Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses

We discuss a recently introduced ECO-driving concept known as SPONGE in the context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realise SPONGE are discussed, paying attention to the privacy implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on Automation Science and Engineerin

Topics in Electromobility and Related Applications

In this thesis, we mainly discuss four topics on Electric Vehicles (EVs) in the context of smart grid and smart transportation systems. The first topic focuses on investigating the impacts of different EV charging strategies on the grid. In Chapter 3, we present a mathematical framework for formulating different EV charging problems and investigate a range of typical EV charging strategies with respect to different actors in the power system. Using this framework, we compare the performances of all charging strategies on a common power system simulation testbed, highlighting in each case positive and negative characteristics. The second topic is concerned with the applications of EVs with Vehicle-to-Grid (V2G) capabilities. In Chapter 4, we apply certain ideas from cooperative control techniques to two V2G applications in different scenarios. In the first scenario, we harness the power of V2G technologies to reduce current imbalance in a three-phase power network. In the second scenario, we design a fair V2G programme to optimally determine the power dispatch from EVs in a microgrid scenario. The effectiveness of the proposed algorithms are verified through a variety of simulation studies. The third topic discusses an optimal distributed energy management strategy for power generation in a microgrid scenario. In Chapter 5, we adapt the synchronised version of the Additive-Increase-Multiplicative-Decrease (AIMD) algorithms to minimise a cost utility function related to the power generation costs of distributed resources. We investigate the AIMD based strategy through simulation studies and we illustrate that the performance of the proposed method is very close to the full communication centralised case. Finally, we show that this idea can be easily extended to another application including thermal balancing requirements. The last topic focuses on a new design of the Speed Advisory System (SAS) for optimising both conventional and electric vehicles networks. In Chapter 6, we demonstrate that, by using simple ideas, one can design an effective SAS for electric vehicles to minimise group energy consumption in a distributed and privacy-aware manner; Matlab simulation are give to illustrate the effectiveness of this approach. Further, we extend this idea to conventional vehicles in Chapter 7 and we show that by using some of the ideas introduced in Chapter 6, group emissions of conventional vehicles can also be minimised under the same SAS framework. SUMO simulation and Hardware-In-the-Loop (HIL) tests involving real vehicles are given to illustrate user acceptability and ease of deployment. Finally, note that many applications in this thesis are based on the theories of a class of nonlinear iterative feedback systems. For completeness, we present a rigorous proof on global convergence of consensus of such systems in Chapter 2

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Control and Optimization of Energy Storage in AC and DC Power Grids

Energy storage attracts attention nowadays due to the critical role it will play in the power generation and transportation sectors. Electric vehicles, as moving energy storage, are going to play a key role in the terrestrial transportation sector and help reduce greenhouse emissions. Bulk hybrid energy storage will play another critical role for feeding the new types of pulsed loads on ship power systems. However, to ensure the successful adoption of energy storage, there is a need to control and optimize the charging/discharging process, taking into consideration the customer preferences and the technical aspects. In this dissertation, novel control and optimization algorithms are developed and presented to address the various challenges that arise with the adoption of energy storage in the electricity and transportation sectors. Different decentralized control algorithms are proposed to manage the charging of a mass number of electric vehicles connected to different points of charging in the power distribution system. The different algorithms successfully satisfy the preferences of the customers without negatively impacting the technical constraints of the power grid. The developed algorithms were experimentally verified at the Energy Systems Research Laboratory at FIU. In addition to the charge control of electric vehicles, the optimal allocation and sizing of commercial parking lots are considered. A bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size a commercial parking lot. The optimization formulation tries to maximize the profits of the parking lot investor, as well as minimize the losses and voltage deviations for the distribution system operator. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location is also performed. Furthermore, in this dissertation, energy management strategies of the onboard hybrid energy storage for a medium voltage direct current (MVDC) ship power system are developed. The objectives of the management strategies were to maintain the voltage of the MVDC bus, ensure proper power sharing, and ensure proper use of resources, where supercapacitors are used during the transient periods and batteries are used during the steady state periods. The management strategies were successfully validated through hardware in the loop simulation

Development of a power monitoring and control system to provide demand side management of electric vehicle charging activity.

Due to the recent inflow of Electric Vehicles (EVs) to the automobile market, new concerns have risen with respect to the additional electrical load and the resultant effects on an overloaded electric grid. Either for convenience purposes or possibly necessity due to limited electric range on EVs, some EV owners may desire to charge their EV while at work in addition to charging at home. These forward-thinking daytime charging providers are typically Commercial and Industrial (C&I) electric ratepayers, or other large electric consumers which constitute the majority of businesses, shopping centers, academic campuses and manufacturing facilities. Increased electricity consumption due to EV charging activity results in higher electricity costs due to differences in the billing structures between residential and C&I electric ratepayers. Therefore, it is beneficial to the EVSE charging provider to minimize charging activity around peak demand periods which would result in lower electrical costs overall. A solution is developed that can provide this control without creating a nuisance to electric vehicle owners since EV charging demand is somewhat inelastic due to range anxiety. The primary objective of the research detailed in this dissertation is to develop a novel demand side management system for monitoring the peak demand of commercial time-of-day electric ratepayers that cost effectively predicts and controls electric vehicle charging during peak demand periods. This objective is achieved, therefore confirming the hypothesis that such a system can provide cost and demand benefits to forward-thinking commercial electric ratepayers that provide daytime charging capabilities. This work proposes and evaluates a novel Power Monitoring and Control System (PMCS) that can be implemented at C&I EV charging locations to minimize or eliminate the negative impacts of charging electric vehicles at the workplace in C&I environments. Operation of the PMCS begins by forecasting electrical demand in advance of every 15 minute demand interval throughout the day. The forecast is generated using an artificial neural network and a number of input data streams. Electrical demand has been shown to correlate well with weather data such as temperature and dew point. Therefore, using those measurements along with a date and time stamp, and historical electrical demand measurements, a highly accurate forecast for the following 15-minute demand interval was achieved. From that forecast, the number of EV charging stations that may be active, without the chance of creating new electrical demand peaks, is calculated. Finally, the forecast is then used to properly schedule EV charging activity so that electrical demand peaks can be avoided but charging activity is maximized. The avoidance of charging activity at or near peaks in electrical demand results in lower total electric costs associated with the charging process. The final design was implemented in an EV charging testbed at the University of Louisville and data was collected to verify the operation and performance of the PMCS. With a properly designed scheduling and prioritization control algorithm, increases in electrical demand and associated costs are limited to the error in the forecasting algorithm used for predicting electrical demand levels. The final design of the forecasting algorithm results in a mean absolute percent error of 0.02% to 0.08% in the electrical demand forecast. This corresponds to approximately 3 to 10 kVA of error in electrical demand. Taking this error into account, total cost of charging several EVs is reduced by nearly 90%. Furthermore, for scenarios where there are several more electric vehicles requiring charge than there are charging stations available, several scheduling algorithms are presented in an attempt to minimize the total processing time required for completing all charging transactions

Modelling dynamic demand response for plug-in hybrid electric vehicles based on real-time charging pricing

© The Institution of Engineering and Technology. Based on the benefits of real-time pricing both to individual users and the society as a whole, this study introduces a real-time charging price (RTCP) mechanism supported by an intelligent charging management module into plug-in hybrid electric vehicles (PHEVs) charging environment. The optimal RTCP is executed by a distributed algorithm using a utility model to maximise the whole charging system welfare. The willingness-to-charge parameter is derived to reflect the charging preferences of PHEV users and their different responses to the RTCP. Several scenarios are established to discuss the effect of both the RTCP and willingness-to-charge on charging load. The simulation results show that reasonable charging will be realised based on the optimal RTCP mechanism