2674results about "Load forecast in ac network" 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.

Systems and methods for energy optimization may receive receiving energy provider data, near-real time individualized energy usage data for each of a plurality of end-uses or near-real time individualized whole premise energy usage data, customer preferences, and near-real time and forecasted weather information. The systems and methods may forecast, for a selected time period, individualized energy usage for each of the plurality of end-uses or individualized whole premise energy usage data for a customer location using: (1) the energy provider data, (2) the near-real time individualized energy usage data, (3) the customer preferences, and (4) the near-real time and forecasted weather information. The systems and methods may optimize, for the selected time period, energy usage at the customer location using (1) the individualized energy usage, (2) the energy provider data, and (3) the customer preferences.

A method for controlling energy consumption within a building includes providing at least one environment sensing device and at least one energy consumption sensing device associated with the building. Current data is collected from the environment sensing device and the energy consumption sensing device along with associated time-of-day data. A value of a future energy consumption parameter is predicted based upon the collected current data, the associated time-of-day data, and historic data collected from the environment sensing device and the energy consumption sensing device. A profile of future costs per unit of energy consumption as a function of time is determined. Energy consumption is controlled dependent upon the predicted future energy consumption parameter value and the determined profile of energy consumption costs.

A utility employs an active load management system (ALMS) to estimate available operating reserve for possible dispatch to the utility or another requesting entity (e.g., an independent system operator). According to one embodiment, the ALMS determines amounts of electric power stored in power storage devices, such as electric or hybrid electric vehicles, distributed throughout the utility's service area. The ALMS stores the stored power data in a repository. Responsive to receiving a request for operating reserve, the ALMS determines whether the stored power data alone or in combination with projected energy savings from a control event is sufficient to meet the operating reserve requirement. If so, the ALMS dispatches power from the power storage devices to the power grid to meet the operating reserve need. The need for operating reserve may also be communicated to mobile power storage devices to allow them to provide operating reserve as market conditions require.

A utility employs a method for estimating available operating reserve. Electric power consumption by at least one device serviced by the utility is determined during at least one period of time to produce power consumption data. The power consumption data is stored in a repository. A determination is made that a control event is to occur during which power is to be reduced to one or more devices. Prior to the control event and under an assumption that it is not to occur, power consumption behavior expected of the device(s) is estimated for a time period during which the control event is expected to occur based on the stored power consumption data. Additionally, prior to the control event, projected energy savings resulting from the control event are determined based on the devices' estimated power consumption behavior. An amount of available operating reserve is determined based on the projected energy savings.

The present disclosure provides for distributed resource scheduling performed by an advanced resource scheduling (ARS) module implemented on a distributed grid management (DGM) server in a power system. The ARS module is configured to automatically generate a resource schedule for controllable distributed energy resources (e.g., resources that are remotely controllable by DGM server and ARS module) in a distribution network of the power system, such as power generation resources and energy storage resources, to provide power in a cost-effective (e.g., optimal) manner. The ARS module is configured to take into account the operating limits of the distributed energy resources (DERs), the cost curves of the DERs, the system load demand, and other operating constraints to determine the most economical operating plan for the DERs, using an optimization technique such as the particle swarm optimization (PSO) algorithm.

An Energy and Cost Savings Calculation System is provided that automates the determination of energy and cost savings due to energy conservation measures. The system provides Multi-Variant, Non-Linear (MVNL) load forecasting techniques, energy and cost savings calculations, and Weather Ranking. The load forecasting technique may accept numerous external parameters as input. The technique may use multiple Baselines. It may also use multiple Basic Reference Periods to reduce the load forecasting error. The load forecasting technique may utilize external parameters that are updated on a daily basis, such as dry bulb temperature, dew point temperature, solar condition, and interval meter data. The technique may use Baseline Extensions to perform forecasts and Reference Period Modifications to enhance accuracy. The system may calculate energy and cost savings using Complex Rates and time-of-use (TOU) energy data. The system may rank a plurality of sources providing weather data to identify the most accurate weather data.

Methods and systems are disclosed for managing a plurality of power resource assets from a central control center, operating the plurality of power resource assets in an automated fashion as a single power plant.

A method and system is provided for optimizing the performance of a power generation and distribution system by forecasting very short term load forecasts through the use of historical load data, demand patterns and short term load forecasts.

Embodiments of the present invention include control methods employed in multiphase distributed energy storage systems that are located behind utility meters typically located at, but not limited to, medium and large commercial and industrial locations. These distributed energy storage systems can operate semi-autonomously, and can be configured to develop energy control solutions for an electric load location based on various data inputs and communicate these energy control solutions to the distributed energy storage systems. In some embodiments, one or more distributed energy storage systems may be used to absorb and/or deliver power to the electric grid in an effort to provide assistance to or correct for power transmission and distribution problems found on the electric grid outside of an electric load location. In some cases, two or more distributed energy storage systems are used to form a controlled and coordinated response to the problems seen on the electric grid.

Provided is a day-ahead load reduction system based on a customer baseline load for inducing a user to efficiently manage energy consumption by applying an incentive (user compensation according to load reduction) to achieve load reduction and load decentralization. The day-ahead load reduction system based on a customer baseline load operates in connection with a provider terminal and a user terminal through a network to induce a reduction in the load of a user and includes an AMI/AMR translator collecting load profile data of the user in real time, converting the load profile data and storing the load profile data in a meter data warehouse; a meter data management system monitoring and analyzing the load profile data stored in the meter data warehouse in real time; a demand response operation system managing the demand of the user by using the load profile data and performing overall management, analysis and verification of a day-ahead load reduction event; a customer energy management system operating in connection with the demand response operation system and providing information on the load to the user through the user terminal in real time to allow the user to control the load; and an account system operating in connection with the demand response operation system and the customer energy management system, calculating an incentive for the day-ahead load reduction event and notifying a provider and the user of the incentive through the provider terminal and the user terminal.

Embodiments of the present invention include control methods employed in multiphase distributed energy storage systems that are located behind utility meters typically located at, but not limited to, medium and large commercial and industrial locations. These distributed energy storage systems can operate semi-autonomously, and can be configured to develop energy control solutions for an electric load location based on various data inputs and communicate these energy control solutions to the distributed energy storage systems. In some embodiments, one or more distributed energy storage systems may be used to absorb and/or deliver power to the electric grid in an effort to provide assistance to or correct for power transmission and distribution problems found on the electric grid outside of an electric load location. In some cases, two or more distributed energy storage systems are used to form a controlled and coordinated response to the problems seen on the electric grid.

A control system for an energy storage system located behind a utility meter uses a unique, feedback-based, communication and control method to reliably and efficiently maximize economic return of the energy storage system. Operating parameters for the energy storage system are calculated at an external, centralized data center, and are selected to prevent electrical power demand of an electric load location from exceeding a specified set-point by discharging energy storage devices, such as DC batteries, through a bidirectional energy converter during peak demand events. The control system can operate autonomously in the case of a communications failure.

A system and method for distributed intelligence of power tracking and power allocation may include: receiving data by at least one computer from a plurality of identified charging stations and vehicles of customers at distributed locations throughout a power grid; analyzing, with at least one processor of the at least one computer, the data with respect to available power for those locations and customer historical usage and profiles; and sending commands, with the at least one processor, to reallocate power to assets of the power grid to handle fluctuations or forecasted fluctuations in power demand based on the analysis. Customer preferences may also be considered in predicting power demand issues and need for demand response. Economic rules may be executed to incentivize the customers to comply with demand response requirements where demand is greater than power supply.

A system, method and computer program product to assist in managing the physical plant mechanisms and market finances for a deregulated electricity grid or regulated utility grid, populated with solar electric generation capacity. This system provides tools to assist grid operators in the scheduling and dispatch of generation resources in an electrical grid populated with solar electric generation capacity, a week in advance, on an hourly basis. It also provides tools to assist companies engaged in generation, distribution and energy marketing, in the electrical power industry, to manage their contractual supply obligations in the day-ahead hourly wholesale market and the spot market, in an electrical grid populated with solar electric generation capacity. This process can also be used to predict solar loading of building structures, using forecast irradiance data as inputs to common building energy modeling programs, a week in advance, on an hourly basis.

A method (300) of managing energy consumption associated with premises includes firstly generating (302) and storing an initial energy profile (304) of the premises. The profile (304) includes information characterising the premises, such as occupancy patterns, function of the premises, geographical location, installed appliances (108), and so forth. An expected energy usage (308) associated with the premises is computed over a predetermined time period based upon the information in the initial energy profile (304). Actual energy usage (312) associated with the premises is then recorded over the predetermined time period and the energy profile (304) is adaptively updated based upon the recorded energy usage (312). The energy profile (304) and the actual energy usage (312) are used to manage energy consumption associated with the premises. An installable system (100) and apparatus (102) for implementing the method at premises are also provided.