The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the opti...
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The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account.
We deal with the conditions which ensure exact penalization in stochastic programming problems under finite discrete distributions. We give several sufficient conditions for problem calmness including graph calmness, ...
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A distributed energy system is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. The optimal design of such a complex system under energy demand and supply unce...
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A distributed energy system is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. The optimal design of such a complex system under energy demand and supply uncertainty poses significant challenges in terms of both modelling and corresponding solution strategies. This paper proposes a two-stage stochastic programming model for the optimal design of distributed energy systems. A two-stage decomposition based solution strategy is used to solve the optimization problem with genetic algorithm performing the search on the first stage variables and a Monte Carlo method dealing with uncertainty in the second stage. The model is applied to the planning of a distributed energy system in a hotel. Detailed computational results are presented and compared with those generated by a deterministic model. The impacts of demand and supply uncertainty on the optimal design of distributed energy systems are systematically investigated using proposed modelling framework and solution approach. (C) 2012 Elsevier Ltd. All rights reserved.
Various energy consumers, such as large energy consumers (LEC), are targeted to procure the demanded energy from various power markets such as the pool market and different energy resources, including renewable energy...
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We develop a Design and Analysis of the Computer Experiments (DACE) approach to the stochastic unit commitment problem for power systems with significant renewable integration. For this purpose, we use a two-stage sto...
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We develop a Design and Analysis of the Computer Experiments (DACE) approach to the stochastic unit commitment problem for power systems with significant renewable integration. For this purpose, we use a two-stage stochastic programming formulation of the stochastic unit commitment-economic dispatch problem. Typically, a sample average approximation of the true problem is solved using a cutting plane method (such as the L-shaped method) or scenario decomposition (such as Progressive Hedging) algorithms. However, when the number of scenarios increases, these solution methods become computationally prohibitive. To address this challenge, we develop a novel DACE approach that exploits the structure of the first-stage unit commitment decision space in a design of experiments, uses features based upon solar generation, and trains a multivariate adaptive regression splines model to approximate the second stage of the stochastic unit commitment-economic dispatch problem. We conduct experiments on two modified IEEE-57 and IEEE-118 test systems and assess the quality of the solutions obtained from both the DACE and the L-shaped methods in a replicated procedure. The results obtained from this approach attest to the significant improvement in the computational performance of the DACE approach over the traditional L-shaped method.
We develop an approach which enables the decision maker to search for a compromise solution to a multiobjective stochastic linear programming (MOSLP) problem where the objective functions depend on parameters which ar...
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We develop an approach which enables the decision maker to search for a compromise solution to a multiobjective stochastic linear programming (MOSLP) problem where the objective functions depend on parameters which are continuous random variables with normal multivariate distributions. The minimum-risk criterion is used to transform the MOSLP problem into its corresponding deterministic equivalent which in turn is reduced to a Chebyshev problem. An algorithm based on the combined use of the bisection method and the probabilities of achieving goals is developed to obtain the optimal or epsilon optimal solution of this specific problem. An illustrated example is included in this paper to clarify the developed theory.
In addressing the challenges of last-mile logistics, the reliability of the supply chain network becomes paramount. These challenges are intensified due to drone performance limitations and various uncertainties in su...
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In addressing the challenges of last-mile logistics, the reliability of the supply chain network becomes paramount. These challenges are intensified due to drone performance limitations and various uncertainties in supply chain operations. While recent literature recognises the potential of drones for last-mile delivery, it falls short in effectively considering these uncertainties in drone-enabled supply chain models. Our research addresses this gap with two major contributions: first, a novel stochastic mixed-integer programming model is developed to construct a feasible delivery network, including warehouses and recharging stations, enhancing both coverage and reliability. Second, a modification in the genetic algorithm by considering each scenario independently improves computational efficiency, outperforming commercial software by an average of 40% and up to 55%. Empirical findings reveal that strategic investments in system hardening can yield substantial improvements in reliability. Despite the absence of real-world stochastic parameters as a limitation, this research pioneers the design of reliable networks under uncertainties and extends drone coverage through strategic charging stations. This work sets a significant milestone for future optimization in drone logistics, offering practical implications for supply chain managers considering drone adoption.
Due to the uncertain situations of the world, considering inventory management in a stochastic environment gains a lot of interest. In this paper, we propose a multi-item economic production quantity (EPQ) model with ...
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Due to the uncertain situations of the world, considering inventory management in a stochastic environment gains a lot of interest. In this paper, we propose a multi-item economic production quantity (EPQ) model with a shortage for a single-vendor, multi-retailer supply chain under vendor managed inventory (VMI) policy in a stochastic environment. Three stochastic constraints are developed in the model. Geometric programming (GP) approach is employed to find the optimal solution of the nonlinear stochastic programming problem to minimize the mean-variance of the total inventory cost of the system. Since the problem is in the Signomial form, first, an algorithm is used to convert the model into the standard GP form. The performance of the addressed model and the solving method are evaluated based on computational experiments and sensitivity analysis. A case study in an Iranian furniture supply chain is conducted to show the applicability of the proposed model and 17.78% improvement in terms of total cost is gained.
The inventory routing problem simultaneously considers both the inventory problem and the delivery problem;it determines the amount of delivery of inventory and the delivery route such that the total cost is minimized...
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Community resilience planning is challenging as it involves several large-scale systems with interdependency, populations with diverse socio-economic characteristics, and numerous stakeholders. This study introduces a...
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Community resilience planning is challenging as it involves several large-scale systems with interdependency, populations with diverse socio-economic characteristics, and numerous stakeholders. This study introduces a new optimization model to decrease a community's burden in developing viable alternative sets of decisions while considering costs and risks associated with uncertain hazard events. The model captures the essential features of a community, and its scope extends beyond infrastructure and buildings to include social goals. Structural engineering and social science approaches are adapted and incorporated into the model formulation to facilitate the identification of engineering decisions meeting the social goals of minimizing population dislocation and time for recovery. A risk-averse approach frames the optimization problem as a two-stage mean-risk stochastic programming model, which enables effective planning for low-probability, high-consequence hazard events. A case study simulating flood hazards in Lumberton, North Carolina, is developed, and the model is run with the generated data set to showcase the model's capability in developing risk-informed mitigation and recovery plans to achieve resilience goals. The insights drawn from the numerical experiments show the effect of changing risk preference on community resilience metrics.
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