This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed meth...
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This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed-integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling-GMDH method, with strong computing speeding-up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33-bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process.
Multi-homing is a technology used by Internet Service Provider (ISP) to connect to the Internet via different network providers. To make full use of the underlying networks with minimum cost, an optimal routing strate...
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Multi-homing is a technology used by Internet Service Provider (ISP) to connect to the Internet via different network providers. To make full use of the underlying networks with minimum cost, an optimal routing strategy is required by ISPs. This study investigates the optimal routing strategy in case where network providers charge ISPs according to top-percentile pricing. We call this problem the Top-percentile Traffic Routing problem (TpTRP). The TpTRP is a multistage stochastic optimisation problem in which routing decision should be made before knowing the amount of traffic that is to be routed in the following time period. The stochastic nature of the problem forms the critical difficulty of this study. In this paper several approaches are investigated in modelling and solving the problem. We begin by modelling the TpTRP as a multi-stage stochasticprogrammingproblem, which is hard to solve due to the integer variables introduced by top-percentile pricing. Several simplifications of the original TpTRP are then explored in the second part of this work. Some of these allow analytical solutions which lead to bounds on the achievable optimal solution. We also establish bounds by investigation several "naive" routing policies. In the end, we explore the solution of the TpTRP as a stochastic dynamic programmingproblem by a discretization of the state space. This SDP model gives us achievable routing policies on medium size instances of TpTRP, which of course improve the naive routing policies. With a classification of the SDP decision table, a crude routing policy for realistic size instances can be developed from the smaller size SDP model.
作者:
Fang, YongChen, LihuaFukushima, MasaoPeking Univ
Guanghua Sch Management Dept Management Sci & Management Informat Syst Beijing 100871 Peoples R China Kyoto Univ
Grad Sch Informat Dept Appl Math & Phys Kyoto 6068501 Japan Chinese Acad Sci
Acad Math & Syst Sci Inst Syst Sci Beijing 100080 Peoples R China
The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper,...
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The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integerstochasticprogramming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model. (C) 2007 Elsevier B.V. All rights reserved.
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