Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/66015
Title: Intelligent microgrid management and EV control under uncertainties in smart grid
Authors: Wang, Ran
Keywords: DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Issue Date: 2016
Source: Wang, R. (2016). Intelligent microgrid management and EV control under uncertainties in smart grid. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: A modern power grid needs to become smarter in order to provide an affordable, reliable, and sustainable supply of electricity. For these reasons, a smart grid is necessary to manage and control the increasingly complex future grid. Certain smart grid elements including renewable energy, storage, microgrid, consumer choice, and smart appliances like electric vehicles increase uncertainty in both supply and demand of electric power. In this thesis, we investigate the intelligent control of two important components in smart grid, namely microgrids (MGs) and electric vehicles (EVs). We focus on developing theoretical frameworks and proposing corresponding algorithms, to optimally schedule virtualized elements (e.g., conventional generators' output, electricity imported, EVs' charging rates, and customers' energy demand) under different uncertainties (e.g., renewable energy generation uncertainty, energy demand uncertainty, EVs' pattern uncertainty, electricity price uncertainty, etc.), so that the total cost of operating the microgrid or the EV charging system can be minimized and the systems maintain stabilized. First, we consider power demand and supply management problem in microgrid with uncertain renewable energy integration. To model the randomness of renewable energy generation, a novel uncertainty model is developed. An optimization problem is then formulated to determine the optimal power consumption and generation scheduling for minimizing the fuel cost. We propose a two-stage optimization approach to solve the problem. The second case considers energy generation scheduling in the microgrid. For this case, we develop robust optimization based techniques to tackle the uncertainties from net demand, heat demand and electricity prices. It is shown that our energy generation scheduling strategy performs well which can effectively reduce the system expenditure. Next, we consider charging scheduling of a large number of EVs at a charging station which is equipped with renewable energy generation devices. Stimulated by the fact that in practical scenario, EV arrival and renewable energy may not follow any determinate process yet obtaining some statistical information of future EVs' arrivals (departures) is possible, we propose a novel two-stage EV charging mechanism to minimize the cost and efficiently utilize renewable energy. Several uncertain quantities such as the arrival and departure times of the EVs, their charging requirements and available renewable energy are taken into account. Finally in the last case, we develop a hybrid centralized-decentralized (HCD) EV charging scheme which offers flexible charging choices for customers. In this charging scheme, EV owners can either assign the charging tasks to system controller or individually choose the charging profiles based on their own preferences. In addition, the stochastic characteristics of EVs such as the arrival and departure times and charging demands are taken into account. The aforementioned microgrid management policies and EV charging schemes can effectively reduce the operational cost of the systems. The proposed approaches and obtained results may provide guidelines to improve the efficiency of the smart grid operation and provide useful insights helping system operators develop rational investment strategies.
URI: https://hdl.handle.net/10356/66015
DOI: 10.32657/10356/66015
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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