Optimization of microgrid energy use and distribution

• US 8,019,697 B2
• Filed: 10/29/2010
• Issued: 09/13/2011
• Est. Priority Date: 01/14/2009
• Status: Active Grant

• Current Assignee: Integral Analytics, Inc. (Willdan Group, Inc.)
• Original Assignee: Michael T. Ozog
• Inventors: Ozog, Michael T.
• Primary Examiner(s): Borissov; Igor
• Application Number:
  US12/915,870
• Publication Number:
  US 20110035073A1
• Time in Patent Office: 319 Days
• Field of Search: 705/412, 705/400, 705/26.1, 705/26.2, 705/26.3, 705/28, 705/29, 705/30, 705/34, 705/7, 705/8, 705/10, 705/11, 705/63, 705/7.25, 705/7.35, 705/7.37, 705/7.24, 705/7.29, 705/7.31, 700286-300, 703/2, 703/13, 703/18
• US Class Current: 705/412
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00022 using wireless data transmission
• CPC Class Codes: H02J 13/00034 the elements or equipment being or involving an electric power substation
• 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: H02J 3/322 the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
• CPC Class Codes: H02J 7/35 with light sensitive cells
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• CPC Class Codes: Y02E 70/30 Systems combining energy storage with energy generation of non-fossil origin
• CPC Class Codes: Y04S 10/123 the energy generation units being or involving renewable energy sources
• CPC Class Codes: Y04S 20/12 Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 40/126 : using wireless data transmission
• CPC Class Codes: Y04S 50/10 : Energy trading, including energy flowing from end-user application to grid
• CPC Class Codes: Y04S 50/14 : Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

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.

Claims

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

a server;

one or more databases in communication with the server;

the server is configured for;

receiving information collector data from at least one information collector, the information collector data comprising near-real time individualized energy usage data for each of a plurality of customers or customer locations;

calculating a cost of service for each of the plurality of customers or customer locations based on their (1) individualized energy usage data, (2) a system generation cost for an energy provider or a locational marginal price of electricity, and (3) characteristics of an electric distribution system used to supply power to each of the plurality of customers or customer locations, the calculating further comprising assigning an individualized relative marginal cost value for each of the customers or customer locations, wherein the relative marginal cost value indicates differences in cost of service for different customers or customer locations, combining the relative marginal cost value with a weighted customer cost, wherein the weighted customer cost indicates an importance of a cost category for individual customers or customer locations;

forecasting individualized energy usage for each of the plurality of customers or customer locations;

forecasting customer specific cost of service for each of the plurality of customers or customer locations using the calculating results; and

optimizing, over a plurality of customers or customer locations, energy use and cost of service using the forecasted individualized energy usage and the forecasted customer specific cost of service.

2. The system of claim 1, wherein the at least one information collector is a third party vendor, and the individualized energy usage data is end-use level information collected through a home area network system or site-level information collected through a smart meter.

3. The system of claim 1, wherein the cost of service is a cost associated with supplying energy to each of the plurality of customers or customer locations.

4. The system of claim 1, further comprising calculating an avoided cost for each of the plurality of customers or customer locations, wherein the avoided cost is cost saved by the energy provider using one particular energy distribution strategy over another energy distribution strategy.

5. The system of claim 1, wherein the method is executed in near real-time, wherein near real-time is a five minute interval or less, and wherein the individualized energy usage data is available at an end-use level.

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

maximizing revenue of the energy provider, minimizing customer discomfort, maximizing avoided costs, minimizing incentive costs, minimizing cost to provide and deliver power, and combinations thereof, while achieving a required total demand reduction or total energy reduction.

7. The system of claim 1, wherein the optimizing comprises minimizing a total system peak demand during a period over the plurality of customers or customer locations subject to end-use kWh requirements and total kWh demand for each of the plurality of customers or customer locations.

8. The system of claim 1, wherein the optimizing comprises maximizing revenue, minimizing a total energy provider cost to provide and deliver energy, or maximizing avoided costs during a period over a plurality of customers or customer locations subject to end-use kWh requirements and total kWh demand for each of the plurality of customers or customer locations during the period.

9. The system of claim 1, wherein the optimizing comprises maximizing avoided cost, minimizing total cost to serve and deliver, or maximizing revenue subject to a predetermined individualized bill level set in advance by each customer for a period.

10. The system of claim 1, wherein the optimizing comprises minimizing renewable generation volatility subject to a generation level during a period and storage system capacity.

11. The system of claim 1, wherein the optimizing comprises maximizing revenue, minimizing total cost to provide and deliver power, or maximizing avoided costs during a period over a plurality of customers or customer locations, a plurality of distributed generation sources and a plurality of distributed storage systems.

12. The system of claim 1, wherein the optimizing considers uncertainty in forecast variables using conditional value at risk or other risk metrics, and provides for least cost planning dispatching solutions applied to end-uses, distributed storage, ancillary services, allocations of cost of service adders or premiums, and combinations thereof.

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

receiving, by a processor, information collector data from at least one information collector, the information collector data comprising near-real time individualized energy usage data for each of a plurality of customers or customer locations;

calculating, by a processor, a cost of service for each of the plurality of customers or customer locations based on their (1) individualized energy usage data, (2) a system generation cost for an energy provider or a locational marginal price of electricity, and (3) characteristics of an electric distribution system used to supply power to each of the plurality of customers or customer locations, the calculating further comprising assigning an individualized relative marginal cost value for each of the customers or customer locations, wherein the relative marginal cost value indicates differences in cost of service for different customers or customer locations, combining the relative marginal cost value with a weighted customer cost, wherein the weighted customer cost indicates an importance of a cost category for individual customers or customer locations;

forecasting, by a processor, individualized energy usage for each of the plurality of customers or customer locations;

forecasting, by a processor, customer specific cost of service for each of the plurality of customers or customer locations using the calculating results; and

optimizing, by a processor, over a plurality of customers or customer locations, energy use and cost of service using the forecasted individualized energy usage and the forecasted customer specific cost of service.

14. The method of claim 13, wherein the method is executed in near real-time, wherein near real-time is a five minute interval or less.

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

maximizing revenue of the energy provider, minimizing customer discomfort, maximizing avoided costs, minimizing incentive costs, minimizing cost to provide and deliver power, and combinations thereof, while achieving a required total demand reduction or total energy reduction.

16. The method of claim 13, wherein the optimizing comprises minimizing a total system peak demand during a period over the plurality of customers or customer locations subject to end-use kWh requirements and total kWh demand for each of the plurality of customers or customer locations.

17. The method of claim 13, wherein the optimizing comprises maximizing revenue, minimizing a total energy provider cost to provide and deliver energy, or maximizing avoided costs during a period over a plurality of customers or customer locations subject to end-use kW requirements and total kWh demand for the plurality of customers or customer locations during the period.

18. The method of claim 13, wherein the optimizing comprises maximizing avoided cost, minimizing total cost to serve and deliver, or maximizing revenue subject to a predetermined individualized bill level set in advance by each customer for a period.

19. The method of claim 13, wherein the optimizing comprises minimizing renewable generation volatility subject to a generation level during a period and storage system capacity.

20. The method of claim 13, wherein the optimizing comprises maximizing revenue, minimizing total cost to provide and deliver power, or maximizing avoided costs during a period over a plurality of customers or customer locations, a plurality of distributed generation sources and a plurality of distributed storage systems.