• Energy Research

Extreme temperatures can push various power grid components to their operational limits. To make matters worse, the available capacity of most assets gets negatively affected as the temperature increases beyond certain thresholds. Unsurprisingly, this temperature-induced reduction in available power generation and transmission capacities coincides with higher electric demand on the system, mostly attributed to the over-utilization of air-conditioning (A/C) systems. This can jeopardize the ability of the power grid to effectively maintain the balance between load and generation. Incorporating temperature into the dispatch model of the grid is not only critical for the reliability and security of the grid operation, but also helps avoid overloading assets, which could otherwise lead to loss of life and premature failure. A unified framework is proposed in this paper to optimally allocate available energy and demand responsive resources in a power distribution system exposed to a heat wave event in order to simultaneously maintain load-generation balance and minimize operational cost. To regulate demand, the proposed solution generates temperature set-points for individual demand responsive A/C units. However, it utilizes this resource conservatively in order to avoid health risks to the residents, particularly children and the elderly. Due to the multi-objective nature of the problem, a goal programming approach is adopted to ensure the Pareto optimality of the final solution.

Abstract Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule.

Abstract Utilities have greatly increased the use of time-of-use (TOU) rate structures for residential customers to more closely match the cost of delivered electricity as well as demand tariffs to incentivize a temporal shift in residential energy use. However current research tends to focus on a particular climate or location. This study analyzes four residences across four different climate zones in Arizona and explores the value of adding battery storage to a net-zero energy (NZE) photovoltaic (PV) system. Using the National Renewable Energy Lab's Building Optimization (BEopt) and System Advisor Model (SAM) tools, this study performs parametric analysis by varying battery size to determine a capacity that produces a maximum net present value (NPV). A sensitivity analysis on three possible future battery price trends is also performed. This study finds the NZE PV systems are only able to mitigate 37–44% of the peak electricity purchases and 4–12% of demand charges due to mismatch between solar potential and on-peak hours. Adding large batteries, 1.5–1.6 times larger than required to meet the annual peak energy purchase requirements, provides a maximum NPV for PV-battery systems at locations with favorable utility rates. We conclude that the best economics are achieved, at both current and expected future battery prices, when batteries are sized to never require replacement during the PV system lifetime.

Abstract Vegetated or green roofs are sustainable roofing systems that have become increasingly widespread across the world in recent decades. However, their design requires accurate numerical modeling to fully realize the benefits of this technology at the building and larger scales. For this reason, several heat and mass transfer models for vegetated roofs have been developed over the last 36 years. This paper provides a critical review of more than 23 heat transfer vegetative roof models developed between 1982 and 2018 that have been used for building energy or urban modeling purposes. Findings of the study include the following: (i) more than 55% of the vegetated roof models have been developed and validated using data from warm temperate climate zones; (ii) green roof validation efforts vary and do not follow a common verification and validation framework; (iii) four of the reviewed models have not been subjected to any simulation process; (iv) no model has been validated for semi-arid conditions or cold climates or during cold winter conditions; (v) the most common variable reported for validation (in more than half of the models) is substrate surface temperature; however, surface temperature does not fully test the accuracy of a model to represent all heat and mass transfer phenomena; (vi) practitioners access to these models is limited since only five of the 23 models have been implemented in whole-building energy models, such as EnergyPlus, TRNSYS, ESP-r, and WUFI; finally, (vii) despite the extensive studies on the impacts of vegetative roofs on building energy performance and urban temperature reduction, no studies have validated the model using whole-building energy data or at larger scales.

Abstract Whole building energy modeling has become extremely important for designers, architects, engineers, and researchers to predict energy performance of buildings. This is particularly important for phase change materials (PCMs) due to their variable properties. For this reason, building energy modeling tools have been developed and validated against different sources of experimental data. However, an IEA Annex 23 surveyed over 250 research publications concluding that the general confidence in currently used numerical models is still too low to use them for designing and code purposes. The objective of this study is to assess the capability of different simulation programs to model the PCMs in building envelope using data from two independent studies using Nano-encapsulated PCMs (Nano-PCM) and shape-stabilized PCMs. The study finds that the investigated PCM models accurately predict the PCM behavior in the building envelope.