A capacity expansion planning model for integrated water desalination and power supply chain problem

摘要

Cogeneration of water and power in integrated cogeneration production plants is a common practice in the Middle East and North Africa (MENA) countries. There are several combinations of water desalination and power technologies which give significant adverse environmental impact. Renewable and alternative energy technologies have been recently proposed as alternative power production paths in the water and power sector. In this study, we examine the optimal capacity expansion of water and power infrastructure over an extended planning horizon. A generic mixed integer linear programming model is developed to assist in the decision making process on: (1) optimal installation of cogeneration expansion capacities; (2) optimal installation of renewable and alternative power plants; (3) optimal operation of the integrated water and power supply chain over large geographical areas. Furthermore, the model considers the installation of carbon capture methods in fossil-based power, plants. A case study will be presented to illustrate the mathematical programming application for the Emirate of Abu Dhabi (AD) in the United Arab Emirates (UAE). The case study is solved reflecting different scenarios: base case scenario, integration of renewable and alternative technologies scenario, and CO2 reduction targets scenario. The results show that increased carbon tax values up to 150 $/ton-CO2 gives a maximum 3% cost increase for the supply chain net present value. The installation of carbon capture methods is not an economical solution due to its high operation energy requirements in the order of 370 kW h per ton of captured CO2. Thus, the Cplex solver in GAMS chooses optimal solutions without installation of carbon capture processes. In addition, higher degree of alternative and renewable energy technologies penetration within the energy mix reduces the overall net present value of the network, and the carbon emissions by 40%, and 12%, respectively. (C) 2016 Elsevier Ltd. All rights reserved.