OPTIMIZATION OF MICROGRID ENERGY USE AND DISTRIBUTION

• US 20140148963A1
• Filed: 01/31/2014
• Published: 05/29/2014
• Est. Priority Date: 01/14/2009
• Status: Abandoned Application

• Current Assignee: Integral Analytics, Inc. (Willdan Group, Inc.)
• Original Assignee: Integral Analytics, Inc. (Willdan Group, Inc.)
• Inventors: OZOG, MICHAEL T.
• Application Number:
  US14/169,577
• Publication Number:
  US 20140148963A1
• Time in Patent Office: Days
• US Class Current: 700/291
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 2310/10 The network having a local or delimited stationary reach
• CPC Class Codes: H02J 2310/64 The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• 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: Y02A 30/00 Adapting or protecting infrastructure or their operation
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• 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

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.

Claims

1. A system for energy optimization, the system comprising:

an energy management server;

one or more databases;

the energy management server executing, at least in near real-time, a method comprising;

receiving energy provider data from an energy provider for a selected time period, wherein the energy provider data is energy rates for a customer location;

receiving 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 for the customer location;

receiving customer preferences from the customer;

receiving near-real time and forecasted weather information for the customer location;

forecasting, for the 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 for each of a plurality of end-uses or near-real time individualized whole premise energy usage data for the customer location, (3) the customer preferences, and (4) the near-real time and forecasted weather information for the customer location;

optimizing, for the selected time period, energy usage at the customer location using (1) the individualized energy usage for each of the plurality of end-uses or individualized whole premise energy usage data for a customer location, (2) the energy provider data, and (3) the customer preferences.

2. The system of claim 1, wherein near real-time is a five minute interval or less.

3. The system of claim 1, wherein the customer preferences comprise additional data selected from the group consisting of:

customer willingness to have the end-use interrupted, customer willingness to have the end-use managed, customer willingness to have the end-use scheduled, desired bill levels, and combinations thereof.

4. The system of claim 1, wherein the optimizing further considers data selected from the group consisting of:

customer or location characteristics, customer overrides, compliance histories, end-use information, end-use usage history, billing information including rates, historical individualized demand, historical and forecasted weather for the customer or customer location, PHEV battery capacity, battery charging and discharge rates, vehicle arrival times, battery fill preferences, battery fill forecasts, desired bill levels, customer energy management server settings, customer responses, and combinations thereof.

5. The system of claim 1, wherein the forecasting of individualized energy usage for each of the plurality of end-uses or individualized whole premise energy usage data for a customer location also uses inputs selected from the group consisting of:

load prediction;

risk given load uncertainty;

customer compliance forecasts;

customer probability of override forecasts;

time of day effects;

day of week effects, and combinations thereof.

6. The system of claim 1, wherein the optimizing comprises at least one of:

minimizing customer discomfort, maximizing customer usage, minimizing customer energy bill, minimizing the customer'"'"'s CO₂ emissions, minimizing the degree of end-use load shifting, and combinations thereof, while achieving a customer'"'"'s targeted energy bill, a customer'"'"'s CO₂ creation target, a customer'"'"'s CO₂ reduction, or a customer'"'"'s targeted energy usage.

7. The system of claim 6, wherein the optimizing uses the customer'"'"'s preferences for interruptions by end-use, total time the customer can be interrupted or rescheduled, probability the customer will override an interruption, a maximum cycling for an end-use, cycling of the end-use within lower and upper bounds, maintaining a predetermined level of end-use settings, end-use cycling constraints based on manufactured limits, staggering end-use starts, and combinations thereof.

8. The system of claim 6, wherein the optimizing uses a predetermined bill level for the customer or customer location.

9. The system of claim 8, wherein the optimizing is represented as:

10. The system of claim 1, further comprising sending instructions for enacting results of the optimizing.

11. A method for energy optimization, the method comprising:

providing an energy management server, wherein the energy management server performs the steps comprising;

receiving a customer'"'"'s whole premise and at least one end-use energy usage data in near real-time;

receiving customer preferences and needs from the customer;

receiving energy prices from an energy provider for a selected time period;

receiving local weather data for the customer location during the selected time period;

storing the customer'"'"'s whole premise and at least one end-use energy usage data, customer preferences and needs, energy prices from the energy supplier, and local weather data;

forecasting, in near-real time, at least one of individualized demand by end-use or individualized demand for the location using;

(1) the customer'"'"'s whole premise and at least one end-use energy usage data, (2) customer preferences and needs, (3) energy prices from the energy supplier, and (4) local weather data;

optimizing, in near-real time future energy use for the customer location during the selected time period using the forecasted individualized demand by end-use or the forecasted individualized demand for the location;

sending instructions, in near-real time, for enacting results of the optimizing; and

controlling, by turning on and off, the at least one end-use at the customer location.

12. The method of claim 11, wherein near real-time is a five minute interval or less.

13. The method of claim 11, wherein the optimizing comprises at least one of:

minimizing customer discomfort, maximizing customer usage, minimizing customer energy bill, minimizing the customer'"'"'s CO₂ emissions, minimizing the degree of end-use load shifting, and combinations thereof, while achieving a customer'"'"'s targeted energy bill, a customer'"'"'s CO₂ creation target, a customer'"'"'s CO₂ reduction, or a customer'"'"'s targeted energy usage.

14. The method of claim 11, wherein the optimizing uses the customers'"'"' preferences for shifting load by appliance, the customer'"'"'s need for running specific appliances at specific times, the customer'"'"'s ability to shed usage at specific time for specific end-uses, the uncertainty associated with the forecast in the forecasted load for each end-use, a maximum cycling for an end-use, cycling of the end-use within lower and upper bounds, maintaining a predetermined level of end-use settings, end-use cycling constraints based on manufactured limits, staggering end-use starts, and combinations thereof.

15. The method of claim 11, wherein the optimizing comprises maximizing the customer'"'"'s total usage or minimizing the customer'"'"'s total bill subject to a predetermined individualized bill level set in advance by each customer for a period.

16. The method of claim 15, wherein the optimizing uses a predetermined bill level for the customer or customer location.