Details

Autor(en) / Beteiligte

• Rogers, Michelle M.
• Wang, Yang
• Wang, Caisheng
• McElmurry, Shawn P.
• Miller, Carol J.

Titel

Evaluation of a rapid LMP-based approach for calculating marginal unit emissions

Ist Teil von

• Applied energy, 2013-11, Vol.111, p.812-820

Ort / Verlag

Kidlington: Elsevier Ltd

Erscheinungsjahr

2013

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• [Display omitted] •Pollutant emissions estimated based on locational marginal price and eGRID data.•Stochastic model using IEEE RTS-96 system used to evaluate LMP approach.•Incorporating membership function enhanced reliability of pollutant estimate.•Error in pollutant estimate typically <20% for CO2 and <40% for NOX and SO2. To evaluate the sustainability of systems that draw power from electrical grids there is a need to rapidly and accurately quantify pollutant emissions associated with power generation. Air emissions resulting from electricity generation vary widely among power plants based on the types of fuel consumed, the efficiency of the plant, and the type of pollution control systems in service. To address this need, methods for estimating real-time air emissions from power generation based on locational marginal prices (LMPs) have been developed. Based on LMPs the type of the marginal generating unit can be identified and pollutant emissions are estimated. While conceptually demonstrated, this LMP approach has not been rigorously tested. The purpose of this paper is to (1) improve the LMP method for predicting pollutant emissions and (2) evaluate the reliability of this technique through power system simulations. Previous LMP methods were expanded to include marginal emissions estimates using an LMP Emissions Estimation Method (LEEM). The accuracy of emission estimates was further improved by incorporating a probability distribution function that characterize generator fuel costs and a membership function (MF) capable of accounting for multiple marginal generation units. Emission estimates were compared to those predicted from power flow simulations. The improved LEEM was found to predict the marginal generation type approximately 70% of the time based on typical system conditions (e.g. loads and fuel costs) without the use of a MF. With the addition of a MF, the LEEM was found to provide emission estimates with errors typically less than 25% for CO2, and less than 50% for SO2 and NOX. Overall, the LEEM presented provides a means of incorporating pollutant emissions into demand side decisions.