(592d) Mixed-Integer Bi-Objective Optimization Algorithm for Solving Energy Supply Chain Problems

Energy supply chains consist of raw materials, production facilities and demands for end products, i.e. energy. According to the Energy Information Administration (EIA)'s Report, over one-third of all primary energy consumption goes into producing and delivering electricity and most of the world's primary energy consumption comes from fossil fuels, such as coal, natural gas and oil. Burning fossil fuels release emissions that are harmful to the environment. These emissions can be classified into two main groups: SOx and CO2 equivalent emissions. SOx equivalent emissions are the particles that are quantified strictly and limited with certain regulations. The case with CO2 equivalent emissions is different; these are gases that have effect on global warming, i.e. greenhouse gases (GHG).

Due to increasing environmental impact and the central role of energy in every industrial activity, the improvement of economical and environmental performance of energy production systems continues to be a major issue. The Kyoto Protocol demands for reductions in greenhouse gas emissions by the industrialized countries. The energy sector will be seriously affected by the Kyoto Protocol since it is one of the leading industries that release harmful substances to the environment. Therefore, the energy production companies and the researchers are in the search for recipe solutions for reducing emissions without sacrificing from the amount of energy production and low prices of production.

In a typical energy production optimization model, the objective is to minimize production cost while satisfying energy requirements with given operational constraints. To extend the model, we introduce a new objective function aimed to minimize GHG emissions as well as production cost. It is essential to include the option to use renewable energy technologies in the existing energy production systems in order to improve environmental performance. For that purpose, bio diesel is added to the model with its own limitations and constraints. The last extension we introduced to our model is the carbon capturing and sequestration (CCS) system. The CCS technology involves capturing CO2 from the gas streams generated by burning fossil fuel and then injecting it underground. The CCS system is not modeled as a new detailed process unit in the system, its existence is modeled by new constraints and its operating cost is model as an energy penalty. As a result, the extended model is an MILP problem with two objectives.

Due to the conflicting nature of these two objectives, it is not possible to find a solution that optimizes both objectives simultaneously. Therefore, the main objective in this bi-objective problem is to find efficient or Pareto optimal solutions. The Pareto optimal is the solution where none of the objective functions can be improved without worsening at least one of the others. In order to solve this challenging problem, a mixed-integer bi-objective optimization algorithm is developed. The developed algorithm is designed to present all efficient solutions to the decision maker and it is based on the combination of Tchebycheff scalarizing and weighted sum methods. In this algorithm, the bi-objective problem is converted into single objective problem and efficient solutions are obtained by solving single objective problem iteratively.

In this work, detailed models and suggestions are developed for energy production systems within emission reduction and cost minimization objectives by using different analysis and modeling techniques. A model is developed for future emission reduction targets of energy production companies. As an advance modeling technique, a new objective function aimed to minimize GHG emissions as well as production cost is introduced. These two contradicting objectives are solved by the developed mixed-integer bi-objective algorithm. Efficient solutions that can be used as production planning tools by decision makers are obtained. In order to improve environmental performance, bio diesel and CCS system are added to the model with their own limitations and constraints. The results showed that up to a 26% reduction of CO2 emission can be achieved by fuel balancing strategy which leads to the 35% increase in production cost. When bio diesel is added to the model, up to a 30% reduction of CO2 emission can be achieved with the same amount of increase in production cost. When both bio diesel and CCS system are added to the model, 90% reduction of CO2 emission is achieved which results in 55% increase in production cost.