Optimization of microgrid energy use and distribution

• US 8,364,609 B2
• Filed: 01/14/2010
• Issued: 01/29/2013
• Est. Priority Date: 01/14/2009
• Status: Active Grant

• Current Assignee: Integral Analytics, Inc. (Willdan Group, Inc.)
• Original Assignee: Integral Analytics, Inc. (Willdan Group, Inc.)
• Inventors: Ozog, Michael T.
• Primary Examiner(s): BORISSOV, IGOR N
• Application Number:
  US12/687,827
• Publication Number:
  US 20100179704A1
• Time in Patent Office: 1,111 Days
• Field of Search: 705/412, 705/400, 705/7.25, 705/7.35, 705/7.37, 705/7.24, 705/7.29, 705/7.31, 705/26.1, 705/26.2, 705/26.3, 705/28, 705/29, 705/30, 705/34, 705/7.11, 705/7.12, 705/7.34, 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 configured to;

receive information collector data from at least one information collector, the information collector data comprising individualized energy usage data regarding energy use of electrical devices of at least one of a plurality of customers or customer locations;

forecast individualized demand by electrical device and individualized demand by location for at least one of the plurality of customers or customer locations using the individualized energy usage data;

determine an optimized energy distribution by minimizing a total system peak demand during a time period over the plurality of customers or customer locations utilizing a constraint of end-use requirements and total demand for each of the plurality of customers or customer locations, the minimizing by calculating at least a fraction over the time period to supply energy by electrical device and the plurality of customers or customer locations that minimizes the total system peak demand during the time period utilizing a constraint of the forecasted individualized demand by electrical device and the forecasted individualized demand for each of the plurality of customers or customer locations; and

produce instructions for controlling energy use of electrical devices of the at least one of the plurality of customers or customer locations based on the determined optimized energy distribution,

wherein the system is configured to determine the optimized energy distribution using the forecasted individualized demand by electrical device, the forecasted individualized demand by location, and a real power of each end-use for the plurality of customers or customer locations, and

wherein the system is configured to determine the optimized energy distribution represented at least partially as;

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.

3. The system of claim 1, wherein the at least one information collector is a third party vendor, and the individualized energy usage data is site-level information collected through a smart meter.

4. The system of claim 1, wherein the at least one information collector is a fourth party vendor, wherein at least one of the plurality of customers or customer locations are not part of a third party vendor customer base.

5. The system of claim 1, wherein the system is configured to operate in near real-time comprising a five minute interval or less.

6. The system of claim 1, wherein the individualized energy usage data is available at an end-use level for each of the plurality of customers or customer locations.

7. The system of claim 1, wherein the information collector data further comprises customer preferences comprising 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.

8. The system of claim 1, wherein the system is configured to determine the optimized energy distribution by considering data selected from the group consisting of:

customer preferences, 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, historical and forecasted avoided costs by each of the plurality of customers or customer locations for commodity and non-commodity cost of service factors, historic and forecasted renewable generation, storage system capacity, storage charge and discharge rates, battery capacity, battery charging and discharge rates, vehicle arrival times, battery fill preferences, battery fill forecasts, desired bill levels, customer management settings per month, customer responses to prior program offerings, research surveys, satisfaction and behavioral/choice preferences, required energy reductions, required demand reductions, and combinations thereof.

9. The system of claim 1, wherein the system is further configured to receive energy provider data comprising overall energy usage data, required energy reductions, required demand reductions, cost of energy data, and data regarding distribution of energy.

10. The system of claim 1, wherein the system is further configured to receive an actual system generation cost from an energy provider or a locational marginal cost of energy from an independent system operator for a bus in near real-time.

11. The system of claim 10, wherein the system is further configured to forecast the system generation cost at a bus or locational marginal cost at a bus using at least one of the following separate, interactive, non-linear, or moving average terms:

temperature, humidity, wind speed, time of day, day of week, month indicator variables, past values of the locational marginal cost at a bus.

12. The system of claim 11, with parameters estimated wherein

φ

(L)[(1−

L)(LMP_t−

X_tβ

]=θ

(L)ε

^t

where

L^jLMP_t=LMP<sub>t−

j</sub>

φ

(L)=1−

φ

₁L−

φ

₂L²−

. . . −

φ

_pL^p

θ

(L)=1−

θ

₁L−

θ

₂L²−

. . . −

θ

_qL^q.

13. The system of claim 1, wherein the system is further configured to estimate the system generation cost for a bus using data including:

generation units, system load, load on the bus, load on other buses, transmission capacity characteristics, microgrid distributed generation, and power flows.

14. The system of claim 13, wherein the system is further configured to forecast the system generation cost at a bus in near real-time using:

forecasted weather conditions, forced outage and transmission congestion inputs, generation units, forecasted system load, microgrid distributed generation forecasts, forecasted demand reductions, and a forecasted load at the bus.

15. The system of claim 1, wherein the system is further configured to receive distributed generation data in at least near real time.

16. The system of claim 1, wherein the system is further configured to receive distributed storage data in at least near real time.

17. The system of claim 1, wherein the system is further configured to:

forecast a cost of service or avoided cost for at least one of the plurality of customers or customer locations using at least one of the individualized energy usage data or a system generation cost at a bus nearest to the at least one of the plurality of customers or customer locations,

wherein the forecasting the cost of service or the avoided cost uses one or more of the following;

primary line losses;

secondary line losses;

one or more voltage adder;

marginal distribution capital costs;

a shaping premium;

a swing premium;

a capacity premium;

ancillary services costs;

non-commodity and non-distribution related adders; and

combinations thereof.

18. The system of claim 1, wherein the system is configured to forecast the individualized demand by electrical device or individualized demand by location using inputs selected from the group consisting of:

load prediction;

weather forecasts;

risk given load uncertainty;

customer compliance forecasts;

customer probability of override forecasts;

time of day effects;

day of week effects, and combinations thereof.

19. The system of claim 1, wherein the system is configured to determine the optimized energy distribution using at least one of:

energy provider data, the information collector data, and data for optimization.

20. The system of claim 1, wherein the system is configured to determine the optimized energy distribution by 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.

21. The system of claim 20, wherein the system is configured to determine the optimized energy distribution using an incentive, a cost of service or avoidable cost, energy rates, the forecasted individualized demand by electrical device, total time of current interruption or scheduling event, each customers'"'"' preferences for interruptions by end-use, total time each customer can be interrupted or rescheduled, probability that each customer will override an interruption, a total allowed number of controls per customer per time period, a maximum cycling for an end-use, soft and hard costs associated with controlling each of the plurality of customers, costs that vary by 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.

22. The system of claim 21, wherein the system is configured to determine the optimized energy distribution represented at least partially as:

23. The system of claim 1, wherein the system is configured to determine the optimized energy distribution by 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.

24. The system of claim 23 wherein the system is configured to determine the optimized energy distribution represented at least partially as:

25. The system of claim 1, wherein the system is configured to determine the optimized energy distribution by minimizing renewable generation volatility subject to a generation level during a period and storage system capacity.

26. The system of claim 25, wherein the system is configured to determine the optimized energy distribution by using a power output of a distributed storage system, a power output of a distributed generation source sent to the distributed storage system, a power generation from the distributed generation source, a storage level of the distributed storage system, an initial storage level of the distributed, and storage system charge and discharge rates.

27. The system of claim 26, wherein the system is configured to determine the optimized energy distribution represented at least partially as:

Max PVBatt

s.t.

PVBatt≦

PVOut_t+BattOut_t tε

T

PVOut_t=PVGen_t−

PVIn_t tε

T

BattLvl_t+1=BattLvl_t+PVIn_t−

BattOut_t tε

T

BattLvl_t=0=K

BattCap≧

BattLvl_t tε

T

Where;

PVBatt is power output of a distributed generation and distributed storage system

PVOut_t is power from the distributed generation that is sent out to an energy distribution system at time t

PVIn_t is power from the distributed generation that is sent out to the distributed storage at time t

PVGen_t is power generation from the distributed generation at time t

BattLvl_t is a storage level of the distributed storage at time t

BattOut_t is power from the distributed storage sent out to the energy distribution system at time t

BattLv_lt=0 is an initial storage level of the distributed storage set to a value K BattCap is the capacity of the distributed storage.

28. The system of claim 26, wherein the system is configured to determine the optimized energy distribution represented at least partially as:

29. The system of claim 1, wherein the system is configured to determine the optimized energy distribution by 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.

30. The system of claim 29, wherein the system is configured to determine the optimized energy distribution by using an energy rate during the period, the forecasted individualized demand by electrical device, a forecasted individualized locational marginal cost of energy at each of the plurality of distributed generation sources and each of the plurality of distributed storage systems, incentives, forecasted individualized cost of service or avoidable cost, forecasted power generation from each of the plurality of distributed generation sources, and the initial storage level of each the plurality of the distributed storage systems.

31. The system of claim 1, wherein the system is configured to determine the optimized energy distribution by considering uncertainty in forecast variables.

32. The system of claim 31, wherein the uncertainty uses a conditional value at risk to incorporate, 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.

33. The system of claim 1, wherein the system is further configured to forecast daily energy prices represented as:

P_t^D=ƒ

₁({right arrow over (P)}_t,{right arrow over (W)}_t,{right arrow over (Y)}_t,{right arrow over (X)}_t,ε

)

Where

ƒ

₁;

is an underlying regression function

P_t^D;

is a daily average electricity price of day t

{right arrow over (P)}_t;

is a past daily average electricity price vector at day t

{right arrow over (W)}_t;

is a past weather condition vector at day t

{right arrow over (Y)}_t;

is a seasonality variables vector at day t

{right arrow over (X)}_t;

is other independent variables vector at day t

ε

_t;

is a white noise error terms at day t.

34. The system of claim 1, wherein the system is further configured to forecast a forward cost of energy represented as:

35. The system of claim 1, wherein the system is further configured to communicate with at least one of the plurality of customers or customer locations through a customer portal.

36. The system of claim 1, wherein the system is further configured to communicate with an energy provider though a utility portal.

37. The system of claim 1, wherein the electrical device comprises an end use comprising at least one of an appliance or an electrical vehicle.

38. The method of claim 1, wherein the electrical device comprises an end use comprising at least one of an appliance or an electrical vehicle.

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

a server configured to;

receive information collector data from at least one information collector, the information collector data comprising individualized energy usage data regarding energy use of electrical devices of at least one of a plurality of customers or customer locations;

forecast individualized demand by electrical device and individualized demand by location for at least one of the plurality of customers or customer locations using the individualized energy usage data;

forecast non-dispatchable energy demand comprising energy demand that is not controllable by the server;

determine an optimized energy distribution by calculating a fraction of a time period to supply energy by electrical device and the plurality of customers or customer locations that maximizes a difference between revenue and total cost to provide and deliver power or avoided costs during the period utilizing constraints of the forecasted non-dispatchable energy demand for each of the plurality of customers or customer locations for the period, the forecasted individualized demand by electrical device for the period, and a predetermined bill level set in advance by the plurality of customers or customer locations for the plurality of customers or customer locations for the time period; and

produce instructions for controlling energy use of electrical devices of the at least one of the plurality of customers or customer locations based on the determined optimized energy distribution,

wherein the system is configured to determine the optimized energy distribution represented at least partially as;

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

a server configured to;

receive information collector data from at least one information collector, the information collector data comprising individualized energy usage data regarding energy use of electrical devices of at least one of a plurality of customers or customer locations;

forecast a cost of service or avoided cost for at least one of the plurality of customers or customer locations using at least one of the individualized energy usage data or a system generation cost at a bus nearest to the at least one of the plurality of customers or customer locations;

forecast individualized demand by location for at least one of the plurality of customers or customer locations using the individualized energy usage data;

determine an optimized energy distribution by determining a supply of energy to each of the plurality of customers or customer locations for a period of time from the distributed generation and distributed storage system that maximizes revenue less the cost of services or avoided cost utilizing constraints of;

the energy rates,

the forecasted individualized demand by each of the plurality of customers or customer locations,

the power from the energy distribution system, the distributed storage, and the distributed generation,

the power from the distributed generation sent out to the distributed storage for storage,

the power generation from the distributed generation,

the storage level of the distributed storage,

the power sent from the energy distribution system to the distributed storage for storage,

the initial storage level of the distributed storage, and

the capacity of the distributed storage; and

produce instructions for controlling energy use of electrical devices of the at least one of the plurality of customers or customer locations based on the determined optimized energy distribution,

wherein the system is configured to determine the optimized energy distribution represented at least partially as;

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

a server;

one or more databases in communication with the server;

the server is configured to;

receive 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;

calculate a cost of service for each of the plurality of customers or customer locations based on at least one of;

individualized energy usage data and at least one of a system generation cost for an energy provider or a locational marginal price of electricity;

or

individualized energy usage data and characteristics of an electric distribution system used to supply power to each of the plurality of customers or customer locations,

further configured to;

assign 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; and

combine 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;

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

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

optimize, 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.

42. The system of claim 41, 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.

43. The system of claim 41, wherein the cost of service is a cost associated with supplying energy to each of the plurality of customers or customer locations.

44. The system of claim 41, the server further configured to calculate 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.

45. The system of claim 41, wherein the system is configured to operate in near real-time comprising a five minute interval or less, and wherein the individualized energy usage data is available at an end-use level.

46. The system of claim 41, wherein the system is configured to optimize by 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.

47. The system of claim 41, wherein the system is configured to optimize by 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.

48. The system of claim 41, wherein the system is configured to optimize by 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.

49. The system of claim 41, wherein the system is configured to optimize by 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.

50. The system of claim 41, wherein the system is configured to optimize by minimizing renewable generation volatility subject to a generation level during a period and storage system capacity.

51. The system of claim 41, wherein the system is configured to optimize by 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.

52. The system of claim 41, wherein the system is configured to optimize by considering 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.

53. 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 by at least one of;

individualized energy usage data and at least one of a system generation cost for an energy provider or a locational marginal price of electricity;

or

individualized energy usage data and 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; and

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.

54. The method of claim 53, wherein the method is executed in near real-time, wherein near real-time is a five minute interval or less.

55. The method of claim 53, 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.

56. The method of claim 53, 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.

57. The method of claim 53, 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.

58. The method of claim 53, 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.

59. The method of claim 53, wherein the optimizing comprises minimizing renewable generation volatility subject to a generation level during a period and storage system capacity.

60. The method of claim 53, 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.