Introduction

It is well known that the existing power grid represents a critical asset essential for the functioning and welfare of modern society. A movement to a smarter power grid promises to enable greater energy delivery, reliability, and efficiency. It also represents a critical foundation for reducing greenhouse gas emissions and transitioning to a lowcarbon economy. The evolution from today's power grid to a smarter grid is only possible through greater dependency on information technology.

There are currently many working definitions for a smart grid. The North American Reliability Corporation (NERC) has defined the smart grid as ‘the integration of realtime monitoring, advanced sensing, and communications, utilizing analytics and control, enabling the dynamic flow of both energy and information to accommodate existing and new forms of supply, delivery, and use in a secure and reliable electric power system, from generation source to end-user’. The movement towards cyber-enabled power systems increases the risk of attacks on information devices and communications systems for several reasons.

From an engineering perspective, there is increased opportunity for cyber attack in a smart grid because of the greater reliance on distributed advanced metering infrastructure (AMI), intelligent electronic devices (IEDs), and wireless and/or off-the-shelf communications components and systems. Such cyber infrastructure increases system connectivity and autonomous decision-making by employing standardized information protocols that often have (or will have in the future) publicly documented vulnerabilities. Motivations for attack also abound.

Introduction

As a core critical infrastructure, the national electric grid is at a crossroads, with modernization efforts driven by advanced cyber-system capabilities on the one hand and risks from cyber attack on the other. All stakeholders are concerned about these risks and view the need to incorporate resilience and adequate cyber security measures into the grid as crucial to all modernization efforts. The Federal Energy Regulatory Commission (FERC) recently released its policy statement [1] on smart grid technologies, which identified cyber security as one of two key priority areas.

As tasked by the Energy Independence and Security Act of 2007, the National Institute of Standards and Technology (NIST) is leading a major effort to develop a comprehensive framework for interoperability in smart grid. In their preliminary Roadmap for Interoperability, the development of a cyber security risk-management framework was identified as a major challenge. The ongoing roadmap to secure energy delivery systems [2] is another example of an important public/private dialogue that has identified milestones and goals for achieving resilience, such as designing, installing, operating, and maintaining control systems by 2015 that can survive an intentional cyber attack without loss of critical function. In addition, over $9 billion has been committed by the electric sector and the Department of Energy as part of ARRA (American Recovery and Reinvestment Act) recovery investment efforts on modernization of the grid, with cyber security being an important focus. This investment offers opportunities and challenges in realizing a resilient electric grid for the future.

Introduction

Demand-side management (DSM) is one of the key components of the future smart grid to enable more efficient and reliable grid operation [1]. To achieve a high level of reliability and robustness in power systems, the grid is usually designed for peak demand rather than for average demand. This usually results in an under-utilized system. To remedy this problem, different programs have been proposed to shape the daily energy consumption pattern of the users in order to reduce the peak-to-average ratio in load demand and use the available generating capacity more efficiently, avoiding the installation of new generation and transmission infrastructures. However, the increasing expectations of the customers both in quantity and quality [2], emerging new types of demand such as plug-in hybrid electric vehicles (PHEVs), which can potentially double the average household energy consumption [3], the limited energy resources, and the lengthy and expensive process of exploiting new resources give rise to the need for developing some more advanced methods for DSM.

Since electricity cannot be stored economically, wholesale prices (i.e., prices set by competing generators to regional electricity retailers) vary drastically between the low-demand times of day and the high-demand periods. However, these changes are usually hidden from retail users. That is, end users are usually charged with some average price. To alleviate this problem, various time-differentiated pricing methods have been proposed in the literature. Some examples include day-ahead pricing, time-of-use pricing, critical-peak-load pricing, and adaptive pricing [4–7].

By equipping users with two-way communication capabilities in smart grid systems and by adopting real-time pricing (RTP) methods, it is possible to reflect the fluctuations of wholesale prices to retail prices.

Introduction

There is a growing communication and computation infrastructure in support of the transfer of electrical energy in both the high-voltage (HV) transmission network and the medium and low-voltage (MV/LV) distribution side. Rather than passively witnessing this trend, research efforts are ongoing to study systematically how information architectures can renew and advance power systems. They go under the umbrella of smart grid.

To advance this field, it is useful to understand why and in what form this information infrastructure has come about in the first place, and what challenges are intrinsic in the network design problem. Then, we can start questioning if new information networks can be a game changer in the energy sector, and can contribute, in more fundamental ways, to advance power-delivery systems. Can cheap information bits and computation flops make greener and cheaper joules flow in the system? That is the question. Some argue that a positive answer may amount to no less than ensuring prosperity for our species [1]. Clearly, the attention on bits and flops cannot replace other parallel investigations. But this research deserves some of the spotlight, along with carbon capture, nuclear fusion and other similarly motivated scientific quests centred around sustainable electrical energy systems.

The aim of this chapter is to envision what possible evolution of the power grid cyber-physical system can address the important issue of scaling up the generation capacity of the system, while relying increasingly on green energy and increasing the transmission efficiency.

Introduction

A wide-area measurement system (WAMS) consists of advanced measurement technology, the latest communication network infrastructure, and integrated operational framework. The supervisory control and data acquisition (SCADA) infrastructure for energy-management system (EMS) has been widely used in power systems for a long time. Some of the functionalities of an EMS are system state monitoring, tie-line bias control, and economic dispatch [1]. However, in recent years, various deficiencies of the existing SCADA-based EMS (such as quasi-steady-state calculation, non-synchronized data acquisition, and relatively low data transmission rate) have been pointed out. These defects make it impossible to sample the global state of a power system in real time. As more and more wide-area blackouts are reported, it is clear that acquiring real-time or wide-area state information would be needed in the future. The state information in terms of phasors of voltages and currents from a distributed wide area in real time is therefore critical for avoiding large-area disturbances by effecting wide-area control based on wide-area measurements.

The main enabler of WAMS is phasor measurement unit (PMU) technology. With the innovation of PMU, the problem of measuring the phasor quantities simultaneously from a wide area of distributed substations, also called ‘synchrophasor’, has been solved. At present, the PMU technology is one of the essential enablers for WAMS. It utilizes the availability of high-precision synchronized clock sources – extracted from global positioning system (GPS) receivers and samples the instantaneous analogue – quantities of voltage and current magnitudes and phase angles.

Introduction

The electrical grid is a critical infrastructure that could have a major impact on human lives, economics, and politics [1]. Hence, any instabilities related to the structural and operational characteristics of the existing power grid, equipment failures, blackouts, poor communication, and lack of effective monitoring of the infrastructure, create additional challenges to the power utilities due to the prospect of vast economic losses, customer dissatisfaction, inefficient electricity usage, and the huge amount of CO2 emissions. The rising costs of new infrastructure and maintaining existing ones, increasing energy demand, and a declining number of skilled personnel, will drive utilities to operate systems more dynamically and efficiently. The need for real-time monitoring and management of transmission and distribution systems will become increasingly important. Such systems can be realized by the utilization of various types of sensors, and possibly actuators (actors).

Sensor systems will help provide the required information to utilities to achieve the goal of dynamic efficiency. The real-time information acquired from these sensors can be analysed to diagnose problems early, serve as a basis for taking remedial action, and thereby reduce service outages. This will reduce lost revenue and minimize person hours required to locate and rectify faults. For example, the Northeast blackout of 2003 in the United States was widespread and adversely affected over 50 million people with a huge economic loss [2]. There are many components of the electrical grid that must be monitored by advanced sensor technologies based on a combination of measurements, such as voltage, current, overhead conductor sag, temperature profile of conductors, power quality disturbances, system frequency, etc. [3–6].

Introduction

The beginning of the 2010s witnessed the rapid development of smart grid. The smart grid initiative (or similar concepts such as Intelligrid, utility of the future, and the Future- Grid) [1–5] is an attempt to modernize the current power grid with digital technology for communication, computing, and control to improve overall performance and to accommodate a high penetration of alternative energy sources and load responses. Among the many objectives related to smart grid, a key goal is to improve the electricity service to all end consumers such as residential houses, commercial buildings, and industrial loads. This calls for two-way communication in smart grid, i.e., power consumption/demand reports from the users to the centre (uplink) and price information from the centre to the users (downlink). Hence, the communication infrastructure for the power market is receiving intensive study, which also brings about a paradigm shift in the communities of communications and networking. Among many proposals for the communication infrastructure, wireless communication is a promising one due to its low cost, large coverage, and fast deployment [6–9].

Although the communication infrastructure can considerably improve the efficiency of the power market, it also brings significant vulnerabilities since malicious users can attack the communication system and thus cause various damages to the smart grid, or even result in large-area blackout. Hence, the security issue is of first priority in the study of smart grid and has attracted substantial attention in industry and academia.

Introduction

Supervisory control and data acquisition (SCADA) systems are widely used to monitor and control large-scale transmission power grids. Monitoring traditionally involves the measurement of voltage magnitudes and power flows; these data are collected by meters located in substations. In order to deliver the measured data from the substations to the control centre, the measurement data measured by meters in the same substation are multiplexed by a remote terminal unit (RTU) [1, 2]. Because electric power transmission systems extend over large geographical areas, typically entire countries, wide-area networks (WANs) are used to deliver the multiplexed measurement data from the substations to the control centre.

For large-scale transmission grids it is often not feasible to measure all power flows and voltages of interest. Furthermore, the measurements are often noisy. Therefore the measurement data are usually fed into a model-based state estimator (SE) at the control centre, which is used to estimate the complete physical state (complex bus voltages) of the power grid. The SE is used to identify faulty equipment and corrupted measurement data through the so-called bad-data detection (BDD) system. Apart from BDD, the state estimate is used by the human operators and by the energy-management systems (EMS) found in modern SCADA systems, such as optimal power flow analysis, and contingency analysis (CA), see for example [1]. Future power grids will be even more dependent on accurate state estimators to fulfil their task of optimally and dynamically routing power flows, because clean renewable power generation tends to be less predictable than nonrenewable power generation.

Shading indicates the ‘best’ vehicle architecture with the darkest shading and the ‘worst’ architecture with the lightest shading for each criterion. Please note that this appendix provides representative results that can be used to compare engine and hybridisation technologies; the MCDA algorithm may select vehicles that are not necessarily the best in this table because it has access to the full design set.

The analyses presented in this book have resulted in several overarching conclusions, which may be briefly summarised as follows:

• Increasing transportation energy security and reducing the environmental impacts of personal vehicles are important high-level goals.

• To achieve these goals, there is no single solution or ‘silver bullet’ pathway.

• Hydrogen will only represent an attractive transportation fuel to meet stringent CO2 reduction targets if it is produced from primary energy resources with zero or low CO2 emissions in the future energy system.

• The paradigm shift that is needed to make the transition from the current, problematic global transportation system will be led by the example of visionary communities, groups, and countries, using innovations that will necessarily be created by the developed world.

• Both short- and long-term policy measures are needed to support the development of future mobility technology, in order to internalise costs and provide a pathway to clean transportation.

In addition, the following themes are central to the role that hydrogen can play as a transportation fuel and have been explored throughout this book:

• The life cycle impacts of the full hydrogen fuel cycle were compared with conventional fuels.

• Changes in the hydrogen emissions due to transport were analysed, along with their effects on atmospheric chemistry.

• The ‘optimum’ design of a vehicle for any single buyer depends strongly on both the desired mix of performance and utility and on the available fuels. A multi-criteria methodology for an individualised assessment of vehicle design options has been developed.

• Introducing hydrogen will require profound changes in the transportation system. The dynamics of the way innovations diffuse into transportation markets was therefore studied, considering barriers, opportunities, and feedbacks.

• Energy–economic models have been used to generate scenarios maximising inter-generational welfare. Resource scarcity and climate protection goals were identified as important drivers promoting changes in the transportation system.

Introduction

Deep cuts in global GHG emissions are required to keep the average increase in global temperature below 2°C (IPPC, 2007c; Sokolov et al., 2009). A strong need for action concerning road traffic as a main originator of GHG emissions through their almost exclusive use of fossil energy carriers is acknowledged (Ribeiro et al., 2007), as discussed in previous chapters. In this chapter, we analyse which technological drivetrain pathways and deployment strategies are required to meet the long-term, global challenge posed by climate change. We implicitly assume that the required technology change must be governed and managed by concerted decision making of entrepreneurial and political leaders. Many policy studies address the mid-term impact of incremental, energy-efficiency improvements of conventional ICE, or hybrid technologies and biofuels (Bandivadekar et al., 2008b; Hankey and Marshall, 2009; Meyer and Wessely, 2009). However, a longer term view on fleet dynamics addressing not-yet-regulated policy measures is missing from the literature. Also, most of the fleet models do not discuss the impact of preference changes on the diffusion process of alternative drivetrain technologies and the effectiveness of corresponding policy measures (Greene et al., 2007; McCollum and Yang, 2009; Thomas, 2009).

In this chapter, we present simulation experiments that illustrate the impact of vehicle technology purchase preferences. The system dynamics fleet model developed and used, therefore, offers a long-term view for the EU, addressing the question of what kind of drivetrain technologies have the potential to meet the scientifically indicated, GHG emission targets. Illustrative diffusion scenarios and the CO2 impact of competing drivetrain technologies, including advanced ICE, hybrid technologies, and vehicles fuelled with gaseous fuel (i.e. LPG, CNG) as well as near zero-emission vehicles (i.e. renewable HFCV) are presented.