Arash Jalil Khabbazi

Advanced control of heating and cooling systems can substantially reduce energy costs and pollution. However, real-world adoption of popular algorithms among researchers, such as model predictive control (MPC) and reinforcement learning (RL), remains limited due in part to their high deployment and commissioning costs. Here, we develop two nearly commissioning-free controllers tailored to objectives that depend linearly on the controlled thermal load, such as energy costs and pollution. The controllers require at most two thermal parameters. In representative heating simulations, controller performance is robust to large parameter specification errors, suggesting potential for deployment with no tuning. The controllers maintain good occupant comfort while achieving 43 to 98% (depending on the electricity pricing and controller variant) of the performance improvement achieved by an omniscient policy with perfect model information and forecasts. These results suggest that simple, structure-exploiting controllers may capture most of the attainable value of advanced control while avoiding the data, modeling, tuning, and computational burdens that can arise with conventional MPC or RL.

How much energy, money, and emissions can advanced control of heating and cooling equipment save in real buildings? To address this question, researchers sometimes control a small number of thermal zones within a larger multi-zone building, then report savings for the controlled zones only. That approach can overestimate savings by neglecting heat transfer between controlled zones and adjacent zones. This paper mathematically characterizes the overestimation error when the dynamics are linear and the objectives are linear in the thermal load, as usually holds when optimizing energy efficiency, energy costs, or emissions. Overestimation errors can be large even in seemingly innocuous situations. For example, when controlling only interior zones that have no direct thermal contact with the outdoors, all perceived savings are fictitious. This paper provides an alternative estimation method based on the controlled and adjacent zones’ temperature measurements. The new method does not require estimating how much energy the building would have used under baseline operations, so it removes the additional measurement and verification challenge of accurate baseline estimation.

A substantial body of simulation research suggests that Model Predictive Control (MPC) and Reinforcement Learning (RL) can reduce energy costs, emissions, and grid stress in residential and commercial buildings. Yet, adoption in practice remains limited. Field demonstrations offer real-world insights and could bridge this gap by validating performance and economic viability. This review examines 104 field studies—24 in residential and 80 in commercial settings—evaluating experiment demographics, protocols, and reported outcomes. We find that 71% of demonstrations rely on potentially unreliable protocols, while the remaining 29%, considered more robust, report weighted-average cost savings of 16% (residential) and 13% (commercial). However, deployment costs remain underreported; only 13 studies address them. We outline five key recommendations to guide future field research, emphasizing protocol rigor, cost transparency, integration with other distributed energy resources, and emerging grid services.

Electrification of appliances, water heating, and space conditioning in homes can significantly reduce emissions and is crucial for achieving decarbonization goals [1, 2]. However, adding more electrical devices increases demand and requires major equipment upgrades on both the distribution and the residential side. One key upgrade is the electrical panel. Raising the panel rating can cost thousands for homeowners and millions for distribution providers, posing an obstacle to rapid electrification. This upgrade challenge impedes fast progress toward widespread electrification. In previous work, we introduced a novel multi-layered control strategy that coordinates heat pump and water heater set-points to keep a building’s net current draw below a safety threshold. Although the results were promising, that study was limited to a single site with specific envelope characteristics and appliance ratings. In this work, a multi-layer RC-based emulator is designed that can be applied at scale to model the feasibility of current limiting control. Preliminary simulations incorporating a heat pump, an electric charger, and an electric water heater for a validated site highlight that the flexibility of hot water and space conditioning can reduce peak current draw when a heat pump schedule is altered to accommodate the EV charging schedule.

Hydrogen injection into natural gas distribution pipeline system through Tee junctions is numerically studied, by considering both ideal and Soave–Redlich–Kwong real gas models. The mixing homogeneity length is quantified for various side pipe sizes and perpendicular configurations for distribution-pressure and intermediate-pressure pipelines. The buoyant nature of hydrogen once blended into natural gas pipelines introduces a challenge as it tends to stratify and become trapped near the top wall. Accordingly, a greater side flow momentum leads to a shorter mixing homogeneity length. This reduces up to three to five times particularly when it penetrates deeply into the main flow without getting trapped from the beginning. Consequently, vertical bottom-side injection yields a mixing homogeneity length that is roughly four times shorter than horizontal and five times shorter than vertical top-side injection. Furthermore, employing a real gas model is critical once the pressure rises to intermediate pressures, resulting in a greater homogeneity length.

Hydrogen, as an alternative source of energy, has the potential to minimize greenhouse gas (GHG) emissions. Through different sources and industrial processes, it is feasible to produce hydrogen. Therefore, blending certain percentages of hydrogen into the existing natural gas pipeline infrastructure can greatly decrease the carbon intensity of current gas grids while also promoting a more sustainable energy system. Nevertheless, the significant density contrast of almost eight to nine times between hydrogen and natural gas introduces a challenge as buoyant hydrogen tends to stratify and become trapped near the top wall once injected via a Tee junction. In this study, the hydrogen blending into the natural gas distribution pipelines using Tee junctions is numerically investigated, and the mixing homogeneity (uniformity) length is quantified through the coefficient of variation (CoV) of the hydrogen mole fraction. The effect of secondary (side) flow jet intensity on turbulent mixing in a distribution-pressure (DP) system is first looked into by altering the secondary pipe diameter while maintaining the same flow rate. Consequently, the differences between vertical top-side, horizontal, and vertical bottom-side injection configurations in an intermediate-pressure (IP) case are studied. The ideal and real gas equations of state (EoS) scenarios are next investigated in the IP case. Overall, a greater secondary flow jet intensity results in a shorter mixing homogeneity length, as investigated in the DP case. The mixing homogeneity length is shortened even further if the secondary flow penetrates deep into the primary (main) flow of natural gas without getting trapped near the top wall from the outset. Vertical bottom-side injection also results in a mixing homogeneity length that is roughly four times shorter than horizontal injection and five times shorter than vertical top-side injection in the IP case. When industry standards are followed, injecting from the bottom definitely outperforms other injection setups. Furthermore, using real gas EoS, such as Soave-Redlich-Kwong (SRK), becomes crucial when the operating pressure rises, resulting in a longer mixing homogeneity length.