RESOURCE CONTROL BY PROBABILITY TREE CONVOLUTION PRODUCTION COST VALUATION BY ITERATIVE EQUIVALENT DEMAND DURATION CURVE EXPANSION (AKA. TREE CONVOLUTION)

• US 20160077507A1
• Filed: 09/11/2015
• Published: 03/17/2016
• Est. Priority Date: 09/11/2014
• Status: Active Grant

• Current Assignee: Gerald Bernard Sheble
• Original Assignee: Gerald Bernard Sheble
• Inventors: Sheble, Gerald Bernard
• Granted Patent: US 10,770,899 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• 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 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02P 90/80 Management or planning
• 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

A method, system and program product for quantifying risk of unserved energy in an energy system using a digital simulation. An energy demand forecast is generated based at least in part on a weather model for near term future periods. A plurality of energy supply resources are committed to meet the plurality of energy demand assisted by a plurality of storage devices and associated ancillary services. A probable operating status is specified for each committed energy supply resource in the energy system. Renewable energy resources such as wind, solar cells, and biofuels are also included in the models for energy supply sources. A determination is made as to whether or not the committed supply resources and storage devices are sufficient to meet the energy demand as well as determine the cost of production above a prespecified LODP and EUE.

Claims

1. A method in a smart grid control system to control a smart energy grid, the method comprising:

receiving, by the smart grid control system, input data that describes one or more operational parameters of each of a plurality of resources of the smart energy grid, the plurality of resources comprising at least a plurality of energy generation resources;

building, by the smart grid control system, a probability tree based at least in part on the received input data, the probability tree comprising a plurality of leaves, each leaf of the plurality of leaves representative of one of the plurality of resources of the smart energy grid;

performing, by the smart grid control system, a plurality of approximation iterations to iteratively revise the probability tree based at least in part on iterative updates to an estimated demand duration curve, the iterative updates to the estimated demand duration curve based at least in part on iterative selections of paths through the probability tree; and

after performing the plurality of approximation iterations, controlling, by the smart grid control system, the smart energy grid to activate or deactivate one or more resources of the smart energy grid based at least in part on the most recently revised probability tree.

2. The method of claim 1 wherein each approximation iteration comprises:

revising, by the smart grid control system, the probability tree based at least in part on an analysis of a most recently updated version of the estimated demand duration curve;

selecting, by the smart grid control system, at least one new path through the revised probability tree;

updating, by the smart grid control system, the estimated demand duration curve based at least in part on the selected at least one new path; and

determining, by the smart grid control system, whether to perform an additional approximation iteration based at least in part on the updated estimated demand duration curve.

3. The method of claim 2 wherein each approximation iteration further comprises determining, by the smart grid control system, at least one of an expected unserved energy, a loss of demand probability, and a loss of load probability for the updated estimated demand duration curve.

4. The method of claim 2 wherein revising, by the smart grid control system, the probability tree comprises revising, by the smart grid control system, the probability tree to increase a number of states associated with at least one of the plurality of leaves traversed by the most recently selected at least one new path.

5. The method of claim 4 wherein revising, by the smart grid control system, the probability tree to increase a number of states associated with at least one of the plurality of leaves comprises revising, by the smart grid control system, the probability tree to include uncertainty regarding one or more of a demand, a transportation network, or a fuel for the resource represented by the at least one of the plurality of leaves.

6. The method of claim 2 wherein revising, by the smart grid control system, the probability tree comprises revising, by the smart grid control system, the probability tree to include at least one new leaf respectively representative of at least one new resource controllably introducible to and operable with the smart energy grid.

7. The method of claim 6, further comprising:

selecting, by the smart grid control system, the at least one new resource from a plurality of available new resources based at least in part on a merit order ranking associated with the plurality of available new resources.

8. The method of claim 6 wherein revising, by the smart grid control system, the probability tree to include at least one new leaf respectively representative of at least one new resource controllably introducible to and operable with the smart energy grid comprises revising, by the smart grid control system, the probability tree to include the at least one new leaf respectively representative of at least one new demand response resource or at least one new transactive energy resource.

9. The method of claim 2 wherein selecting, by the smart grid control system, at least one new path through the revised probability tree comprises at least one of adding, by the smart grid control system, a new path to the probability tree and revising, by the smart grid control system, a previous path to follow a new route.

10. The method of claim 2 wherein determining, by the smart grid control system, whether to perform an additional approximation iteration based at least in part on the updated estimated demand duration curve comprises determining, by the smart grid control system, whether the updated estimated demand duration curve satisfies one or more accuracy parameters.

11. The method of claim 10 wherein determining, by the smart grid control system, whether the updated estimated demand duration curve satisfies one or more accuracy parameters comprises comparing, by the smart grid control system, the updated estimated demand duration curve to an expected demand duration curve descriptive of expected energy demand for at least a first period of time.

12. The method of claim 11, further comprising:

generating, by the smart grid control system, the expected demand duration curve for at least the first period of time based at least in part on a weather forecast for at least the first period of time.

13. The method of claim 2 wherein determining, by the smart grid control system, whether to perform an additional approximation iteration based at least in part on the updated estimated demand duration curve comprises determining, by the smart grid control system, whether the updated estimated demand duration curve satisfies one or more user-inputted accuracy parameters.

14. The method of claim 2 wherein each approximation iteration further comprises performing, by the smart grid control system, a deterministic optimization technique for the selected at least one new path to simulate or value the selected at least one new path.

15. The method of claim 2 wherein revising, by the smart grid control system, the probability tree based at least in part on an analysis of a most recently updated version of the estimated demand duration curve comprises determining, by the smart grid control system, a sensitivity factor for each resource included in the probability tree and revising, by the smart grid control system, a portion of the probability tree that corresponds to the resource with the smallest sensitivity factor that is still greater than a threshold value.

16. The method of claim 1, further comprising:

generating a plurality of expected demand duration curves respectively for a plurality of different time periods; and

performing, by the smart grid control system, the plurality of approximation iterations for each of the plurality of different time periods, the plurality of approximation iterations performed for each respective time period including iterative updates to an estimated demand duration curve for such time period based at least in part on the respective expected demand duration curve generated for such time period.

17. The method of claim 1 wherein building, by the smart grid control system, a probability tree comprises building, by the smart grid control system, the probability tree comprising the plurality of leaves, at least some of the plurality of leaves respectively representative of energy transmission and transportation resources or energy generation fuel resources.

18. The method of claim 1 wherein controlling, by the smart grid control system, the smart energy grid to activate or deactivate one or more resources of the smart energy grid based at least in part on the most recently revised probability tree comprises causing, by the smart grid control system, one or more of the plurality of resources to come online or offline based at least in part on the most recently revised probability tree.

19. A smart energy grid control system to control a smart energy grid, the smart energy grid control system comprising:

at least one processor; and

at least one non-transitory processor-readable medium storing at least one of data and instructions that, when executed by the at least one processor, cause the smart energy grid control system to;

receive input data that describes one or more operational parameters of each of a plurality of resources of the smart energy grid, the plurality of resources comprising at least a plurality of energy generation resources;

build a probability tree based at least in part on the received input data, the probability tree comprising a plurality of leaves, each leaf of the plurality of leaves representative of one of the plurality of resources of the smart energy grid;

select at least one initial path through the probability tree;

determine an estimated demand duration curve based on the at least one initial path through the probability tree;

determine whether the estimated demand duration curve for the at least one initial path satisfies one or more accuracy requirements;

responsive to a determination that the estimated demand duration curve does not satisfy the one or more accuracy requirements, perform one or more approximation iterations in which the smart energy grid control system iteratively revises the probability tree based at least in part on an analysis of the estimated demand duration curve, iteratively selects at least one revised path through the revised probability tree, and iteratively updates the estimated demand duration curve; and

responsive to a determination that the estimated demand duration curve satisfies the one or more accuracy requirements, control the smart energy grid to respectively activate for at least a first period of time at least one of the particular resources of the smart energy grid that respectively correspond to the leaves of the probability tree included in a most recently selected path.

20. The smart energy grid control system of claim 19 wherein the data or instructions that cause the smart energy grid control system to perform one or more approximation iterations cause, for each of the one or more approximation iterations, the smart energy grid control system to:

revise the probability tree based at least in part on the estimated demand duration curve;

select at least one revised path through the revised probability tree;

update the estimated demand duration curve based at least in part on the selected at least one revised path; and

determine whether the updated estimated demand duration curve satisfies the one or more accuracy requirements.

21. The smart energy grid control system of claim 20 wherein the data or instructions that cause the smart energy grid control system to revise the probability tree based at least in part on the estimated demand duration curve cause the smart energy grid control system to:

determine a plurality of sensitivity factors respectively for the plurality of resources, wherein the sensitivity factor determined for each resource indicates a magnitude of impact that operational unavailability of such resource will have on the estimated demand duration curve for the smart energy grid; and

revise a portion of the probability tree associated with at least one of the plurality of resources selected based on the sensitivity factors.

22. The smart energy grid control system of claim 21 wherein the data or instructions that cause the smart energy grid control system to revise a portion of the probability tree associated with at least one of the plurality of resources selected based on the sensitivity factors cause the smart energy grid control system to revise the portion of the probability tree to increase a number of states associated with the at least one of the plurality of resources selected based on the sensitivity factors.

23. The smart energy grid control system of claim 20 wherein the data or instructions further cause the smart energy grid control system to:

generate an expected demand duration curve for the at least one period of time, the expected demand duration curve descriptive of an expected amount of energy demand for the at least one period of time;

wherein the data or instructions that cause the smart energy grid control system to determine whether the updated estimated demand duration curve satisfies the one or more accuracy requirements cause the smart energy grid control system to compare the updated estimated demand duration curve to the expected demand duration curve for the at least one period.

24. A smart energy grid, comprising:

a plurality of resources, at least some of the plurality of resources comprising energy generation resources; and

a smart grid controller respectively controllingly coupled to the plurality of resources, the smart grid controller comprising at least one processor, wherein the smart grid controller;

generates a plurality of operational models respectively for the plurality of resources for at least one time period, the plurality of operational models respectively descriptive of operational availability of the plurality of resources during the at least one time period;

builds a probability tree representative of the plurality of resources during the at least one time period, the probability tree including respective probabilities of operational availability for the plurality of resources based on the respective operational models;

evaluates an availability for each of the plurality of resources according to at least one initial path through the probability tree;

determines a plurality of sensitivity factors respectively for the plurality of resources, wherein the sensitivity factor determined for each resource indicates a magnitude of impact that operational unavailability of such resource will have on an estimated demand duration curve for the smart energy grid for the at least one time period;

revises a portion of the probability tree associated with at least one of the plurality of resources selected based on the sensitivity factors; and

controls one or more of the plurality of resources based at least in part on the revised probability tree.

25. The smart energy grid of claim 24 wherein, prior to determination of the plurality of sensitivity factors, the smart grid controller further determines whether the plurality of resources according to the at least one initial path through the probability tree provide sufficient energy to meet an expected energy demand, and, responsive to a determination that the plurality of resources according to the at least one initial path through the probability tree do not provide sufficient energy to meet the expected energy demand, supplements the probability tree to include at least one additional representation of at least one additional resource controllably coupleable to the smart energy grid.

26. The smart energy grid of claim 24 wherein the smart grid controller further:

evaluates the availability for each of the plurality of resources according to at least one additional path through the revised probability tree; and

generates an estimated demand duration curve based at least in part on at least one additional path through the revised probability tree.

27. The smart energy grid of claim 26 wherein the smart grid controller further:

calculates at least one of an expected unserved energy, a loss of demand probability, and a loss of load probability based at least in part on the estimated demand duration curve.