OPTIMIZED ENERGY MANAGEMENT SYSTEM

• US 20110208365A1
• Filed: 08/24/2010
• Published: 08/25/2011
• Est. Priority Date: 06/06/2005
• Status: Active Grant

• Current Assignee: Craig Howard Miller
• Original Assignee: Craig Howard Miller
• Inventors: Miller, Craig Howard
• Granted Patent: US 8,903,560 B2
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/008 involving trading of energy or energy transmission rights
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H02J 3/32 using batteries with converting means
• CPC Class Codes: H02J 7/34 Parallel operation in networks using both storage and other dc sources, e.g. providing buffering H02J7/14 takes precedence
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02E 10/56 Power conversion systems, e.g. maximum power point trackers
• CPC Class Codes: Y02E 70/30 Systems combining energy storage with energy generation of non-fossil origin
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 50/10 Energy trading, including energy flowing from end-user application to grid

Abstract

Methods and systems are provided for optimizing the control of energy supply and demand. An energy control unit includes one or more algorithms for scheduling the control of energy consumption devices on the basis of variables relating to forecast energy supply and demand. Devices for which energy consumption can be scheduled or deferred are activated during periods of cheapest energy usage. Battery storage and alternative energy sources (e.g., photovoltaic cells) are activated to sell energy to the power grid during periods that are determined to correspond to favorable cost conditions.

Claims

1. A method for deferring demand for electrical energy, comprising the computer-implemented steps of:

(1) automatically estimating a cost over a forecasting period for electrical energy at a location, wherein the forecasting period is divided into multiple zones, and wherein the cost of electrical energy is estimated based on a forecast of demand for electrical energy in each zone;

(2) determining a demand for electrical energy by one or more devices at the location for which energy consumption can be deferred;

(3) identifying a zone of the multiple zones in which an available marginal power supply capacity is greater that an electrical load of at least one device of the one or more devices;

(4) repeating step (3) to identify contiguous zones having a total duration exceeding a duration of time during which the at least one device is to be operated;

(5) selecting a plurality of the identified contiguous zones as a lower-cost time period; and

(6) automatically deferring electrical consumption for the at least one device from a higher-cost time period to the lower-cost time period, including the computer-implemented step of issuing a command to the at least one device to cause the deferral to occur.

2. The method of claim 1, wherein the available marginal power supply capacity represents a capacity of a non-grid-based power source.

3. The method of claim 2, wherein the available marginal power supply capacity represents a capacity of a photovoltaic power source.

4. The method of claim 1, wherein step (5) includes

(5a) identifying, from the multiple zones, a plurality of candidate intervals for selection as the lower-cost time period, and

(5b) selecting as the lower-cost time period the candidate interval resulting in the lowest cost.

5. The method of claim 1, wherein step (5) includes

(5a) determining that there is insufficient available marginal power supply capacity to provide the electrical load of the at least one device for an amount of time during which the at least one device is to be operated,

(5b) incrementing demand in each zone by an amount sufficient to provide the electrical load of the at least one device,

(5c) re-estimating the cost over the forecasting period for electrical energy based on the incremented demand in each zone, and

(5d) selecting the portion of the forecasting period having the lowest re-estimated cost as the lower-cost time period.

6. The method of claim 1, wherein the forecasting period is divided into multiple zones, and wherein step (5) includes

(5a) determining, using a configuration specifying temporal limits on deferral of operation of the at least one device, a candidate interval comprising zones of the multiple zones during which the at least one device may be operated,

(5b) repeating step (5a) so as to identify a plurality of candidate intervals for selection as the lower-cost time period, and

(5c) selecting as the lower-cost time period the candidate interval resulting in the lowest cost.

7. A computer-readable medium comprising computer-readable instructions that, when executed by a computer, perform the steps of:

(1) determining a marginal cost for each of a plurality of energy sources available at a location, at least one of which is a non-grid source of electricity, wherein at least one of the plurality of energy sources has a non-constant marginal cost that varies based on the capacity drawn by a single location from that energy source;

(2) determining a capacity of electrical energy available from each non-grid energy source;

(3) determining a demand for electrical energy at the single location; and

(4) dynamically allocating, in order of lowest marginal cost to highest marginal cost, electrical energy capacity from each of the plurality of energy sources to meet the demand.

8. The computer readable medium of claim 7, wherein

the plurality of energy sources comprise a grid-based source of electrical energy having a time-varying marginal cost, a photovoltaic source having a time-varying capacity, a backup generator and a battery,

step (1) comprises the step of approximating a cost production curve for the at least one energy source using piecewise linear segments,

step (2) comprises the steps of determining the capacity of electrical energy available from the photovoltaic source, and

step (4) comprises the steps of allocating electrical energy from at least two of the plurality of energy sources concurrently, treating at least one of the plurality of sources as exceeding demand and dynamically allocating electrical energy from a battery, and further comprising the steps of

(5) receiving at the location via electronic means the time-varying marginal cost of the grid-based source;

(6) determining whether electrical energy in excess of the demand can be economically sold back to the power grid, said determination including an adjustment for loss of battery capacity due to repeated charging cycles;

(7) reducing demand at the location by automatically deferring electrical consumption for a device for which consumption can be deferred from a higher-cost time period to a lower-cost time period, including the computer-implemented step of issuing a command to the device to cause the deferral to occur, and further including determining projected marginal costs in each of a plurality of future time frames and deferring electrical consumption for the device to one of the plurality of future time frames while conforming to an operational constraint for the device, the operational constraint for the device comprising a maximum time duration for which the device can be switched off, and wherein an operational constraint for a second device comprises an end time during which the second device must have completed its operating cycle; and

(8) projecting demand in each of a plurality of future periods based on historical data and on weather forecast data and using the projected demand in making deferral determinations.

9. A computer-readable medium comprising computer-readable instructions that, when executed by a computer, perform the steps of:

(1) defining a plurality of energy consumption patterns for a forecasting period comprising a plurality of contiguous time intervals;

(2) forecasting, for each of the plurality of energy consumption patterns, the electrical load for each of the plurality of contiguous time intervals;

(3) selecting one of the plurality of energy consumption patterns, whereby the selected one of the energy consumption patterns represents an estimate of demand for electrical energy over the forecasting period;

(4) for each of the each of the plurality of contiguous time intervals, as each interval elapses in real-time;

(a.) receiving data relating to actual energy consumption;

(b.) comparing actual energy consumption for a subset of the plurality of contiguous time intervals to each of the plurality of energy consumption patterns to identify a second one of the plurality of energy consumption patterns that is the closest fit to the actual energy consumption of the subset of the plurality of contiguous time intervals;

(c.) scaling the second one of the plurality of energy consumption patterns to reflect the actual energy consumption for the subset of the plurality of contiguous time intervals, whereby the scaled second one of the energy consumption patterns represents an re-estimate of demand for electrical energy over the forecasting period.

10. A computer-readable medium comprising computer-readable instructions that, when executed by a computer, perform the steps of:

(1) determining a marginal cost for each of a plurality of energy sources available at a location, at least one of which is a non-grid source of electricity, wherein at least one of the plurality of energy sources has a non-constant marginal cost that varies based on the capacity drawn by a single location from that energy source, wherein a cost production curve for the at least one energy source is approximated using piecewise linear segments,

(2) determining a capacity of electrical energy available from each non-grid energy source;

(3) determining a demand for electrical energy at the single location; and

(4) dynamically allocating, in order of lowest marginal cost to highest marginal cost, electrical energy capacity from each of the plurality of energy sources to meet the demand.