Optimal renewable energy integration into the process industry using multi-energy hub approach with economic and environmental considerations: Refinery-wide case study

https://doi.org/10.1016/j.compchemeng.2021.107345Get rights and content

Highlights

  • A framework for integrating renewable energy in the process industry is introduced.

  • The multi-energy hub is used in the framework development.

  • A refinery-wide case study was used to illustrate the framework.

  • Costs and carbon emissions resulting from different configurations of energy generation technologies were determined.

Abstract

With growing research interest in renewable energy generation and storage technologies, a need arises for a framework integrating renewable energy within the process industry. In this study, the multi-energy hub approach was used to develop a model to achieve economic gains and carbon emissions reduction. Furthermore, a case study on a refinery was carried out to investigate the applicability of the proposed model. Lowest carbon emissions were realized when utilizing wind coupled with concentrated solar power technologies for electricity and heat generation, respectively. This configuration, for the particular refinery case study, mitigated about 9.8 ktonnes carbon dioxide emissions at an additional annual cost of about $88,000, as compared to the assumed case of utilizing grid energy. Whilst considering energy storage, a further reduction of 1.94 ktonnes of carbon emissions can be attained by employing 9.8 MWh of thermal energy storage, at an added annual cost of $275,000. Moreover, different scenarios were investigated to study the impact of schemes such as carbon cap-and-trade and carbon capture and storage on economic costs and carbon dioxide emissions. Under the carbon cap-and-trade scenario, the lowest annual cost while minimizing annual carbon dioxide emissions was realized when a high carbon credit value of $ 0.00014 gCO2−1 was utilized. A Pareto front was generated, outlining the optimal cost and carbon emissions when employing different configurations and storage technologies.

Introduction

Fossil-based fuels have been the major source of energy for electricity production worldwide since their discovery in the 1950s. Their relatively low capital cost for electricity generation, ease of transportation and storage has paved their way to being the key stakeholder in the energy industry for decades (Forsberg, 2009). Moreover, they are forecasted to continue playing a pivotal role in meeting at least 60% of global energy demand over the next two decades (Absi Halabi et al., 2015). On the other hand, due to the enormous amount of greenhouse gas emissions associated with their use, they are regarded as environmentally hostile. This has resulted in significant investment on alternate sources of ‘cleaner’ energy. Electrification via renewable sources has significantly increased through power mix and/or direct substitution from the grid (Philibert, 2017). Still, 81% of the global electricity was produced from non-renewable energy sources in 2018, as seen from Fig. 1 (International Energy Agency, 2018).

In the past, renewable energy sources were not as competitive, economically and/or technically, as fossil-based energy sources. Now, they are unanimously agreed as the ‘favorable’ source of future energy due to their cleaner nature. However, intermittent nature of certain renewables, such as solar and wind, has raised concerns regarding their reliability. Several other barriers such as high capital cost, lack of sufficient financial incentives and abundance of fossil fuel resources prevented mass adoption of these resources (Martin & Rice, 2012). Many of these challenges have been successfully conquered to a certain degree in recent years. Reliability issues, in these renewables, arise mainly due to seasonal imbalances. Advancement of energy storage technologies has tackled this matter to a great extent. Additionally, the levelized cost of energy (LCOE) of Photovoltaic (PV) systems was averaged to $0.62/kWh in 2004 (Open Energy Information, 2016). In comparison, the LCOE associated with natural gas was averaged to $0.07/kWh (Logan et al., 2009). However, in 2014, LCOE of PV systems was reported to be as low as $0.056/kWh while LCOE of natural gas was reported to be as low as $0.049/kWh (Lazard, 2014). Several countries have taken different initiatives, incorporated diverse strategies, and set targets to significantly increase their renewable energy share to decrease greenhouse gas emissions.

Modern energy crisis researchers have regarded the strategy of systematic coupling different energy sources more effective as opposed to the direct substitution of fossil-based energy. In addition, they deem the conventional approach of incremental alterations in technology, especially in energy intensive sectors, inefficient (Forsberg, 2009). Only, a few researchers suggest a complete redesign of the whole system based wholly on renewable energy (Mathiesen et al., 2015). On the other hand, the conventional approach is limited and depends on the gradual depletion of reserves as well as monotone increase in oil costs and prices. In 2006, the concept of alternative energy systems was introduced where oil was proposed to be integrated with other energy sources to pave way for new systems. The oil industry is experiencing a persistent decline in the energy return to the energy invested (EROEI) (Taqvi et al., 2019). According to a study conducted in 2013, nearly 10% of produced oil was consumed during the production and refining of oil stage. Around 50% of that consumption is attributed with petroleum refining (Absi Halabi et al., 2015). Yet, the oil industry has economies of scales, giving it the economical advantage of vertical integration. This is crucial for driving energy services of the modern society (Szklo & Schaeffer, 2006).

Petroleum refining is one of the most complex processes in the oil industry that requires tremendous energy. In addition, it is one of the largest carbon dioxide emitters (Alnifro et al., 2017; Dbaberi & Diabat, 2010; Szklo & Schaeffer, 2007). Several studies have been conducted that present the opportunity of fuel mix, especially with biofuel, to gain extra environmental benefits and considerable capital savings (Tong et al., 2014). However, other avenues exist that can yield greater benefits, economically and environmentally. For instance, focusing on electrification offers flexible options to integrate a large share of variable RE; hence, resulting in greater carbon emission reduction (Philibert, 2017). Utilizing solar heat to generate steam is another promising method to help refineries drastically reduce costs. It may be argued that renewable energy technologies can directly be integrated within the grid to experience similar gains. However, Philibert screened over 200 renewable uptake projects realized by industries. Findings suggested that integrating renewables into industrial assets directly result in greater benefits than purchasing renewable power (Philibert, 2017). In addition, it was observed that oil and gas industry has growing needs that could be partly fed on solar heat/power in regions with high solar potential; but such systems need to be onshore. According to Philibert, up to 64% of energy demand of refineries can be fed with solar energy (Philibert, 2017). Similarly, Wang et al. considered 75 crude oil refineries in a study and determined the solar PV and thermal potential to be 21-95 GWthermal and 17-91 GWelectricity, respectively (Wang et al., 2017). Moreover, no economic analysis was carried out and was recommended as future work. When studying the current status and future prospects of solar energy in industry, Absi Halabi et al revealed that no industrial scale of applications was attempted in the refining industry despite the vast potential (Absi Halabi et al, 2015). Alnifro et al. had conducted a preliminary study, developing a simple LP program to determine the feasibility of such an integration; however, a detailed study was recommended that incorporated storage technologies to tackle with intermittency of renewables (Alnifro et al., 2017).

The aim of this study is to develop a general mixed integer linear programming (MILP) mathematical model for optimal renewable energy integration within the process industry with economic and environmental considerations. To tackle seasonal imbalances of renewables, a multi-period model, based on hourly periods, is proposed that incorporates energy storage. This study focuses on studying all essential processes that take place within the industry and the associated CO2 emissions, while considering the potential energy producers. Specifically, this developed model identifies areas of renewable energy integration, calculates the total profit/cost and total CO2 emissions. Different possible constraints are defined on product supply and demand, energy supply and demand, and CO2 management constraints due to possible limitations (e.g., upper and lower bounds) on the production limit, energy availability and/or technological restrictions. Based on these conditions, the model will propose areas of integration of renewable energy within the process industry for optimal operation. Specifically speaking, it will depict the benefit of employing a particular technology at a particular time period. In all, with the aid of this study, decisions/policy makers will be able to assess the economic and environmental impact of integrating renewable energy to their current system and make informed decisions accordingly.

Section snippets

Model framework

Various methodologies are commonly utilized when effectively modeling multi-energy systems such as virtual power plants (VPP), integrated energy systems (IES), micro-grids, energy hubs (EH), intelligent power grids and other Buehler, 2010; Chicco & Mancarella, 2009; Mancarella, 2014; Mohammadi et al., 2017; van Beuzekom et al., 2015). In a comprehensive review of such modeling approaches, Mancarella describes the energy hub approach as the “the most elegant way to describe energy flows in a

Refinery-wide case study

The proposed model is applied to a refinery-wide case study to investigate the applicability of the formulated model. Life-cycle emissions are not included in this study; rather the direct emissions that arise from utilizing the particular technology. Therefore, emissions from renewable energy generation and storage technologies were considered negligible. Several measures have been taken globally to mitigate carbon emissions in almost all industries. Integration of renewable energy within

Results and discussion

Different scenarios were considered in this case study. This section presents the results generated from each of these scenarios and discusses each of them in detail. General Algebraic Modeling System (GAMS) v.24.5.6 was used to solve the model, utilizing the CPLEX 12.6.2.0 solver. The model comprised of 11,773,597 single variables and 10,249,353 single equations. Furthermore, the computation time to solve the base case was about 53.8 minutes on an Intel(R) Core(TM) i7-8565U with 16.0 GB

Conclusion and future work

In this study, a generic framework was developed to optimally integrate renewable energy technologies into the process industry with economic and environmental considerations. To investigate the applicability of the model, a refinery-wide case study was successfully examined. With the help of the developed model, the annual costs and carbon emissions resulting from different types of configurations of energy generation technologies were determined. For the presented case study, up to 10 ktonnes

CRediT authorship contribution statement

Syed Taqvi: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. Ali Almansoori: Conceptualization, Methodology, Formal analysis, Supervision, Writing - review & editing. Ali Elkamel: Project administration, Conceptualization, Methodology, Formal analysis, Supervision, Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

Reprinted Figs. 2 and 3 from Computers & Chemical Engineering, Vol 130, Syed Taqvi, Mohamed Alkatheri, Ali Elkamel, Ali Almansoori, Generic modeling framework of Multi-Energy Systems (MES) within the Upstream Oil Supply Chain (USOSC) network, Page No. 5, Copyright (2019), with permission from Elsevier.

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