Advances in Near-Optimal Control of Passive Building Thermal Storage

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Gregor P. Henze ◽  
Anthony R. Florita ◽  
Michael J. Brandemuehl ◽  
Clemens Felsmann ◽  
Hwakong Cheng
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Thermal Storage ◽  
Thermal Mass ◽  
Commercial Building ◽  
Simulation And Optimization ◽  
Rate Structure ◽  
Starting Point ◽  
Utility Rate ◽  
And Control

Using a simulation and optimization environment, this paper presents advances toward near-optimal building thermal mass control derived from full factorial analyses of the important parameters influencing the passive thermal storage process for a range of buildings and climate/utility rate structure combinations. Guidelines for the application of, and expected savings from, building thermal mass control strategies that can be easily implemented and result in a significant reduction in building operating costs and peak electrical demand are sought. In response to the actual utility rates imposed in the investigated cities, fundamental insights and control simplifications are derived from those buildings deemed suitable candidates. The near-optimal strategies are derived from the optimal control trajectory, consisting of four variables, and then tested for effectiveness and validated with respect to uncertainty regarding building parameters and climate variations. Due to the overriding impact of the utility rate structure on both savings and control strategy, combined with the overwhelming diversity of utility rates offered to commercial building customers, this study cannot offer universally valid control guidelines. Nevertheless, a significant number of cases, i.e., combinations of buildings, weather, and utility rate structure, have been investigated, which offer both insights and recommendations for simplified control strategies. These guidelines represent a good starting point for experimentation with building thermal mass control for a substantial range of building types, equipments, climates, and utility rates.

Advances in Near-Optimal Control of Passive Building Thermal Storage

2009 ◽  
Author(s):  
Gregor P. Henze ◽  
Anthony R. Florita ◽  
Michael J. Brandemuehl ◽  
Clemens Felsmann ◽  
Hwakong Cheng
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Thermal Storage ◽  
Thermal Mass ◽  
Commercial Building ◽  
Simulation And Optimization ◽  
Rate Structure ◽  
Starting Point ◽  
Utility Rate ◽  
And Control

Using a simulation and optimization environment, this paper presents advances towards near-optimal building thermal mass control derived from full factorial analyses of the important parameters influencing the passive thermal storage process for a range of buildings and climate/utility rate structure combinations. Guidelines for the application of, and expected savings from, building thermal mass control strategies that can be easily implemented and result in a significant reduction in building operating costs and peak electrical demand are sought. In response to the actual utility rates imposed in the investigated cities, fundamental insights and control simplifications are derived from those buildings deemed suitable candidates. The near-optimal strategies are derived from the optimal control trajectory, consisting of four variables, and then tested for effectiveness and validated with respect to uncertainty regarding building parameters and climate variations. Due to the overriding impact of the utility rate structure on both savings and control strategy, combined with the overwhelming diversity of utility rates offered to commercial building customers, the study cannot offer universally valid control guidelines. Nevertheless, a significant number of cases, i.e. combinations of buildings, weather, and utility rate structure, have been investigated, which offer both insight and recommendations for simplified control strategies. These guidelines represent a good starting point for experimentation with building thermal mass control for a substantial range of building types, equipment, climates, and utility rates.

Distributed control strategies for high-penetration commercial-building-scale thermal storage

PES T&D 2012 ◽  
2012 ◽  
Author(s):  
Andrea Mammoli ◽  
C. Birk Jones ◽  
Hans Barsun ◽  
David Dreisigmeyer ◽  
Gary Goddard ◽  
...  
Keyword(s):  
Distributed Control ◽  
Control Strategies ◽  
Thermal Storage ◽  
Commercial Building ◽  
High Penetration

Trade-Off Between Energy Consumption and Utility Cost in the Optimal Control of Active and Passive Building Thermal Storage Inventory

Solar Energy ◽  
2004 ◽  
Author(s):  
Gregor P. Henze
Keyword(s):  
Optimal Control ◽  
Energy Consumption ◽  
Thermal Energy ◽  
Peak Load ◽  
Electrical Energy ◽  
Thermal Storage ◽  
Commercial Building ◽  
Trade Off ◽  
Massive Structure ◽  
Utility Cost

In contrast to building energy conversion equipment, less improvement has been achieved in thermal energy distribution, storage and control systems in terms of energy efficiency and peak load reduction potential. Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates are designed to encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building’s massive structure (passive storage) or by using active thermal energy storage systems such as ice storage. Recent theoretical and experimental work showed that the simultaneous utilization of active and passive building thermal storage inventory can save significant amounts of utility costs to the building operator, yet in many cases at the expense of increased electrical energy consumption. This article investigates an approach to ensure that a commercial building utilizing both thermal batteries does not incur excessive energy consumption. The model-based predictive building controller is modified to trade off energy cost against energy consumption. This work shows that buildings can be operated in a demand-responsive fashion to substantially reduce utility costs, however, at the expense of increased energy consumption. Placing a greater emphasis on energy consumption led to a reduction in the savings potential. In the limiting case of energy-optimal control, the reference control was replicated, i.e., if only energy consumption is of concern, neither active nor passive building thermal storage should be utilized. On the other hand, cost-optimal control suggests strongly utilizing both thermal storage inventories.

Load Control Using Building Thermal Mass

2003 ◽  
Vol 125 (3) ◽  
pp. 292-301 ◽  
Author(s):  
James E. Braun
Keyword(s):  
Control Strategies ◽  
Field Studies ◽  
Commercial Buildings ◽  
Load Control ◽  
Thermal Mass ◽  
Widespread Application ◽  
Climate Conditions ◽  
Cooling Loads ◽  
And Control ◽  
Zone Temperature

This paper provides an overview of research related to use of building thermal mass for shifting and reducing peak cooling loads in commercial buildings. The paper presents background on the concept and the problem of optimizing zone temperature setpoints and provides specific results that have been obtained through simulations, controlled laboratory testing, and field studies. The studies have demonstrated significant savings potential for use of building thermal mass in commercial buildings. However, the savings are sensitive to many factors, including utility rates, type of equipment, occupancy schedule, building construction, climate conditions, and control strategy. The paper also attempts to provide an assessment of the state of the art in load control using building thermal mass and to identify the steps necessary to achieve widespread application of appropriate control strategies.

Optimization of Thermal Mass in Commercial Building Applications

1990 ◽  
Vol 112 (4) ◽  
pp. 273-279 ◽  
Author(s):  
M. Judson Brown
Keyword(s):  
Energy Savings ◽  
Thermal Storage ◽  
Office Building ◽  
Base Case ◽  
Thermal Mass ◽  
Commercial Building ◽  
Heating And Cooling ◽  
Initial Cost ◽  
Heavy Construction ◽  
Cooling Loads

Based on results from a one-year intensive monitoring project of a Northern New York commercial building with energy-conserving design features, a thermal storage project was undertaken to optimize the design of a thermal mass storage system for a moderately sized commercial building and transfer the technology to the commercial building sector. A generic commercial building design of 27,000 square feet (2508 m2) was selected for the optimization project. Several different types of thermal mass designs were considered as potentially practical for a commercial building. These included a “sandmass” design such as the mass incorporated in the previously monitored commercial building mentioned above, a foundation slab of sufficient thickness to serve as a significant building thermal mass, and the use of poured cement in interior wall and floor construction. Five different office building thermal designs were selected which represented various thermal storage features and two different building insulation levels (R10 and R20). Energy performance of the five thermal designs was modeled in building energy simulations using DOE 2.1C (Department of Energy 2.1C) energy simulation code. Results of the simulations showed a reduction in peak heating and cooling loads would be experienced by the HVAC equipment. The reduction in peak heating and cooling loads was anticipated because thermal mass within a building serves to average peak heating and cooling loads due to the capacity of the thermal mass to store and release heat from all building heat sources over a period of time. Peak heating loads varied from 1972 kBtuh (578 kW) for the R-10 light construction base case to a minimum of 980 kBtuh (287 kW) for the R-20 heavy construction sandmass storage case. Peak cooling loads dropped from 772 kBtuh (226 kW) for the R-20 light construction case to 588 kBtuh (172 kW) for the R-20 heavy construction sandmass storage case. Results of the simulations also showed annual energy savings for the high thermal mass designs. Energy savings varied from 20 percent [16.0 kBtu/ft2 (50 kWh/m2)] for the R-10 high thermal mass design in comparison to its base case to 18 percent [12.2 kBtu/ft2 (39 kWh/m2)] for the R-20 high thermal mass design in comparison to its base case. The annual energy savings are due to the ability of the thermal mass to absorb heat from all sources of heat generation (lights, occupancy, solar, and auxiliary) during occupied periods and release the heat during unoccupied periods. An optimized thermal design was developed based on results from the DOE 2.1C simulations. The initial cost for the optimized thermal storage design is lower than the initial costs for light construction office buildings, since the lower initial cost of the down-sized HVAC system for the optimized thermal storage design more than offsets the increased cost of wall and floor systems incorporated in the optimized design. Annual energy savings are realized from the high thermal mass system in both cooling and heating modes due to the interaction of building HVAC systems operation in the simulated 27000 ft2 (2508 m2) office building. Annual operating savings of $3781 to $4465 per year are estimated based on simulation results.

Evaluation of Reinforcement Learning for Optimal Control of Building Active and Passive Thermal Storage Inventory

2006 ◽  
Vol 129 (2) ◽  
pp. 215-225 ◽  
Author(s):  
Simeng Liu ◽  
Gregor P. Henze
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Storage Capacity ◽  
Control Strategies ◽  
Cost Savings ◽  
Thermal Storage ◽  
Learning Control ◽  
Learning Performance ◽  
Optimal Control Strategy ◽  
Model Free

This paper describes an investigation of machine learning for supervisory control of active and passive thermal storage capacity in buildings. Previous studies show that the utilization of active or passive thermal storage, or both, can yield significant peak cooling load reduction and associated electrical demand and operational cost savings. In this study, a model-free learning control is investigated for the operation of electrically driven chilled water systems in heavy-mass commercial buildings. The reinforcement learning controller learns to operate the building and cooling plant based on the reinforcement feedback (monetary cost of each action, in this study) it receives for past control actions. The learning agent interacts with its environment by commanding the global zone temperature setpoints and thermal energy storage charging∕discharging rate. The controller extracts information about the environment based solely on the reinforcement signal; the controller does not contain a predictive or system model. Over time and by exploring the environment, the reinforcement learning controller establishes a statistical summary of plant operation, which is continuously updated as operation continues. The present analysis shows that learning control is a feasible methodology to find a near-optimal control strategy for exploiting the active and passive building thermal storage capacity, and also shows that the learning performance is affected by the dimensionality of the action and state space, the learning rate and several other factors. It is found that it takes a long time to learn control strategies for tasks associated with large state and action spaces.

scholarly journals Suboptimal Control Strategies for Finite-Time Nonlinear Processes with Input Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Pablo S. Rivadeneira ◽  
Eduardo J. Adam
Keyword(s):  
Optimal Control ◽  
Finite Time ◽  
Control Strategies ◽  
Batch Reactor ◽  
Infinite Horizon ◽  
Optimization Method ◽  
Suboptimal Control ◽  
Linear Matrix ◽  
Input Constraints ◽  
And Control

Novel techniques for the optimization and control of finite-time processes in real-time are pursued. These are developed in the framework of the Hamiltonian optimal control. Two methods are designed. The first one constructs the reference control trajectory as an approximation of the optimal control via the Riccati equations in an adaptive fashion based on the solutions of a set of partial differential equations called the α and β matrices. These allow calculating the Riccati gain for a range of the duration of the process T and the final penalization S. The second method introduces input constraints to the general optimization formulation. The notions of linear matrix inequalities allow us to recuperate the Riccati gain as in the first method, but using an infinite horizon optimization method. Finally, the performance of the proposed strategies is illustrated through numerical simulations applied to a batch reactor and a penicillin fed-batch reactor.

Dynamics and optimal control in transmission of tungiasis diseases

2021 ◽  
pp. 2150076
Author(s):  
Wei Lv ◽  
Lu Liu ◽  
Shi-Jia Zhuang
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Development Stage ◽  
Latent Stage ◽  
Flea Control ◽  
Control Schemes ◽  
Lyapunov Function Method ◽  
Individual Protection ◽  
And Control ◽  
The Impact

This paper aims to model the transmission of tungiasis disease and assess the optimal control schemes to stop its occurrence. Based on the development stage of fleas and propagation process of diseases, we propose a human-flea model without control, in which the susceptible-infected in latent stage-infectious populations and the egg-larva-pupa-adult stage of fleas are all in involved. In the light of the Lyapunov function method, we prove global stability of equilibria. The model is extended by reformulating it as an optimal control problem, with the use of four time-dependent controls, to assess the impact of individual protection, treatment and two flea control strategies (killing adult fleas and reduction of eggs and larvae). By using Pontryagin’s maximum principle, we characterize the optimal control. Using the data of human and flea in Brazil and Nigeri, numerical simulations are performed. The numerical results show that enhancing the protection and treatment of people and increasing the killing efficacy of flea adults would contribute to prevent and control the spread of the disease appreciably.

scholarly journals Effect of an antiviral drug control in host COVID-19 kinetics

2020 ◽  
Author(s):  
AMAR NATH CHATTERJEE ◽  
JAYANTA MONDAL ◽  
PIU SAMUI
Keyword(s):  
Optimal Control ◽  
Control Strategies ◽  
Low Cost ◽  
Antiviral Drug ◽  
Infection Process ◽  
Control Mechanisms ◽  
Effective Strategies ◽  
Effectiveness Analysis ◽  
Human Lungs ◽  
And Control

Abstract The article proposes and analyzes a system of differential equations modeling the interaction of the SARS-CoV-2 virus and the epithelial cells of the human lungs. Optimal control strategies representing antiviral drug treatment effects of this model are explored here. The Pontryagin’s max-imum principle is used to clarify the optimal control strategies. The exis-tence of optimal control is proved and effective strategies are illustrated. Numerical simulations, efficiency analysis, and cost-effectiveness analysis reveal that time-dependent antiviral drug with other control mechanisms, would reduce the viral load and control the infection process at low cost.

scholarly journals A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building Through Predictive Control of Passive and Active Storage

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1387
Author(s):  
Jennifer Date ◽  
José A. Candanedo ◽  
Andreas K. Athienitis
Keyword(s):  
Predictive Control ◽  
Thermal Energy ◽  
Control Strategies ◽  
Thermal Storage ◽  
Building Energy ◽  
Load Management ◽  
Active Storage ◽  
Rate Structure ◽  
Heating Load ◽  
Zone Temperature

Optimal management of thermal energy storage in a building is essential to provide predictable energy flexibility to a smart grid. Active technologies such as Electric Thermal Storage (ETS) can assist in building heating load management and can complement the building’s passive thermal storage capacity. The presented paper outlines a methodology that utilizes the concept of Building Energy Flexibility Index (BEFI) and shows that implementing Model Predictive Control (MPC) with dedicated thermal storage can provide predictable energy flexibility to the grid during critical times. When the utility notifies the customer 12 h before a Demand Response (DR) event, a BEFI up to 65 kW (100% reduction) can be achieved. A dynamic rate structure as the objective function is shown to be successful in reducing the peak demand, while a greater reduction in energy consumption in a 24-hour period is seen with a rate structure with a demand charge. Contingency reserve participation was also studied and strategies included reducing the zone temperature setpoint by 2∘C for 3 h or using the stored thermal energy by discharging the device for 3 h. Favourable results were found for both options, where a BEFI of up to 47 kW (96%) is achieved. The proposed methodology for modeling and evaluation of control strategies is suitable for other similar convectively conditioned buildings equipped with active and passive storage.

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