Even in a static setting, the economic load dispatch problem (ELDP) - namely the cost-optimal distribution of power among generating units to meet a specific demand subject to system constraints - turns out to be a ch...
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Even in a static setting, the economic load dispatch problem (ELDP) - namely the cost-optimal distribution of power among generating units to meet a specific demand subject to system constraints - turns out to be a challenge owing to the consideration of valve-point effects (VPE), which make the cost function nonsmooth and nonconvex. We present a new method, termed Adaptive Piecewise-Quadratic Under-Approximation (APQUA), for the global solution of the ELDP with VPE. Unlike the many existing methods for this problem, APQUA produces at each iteration an upper and a lower bound on the globally optimal cost, and the gap between the two bounds is guaranteed to converge to zero as the iteration number grows. Consequently, APQUA is guaranteed to compute the global optimum of the ELDP within any user-prescribed accuracy. Even though APQUA has to call an MIQP solver on increasingly large surrogate problems in order to achieve this unprecedented optimality guarantee, our experiments indicate that the total computation time remains reasonable even when the prescribed accuracy is very high.
A mixed-integer nonlinear programming model is proposed in this paper for reducing excessive standing phase angle difference (SPA) across open circuit breakers in transmission loop paralleling operation. The model ado...
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ISBN:
(纸本)9781479932559
A mixed-integer nonlinear programming model is proposed in this paper for reducing excessive standing phase angle difference (SPA) across open circuit breakers in transmission loop paralleling operation. The model adopts both the active power generation increment and the load switch as control variables and takes the relative importance of the loads to be restored into account, resulting in fewer rescheduled generators and less adjustment of active power generation. The condition for transmission loop paralleling operation can be reached from two ways of reducing generator adjustment time and optimally restoring load. The method can improve power restoration efficiency, speed up restoration progress, and hence reduce the loss of outage. Simulation results of New England 10-machine 39-bus test system demonstrate the effectiveness of the method.
This article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epi...
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This article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location–allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework co
This paper presents a vendor selection model for buyers practicing Just-in-time management strategy. Five vendor evaluation criteria including quality, delivery, net price, geographical location, and production capaci...
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This paper presents a vendor selection model for buyers practicing Just-in-time management strategy. Five vendor evaluation criteria including quality, delivery, net price, geographical location, and production capacity are utilized for the selection of vendors and determination of associated purchasing quantities. A soft time window mechanism is incorporated to allow purchased parts been delivered within a grace period of time with a specified penalty function. A mixed-integer programming model is developed for this vendor selection problem. The proposed model encourages the buyer to select vendors capable of providing quality in-time products which will minimize the total cost of purchasing, quality, transportation, and penalty charge for violating the delivery time window. An illustrative example is given to demonstrate the implementation of the time-window model.
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