Clustering of Usage Profiles for Electric Vehicle Behaviour Analysis

Accurately predicting the behaviour of electric vehicles is going to be imperative for network operators. In order for vehicles to participate in either smart charging schemes or providing grid services, their availability and charge requirements must be forecasted. Their relative novelty means that data concerning electric vehicles is scarce and biased, however we have been collecting data on conventional vehicles for many years. This paper uses cluster analysis of travel survey data from the UK to identify typical conventional vehicle usage profiles. To this end, we determine the feature vector, introduce an appropriate distance metric, and choose a number of clusters. Five clusters are identified, and their suitability for electrification is discussed. A smaller data set of electric vehicles is then used to compare the current electric fleet behaviour with the conventional one

Improving the Effect of Electric Vehicle Charging on Imbalance Index in the Unbalanced Distribution Network Using Demand Response Considering Data Mining Techniques

With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25 and 75 for electric vehicles in the network, charging/discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects