Meysam Razmara

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.

Selecting building predictive control based on model uncertainty

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%).

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.

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’s 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.

Hierarchical optimization framework for demand dispatch in building-grid systems

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.

Novel Exergy-wise predictive control of Internal Combustion Engines

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.

Enabling Demand Response programs via Predictive Control of Building-to-Grid systems integrated with PV Panels and Energy Storage Systems

Demand Response (DR) program is one of the ancillary services to reduce the peak load contribution of buildings by altering the operation of dispatchable load including Heating, Cooling and Air-Conditioning (HVAC) load. In this paper, a Model Predictive Controller (MPC) is designed to optimize the power flows from the grid and Energy Storage Systems (ESS) to a commercial building equipped with HVAC systems and PV panels. The MPC framework uses the inherent thermal storage of the building and the ESS as a means to provide DR. Our results show that the proposed control framework for Building-to-Grid (B2G) systems can significantly reduce the maximum load ramp-rate of the electric grid to prevent duck-curve issues associated with increase in solar PV penetration into the grid. The B2G simulation testbed in this paper is based on the experimental data obtained from an office building, PV panels, and battery packs at Michigan Technological University integrated with a 3-phase distribution test feeder. Compared to the rule-based controller, the proposed predictive control approach can decrease the building operation electricity cost by 28% while decreasing maximum load ramp-rates by more than 70%.