Multiobjective Strategy for an Industrial Gas Turbine: Absorption Chiller System

Multiobjective Strategy for an Industrial Gas Turbine: Absorption Chiller System

T. Ganesan (TNB Research, Malaysia), Mohd Shiraz Aris (TNB Research, Malaysia) and Elamvazuthi I. (Universiti Teknologi Petronas, Malaysia)
DOI: 10.4018/978-1-5225-2990-3.ch023
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Abstract

In gas power plants, the overall efficiency of the generation system plays a key role in ensuring stable and efficient power supply. Terms of power supply are usually detailed in power purchase agreements (PPA). Current requirements set by PPAs limit the net power produced by the supplier. This creates opportunities for plant optimization to focus on system efficiency - aiming to increase system life-time with lower operational costs. In this chapter, a gas turbine (GT) system is considered to demonstrate certain features of power plant optimization. Waste heat from the GT exhaust stack is fed into an absorption chiller (AC). The AC cools the air intake at the GT compressor. This cooling reduces the heat rate and increases the GT efficiency. This combined GT-AC system is optimized in a multiobjective setting while considering power limitations (imposed by the PPA). The enhanced chaotic differential evolution (CEDE) is employed for optimizing this system. The obtained results are presented and analyzed in detail.
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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).

Key Terms in this Chapter

Weighted Sum Approach: A solution approach in multiobjective optimization where the objective functions are aggregated by multiplying them to weights (level of importance) and summing them over.

Power Purchase Agreement: A contract signed between the independent power supplier and the consumer (purchaser) consisting detailed terms involving the purchase of power.

Gas Turbines: A type of internal combustion jet-engine driven by the expansion of hot gases consisting four major components: compressor, combustion chamber, turbine, exhaust stack/outlet.

Measurement Metrics: Mathematical metrics used to identify and measure solution characteristics such as; degree of convergence, diversity and dominance.

Multiobjective Optimization: A class of optimization problems formulated with more than one objective functions.

Differential Evolution (DE): A type of metaheuristic that uses concepts from evolutionary algorithms to search for optimal solutions.

Absorption Chillers: A refrigeration system operating on the principle of absorption cooling usually using a liquid like LiBr as a refrigerant.

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