Smart grid

All over the world, electrical power systems are encountering radical change stimulated by the urgent need to decarbonize electricity supply, to swap aging resources and to make effective application of swiftly evolving information and communication technologies (ICTs). All of these goals converge toward one direction; ‘Smart Grid.’ The Smart Grid can be described as the transparent, seamless, and instantaneous two-way delivery of energy information, enabling the electricity industry to better manage energy delivery and transmission and empowering consumers to have more control over energy decisions. Basically, the vision of Smart Grid is to provide much better visibility to lower-voltage networks as well as to permit the involvement of consumers in the function of the power system, mostly through smart meters and Smart Homes. A Smart Grid incorporates the features of advanced ICTs to convey real-time information and facilitate the almost instantaneous stability of supply and demand on the electrical grid. The operational data collected by Smart Grid and its sub-systems will allow system operators to quickly recognize the best line of attack to protect against attacks, susceptibility, and so on, sourced by a variety of incidents. However, Smart Grid initially depends upon knowing and researching key performance components and developing the proper education program to equip current and future workforce with the knowledge and skills for exploitation of this greatly advanced system. The aim of this chapter is to provide a basic discussion of the Smart Grid concept, evolution and components of Smart Grid, environmental impacts of Smart Grid and then in some detail, to describe the technologies that are required for its realization. Even though the Smart Grid concept is not yet fully defined, the chapter will be helpful in describing the key enabling technologies and thus allowing the reader to play a part in the debate over the future of the Smart Grid. The chapter concludes with the experimental description and results of developing a hybrid prediction method for solar power which is applicable to successfully implement the ‘Smart Grid.’

Empowering the smart grid : can redox batteries be matched to renewable energy systems for energy storage?

The charging of an undivided cerium–zinc redox battery by various current waveforms some of which mimic the output of renewable energy (solar, wind, tidal, biofuel burning) to electricity transducers is considered in this work, where the battery operates through diffusion-only conditions, and is discharged galvanostatically. Under reasonable assumption, the mathematical model developed enables the observation that the performance characteristic of the cells charged with a constant power input differentiates between the various current–charge waveforms, with cell geometry and electrode kinetics playing subtle, but significant, roles; in particular, high efficiency is observed for sunlight-charged batteries which are thin and suffer no corrosion of the sacrificial electrode, and which have already experienced a charge–discharge cycle. The performance characteristics of the systems are interpreted in the light of consequences for smart grid realisation, and indicate that, for a constant power input, the most matched renewable is biofuel burning with a current output that linearly increases with time.

A novel distributed algorithm for power loss minimizing in Smart Grid

Because power generation of renewable resources are unstable and demands of the customers are time-varying, the supply power and demands of the customers are always unequal. To meet the demands of the customers, power is transmitted from primary power generation to secondary power generation. It will cause high power loss. To solve this problem, a distributed algorithm is proposed in this paper. By using the algorithm, the micro-grids are able to exchange power with their neighbors so as to minimize the total power losses of the smart grid. Moreover, communication overhead (bandwidth) is reduced, comparing with centralized algorithm. Through computer simulations, we demonstrate that the proposed algorithm can lead to near-optimal result for alleviating the average power loss per micro-grid and reduce the communication overhead significantly in contrast with the centralized approach.

Agent-based smart grid protection and security

The vision of a smart grid is to provide a modern, resilient, and secure electric power grid as it boasts up with a highly reliable and efficient environment through effective use of its information and communication technology (ICT). Generally, the control and operation of a smart grid which integrate the distributed energy resources (DERs) such as, wind power, solar power, energy storage, etc., largely depends on a complex network of computers, softwares, and communication infrastructure superimposed on its physical grid architecture facilitated with the deployment of intelligent decision support system applications. In recent years, multi-agent system (MAS) has been well investigated for wide area power system applications and specially gained a significant attention in smart grid protection and security due to its distributed characteristics. In this chapter, a MAS framework for smart grid protection relay coordination is proposed, which consists of a number of intelligent autonomous agents each of which are embedded with the protection relays. Each agent has its own thread of control that provides it with a capability to operate the circuit breakers (CBs) using the critical clearing time (CCT) information as well as communicate with each other through high speed communication network. Besides physical failure, since smart grid highly depends on communication infrastructure, it is vulnerable to several cyber threats on its information and communication channel. An attacker who has knowledge about a certain smart grid communication framework can easily compromise its appliances and components by corrupting the information which may destabilize a system results a widespread blackout. To mitigate such risk of cyber attacks, a few innovative counter measuring techniques are discussed in this chapter.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

GTES: An optimized game-theoretic demand-side management scheme for smart grid

Demand-side management in smart grids has emerged as a hot topic for optimizing energy consumption. In conventional research works, energy consumption is optimized from the perspective of either the users or the power company. In this paper, we investigate how energy consumption may be optimized by taking into consideration the interaction between both parties. We propose a new energy price model as a function of total energy consumption. Also, we propose a new objective function, which optimizes the difference between the value and cost of energy. The power supplier pulls consumers in a round-robin fashion and provides them with energy price parameter and current consumption summary vector. Each user then optimizes his own schedule and reports it to the supplier, which, in turn, updates its energy price parameter before pulling the next consumers. This interaction between the power company and its consumers is modeled through a two-step centralized game, based on which we propose our game-theoretic energy schedule (GTES) method. The objective of our GTES method is to reduce the peak-to-average power ratio by optimizing the users' energy schedules. The performance of the GTES approach is evaluated through computer-based simulations. © 2014 IEEE.

Constrained coordinated distributed control of smart grid with asynchronous information exchange

Smart grid constrained optimal control is a complex issue due to the constant growth of grid complexity and the large volume of data available as input to smart device control. In this context, traditional centralized control paradigms may suffer in terms of the timeliness of optimization results due to the volume of data to be processed and the delayed asynchronous nature of the data transmission. To address these limits of centralized control, this paper presents a coordinated, distributed algorithm based on distributed, local controllers and a central coordinator for exchanging summarized global state information. The proposed model for exchanging global state information is resistant to fluctuations caused by the inherent interdependence between local controllers, and is robust to delays in information exchange. In addition, the algorithm features iterative refinement of local state estimations that is able to improve local controller ability to operate within network constraints. Application of the proposed coordinated, distributed algorithm through simulation shows its effectiveness in optimizing a global goal within a complex distribution system operating under constraints, while ensuring network operation stability under varying levels of information exchange delay, and with a range of network sizes.