# US 9,438,041 B2 · System and method for energy distribution

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- Number: US 9,438,041 B2
- Document ID: US9438041B2
- Status: Granted
- Kind: B2
- Application number: 14/211,284
- Filed: 2014-03-14
- Published: 2014-07-17
- Granted: 2016-09-06
- Earliest claimed: 2012-12-19
- Assignees: Bosch Energy Storage Solutions LLC; Robert Bosch GmbH
- Applicants: Bosch Energy Storage Solutions LLC; Robert Bosch GmbH
- Inventors: Binayak Roy; Maksim Subbotin; Ashish S. Krupadanam; Jasim Ahmed
- Web page: https://patentgrep.com/patent/US9438041B2

## CPC

- H02J 3/28: Arrangements for balancing of the load in networks by storage of energy
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06Q 50/06: Energy or water supply
- H02J 3/32: using batteries or super capacitors with converting means
- H02J 3/381: {Dispersed generators}
- H02J 3/46: Controlling the sharing of generated power between the generators, sources or networks
- H02J 2101/10: Dispersed power generation using fossil fuels, e.g. diesel generators
- H02J 2101/20: Dispersed power generation using renewable energy sources
- H02J 2101/24: Photovoltaics
- H02J 2101/28: Wind energy
- Y02B 70/3225: Demand response systems, e.g. load shedding, peak shaving
- Y02E 10/56: Power conversion systems, e.g. maximum power point trackers
- Y02E 10/76: Power conversion electric or electronic aspects
- Y02E 40/70: Smart grids as climate change mitigation technology in the energy generation sector
- Y02E 70/30: Systems combining energy storage with energy generation of non-fossil origin
- Y04S 10/50: Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S 20/222: Demand response systems, e.g. load shedding, peak shaving
- H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS

## Abstract

A Dispatch Planner (DP) is a component in an Energy System Controller that controls the operation of energy resources interconnected into one energy system to provide optimal energy management for a customer. In one embodiment, the energy storage system includes an electric load, dispatchable sources of energy such as an electrical grid, diesel generators, combined heat and power generators; renewable sources of energy such as photo-voltaic cells and wind turbines; and stored energy resources such as electrochemical batteries or pumped hydro reserves.

## Claims

### Claim 1

A method of controlling energy generation and distribution comprising:
receiving with a controller first prediction data corresponding to available power generated from a renewable energy resource over a predetermined first time period and second prediction data corresponding to power demand for at least one load over the predetermined first time period;
generating with the controller a first constraint model corresponding to power generation capacity of the renewable energy resource over the predetermined first time period with reference to the first prediction data;
generating with the controller a second constraint model corresponding to power demand of the at least one load over the predetermined first time period with reference to the second prediction data;
generating with the controller a first plurality of power control commands with reference to the first constraint model and the second constraint model, the first plurality of power control commands being generated to control operation of the renewable energy resource within constraints in the first constraint model to supply power to the at least one load within constraints in the second constraint model over the predetermined first time period; and
operating with the controller the renewable energy resource with reference to the first plurality of power control commands to adjust a level of power output from the renewable energy resource within a first plurality of constraints in the first constraint model to provide power to the at least one load within a second plurality of constraints in the second constraint model.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
generating with the controller a third constraint model corresponding to power generation from a non-renewable energy resource;
generating with the controller the first plurality of power control commands to generate first portion of the power for the at least one load with the renewable energy resource;
generating with the controller a second plurality of power control commands for the non-renewable energy resource to generate a second portion of the power for the at least one load with the non-renewable energy resource; and
operating with the controller the non-renewable energy resource with reference to the second plurality of power control commands to adjust a level of power output from the non-renewable energy resource within a third plurality of constraints in the third constraint model to provide power to the at least one load within the second plurality of constraints in the second constraint model.

### Claim 3 (depends on 2)

The method of claim 2, the generation of the third constraint model further comprising:
generating with the controller the third constraint model with a predetermined load range for the non-renewable energy resource to operate the non-renewable energy resource continuously without deactivating the non-renewable energy resource or operating the non-renewable energy resource at a maximum capacity during the predetermined first time period.

### Claim 4 (depends on 1)

The method of claim 1 further comprising:
receiving with the controller the second prediction data for the at least one load including prediction data for power demand of a deferrable load;
identifying with the controller a time deadline during the predetermined first time period for delivery of power to the deferrable load;
identifying with the controller a second time period prior to the time deadline during which the renewable energy resource is predicted to generate power with an available power level that is above a predicted demand level of the at least one load; and
generating with the controller the first plurality of power control commands to operate the renewable energy resource to generate power at a level that is greater than the predicted demand level during the second time period to provide power to the deferrable load.

### Claim 5 (depends on 1)

The method of claim 1 further comprising:
receiving with the controller third prediction data for a capacity of an energy storage resource over the predetermined first time period, the energy storage resource being configured to receive power from the renewable energy resource at a first time and provide power to the at least one load at a second time after the first time;
identifying with the controller a second time period prior during which the renewable energy resource is predicted to generate power with an available power level that is above a predicted demand level of the at least one load; and
generating with the controller the first plurality of power control commands to operate the renewable energy resource to generate power for at a level that is greater than the predicted demand level during the second time period to provide power to the energy storage resource.

### Claim 6 (depends on 5)

The method of claim 5, the generation of the first plurality of power control commands further comprising:
generating with the controller the first plurality of power control commands to reduce generation of power from the renewable energy resource below a predicted power generation capacity for the renewable energy resource in the first prediction data in response to the predicted power generation capacity exceeding a first constraint for maximum power demand for the at least one load in the second constraint model and a second constraint for available storage capacity in the energy storage resource in the third constraint model.

### Claim 7 (depends on 1)

The method of claim 1, the generation of the first constraint model further comprising:
generating with the controller the first constraint model for the renewable energy resource with reference to a plurality of constraints corresponding to available power generation levels for the renewable energy resource over the predetermined first time period and to the first prediction data.

### Claim 8 (depends on 1)

The method of claim 1, the generation of the second constraint model further comprising:
generating with the controller the second constraint model for the at least one load with reference to a plurality of constraints corresponding to a range of load demand levels for the at least one load over the predetermined first time period and to the second prediction data.

### Claim 9 (depends on 1)

The method of claim 1 the receiving of the first prediction data further comprising:
receiving weather prediction data corresponding to a geographic location of the renewable energy resource.

### Claim 10 (depends on 1)

The method of claim 1 the receiving of the second prediction data further comprising:
receiving weather prediction data corresponding to a geographic location of the at least one load.

### Claim 11 (depends on 1)

The method of claim 1, the generation of the first constraint model further comprising
generating with the controller the first constraint model for the renewable energy resource with reference to the renewable energy resource being one of a photovoltaic array, a wind turbine, a hydroelectric power plant, a tidal power installation, and a wave power installation.