Analysis of the correlating or competing nature of cost-driven and emissions-driven demand response

Multi-objective optimization is an important tool for sustainable decision making. In process operations, the nature of tradeoffs between objectives may change over time, due to, for example, changes in power cost and emissions signals. This work seeks to understand the correlating vs. competing nature of demand response objectives, and their variation in time, through a time-varying ammonia production case study. Results show that correlation between cost and emissions is near perfect when the time-varying cost and emissions factor profiles are in phase, and low when they are anti-phase. Furthermore, analysis of real historical data from different locations around the world suggests that for the majority of the time, cost and emissions objectives are strongly correlated. Trends observed are robust over different times of the year and different locations in the same ISO; however, considering ISOs in different parts of the world can provide different results due to variation in generator mix. An approach for determining compromise operating strategies when cost and emissions are competing is also presented.