Reinforcement learning method for plug-in electric vehicle bidding

This study proposes a novel multi-agent method for electric vehicle (EV) owners who will take part in the electricity market. Each EV is considered as an agent, and all the EVs have vehicle-to-grid capability. These agents aim to minimise the charging cost and to increase the privacy of EV owners due to omitting the aggregator role in the system. Each agent has two independent decision cores for buying and selling energy. These cores are developed based on a reinforcement learning (RL) algorithm, i.e. Q-learning algorithm, due to its high efficiency and appropriate performance in multi-agent methods. Based on the proposed method, agents can buy and sell energy with the cost minimisation goal, while they should always have enough energy for the trip, considering the uncertain behaviours of EV owners. Numeric simulations on an illustrative example with one agent and a testing system with 500 agents demonstrate the effectiveness of the proposed method

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies

Distributed Power Generation Scheduling, Modelling and Expansion Planning

Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. This book deals with distributed energy resources, such as renewable-based distributed generators and energy storage units, among others, considering their operation, scheduling, and planning. Moreover, other interesting aspects such as demand response, electric vehicles, aggregators, and microgrid are also analyzed. All these aspects constitute a new paradigm that is explored in this Special Issue

Improving the Reliability of Optimised Link State Routing Protocol in Smart Grid’s Neighbour Area Network

A reliable and resilient communication infrastructure that can cope with variable application traffic types and delay objectives is one of the prerequisites that differentiates a Smart Grid from the conventional electrical grid. However, the legacy communication infrastructure in the existing electrical grid is insufficient, if not incapable of satisfying the diverse communication requirements of the Smart Grid. The IEEE 802.11 ad hoc Wireless Mesh Network (WMN) is re-emerging as one of the communication networks that can significantly extend the reach of Smart Grid to backend devices through the Advanced Metering Infrastructure (AMI). However, the unique characteristics of AMI application traffic in the Smart Grid poses some interesting challenges to conventional communication networks including the ad hoc WMN. Hence, there is a need to modify the conventional ad hoc WMN, to address the uncertainties that may exist in its applicability in a Smart Grid environment. This research carries out an in-depth study of the communication of Smart Grid application traffic types over ad hoc WMN deployed in the Neighbour Area Network (NAN). It begins by conducting a critical review of the application characteristics and traffic requirements of several Smart Grid applications and highlighting some key challenges. Based on the reviews, and assuming that the application traffic types use the internet protocol (IP) as a transport protocol, a number of Smart Grid application traffic profiles were developed. Through experimental and simulation studies, a performance evaluation of an ad hoc WMN using the Optimised Link State Routing (OLSR) routing protocol was carried out. This highlighted some capacity and reliability issues that routing AMI application traffic may face within a conventional ad hoc WMN in a Smart Grid NAN. Given the fact that conventional routing solutions do not consider the traffic requirements when making routing decisions, another key observation is the inability of link metrics in routing protocols to select good quality links across multiple hops to a destination and also provide Quality of Service (QoS) support for target application traffic. As with most routing protocols, OLSR protocol uses a single routing metric acquired at the network layer, which may not be able to accommodate different QoS requirements for application traffic in Smart Grid. To address these problems, a novel multiple link metrics approach to improve the reliability performance of routing in ad hoc WMN when deployed for Smart Grid is presented. It is based on the OLSR protocol and explores the possibility of applying QoS routing for application traffic types in NAN based ad hoc WMN. Though routing in multiple metrics has been identified as a complex problem, Multi-Criteria Decision Making (MCDM) techniques such as the Analytical Hierarchy Process (AHP) and pruning have been used to perform such routing on wired and wireless multimedia applications. The proposed multiple metrics OLSR with AHP is used to offer the best available route, based on a number of considered metric parameters. To accommodate the variable application traffic requirements, a study that allows application traffic to use the most appropriate routing metric is presented. The multiple metrics development is then evaluated in Network Simulator 2.34; the simulation results demonstrate that it outperforms existing routing methods that are based on single metrics in OLSR. It also shows that it can be used to improve the reliability of application traffic types, thereby overcoming some weaknesses of existing single metric routing across multiple hops in NAN. The IEEE 802.11g was used to compare and analyse the performance of OLSR and the IEEE 802.11b was used to implement the multiple metrics framework which demonstrate a better performance than the single metric. However, the multiple metrics can also be applied for routing on different IEEE wireless standards, as well as other communication technologies such as Power Line Communication (PLC) when deployed in Smart Grid NAN

Planning Model for Implementing Electric Vehicle Charging Infrastructure in Distribution System

Plug-in electric vehicles (PEVs) are growing in popularity in developed countries in an attempt to overcome the problems of pollution, depleting natural oil and fossil fuel reserves and rising petrol costs. In addition, automotive industries are facing increasing community pressure and governmental regulations to reduce emissions and adopt cleaner, more sustainable technologies such as PEVs. However, accepting this new technology depends primarily on the economic aspects for individuals and the development of adequate PEV technologies. The reliability and dependability of the new vehicles (PEVs) are considered the main public concerns due to range anxiety. The limited driving range of PEVs makes public charging a requirement for long-distance trips, and therefore, the availability of convenient and fast charging infrastructure is a crucial factor in bolstering the adoption of PEVs. The goal of the work presented in this thesis was to address the challenges associated with implementing electric vehicle fast charging stations (FCSs) in distribution system. Installing electric vehicle charging infrastructure without planning (free entry) can cause some complications that affect the FCS network performance negatively. First, the number of charging stations with the free entry can be less or more than the required charging facilities, which leads to either waste resources by overestimating the number of PEVs or disturb the drivers’ convenience by underestimate the number of PEVs. In addition, it is likely that high traffic areas are selected to locate charging stations; accordingly, other areas could have a lack of charging facilities, which will have a negative impact on the ability of PEVs to travel in the whole transportation network. Moreover, concentrating charging stations in specific areas can increase both the risk of local overloads and the business competition from technical and economic perspectives respectively. Technically, electrical utilities require that the extra load of adopting PEV demand on the power system be managed. Utilities strive for the implementation of FCSs to follow existing electrical standards in order to maintain a reliable and robust electrical system. Economically, the low PEV penetration level at the early adoption stage makes high competition market less attractive for investors; however, regulated market can manage the distance between charging stations in order to enhance the potential profit of the market. As a means of facilitating the deployment of FCSs, this thesis presents a comprehensive planning model for implementing plug-in electric vehicle charging infrastructure. The plan consists of four main steps: estimating number of PEVs as well as the number of required charging facilities in the network; selecting the strategic points in transportation network to be FCS target locations; investigating the maximum capability of distribution system current structure to accommodate PEV loads; and developing an economical staging model for installing PEV charging stations. The development of the comprehensive planning begins with estimating the PEV market share. This objective is achieved using a forecasting model for PEV market sales that includes the parameters influencing PEV market sales. After estimating the PEV market size, a new charging station allocation approach is developed based on a Trip Success Ratio (TSR) to enhance PEV drivers’ convenience. The proposed allocation approach improves PEV drivers’ accessibility to charging stations by choosing target locations in transportation network that increase the possibility of completing PEVs trips successfully. This model takes into consideration variations in driving behaviors, battery capacities, States of Charge (SOC), and trip classes. The estimation of PEV penetration level and the target locations of charging stations obtained from the previous two steps are utilized to investigate the capability of existing distribution systems to serve PEV demand. The Optimal Power Flow (OPF) model is utilized to determine the maximum PEV penetration level that the existing electrical system can serve with minimum system enhancement, which makes it suitable for practical implementation even at the early adoption rates. After that, the determination of charging station size, number of chargers and charger installation time are addressed in order to meet the forecasted public PEV demand with the minimum associated cost. This part of the work led to the development of an optimization methodology for determining the optimal economical staging plan for installing FCSs. The proposed staging plan utilizes the forecasted PEV sales to produce the public PEV charging demand by considering the traffic flow in the transportation network, and the public PEV charging demand is distributed between the FCSs based on the traffic flow ratio considering distribution system margins of PEV penetration level. Then, the least-cost fast chargers that satisfy the quality of service requirements in terms of waiting and processing times are selected to match the public PEV demand. The proposed planning model is capable to provide an extensive economic assessment of FCS projects by including PEV demand, price markup, and different market structure models. The presented staging plan model is also capable to give investors the opportunity to make a proper trade-off between overall annual cost and the convenience of PEV charging, as well as the proper pricing for public charging services.