Biblio

Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form of tampering with EV battery's State of Charge (SOC) was explored. A Back Propagation (BP) Neural Network (NN) was trained and tested based on experimental data to estimate SOC of battery under normal operation and cyber-attack scenarios. NeuralWare software was used to run scenarios. Different statistic metrics of the predicted values were compared against the actual values of the specific battery tested to measure the stability and accuracy of the proposed BP network under different operating conditions. The results showed that BP NN was able to capture and detect the false entries due to a cyber-attack on its network.

The United States and European Union have an increasing number of projects that are engaging end-use devices for improved grid capabilities. Areas such as building-to-grid and vehicle-to-grid are simple examples of these advanced capabilities. In this paper, we present an innovative concept study for a ship-to-grid integration. The goal of this study is to simulate a two-way power flow between ship(s) and the grid with GridLAB-D for the port of Kyllini in Greece, where a ship-to-shore interconnection was recently implemented. Extending this further, we explore: (a) the ability of ships to meet their load demand needs, while at berth, by being supplied with energy from the electric grid and thus powering off their diesel engines; and (b) the ability of ships to provide power to critical loads onshore. As a result, the ship-to-grid integration helps (a) mitigate environmental pollutants from the ships' diesel engines and (b) provide resilience to nearby communities during a power disruption due to natural disasters or man-made threats.