Fresnel solar cooling plant for buildings: Optimal operation of an absorption chiller through inverse modelling

Increasing comfort conditions in buildings imply higher energy demands. However, these needs can be mitigated by solar cooling solutions. These systems, such as absorption chillers, are complex and require stable operation, with strict control to maximise the solar fraction and minimise gas consumption. This is incompatible with the variability of renewable resources, so they are often coupled with auxiliary gas systems. Although gas-free operation is possible if these systems are optimally controlled, they would require special supervision. This paper aims to develop an experimental validation of an inverse model to manage an absorption chiller coupled with a solar cooling plant. To know its real behaviour, long-term experiments have been performed using this plant, which consists of a linear Fresnel solar collector and an auxiliary natural gas boiler. The inverse model is used as a predictive control tool to decide the auxiliary boiler commands of the absorption chiller to optimise its operation: maximum cooling production by minimising gas consumption and maximising solar contribution. It has been identified with data from two weeks and validated with data from one summer month. Results show that the model estimates, on a time base of fewer than 30 min, are acceptable with errors of less than 5%. In addition, the maximum error of the estimated seasonal COP and the renewable fraction are less than 6% per day. Therefore, the results prove the usefulness of the proposal as a predictive control for optimal operation. Furthermore, it could be used as a baseline for preventive maintenance. If the proposed model is used for optimal management of the absorption chiller, the thermal efficiency of the plant increases significantly, doubling the solar contribution. As a result, the gas consumption of the solar cooling plant is halved and the total cost of air conditioning the building decreases by 16%.Comisión Europea A_B.4.3_02

Stochastic Model Predictive Control of Mixed-mode Buildings Based on Probabilistic Interactions of Occupants With Window Blinds

Between 4% to 20% of energy used for HVAC, lighting and refrigeration in a building is wasted due to issues associated with systems operations. It is estimated that proper building energy load control and operation can result in up to 40% utility cost savings. Current heuristic rules based on decision trees are difficult to define, manage and optimize as buildings become more complex. Advanced control strategies with weather forecast and cooling load anticipation, known as model predictive control (MPC), offer an attractive alternative for buildings with slow dynamics. However, MPC is mostly practiced through deterministic approaches. Deterministic MPC implicitly assumes that a dynamic model is able to perfectly predict the future behavior of the building over the desired control window, or prediction horizon. However, this assumption is clearly not rational because there will be both modeling errors and disturbances acting on the system over this period. One of these disturbances is associated with building occupant behaviors which interfere with deterministic assumptions. In this study, a probabilistic model of occupants’ behavior on window blind closing event is used to represent the disturbance coming from interactions of building residents with window blinds. This model is a multiple logistic regression analysis, based on a field study in an office building at the University of California, Berkeley (Inkarojrit, 2005). It considers the incident solar radiation on window surface and occupants’ self-reported brightness sensitivity as variable parameters to predict the closing event of blinds with 86.3% of accuracy. The probability of closing event is compared with a random number from the uniform distribution on the interval [0,1] at each time step and if it is greater than the random number, some indicator function will be equal to 1 (closing action) and vice versa. In order to implement the stochastic MPC, Monte Carlo simulation needs to be conducted due to the randomness of occupants’ behavior in closing the blinds. A test-building with mixed-mode cooling and high solar gains is considered as a test-bed. In our methodology, a detailed dynamic building model is developed and it is then used to identify the parameters of a 4th order linear time-variant state-space model. In the MPC formulation, the window opening schedule is optimized for the upcoming prediction horizon and the cost function is the minimization of energy usage subject to thermal comfort constraints during this horizon. Optimal control sequences based on the proposed stochastic MPC framework will be compared with deterministic MPC approaches to investigate possible advantages of considering uncertainties of occupant actions in model predictive controllers of buildings. References: Inkarojrit V., 2005. Balancing Comfort: Occupants’ Control of Window Blinds in Private Offices. PhD thesis, School of Architecture, University of California Berkeley

Cooling load forecasting-based predictive optimisation for chiller plants

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced

Investigation of Methodologies for Minimizing Buildings Electricity Demand and Cost

Heating, ventilating, and air conditioning (HVAC) systems are the largest consumer of electricity in buildings. The HVAC system is complex in terms of components that make them up and their different time scales. The inefficient operation of HVAC system leads to unreasonable electricity consumption during peak periods, which is accompanied by high cost of electricity use. The dynamic changes in building demand, contributions from exogenous inputs such as solar radiation and ambient temperature, and phenomenon such as radiative delays, thermal storage, internal mass etc. are some of the current challenges in buildings systems operation. Due to dynamic and thermal coupling between the conditioned building and HVAC systems components, optimal control is difficult to achieve. In a multi-zone building, multiple thermal interactions among the different thermal zones and the effects on electricity demand and cost are not well understood, due to lack of fundamental knowledge. The existing strategies for electricity demand and cost control do not consider the dynamics of building construction and multi-zone interactions in their formulation. As a result, existing demand and electricity minimization strategies are not consistent in their conclusions. Meanwhile, multi-zone interactions and building dynamics play a crucial role in the overall electricity demand, cost, and load profiles due to the dependency of states of each individual zone on the thermal characteristics and states of the adjacent zones. The objective of this research is to understand multi-zone and equipment interactions in buildings energy systems, in order to minimize electricity cost. This is the first research to integrate building dynamics into controller formulation and design through the use of a physically representative thermal model that captures important phenomenon of building load and cooling coil operations. The intellectual contribution of this research is the understanding of multiple-zone interactions in buildings to aid in effective decision making regarding the operational states of HVAC equipment that minimizes overall electricity cost. Other original contributions are identification of critical thermal zones in a multi-zone building, extension of the R-C thermal network approach for transient modeling of cooling coils, identification of new methods (near constant cooling and temperature recovery/optimal start) for minimizing buildings electricity demand and cost, downsizing of heating system size based on passive thermal storage properties of building construction, and demonstration of the electricity cost savings capabilities in air handling units operations through the use of Model predictive control (MPC) methods. This is the first research to demonstrate predictive control that utilizes building dynamics through the use of models that represent the building physically and captures important phenomenon e.g. radiative delays and thermal storage. Therefore, it provides opportunities to strategically maximize curtailment potentials and human comfort through optimization, and contributes to knowledge through the development of step by step approach to achieve system-wide optimal operation of the air handling unit, based on consideration of time of use electricity tariff. Therefore, the developed framework in this dissertation is useful for smart grid integration, and for building modelers in the areas of fault detection and diagnosis (FDD) and control. As such, the developed framework is very promising for existing and future building automation system (BAS) and emerging technologies in the building sector

Comparison of Model Predictive Control performance using grey-box and white box controller models

Model predictive control (MPC) for building climate control has received increasing attention the last decade. Its large scale implementation is, however, still hampered by the difficulty of obtaining accurate but computationally efficient multi-zone building controller models. This paper compares an existing grey-box approach with a novel white-box approach to obtain a controller model of the building envelope and it compares the performance achieved by using these two approaches. The comparison is made for an existing office building, which is currently controlled using a grey-box MPC [1].  The building envelope and its heating, cooling and air conditioning systems  (HVAC) are modelled using the Modelica building energy simulation library IDEAS. The model is validated using measurement data from the real building. This detailed simulation model is composed of discretised partial differential equations, ordinary differential equations and algebraic equations. The model is therefore too complex to be used as controller model for MPC. Two MPC approaches are compared. On the one hand, the white-box controller model is obtained by linearizing the building envelope part of the simulation model and by pre-computing model inputs such as solar gains through each window [2]. The method generates a linear state space model, which produces very similar temperatures as the original non-linear model. On the other hand, the grey-box identification method that was used to obtain the current controller model, is also applied to the detailed simulation model. Both white-box and grey-box MPC are applied to the detailed simulation model. The dynamics of the HVAC systems are not included in the MPC model but the efficiencies, constraints, cost function and boundary conditions are included. The energy use, the achieved thermal zone comfort and the prediction performance are compared. Finally, a new grey-box model is identified with operation data of the real building and the multi-step ahead prediction performance of the white-box and of both the grey-box models obtained with the simulation data and obtained with the measured data is computed for the real building using the measurement data and the weather forecast, which are used by the current MPC implementation.  [1] Zdenek Vana, Jiri Cigler, Jan Siroky, Eva Zacekova, Lukas Ferkl. Model-based energy efficient control applied to an office building. J. Process Control (2014).  [2] Picard, D., Jorissen, F., and Helsen, L. 2015. Methodology for Obtaining Linear State Space Building Energy Simulation Models. In 11th International Modelica Conference, pages 51–58, Paris

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions

Simulation-assisted control in building energy management systems

Technological advances in real-time data collection, data transfer and ever-increasing computational power are bringing simulation-assisted control and on-line fault detection and diagnosis (FDD) closer to reality than was imagined when building energy management systems (BEMSs) were introduced in the 1970s. This paper describes the development and testing of a prototype simulation-assisted controller, in which a detailed simulation program is embedded in real-time control decision making. Results from an experiment in a full-scale environmental test facility demonstrate the feasibility of predictive control using a physically-based thermal simulation program