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We study a novel chemical tanker port call optimization framework to improve the efficiency of waterway traffic control, in which chemical tankers load and unload cargoes while visiting multiple terminals. We formulate this problem as a mixed-integer program to minimize the total cost of vessel operations while compressing the schedule of cargo operations. The port call optimization framework integrates decentralized scheduling efforts by different stakeholders to minimize the traffic congestion satisfying policy, operational, and technical requirements, while embracing the current competitive scheduling discipline. Solving real-world problems can be computationally challenging. Hence we propose efficient solution techniques to promote fast convergence to optimum solutions. As we compare the vessel operational schedule from the proposed port call optimization model with monthly transit data in the past, terminal utilization is improved by more than 250% and the number of individual tanker’s transit is reduced by 28%. Suggested computational techniques ensured the solving of real-world problems in approximately 10 minutes.

This paper presents a novel distributed reconfiguration strategy to enable the secure and reliable operation of the zonal shipboard power system (SPS). To adapt to the latest distributed control structure, the proposed strategy features two levels of reconfiguration: zonal reconfiguration and global reconfiguration. An extended hybrid model of SPS is first developed to accurately capture the interactions between the discrete events and the continuously evolving system dynamics involved in the reconfiguration process. The concept of zonal and global reconfigurability is then proposed along with the evaluation criteria to examine if the system can be reconfigured to return to steady-state operation. Provided that the SPS is reconfigurable, executable algorithms are proposed to determine the optimal sequence of operation events for both zonal and global reconfiguration. To evaluate the performance of the proposed strategy, four case studies are presented in which a four-zone SPS is faced with multiple random faults and requires reconfiguration. Simulation results demonstrate that the proposed reconfiguration strategy outperformed previous algorithms in examining the reconfigurability of the system and determining the optimal reconfiguration solution.

This paper develops a novel co-optimized distribution system management algorithm for the distribution system operator to optimally manage the ever-increasing penetration of networked microgrids that are participating in the transactive distribution market as prosumers. For the first time, we evaluate how the incorporation of joint bidirectional energy and ancillary service exchanges could quantitatively increase the operation economy, flexibility, and reliability for both the microgrids and the distribution grid. We formulate the joint distribution system management problem using a bilevel programming approach, in which the upper level is a distribution system optimal management problem for the distribution system operator and the lower level is a microgrid optimal scheduling problem for each participating microgrid operator. Uncertainties from renewable energy generation and responsive loads within the distribution system are also incorporated in the problem formulation. The bilevel stochastic programming model is then reformulated as a single mixed-integer model to solve. The simulation-based case study supports that the proposed management scheme is capable of enhancing energy independency, system-wide efficiency, operational reliability, and economy of the distribution system in comparison with conventional transactive market management schemes.

Purpose Linear energy transfer (LET)-guided methods have been applied to intensity-modulated proton therapy (IMPT) to improve its biological effect. However, using LET as a surrogate for biological effect ignores the topological relationship of the scanning spot to different structures of interest. In this study, we developed an optimization method that takes advantage of the continuing increase in LET beyond the physical dose Bragg peak. This method avoids placing high biological effect values in critical structures and increases biological effect in the tumor area without compromising target coverage. Methods We selected the cases of two patients with brain tumors and two patients with head and neck tumors who had been treated with proton therapy at our institution. Three plans were created for each case: a plan based on conventional dose-based optimization (DoseOpt), one based on LET-incorporating optimization (LETOpt), and one based on the proposed distal-edge avoidance-guided optimization method (DEAOpt). In DEAOpt, an L1 -norm sparsity term, in which the penalty of each scanning spot was set according to the topological relationship between the organ positions and the location of the peak scaled LET-weighted dose (c LETxD) was added to a conventional dose-based optimization objective function. All plans were normalized to give the same target dose coverage. Dose (assuming a constant relative biological effectiveness value of 1.1, as in clinical practice), biological effect (c LETxD), and computing time consumption were evaluated and compared among the three optimization approaches for each patient case. Results For all four cases, all three optimization methods generated comparable dose coverage in both target and critical structures. The LETOpt plans and DEAOpt plans reduced biological effect hot spots in critical structures and increased biological effect in the target volumes to a similar extent. For the target, the c LETxD98% and c LETxD2% in the DEAOpt plans were on average 7.2% and 11.74% higher than in the DoseOpt plans, respectively. For the brainstem, the c LETxDmean in the DEAOpt plans was on average 33.38% lower than in the DoseOpt plans. In addition, the DEAOpt method saved 30.37% of the computation cost over the LETOpt method. Conclusions DEAOpt is an alternative IMPT optimization approach that correlates the location of scanning spots with biological effect distribution. IMPT could benefit from the use of DEAOpt because this method not only delivers comparable biological effects to LETOpt plans, but also is faster.

This paper presents a two-stage microgrid scheduling strategy in which the frequency control reserve (FCR) is incorporated to ensure economic, reliable, and stable microgrid operation in a joint energy and ancillary service market environment. In addition, to using such reserve to limit frequency excursions caused by uncertainties of active loads and renewable generation, this paper is focused on determining the optimal provision of reserve for microgrid operation mode switching to assure sufficient reserve is available and readily deployable at the disposal of the microgrid operator. The scheduling problem is formulated as a two-stage chance-constrained programming (CCP) model where the power balance chance constraints are used to determine the trade-off between operational economy and stability risk exposure. The chance-constrained formulation is then approximated as a deterministic mixed-integer linear programming (MILP) model for solution. Two sets of policy studies are performed through simulation to evaluate the effectiveness of the proposed strategy in enhancing the operational performance of a microgrid through optimal scheduling.