Rajasthan Issues Tender For AI-Based Energy Portfolio Management
The implementation agency is expected to provide end-to-end demand and renewable energy forecasting from intra-day to long-term horizons with block-level accuracy and perform least-cost portfolio optimisation across conventional generation, renewable energy, bilateral contracts and power exchanges to reduce deviation settlement mechanism charges and procurement costs. It will also offer round-the-clock operational support for participation in electricity markets including the Day-Ahead Market (DAM), the Real-Time Market (RTM), the Term-Ahead Market (TAM) and the Green Day-Ahead Market (GDAM), covering bid strategy, submission, approvals and monitoring.
The platform will deploy artificial intelligence and machine learning based scheduling and dispatch optimisation that accounts for technical, transmission, regulatory and market constraints alongside real-time DSM monitoring and deviation analytics. The tender calls for block-wise power availability assessment across central and state generating stations, independent power producers, captive plants and renewable sources to manage surplus and deficit situations. The partner will be required to provide market intelligence and price forecasting including analysis of market-clearing price trends and volatility with advisory services for bilateral and medium-term power procurement.
The project includes the development of a centralised data platform integrating Supervisory Control and Data Acquisition (SCADA), load dispatch centres, weather data, power exchanges and contract systems, with IT infrastructure cloud-hosted in India and compliant with MeitY empanelment and CERT-In cybersecurity requirements. RUVITL has invited bidders and stakeholders to provide feedback during the pre-bid meeting, reflecting a move to replace manual and fragmented portfolio planning. The initiative is intended to shift state-level power management from administrative practice to analytical, market-based portfolio management that other states may replicate.