The COVID-19 pandemic presents unprecedented challenges for the US healthcare system, and the critical care settings are heavily impacted by the pressures of caring for COVID-19 patients. However, hospital pandemic preparedness has been hampered by a lack of disease specific planning guidelines. In this paper, we proposed a holistic modeling and analysis approach, with a system dynamics model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization, to support the hospital planning decisions. Our model was trained using the public data from the JHU Coronavirus Resource Center and was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL and public data from the Florida Department of Health (FDOH). Various experiments were conducted to investigate different control measures and the variants of the virus and their impact on the disease transmission, and subsequently, the hospital planning needs. Our proposed approach can be tailored to a given hospital setting of interest and is also generalizable to other hospitals to tackle the pandemic planning challenge.Note to Practitioners—We proposed a holistic modeling and analysis approach to support hospital preparedness and resource planning during the COVID-19 pandemic. To capture the highly dynamic pandemic environment, we developed a numerical method to estimate$R_{0}$, the effective basic reproductive rate, and used the most recent estimated data series of daily$R_{0}$to project the change in$R_{0}$in a short-term forecast window. The prediction of the daily confirmed cases in that forecast window were then obtained based on recursively solving the system dynamics model, and was validated to be very close to the real confirmed cases from the public record. This data-driven approach allows us to gain a systematic understanding of the common trends across different states and regions, and to evaluate the effect of the control measures like the stay-at-home order and the impact of the virus variants on the disease transmission behavior. Furthermore, the dynamic prediction allows us to evaluate the hospital resource needs during different stages of the pandemic. The insights obtained through this effort shed light on the impact of interventions (e.g., vaccines and control measures) on the hospital preparedness to support appropriate hospital resource allocation.

The complexity of current electricity wholesale markets and the increased volatility of electricity prices because of the intermittent nature of renewable generation make independent power producers (IPPs) face significant challenges to submit offers. This challenge increases for those owning traditional coal-fired thermal generators and renewable generation. In this paper, an integrated stochastic optimal strategy is proposed for an IPP using the self-scheduling approach through its participation in both day-ahead and real-time markets (i.e., two-settlement electricity markets) as a price taker. In the proposed approach, the IPP submits an offer for all periods to the day-ahead market for which a multistage stochastic programming setting is explored for providing real-time market offers for each period as a recourse. This strategy has the advantage of achieving overall maximum profits for both markets in the given operational time horizon. Such a strategy is theoretically proved to be more profitable than alternative self-scheduling strategies as it takes advantage of the continuously realized scenario information of the renewable energy output and real-time prices over time. To improve computational efficiency, we explore polyhedral structures to derive strong valid inequalities, including convex hull descriptions for certain special cases, thus strengthening the formulation of our proposed model. Polynomial-time separation algorithms are then established for the derived exponential-sized inequalities to speed up the branch-and-cut process. Finally, both numerical and real case studies demonstrate the potential of the proposed strategy. Summary of Contribution: This paper develops innovative models and methods to study a family of practically important problems via the interactions of operations research and computing. Specifically, this paper provides in-depth analyses of innovative stochastic optimization modeling approaches and develops computationally efficient polyhedral results. The paper also verifies the effectiveness of proposed analyses via extensive computing experiments.

With the increasing penetration of intermittent renewable energy and fluctuating electricity loads, power system operators are facing significant challenges in maintaining system load balance and reliability. In addition to traditional energy markets that are designed to balance power generation and load, ancillary service markets have been recently introduced to help manage the considerable uncertainty by reserving certain generation capacities against unexpected events. In this paper, we develop a multistage stochastic optimization model for system operators to efficiently schedule power-generation assets to co-optimize power generation and regulation reserve service (a critical ancillary service product) under uncertainty. In addition, to improve the computational efficiency of the proposed multistage stochastic integer program, we explore its polyhedral structure by investigating physical characteristics of individual generators, the system-wide requirements that couple all of the generators, and the scenario tree structure for our proposed multistage model. We start with the single-generator polytope and provide convex hull descriptions for the two-period case under different parameter settings. We then provide several families of multiperiod strong valid inequalities linking different scenarios and covering decision variables that represent both power generation and regulation reserve amounts. We further extend our study by exploring the multigenerator polytope and derive strong valid inequalities linking different generators and covering multiple periods. To enhance computational performance, polynomial-time separation algorithms are developed for the exponential number of inequalities. Finally, we verify the effectiveness of our proposed strong valid inequalities by applying them as user cuts under the branch-and-cut scheme to solve multistage stochastic network-constrained power generation scheduling problems.

With fewer emissions, higher efficiency, and quicker response than traditional coal-fired thermal power plants, the combined-cycle units (CCUs), as gas-fired generators, have been increasingly adapted in the U.S. power system to enhance the smart grids operations. Meanwhile, due to the inherent uncertainties in the deregulated electricity market, e.g., intermittent renewable energy output, unexpected outages of generators and transmissions, and fluctuating electricity demands, the electricity price is volatile. As a result, this brings challenges for an independent power producer (served in the self-scheduling mode) owning CCUs to maximize the total profit when facing the significant price uncertainties. In this paper, a data-driven risk-averse stochastic self-scheduling approach is presented for the CCUs that participate in the real-time market. The proposed approach does not require the specific distribution of the uncertain real-time price. Instead, a confidence set for the unknown distribution is constructed based on the historical data. The conservatism of the proposed approach is adjustable based on the amount of available data. Finally, numerical studies show the effectiveness of the proposed approach.

We use pricing as an effective strategy to allocate network resources in an efficient way so as to maximize a service provider's revenue. Among all static and dynamic pricing strategies, an auction approach is a widely proposed decentralized mechanism. We propose a scenario where all clients can bid for their required bandwidth as well as the price they are willing to pay. The service provider decides on the admission price and differentiated service provided for each class. These thresholds also provide a future reference for admitting new flows later.