3,752 research outputs found
Optimal Carbon Taxes for Emissions Targets in the Electricity Sector
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
Decomposition-Coordinating Method for Parallel Solution of a Multi-area Combined Economic Emission Dispatch Problem
Multi-area Combined Economic Emission Dispatch (MACEED) problem is an optimization task in power system operation for allocating the amount of generation to the committed units within the system areas. Its objective is to minimize the fuel cost and the quantity of emissions subject to the power balance, generator limits, transmission line and tie-line constraints. The solutions of the MACEED problem in the conditions of deregulation are difficult, due to the model size, nonlinearities, and the big number of interconnections, and require intensive computations in real-time. High-Performance Computing (HPC) gives possibilities for the reduction of the problem complexity and the time for calculation by the use of parallel processing techniques for running advanced application programs efficiently, reliably and quickly. These applications are considered as very new in the power system control centers because there are not available optimization methods and software based on them that can solve the MACEED problem in parallel, paying attention to the existence of the power system areas and the tie-lines between them. A decomposition-coordinating method based on Lagrange’s function is developed in this paper. Investigations of the performance of the method are done using IEEE benchmark power system models
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