Construction and Application of a Decision Support Platform for Electricity Spot Trading Based on Multi-source Information Fusion

With the deepening of electricity market-oriented reforms, electricity spot trading has become a critical avenue for power generation companies to participate in market competition and enhance economic efficiency. However, traditional trading decision-making processes suffer from issues such as information silos, difficulties in data integration, and reliance on manual experience, which constrain enterprises' ability to respond rapidly and make precise decisions. Building upon the previously developed automatic generation dispatch optimization platform, this paper proposes a decision support platform for electricity spot trading based on multi-source information fusion. The platform integrates multi-source data, including real-time in-plant PI system data, publicly available electricity market information, and weather forecasts, to construct a decision engine incorporating core algorithms such as marginal cost analysis, energy storage optimization decisions, and electricity price forecasting. A visualized dashboard is implemented using the RCV intelligence tool. Practical applications demonstrate that the platform effectively addresses the issue of information fragmentation, enhances the intelligence level of trading decisions and market competitiveness, and provides robust support for power generation companies to maximize economic benefits in the spot market.