We present a mixed integer non-linear programming (MINLP) model capable of choosing the best design considering economic profit, availability, and safety. The model takes into account the probability of suffering a fa...
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We present a mixed integer non-linear programming (MINLP) model capable of choosing the best design considering economic profit, availability, and safety. The model takes into account the probability of suffering a failure in a year of operation, as well as the revenue generated and the probability of the process units of being in a non-functional state. The inclusion of programmed maintenances of a specified duration is considered in the model, assuming an equal distribution in the maintenances time. The performance of the model is illustrated by small examples to help the reader to better understand the model, before applying it to the methanol synthesis case study, where the economic and safety objectives are represented in a Pareto front. The results showcase the possibility of considering safety during the early design stage. ? 2021 Elsevier Ltd. All rights reserved.
Analysis of the energy transportation cost has a wide range of scope nowadays. Satisfying the demand for minimum cost is a great challenge for the power system. The planning and modeling of the production system shoul...
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ISBN:
(纸本)9781538673287
Analysis of the energy transportation cost has a wide range of scope nowadays. Satisfying the demand for minimum cost is a great challenge for the power system. The planning and modeling of the production system should put forward the objectives of greenhouse gas emission reduction and promote the deployment of renewable energy. These objectives are designed to achieve significant energy savings in the future. In this work, a mixed integer non-linear programming is used to minimize the energy transportation cost using commercial software GAMS. The obtained results show the effectiveness of the proposed method.
Reliability-Security Constrained Unit Commitment (RSCUC) with emphasis on mixedintegernonlinearprogramming (MINLP) and Benders Decomposition (BD) based on thermal generating units are presented in this paper. To so...
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ISBN:
(纸本)9781509047086
Reliability-Security Constrained Unit Commitment (RSCUC) with emphasis on mixedintegernonlinearprogramming (MINLP) and Benders Decomposition (BD) based on thermal generating units are presented in this paper. To solve unit commitment problem generalized BD along with reliability issues are considered. The approach presented in this work allows the decomposition of the whole program in quadratic mixedinteger master program and network security check non-linear subproblem. The case study demonstrates the effectiveness of the proposed approach.
Coordinated voltage and VAR control (VVC) can provide major economic benefits for distribution utilities. Incorporating distributed generations (DG) for VVC can improve the efficiency and reliability of distribution s...
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Coordinated voltage and VAR control (VVC) can provide major economic benefits for distribution utilities. Incorporating distributed generations (DG) for VVC can improve the efficiency and reliability of distribution systems. This paper presents an approach to formulate and solve distribution system VVC with DG units as a mixed integer non-linear programming (MINLP) problem. The method can be utilized to create an effective control scheme for both the traditional VVC devices and DG units. The MINLP formulation is based on three-phase power flow formulation, and is solved with an open-source BONMIN optimization solver with outer approximation (OA) algorithm. BONMIN is interfaced with Matlab via a third-party optimization toolbox. The proposed approach is applied to several distribution feeder models with promising results. (C) 2013 Elsevier B.V. All rights reserved.
Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted...
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Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids;(2) dispatch considering complex bids. (c) 2013 Elsevier Ltd. All rights reserved.
Delivery by drones holds significant potential to solve issues (such as high costs, access to remote areas, etc.) faced in last-mile delivery operations, particularly in the e-commerce industry. Still, it involves com...
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Delivery by drones holds significant potential to solve issues (such as high costs, access to remote areas, etc.) faced in last-mile delivery operations, particularly in the e-commerce industry. Still, it involves complex issues such as multi-trip operations, energy estimation, and battery recharge planning. A sound drone delivery problem entails an optimal drone deployment plan with routing details at the lowest possible cost. To this end, this study focuses on formulating a delivery problem that involves multi-trip drone routing, energy optimization, and travel time optimization problems where energy consumption by drones is modeled as a non-linear function. We develop a mixed integer non-linear programming model as an integrated optimization model. This model aims to: (a) maximize revenue by meeting demand completely without leaving idle drones, (b) optimize energy use by drones, and (c) minimize the required drone fleet size for an optimal plan. The proposed model is solved using the Gurobi Solver, which employs data supplied by a well-known e-commerce firm. We introduce a two-phase heuristic solution methodology to tackle larger networks' complexities. This method consists of the clustering phase (K-means clustering method) and the optimization phase. The robustness of the developed mathematical modeling is demonstrated by testing with varied large problem instances. The evaluation shows that expanding destination options boosts drone demand until saturation, necessitating more drones. Efficient route planning and fleet adjustments are crucial for meeting rising demand and satisfying customers amidst dense clustering. This model helps e-commerce manage daily last-mile drone deliveries and anticipate future growth.
Electric vehicle charging stations are widespread but suffer from long charging times. In contrast, battery swapping stations have gained attention due to their efficiency and small footprint. However, there is a lack...
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Electric vehicle charging stations are widespread but suffer from long charging times. In contrast, battery swapping stations have gained attention due to their efficiency and small footprint. However, there is a lack of extensive discussion on their layout, operation modes, and scheduling algorithms. This paper discusses the layout-dispatching-scheduling model of battery swapping stations and super battery swapping stations under centralized charging and unified dispatch. Considering battery swapping stations service time and electric vehicles queuing, a queuing-aware location-routing problem is proposed and solved using Gurobi. This study tackles the uncertainty in electric vehicle spatio-temporal dispatch by formulating the battery scheduling process between super battery swapping stations and battery swapping stations as a vehicle routing problem with time windows and uncertain demand. To address this challenge, the study proposes an adaptive routing optimization method based on an improved proximal policy optimization algorithm. Additionally, it investigates a flexible charging strategy for super battery swapping stations, where the battery charging and discharging process is modeled as a Markov decision process. To optimize operational revenue, meet demand, enable grid interactions, and contribute to peak shaving, the study employs a deep reinforcement learning approach that utilizes the twin delayed deep deterministic policy gradient algorithm. The system design is proven to be feasible and capable of meeting operational requirements.
PurposeSupply chain risk management can effectively reduce the loss of retailers. In this regard, retailers need to consider the competition risks of competitors in addition to the disruption risks. This paper designs...
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PurposeSupply chain risk management can effectively reduce the loss of retailers. In this regard, retailers need to consider the competition risks of competitors in addition to the disruption risks. This paper designs a resilient retail supply chain network for perishable foods under the dynamic competition to maximize retailer's ***/methodology/approachA two-stage mixed-integernon-linear model is presented for designing the supply chain network. In the first stage, an equilibrium model that considers the characteristics of perishable foods is developed. In the second stage, a mixed integer non-linear programming model is presented to deal with the strategic decisions. Finally, an efficient memetic algorithm is designed to deal with large-scale *** optimal the selection of suppliers, distribution centers and the order allocation are found among the supply chain entities. Considering the perishability of agri-food products, the equilibrium retail price and selling quantity are determined. Through a numerical example, the optimal inventory period under different maximum shelf life and the impact of three resilient strategies on retailer's profit, selling price and selling quantity are *** limitations/implicationsAs for future research, the research can be extended in a number of directions. First, this paper studies the retail supply chain network design problem under competition among retailers. It can be an interesting direction to consider retailers competing with suppliers. Second, the authors can try to linearize the non-linear model and solve the large-scale integerprogramming problem by exact algorithm. Finally, the freshness of perishable foods gradually declines linearly to zero as the maximum shelf life approaches, and it would be a meaningful attempt to consider the freshness of perishable foods declines ***/valueThis paper innovatively designs the resilient supply chain network for perishabl
In this article, we discuss an exact algorithm for solving mixedinteger concave minimization problems. A piecewise inner-approximation of the concave function is achieved using an auxiliary linear program that leads ...
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In this article, we discuss an exact algorithm for solving mixedinteger concave minimization problems. A piecewise inner-approximation of the concave function is achieved using an auxiliary linear program that leads to a bilevel program, which provides a lower bound to the original problem. The bilevel program is reduced to a single level formulation with the help of Karush-Kuhn-Tucker (KKT) conditions. Incorporating the KKT conditions lead to complementary slackness conditions that are linearized using BigM, for which we identify a tight value for general problems. Multiple bilevel programs, when solved over iterations, guarantee convergence to the exact optimum of the original problem. Though the algorithm is general and can be applied to any optimization problem with concave function(s), in this paper, we solve two common classes of operations and supply chain problems;namely, the concave knapsack problem, and the concave production-transportation problem. The computational experiments indicate that our proposed approach outperforms the customized methods that have been used in the literature to solve the two classes of problems by an order of magnitude in most of the test cases.(c) 2023 Elsevier B.V. All rights reserved.
Photovoltaic (PV) technology is one of the most popular means of renewable generation, whose applications range from commercial and residential buildings to industrial facilities and grid infrastructures. The problem ...
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Photovoltaic (PV) technology is one of the most popular means of renewable generation, whose applications range from commercial and residential buildings to industrial facilities and grid infrastructures. The problem of determining a suitable layout for the PV arrays, on a given deployment region, is generally non-trivial and has a crucial importance in the planning phase of solar plants design and development. In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays' layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window. Subsequently, we formalize the mathematical optimization problem, whose constraints' set is characterized by non-convexities. In order to propose a computationally tractable approach, we provide a tight parametrized convex relaxation. The resulting optimization resolution procedure is tested numerically, using realistic data, and benchmarked against the traditional global resolution approach, showing that the proposed methodology yields near-optimal solutions in lower computational time. Note to Practitioners-The paper is motivated by the need for efficient algorithmic procedures which can yield near-optimal solutions to the PV arrays layout problem. Due to the strong non-convexity of even simple instances, the existing methods heavily rely on global or stochastic solvers, which are computationally demanding, both in terms of resources and run-time. Our approach acts as a baseline, from which practitioners can derive more elaborate instances, by suitably modifying both the objective function and/or the constraints. In fact, we focus on the minimum set of necessary geometrical (e.g., arrays position model), astronomical (e.g., irradiance variation), and operational (e.g., power requirements) constraints which make the overall problem ha
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