Optimal Carbon Taxes for Emissions Targets in the Electricity Sector

TL;DR

This paper introduces a novel Weighted Sum Bisection method to determine the minimal carbon tax rate needed to meet emissions targets in the power sector, accounting for operational variability and investment effects.

Contribution

It presents a new bi-level stochastic programming approach for setting optimal carbon taxes that ensure emissions reduction in large, realistic power systems.

Findings

The method reliably finds the minimum tax rate to meet emissions targets.

Increasing investments in clean energy reduces the required carbon tax.

The approach remains computationally feasible for complex, multi-period systems.

Abstract

The most dangerous effects of anthropogenic climate change can be mitigated by using emissions taxes or other regulatory interventions to reduce greenhouse gas (GHG) emissions. This paper takes a regulatory viewpoint and describes the Weighted Sum Bisection method to determine the lowest emission tax rate that can reduce the anticipated emissions of the power sector below a prescribed, regulatorily-defined target. This bi-level method accounts for a variety of operating conditions via stochastic programming and remains computationally tractable for realistically large planning test systems, even when binary commitment decisions and multi-period constraints on conventional generators are considered. Case studies on a modified ISO New England test system demonstrate that this method reliably finds the minimum tax rate that meets emissions targets. In addition, it investigates the relationship between system investments and the tax-setting process. Introducing GHG emissions taxes increases the value proposition for investment in new cleaner generation, transmission, and energy efficiency; conversely, investing in these technologies reduces the tax rate required to reach a given emissions target.