(204s) Multi-Objective Optimization of a Bio-Based Supply Chain for Feeding Co-Combustion Power Plants

Biomass can contribute to centralized and / or distributed energy systems, depending on its intrinsic characteristics, and meet the different user needs. It can be supplied as a liquid, solid or gas energy carrier [1]. The electricity generation sector today needs to reduce its environmental impact and coal dependence. In terms of immediacy and cost of the solution, co-combustion of biomass and coal is presented as an alternative of transition, towards a renewable energy sector [2,3,4]. The use of biomass in large centralized systems, requires the establishment of supply channels to provide adequate feedstocks with the necessary attributes. Hence, there is a need for methods to evaluate the effective introduction of this type of projects in the energy market, to help policy makers and investors in clarifying the potential of biomass and biomass waste in particular.

The purpose of this work is to investigate the application of a supply chain management approach in providing biomass, considering its highly distributed nature, seasonality, low heating value and low bulk density. Sustainability is crucial for this sort of energy projects [5]. Within this work approach, we integrate economic and environmental criteria, taking into account the net present value (NPV), measured in €₂₀₁₀, and a life cycle assessment (LCA) based metric (Impact 2002+), respectively. Particularly important for bioenergy problems are transportation and storage, as well as biomass properties modification through pre-treatment techniques. Torrefaction, torrefaction combined with pelletization, pelletization, fast pyrolysis and fast pyrolysis combined with char grinding compose the superstructure of pre-treatment technologies considered in this approach. For the granularity of the problem, monthly planning periods are used to evaluate feedstock and demand seasonality. The problem is formulated as a multi-objective mixed integer linear program (MO-MILP) to elucidate tailor-made solutions for the different case studies that can be solved with the developed formulation.

The model variables and constraints can be classified into three groups: (i) process operations constraints given by the design-planning sub-model, (ii) the economic metric formulation and  (iii) the environmental sub-model. The specific case study result provides design and planning solutions: (i) locates and identifies the most appropriate pre-treatment technologies and feedstock suppliers, (ii) evaluates matter flows between nodes and echelons, and (iii) gives preliminary approach to the monthly working sequence of all the units. Specifically, it defines the network structure by setting the unit capacities, monthly flows and inventories, breakdown of costs and environmental impacts per echelon and equipment. The MO-MILP problem is solved by using the ε-constraint method, which allows for multi-objective trade-off consideration.

To demonstrate the validity of the developed model approach, it is applied to a retrofitting proposal for a co-combustion case study placed in Spain. Forest and agricultural woody residues are proposed to replace the 10% of the total thermal inlet power provided by coal in the existing network of conventional power plants. The objective functions quantify the difference between the only-coal towards the coal-biomass supply chain. The selected boundaries are from cradle-to-gate, with the co-combustion plant serving as the last echelon. To define the possible different supply chains, the biomass collection sites and amounts are defined [6], together with the currently operating conventional power plants location. Also a list of potential sites for pre-treatment technologies and intermediate storage is proposed.

The results suggest that biomass use is feasible and can be competitive in the energy market, once provided with the appropriate policies. The performance of the model also indicates that pre-treatment technologies of biomass are crucial for the deployment of the bioenergy sector. The model can be adapted to other case studies, at large and small scale, to elucidate the best network configurations according to the criteria of the decision-maker.

References

[1] Highman, C. and van der Burght M., Gasification. Elsevier Science, 2003.

[2] Faaij, A., Bioenergy in Europe: changing technology choices. Energy Policy 34 (2006) 322-342.

[3] Berndes, G., Hansson, J., Egeskog, A. and Johnson, F. Strategies for 2^nd generation biofuels in EU – Co-firing to stimulate feedstock supply development and process integration to improve energy efficiency and economic competitiveness. Biomass and Bioenergy 34 (2010) 227-236. 

[4] Gómez, A., Zubizarreta, J., Rodrigues, M., Dopazo, C. and Fueyo, N., An estimation of the energy potential of agro-industrial residues in Spain. Resources, Conservation and Recycling 54 (2010) 227-236.

[5] Pérez-Fortes, M., Laínez-Aguirre, JM., Arranz-Piera, P., Velo, E. and Puigjaner, L., Design of regional and sustainable bio-based networks for electricity generation using a multi-objective MILP approach. Energy 44 (2012) 79-95.

[6] Gómez, A., Rodrigues, M., Montañés, C., Dopazo, C. and Fueyo, N., The potential for electricity generation from crop and forestry residues in Spain. Biomass and Bioenergy 34 (2010) 703-719.