluation of carbon emission of a power system. The method comprises the following steps: generating typical curves of each month according to a prediction curve of a new energy base; generating a two-step delivery curve according to the typical curve of each month based on the output characteristics of new energy wind power and photovoltaic power and the requirements of a direct current delivery curve; and based on the peak demand of the receiving end power grid, correcting the two-step outward transmission curve to generate a three-step outward transmission curve, namely the direct current outward transmission curve of the new energy base. According to the method, the direct current delivery curve of the new energy base can be constructed on the basis of the operation characteristics of various power supplies of the wind-solar energy storage and the electric quantity characteristics of the wind-solar energy storage, and the method can be used for conveniently and rapidly carrying out planning operation and power supply configuration of the whole system and improving the calculation efficiency when processing direct current delivery and multiple receiving end power grids of multiple new energy bases.

The application belongs to the technical field of charging scheduling, and proposes a charging scheduling method and system for electric vehicle fast charging stations based on safety reinforcement learning. A state-by-state safety value function based on a control barrier function is proposed to ensure that constraints are formally enforced during the entire training and execution process. The state-by-state neural network method based on Lagrange can adaptively balance reward maximization and safety protection. This method effectively reduces peak demand during high load periods while achieving zero voltage violations.

The invention discloses a cogeneration unit auxiliary peak regulation method and device, a storage medium and electronic equipment. The method comprises the following steps: processing and analyzing meteorological data to obtain a meteorological prediction result; obtaining a new energy output prediction result based on the weather prediction result and the new energy operation data; determining an electric heating load demand prediction result according to the electric heating load data, the weather prediction result and the operation state of the thermodynamic system; according to the new energy output prediction result and the electric heating load demand prediction result, peak regulation or peak demand parameters are generated; according to the real-time operation parameters, the configuration parameters of the multiple thermoelectric decoupling modes and the application scene, the peak regulation and peak potential of the cogeneration unit is determined; and according to the weather prediction result, the new energy output prediction result, the electric heating load demand prediction result, the peak regulation or peak related demand parameters and the peak regulation and peak potential of the cogeneration unit, an auxiliary peak regulation scheme of the cogeneration unit is determined in combination with the supply and demand change of the electric power spot market and the electric heating load fluctuation condition.