The advent of greater concern for climate change and specifically CO2 emissions has led to a number of studies and methodologies attempting to quantify the environmental impact of individual actions and policies of the electric power sector. The result has been a myriad of papers focused on average emissions; discussions of “Green Products” and “Green Companies.” In 2010–2012 [1,2,3], Pablo Ruiz and Alex Rudkevich developed the theory and implementable calculation of Locational Marginal Emission Rates (LMER), which provided the first physically-based, mathematically rigorous methodology applicable to the electric power sector.
The mathematically defendable Locational Marginal Emission Rate calculation methodology for both electric consumption and renewables generation is based on the physics and the economics of carbon dioxide generated by the power system. It derives from the same logic and underlying mathematics as Locational Marginal Pricing (LMP). The critical understanding in MER as in LMP is that the physical (quantity of CO2) and economic (dollar) value in both measures is a function of Where, When and What.
The calculation of electric production or consumption or their generation of CO2, their an electricity carbon foot print is a function of the marginal change in power system cost or emissions caused by an incremental or decremental unit of energy produced or consumed at every location – the WHERE – and at that instant in time – the WHEN. The WHAT is quantity – the change in system-wide cost or carbon emissions attributable to an incremental or decremental change in the entity’s generation or consumption expressed as the LMP in dollars per MWh or as LMER tons of carbon. These interval or hourly specific marginal changes can be summed over an hour a week or a year to identify the total impact – a carbon footprint or evaluated to identify the impact of specific policies or investments.
Today, both PJM (for the past several years) and MISO (more recently) report the LMER nodally and on a 5 minute basis (along with LMP) on their publicly available web pages. NYISO and ISO NE have both developed the technology but as yet have not implemented or published the results. All have adapted the logic and the mathematics of the calculation of LMP to directly calculate LMER.
LMP = LMER x PC (the assumed price of carbon)
The conceptual structure of LMER provides the analytic structure needed to calculate the locational carbon footprint, i.e., the impact, of all loads, generators and constrained transmission facilities within a power system. The starting point is to recognize that the electrical grid is a single, integrated entity that at any point in time releases a measurable mass of CO2. LMER represents the total mass of carbon emissions released by all interconnected generators. LMER is equal to the increase or decrease in CO2 emissions in the electrical network in response to an infinitesimal increase or decrease in electricity supply or demand measured in short tons /MWh.
A larger LMER for a given location and time indicates a greater sensitivity in the total carbon emission volume in response to a change in electricity supply or demand. A positive value of MER implies that at a given location and time an increase/decrease in electricity demand causes increase/decrease in CO2 emissions in the power system. A negative value of MER implies that at a given location and time, changes in electricity supply or demand and CO2 emissions move in opposite directions.
The theoretical underpinning and applications of LMER were first published by Alex Rudkevich and Pablo Ruiz [1] in the Proceedings of Hawaii International Conference on System Sciences (HICSS) in 2010-2011 [1,3] and in the Handbook of CO2 in Power Systems in 2012 [2]. The referenced papers provide the mathematical derivations underlying the calculation of the grid’s marginal emission rates at the maximum level of temporal and spatial granularity.
To forecast the potential impact on LMER at any location at a future point in time or over time it is necessary to be able to simulate the emission levels of the power system at that location and over the time period of interest. Thus LMER could, in theory, be computed through the analysis of marginal generating units and binding constraints on transmission using the mathematics of shift factor and loss factor decomposition. In practice, this approach is very difficult to implement because marginal generating units are not often easily identifiable as they may be marginal either for energy or for reserves. The marginal units may be constrained based on energy-limited hydro or pumped storage units and it may not be possible to capture the optimized operation of phase shifters.
The good news is that these difficulties in modeling can be overcome given the basic relationship between LMER and LMP. Stated simply, at each location LMP changes to small variations in CO2 in proportion to LMER at that location. For this reason it is possible to estimate LMER values using a production cost modeling approach.
The process requires:
Running the simulation for the system and computing the LMPs for each location (all generators and all load areas)
Rerunning the dispatch using the same unit commitment as above with a very small increase in cost reflecting, effectively, a price for CO2. and recomputing the resulting LMPs.
From the differences between the two runs estimate the LMERs using:
See LMER in a Production Cost Model
EnCompass calculates LMER as a parallel output of nodal LMP simulation, so you can forecast locational carbon impact alongside price. Explore EnCompass.
LMER calculations are a parallel output of the process of calculating Locational Marginal Prices in a detailed production cost modeling platform. Both LMP and LMER reflect the reality that the electric power system is fully integrated and that any changes with the power system are reflected in changes throughout the system to retain the operational balance. LMER like LMP is a metric measuring the emissions (as opposed to the cost) of the power system as a whole.
[1] Pablo Ruiz and Aleksandr Rudkevich, “Analysis of Marginal Carbon Intensities in Constrained Power Networks.” IEEE Proceedings of the 43rd Hawaiian Conference on System Sciences, January 2010.
[2] Aleksandr Rudkevich and Pablo Ruiz, "Locational Carbon Footprint of the Power Industry: Implications for Operations, Planning and Policy Making” in Q. P. Zheng et al (eds.) Handbook of Co2 in Power Systems DOI 10.1007/978-3-642-27431-2_8 Springer Verlag Berlin Heidelberg, 2012
[3] Aleksandr Rudkevich, Pablo Ruiz and Rebecca Carroll, “Locational Carbon Footprint and Renewable Portfolio Policies: A theory and its Implications for the Eastern Interconnection of the US. IEEE Proceedings of the 44th Hawaiian Conference on System Sciences, January 2011.