Toward Realistic Energy Forecasting: A Delay-Enhanced Fractional-Order Supply-Demand Model

Abstract

In order to represent the complex dynamics of contemporary energy systems, a novel fractional-order energy supply-demand model with time delay is presented in this investigation. The fractional model, compared to traditional integer-order models, naturally accommodates for memory and genetic effects, and the incorporation of delay component takes into account unavoidable lags in energy production, transmission, and consumption. To ensure the mathematical rigor of the proposed model, we prove the existence and uniqueness of solutions. We additionally examine into the model's stability within the Ulam-Hyers concept and demonstrate that it is resilient to minor uncertainties and perturbations. To handle fractional derivatives with delay systems, we utilize the Grnwald-Letnikov (GL) discretization scheme, which offers a straightforward and effective method for approximating the solutions. The impact of delay parameters and fractional orders on system behavior is investigated numerically, demonstrating how they shape oscillations, convergence rates, and equilibrium states. According to the results, fractional-order modeling with delay, reinforced by the GL discretization scheme, provides a flexible and realistic framework for examining energy dynamics. This framework gives important insights for long-term policy planning, supply management, and demand forecasting. Additionally, the framework lays the groundwork for upcoming additions that incorporate optimization techniques, stochastic effects, and the integration of renewable energy sources, all of which will further the development of effective and sustainable energy systems. We also discuss three sensitivity analysis scenarios including high demand combined with low supply, high renewable and reduced and imports low demand with high imports which the results show stable and efficient results.