Electricity generation increasingly relies on natural gas for fuel. The competing demands for gas by other users who may have higher priority, the lack of coordination between gas and electricity markets, and extreme weather events all pose risks to systems with high dependence on gas. When the gas supply on which generators have planned is limited, operators may dispatch more costly units and generators may switch to alternative fuels or procure gas at high spot prices. All these efforts to avoid load-shedding result in higher electricity costs. To assess this economic risk we approximate the distribution of the daily operational cost to satisfy demand for electricity by conducting simulations of dispatch. Input parameters are sampled from a temporal and weather conditional joint distribution of daily electric load and spot price of gas. To isolate the impact of uncertainty in the gas price, we generate a benchmark distribution of the dispatch cost by fixing the gas price at its expectation while sampling from the marginal distribution for load, and compare the distribution generated from uncertainty in both the gas price and the load against this benchmark. The risk is quantified by alternatively computing the distance between dispatch cost distributions or by the difference between the values of a risk measure applied to each distribution. In a numerical case study we demonstrate how such risk quantification can be used to evaluate alternative risk-mitigation strategies at the system level.