Optimizing the electricity value of sugarcane production systems in Mauritius

Shingirirai Savious Mutanga of the University of Pretoria, South Africa, examined how using sugar cane to produce electricity could reduce greenhouse gas emissions.

Shingirirai Savious Mutanga

Shingirirai Savious Mutanga

Introduction

Current global energy systems have proven unsustainable. Among several renewable energy options, sugar cane, grown widely in African countries, is known to be one of the most productive species in terms of its conversion of solar energy to chemical potential energy. However, the supply for electricity generation is limited to the crop harvest season. The supply is also threatened by a range of factors including declining sugar prices, and competing priorities for land and water which hinder growth of this sector. Nonetheless, the opportunity of harnessing electricity is becoming increasingly attractive. Using Mauritius as an example this study seeks to optimize the electricity value of sugarcane production systems. The study develop an integrated energy model based on systems dynamics, and spatial analysis to: 1) Examine the effects of land use change dynamics on the current and future potential of cogeneration. 2) Explore the potential of preserving surplus bagasse, and trash for off-crop season electricity generation. 3) Determine the emission savings from sugar-based electricity production optimization.

Methods

We applied GIS and Remote Sensing to map land use change dynamics and sugarcane production over the three year period of 1972, 1991, and 2010. Schematic approaches developed by [1] were applied. The datasets derived were used for modeling and simulation of the land sub model. The study then applied systems thinking to unpack the complexity throughout the entire sugar-based electricity production system. A systems dynamics model with four key sub models namely land use dynamics-sugarcane production, bagasse and trash supply, electricity production, and emission savings was developed.

Results and conclusions








References

[1] Lillesand T M, Keifer R W, and C J W (2004). Remote Sensing and Image Interpretation. (C J W Lillisand; T, M, Keifer R W, Ed.) (Fifth Edti.). New York: John Wiley and Sons.

Supervisors

Charles Mbohwa, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

Manfred Strubegger, Energy Program, IIASA

Holger Rogner, Energy Program, IIASA

Note

Shingirirai Savious Mutanga of the University of Pretoria, South Africa, is a citizen of Zimbabwe and was funded by the IIASA South African National Member Organization during the SA-YSSP.

Please note these Proceedings have received limited or no review from supervisors and IIASA program directors, and the views and results expressed therein do not necessarily represent IIASA, its National Member Organizations, or other organizations supporting the work.   


Print this page

Last edited: 02 February 2016

CONTACT DETAILS

Ulf Dieckmann

Program Director

Evolution and Ecology

SA-YSSP Dean (IIASA)

Capacity Building and Academic Training

T +43(0) 2236 807 386

Further information

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313