A decomposable linear programming model for energy supply chains

• Main Authors: Ng, Tsan Sheng, Aras, Buse, Sy, Charlle Lee, Biao, Wu
• Format: text
• Published: Animo Repository 2014
• Subjects: Fuel; Power resources; Linear programming; Oceanography and Atmospheric Sciences and Meteorology
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/744
• Institution: De La Salle University

This chapter presents an application of linear programming to an energy supply chain problem. The energy supply chain is composed of the fuel supply market, electricity generators and electricity consumers. A linear programming model is developed to determine the optimal fuel mix for the system under the constraints of suppliers' and generators' capacities, CO2 emission quota, generators' profit targets and other technical issues. In particular, the Berger Parker Index is considered to measure the fuel diversity index of the supply chain. Then, the model is solved using the Dantzig-Wolfe Decomposition algorithm. The performance of the algorithm, with respect to its computational speed and efficiency, is investigated using various sizes of the energy supply chain model. Numerical studies have also been performed on a case study concerning four suppliers, four electricity generators, four consumers and four fuel types. The case study shows the capability of the energy supply chain model to determine the best aggregate strategy of fuel mix that would generate electricity according to changing fuel prices, customer demands and profit targets. The model is also used to provide Pareto frontiers, which illustrate tradeoffs between diversity index, profit and emission quota. Finally, it is found that the model can be applied for any areas of energy planning and could also be easily extended to meet specific scenarios. © 2014 Nova Science Publishers, Inc.