According to the European Technology Platform for the Electricity Networks of the Future, a smart grid is an electricity network that can intelligently integrate the actions of all users connected to it - generators, consumers and those that do both - in order to efficiently deliver sustainable, economic and secure electricity supplies. The aim of this chapter is to give an overview of the recent and ongoing research activities that are focused on the smart grid domain. The international literature on this subject is particularly rich and growing. With this in mind, the motivation and key objective for the authors when preparing this chapter was an attempt to collate the distribution grid related research and innovation activities co-funded by the European Commission, in the context of the Horizon 2020 Work Programme 2014-2015 with activities co-funded by similar initiatives in the USA and Asian and Pacific countries. This material is accompanied by information about the recent past and the near future of smart grid research and innovation in Europe. On top of this mapping of facts and trends, this chapter presents the key objectives and functional characteristics of a set of information and communications technology (ICT) tools for the distribution grid that are being implemented in the context of SmarterEMC2, a collaborative project co-funded by the European Commission.

In this chapter, a survey of protocols for privacy-preserving data aggregation in smart metering systems is presented. They are necessary to protect customers'profiles from adversaries, who can predict customers' behaviour and manipulate them. The state of the art covers the entire cycle of a smart grid with real-time consolidated consumption, billing processes and verifications. However, the real-time consolidated consumption is public in the best protocol. In this work, we find an improved protocol that can hide real-time consolidated consumptions keeping them accessible only to the public utility. In addition, a complexity analysis of algorithms is presented and a simulation with millions of real-world measurements collected by thousands of smart meters is run to validate the theoretical evaluation. Furthermore, the improved protocol slightly outperforms the state of the art in the encryption algorithm, which runs in constrained smart meters. Moreover, such techniques can be applied in several other research areas.

This chapter presents, analyses, and compares three different smart measures to avoid such concentration effects and achieve more efficient system operation without centralised knowledge of residential appliances' characteristics. The design of these measures is customised to the specific operating properties of different types of flexible residential appliances, namely appliances with continuously adjustable power levels and appliances with shiftable cycles. Smart-charging electric vehicles (EV) and wet appliances (WA) with delay functionality are used as representative examples of these two types, due to significant level of expected penetration and flexibility potential.

Smart tariffs, as incentives to trigger DSR, play a critical role in the energy sector to exploit the huge resources from the customer side. Load shifting in response to appropriate pricing signals could produce energy cost savings and network investment deferral in addition to other benefits. This chapter investigates smart tariff design enabled by smart metering platform and its application to end customers. The variable ToU tariffs are designed based on energy price variations, which are then scaled to include network transportation costs. In detail, RTP tariffs are developed based on annual energy price variations, where two novel approaches are proposed to convert the tariffs into ToU tariffs. The two new approaches are equal interval grouping and hierarchical clustering. The developed tariffs by both approaches are for eight scenarios/days, i.e. weekdays and weekends in four seasons, to reflect the tariff diversity throughout a calendar year. The DSR to these tariffs are therefore able to reduce energy cost by moving part of energy consumption from periods with expensive energy, where demand is met by expensive generation units.

In this chapter, we describe a decentralized model of future grid, formulate a problem to maximize real-time renewable integration, and introduce approaches for solving the problem in a decentralized manner.

This chapter reviews the traditional power systems control structure and discusses how it could evolve to incorporate all the new controllable devices which will be available in the future, as well as cope with the potential difficulties these may create. Given the scale of these problems, we place particular emphasis on distributed solutions. We provide an overview of relevant approaches and put them into perspective with regards to when and how they could be applied. Rather than a rigorous mathematical treatment of the presented methods, we use simple, indicative, power system based formulations and examples to illustrate how they work, establish similarities between them, and identify the challenges - both in mathematics and implementation - that still lie ahead. For the reader interested in mathematical proofs or extended results regarding a particular method, we provide an adequate number of relevant bibliographic references.

This chapter proposes a framework for designing smart grid systems that considers multiple objectives. A grid is considered a combination of an electric grid and a network of transmission lines, substations, and transformers that delivers electricity from a power plant to consumers. A smart grid renders the grid efficient, provides a friendly environment for active grid participants, and improves the energy efficiency of the underlying power system. In a smart grid, it is desirable to optimize various objectives, such as minimizing the power consumption, maximizing the quality of service, and optimizing a stored energy level for emergency operations. To achieve these objectives, a multiobjective approach is investigated. First, objectives of the smart grid system are formulated as a multiobjective optimization problem (MOP). Multiobjective evolutionary algorithms are then employed to solve the MOP, yielding a set of approximate Pareto optimal solutions and an approximate Pareto front (APF). Based on the preference of a decision maker, the final solution is selected from among the obtained solutions according to the associated performance represented by the APF. A multiobjective approach to smart grid system designs is thus provided.

Balancing the active power between the generation side and the demand side to maintain the frequency is one of the main challenging problems of integrating the increased intermittent wind power to the smart grid. Although the energy storage system, such as battery energy storage system (BESS), has potential to solve this problem, the installation of the BESS with large capacity is limited by its high cost. This chapter investigates the frequency regulation of the smart grid working in the isolated mode with wind farms by introducing not only the BESS but also dynamic demand control (DDC) via controllable loads and the plug-in electric vehicles (PEVs) with vehicle-to-grid (V2G) service. First, modelling of a single-area load frequency control (LFC) system is obtained, which includes the wind farms equipped with variable-speed wind turbines, the simplified BESS, the air conditioner based DDC and the distributed PEVs. The LFC system contains traditional primary and supplementary control loops and three additional control loops of the BESS, the PEVs and the DDC, respectively. Then, state-space models of the closed-loop LFC scheme with/without communication delays in the control loops are derived, and the stability of the closed-loop system with time delays is investigated via the Lyapunov functional based method. Third, gains of proportional integral derivative (PID)-type controllers are tuned based on the H∞ performance analysis and the particle swarm optimization searching algorithm. Case studies are carried out for the single-area smart power grid through the MATLAB®/Simulink platform. Both the theoretical analysis and the simulation studies demonstrate the contribution of the DDC, the BESS, and the PEVs to frequency regulation, and the robustness of the designed PID-type LFC against the disturbances caused by the load changes and the intermittent wind power and the delays arising in the control loops via theoretical analysis and the simulation studies.

Reliable and efficient control of bus frequencies is crucial to the operation of any power grid. Classically, frequency control is implemented on the generation side via primary and secondary control schemes, with issues of optimality addressed at a much slower timescale via the optimal power flow (OPF) problem. In recent years, power systems have undergone significant changes, such as the liberalisation of the electricity markets, the introduction of new generation technologies, and the increased penetration of renewable energy sources. This expansion ofpower systems along with the stochastic nature of renewable energy inevitably lead to a need for faster, more efficient, and more reliable frequency control mechanisms. Furthermore, in a smart grid paradigm, frequency control schemes can be highly distributed due to the participation of the demand-side. Such approaches, incorporating control on both generators and loads, have the potential to reduce operational costs, improve system security, and increase the overall economic efficiency of the network's operation. In this chapter, we will discuss various approaches to distributed frequency control, paying particular attention to the incorporation of demand-side management and to the economic optimality ofthe schemes. We begin by describing the key concepts that will be considered.

For every entity not indifferent to or unaffected by changes in the environment in which it operates, any decision taken locally inevitably reflects the inherent characteristics as well as the influence of extrinsic parameters. In turn, the coexistence of various entities, possibly with conflicting goals, forms an environment in which each individual is called upon to act. The interrelation between the decision-making process of interacting entities formulates a complex environment, where the behavior of each entity influences and is influenced by the behavior of the rest. In this respect, game theory is considered particularly useful for describing situations such as these, as it offers the appropriate normative framework for giving form to problems that would have been otherwise difficult to cast in a mathematical manner. One category includes hierarchical decision-making, that corresponds to a Stackelberg (leader-follower) game, which can be described mathematically by a bilevel programming problem.

In this chapter, we looked into the problem of malicious FDIAs in power grid state estimation. We proposed stealth attack construction strategies for different scenarios and also introduced the countermeasures. The results demonstrate that the proposed random attack construction algorithm can generate extremely sparse attack vectors with high probabilities with consideration of the noise in measurements. Traditional successful attacks tend to compromise a number of measurements, which exceeds a certain value. The proposed algorithm can construct undetectable attacks which only compromise a much smaller numbers of measurements than this known value. The targeted attack construction method is evaluated considering different percentages of state variables are targeted and different number of measurements are protected. The results show that attack vectors in this scenario cannot be extremely sparse, unless only an extremely small number of state variables are targeted. It also demonstrates that targeted stealth attack vectors do not exist when a number of measurements are protected from being modified. An efficient protection scheme is proposed in this chapter to find an effective measurement protection subset to defend from the stealth attacks. The simulation results have demonstrated that the proposed algorithm can find protection subsets with the same size as that from brute-force method in nearly all cases. More importantly, the algorithm is quick, and thus feasible in practice when the power system is large. Additionally, a detection algorithm is introduced to detect the stealth attacks as well as other false data. This algorithm considers the case in which only partial measurements are collected in the presence of noise. The performance is demonstrated via the simulation results based on IEEE test power systems.

The European Marie Curie Project ADVANTAGE (Advanced Communications and Information processing in smart grid systems) was launched in 2014. It represents a major inter-disciplinary research project into the topic of Smart Grid technology. A key aspect of the project is to bring together and train 13 early stage researchers from the traditionally separate fields of power systems and communications engineering research. This chapter describes some of the initial research results that have arisen from the project and to highlight some of the key advances and developments that are being studied in the project. The major research focus of the ADVANTAGE project is on advancing technologies for the smart grid, providing architectural solutions and developing innovative information and communications technology (ICT) solutions to support its operation.

Data will become a strategic resource and even prime driving force for future grid. Essentially, rather than massive data themselves, we are much more interested in the potential contained in the data. In other words, how to mine the value from the 4Vs data (data with features of volume, variety, velocity, and veracity) within tolerant resources (time, hardware, human, etc.) is the key challenge. This chapter studies the methodology of applying big data analytics to power grids. First, the definition of big data and random matrix theories (RMTs), as well as related system mapping framework and data processing methods are introduced as foundations. Especially, some mathematical contents, such as random matrix models (RMMs), probability in high dimension, and linear eigenvalue statistics (LES), are discussed in detail. Then, a series of functions related to situation awareness (SA) of power grids, including early event detection (EED), fault diagnosis and location, correlation analysis, high-dimensional indicator system and its visualization (i.e., auxiliary 3D power-map), are developed as concrete applications. In this way, a typical data-driven methodology, mainly based on RMT, is proposed to cognize power grids. Three main procedures are essential: (1) big data model-to build the RMMs with raw data; (2) big data analysis-to conduct high-dimensional analyses to construct the indicator system via statistical transformations; and (3) engineering interpretation-to visualize and interpret the statistical results to human beings. This methodology is a more precise and natural way to gain insight into the large-scale interconnected systems. Furthermore, the indicator system will build a new epistemology to reveal the physical systems; it will open a new ear for the SA.

Balancing the smart grid is a continuous process that requires to equate the energy production with the consumption of electricity in the grid. Demand response can support the stability of the grid by incorporating the customer side. In order to communicate with the customers, standardized demand response protocols are used. However, the performance of such protocols varies based on the application, demand response strategy, and their tuning parameters. Therefore, this chapter presents a methodology for evaluating the performance of demand response protocols combined with a demand response strategy for the smart grid.

Information and communication technologies (ICTs) are playing an important role in the modernization of the power grid. Communication networks for the smart grid should be energy-efficient (EE), so that these extra associated networks themselves will not significantly increase energy consumption of the total grid. More importantly, communication devices should not have to be recharged frequently and ideally would keep working even during extreme situations such as blackouts. A significant amount of research efforts have been put into reducing energy consumption of networks powered by the smart grid. However, the impact of green ICTs on the smart grid communications and applications remains to be explored. In this chapter, we surveyed EE smart grid communication networks, which are divided into three domains: smart grid home area networks (SG-HANs), smart grid neighborhood area networks (SG-NANs), and smart grid wide area networks (SG-WANs). To the best of our knowledge, this is the first survey of EE smart communications networks. Due to the importance of SG-NANs, this chapter focuses on SG-NANs, where data aggregation units (DAUs) communicate with home gateways (HGWs). Moreover, a multicell orthogonal frequency-division multiple access (OFDMA) cellular network is proposed for an SG-NAN. In order to improve energy-efficiency of the SG-NAN, a distributed resource allocation scheme is proposed, which also takes HGW fairness and priority into consideration. Average data rates are considered, since they are more appropriate from the HGWs' perspective. The EE resource allocation with fairness optimization problem is transformed from a fractional to equivalent subtractive form, which is subsequently modeled as a non-cooperative game. Interference pricing functions are used to drive the Nash equilibrium (NE) to Pareto optimal. An EE resource allocation iterative algorithm is designed for the resource allocation optimization problem. Simulation results show the effectiveness of the proposed scheme on energy-efficiency and HGW fairness.