Optimizing Electricity Market Bidding Strategies Through Deep Learning: Case Study of Nepal’s Energy Exchange Trends and Market Dynamics

Ensuring a real-time balance between electricity demand and supply is crucial for grid safety. Pre-trading in energy markets can cause significant imbalances, leading to the introduction of Real-Time Market (RTM) mechanisms and penalty-based Deviation Settlement Mechanisms (DSM) for deviations from scheduled energy transactions. Nepal’s involvement in the Day-Ahead Market (DAM) of the Indian Energy Exchange (IEX) showcases regional integration but also results in substantial deviation charges due to reliance on intermittent Run-off-River (RoR) hydropower, inaccurate load forecasting, and inefficient bidding strategies. Minimizing these charges necessitates exploring RTM participation and optimal bidding strategies. Accurate load forecasting is vital for resource management, cost minimization, grid stability, and regulatory compliance. Market participants in the IEX must predict their loads in 15-minute intervals with high accuracy to avoid penalties and maintain reliability. This paper addresses load forecasting and RTM trading challenges in Nepal’s energy market by proposing advanced deep learning models. A Deep Neural Network (DNN) model is recommended for DAM bidding, and a Long Short-Term Memory (LSTM) model for RTM bidding and HeADR algorithm is presented for optimal bidding. Our analysis shows that these models significantly improve forecasting accuracy: DNN achieves 96% for generation, 90% for load, 92% for Market Clearing Price (MCP), and 87% for Tanakpur import/export. Accurate DAM forecasts reduce deviation charges, while RTM trading offers flexibility for unforeseen events, enhancing grid stability. The LSTM model achieves 98.62% accuracy for Tanakpur, 98.9% for Dhalkebar import/export, and 92.06% for RT MCP, the HeADR algorithm results in 85% savings compared to actual.