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The growing complexity of electrical systems, driven by intermittent renewable generation and flexible demand, has led operators to seek more robust approaches for energy dispatch. Traditionally, scenario generation has been optimized solely to fit historical distributions, ignoring spatial correlations between uncertainties and, more critically, the actual impact on the final operating cost. A new paradigm, decision-oriented scenario generation, proposes training generative models —such as generative adversarial networks, variational autoencoders, or diffusion models— by directly maximizing the economic efficiency of robust dispatch. This approach not only captures dependencies between system nodes but also incorporates a differentiable selector of relevant scenarios, reducing operating costs by between 0.80% and 2.02% compared to classical methods.
From a business and technological perspective, implementing this type of solution requires a platform of artificial intelligence for businesses that integrates advanced models with scalable infrastructure. At Q2BSTUDIO, we develop custom applications that combine tailored software, AWS and Azure cloud services, and business intelligence services such as Power BI to visualize and optimize network operations. Additionally, our solutions include AI agents that automate scenario selection and cybersecurity to protect critical system data. These developments transform theory into practical tools that reduce costs and improve the resilience of energy dispatch.
