Integrated Modeling of Electric Power System Operations and Electricity Market Risks with Applications
Because of the physical nature of electricity and the complexity of system operations, many challenging problems have arisen from the restructuring of electric power industry. Tremendous uncertainties put market participants in the midst of unprecedented risks when making the planning, operating, and trading decisions. This situation has motivated the reported research work on modeling, evaluating, and constructing strategies to hedge against the market risks involved. Through integrated modeling of power system operations and market risks, this thesis addresses a variety of important issues on market signals modeling, generation capacity scheduling, and electricity forward trading.
The level of investment in electricity transmission networks has been lagging behind those in the generation and distribution sectors amid the industry restructuring. It affects economic efficiency and impairs system reliability. The first part of the thesis addresses a central problem of transmission investment which is to model market signals for transmission adequacy. The proposed system simulation framework, combined with the stochastic price model, provides a powerful tool for capturing the characteristics of market prices dynamics and evaluating transmission investment. Numerical experiments with the IEEE RTS24 system yield interesting insights. In contrast with the common practice of using DC power flow formulations for market dispatch, we advocate the use of an AC power flow formulations instead since it allocates transmission losses correctly and reveals the economic incentives of voltage requirements. By incorporating reliability constraints in the market dispatch, the resulting market prices yield incentives for market participants to invest in additional transmission capacity.
In electricity markets, generators seek to maximize their profits by simultaneously participating in multiple markets simultaneously. The uncertainties in market prices and different extents of market participations make generation capacity allocation a challenging task. The second part of the thesis presents a co-optimization modeling framework that incorporates market participation and market price uncertainties into the capacity allocation decision-making problem through a stochastic programming formulation. Optimal scenario-dependent generation scheduling strategies are obtained. The advantages of the proposed model are illustrated through a computational study with realistic data provided by a hydroelectric producer.
The third part of the thesis is devoted to analyzing the risk premium present in the electricity day-ahead forward price over the real-time spot price. This study establishes a quantitative model for incorporating transmission congestion into the analysis of electricity day-ahead forward risk premium. Through simulations with a three-bus study-system, it is illustrated that the more frequently transmission congestion happens, the higher the forward prices get at the load buses. Evidences from empirical studies with the New York electricity market data confirm the significant statistical relationship between the day-ahead forward risk premium and the shadow price premiums on transmission flowgates.