No abstract

While biodiesel production and consumption for use in transportation has risen considerably over the last decade, its competiveness in the marketplace is largely due to regulatory and fiscal support from governmental bodies, exceeding $25 billion in 2010 in the EU and US alone. The price of feedstocks represent 80-85% of the total biodiesel cost, and with over 350 different oil feedstocks available for blending, there is potential to optimize feedstock blends to reduce costs. This paper presents a chance-constrained optimization model that considers the technical constraints of conventional, first generation feedstocks, pricing trends, as well as the uncertainty and variation latent within these numbers. Further, the frequency with which a feedstock blend portfolio should be re-evaluated is considered through a case study. The model is then applied to a second case study for actual fuel constraint scenarios used in the EU and US. The results demonstrate the potential for substantial cost savings through targeted feedstock diversification, minimizing risks to producers from price fluctuations while still meeting technical fuel standards.

As an alternative transportation fuel to petrodiesel, biodiesel has been promoted within national energy portfolio targets across the world. Early estimations of low lifecycle greenhouse gas (GHG) emissions of biodiesel were a driver behind extensive government support in the form of financial incentives for the industry. However, studies consistently report a high degree of uncertainty in these emissions estimates, raising questions concerning the carbon benefits of biodiesel. Furthermore, the implications of feedstock blending on GHG emissions uncertainty have not been explicitly addressed despite broad practice by the industry to meet fuel quality standards and to control costs. This work investigated the impact of feedstock blending on the characteristics of biodiesel by using a chance-constrained (CC) blend optimization method. The objective of the optimization is minimization of feedstock costs subject to fuel standards and emissions constraints. Results indicate that blending can be used to manage GHG emissions uncertainty characteristics of biodiesel, and to achieve cost reductions through feedstock diversification. Simulations suggest that emissions control policies that restrict the use of certain feedstocks based on their GHG estimates overlook blending practices and benefits, increasing the cost of biodiesel. In contrast, emissions control policies which recognize the multifeedstock nature of biodiesel provide producers with feedstock selection flexibility, enabling them to manage their blend portfolios cost effectively, potentially without compromising fuel quality or emissions reductions.