A data‐driven approach to estimate emissions for market‐based power system test cases

Abstract

A data-driven technique to determine greenhouse gas (GHG) and air-pollutant (AP) emissions from bulk-power system simulations is proposed. The proposed technique emulates the dispatch of a bulk-power system using open-source hourly fuel-energy data from an actual U.S. electricity market (i.e. PJM). Sixteen different fuel types were analysed from real generator data and dynamically assigned to power system test cases to statistically represent the real fuel-energy mix. Each test case generator is assigned a heat-rate function based on open-source real generator data to estimate realistic emissions from power system test case simulations. These augmented test cases can be used to simulate how changes in load and generation impact power system emissions to determine the environmental sustainability of new technologies (e.g. demand-side management). The proposed technique is implemented on three different power system test cases, and the simulated emissions are compared with the actual emissions of the PJM system. The results from the test systems are found to accurately emulate the time-series values of fuel-mix, emissions, and marginal costs of PJM.