Thermo-economic modeling and optimization of a combined cooling, heating, and power plant

Patent metadata

Field	Value
Publication number	US-9709966-B2
Application number	US-201214236500-A
Country	US
Kind code	B2
Filing date	Aug 17, 2012
Priority date	Aug 18, 2011
Publication date	Jul 18, 2017
Grant date	Jul 18, 2017

Abstract

A method to manage operating costs of a combined cooling heating and power (CCHP) plant that includes converting complex models of underlying components of the plant into simplified models (S 101 ), performing an optimization that uses the simplified models as constraints of the optimization to output at least one decision variable (S 102 ), and adjusting controls of the plant based on one or more of the output decision variables (S 103 ).

First claim

What is claimed is:

1. A method to manage operating costs of a combined cooling heating and power (CCHP) plant, the method comprising:
converting, by a processor, complex models of underlying components of the plant into simplified models;
performing, by the processor, an optimization that uses the simplified models as constraints of the optimization to output decision variables; and
adjusting, by the processor, controls of the plant based on one or more of the output decision variables;
wherein one of the simplified models of a thermal energy storage of the plant is a two layer model of the tank and the simplified model of the tank includes first constraints representative of a charging mode of the tank and second constraints representative of a discharging mode of the tank.

2. The method of claim 1 , wherein return and supply water temperatures of a chiller of the plant are held constant in one of the simplified models that represent a chiller of the plant.

3. The method of claim 2 , wherein conversion into the simplified model of the chiller comprises:
generating a polynomial representative of a power requirement of the chiller using the amount of cooling and one or more coefficients; and
performing a linear regression on the polynomial to determine values of the coefficients.

4. The method of claim 1 , wherein the first set of constraints include a charging temperature of the tank based on a temperature of the first layer and the second set of constraints include a discharging temperature of the tank based on a temperature of the second layer.

5. The method of claim 4 , wherein the charging and discharging temperatures are based on time delays determined by mass flow rates in and out of the tank, respectively.

6. The method of claim 5 , wherein conversion into the simplified model of the tank comprises:
performing a global optimization based on the charging temperature subject to the first set of constraints; and
performing a global optimization based on the discharging temperature subject to the second set of constraints.

7. The method of claim 1 , wherein one of the simplified models represents a gas turbine of the plant and is a polynomial based on a desired electrical power produced by the gas turbine.

8. The method of claim 7 , wherein the polynomial represents one of a fuel consumption rate, an exhaust flow rate, or a turbine exit temperature of the gas turbine.

9. The method of claim 1 , wherein one of the simplified models represents a heat recovery steam generator (HRSG) that is modeled as a lumped, counter-flow heat exchanger.

10. The method of claim 9 , wherein the simplified model of the HRSG includes constraints of i) there is no pressure drop from inlet to outlet of the HRSG, ii) pressure drops by a constant factor within the HRSG, and iii) a saturation temperature and specific enthalpy of vaporization for water are linearly dependent on a saturation pressure in an operating range of the HRSG.

11. The method of claim 1 , wherein one of the simplified models represents a steam loop of the plant that comprises a steam turbine, the simplified model including constraints that i) a deaerator and condenser operate at constant pressures, ii) pressure and temperature of water exiting and entering the steam loop are constant, iii) steam driving the steam turbine behaves like an ideal gas, and iv) a mass flow rate of steam in the loop is proportional to a fuel mass flow rate in a gas turbine supplying exhaust to the steam turbine.

12. The method of claim 1 , wherein performing the optimization comprises:
minimizing an objective function including a summation of a term representative of grid electricity cost and a term representative of a fuel cost; and
using the simplified models as constraints in the minimization.

13. The method of claim 12 , wherein data for the terms are sampled during at least two consecutive periods of time.

14. The method of claim 1 , where the decision variables include at least one of water mass flow rate of chilled water, total chilled water supply, chilled water supply temperature, temperature of return stream, operating power level of a gas turbine, and an amount of power purchased.

15. A computer readable storage medium embodying instructions executable by a processor to perform method steps for managing operating costs of a combined cooling heating and power (CCHP) plant, the method steps comprising instructions for:
converting complex models of underlying components of the plant into simplified models;
performing an optimization that uses the simplified models as constraints of the optimization to output at least one decision variable; and
adjusting controllers of the plant based on one or more of the output decision variables, wherein one of the simplified models of a thermal energy storage tank of the plant is a two layer model of the tank and the simplified model of the tank includes first constraints representative of a charging mode of the tank and second constraints representative of a discharging mode of the tank.

16. A Combined Cooling Heating Power (CCHP) Plant comprising:
a cooling element;
a heating element;
a power supplying element; and
a computer comprising a memory including a program, and a processor configured to execute the program,
wherein the program comprises instructions to convert complex models of underlying components of the elements into simplified models, perform an optimization that uses the simplified models as constraints of the optimization to output decision variables, and adjust controls of at least one of the elements of the Plant based on one or more of the output decision variables and wherein one of the simplified models of the cooling element is a thermal energy storage tank modeled as two layers, the simplified model of the tank including first constraints representative of a charging mode of the tank and second constraints representative of a discharging mode of the tank.

17. The CCHP Plant of claim 16 , wherein the cooling element includes at least one chiller, the heating element comprises a heat recovery steam generator, and the power element comprises a gas turbine or a steam turbine.

18. The CCHP Plant of claim 16 , wherein return and supply water temperatures of a chiller of the Plant are held constant in one of the simplified models that represent the corresponding chiller.

19. The CCHP Plant of claim 16 , wherein the program is configured to convert one of the complex models of a chiller of the cooling element into a simplified model of the chiller by generating a polynomial representative of a power requirement of the chiller using an amount of cooling and one or more coefficients, and performing a linear regression on the polynomial to determine values of the coefficients.

20. The CCHP Plant of claim 16 , wherein the first set of constraints include a charging temperature of the tank based on a temperature of the first layer and the second set of constraints include a discharging temperature of the tank based on a temperature of the second layer.

21. The CCHP Plant of claim 20 , wherein the charging and discharging temperatures are based on time delays determined by mass flow rates in and out of the tank, respectively.

22. The CCHP Plant of claim 21 , wherein the program is configured to convert one of the complex models of the tank into the simplified model by performing a global optimization based on the charging temperature subject to the first set of constraint, and
perf

Assignees

• Chandan Vikas
• Akrotirianakis Ioannis
• Chakraborty Amit
• Siemens Ag

Inventors

• Chandan Vikas
• Akrotirianakis Ioannis
• Chakraborty Amit

Classifications

• G05B13/042 Primary

  in which a parameter or coefficient is automatically adjusted to optimise the performance · CPC title

• G05B13/04 Primary

  involving the use of models or simulators · CPC title