Decarbonization is structurally changing the electricity generation mix and shifting market operation toward real time. As renewable penetration increases, short-term markets are characterized by higher price volatility and continuous position adjustment.

Two classes of models are commonly used to analyze these markets: fundamental and statistical electricity price forecasting (EPF) models. Both face limitations in representing modern market dynamics. Fundamental models are developed for stable, dispatchable systems and are unable to capture highly dynamic market behavior, while statistical models rely on historical data and lose validity under structural change.

To address these limitations, this thesis develops a dynamical systems model using economic engineering. The day-ahead, intraday, and balancing stages are consolidated into a single formulation, enabling the representation of real-time market dynamics. The model remains
valid under structural change by restricting exogenous inputs to renewable generation forecasts and demand profiles. Price volatility and trading behavior emerge endogenously from the system dynamics.

The dynamical formulation enables real-time market management using control theory. The transmission system operator (TSO) is modeled as an incentive-based feedback controller that steers trading behavior and promotes proactive imbalance resolution. Similarly, generator-level control mitigates the impact of forecast errors.

The resulting closed-loop system is constructed using economic circuit theory. The controllers are shown to reduce reliance on balancing reserves and improve system stability under high renewable penetration and supply shocks. Using dynamic scenario analysis, this thesis further evaluates how system flexibility and sector heterogeneity affect prices and market liquidity
across market stages. ... Read more