Participation of Nepal in Real Time Market in Indian Energy Exchange: Analysis of Optimal Bidding Strategies using Machine Learning
Date
2024-06
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E
Abstract
Precise load prediction is vital for electric utilities, as it provides crucial information
for scheduling power generation, managing energy distribution, and optimizing
resources. Nepal Electricity Authority, is currently participating in Indian Energy
Exchange’s (IEX) Day Ahead Market (DAM) for the import/export of electricity
which is itself a huge milestone for Nepal but is also required to forecast their
loads in 15-minute intervals a day in advance with high accuracy to bid in the energy
market. To discourage excessive power drawl or insufficient power injection, a
frequency-based component called the deviation settlement charge (DSM) is incorporated
into the bulk electricity pricing in the IEX market. Due to in accurate load
forecasting, weather changes, holidays, Nepal has also been subjected to deviation
charges to account for any deviations from the agreed-upon energy transactions.
RTM provide a mechanism for balancing the fluctuations in supply and demand by
allowing market participants to adjust their electricity purchases or sales in response
to changing conditions. It also provides more flexibility and responsiveness compared
to day-ahead markets, which can help grid operators manage their systems more
effectively. Therefore, RTM bidding in addition to Day Ahead Market bidding is of
paramount importance to handle this deviation.
Description
Precise load prediction is vital for electric utilities, as it provides crucial information
for scheduling power generation, managing energy distribution, and optimizing
resources. Nepal Electricity Authority, is currently participating in Indian Energy
Exchange’s (IEX) Day Ahead Market (DAM) for the import/export of electricity
which is itself a huge milestone for Nepal but is also required to forecast their
loads in 15-minute intervals a day in advance with high accuracy to bid in the energy
market.
Keywords
Real Time Market, Energy Exchange, Machine Learning