US-20260128590-A1 - BUILDING ENERGY SYSTEM WITH STOCHASTIC OPTIMIZATION

Abstract

A building energy system includes equipment operable to consume, store, or discharge energy resources purchased from a utility supplier to satisfy energy loads and a controller configured to allocate the energy resources across the equipment over a prediction horizon by performing a stochastic optimization of an objective function. The objective function includes a first cost of purchasing the energy resources based on first decision variables to satisfy a first alternative set of energy loads over the prediction horizon and a second cost of purchasing the energy resources based on second decision variables to satisfy a second alternative set of energy loads over the same prediction horizon. The controller performs the stochastic optimization to determine values of the first and second decision variables subject to corresponding constraints based on the first and second alternative sets of energy loads and controls the equipment based on a result of the stochastic optimization.

Inventors

• Ranjeet Kumar
• Michael J. Wenzel
• Matthew J. Ellis
• Mohammad N. ElBsat
• Kirk H. Drees
• VICTOR MANUEL ZAVALA TEJEDA

Assignees

• TYCO FIRE & SECURITY GMBH

Dates

Publication Date

20260507

Application Date

20251009

Claims (20)

1. 1 . A building energy system comprising: equipment operable to consume, store, or discharge one or more energy resources purchased from a utility supplier to satisfy one or more energy loads; and a controller configured to determine an allocation of the energy resources across the equipment over a prediction horizon, the controller configured to: obtain a plurality of different alternative sets of energy loads over the prediction horizon, the plurality of different alternative sets of energy loads comprising at least a first alternative set of energy loads and a second alternative set of energy loads; generate an objective function comprising at least (i) a first cost of purchasing the energy resources over the prediction horizon based on first decision variables for the energy resources purchased during the prediction horizon to satisfy the first alternative set of energy loads and (ii) a second cost of purchasing the energy resources over the prediction horizon based on second decision variables for the energy resources purchased during the prediction horizon to satisfy the second alternative set of energy loads; perform a stochastic optimization of the objective function to determine (i) first values for the first decision variables subject to first constraints based on the first alternative set of energy loads and (ii) second values for the second decision variables subject to second constraints based on the second alternative set of energy loads; and control the equipment to achieve the allocation of the energy resources across the equipment based on the stochastic optimization.
2. 2 . The building energy system of claim 1 , wherein the equipment comprise HVAC equipment, the first alternative set of energy loads comprises a first alternative set of cooling loads to be satisfied by operating the HVAC equipment, and the second alternative set of energy loads comprises a second alternative set of cooling loads to be satisfied by operating the HVAC equipment.
3. 3 . The building energy system of claim 1 , wherein the equipment comprise one or more computers or electronics, the first alternative set of energy loads comprises a first alternative set of electric loads to be consumed by the computers or electronics, and the second alternative set of energy loads comprises a second alternative set of electric loads to be consumed by the computers or electronics.
4. 4 . The building energy system of claim 1 , wherein the objective function comprises a demand charge term based on a value for a peak demand target for an energy resource subject to a demand charge; wherein the controller is configured to determine the value for the peak demand target by performing the stochastic optimization subject to constraints ensuring that the value for the peak demand target is greater than or equal to the first values and the second values over the prediction horizon.
5. 5 . The building energy system of claim 1 , wherein the controller is configured to obtain a plurality of different alternative sets of rates for the one or more energy resources purchased from the utility supplier, the plurality of different alternative sets of rates comprising at least a first alternative set of rates and a second alternative set of rates; wherein the first cost in the objective function is further based on the first alternative set of rates and the second cost in the objective function is further based on the second alternative set of rates.
6. 6 . The building energy system of claim 1 , wherein the controller is configured to perform the stochastic optimization subject to a constraint requiring equality between (i) a first state value for a state of the building energy system at a time during the prediction horizon and resulting from the first decision variables and (ii) a second state value for the state of the building energy system at the time during the prediction horizon and resulting from the second decision variables.
7. 7 . The building energy system of claim 1 , wherein the equipment comprise one or more storage devices configured to store or discharge the energy resources or one or more generators configured to generate the energy resources; wherein the first values for the first decision variables and the second values for the second variables determined by the controller by performing the stochastic optimization comprise amounts of the energy resources to be stored in or discharged from storage devices or generated by the generators over the prediction horizon.
8. 8 . The building energy system of claim 1 , wherein the first values for the first decision variables and the second values for the second decision variables are alternative values for a same set of decision variables at a same time step of the prediction horizon; wherein the first values for the first decision variables represent optimal values of the same set of decision variables at the same time step of the prediction horizon resulting from the first alternative set of energy loads; and wherein the second values for the second decision variables represent optimal values of the same set of decision variables at the same time step of the prediction horizon resulting from the second alternative set of energy loads.
9. 9 . A method for operating a building energy system comprising: obtaining a plurality of different alternative sets of one or more energy loads over a prediction horizon, the plurality of different alternative sets comprising at least a first alternative set of energy loads and a second alternative set of energy loads; generating an objective function comprising at least (i) a first cost of purchasing one or more energy resources over the prediction horizon based on first decision variables for the energy resources purchased during the prediction horizon to satisfy the first alternative set of energy loads and (ii) a second cost of purchasing the energy resources over the prediction horizon based on second decision variables for the energy resources purchased during the prediction horizon to satisfy the second alternative set of energy loads; performing a stochastic optimization of the objective function to determine (i) first values for the first decision variables subject to first constraints based on the first alternative set of energy loads and (ii) second values for the second decision variables subject to second constraints based on the second alternative set of energy loads; and operating equipment of the building energy system to consume, store, or discharge the energy resources during the prediction horizon based on the stochastic optimization.
10. 10 . The method of claim 9 , wherein the equipment comprise HVAC equipment, the first alternative set of energy loads comprises a first alternative set of cooling loads to be satisfied by operating the HVAC equipment, and the second alternative set of energy loads comprises a second alternative set of cooling loads to be satisfied by operating the HVAC equipment.
11. 11 . The method of claim 9 , wherein the equipment comprise one or more computers or electronics, the first alternative set of energy loads comprises a first alternative set of electric loads to be consumed by the computers or electronics, and the second alternative set of energy loads comprises a second alternative set of electric loads to be consumed by the computers or electronics.
12. 12 . The method of claim 9 , wherein the objective function comprises a demand charge term based on a value for a peak demand target for an energy resource subject to a demand charge; wherein the method comprises determining the value for the peak demand target by performing the stochastic optimization subject to constraints ensuring that the value for the peak demand target is greater than or equal to the first values and the second values over the prediction horizon.
13. 13 . The method of claim 9 , comprising obtaining a plurality of different alternative sets of rates for the one or more energy resources, the plurality of different alternative sets of rates comprising at least a first alternative set of rates and a second alternative set of rates; wherein the first cost in the objective function is further based on the first alternative set of rates and the second cost in the objective function is further based on the second alternative set of rates.
14. 14 . The method of claim 9 , comprising performing the stochastic optimization subject to a constraint requiring equality between (i) a first state value for a state of the building energy system at a time during the prediction horizon and resulting from the first decision variables and (ii) a second state value for the state of the building energy system at the time during the prediction horizon and resulting from the second decision variables.
15. 15 . The method of claim 9 , wherein the equipment comprise one or more storage devices configured to store or discharge the energy resources or one or more generators configured to generate the energy resources; wherein the first values for the first decision variables and the second values for the second variables determined by performing the stochastic optimization comprise amounts of the energy resources to be stored in or discharged from storage devices or generated by the generators over the prediction horizon.
16. 16 . A controller for a building energy system, the controller comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining a plurality of different alternative sets of one or more energy loads over a prediction horizon, the plurality of different alternative sets comprising at least a first alternative set of energy loads and a second alternative set of energy loads; generating an objective function comprising at least (i) a first cost of purchasing one or more energy resources over the prediction horizon based on first decision variables for the energy resources purchased during the prediction horizon to satisfy the first alternative set of energy loads and (ii) a second cost of purchasing the energy resources over the prediction horizon based on second decision variables for the energy resources purchased during the prediction horizon to satisfy the second alternative set of energy loads; performing a stochastic optimization of the objective function to determine (i) first values for the first decision variables subject to first constraints based on the first alternative set of energy loads and (ii) second values for the second decision variables subject to second constraints based on the second alternative set of energy loads; and operating equipment of the building energy system to consume, store, or discharge the energy resources during the prediction horizon based on the stochastic optimization.
17. 17 . The controller of claim 16 , wherein the equipment comprise HVAC equipment, the first alternative set of energy loads comprises a first alternative set of cooling loads to be satisfied by operating the HVAC equipment, and the second alternative set of energy loads comprises a second alternative set of cooling loads to be satisfied by operating the HVAC equipment.
18. 18 . The controller of claim 16 , wherein the equipment comprise one or more computers or electronics, the first alternative set of energy loads comprises a first alternative set of electric loads to be consumed by the computers or electronics, and the second alternative set of energy loads comprises a second alternative set of electric loads to be consumed by the computers or electronics.
19. 19 . The controller of claim 16 , the operations comprising obtaining a plurality of different alternative sets of rates for the one or more energy resources, the plurality of different alternative sets of rates comprising at least a first alternative set of rates and a second alternative set of rates; wherein the first cost in the objective function is further based on the first alternative set of rates and the second cost in the objective function is further based on the second alternative set of rates.
20. 20 . The controller of claim 16 , the operations comprising performing the stochastic optimization subject to a constraint requiring equality between (i) a first state value for a state of the building energy system at a time during the prediction horizon and resulting from the first decision variables and (ii) a second state value for the state of the building energy system at the time during the prediction horizon and resulting from the second decision variables.

Description

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 16/115,290 filed Aug. 28, 2018, which claims the benefit of and priority to U.S. Provisional Patent Application No. 62/558,135 filed Sep. 13, 2017, each of which is incorporated by reference herein in its entirety. BACKGROUND The present disclosure relates generally to model predictive control (MPC) system for a building. The present disclosure relates more particularly to a stochastic MPC system that determines optimal participation commitments for stationary battery systems in ISO frequency regulation markets while simultaneously mitigating demand charges for a modulated load. SUMMARY One implementation of the present disclosure is a building energy system configured to serve energy loads of a building or campus. The system includes equipment configured to consume, store, or discharge one or more energy resources purchased from a utility supplier. At least one of the energy resources is subject to a demand charge. The system further includes a controller configured to determine an optimal allocation of the energy resources across the equipment over a demand charge period. The controller includes a stochastic optimizer configured to obtain representative loads and rates for the building or campus for each of a plurality of scenarios, generate a first objective function comprising a cost of purchasing the energy resources over a portion of the demand charge period, and perform a first optimization to determine a peak demand target for the optimal allocation of the energy resources. The peak demand target minimizes a risk attribute of the first objective function over the plurality of the scenarios. The controller is configured to control the equipment to achieve the optimal allocation of energy resources. In some embodiments, the controller includes a model predictive controller configured to generate a second objective function comprising a cost of purchasing the energy resources over an optimization period, use the peak demand target to implement a peak demand constraint that limits a maximum purchase of the energy resource subject to the demand charge during the optimization period, and perform a second optimization, subject to the peak demand constraint, to determine the optimal allocation of the energy resources across the equipment over the optimization period. In some embodiments, the model predictive controller is configured to implement the peak demand constraint as a soft constraint on the maximum purchase of the energy resource subject to the demand charge. In some embodiments, the model predictive controller is configured to perform the second optimization a plurality of times. Each of the second optimizations may determine the optimal allocation of the energy resources for one of a plurality of optimization periods. The model predictive controller may use the same peak demand constraint to constrain each of the second optimizations. In some embodiments, the risk attribute of the first objective function includes at least one of a conditional value at risk, a value at risk, or an expected cost. In some embodiments, the first objective function includes a frequency regulation revenue term that accounts for revenue generated by operating the equipment to participate in a frequency regulation program for an energy grid. In some embodiments, the stochastic optimizer is configured to obtain the representative loads and rates by receiving user input defining the loads and rates for several scenarios, generating an estimated distribution based on the user input, and sampling the representative loads and rates from the estimated distribution. In some embodiments, the stochastic optimizer is configured to obtain the representative loads and rates by receiving user input defining the loads and rates for several scenarios and sampling the representative loads and rates from the user input defining the loads and rates for several scenarios. In some embodiments, stochastic optimizer is configured to obtain the representative loads and rates by receiving input defining loads and rates for several scenarios. Each of the user-defined loads and rates corresponds to a different time period used by a planning tool. The stochastic optimizer may be configured to sample the representative loads and rates for each scenario from the loads and rates for the corresponding time period used by the planning tool. In some embodiments, the stochastic optimizer is configured to obtain the representative loads and rates by storing a history of past scenarios comprising actual values for historical loads and rates and sampling the representative loads and rates from the history of past scenarios. In some embodiments, the stochastic optimizer is configured to obtain the representative loads and rates by storing a history of past scenarios comprising actual values for historical loads and rates, generating an estim