José A. Candanedo

Load matching and grid interaction of net zero energy buildings

Net Zero Energy Building” has become a prominent wording to describe the synergy of energy efficient building and renewable energy utilization to reach a balanced energy budget over a yearly cycle. Taking into account the energy exchange with a grid infrastructure overcomes the limitations of seasonal energy storage on-site. Even though the wording “Net Zero Energy Building” focuses on the annual energy balance, large differences may occur between solution sets in the amount of grid interaction needed to reach the goal. The paper reports on the analysis of example buildings concerning the load matching and grid interaction. Indices to describe both issues are proposed and foreseen as part of a harmonized definition framework. The work is part of subtask A of the IEA SHCP Task40/ECBCS Annex 52: “Towards Net Zero Energy Solar Buildings”.

Predictive control of radiant floor heating and solar-source heat pump operation in a solar house

Solar radiation can supply a significant portion of the energy requirements of a house through the harmonized use of passive solar design and building-integrated active solar energy systems (e.g., building-integrated photovoltaic, photovoltaic/thermal systems, or solar thermal collectors). Given the variability of solar radiation, energy storage technologies, along with carefully planned control strategies, can offer significant benefits for the performance of these systems in terms of energy consumption, peak load reduction, and thermal comfort for the occupants. This article investigates the application of a predictive control methodology for a solar house. The case study is a room with a simple geometry with high insulation and air-tightness values and large windows (i.e., a typical room found in a passive solar house). Predictive control is applied at two different, but closely linked, levels: (a) local-loop control of a radiant floor heating system and (b) supervisory control of the temperature of a water tank—used for thermal energy storage—heated with a solar-source heat pump. The development of control strategies is facilitated by the use of simplified building models obtained from more detailed models appropriate for building simulation. This methodology provides insight into the relevance of different design and control parameters and makes it easier to apply software tools designed specifically for testing control algorithms.

Analysis of grid interaction indicators in net zero-energy buildings with sub-hourly collected data

This paper aimed to contribute to the discussion about the role of net zero-energy buildings (ZEBs) or nearly ZEBs in future energy systems, from the perspective of the resulting import/export interaction with the surrounding energy grid (commonly named grid interaction (GI)). This investigation analyses three buildings with measured data at sub-hourly time resolution. The goal of this paper was to quantify the effect of using high-resolution data (one or a few minutes) versus hourly resolution in the GI analysis of buildings with an on-site generation system. A limited set of quantitative GI indicators have been selected: the generation multiple, the dimensioning rate and the connection capacity credit. Additionally, this paper presents graphical representations describing in an intuitive way the yearly or daily variation of the indicators. Some general trends have been identified and the usefulness of the selected indicators is demonstrated. Findings show conclusively that sub-hourly analysis will give more accurate information. Differences between peak values measured with hourly and sub-hourly time resolution can be significant. If detailed GI analysis at the individual building level is required, one should consider going for detailed sub-hourly analysis.

A systematic approach for energy design of advanced solar houses

Designing a net-zero energy solar house (defined here as a house that generates on site as much energy as it consumes over an average year) requires an all-inclusive methodology exploiting synergies between passive solar design and the operation of renewable energy systems. By following this method, it is also possible to build a house whose energy production substantially exceeds its consumption, so that the additional available power can be used for other purposes (e.g., for a plug-in electric vehicle or in a greenhouse). This paper describes a general methodology for the design of such an advanced solar house.

Modeling approaches for the characterization of building thermal dynamics and model-based control: A case study

This article investigates two building thermal modeling approaches: (a) frequency response analysis and (b) a low-order gray-box resistance—capacitance thermal network. Frequency response analysis is a valuable tool for building dynamic response characterization and model-based control studies. Low-order models reflecting the dynamic response of the building are essential for practical model-based control. A case study used for investigation is conducted on a large environmental chamber with a high thermal capacity concrete floor. The chamber represents a typical building zone with a thermal capacity floor. A detailed frequency-domain model is first developed for the environmental chamber and verified with experimental measurements. In addition, three different low-order gray-box resistance–capacitance thermal network models (of the second, third, and fourth orders) are also developed for the environmental chamber. The effective parameters of the low-order models are obtained through an optimization routine. Then, from these detailed and low-order models, transfer functions linking indoor air temperature to the convective cooling/heating source are obtained and studied. The third- and fourth-order resistance–capacitance models are shown to represent with acceptable accuracy the thermal dynamics of a high thermal mass zone with a convective heating/cooling source. The presented investigation approach can be applicable to other types of zones and buildings.

A Model-Based Predictive Control Approach for a Building Cooling System With Ice Storage

This paper presents a model predictive control (MPC) algorithm designed for the cooling system of a small commercial building under a time-dependent electricity price profile. The proposed approach includes a problem formulation in terms of cooling power, a variable-length prediction horizon and the consideration of the equipment duty cycle as a constraint in the optimization algorithm. The cooling system is equipped with an ice bank for thermal energy storage. A simple linear building thermal model is used to calculate the required amount of cooling power to maintain thermal comfort. The MPC algorithm uses this information to find the optimal operating points for the chiller and the ice bank to minimize the electric energy cost. The results of the MPC algorithm are compared against those of the reactive rule-based control algorithm currently in use in the building.Copyright

A model-based strategy for self-correction of sensor faults in variable air volume air handling units

This article presents an on-line model-based self-correction strategy intended to handle problems associated with faulty sensors in variable air volume air-handling units. Data-driven gray-box models were used to create “virtual sensors” aimed at substituting for defective supply air temperature and static pressure sensors. Two types of models were used: fixed-parameter models, based on data collected over a long term, and “self-tuning” models, based on data collected on the previous day. The parameters of the self-tuning models were adjusted with a genetic algorithm to reduce the residual between predictions and measurements. The proposed self-correction strategy was tested in a real variable air volume air-conditioning system. Test results show that virtual sensors can be an effective tool to temporarily replace missing sensor data in air-handling units.

Building integration of photovoltaic systems in cold climates

This paper presents some of the research activities on building-integrated photovoltaic (BIPV) systems developed by the Solar and Daylighting Laboratory at Concordia University. BIPV systems offer considerable advantages as compared to stand-alone PV installations. For example, BIPV systems can play a role as essential components of the building envelope. BIPV systems operate as distributed power generators using the most widely available renewable source. Since BIPV systems do not require additional space, they are especially appropriate for urban environments. BIPV/Thermal (BIPV/T) systems may use exterior air to extract useful heat from the PV panels, cooling them and thereby improving their electric performance. The recovered thermal energy can then be used for space heating and domestic hot water (DHW) heating, supporting the utilization of BIVP/T as an appropriate technology for cold climates. BIPV and BIPV/T systems are the subject of several ongoing research and demonstration projects (in both residential and commercial buildings) led by Concordia University. The concept of integrated building design and operation is at the centre of these efforts: BIPV and BIPV/T systems must be treated as part of a comprehensive strategy taking into account energy conservation measures, passive solar design, efficient lighting and HVAC systems, and integration of other renewable energy systems (solar thermal, heat pumps, etc.). Concordia Solar Laboratory performs fundamental research on heat transfer and modeling of BIPV/T systems, numerical and experimental investigations on BIPV and BIPV/T in building energy systems and non-conventional applications (building-attached greenhouses), and the design and optimization of buildings and communities.

A numerical and experimental study of a simple model-based predictive control strategy in a perimeter zone with phase change material

The current article presents a numerical and experimental study of predictive control strategies based on a low-order model in a test cell that emulates a perimeter zone of a building. The test cell uses a phase change material as a means of thermal storage. The phase change material, embedded in the wall of the test cell furthest away from the window, is thermally actively charged through forced air circulation. The objective of the study is to investigate how model-based predictive control can be used to optimize the performance of a phase change material wall. The present article also shows how a low-order thermal network model can be used as an effective tool in the design and implementation of the model-based predictive control strategy. The proposed model predictive control algorithm uses a set of linear ramp functions to change the room temperature set-point to reduce and shift peak power demand. These ramp set-point profiles allow the effective charging and discharging of the wall-integrated phase change material. The algorithm applied in the experimental facility uses the outdoor temperature as an input to select the best charging and discharging rates over a prediction horizon. A low-order model of the room and the phase change material wall is used in the predictive control algorithm. It was found that this model can accurately predict the peak power demand (coefficient of variation of the root-mean-square error 28.2% and normalized mean bias error 3.4%) and the room temperature profile. As the process moves forward in time, the weather profile is updated periodically and the algorithm calculates the new outputs over the new control horizon. The whole procedure is automated and the outputs of the algorithm are transferred to the test room controller through BACnet.

Control-oriented model of a solar community with seasonal thermal energy storage: development, calibration and validation

The development of a control-oriented model of a solar community with seasonal storage (the Drake Landing Solar Community) is investigated. The proposed approach, intended to facilitate the development and testing of control strategies and targeting an actual predictive control implementation, is based on grey-box models, and enables the prediction of the system state (temperatures at key locations). This paper discusses the concept of state update procedure (whereby the system state is periodically corrected with measurements), which plays a fundamental role for control purposes. Firstly, the DLSC is presented and both operation and monitoring system are described. Secondly, a simplified model is developed for each sub-system: district and solar loops, short-term (water tanks) and seasonal (borehole) thermal energy storage, and existing operation rules are encoded. Finally, the model is calibrated and validated by using measurements at 10-min intervals over two years of operation (2015–2016, 2016–2017) and accurately predicts the system performance.