Model Predictive Control of Commercial Office Plug-Loads and Battery Storage Systems

Volume 1: Active Control of Aerospace Structure; Motion Control; Aerospace Control; Assistive Robotic Systems; Bio-Inspired Systems; Biomedical/Bioengineering Applications; Building Energy Systems; Condition Based Monitoring; Control Design for Drilling Automation; Control of Ground Vehicles, Manipulators, Mechatronic Systems; Controls for Manufacturing; Distributed Control; Dynamic Modeling for Vehicle Systems; Dynamics and Control of Mobile and Locomotion Robots; Electrochemical Energy Systems ◽

10.1115/dscc2014-6051 ◽

2014 ◽

Author(s):

Michael D. Sankur ◽

Daniel Arnold ◽

David M. Auslander

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Demand Response ◽

Storage System ◽

Battery Storage ◽

Smart Power ◽

Load Following ◽

Plug Loads ◽

Lighting Systems ◽

Energy Information

Commercial demand response (DR) has traditionally relied on HVAC and lighting systems as load-shed resources in buildings. However, improvements in technologies such as Energy Information Gateways and smart power strips are making it possible to incorporate distributed plug-loads as an actionable resource. In this paper we explore the addition of a battery storage system (BSS) as a load-shed resource to supplement plug-loads in an office setting. Furthermore we investigate the value of control of BSS battery charging. We develop a model predictive control (MPC) framework for office plug-loads and a BSS. An experimentally derived model of a BSS is presented along with numerical methods for solving the MPC optimization program. Simulations demonstrate the efficacy of a BSS as a load-shed resource. Simulation results also quantify the benefit of BSS controllable charging for DR and load-following scenarios.

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Optimal Control of Office Plug-Loads for Commercial Building Demand Response

Volume 1: Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; Bio-Medical and Bio-Mechanical Systems; Biomedical Robots and Rehab; Bipeds and Locomotion; Control Design Methods for Adv. Powertrain Systems and Components; Control of Adv. Combustion Engines, Building Energy Systems, Mechanical Systems; Control, Monitoring, and Energy Harvesting of Vibratory Systems ◽

10.1115/dscc2013-3827 ◽

2013 ◽

Cited By ~ 2

Author(s):

Daniel B. Arnold ◽

Michael D. Sankur ◽

David M. Auslander

Keyword(s):

Optimal Control ◽

Demand Response ◽

Energy Resource ◽

Electrical Energy ◽

Commercial Building ◽

Smart Power ◽

Plug Loads ◽

Lighting Systems ◽

The Individual ◽

Energy Information

While historically the electrical energy resource a commercial building may utilize during a Demand Response (DR) event has been limited to building HVAC and/or lighting systems, enabling technologies such as smart power strips (SPSs) and Energy Information Gateways (EIGs) have made distributed control over commercial office plug loads a reality. This paper investigates coordinated optimal control over plug loads in a commercial building during a DR event. Control over local office plug loads is accomplished via the use of a software entity, the Energy Information Gateway, which employs a binary integer linear program to determine which loads are shed. Individual EIGs are managed by another piece of software, the Central Building Controller (CBC), which can adjust parameters in the individual EIG optimal control algorithm. The proposed control structure is tested via the EIGs managing physical appliances in an actual office, and in simulations of the CBC managing virtual Gateways.

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Model Predictive Control of Micro-CSP Integrated Into a Building HVAC System for Load Following Demand Response Programs

Volume 2: Modeling and Control of Engine and Aftertreatment Systems; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Validation; Motion Planning and Tracking Control; Multi-Agent and Networked Systems; Renewable and Smart Energy Systems; Thermal Energy Systems; Uncertain Systems and Robustness; Unmanned Ground and Aerial Vehicles; Vehicle Dynamics and Stability; Vibrations: Modeling, Analysis, and Control ◽

10.1115/dscc2019-9106 ◽

2019 ◽

Author(s):

Mohamed Toub ◽

Mahdi Shahbakhti ◽

Rush D. Robinett ◽

Ghassane Aniba

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Demand Response ◽

Dynamic Pricing ◽

Power Flow ◽

Voltage Regulation ◽

Ancillary Services ◽

Hvac System ◽

Time Model ◽

Load Following

Abstract Building heat, ventilation and air conditioning (HVAC) systems are good candidates for demand response (DR) programs as they can flexibly alter their consumption to provide ancillary services to the grid and contribute to frequency and voltage regulation. One of the major ancillary services is the load following demand response (DR) program where the demand side tries to track a DR load profile required by the grid. This paper presents a real-time Model Predictive Control (MPC) framework for optimal operations of a micro-scale concentrated solar power (MicroCSP) system integrated into an office building HVAC system providing ancillary services to the grid. To decrease the energy cost of the building, the designed MPC exploits, along with the flexibility of the building’s HVAC system, the dispatching capabilities of the MicroCSP with thermal energy storage (TES) in order to control the power flow in the building and respond to the DR incentives sent by the grid. The results show the effect of incentives in the building participation to the load following DR program in the presence of a MicroCSP system and to what extent this participation is affected by seasonal weather variations and dynamic pricing.

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