Technical advances and sustainable development tendency accelerate the implementation of electric ***,the penetration of dynamic charging tariff policy poses a huge challenge to the cost-optimal operation of the elect...
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Technical advances and sustainable development tendency accelerate the implementation of electric ***,the penetration of dynamic charging tariff policy poses a huge challenge to the cost-optimal operation of the electric truck *** this end,a two-stage stochastic electric vehicle routing model is formulated to support cost-efficient routing and charging ***,an experimental study based on a real-world distribution network is conducted to evaluate impacts of dynamic charging tariffs on logistics *** results show that the daily operation cost can reduce by 3.57%to 5.55%as the number of dynamic charging stations *** value of stochastic solution confirms the benefits of implementing stochastic programming model,which will ensure a lower operation cost in the long-term through robust route planning.
PV power generation has significantly penetrated distribution networks, and inverter based local voltage control has been applied in practice. However, volt-var and volt-watt droop control functions are not optimized ...
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PV power generation has significantly penetrated distribution networks, and inverter based local voltage control has been applied in practice. However, volt-var and volt-watt droop control functions are not optimized or coordinated. This letter proposes an effective optimization model for inverter based local voltage control, systematically optimizing both volt-var and volt-watt droop functions under uncertainties and adopts a new solution method. The simulation results show high efficiency of the optimized droop control functions in minimizing power losses and PV power curtailment and addressing the overvoltage issue.
In this paper, we derive deterministic inner approximations for single and joint independent or dependent probabilistic constraints based on classical inequalities from probability theory such as the onesided Chebyshe...
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In this paper, we derive deterministic inner approximations for single and joint independent or dependent probabilistic constraints based on classical inequalities from probability theory such as the onesided Chebyshev inequality, Bernstein inequality, Chernoff inequality and Hoeffding inequality (see Pinter, 1989). The dependent case has been modelled via copulas. New assumptions under which the bounds based approximations are convex allowing to solve the problem efficiently are derived. When the convexity condition can not hold, an efficient sequential convex approximation approach is further proposed to solve the approximated problem. Piecewise linear and tangent approximations are also provided for Chernoff and Hoeffding inequalities allowing to reduce the computational complexity of the associated optimization problem. Extensive numerical results on a blend planning problem under uncertainty are finally provided allowing to compare the proposed bounds with the Second Order Cone (SOCP) formulation and Sample Average Approximation (SAA). (c) 2021 Elsevier B.V. All rights reserved.
We develop a dynamic stochastic model of military workforce planning that incorporates uncertainties about personnel gains and losses across ranks. We then apply it to determine the probability of not meeting required...
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We develop a dynamic stochastic model of military workforce planning that incorporates uncertainties about personnel gains and losses across ranks. We then apply it to determine the probability of not meeting required targets as well as the resulting shortages and overages in the short, medium, and long terms along with the evaluation of policies to mitigate these risks. Our model allows decision makers to adjust recruiting and training practices to minimize the risk of not meeting target personnel levels as well as to value retention and reenlistment policies by calculating the expected marginal value of retaining additional service members. Moreover, it allows us to create a penalty function to optimize recruiting and training levels. The outcome is a tool to evaluate and ensure comprehensive force readiness.
Managing environmental performance of waste dump facilities in mining complexes is an integral part of long-term production planning. Sustainable long-term production scheduling solutions are desired to mitigate risk ...
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Managing environmental performance of waste dump facilities in mining complexes is an integral part of long-term production planning. Sustainable long-term production scheduling solutions are desired to mitigate risk and return the environment to a productive post-mining state. A simultaneous stochastic optimisation framework for long-term production scheduling in mining complexes is developed that integrates waste management and progressive reclamation. The waste dump placement schedule is jointly optimised with the extraction sequence, destination policy, and stockpiling decisions in a single stochastic mathematical programming framework. This includes the timing of progressive reclamation activities in parallel with production to enhance waste dump rehabilitation. Uncertainty related to the production of acid rock drainage is quantified by simulating geochemical properties of waste and managing the blending of uncertain waste properties within the optimisation framework. With respect to the framework for simultaneous stochastic optimisation, contextual bandits are explored to improve the metaheuristic solution approach and solve the corresponding large-scale optimisation model. The framework is tested in a multi-mine copper-gold mining complex leading to improved environmental performance. Risk of acid rock drainage is decreased by 52.5% in the waste dump facilities. Reclamation planning activities for meeting environmental requirements are scheduled prior to closure. The solution approach more effectively improves the objective function with contextual bandits leading to a 24% improvement in the study presented.
In this paper, we present a two-stage stochastic programming and simulation-based framework for tackling large-scale planning and operational problems that arise in power systems with significant renewable generation....
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In this paper, we present a two-stage stochastic programming and simulation-based framework for tackling large-scale planning and operational problems that arise in power systems with significant renewable generation. Traditional algorithms (the L-shaped method, for example) used to solve the sample average approximation of the true problem suffer from computational difficulties when the number of scenarios or the size of the subproblem increases. To address this, we develop a cutting plane method that uses sampling internally within optimization to select only a random subset of subproblems to solve in any iteration. We analyze the convergence property of the subproblem sampling-based method and demonstrate its computational advantages on two alternative formulations of the stochastic unit commitment-economic dispatch problem. We conduct the numerical experiments on modified IEEE-30 and IEEE-118 test systems. We also present detailed steps for assessing the quality of solutions obtained from sampling-based stochastic programming methods and determining a solution to prescribe to the system operators.
This study develops a stochastic Mixed Integer Linear programming (SMILP) model to optimize the reverse logistics network for debris generated from proactive demolition of end-of-life buildings. Unlike most existing r...
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This study develops a stochastic Mixed Integer Linear programming (SMILP) model to optimize the reverse logistics network for debris generated from proactive demolition of end-of-life buildings. Unlike most existing research, this study addresses proactive strategies for the Mitigation stage of disaster management. Our model identifies the optimal number of landfilling areas and sorting facilities, factoring in uncertainties in debris quantity and material quality. It incorporates environmental constraints, such as mandatory sorting processes and recycling thresholds. Multiple scenarios are considered, each with unique parameter values and occurrence probabilities, with the overall objective of minimizing net costs across all scenarios. A realistic case study is used to illustrate the model, demonstrating its capacity to reduce post-disaster recovery costs, improve operational efficiency, and balance financial and environmental considerations. This study offers insights for decision-makers, advocating proactive end-of-life buildings' management as a disaster preparedness and sustainable practices.
This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding *** goal is to minimize the total operation and maintenance costs,while maximiz...
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This paper proposes an optimal risk-constrained energy management strategy for commercial buildings in a commercial campus with islanding *** goal is to minimize the total operation and maintenance costs,while maximizing comprehensive comfort levels for the occupants.A two-stage riskconstrained,scenario-based stochastic optimization approach is adopted to handle various uncertainties associated with the energy management process,such as power generation of rooftop solar panels,arrival state-of-charges,and arrival/departure time of plug-in electric vehicles,intermittent load demand,and uncertain grid-connection conditions.A conditional-valueat-risk method is introduced to provide a risk-averse energy management *** face the challenge of both reducing the computational burden and maintaining the accuracy of the stochastic programming,an advanced scenario reduction method is *** simulation results validate the effectiveness of the proposed energy management strategy for minimizing total operating and maintenance costs of commercial buildings with islanding capabilities,while maximizing comprehensive comfort levels of the occupants.
The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develo...
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The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions.
Multistage stochastic programs arise in many applications from engineering whenever a set of inventories or stocks has to be valued. Such is the case in seasonal storage valuation of a set of cascaded reservoir chains...
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Multistage stochastic programs arise in many applications from engineering whenever a set of inventories or stocks has to be valued. Such is the case in seasonal storage valuation of a set of cascaded reservoir chains in hydro management. A popular method is stochastic dual dynamic programming (SDDP), especially when the dimensionality of the problem is large and dynamic programming is no longer an option. The usual assumption of SDDP is that uncertainty is stage-wise independent, which is highly restrictive from a practical viewpoint. When possible, the usual remedy is to increase the state-space to account for some degree of dependency. In applications, this may not be possible or it may increase the state-space by too much. In this paper, we present an alternative based on keeping a functional dependency in the SDDP-cuts related to the conditional expectations in the dynamic programming equations. Our method is based on popular methodology in mathematical finance, where it has progressively replaced scenario trees due to superior numerical performance. We demonstrate the interest of combining this way of handling dependency in uncertainty and SDDP on a set of numerical examples. Our method is readily available in the open-source software package StOpt.
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