A mixed-integer optimization approach for polygeneration energy systems design

Abstract

A mixed-integer nonlinear programming (MINLP) model is developed for the design optimization of polygeneration energy systems. A suitable superstructure is introduced, based on partitioning a general polygeneration energy system into four major blocks, for each of which alternative available technologies and types of equipment are considered. A detailed case study, involving a coal-based polygeneration plant producing electricity and methanol, is presented to demonstrate the key features and applicability of the proposed approach.

Introduction

Polygeneration is considered a potentially attractive technology for energy utilization, as it could provide feasible solutions to the worldwide problems of excessive green house gas (GHG) emissions and ever-increasing depletion of fossil fuels (Li, Ni, Zheng, & Ma, 2003; Ni & Johansson, 2004; Ni, Li, & Yuan, 2000; U.S. Department of Energy, 1999, U.S. Department of Energy, 2001; Yamashita & Barreto, 2005).

A typical polygeneration plant produces electricity and chemical synthesis products, in particular alternative fuels, such as methanol, dimethyl ether (DME) and hydrogen. Fig. 1 shows an example of a polygeneration process producing electricity and methanol (National Energy Technology Laboratory, 2003).

Polygeneration energy systems are considered to be superior to conventional stand-alone plants. Their advantages lie in three main aspects:

• Energy efficiency: due to the tight integration of the power generation and the chemical synthesis sections, the overall energy utilization of a polygeneration plant is expected to be higher than the overall efficiency of stand-alone plants, producing the same products.

• Alternative fuels and energy carriers: chemical products produced by a typical polygeneration plant can be used as substitutions for traditional liquid fuels; for example, methanol for gasoline, DME for diesel oil. Hydrogen can also be a product.

• Cost-effective emissions reduction: the large-scale of polygeneration energy systems is expected to result in cost-effective solutions for the implementation of CO₂ capture and sequestration (CCS) units.

Due to the high degree of integration and coupling between the power generation and chemical synthesis parts, determining the optimal configuration and design of a polygeneration energy system is quite a challenging task. Different process designs have been reported in literature. Ma, Ni, Li, and Ren (2004) and Ma, Ni, Li, and Ren (2004) proposed a group of sequential and parallel process designs for a coal-based polygeneration plant producing electricity and methanol. By comparing energy efficiency and economic characteristics, they concluded that the sequential design with a once-through methanol synthesis unit exhibits optimal overall performance. Liu, Gao, and Li (2006) tested the dynamic behaviour of the processes designed by Ma et al. under varying power loads, and concluded that a parallel process design will have better performance under certain operating conditions. Liu, Liu, Li, Ni, and Xu (2006) developed a novel process design producing electricity and DME from natural gas, in the context of determining a better way to transport natural gas from West China to East China. Chen, Jin, and Gao (2006) compared the energy and energy efficiencies between polygeneration plants producing electricity and DME and stand-alone DME plants, and concluded that the energy saving ratio in a polygeneration plant could be as high as 16.6%. Besides general processes producing electricity and chemical fuels, there are also other forms of polygeneration process designs for specific purposes, such as exploring the potential of coal-gas generated in coke ovens in iron and steel industry, and combining an ammonia process with a coal-based power generation process for higher energy utilization rates (Zhang, Ni, & Li, 2004; Zhang, Gao, Jin, & Cai, 2006).

While the reported works above have significantly advanced our understanding of polygeneration from a design perspective, they share a common limitation—they either focus on specific technologies, or mostly focus on specific requirements/conditions.

In this context, it is important to provide a general systematic methodology for the design of coal-based polygeneration energy systems, which could be applicable for different technology, design and operational requirements.

In this work, building on our earlier work for the strategic planning of polygeneration energy system (Liu, Gerogiorgis, & Pistikopoulos, 2007), we present the building blocks of such a general methodology, featuring a superstructure representation and a comprehensive mixed integer optimization model formulation.

The paper is structured as follows. The superstructure representation is described. The mathematical model is presented then, followed by detailed study of a polygeneration plant for the production of methanol and electricity.