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1. Strategic Behavior of Distributed Energy Resources in Energy and Reserves Co-Optimizing Markets

   https://doi.org/10.1109/CDC.2018.8619550  

   Yanikara, Fatma Selin; Andrianesis, Panagiotis; Caramanis, Michael (January 2019, 2018 IEEE Conference on Decision and Control (CDC), Miami Beach, FL, 2018, pp. 4875-4881. doi: 10.1109/CDC.2018.8619550)

   null (Ed.)

   We consider decentralized scheduling of Distributed Energy Resources (DERs) in a day-ahead market that clears energy and reserves offered by both centralized generators and DERs. Recognizing the difficulty of scheduling transmission network connected generators together with distribution feeder connected DERs that have complex intertemporal preferences and dynamics, we propose a tractable distributed algorithm where DERs self-schedule based on granular Distribution Locational Marginal Prices (DLMPs) derived from LMPs augmented by distribution network costs. For the resulting iterative DER self-scheduling process, we examine the opportunity of DERs to engage in strategic behavior depending on whether DERs do or do not have access to detailed distribution feeder information. Although the proposed distributed algorithm is tractable on detailed real-life network models, we utilize a simplified T&D network model to derive instructive analytical and numerical results on the impact of strategic DER behavior on social welfare loss, and the distribution of costs and benefits to various market participants. 

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2. An approximate dynamic programming approach to food security of communities following hazards

   https://doi.org/10.22725/ICASP13.096  

   Nozhati, S.; Sarkale, Y.; Ellingwood, B. R.; Chong, E. K.; Mahmoud, H. (May 2019, 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13))

   Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake. 

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3. An approximate dynamic programming approach to food security of communities following hazards

   Nozhati, S. (May 2019, 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13))

   Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake. 

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4. A modified approximate dynamic programming algorithm for community-level food security following disasters

   Nozhati, S.; Sarkale, Y.; Ellingwood, B. R.; Chong, E. K.; Mahmoud, H. (June 2018, 2018 9th International Congress on Environmental Modelling and Software (iEMSs))

   In the aftermath of an extreme natural hazard, community residents must have access to functioning food retailers to maintain food security. Food security is dependent on supporting critical infrastructure systems, including electricity, potable water, and transportation. An understanding of the response of such interdependent networks and the process of post-disaster recovery is the cornerstone of an efficient emergency management plan. In this study, the interconnectedness among different critical facilities, such as electrical power networks, water networks, highway bridges, and food retailers, is modeled. The study considers various sources of uncertainty and complexity in the recovery process of a community to capture the stochastic behavior of the spatially distributed infrastructure systems. The study utilizes an approximate dynamic programming (ADP) framework to allocate resources to restore infrastructure components efficiently. The proposed ADP scheme enables us to identify near-optimal restoration decisions at the community level. Furthermore, we employ a simulated annealing (SA) algorithm to complement the proposed ADP framework and to identify near-optimal actions accurately. In the sequel, we use the City of Gilroy, California, USA to illustrate the applicability of the proposed methodology following a severe earthquake. The approach can be implemented efficiently to identify practical policy interventions to hasten recovery of food systems and to reduce adverse food-insecurity impacts for other hazards and communities. 

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5. Optimizing microgrid deployment for community resilience

   https://doi.org/10.1007/s11081-023-09844-6  

   Grymes, James; Newman, Alexandra; Cranmer, Zana; Nock, Destenie (November 2023, Optimization and Engineering)

   The ability to (re)establish basic community infrastructure and governmental functions, such as medical and communication systems, after the occurrence of a natural disaster rests on a continuous supply of electricity. Traditional energy-generation systems consisting of power plants, transmission lines, and distribution feeders are becoming more vulnerable, given the increasing magnitude and frequency of climate-related natural disasters. We investigate the role that fuel cells, along with other distributed energy resources, play in post-disaster recovery efforts. We present a mixed-integer, non-linear optimization model that takes load and power-technology data as inputs and determines a cost-minimizing design and dispatch strategy while considering operational constraints. The model fails to achieve gaps of less than 15%, on average, after two hours for realistic instances encompassing five technologies and a year-long time horizon at hourly fidelity. Therefore, we devise a multi-phase methodology to expedite solutions, resulting in run times to obtain the best solution in fewer than two minutes; after two hours, we provide proof of near-optimality, i.e., gaps averaging 5%. Solutions obtained from this methodology yield, on average, an 8% decrease in objective function value and utilize fuel cells three times more often than solutions obtained with a straight-forward implementation employing a commercial solver. 

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