Application of Data Mining in Smart Grid Technology

Smart grid technology is a radical approach for improvisation in existing power grid. Some of the significant features of smart grid technology are bidirectional communication, AMI, SCADA, renewable integration, active consumer participation, distribution automation, and complete management of entire grid through wireless communication standards and technologies. Management of complex, hierarchical, and heterogeneous smart grid infrastructure requires data collection, storage, processing, analysis, retrieval, and communication for self-healing and complete automation. Data mining techniques can be an effective solution for smart grid operation and management. Data mining is a computational process for data analysis. Data scrutiny is unavoidable for unambiguous knowledge discovery as well as decision making practices. Data mining is inevitable for analysis of various statistics associated with power generation, distribution automation, data communications, billing, consumer participation, and fault diagnosis in smart power grid.

Economic appraisal of Power-to-Liquid Fischer-Tropsch plants exploiting renewable electricity, green hydrogen, and CO2 from biogas in Europe

The present work analyses the techno-economic potential of Power-to-Liquid routes to synthesize Fischer-Tropsch paraffin waxes for the chemical sector. The Fischer-Tropsch production unit is supplied with hydrogen produced by electrolysis and CO2 from biogas upgrading. In the analysis, 17 preferential locations were identified in Germany and Italy, where a flow of 1 t/h of carbon dioxide was ensured. For each location, the available flow of CO2 and the capacity factors for both wind and solar PV were estimated. A metaheuristic-based approach was used to identify the cost-optimal process design of the proposed system. Accordingly, the sizes of the hydrogen storage, electrolyzer, PV field, and wind park were evaluated. The analysis studied the possibility of having different percentage of electricity coming from the electric grid, going from full-grid to full-RES configurations. Results show that the lowest cost of Fischer-Tropsch wax production is 6.00 €/kg at full-grid operation and 25.1 €/kg for the full-RES solution. Wind availability has a key role in lowering the wax cost.

Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System with IoTs

<div>The electric grid operation is constantly threatened with natural disasters and cyber intrusions. The introduction of Internet of Things (IoTs) based distributed energy resources (DERs) in the distribution system provides opportunities for flexible services to enable efficient, reliable and resilient operation. At the same time, IoT based DERs comes with cyber vulnerabilities and requires cyber-power resiliency analysis of the IoT-integrated distribution system. This work focuses on developing metrics for monitoring resiliency of cyber-power distribution system, while maintaining consumers’ privacy. Here, resiliency refers to the system’s ability to keep providing energy to the critical load even with adverse events. In the developed cyber-power Distribution System Resiliency (DSR) metric, the IoT Trustability Score (ITS) considers the effects of IoTs using a neural network with federated learning. ITS and other factors impacting resiliency are integrated into a single metric using Fuzzy Multiple-Criteria Decision Making (F-MCDM) to compute Primary level Node Resiliency (PNR). Finally, DSR is computed by aggregating PNR of all primary nodes and attributes of distribution level network topology and vulnerabilities utilizing game-theoretic Data Envelopment Analysis (DEA) based optimization. The developed metrics will be valuable for i) monitoring the distribution system resiliency considering a holistic cyber-power model; ii) enabling data privacy by not utilizing the raw user data; and iii) enabling better decision-making to select the best possible mitigation strategies towards resilient distribution system. The developed ITS, PNR, and DSR metrics have been validated using multiple case studies for the IoTs-integrated IEEE 123 node distribution system with satisfactory results.</div>

Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System with IoTs

<div>The electric grid operation is constantly threatened with natural disasters and cyber intrusions. The introduction of Internet of Things (IoTs) based distributed energy resources (DERs) in the distribution system provides opportunities for flexible services to enable efficient, reliable and resilient operation. At the same time, IoT based DERs comes with cyber vulnerabilities and requires cyber-power resiliency analysis of the IoT-integrated distribution system. This work focuses on developing metrics for monitoring resiliency of cyber-power distribution system, while maintaining consumers’ privacy. Here, resiliency refers to the system’s ability to keep providing energy to the critical load even with adverse events. In the developed cyber-power Distribution System Resiliency (DSR) metric, the IoT Trustability Score (ITS) considers the effects of IoTs using a neural network with federated learning. ITS and other factors impacting resiliency are integrated into a single metric using Fuzzy Multiple-Criteria Decision Making (F-MCDM) to compute Primary level Node Resiliency (PNR). Finally, DSR is computed by aggregating PNR of all primary nodes and attributes of distribution level network topology and vulnerabilities utilizing game-theoretic Data Envelopment Analysis (DEA) based optimization. The developed metrics will be valuable for i) monitoring the distribution system resiliency considering a holistic cyber-power model; ii) enabling data privacy by not utilizing the raw user data; and iii) enabling better decision-making to select the best possible mitigation strategies towards resilient distribution system. The developed ITS, PNR, and DSR metrics have been validated using multiple case studies for the IoTs-integrated IEEE 123 node distribution system with satisfactory results.</div>

Research on Charging-Discharging Operation Strategy for Electric Vehicles Based on Different Trip Patterns for Various City Types in China

Connecting large numbers of electric vehicles to the power grid creates challenges for the operation of the power distribution network, but also provides a new method for supporting grid operation. This paper considers the trip patterns of electric vehicle users in China, including their trip starting time, traffic congestion, vehicle energy consumption, and other factors. We develop a charging–discharging operation strategy for electric vehicles in different functional areas with the goal of minimizing the cost of distribution network, which considers the distribution patterns of electric vehicles in different functional areas. As different types of cities in China have different proportions of electric vehicle users who follow different travel chains, we provide multiple examples showing the effectiveness of our proposed V2G method in different cities.

Economic and Environmental Benefits for Electricity Grids from Spatiotemporal Optimization of Electric Vehicle Charging

This article addresses the problem of estimating the potential economic and environmental gains for utility grids of shifting the electric-vehicle (EV) charging time and location. The current literature on shifting EV charging loads has been limited by real-world data availability and has typically therefore relied on simulated studies. Collaborating with a large automobile company and a major utility grid operator in California, this research used actual EV operational data and grid-operation data including locational marginal prices, marginal-grid-emission-rate data, and renewable-energy-generation ratio information. With assumptions about the future potential availability of EV charging stations, this research estimated the maximum potential gains in the economic and environmental performance of the electrical-grid operation by optimizing the time and location of EV charging. For the problem of rescheduling the charging sessions, the optimization models and objective functions were specifically designed based on the information available to the energy system operators that influence their economic and environmental performance like grid congestion, emissions, and renewable energy. The results present the maximum potential in reducing the operational costs and the marginal emissions and increasing the renewable energy use in the utility grid by rescheduling the EV charging load with respect to its time and location. The analysis showed that the objective functions of minimizing the marginal cost or the marginal emission rate performed the best overall.