Abstract-Decentralized inverter control is essential in distributed generation (DG) microgrids for low deployment/operation cost and high reliability. However, decentralized inverter control suffers from a limited system stability mainly because of the lack of communications among different inverters. In this paper, we investigate stability enhancement of the droop based decentralized inverter control in microgrids. Specifically, we propose a power sharing based control strategy which incorporates the information of the total real and reactive power generation of all DG units. The information is acquired by a wireless network (such as a WiFi, ZigBee, and/or cellular communication network) in a decentralized manner. Based on the desired power sharing of each DG unit and the acquired information of total generation, additional control terms are added to the traditional droop controller. We evaluate the performance of the proposed control strategy based on smallsignal stability analysis. As timely communication may not be established for a microgrid with low-cost wireless communication devices, two kinds of analytical models are developed with respect to negligible and nonnegligible communication delays, respectively. Extensive numerical results are presented to demonstrate the system stability under the proposed control strategy with respect to different communication delays.

Abstract-As essential building blocks of the future smart grid, microgrids can efficiently integrate various types of distributed generation (DG) units to supply the electric loads at the minimum cost based on the economic dispatch. In this paper, we introduce a decentralized economic dispatch approach such that the optimal decision on power generation is made by each DG unit locally without a central controller.

Abstract-Navigating security and privacy challenges is one of the crucial requirements in the Vehicle-to-Grid (V2G) network. Since Electric Vehicles (EV) need to provide their private information to aggregators/servers when charging/discharging at different charging stations, privacy of the vehicle owners can be compromised if the information is misused, traced, or revealed. In a wide V2G network, where vehicles can move outside of their home network to visiting networks, security and privacy becomes even more challenging due to untrusted entities in the visiting networks. Although some privacy-preserving solutions were proposed in literature to tackle this problem, they do not protect against well-known security attacks and generate a huge overhead. Therefore, we propose a mutual authentication scheme to preserve privacy of the EV's information from aggregators/servers in the home as well as distributed visiting V2G networks. Our scheme, based on a bilinear pairing technique with an accumulator performing batch verification, yields higher system efficiency, defeats various security attacks, and maintains untraceability, forward privacy, and identity anonymity. Performance analysis shows that our scheme, in comparison with existing solutions, generates significantly lower communication and computation overheads in the home and centralized V2G networks, and comparable overheads in the distributed visiting V2G networks.

Abstract-The smart grid, as the next generation of the power grid, is characterized by employing many different types of intelligent devices, such as intelligent electronic devices located at substations, smart meters positioned in the home area network, and outdoor field equipment deployed in the fields. Also, there are various users in the smart grid network, including customers, operators, maintenance personnel, and etc., who use these devices for various purposes. Therefore, a secure and efficient mutual authentication and authorization scheme is needed in the smart grid to prevent various insider and outsider attacks on many different devices. In this paper, we propose an authentication and authorization scheme for mitigating outsider and insider threats in the smart grid by verifying the user authorization and performing the user authentication together whenever a user accesses the devices. The proposed scheme computes each user-role dynamically using an attribute-based access control and verifies the identity of user together with the device. Security and performance analysis show that the proposed scheme resists various insider as well as outsider attacks, and is more efficient in terms of communication and computation costs in comparison with the existing schemes. The correctness of the proposed scheme is also proved using BAN-Logic and Proverif.

The Internet-of-things (IoT) has been gradually paving the way for the pervasive connectivity of wireless networks. Due to the ability to connect a number of devices to the Internet, many applications of IoT networks have recently been proposed. Though these applications range from industrial automation to smart homes, healthcare applications are the most critical. Providing reliable connectivity among wearables and other monitoring devices is one of the major tasks of such healthcare networks. The main source of power for such low-powered IoT devices is the batteries, which have a limited lifetime and need to be replaced or recharged periodically. In order to improve their lifecycle, one of the most promising proposals is to harvest energy from the ambient resources in the environment. For this purpose, we designed an energy harvesting protocol that harvests energy from two ambient energy sources, namely radio frequency (RF) at 2.4 GHz and thermal energy. A rectenna is used to harvest RF energy, while the thermoelectric generator (TEG) is employed to harvest human thermal energy. To verify the proposed design, extensive simulations are performed in Green Castalia, which is a framework that is used with the Castalia simulator in OMNeT++. The results show significant improvements in terms of the harvested energy and lifecycle improvement of IoT devices.

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As an important component of smart grid, the vehicle-to-grid (V2G) system is recently introduced to enable bidirectional energy delivery between the power grid and plugin electric vehicles. Communication technology is incorporated to facilitate the energy delivery by providing electricity pricing and energy demand information. However, different from the stationary energy storage systems, the energy store-carry-anddeliver mechanism for a V2G system poses new challenges for performance optimization, such as bi-directional energy flow and non-stationary energy demand. How to utilize the statistical information provided by the communication system to achieve efficient energy delivery is critical for a V2G system and is still an open issue. In this paper, we address a specific problem in this new research area, i.e., daily energy cost minimization of vehicle owners under time-of-use (TOU) electricity pricing. We investigate a plug-in hybrid electric vehicle (PHEV) with a realistic battery model, which is general for both battery electric cars and plug-in hybrids. A dynamic programming formulation is established by considering the bidirectional energy flow, nonstationary energy demand, battery characteristics, and TOU electricity price. We prove the optimality of a state-dependent doublethreshold (or (S, S ′ )) policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average (EWMA) algorithm is used to estimate the statistics of PHEV mobility and energy demand. The performance of the proposed scheme is demonstrated by simulations based on survey and real data collected from Canadian households. Numerical results indicate that our proposed scheme performs closely to a scheme with a priori knowledge of the PHEV mobility and energy demand information. Compared with the existing approaches, the proposed scheme can achieve energy cost reduction, which increases with the battery capacity.

The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.