Determining energy and climate market policy using multiobjective programs with equilibrium constraints. (1st January 2016)

• Record Type: Journal Article
• Authors: Siddiqui, Sauleh; Christensen, Adam
• Is Part Of: Energy. Volume 94(2016)
• Page Start: 316
• Page End: 325
• Publication Date: 2016-01-01
• Subjects: Multiobjective optimization -- Energy markets -- Climate markets -- Mathematical programs with equilibrium constraints -- Cap and trade -- Taxes; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.11.002
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Energy and climate market policy is inherently multiobjective and multilevel, in that desired choices often conflict and are made at a higher level than influenced actors. Analyzing tradeoff between reducing emissions and keeping fuel prices low, while seeking compromise among producers, traders, and consumers is the crux of the policy problem. This paper aims to address this issue by combining multiobjective optimization problems, which allow the study of tradeoff between choices, with equilibrium problems that model the networks and players over which these policies are chosen, to produce a formulation called a Multiobjective Program with Equilibrium Constraints. We apply this formulation to the United States renewable fuel market to help understand why it has been so difficult in releasing the 2014 mandate for the RFS (Renewable Fuel Standard). The RFS ensures that a minimum volume of renewable fuel is included in transportation fuel sold in the United States. Determining the RFS volume requirements involves anticipating market reaction as well as balancing policy objectives. We provide policy alternatives to aid in setting these volume obligations that are applicable to a wide variety of climate and energy market settings and explain why the RFS is not an optimal policy for reducing emissions. Highlights: First time a MOPEC has been used to model energy markets and climate policy. Method to endogenously determine energy policy along with associated tradeoff. Computationally efficient algorithm for MOPECs and compare to methods. Explain why the RFS is not an optimal policy for emission reduction.