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 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. 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. 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 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. Evidences from empirical studies confirm the significant statistical relationship between the day-ahead forward risk premium and the shadow price premiums on transmission flowgates.