This paper proposes a multi-time collaborative restoration model for integrated electricity-gas distribution sys-tems(IEGDSs)considering multiple resources after extreme weather *** on the linearized power flow constr...
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This paper proposes a multi-time collaborative restoration model for integrated electricity-gas distribution sys-tems(IEGDSs)considering multiple resources after extreme weather *** on the linearized power flow constraints of the unbalanced electrical distribution system(EDS)and gas distribution system(GDS),this problem can be formulated as a mixed-integerlinearprogramming(MILP)*** improve the efficiency and veracity of the solution,a rolling optimiza-tion based two-stage method is developed with the first stage solved by a linear approximation model,and the second stage solved by real-time updated rolling *** solving the MILP problem using rolling optimization,the proposed model and solution method achieve efficient and reliable collaborative restoration of IEGDS considering multiple resources and unbal-anced operation characteristics of *** effectiveness of the proposed model and method is validated by using an IEGDS made of a 37-bus unbalanced EDS and 11-node *** Terms-Electricity-gas system,mix-integer linear programming,power system restoration.
The competitiveness of forest companies is strongly affected by the costs associated with getting the raw material to the mills. As harvesting costs contribute significantly to this cost, mathematical programming mode...
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The competitiveness of forest companies is strongly affected by the costs associated with getting the raw material to the mills. As harvesting costs contribute significantly to this cost, mathematical programming models were developed to optimize the scheduling of harvest activities within and between cut blocks to reduce the overall cost. However, the precedence relationship between harvesting activities occurring concurrently across multiple cut blocks has not been considered in the existing literature. In this paper, a mixed-integerlinearprogramming model is developed to optimize the scheduling of harvesting activities, considering the precedence relationship among harvesting activities. The objective of the model is to minimize the total costs. The model determines the start time and end time of each harvesting activity at each cut block, considering the movement time of machines between cut blocks. The model is applied to the case of a large forest company in British Columbia, Canada. The model's harvesting cost is only 1.37% higher than the lowest possible harvesting cost, and only 3 assigned machines have an idle time. The detailed harvesting schedule is generated based on the start time, the end time, and the operating time for each activity at each cut block.
With growing interdependence among various energy forms, such as electricity, heat, and cooling, multi-energy system (MES) is playing an increasingly important role. As a focal point in MES, energy hub (EH) needs to b...
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ISBN:
(纸本)9798350315097
With growing interdependence among various energy forms, such as electricity, heat, and cooling, multi-energy system (MES) is playing an increasingly important role. As a focal point in MES, energy hub (EH) needs to be properly planned for increased efficiency of energy utilization. This paper presents a generic mixed-integerlinear model for EH planning to minimize the overall investment and operation cost given the demand and price of each energy. Our approach uses a graph with multiple rows and layers to represent the energy conversion topology in an EH. In this work, we formulate the EH optimal planning problem with a single combined model to implement the following two steps: 1) optimize the device investments represented in system topology graph;2) manage the energy conversion and flow to meet the end-use demands meanwhile minimizing the cost. The numerical study shows the efficacy of our proposed model.
This paper concerns the optimization model for a multi-product multi-period raw-material selec-tion and composition, and order quantity problem faced by a beverage company. center dot There are some criteria in raw ma...
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This paper concerns the optimization model for a multi-product multi-period raw-material selec-tion and composition, and order quantity problem faced by a beverage company. center dot There are some criteria in raw material selection, which we accommodate all the criteria in the objective function. There are several suppliers, and one of the decision criteria is a one-year minimum order quantity contract between the company and the suppliers. The actual one-year demand for raw materials may deviate significantly from the one-year minimum order quantities. center dot We derive a function that can be regarded as a penalty function to maintain the total order quantities in one year to fulfill the minimum one-year order quantity contracts. This penalty function is a part of the objective function and can be relaxed once the one-year minimum order quantity contracts are fulfilled. center dot We performed several numerical experiments to check the optimal solutions for various de-mands and for various objective functions. These experiments show our MILP (mixed integerlinearprogramming) gives the desired optimal solutions and show the influence of decision criteria on the optimal solution.
This paper used different risk management indicators applied to the investment optimization performed by consumers in Distributed Generation (DG). The objective function is the total cost incurred by the consumer incl...
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This paper used different risk management indicators applied to the investment optimization performed by consumers in Distributed Generation (DG). The objective function is the total cost incurred by the consumer including the energy and capacity payments, the savings, and the revenues from the installation of DG, alongside the operation and maintenance (O&M) and investment costs. Probability density function (PDF) was used to model the price volatility in the long-term. The mathematical model uses a two-stage stochastic approach: investment and operational stages. The investment decisions are included in the first stage and which do not change with the scenarios of the uncertainty. The operation variables are in the second stage and, therefore, take different values with every realization. Three risk indicators were used to assess the uncertainty risk: Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Expected Value (EV). The results showed the importance of migration from deterministic models to stochastic ones and, most importantly, the understanding of the ramifications of every risk indicator.
A considerable amount of desired operation points for vehicle engines can be obtained from vehicle *** desired operations points are in need to guide the design of engines with preferred *** this paper,an effective me...
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A considerable amount of desired operation points for vehicle engines can be obtained from vehicle *** desired operations points are in need to guide the design of engines with preferred *** this paper,an effective method to cluster the operating points and generate condensation maps of engines is proposed to meet this urgent ***,we divide the area where the simulation points are located according to the geometric *** operation points in one area belong to the gravity center of this *** then reallocate the simulation points to the gravity centers based on the distance between the simulation points and the gravity *** process is formulated as a mixed-integerlinearprogramming ***,we determine representative points representing different groups of operation points by solving a nonlinear optimization *** method has been successfully utilized in our design of vehicle engines.
Flight arrival and departure scheduling (FAADS) problem, one of the critical tasks in terminal air traffic operation, faces new challenges as the terminal air traffic management has transformed to adapt to the perform...
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Flight arrival and departure scheduling (FAADS) problem, one of the critical tasks in terminal air traffic operation, faces new challenges as the terminal air traffic management has transformed to adapt to the performance-based navigation (PBN) environment, beside of that, the terminal system uncertainties which are usually due to the time-varying interference of the convective weather and flight arrival time will also exacerbate the difficulty to realize an efficient terminal air traffic operation. In order to effectively address the above issues, we propose the formulation and solution approach for a stochastic terminal FAADS problem under PBN environment. The proposed FAADS problem formulation combines the flexible 4-dimensional trajectory requirement under PBN environment and the stochastic quantifications both from the convective weather and flight arrival time uncertainties, which can finally make the FAADS results realize the avoidance of the convective weather and immunity of the flight arrival time variations within tolerable risk probabilities. We provide an efficient solution approach to tackle this problem with complex mixed integer and nonlinearprogramming basics and illustrate the capabilities of the solution approach by a test case on real terminal system in Shanghai Metroplex. Numerical results demonstrate the effectiveness of proposed problem formulation and solution approach.
Strategic air traffic management (SATM) problem, one of the critical tasks in air traffic operations, faces new challenges as the increased concerns on environmental impact and operation efficiency of the air transpor...
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Strategic air traffic management (SATM) problem, one of the critical tasks in air traffic operations, faces new challenges as the increased concerns on environmental impact and operation efficiency of the air transportation, and the system uncertainty increases due to the time-varying interference, such as the convective weather. To effectively address these challenges, we propose the formulation and solution of a generalized 4-D SATM problem for an air traffic system with convective weather uncertainty. The proposed SATM problem formulation is computationally tractable and the schedule results can realize the avoidance of the convective weather with more than a tolerable risk probability. We provide a solution approach to tackle this problem with complex mathematical basics and illustrate the capabilities of the proposed mixed integer solution approach on the real regional air traffic system operated by the China civil aviation administration. Numerical results demonstrate the effectiveness of the proposed solution approach.
Large-scale wind power integration will inevitably affect the dispatch of conventional resources, which could result in transmission line overload. Thyristor-controlled series capacitors (TCSCs) can be therefore insta...
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Large-scale wind power integration will inevitably affect the dispatch of conventional resources, which could result in transmission line overload. Thyristor-controlled series capacitors (TCSCs) can be therefore installed in the transmission lines to change their series impedance so as to increase the loadability of the power system and reduce transmission losses. To address the highly uncertain characteristic of wind power output, a stochastic optimisation-based optimal TCSC planning model is proposed here. This model minimises the expected value of power loss cost and the investment cost of TCSC considering the probability of different scenarios, which are developed by using the classical copula theory, where the temporal interdependence between wind and load is taken into account. Mathematically, this optimal TCSC placement problem is formulated as a two-stage non-linear programme. Then the linearisation methods are adopted to transform the model to a mix-integerlinear programme. Comprehensive case studies are carried out on the modified IEEE 57-bus test system, which demonstrates the effectiveness and efficiency of the proposed model.
This paper solves stochastic unit commitment problems using multi-cut decomposition algorithm with partial aggregation. The generation unavailability and load uncertainty are both incorporated into the unit commitment...
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ISBN:
(纸本)9781457710018
This paper solves stochastic unit commitment problems using multi-cut decomposition algorithm with partial aggregation. The generation unavailability and load uncertainty are both incorporated into the unit commitment formulation, and it is demonstrated by simulations that this method has better performance in economic and reliability perspective than its deterministic counterpart. The modified version of multi-cut algorithm is used to solve this large-scale unit commitment problem. The efficiency of this modified multi-cut algorithm with different levels of aggregation is examined by numerical studies, which show that the computational cost is significantly reduced by the algorithm.
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