Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

Microgrid for SCU with Vehicle-to-Grid

Santa Clara University is a large loads in Santa Clara needed two finders and a maximum of over 8MW peak demand; however, this consumption will only increase as the student body and electric vehicles on campus continue to grow. To meet this rising demand in both a sustainable and environmentally friendly manner, we proposed and simulated a complete energy management system with cost analysis of energy savings of a microgrid capable of reducing the power supplied to Santa Clara University’s campus from the grid by 40% using renewable energy, vehicle-to-grid (V2G) functionality, and real SCU energy data. The project further used machine learning to match SCU’s energy demand with the renewable generation for future use of optimizing the proposed system. The microgrid was simulated in MATLAB while the machine learning algorithm was developed in python. The benefits of this project provide SCU with a path to 100% clean energy, increased power reliability, and reduced operating cost for SCU. Increasing solar output on campus is the best way to achieve 100% renewable energy because the fuel cells on campus have a byproduct of carbon dioxide and are therefore not 100% renewable. Our vehicle to grid analysis showed that it is not currently a viable solution to help SCU run on 100% renewable energy; however, as electric vehicle charging capacity at SCU increases, vehicle to grid could become an important part of SCU achieving carbon neutrality

Smart Procurement Of Naturally Generated Energy (SPONGE) for PHEV's

In this paper we propose a new engine management system for hybrid vehicles to enable energy providers and car manufacturers to provide new services. Energy forecasts are used to collaboratively orchestrate the behaviour of engine management systems of a fleet of PHEV's to absorb oncoming energy in an smart manner. Cooperative algorithms are suggested to manage the energy absorption in an optimal manner for a fleet of vehicles, and the mobility simulator SUMO is used to show simple simulations to support the efficacy of the proposed idea.Comment: Updated typos with respect to previous versio

Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services

In this paper, we study the expanding attack surface of Adversarial Machine Learning (AML) and the potential attacks against Vehicle-to-Microgrid (V2M) services. We present an anticipatory study of a multi-stage gray-box attack that can achieve a comparable result to a white-box attack. Adversaries aim to deceive the targeted Machine Learning (ML) classifier at the network edge to misclassify the incoming energy requests from microgrids. With an inference attack, an adversary can collect real-time data from the communication between smart microgrids and a 5G gNodeB to train a surrogate (i.e., shadow) model of the targeted classifier at the edge. To anticipate the associated impact of an adversary's capability to collect real-time data instances, we study five different cases, each representing different amounts of real-time data instances collected by an adversary. Out of six ML models trained on the complete dataset, K-Nearest Neighbour (K-NN) is selected as the surrogate model, and through simulations, we demonstrate that the multi-stage gray-box attack is able to mislead the ML classifier and cause an Evasion Increase Rate (EIR) up to 73.2% using 40% less data than what a white-box attack needs to achieve a similar EIR.Comment: IEEE Global Communications Conference (Globecom), 2022, 6 pages, 2 Figures, 4 Table