Identifying households with electrical vehicle for demand response participation.

• Electric Vehicle's usage has been disaggregated from energy meter's data. • EV's charging state as well as State of Charge (SOC) value has been estimated. • A Hidden Markov Model (HMM) based NILM algorithm has been used for this purpose. • Model probabilities were estimated using the Baum-Welch algorithm. • The state sequence was found out using the Viterbi algorithm. Due to a surge in the usage of Electric Vehicles (EV), the burden on the power grid utilities have increased in the recent past. But the time-shiftable nature of the charging process of these EVs allow them to participate in Demand Response Programs (DRP) which offers a whole new dimension of flexibility in a Power system. In order to identify the availability of EV in a household, the grid operator can employ the concerned household's consumption data that is available through the smart meter installed there. A Hidden Markov Model (HMM) based approach similar to Machine Learning has been proposed in this paper for identifying houses with EV. For operational purpose, this algorithm helps in desegregation of EV charging from smart meter's energy consumption data along with the determination of their charging/discharging state, and SOC estimation. This has been done in a non-intrusive way and is aimed towards the identification of time-flexible or shiftable loads for Demand Response (DR) participation. Data from the smart meters of over 1000 households have been taken into account at different sampling frequencies. Several of these households have also incorporated roof top PV to make them self-reliant. The practicality of the proposed approach is ascertained by the simulation results that are based on real-world situations. An error analysis has been performed to observe the effectiveness of this method. The scope of participation for the identified EVs in DR programs has been discussed in the end. [ABSTRACT FROM AUTHOR]