PREDICTIVE CONTROL OF POWER GRID-CONNECTED ENERGY SYSTEMS BASED ON ENERGY AND EXERGY METRICS

Building and transportation sectors account for 41% and 27% of total energy consumption in the US, respectively. Designing smart controllers for Heating, Ventilation and Air-Conditioning (HVAC) systems and Internal Combustion Engines (ICEs) can play a key role in reducing energy consumption. Exergy or availability is based on the First and Second Laws of Thermodynamics and is a more precise metric to evaluate energy systems including HVAC and ICE systems. This dissertation centers on development of exergy models and design of model-based controllers based on exergy and energy metrics for grid-connected energy systems including HVAC and ICEs. In this PhD dissertation, effectiveness of smart controllers such as Model Predictive Controller (MPC) for HVAC system in reducing energy consumption in buildings has been shown. Given the unknown and varying behavior of buildings parameters, this dissertation proposes a modeling framework for online estimation of states and unknown parameters. This method leads to a Parameter Adaptive Building (PAB) model which is used for MPC. Exergy destruction/loss in a system or process indicates the loss of work potential. In this dissertation, exergy destruction is formulated as the cost function for MPC problem. Compared to RBC, exergy-based MPC achieve 22% reduction in exergy destruction and 36% reduction in electrical energy consumption by HVAC system. In addition, the results show that exergy-based MPC outperforms energy-based MPC by 12% less energy consumption. Furthermore, the similar exergy-based approach for building is developed to control ICE operation. A detailed ICE exergy model is developed for a single cylinder engine. Then, an optimal control method based on the exergy model of the ICE is introduced for transient and steady state operations of the ICE. The proposed exergy-based controller can be applied for two applications including (i) automotive (ii) Combined Heat and Power (CHP) systems to produce electric power and thermal energy for heating purposes in buildings. The results show that using the exergy-based optimal control strategy leads to an average of 6.7% fuel saving and 8.3% exergy saving compared to commonly used FLT based combustion control. After developing thermal and exergy models for building and ICE testbeds, a framework is proposed for bilevel optimization in a system of commercial buildings integrated to smart distribution grid. The proposed framework optimizes the operation of both entities involved in the building-to-grid (B2G) integration. The framework achieves two objectives: (i) increases load penetration by maximizing the distribution system load factor and (ii) reduces energy cost for the buildings. The results show that this framework reduces commercial buildings electricity cost by 25% compared to the unoptimized case, while improving the system load factor up to 17%

MODEL PREDICTIVE CONTROL OF BUILDING HVAC SYSTEMS

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, although not all the states of a building model are measurable. In this PhD proposal, I propose a modeling framework for “on-line estimation of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. The results indicate that the new framework can accurately predict states and parameters of the building thermal model. Model uncertainty is unavoidable for building HVAC systems. In this PhD proposal, the impact of model uncertainty is characterized on model-based controllers, e.g. model predictive control (MPC), and robust model predictive control (RMPC). Closed-loop RMPC uses uncertainty knowledge to enhance the nominal MPC. The RMPC is shown capable of maintaining the temperature within the comfort zone for model uncertainty up to 70%. Exergy is relevant to quality of energy and is also a measure of sustainability. Less exergy destruction leads to less of a footprint on the built environment. In this PhD proposal, the exergy concept will be used in the model predictive control cost function (XMPC). The critically new aspects of MPC problem formulation based on exergy are using low quality energy for HVAC systems. In this proposal, exergy destruction is formulated as a cost function of the physical parameters of the model, and the objective is minimization of the calculated exergy destruction rate. Exergy destruction addresses energy consumption, irreversibility and heat losses due to heat transfer. The results show that using the exegy-based objective function is promising, resulting in less electrical energy consumption and exergy destruction. Many studies have been done to reduce energy consumption and price in buildings and to decrease distribution losses and increase load factor. In this PhD proposal, a new methodology for bidirectional Building to grid (B2G) optimization will be proposed. This bidirectional optimization will lead to bilateral benefits for both buildings and the distribution system

Selecting building predictive control based on model uncertainty

Abstract — Model uncertainty limits the utilization of Model Predictive Controllers (MPC) to minimize building energy consumption. We propose a new Robust Model Predictive Control (RMPC) structure to make a building controller robust to model uncertainty. The results from RMPC are compared with those from a nominal MPC and a common building Rule Based Control (RBC). The results are then used to develop a methodology for selecting a controller type (i.e. RMPC, MPC, and RBC) as a function of building model uncertainty. RMPC is found to be the desirable controller for the cases with an intermediate level (30%-67%) of model uncertainty, while MPC is preferred for the cases with a low level (0-30%) of model uncertainty. A common RBC is found to outperform MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%). I

Novel Exergy-wise predictive control of Internal Combustion Engines

© 2016 American Automatic Control Council (AACC). Exergy is an effective metric to evaluate the performance of energy systems. Exergy analysis has been extensively used to study and understand loss mechanisms of Internal Combustion Engines (ICEs). However knowledge from exergy analysis has not been used for control of ICEs. This paper presents the first application of exergy-based control to ICEs. In this paper, an exergy model is developed for an advanced ICE with low temperature combustion mode that has higher efficiency compared to conventional diesel and spark ignition engines. The exergy model is based on quantification of the Second Law of Thermodynamic (SLT) and irreversibilities which are not identified in commonly used First Law of Thermodynamics (FLT) analysis. An optimal control method is developed based on minimizing irreversibilities and exergy losses. The new controller finds the optimum combustion phasing at every given engine load to minimize exergy destruction/loss. Application of the new developed control algorithm is demonstrated for a Combined Heat and Power (CHP) case study. The results show that by using the exergy-based optimal control strategy, the engine output power and exhaust exergies are maximized

Optimal exergy-wise predictive control for a combined MicroCSP and HVAC system in a building

This paper presents a new control method to minimize the energy consumption of a micro-scale concentrated solar power (MicroCSP) system and building heating, ventilation, and air conditioning (HVAC) system. A new realtime optimal control method is proposed using the concept of “exergy” and model predictive control (MPC) techniques. To achieve this, first law of thermodynamics (FLT) and second law of thermodynamics (SLT) based mathematical models of MicroCSP are developed and integrated into a model of an office building located at Michigan Technological University. Then, an exergy-wise MPC framework is designed to optimize MicroCSP operation in accordance with the building HVAC needs. The new controller reduces exergy destruction by 28%, compared to a common rule-based controller (RBC). This leads to 23% energy saving, compared to the applied RBC

Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for “on-line estimation ” of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model

Bidirectional optimal operation of smart building-to-grid systems

This paper proposes a novel bidirectional optimization of buildings integrated to the smart distribution grid, which possess potential benefits to the customers and utilities both. Mathematical models required for the optimal operations of buildings and grids are developed and a new method is proposed to obtain the solution of the bidirectional optimization. In this work, minimization of the cost of energy is chosen as an objective for the building load management, while the distribution utilities aim to increase load penetration by maximizing the load factor. Case studies are carried out based on actual data collected from an office building at Michigan Technological University, and using a standard distribution test feeder. Studies demonstrate that the proposed bidirectional optimization is beneficial to both the customer and the distribution grid as it shows significant saving in the energy costs and improvement on the system load factor

Online simultaneous state estimation and parameter adaptation for building predictive control

Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, model-based controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose a modeling framework for on-line estimation of states and unknown parameters of buildings, leading to the Parameter-Adaptive Building (PAB) model. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design the PAB model which simultaneously tunes the parameters of themodel and provides an estimate for all states of the model. The proposed PAB model is tested against experimental data collected from Lakeshore Center building at Michigan Tech University. Our results indicate that the new framework can accurately predict states and parameters of the building thermal model. Copyright © 2013 by ASME

Hierarchical optimization framework for demand dispatch in building-grid systems

© 2016 IEEE. This paper develops a hierarchical framework required to solve optimal demand dispatch of multiple buildings coordinating building energy management systems (BEMSs) and distribution system operation (DSO) control center. The proposed framework consists of mathematical model of heating, ventilation and air-conditioning (HVAC) load in buildings, model of distribution grid, objectives of BEMSs and DSO, operational requirements at building and grid levels, and a coordination algorithm. Usefulness of the proposed framework is demonstrated through HVAC loads in 27 commercial buildings connected to the IEEE 13-node test feeder. In the study, the objectives of the BEMSs and DSO are set to minimize the energy costs in dynamic pricing and power losses in distribution network, respectively. Results demonstrate that coordinated demand dispatch process honors objectives and operational constraints set by both entities, i.e., BEMSs and DSO, and benefits both entities involved in the demand dispatch process

Bilevel optimization framework for smart building-to-grid systems

This paper proposes a novel framework suitable for bilevel optimization in a system of commercial buildings integrated to smart distribution grid. The proposed optimization framework consists of comprehensive mathematical models of commercial buildings and underlying distribution grid, their operational constraints, and a bilevel solution approach which is based on the information exchange between the two levels. The proposed framework benefits both entities involved in the building-to-grid (B2G) system, i.e., the operations of the buildings and the distribution grid. The framework achieves two distinct objectives: increased load penetration by maximizing the distribution system load factor and reduced energy cost for the buildings. This study also proposes a novel B2G index, which is based on building\u27s energy cost and nodal load factor, and represents a metric of combined optimal operations of the commercial buildings and distribution grid. The usefulness of the proposed framework is demonstrated in a B2G system that consists of several commercial buildings connected to a 33-node distribution test feeder, where the building parameters are obtained from actual measurements at an office building at Michigan Technological University