Demand Response in Electric Vehicles Management Optimal Use of End-User Contracts

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI) Pub Date : 2015-10-25 DOI:10.1109/MICAI.2015.25

J. Soares, Z. Vale, H. Morais, Nuno Borges

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引用次数: 4

Abstract

Demand Response (DR) has gained attention in the past few years. With the mass introduction of Electric Vehicles (EVs) the opportunity for DR programs can grow even more. This paper concentrates on a specific type of DR, namely evaluating different scenarios with respect to end-user EVs tariffs. The effects of different tariffs are compared in several scenarios in terms of the Virtual Power Plant (VPP) operation costs and also the EVs' perspective, i.e. considering the optimal use of end-user contracts. To solve the Mixed Integer Non-Linear Problem (MINLP) a modernized optimization approach is used by combining a deterministic method in the first stage, relaxing the problem to a Mixed Integer Linear Problem (MILP) with the use of a computational intelligence method in the second stage, namely Particle Swarm Optimization (PSO). The case study presents different price scenarios, namely, singletariff, bi-tariff, tri-tariff, tetra-tariff, and Real-Time Pricing (RTP). The network used for this application is a 33-bus distribution network with high penetration of renewables and a fleet of 30 electric buses.

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基于终端用户契约的电动汽车需求响应管理

需求响应(DR)在过去几年中得到了广泛的关注。随着电动汽车(ev)的大规模引入，DR项目的机会可能会增加更多。本文主要关注一种特定类型的DR，即评估与最终用户电动汽车关税相关的不同情景。从虚拟电厂(VPP)运营成本和电动汽车的角度，即考虑最终用户合同的最佳使用，在几种情况下比较了不同关税的影响。为了解决混合整数非线性问题(MINLP)，采用了一种现代化的优化方法，将第一阶段的确定性方法结合起来，将问题简化为混合整数线性问题(MILP)，并在第二阶段使用计算智能方法，即粒子群优化(PSO)。本案例研究展示了不同的价格情景，即单费率、双费率、三费率、四费率和实时定价(RTP)。该应用程序使用的网络是一个33辆公交车的配电网络，可再生能源的渗透率很高，车队由30辆电动公交车组成。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)

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