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1. Reinforcement Learning-Based Home Energy Management System for Resiliency

   https://doi.org/10.23919/ACC50511.2021.9483162  

   Raman, Naren Srivaths ; Gaikwad, Ninad ; Barooah, Prabir ; Meyn, Sean P. ( May 2021 , American Control Conference)

   With increase in the frequency of natural disasters such as hurricanes that disrupt the supply from the grid, there is a greater need for resiliency in electric supply. Rooftop solar photovoltaic (PV) panels along with batteries can provide resiliency to a house in a blackout due to a natural disaster. Our previous work showed that intelligence can reduce the size of a PV+battery system for the same level of post-blackout service compared to a conventional system that does not employ intelligent control. The intelligent controller proposed is based on model predictive control (MPC), which has two main challenges. One, itmore »requires simple yet accurate models as it involves real-time optimization. Two, the discrete actuation for residential loads (on/off) makes the underlying optimization problem a mixed-integer program (MIP) which is challenging to solve. An attractive alternative to MPC is reinforcement learning (RL) as the real-time control computation is both model-free and simple. These points of interest accompany certain trade-offs; RL requires computationally expensive offline learning, and its performance is sensitive to various design choices. In this work, we propose an RL-based controller. We compare its performance with the MPC controller proposed in our prior work and a non-intelligent baseline controller. The RL controller is found to provide a resiliency performance — by commanding critical loads and batteries—similar to MPC with a significant reduction in computational effort.« less
2. Combining Renewable Solar and Open Air Cooling for Greening Internet-Scale Distributed Networks

   https://doi.org/10.1145/3307772.3328307  

   Gupta, Vani ; Shenoy, Prashant ; Sitaraman, Ramesh K. ( June 2019 , Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy'19))

   The widespread adoption and popularity of Internet-scale Distributed Networks (IDNs) has led to an explosive growth in the infrastructure of these networks. Unfortunately, this growth has also led to a rapid increase in energy consumption with its accompanying environmental impact. Therefore, energy efficiency is a key consideration in operating and designing these power-hungry networks. In this paper, we study the greening potential of combining two contrasting sources of renewable energy, namely solar energy and Open Air Cooling (OAC). OAC involves the use of outside air to cool data centers if the weather outside is cold and dry enough. Therefore OACmore »is likely to be abundant in colder weather and at night-time. In contrast, solar energy is correlated with sunny weather and day-time. Given their contrasting natures, we study whether synthesizing these two renewable sources of energy can yield complementary benefits. Given the intermittent nature of renewable energy, we use batteries and load shifting to facilitate the use of green energy and study trade-offs in brown energy reduction based on key parameters like battery size, number of solar panels, and radius of load movement. We do a detailed cost analysis, including amortized cost savings as well as a break-even analysis for different energy prices. Our results look encouraging and we find that we can significantly reduce brown energy consumption by about 55% to 59% just by combining the two technologies. We can increase our savings further to between 60% to 65% by adding load movement within a radius of 5000kms, and to between 73% to 89% by adding batteries.« less
3. The extent of vegetation-driven panel cooling and consequent increase in electricity generation from solar PV sites depend on climate and soil properties

   Bertel, R. ; Choi, C. S. ; Macknick, J. ; McCall, J. ; Ravi, S ( December 2021 , AGU 2021 Fall Meeting Meeting New Orleans, LA)

   Co-locating solar photovoltaics (PV) with agriculture or natural vegetation could provide a sustainable solution to meeting growing food and energy demands, particularly considering the recent concerns of solar PV encroaching on agricultural and natural areas. However, the identification and quantification of the mutual interactions between the solar panels and the underlying soil-vegetation system are scarce. This is a critical research gap, as understanding these feedbacks are important for minimizing environmental impacts and for designing resource conserving and climate-resilient food-energy production systems. We monitored the microclimate, soil moisture distribution, and soil properties at three utility-scale solar facilities (MN, USA) with differentmore »site management practices, with an emphasis on verifying previously hypothesized vegetation-driven cooling of solar panels. The microclimatic variables (air and soil temperature, relative humidity, wind speed and direction) and soil moisture were significantly different between the PV site with bare soil (bare-PV) and vegetated PV (veg.-PV) site. Compared to the bare-PV site, the veg.-PV site also had significantly higher levels of total soil carbon and total soil nitrogen, as well as higher humidity and lower air and soil temperatures. Further, soil moisture heterogeneity created by the solar panels was homogenized by vegetation at the veg.-PV sites. However, we found no significant panel cooling or increase in electricity output that could be linked to co-location of the panels with vegetation in these facilities. We link these outcomes to the background climatic conditions (not water limited system) and soil moisture conditions. In regions with persistent high soil moisture (more frequent rainfall events) soil evaporation from wet bare soil may be comparable or even higher than from a vegetated surface. Thus, the cooling effects of vegetation on solar panels are not universal but rather site-specific depending on the background climate and soil properties. Regardless, the other co-benefits of maintaining vegetation at solar PV sites including the impacts on microclimate, soil moisture distribution, and soil quality support the case for solar PV–vegetation co-located systems.« less
4. Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification

   https://doi.org/10.3390/en14051481  

   Roth, Jonathan ; Chadalawada, Jayashree ; Jain, Rishee K. ; Miller, Clayton ( March 2021 , Energies)

   As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to better forecast building-level demand. This paper uses Bayesian Structural Time Series for probabilistic load forecasting at the residential building level to capture uncertainties in forecasting. We use sub-hourly electrical submeter data from 120 residential apartments in Singapore that were part of a behavioral intervention study. The proposed model addresses several fundamentalmore »limitations through its flexibility to handle univariate and multivariate scenarios, perform feature selection, and include either static or dynamic effects, as well as its inherent applicability for measurement and verification. We highlight the benefits of this process in three main application areas: (1) Probabilistic Load Forecasting for Apartment-Level Hourly Loads; (2) Submeter Load Forecasting and Segmentation; (3) Measurement and Verification for Behavioral Demand Response. Results show the model achieves a similar performance to ARIMA, another popular time series model, when predicting individual apartment loads, and superior performance when predicting aggregate loads. Furthermore, we show that the model robustly captures uncertainties in the forecasts while providing interpretable results, indicating the importance of, for example, temperature data in its predictions. Finally, our estimates for a behavioral demand response program indicate that it achieved energy savings; however, the confidence interval provided by the probabilistic model is wide. Overall, this probabilistic forecasting model accurately measures uncertainties in forecasts and provides interpretable results that can support building managers and policymakers with the goal of reducing energy use.« less
5. AutoShare: Virtual community solar and storage for energy sharing

   https://doi.org/10.1186/s42162-021-00144-w  

   Lee, Stephen ; Shenoy, Prashant ; Ramamritham, Krithi ; Irwin, David ( July 2021 , Energy Informatics)

   Residential solar installations are becoming increasingly popular among homeowners. However, renters and homeowners living in shared buildings cannot go solar as they do not own the shared spaces. Community-owned solar arrays and energy storage have emerged as a solution, which enables ownership even when they do not own the property or roof. However, such community-owned systems do not allow individuals to control their share for optimizing a home’s electricity bill. To overcome this limitation, inspired by the concept of virtualization in operating systems, we propose virtual community-owned solar and storage—a logical abstraction to allow individuals to independently control theirmore »share of the system. We argue that such individual control can benefit all owners and reduce their reliance on grid power. We present mechanisms and algorithms to provide a virtual solar and battery abstraction to users and understand their cost benefits. In doing so, our comparison with a traditional community-owned system shows that our AutoShare approach can achieve the same global savings of 43% while providing independent control of the virtual system. Further, we show that independent energy sharing through virtualization provides an additional 8% increase in savings to individual owners.

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