A multi-agent approach for enhancing transient stability of smart grids

Transient stability, an important issue to avoid the loss of synchronous operation in power systems, can be achieved through proper coordination and operation of protective devices within the critical clearing time (CCT). In view of this, the development of an intelligent decision support system is useful for providing better protection relay coordination. This paper presents an intelligent distributed agent-based scheme to enhance the transient stability of smart grids in light of CCT where a multi-agent framework (MAF) is developed and the agents are represented in such a way that they are equipped with protection relays (PRs). In addition to this, an algorithm is developed which assists the agents to make autonomous decision for controlling circuit breakers (CBs) independently. The proposed agents are responsible for the coordination of protection devices which is done through the precise detection and isolation of faults within the CCT. The agents also perform the duty of reclosing CBs after the clearance of faults. The performance of the proposed approach is demonstrated on a standard IEEE 39-bus test system by considering short-circuit faults at different locations under various load conditions. To further validate the suitability of the proposed scheme a benchmark 16-machine 68-bus power system is also considered. Simulation results show that MAF exhibits full flexibility to adapt the changes in system configurations and increase the stability margin for both test systems.

Integration of electric vehicles for load frequency output feedback H ∞ control of smart grids

This study considers a novel application of electric vehicles (EVs) to quickly help reheated thermal turbine units to provide the stability fluctuated by load demands. A mathematical model of a power system with EVs is first derived. This model contains the dynamic interactions of EVs and multiple network-induced time delays. Then, a dynamic output feedback H∞ controller for load frequency control of power systems with multiple time delays in the control input is proposed. To address the multiple time delays issue, a refined Jensen-based inequality, which encompasses the Jensen inequality, is used to derive less conservative synthesis conditions in terms of tractable linear matrix inequalities. A procedure is given to parameterise an output feedback controller to guarantee stability and H∞ performance of the closed-loop system. Extensive simulations are conducted to validate the proposed control method.

Distributed multi-agent scheme to enhance cyber security of smart power grids

This paper presents a distributed multi-agent scheme for enhancing the cyber security of smart grids which integrates computational resources, physical processes, and communication capabilities. Smart grid infrastructures are vulnerable to various cyber attacks and noises whose influences are significant for reliable and secure operations. A distributed agent-based framework is developed to investigate the interactions between physical processes and cyber activities where the attacks are considered as additive sensor fault signals and noises as randomly generated disturbance signals. A model of innovative physical process-oriented counter-measure and abnormal angle-state observer is designed for detection and mitigation against integrity attacks. Furthermore, this model helps to identify if the observation errors are caused either by attacks or noises.

Distributed multi-agent scheme to enhance cyber security of smart power grids

This paper presents a distributed multi-agent scheme for enhancing the cyber security of smart grids which integrates computational resources, physical processes, and communication capabilities. Smart grid infrastructures are vulnerable to various cyber attacks and noises whose influences are significant for reliable and secure operations. A distributed agent-based framework is developed to investigate the interactions between physical processes and cyber activities where the attacks are considered as additive sensor fault signals and noises as randomly generated disturbance signals. A model of innovative physical process-oriented counter-measure and abnormal angle-state observer is designed for detection and mitigation against integrity attacks. Furthermore, this model helps to identify if the observation errors are caused either by attacks or noises.

Fog computing: a cloud to the ground support for smart things and machine-to-machine networks

Cloud services to smart things face latency and intermittent connectivity issues. Fog devices are positioned between cloud and smart devices. Their high speed Internet connection to the cloud, and physical proximity to users, enable real time applications and location based services, and mobility support. Cisco promoted fog computing concept in the areas of smart grid, connected vehicles and wireless sensor and actuator networks. This survey article expands this concept to the decentralized smart building control, recognizes cloudlets as special case of fog computing, and relates it to the software defined networks (SDN) scenarios. Our literature review identifies a handful number of articles. Cooperative data scheduling and adaptive traffic light problems in SDN based vehicular networks, and demand response management in macro station and micro-grid based smart grids are discussed. Security, privacy and trust issues, control information overhead and network control policies do not seem to be studied so far within the fog computing concept.

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.

Power conversion technology in the smart grid and EV

The chapter presents a dynamical model and power conversion technology of electric vehicles (EVs) used in smart grids. The efficient power conversion of EVs in smart grids depends on the operation of bi-directional converters as these EVs need to be either charged or discharged. In this chapter, the mathematical model of a bi-directional converter used in EVs is developed and a nonlinear controller is designed to facilitate the power conversion in the smart grid environments. Since the power conversion of EVs in smart grids requires the communication, a nonlinear partial feedback linearising distributed controller based on the communication with different EVs is proposed to ensure high power quality and system stability.

Viable approaches for increasing the efficiency of buiding integrated photovoltaic systems

Nowadays, there is growing interest towards the area of building integrated photovoltaic (BIPV) systems and PV microgrids (MGs) in the field of power generation and distribution systems. This is mainly due to the higher adaptability and compatibility of these systems with preplanned sustainable development strategies in the most urban areas. The quiet operating process and movement free characteristic of photovoltaic systems brought them to the roof tops of the buildings in urban and rural areas and made them the most demanded means of renewable energy systems. This study highlights the problems affecting the performance and efficiency of BIPV systems and presents miscellaneous solutions and recommendations to solve these problems.

Renewable energy integration : challenges and solutions

This book presents different aspects of renewable energy integration, from the latest developments in renewable energy technologies to the currently growing smart grids. The importance of different renewable energy sources is discussed, in order to identify the advantages and challenges for each technology. The rules of connecting the renewable energy sources have also been covered along with practical examples. Since solar and wind energy are the most popular forms of renewable energy sources, this book provides the challenges of integrating these renewable generators along with some innovative solutions. As the complexity of power system operation has been raised due to the renewable energy integration, this book also includes some analysis to investigate the characteristics of power systems in a smarter way. This book is intended for those working in the area of renewable energy integration in distribution networks.

Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

On optimally reducing power loss in micro-grids with power storage devices

Smart micro-grids can produce 'renewable' energy and store them in power storage devices. Power loss, however, is a significant problem in power exchange among the micro-grids and between the macro-station and individual micro-grids. To optimally reduce the total power losses in such a power grid system, in this paper, a greedy coalition formation algorithm is proposed, which allows the macro-station to coordinate mutual power exchange among the micro-grids and between each micro-grid and the macro-station. Our algorithm optimizes the total power losses across the entire power grid, including the cost of charging and discharging power storage devices and power losses due to power transfers. The algorithm creates exchange pairs among the micro-grids, giving priority to pairs with higher power loss reduction per exchanged power unit. Through computer-based simulations, we demonstrate that the proposed approach significantly reduces the average power loss compared with the conventional noncooperative method. The simulations also demonstrate that the communications overhead of our proposal (due to negotiations aimed at forming coalitions) does not significantly affect the available communication resource. © 2014 IEEE.

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.