Battery energy storage system controller systems and methods

Patent metadata

Field	Value
Publication number	US-10554048-B2
Application number	US-201615370464-A
Country	US
Kind code	B2
Filing date	Dec 6, 2016
Priority date	May 18, 2015
Publication date	Feb 4, 2020
Grant date	Feb 4, 2020

Abstract

An energy storage system controller, including: an energy storage system coupled to a power distribution system; and a processor in communication with the energy storage system, wherein the processor executes: a renewables capacity firming algorithm operable for conditioning intermittent power of a renewable energy station using real time and historical input data such that it is made more stable and non-intermittent, optionally utilizing one or more parameter values associated with comparable time periods taking into account one or more factors comprising cloud state; and a peak load shaving algorithm operable for ensuring that the energy storage system is capable of transmitting full power capacity at a predicted feeder peak load time determined by the processor from real time and historical input data; wherein the performance of the renewables capacity firming algorithm and the performance of the peak load shaving algorithm are optimized in parallel.

First claim

What is claimed is:

1. An energy storage system controller, comprising:
an energy storage system coupled to a power distribution system including renewable generation resources; and
a processor in communication with the energy storage system and the power distribution system, wherein the processor obtains real time data streams from different points of the power distribution system and records the real time data streams locally to establish a historical database for the different points of the power distribution system to deduce a required energy storage system active power reference to achieve an optimal firming degree for distribution feeder power by executing:
a renewables capacity firming algorithm operable for conditioning intermittent power of a renewable energy station using real time and recorded historical input data such that it is made more stable and non-intermittent, wherein the renewables capacity firming algorithm comprises an intermittency detection algorithm that tracks a rate of change of a difference between output power and a power reference curve and, if dual thresholds are exceeded, a power swing is identified and firming is commenced, and, if the output power is non-intermittent for a predetermined period of time, firming is rested; and
a peak load shaving algorithm operable for ensuring that the energy storage system is capable of transmitting full power capacity at a predicted feeder peak load time determined by the processor from real time and recorded historical input data;

wherein the performance of the renewables capacity firming algorithm and the performance of the peak load shaving algorithm are optimized in parallel; and
wherein energy is selectively transferred between the energy storage system and the power distribution system at the direction of the processor.

2. The energy storage system controller of claim 1 , wherein the renewable energy station comprises one of a photovoltaic energy station and a wind energy station.

3. The energy storage system controller of claim 1 , wherein the renewables capacity firming algorithm is operable for determining a reference power value to determine optimal power output of the energy storage system during power swings taking into account renewable energy station characteristics.

4. The energy storage system controller of claim 1 , wherein the renewables capacity firming algorithm implements an adaptive filtering control methodology.

5. The energy storage system controller of claim 1 , wherein the renewables capacity firming algorithm is operable for developing a characteristic power curve based on daily power output derived from the recorded historical input data of a renewable energy source.

6. The energy storage system controller of claim 1 , wherein the renewables capacity firming algorithm is operable for developing a firming power reference that considers real time power swing magnitudes, energy storage system capacity, and target state-of-charge at an end of a firming period.

7. The energy storage system controller of claim 1 , wherein the renewables capacity firming algorithm is operable for implementing an intermittency detection algorithm that triggers the energy storage system to commence and halt firming based on a renewable energy facility output ramp rate.

8. An energy storage system controller, comprising:
an energy storage system coupled to a power distribution system including renewable generation resources; and
a processor in communication with the energy storage system and the power distribution system, wherein the processor obtains real time data streams from different points of the power distribution system and records the real time data streams locally to establish a historical database for the different points of the power distribution system to deduce a required energy storage system active power reference to achieve an optimal firming degree for distribution feeder power by executing:
a renewables capacity firming algorithm operable for conditioning intermittent power of a renewable energy station using real time and recorded historical input data such that it is made more stable and non-intermittent, wherein the renewables capacity firming algorithm utilizes one or more parameter values associated with comparable time periods taking into account one or more factors comprising cloud state, wherein the renewables capacity firming algorithm comprises an intermittency detection algorithm that tracks a rate of change of a difference between output power and a power reference curve and, if dual thresholds are exceeded, a power swing is identified and firming is commenced, and, if the output power is non-intermittent for a predetermined period of time, firming is rested; and
a peak load shaving algorithm operable for ensuring that the energy storage system is capable of transmitting full power capacity at a predicted feeder peak load time determined by the processor from real time and recorded historical input data;

wherein the performance of the renewables capacity firming algorithm and the performance of the peak load shaving algorithm are optimized in parallel; and
wherein energy is selectively transferred between the energy storage system and the power distribution system at the direction of the processor.

9. The energy storage system controller of claim 8 , wherein the renewable energy station comprises one of a photovoltaic energy station and a wind energy station.

10. The energy storage system controller of claim 8 , wherein the renewables capacity firming algorithm is operable for determining a reference power value to determine optimal power output of the energy storage system during power swings taking into account renewable energy station characteristics.

11. The energy storage system controller of claim 8 , wherein the renewables capacity firming algorithm implements an adaptive filtering control methodology.

12. The energy storage system controller of claim 8 , wherein the renewables capacity firming algorithm is operable for developing a characteristic power curve based on daily power output derived from the recorded historical input data of a renewable energy source.

13. The energy storage system controller of claim 8 , wherein the renewables capacity firming algorithm is operable for developing a firming power reference that considers real time power swing magnitudes, energy storage system capacity, and target state-of-charge at an end of a firming period.

14. The energy storage system controller of claim 8 , wherein the renewables capacity firming algorithm is operable for implementing an intermittency detection algorithm that triggers the energy storage system to commence and halt firming based on a renewable energy facility output ramp rate.

15. The energy storage system controller of claim 8 , wherein the renewables capacity firming algorithm utilizes one or more parameters based on predefined day types and associated cloud cover characteristics correlating to power requirements.

Assignees

• Abdelrazek Sherif Abdelmageed
• Kamalasadan Sukumar
• Enslin Johan H R
• SOWDER Daniel Blair
• Univ Of North Carolina At Charlotte
• Duke Energy Corp

Inventors

• Abdelrazek Sherif Abdelmageed
• Kamalasadan Sukumar
• Enslin Johan H R
• SOWDER Daniel Blair

Classifications

• H02J3/003

  Load forecast, e.g. methods or systems for forecasting future load demand · CPC title

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• H02J2101/28

  Wind energy · CPC title

• H02J2101/24

  Photovoltaics · CPC title

• H02J3/38

  Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel · CPC title