Salman Mohagheghi

Long-duration power outages caused by natural disasters can severely disrupt society, leading to widespread social and economic losses. Although such outages can be considered a type of disaster, the response to them is not typically coordinated with other disaster response activities. Previous work has overlooked the opportunities that arise from enhanced coordination between evacuation logistics and backup power support. To fill this gap, this paper develops an integrated framework of power outage disaster response that coordinates localized battery-based power restoration with emergency evacuation and prioritizes support to socially vulnerable populations who are most at risk during an outage. The proposed sequential optimization models generate an evacuation collection schedule that informs temporary power grid restoration with mobile energy storage in an isolated distribution system. The model is demonstrated through a case study of the city of Greeley, Colorado during a flooding event. Sensitivity analyses examine the impact of evacuation compliance uncertainty and road network congestion on the modeling results, demonstrating the robustness of the framework to uncertainty and the benefits of mobile resources over stationary counterparts. Results indicate that evacuation-informed localized power restoration can provide additional support for vulnerable residents who need it the most without compromising the overall power restoration performance.

Ensuring affordable and reliable electricity access to areas with low population density is challenging, as network sparsity and lower connectivity rates can make it nearly impossible for electric utilities to cover the cost of interconnection without raising electricity tariffs. Utility providers that consider extending their networks to remote households must balance multiple and often conflicting objectives, including investment cost, grid resilience, geographical coverage, and environmental impacts. In this paper, a multi-objective decision-making framework is proposed for the electrification of rural households, considering traditional distribution network extension as well as microgrid deployment. In order to condense a wide range of spatial inputs into a tractable problem, a multi-criteria decision-making approach is adopted to identify and rank candidate sites for microgrid deployment that offer superior performance over a variety of technical, environmental, and economic criteria. A novel optimization model is then proposed using multi-objective Chebyshev goal programming, in which project costs, environmental impacts, and energy justice criteria are jointly optimized. The applicability of this framework is demonstrated through a case study of the Shiprock region within the Navajo Nation. The results indicate that the proposed methodology provides a balanced trade-off among conflicting objectives and identifies a priority order of loads to energize first under marginally increasing budgets.

Climate change has increased the frequency and severity of extreme weather events such as wildfires, storms, high winds, and floods. Overhead lines are particularly vulnerable to these hazards, prompting utilities to consider reinforcement solutions through undergrounding overhead lines or structural hardening. However, these mitigation strategies are expensive and should be used selectively, prioritized for areas that are most at risk. This necessitates a framework to concurrently balance cost and resilience. In addition, the adopted reinforcement strategy must consider the consequences of possible outages on communities. This paper presents a multi-objective optimization framework to identify overhead line reinforcement strategies in a distribution system exposed to different hazards. A case study is presented for the city of Greeley, CO, which is prone to both wildfire and flood risks. Undergrounding overhead lines and reinforcing tower structures are considered as possible solutions for wildfire-prone areas and flood-prone areas, respectively. The proposed model is adaptable and can be applied to other hazard types and/or geographic regions. The proposed framework incorporates energy justice by prioritizing vulnerable populations and ensuring equitable distribution of reinforcement benefits. The results indicate that targeted hardening can reduce load shedding, improve outage response, and support equitable resilience planning.

Recent advancements in photovoltaic (PV) and battery technologies, combined with improvements in power electronic converters, have accelerated the adoption of rooftop PV systems and electric vehicles (EVs) in distribution networks, while these technologies offer economic and environmental benefits and support the transition to sustainable energy systems, they also introduce operational challenges, including voltage fluctuations, increased system losses, and voltage regulation issues under high penetration levels. Traditional Voltage and Var Control (VVC) strategies, which rely on substation on-load tap changers, voltage regulators, and shunt capacitors, are insufficient to fully manage these challenges. This study proposes a novel Voltage, Var, and Watt Control (VVWC) framework that coordinates the operation of PV and EV resources, conventional devices, and demand responsive loads. A mixed-integer nonlinear multi-objective optimization model is developed, applying a Chebyshev goal programming approach to balance objectives that include minimizing PV curtailment, reducing system losses, flattening voltage profile, and minimizing demand not met. Unserved demand has, in particular, been modeled while incorporating the concepts of distributional and recognition energy justice. The proposed method is validated using a modified version of the IEEE 123-bus test distribution system. The results indicate that the proposed framework allows for high levels of PV and EV integration in the grid, while ensuring that EV demand is met and PV curtailment is negligible. This demonstrates an equitable access to energy, while maximizing renewable energy usage.

As renewable energy is increasingly integrated into our electricity supply, it becomes more challenging to ensure reliability and security due to the intermittent nature of these resources. With the electrification of buildings and technological advancements across various aspects of their operations, the building sector is expected to play a key role in reducing emissions while supporting the needs of the grid. Buildings and the loads they house can provide grid resources via demand flexibility, shifting, and shedding electric load, as necessary. This key resource has received increased attention from researchers, building operators, electric utilities, policymakers, and system operators as a tool to improve power grid reliability and reduce system costs. Before increasing reliance on demand flexibility, however, a better understanding of its availability is needed to inform planning efforts. This paper includes a review of the literature on current methods and data used to model the available flexibility of power delivered to customers. This review also summarizes how demand flexibility is defined and quantified to help inform future studies in this field. The results of this review illustrate the diversity found within this field of research and the innovation that researchers are employing to solve this complex problem.

The problem of power grid capacity expansion focuses on adding or modernizing generation and transmission resources to respond to the rise in demand over a long-term planning period. Traditionally, the problem has been mainly viewed from technical and financial perspectives. However, with the rise in the frequency and severity of natural disasters and their dire impacts on society, it is paramount to consider the problem from a nexus of resilience, sustainability, and equity. This paper presents a novel multi-objective optimization framework to perform power grid capacity planning, while balancing the cost of operation and expansion with the life cycle impacts of various technologies. Further, to ensure equity in grid resilience, a social vulnerability metric is used to weigh the energy not served based on the capabilities (or lack thereof) of communities affected by long-duration power outages. A case study is developed for part of the bulk power system in the state of Colorado. The findings of the study show that, by considering life cycle impacts alongside cost, grid expansion solutions move towards greener alternatives because the benefits of decommissioning fossil-fuel-based generation outweigh the costs associated with deploying new generation resources. Furthermore, an equity-based approach ensures that socially vulnerable populations are less impacted by disaster-induced, long-duration power outages.

Electric demand is steadily increasing, hence requiring continuous investments in modernizing, and expanding power grids worldwide. Traditionally, power system planning projects have considered minimizing the costs of capacity expansion and minimizing the amount of energy not served as the main objectives. With climate change policies enforcing the decommissioning of fossil-fuel-based generation, new clean and renewable generation technologies are being considered for power system capacity expansion projects. However, the environmental impacts of energy resources are not limited to carbon emissions and their contribution to global warming. In fact, every power generation technology can result in undesired impacts during its entire life cycle, which could negatively affect air quality, water resources, material resources, and/or human health. This paper provides an overview of how to assess the sustainability of power systems and power generation technologies based on life cycle assessment (LCA). A review of LCA, as applied to power systems and generation technologies, is presented with a discussion of general findings, challenges, and limitations. A review of the literature is then provided related to how sustainability objectives are currently incorporated in power grid design and capacity expansion models. Finally, shortcomings of the current models are discussed, along with opportunities for future research.