Buildings as a group contribute the highest amount of energy use and man-made carbon di oxide emission in U.S. Energy efficient measures and optimal use of distributed energy resources can help to improve sustain ability to a great extent. This paper presents a comprehensive solution for an existing standard office building to optimize the energy purchased from the grid equipped with a variety of distributed energy resources (DERs). The building includes 180 kW solar Photovoltaic (PV), 150 kWh Battery Energy Storage System (BESS) and five Electric Vehicle (EV) charging stations. Two Plug-in Electric Vehicles (PEV) are available for level II grid to vehicle (G2V) activities and one fast charging level III electric trolley is available for both G2V and vehicle to grid (V2G) operation. A multi objective Mixed Integer Liner Programming (MILP) problem has been formulated to reduce the total cost of grid energy and life cycle degradation cost due to BESS and EV activities. It considers and models all the components associated with building such as Heating Ventilation and Air Conditioning (HVAC), comfort assessment, lighting and miscellaneous plug loads as constraints to solve the optimization model. This exhaustive model results in an optimal schedule for HVAC, lighting, BESS and EV to provide the maximum cost benefit to the building end user. It also shows that level III EV provides higher cost savings compared to level II EV operation.

Wildfires are projected to increase in severity and frequency due to climate change, and the electric grid is both a cause of wildfires and is vulnerable to wildfires. Equipment from the electric grid accounts for 10% of fires burned in California and 3% of fires nationally. Recent catastrophic wildfires, such as the Lahaina Fire, Camp Fire, Marshall Fire, and Smokehouse Creek fires, were all started by electrical equipment and show how devastating these events can be because they threaten lives and structures. Vegetation structure, weather and winds, climate and vegetation response, land use, and human activities all impact the likelihood of severe wildfires. We explore the relationship between the built environment, electric grid infrastructure specifically, and its role in causing catastrophic wildfires to find lessons learned for increasing resilience. Electric grid utility companies currently employ multiple methods to mitigate fire, including (1) early detection, (2) grid hardening, (3) vegetation management, and (4) pre-emptive shutoffs. Utility companies need to consider the conditions for wildfire and the impact that each mitigation strategy has on drivers of wildfire behavior, as a single solution will not be adequate. Utility companies need to work with stakeholders to develop a holistic strategy to reduce ignition likelihood and spread likelihood to reduce catastrophic wildfires and improve resiliency.

As the world is moving towards using renewable energy sources for a greener environment, battery energy storage systems (BESS) has become an important issue because of the intermittent nature of renewable sources. The quantity associated with the amount of energy stored in battery at a certain time is called the battery’s State of Charge or SOC. Typically it is estimated with the assumption that it changes linearly with the quantities such as voltage, current etc. which can be measured effortlessly. But actually no such linear relationship exists. Machine learning techniques have proven to be useful for measuring SOC. Though the features that are conventionally used often suffer from a phenomenon called multicollinearity. Ridge and lasso regression methods have shown to counter the effects of multicollinearity. In this paper, these methods have been used along with linear regression, support vector machine (SVM) and artificial neural network (ANN). Besides readily available conventional features some nonconventional features are explored too that can be derived from conventional features. Results exhibit that nonconventional features boost the performance of all the methods except for SVM. Ridge regression method with nonconventional features has shown to generate the best outcome among all the scenarios taking both accuracy and speed of computation into account.

Plug-in Electric Vehicles (PEVs) have been transforming the fossil fueled transportation system into a sustainable and emission free transportation system. This paper studies the integration of electric vehicles and associated complexities involved with energy management in a distribution level microgrid. The microgrid is situated at University of California Riverside College of Engineering- Center for Environmental Research and Technology (UCR CE-CERT). Efficiency issues of electric vehicles in comparison to conventional gasoline engine vehicles have been discussed from the source of energy viewpoint. The real time charging characteristics for different types of electric vehicles have been addressed considering level II and level III chargers. The charging impacts on building load and distribution grid of the system are also shown to assess the future challenges of large scale EV integration.