31 results about "Energy pricing" patented technology

A “Smart Grid Pricer” enables automated balancing of the supply and demand of energy supply and consumption, such as the generation and consumption of electricity between electricity providers and electricity consumers. The Smart Grid Pricer automatically computes and delivers real-time energy pricing information to consumers on behalf of energy retailers (e.g., electricity utilities) to help drive the balance of demand with supply. In various embodiments, real-time pricing is determined by using various probabilistic models to estimate overall consumer demand as a function of factors such as energy price, time of day, region, weather, etc. to compute a price that will result in an energy demand that is closely balanced to the available supply. On the consumer side, various components of the Smart Grid Pricer automatically respond to such pricing information to optimize energy consumption in accordance with a variety of automated and/or user defined rules and preferences.

A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

According to various implementations, a demand response (DR) strategy system is described that can effectively model the HVAC energy consumption of a house using a learning based approach that is based on actual energy usage data collected over a period of days. This modeled energy consumption may be used with day-ahead energy pricing and the weather forecast for the location of the house to develop a DR strategy that is more effective than prior DR strategies. In addition, a computational experiment system is described that generates DR strategies based on various energy consumption models and simulated energy usage data for the house and compares the cost effectiveness and energy usage of the generated DR strategies.

In one embodiment, an upper power limit and an average power limit are specified for each server of a computer system. Power to each server is controlled so that the instantaneous power consumption does not exceed the upper power limit and the average power consumption does not exceed the average power limit. Servers whose average power consumption is currently less than the average power limit are identified. The instantaneous power consumption of each identified server is temporarily allowed to exceed its average power limit, to maximize server throughput. In cases where the average power limit of a device is reduced, such as may occur during peak energy pricing hours, the instantaneous power consumption of the device may be throttled down below the average power limit until the average power consumption no longer exceeds the average power limit.

Energy-related devices such as heating, ventilation, and air conditioning (HVAC) units, electric vehicle charging platforms, and solar panels are becoming increasingly networkable within a home or business environment. Furthermore, utility providers are offering flexible pricing schemes that adjust the cost of energy over time based on overall demand, and the energy pricing data is made publically available. Provided are exemplary techniques that utilize this pricing data as well as exploit various synergies between the networked energy-related devices to develop automated and cost-effective energy control solutions.

The present invention provides a METHOD AND APPARATUS FOR HOLISTIC POWER MANAGEMENT TO DYNAMICALLY AND AUTOMATICALLY TURN SERVERS, NETWORK EQUIPMENT AND FACILITY COMPONENTS ON AND OFF INSIDE AND ACROSS MULTIPLE DATA CENTERS BASED ON A VARIETY OF PARAMETERS WITHOUT VIOLATING EXISTING SERVICE LEVELS. This method and apparatus pertains specifically to a method and apparatus for power management in data centers and large server environments.

Until today all servers running a specific application or virtualized environment are kept turned on to support various levels of application demand. With the invention of Holistic Power Management this invention completely automates the tasks required to shutdown and turn off servers not needed and to turn them on and start them up independent of their physical location. Furthermore this invention makes such changes dynamically based on application load, environmental conditions and energy pricing and availability and can adjust cooling services accordingly.

An electric vehicle supply equipment (EVSE) charging system includes an EVSE charging station that is configured to switch a power source between a first power source and a second power source based on a time of use or real-time energy pricing data from a utility. Switching additionally and/or optionally may be based on at least one of charging information and metering information. The first power source may be a renewable source and the second source may be the power grid. In this way, a smart electric vehicle supply equipment (EVSE) charging station may manage and optimize energy supply from a grid and a renewable source when charging an electric vehicle (EV) with the smart the EVSE charging station.

This disclosure enables energy providers and users to establish variable pricing or maximum thresholds for individual components (aka appliances) or groups of components. The consumer (individual or business) defines a usage policy for each device or component which defines limits the consumer is willing to pay in a supply shortage. The usage policy also defines safety parameters as well as energy limiting capabilities of the appliance or component. Energy suppliers use this usage policy to determine which devices get managed or powered off in a shortage situation. With the detailed consumer supply and demand data available from this invention, energy providers may use economic analysis to better understand customer price sensitivity. This information will enable the provider to run simulations of different energy shortage scenarios.

Techniques for real-time pricing of electrical energy are provided. The techniques include receiving electrical energy data, wherein the electrical energy data comprises one or more energy pricing parameters specified by an energy supplier, measuring power grid frequency, wherein the power grid comprises the current frequency of the power grid, measuring current energy consumption, wherein current energy consumption comprises total energy consumption in a sampling period, retrieving consumption history, wherein consumption history comprises energy consumed by a customer over a time period, computing a unit energy rate as a function of customer type, the one or more pricing parameters, frequency and past history of consumption, and using the computed rate to compute a total charge as a product of the unit energy rate and the total energy consumption.

The invention provides an energy trading model by means of energy Internet. The model comprises a formula as described in the specifications, xn=fn(t) and pn=Fn(p'n); y is total expenditure for buying energy by a user; xn is an effective energy use price and is determined by a time function fn(t); n is a type of energy input in the energy Internet; t is a time period; pn is an effective energy use amount; Fn(p'n) is a self characteristic function by a certain energy; and N is a total energy type number. According to the trading model of the invention, an energy price metering form is updated in the basis of the current sectional electricity price, a good foundation is laid for wide interconnection by the future energy. Even if in a single energy trading mode, compared with the current ladder-type electricity price trading mode, the energy trading mode of the invention can increase optimization paths for energy pricing and trading.

A method and apparatus for calculating energy revenues of electric power devices based on real time pricing is provided. The energy revenue calculating apparatus may synchronize time between the energy revenue calculating apparatus and at least one of a real time energy pricing information providing server and a time information providing server, based on time information received from at least one of the real time energy pricing information providing server and the time information providing server, synchronize an energy sale price stored in the energy revenue calculating apparatus with an energy sale price, based on energy sale price information received from the real time energy pricing information providing server, and calculate energy revenues for a predetermined time period, based on the received energy sale price information and information about an amount of energy that flows into and out of the plurality of electric power devices.

Disclosed is an energy metering system, apparatus and method that enables an energy meter to reduce power consumed in communication when remotely inspecting the energy meter under an energy pricing system changed depending on time. To this end, the energy meter transmits information on energy consumption at only a changed time of energy price, and operates in a power saving mode except the changed time. If the information on the energy consumption is transmitted at the changed time, the information on the energy consumption in each period having the same energy price is secured, and therefore, a central server has no difficulty in calculating an energy consumption rate. Thus, it is possible to minimize power consumed in communication while transmitting all the information on the energy consumption, required in the central server. Particularly, a battery is used longer in an environment using the battery, thereby reducing maintenance/repair cost.

A system and method for monitoring a heating, ventilation, or air conditioning (HVAC) system of a building is provided. A monitoring server, located remotely from the building, is configured to receive power consumption data and control signal data from a monitoring device at the building and to receive energy pricing data from a customer device associated with the HVAC system or a database that stores energy pricing data. The monitoring server is configured to determine energy cost data for segments of a selected time period based on the power consumption data, the control signal data, and the energy pricing data. The monitoring server is also configured to communicate the energy cost data to the customer device for display on the customer device.

Disclosed are an energy management system, an energy management method and a repeater for an energy management system. The repeater receives information on an amount of energy consumption of the individual user from the meter, and receives information on an energy pricing system from the central management server. Then, the repeater analyzes and manages the energy consumption rate of the individual user using the received information, and transmits information on the energy consumption rate to the meter or the central management server. The repeater may also analyze various information such as information on an energy consumption propensity of the individual user. The repeater immediately transmits essential information but transmits incidental information only when there is a request of the meter or the central management server. Accordingly, it is possible to reduce communication traffic between the central management server and the plurality of meters.

Distributed energy demand management mechanisms for a distributed energy network (120) are disclosed. In one example, a local energy controller (150) of a local energy system (120) determines projected energy demand information for a plurality of energy devices (122) of the local energy system based on energy pricing information (112) received from a central energy coordinator (110). The local energy controller modifies an energy consumption schedule during an energy pricing transition period based on energy access control information received from the central energy coordinator to manage a projected energy demand increase during the energy pricing transition period of the distributed energy network. In one example, the central energy coordinator determines the energy pricing information for the distributed energy network based on energy supply information and the projected energy demand information. The central energy coordinator determines the energy access control information based on the projected energy demand increase during the energy pricing transition period.

The invention provides an optimization method for energy pricing of a regional comprehensive energy service provider, which comprises the following steps: obtaining historical data of renewable energypower generation output conditions, and preprocessing the historical data to obtain preprocessed data; further obtaining a renewable energy power generation output prediction value according to the preprocessed data; enabling the user to report an energy consumption plan to the regional comprehensive energy service provider; formulating a day-ahead pricing scheme according to the predicted valueand the energy consumption plan, and performing electric energy sales and natural gas sales according to the pricing scheme; making an energy purchasing and selling real-time pricing strategy, and prompting a user to adjust an energy consumption strategy; calculating profits of electric energy sales and natural gas sales and expenditures of users; and judging whether the operation profit of the regional comprehensive energy service provider and the expenditure cost of the user can reach the equilibrium point of the master-slave game or not under the pricing strategy, if not, updating the pricing scheme according to the energy consumption strategy, and if yes, determining that the pricing scheme at the moment is an optimal pricing scheme.

The invention discloses a comprehensive demand response method based on a node energy price strategy of a comprehensive energy system. The method comprises the following steps: constructing an electricity-gas-heat comprehensive energy system operation framework; providing an energy price calculation method for an electricity-gas-heat coupling multi-energy flow network node containing fan output; obtaining node energy prices, building an energy consumption cost model considering load change of user comprehensive demand response, and analyzing response behaviors of various flexible loads under the guidance of an energy pricing mechanism of the comprehensive energy system. According to the comprehensive demand response method based on the node energy price strategy of the comprehensive energy system provided by the invention, the comprehensive energy consumption behavior of the multi-energy user is guided through the node energy prices, the response potential of the multi-energy user can be further mined, the energy consumption of the user is more economical and flexible, and the win-win situation of an energy network and the user is realized.

The invention discloses an energy pricing method for an electricity-gas combined system, and the method comprises the steps: building and considering an energy contract, and taking a power network constraint, a natural gas network constraint and an electricity-gas combined network coupling constraint as constraint conditions; a day-ahead market clearing model of the electricity-gas combined system is obtained by taking minimization of the sum of the electricity purchase cost of a power grid company and the gas purchase cost of a natural gas company as an objective function, and the sum of the electricity purchase cost and the gas purchase cost of other members when each member participates in and does not participate in market day-ahead clearing is calculated through the day-ahead market clearing model. And the energy price is formulated by taking the variable quantity of the sum of the electricity/gas purchasing fees of other members as the value of the member. According to the technical scheme, an energy operator formulates an energy price mechanism according to the value of each member obtained by the method, the market members can be prompted to report real prices in bidding in order to achieve the maximum benefits of the market members, and effective scheduling and stable operation of the electricity market are ensured.

The invention relates to the technical field of node energy pricing of electric power and heating power combined transaction, in particular to a heating and power combined market node energy price calculation method based on a master-slave game. The method comprises the following steps: S1, calculating a node heat price covering heat production cost and water pump operation cost by adopting a node heat price calculation method according to a proportion sharing principle; s2, calculating heat transmission loss, and correcting a heat demand function of a user by adopting a correction calculation method; s3, designing an interactive iterative algorithm for calculating the node electricity price and the node heat price under the master-slave game model. The invention aims to provide a heat and power combined market node energy price calculation method based on the master-slave game aiming at the defects in the prior art, and the practicability is improved.