Multi-objective energy management methods for micro-grids

• US 9,568,901 B2
• Filed: 04/06/2013
• Issued: 02/14/2017
• Est. Priority Date: 08/27/2012
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Corporation
• Inventors: Hooshmand, Ali, Sharma, Ratnesh, Asghari, Babak
• Primary Examiner(s): Huang, Miranda
• Assistant Examiner(s): Chu, Alan
• Application Number:
  US13/858,033
• Publication Number:
  US 20140058571A1
• Time in Patent Office: 1,410 Days
• Field of Search: 700/286
• US Class Current: 1/1
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: 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

Abstract

Systems and methods are disclosed for multi-objective energy management of micro-grids. A two-layer control method is used. In the first layer which is the advisory layer, a Model Predictive Control (MPC) method is used as a long term scheduler. The result of this layer will be used as optimality constraints in the second layer. In the second layer, a real-time controller guarantees a second-by-second balance between supply and demand subject to the constraints provided by the advisory layer.

Claims

1. A method to perform multi-objective energy management of micro-grids, comprising:

controlling a charge or discharge of a battery cell;

providing an advisory layer with a Model Predictive Control (MPC) as a long term scheduler, wherein the advisory layer determines an optimal set point or reference trajectory to reduce cost of energy;

determining battery off-peak charging level by the MPC;

providing a real-time layer coupled to the advisory layer with a real-time controller that guarantees a real-time second-by-second balance between supply and demand, subject to the optimal setpoint or trajectory generated by the advisory layer;

optimizing energy cost using forecasted renewable generation, load, time-of-use electricity price, battery depth of discharge, and battery power price;

maximizing battery lifetime and integrating with energy cost minimization, wherein the battery cell which has been operated for a certain period of time and experienced k discharge events, has an estimated lifetime, BL, as follows;

2. The method of claim 1, comprising time-triggering the operation of the MPC.

3. The method of claim 1, comprising event-triggering the operation of the MPC.

4. The method of claim 1, wherein the advisory layer receives feedback from the real-time layer in a closed loop.

5. The method of claim 1, wherein the real-time controller applies a battery ampere-hour cost model.

6. The method of claim 1 comprising sending optimal commands for the micro-grid to the real-time layer.

7. A method for multi-objective management of a storage device, comprising:

generating a battery power cost model;

generating an energy cost model;

controlling a charge or discharge of the battery storage device;

dispatching energy sources from a micro-grid to minimize a marginal cost of operation and maximize a storage unit lifetime over a time horizon;

utilizing a peak-shaving inequality constraint such that the total extracted power from the grid at each time is less than a predetermined constant value;

optimizing energy cost using forecasted renewable generation, load, time-of-use electricity price, battery depth of discharge, and battery power price;

maximizing battery lifetime and integrating with energy cost minimization, wherein a battery cell which has been operated for a certain period of time and experienced k discharge events, has an estimated lifetime, BL, as follows;

8. The method of claim 7, comprising performing peak shaving wherein an inequality constraint is added to restrict a total power extracted from the grid for both load supplying and battery charging at each time instance.

9. The method of claim 7, comprising charging the battery at a constant level by considering an equality constraint in an optimization problem which lets a charging power to be either a constant level or zero.

10. The method of claim 7, comprising determining a first objective function J₁ as a grid power cost over an optimization window as:

11. The method of claim 7, comprising determining a second objective function J₂ as a grid power cost over an optimization window as:

12. A multi-objective energy management system, comprising:

a processor coupled to a micro-grid;

computer code for utilizing a peak-shaving inequality constraint such that the total extracted power from the grid at each time is less than a predetermined constant value;

computer code for optimizing energy cost using forecasted renewable generation, load, time-of-use electricity price, battery depth of discharge, and battery power price;

computer code for controlling a charge or discharge of a battery cell

computer code for maximizing battery lifetime and integrating with energy cost minimization, wherein the battery cell which has been operated for a certain period of time and experienced k discharge events, has an estimated lifetime, BL, as follows;

13. An energy storage system with multi-objective management, comprising:

an energy storage system;

a computer to control the charging of the energy storage system, the computer executing;

computer code for generating a battery power cost model and an energy cost model;

computer code for controlling a charge or discharge of the battery storage device;

computer code for integrating an energy cost minimization and battery life maximization objective functions for optimizing a Model Predictive Control (MPC);

computer code for dispatching energy sources from a micro-grid to minimize a marginal cost of operation and maximize the storage unit lifetime;

computer code for utilizing a peak-shaving inequality constraint such that the total extracted power from the grid at each time is less than a predetermined constant value;

computer code for optimizing energy cost using forecasted renewable generation, load, time-of-use electricity price, battery depth of discharge, and battery power price; and

computer code for maximizing battery lifetime and integrating with energy cost minimization, wherein a battery cell which has been operated for a certain period of time and experienced k discharge events, has an estimated lifetime, BL, as follows;

14. The system of claim 13, comprising computer code for performing peak shaving wherein an inequality constraint is added to restrict a total power extracted from the grid for both load supplying and battery charging at each time instance.

15. The system of claim 13, comprising computer code for charging the battery at a constant level by considering an equality constraint in an optimization problem which lets a charging power to be either a constant level or zero.

16. The system of claim 13, comprising computer code for determining a first objective function Ji as a grid power cost over an optimization window as:

17. The method of claim 13, comprising computer code for determining a second objective function J₂ as a grid power cost over an optimization window as: