Multiobjective Strategy for an Industrial Gas Turbine: Absorption Chiller System

Introduction

In most power production industries, the overall efficiency of the generation system plays a key role. Optimizing the system increases its efficiency resulting in higher power production. Most current power producers are bound contractually by a Power Purchase Agreement (PPA) which details the amount of power they are allowed to produce (Wu and Babich, 2012; Cory et al., 2009). The strategies involved in drawing up the contract vary from country to country and represent an alignment towards local policies and scenarios. Some of these contracts require that the amount of power supplied remains fixed throughout the period of purchase. Nevertheless, there may be requirements specified in the contracts to combine fixed generation with additional available power. This is done to address concerns over unplanned outages. For fixed amount contracts, although engineers and plant personnel manage to optimize the plant efficiency significantly, the surplus power produced could not be sold due to the terms in the PPA. The motivation to pursue power generation efficiency can also relate to savings in fuel consumptions without the need to increase power. This work aims to optimize the design and operation parameters for the combined Gas Turbine (GT) and Absorption Chiller (AC) system without maximizing the total power supplied to the consumer. The optimization model for the combined system would hence contain multiple objectives (which represent key indicators of efficiency) while constraining the GT power output such that it adheres to PPA requirements.

By optimizing the design and operations of the combined GT-AC system, the cost efficiency of the plant would significantly increase. Additionally, this optimization would also enable the GT to operate at its maximum efficiency as per design. This would contribute to the increase of its active lifetime. Recovering the waste heat works in favor of the environment since the plant does not dispose large amounts of heat to its surrounding.

Real-world engineering applications often present scenarios with multiple target objectives. Such classes of problems are often difficult to solve. Even if solved, these solutions are difficult to analyze due to their multidimensional and complex nature. However, as industrial systems become more complex, engineers and decision makers often find themselves in situations involving multiple objectives (Ganesan et al., 2014; Ganesan et al., 2015; Ganesan et al., 2016). The idea of Pareto-optimality is prevalent when it comes to tracing-out the non-dominated solution options at the Pareto curve (Deb et al., 2002). An alternative to non-dominated solution tracing is the function aggregation method – where multiple objective functions are merged into a single master function effectively transforming the problem into a weighted single-objective problem (Marler and Arora, 2010; Brito et al., 2014; Naidu et al., 2014). Detail examples and analyses on MO techniques for problems in engineering optimization are presented (Oliveira and Saramago, 2010; Rao and Rao, 2009).