The increasing industrialization since its inception has drawn attention towards the impact of industrial activities on the global environment. The increasing concern of global warming and rising earth's temperatu...
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ISBN:
(纸本)9781538672204
The increasing industrialization since its inception has drawn attention towards the impact of industrial activities on the global environment. The increasing concern of global warming and rising earth's temperature has driven the institution of the Paris Agreement to examine the threshold limit of emission of carbon dioxide, which is the major component of greenhouse gases (GHG). Carbon capture and storage (CCS) technologies play a vital role in achieving net-zero carbon emission. The motivation behind this paper is to review the execution of CCS innovation in thermal power plants with the help of a mixed-integer nonlinear programming model. The problem of uncertainty of emission information in the thermal power plant is solved using the fuzzy technique. The results presented here demonstrate the option of selection of technology in a coalfired power plant.
This paper presents a stochastic mixed-integer nonlinear programming model (MINLP) for the security-constrained energy management system (EMS) of microgrids under demand and renewable generation uncertainties. Conside...
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ISBN:
(纸本)9781728141558
This paper presents a stochastic mixed-integer nonlinear programming model (MINLP) for the security-constrained energy management system (EMS) of microgrids under demand and renewable generation uncertainties. Considering a balanced single-phase representation of the network, the proposed MINLP model is transformed into a mixed-integer second-order cone programming (MISOCP) model that can be solved via off-the-shelf convex programming solvers. The proposed formulation considers photovoltaic (PV) generation, energy storage systems (ESS), direct load control (DLC) and a diesel generator (genset), which can be turned on when the microgrid is operating in isolated mode. The proposed model minimizes the average operational costs for the day-ahead scheduling. Constraints consider the operation in either grid-connected or isolated mode due to a predefined set of plausible contingencies, hence the term security-constrained. The proposed model is tested using data of the real microgrid Laboratory of Intelligent Electrical Networks (LabREI), located at the UNICAMP facilities. Results show that the proposed model is suitable for real-world applications since it provides cost-efficient and contingency-robust solutions.
Drones are projected to alter last-mile delivery, but their short travel range is a concern. This study proposes a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The ...
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Drones are projected to alter last-mile delivery, but their short travel range is a concern. This study proposes a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integernonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). A sensitivity analysis identifies that ABSM service times and costs are the key parameters impacting the long-term adoption of drone delivery. (C) 2020 Elsevier Ltd. All rights reserved.
Integrated planning for urban rail transit (URT) systems is a significant technique in URT operation to balance service level and operating cost. It is generally oriented towards transportation efficiency and solved i...
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Integrated planning for urban rail transit (URT) systems is a significant technique in URT operation to balance service level and operating cost. It is generally oriented towards transportation efficiency and solved in a sequential way due to the complexity, but ignores the service experiences among equitable passengers and the utilization balance among equal rolling stocks. This paper aims to study the integrated planning for an oversaturated URT line, incorporating the fairness in train scheduling and workload balance in rolling stock circulation that are measured by two defined min-max indexes. A mixed-integer nonlinear programming (MINLP) model is established to formulate the integrated planning of stop planning, train scheduling and rolling stock circulation, explicitly considering time-dependent origin-destination demand, first-come-first-serve principle and rigid capacity constraints. Due to the model complexity, an iterative searching approach (ISA) is proposed to solve the model, involving adjusting stop patterns, departure times and connections at the depot terminal. The performance of the ISA is evaluated by three cases with different time lengths based on the practical data from No.8 URT line in Guangzhou. The results are compared with the practical plans and the solutions solved by the two-stage approach respectively. It shows that the ISA can not only mitigate unfairness and balance workload, but also improve transportation efficiency. We also investigate the optimization effects and application conditions of different combinations of searching strategies in the ISA. Furthermore, the study on the typical case during peak hours shows more details about the optimization effects and mechanism of the ISA, which can reach a uniform state in both service level and resource utilization.
Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing ...
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Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.
During early phases of oil field development, field planners must decide upon the optimal number of wells and optimal field plateau rate, usually by performing sensitivity studies. These design choices are then "...
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During early phases of oil field development, field planners must decide upon the optimal number of wells and optimal field plateau rate, usually by performing sensitivity studies. These design choices are then "frozen"in subsequent development stages. However, they often end up being suboptimal when the field is built and produced and the uncertainty is reduced. In this work, we employ non-linear numerical optimisation, latin hypercube sampling and the Schwartz & Smith oil price model to compute probability distributions of the optimal number of wells, plateau rate and project value. We also employ an analytical model to compute production profiles and project value and consider uncertainties in in-place oil volume, well productivity and oil price. Then, we study how do these distributions change from early field planning until when the field is abandoned, when uncertainties are reduced to a minimum. The variation in time of the in-place oil volume uncertainty is modelled with a random walk. The well productivity is a step function altered randomly after production startup. The actual oil price trajectory is picked randomly from possible trajectories computed with the Schwartz and Smith model. The results show that the distributions of the optimal number of wells, plateau rate and project value depend greatly on the uncertainties in the input data. Field designs based on the average of the distributions during the early phase are profitable, but suboptimal. A potential upside of such designs is that they entail less capital investment and therefore less financial risk when compared against the optimal field design.
In this paper, to design a Cellular Manufacturing System (CMS) under a dynamic situation and make Aggregate Planning (AP) decisions simultaneously, a mixed-integer nonlinear programming (MINLP) model is designed. The ...
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In this paper, to design a Cellular Manufacturing System (CMS) under a dynamic situation and make Aggregate Planning (AP) decisions simultaneously, a mixed-integer nonlinear programming (MINLP) model is designed. The distinctive features of the comprehensive Dynamic CMS (DCMS) model under consideration are: i) an extensive coverage of significant manufacturing characteristics in designing a DCMS in addition to the main strategies of AP, ii) integration of cost elements addressing structural, operational and planning issues in the design of DCMS, and iii) capable of developing better DCMS design decisions by incorporating more detailed and realistic parameters when compared to the literature. An illustrative numerical example is solved by CPLEX 12.6 to demonstrate the achievements obtained by the integrated model. Since the proposed model belongs to NP-hard category, a Genetic Algorithm (GA) improved by an elaborately designed matrix-based chromosome representation to represent all decision variables, as well as a sequential procedure generating initial solutions is developed. Several test problems either generated randomly or taken from the literature with various sizes are solved and the results are compared with the solutions gained using CPLEX solver. The comparisons results show that the designed GA is capable of evolving optimal or near-optimal solutions with relative gap less than 1% in a computationally satisfactory manner.
This paper proposes a mixed-integer nonlinear programming model (MINLP) to address the battery energy storage system placement problem (BESP). The solution provides the battery energy storage system (BES) operational ...
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ISBN:
(纸本)9781728131030
This paper proposes a mixed-integer nonlinear programming model (MINLP) to address the battery energy storage system placement problem (BESP). The solution provides the battery energy storage system (BES) operational policy and a solution for the BESP in order to achieve the reduction of energy losses cost. The model considers AC power flow and voltage magnitude constraints;the battery equations are included into the model in order to provide its daily operation. It also considers a photovoltaic energy plant connected to the distribution feeder. The computational studies evaluate the energy costs and the maximum voltage in each bus of a 34-bus test feeder. The results show that the BES policy can reduce up to 24% of the costs with energy losses depending on the result of BESP;besides, the BES operation allows a reduction in the maximum voltage for all scenarios.
In 2013, Buchheim and Wiegele introduced a quadratic optimisation problem, in which the domain of each variable is a closed subset of the reals. This problem includes several other important problems as special cases....
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In 2013, Buchheim and Wiegele introduced a quadratic optimisation problem, in which the domain of each variable is a closed subset of the reals. This problem includes several other important problems as special cases. We study some convex sets and polyhedra associated with the problem, and derive several families of strong valid inequalities. We also present some encouraging computational results, obtained by applying our inequalities to (a) integer quadratic programs with box constraints and (b) portfolio optimisation problems with semi-continuous variables. (C) 2021 Elsevier B.V. All rights reserved.
In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and contro...
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In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and control of power distribution grids. In this paper, a control scheme for flexible asset management is proposed with the aim of closing the gap between power supply and demand in a suburban low-voltage power distribution grid with significant penetration of solar PV power generation while respecting the different systems' operational constraints, in addition to the voltage constraints prescribed by the French distribution grid operator (ENEDIS). The premise of the proposed strategy is the use of a model-based predictive control (MPC) scheme. The flexible assets used in the case study are a biogas plant and a water tower. The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. Thus, one of the contributions of the paper is a new formulation that solves the MINLP problem as a smooth continuous one without having recourse to relaxation. To determine the most adequate size for the proposed scheme's sliding window, a sensitivity analysis is carried out. Then, results given by the scheme using the previously determined window size are analysed and compared to two reference strategies based on a relaxed problem formulation: a single optimisation yielding a weekly operation planning and a MPC scheme. The proposed problem formulation proves effective in terms of performance and maintenance of acceptable computational complexity. For the chosen sliding window, the control scheme drives the power supply/demand gap down from the initial one up to 38%.
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