A decision-making framework for the optimal design of renewable energy systems under energy-water-land nexus considerations

https://doi.org/10.1016/j.scitotenv.2022.154185Get rights and content

Highlights

  • Detailed models of key components of energy systems with water-land considerations

  • Integrated optimization model for infrastructure planning of energy systems

  • Multi-objective optimization approach for energy-water-land nexus trade-off analysis

  • Derivation of cost power output surrogate models for renewable energy technologies

Abstract

The optimal allocation of land for energy generation is of emergent concern due to an increasing demand for renewable power capacity, land scarcity, and the diminishing supply of water. Therefore, economically, socially and environmentally optimal design of new energy infrastructure systems require the holistic consideration of water, food and land resources. Despite huge efforts on the modeling and optimization of renewable energy systems, studies navigating the multi-faceted and interconnected food-energy-water-land nexus space, identifying opportunities for beneficial improvement, and systematically exploring interactions and trade-offs are still limited. In this work we present the foundations of a systems engineering decision-making framework for the trade-off analysis and optimization of water and land stressed renewable energy systems. The developed framework combines mathematical modeling, optimization, and data analytics to capture the interdependencies of the nexus elements and therefore facilitate informed decision making. The proposed framework has been adopted for a water-stressed region in south-central Texas. The optimal solutions of this case study highlight the significance of geographic factors and resource availability on the transition towards renewable energy generation.

Introduction

Global population is projected to continuously increase (Gu and Andreev K, 2021), and with that comes an increased demand for energy, food, water and land (Garcia and You, 2016; Vakilifard et al., 2018). Land is becoming a scarce resource in many countries, which gives rise to the need for more efficient land use allocation both for food and energy production (Lambin and Meyfroidt, 2011). Furthermore, water usage has been growing globally at more than twice the rate of population (Boretti and Rosa, 2019; United Nations, 2019). Moreover, an increasing number of regions have reached the limit at which water services can be sustainably delivered, especially in arid regions such as Texas (Di Martino et al., 2021). Consequently, achieving food, energy, and water security in the future, while using resources in a sustainable manner is a major challenge we need to address (Avraamidou et al., 2020; Baratsas et al., 2021).

The conventional fossil-based energy provision systems are water intensive and responsible for the majority of global greenhouse gas (GHG) emissions (Bogdanov et al., 2021). A transition towards renewable energy generation could result in providing universal access to clean and affordable energy, reduce GHG emissions, and also decrease water scarcity by eliminating freshwater usage in thermal power plants (Lohrmann et al., 2019). Although, this transition comes with many challenges. Renewable energy sources, such as wind and solar, are inconsistent throughout the day as well as seasonally and spatially. This presents the need for large capacity storage systems to store the intermittent renewable energy for use during low sunlight or wind hours (Allen et al., 2021, Allen et al., 2022; Joskow, 2019). Furthermore, renewable energy generation tends to be much less energy dense than fossil-based methods, requiring much more land area to produce the same amount of energy (Nie et al., 2019a). Another challenge is that growing crops to use for bio-energy requires a large amount of water and land resources (Drews et al., 2020). Therefore, even though renewable energy systems do not have the same environmental impact as fossil fuel based methods, they can still place huge stresses on food, land and water resources, especially in semi-arid areas.

To tackle these challenges, a holistic food-energy-water-land nexus approach needs to be followed to systematically evaluate the interdependence and trade-offs of different renewable energy system solutions (Finley and Seiber, 2014). That is, for the energy system optimization problem, solutions considering the scope of the nexus rather than the individual food, energy, water, and land elements would provide more environmentally and socially sustainable decisions due to the very nature of the interconnected nexus in renewable energy systems. Many challenges emerge when considering nexus wide decision-making approaches, including: (i) the identification and modeling of interactions among nexus elements; (ii) the solution of the highly complex and interconnect nexus system models; (iii) the multiple, often conflicting stakeholder interests and objectives; and (iv) the choice of system boundaries. Therefore, typically only two of the interconnected nexus elements receive direct study due to the complexities that can arise in the multi-faceted, multi-spatial and multi-temporal nexus systems (Garcia and You, 2016), with the energy-water nexus being well studied for renewable energy systems (Allen et al., 2019; Chen et al., 2021; Martín and Grossmann, 2015; Di Martino et al., 2020; Garcia and You, 2015).

Current methodologies used in nexus studies mainly include data-intensive modeling and life cycle analysis for specific technologies or products (Albrecht et al., 2018). These approaches can provide some essential knowledge and are useful for expanding our understanding of food-energy-water-land nexus interactions and addressing social and economic concerns of energy systems under nexus considerations. Although, to achieve a quantitative understanding of the multi-faceted interconnected nexus systems and make technically, environmentally and socially optimal decisions for renewable energy infrastructure, it is required to holistically solve modeling and data challenges using appropriate predictive modeling approaches (Nie et al., 2019b), effective integration of data and models at different scales (Demirhan et al., 2021; Biegler and Lang, 2012), mathematical optimization of trade-offs (Di Martino et al., 2022; Pappas et al., 2021; Avraamidou et al., 2018a), and generic metrics for assessing nexus interconnections in the system (Mohtar and Daher, 2019; Avraamidou et al., 2018b; Baratsas et al., 2022).

The tools and approaches developed by the Process Systems Engineering research community can aid in the solution of the aforementioned challenges, with a number of recent developments tackling the food-energy-water nexus through multi-scale modeling, optimization and trade-off analysis (Garcia and You, 2016; Namany et al., 2021; Nie et al., 2019a; Gao et al., 2021; Garcia and You, 2017; Ahmetovic et al., 2010; Beykal et al., 2020).

In this work, a multi-scale mathematical modeling and optimization framework is developed, capable of holistically addressing energy-water-land nexus interactions in renewable energy systems, and therefore facilitating informed land use allocation decisions for new renewable infrastructure developments. The proposed framework can determine the optimal mix of renewable energy generation and storage systems under different scenarios and objectives, including cost, energy production, water use and land use. The types of solutions generated by the proposed framework include the optimal type and size of energy storage and generation units in order to meet a specified energy demand profile over the course of a given time period. The proposed framework utilizes multi-scale energy systems engineering approaches, along with data analytics and hybrid modeling to capture nexus interactions and uncertainties to facilitate decision making for land-use allocation.

The remainder of this paper is structured as follows: the next section defines the problem under consideration; Section 3 describes the developed framework and the methodology used for its derivation; Section 4 introduces the Amarillo-Texas case study and presents results for different supply and demand scenarios; and finally Section 5 concludes this work.

Section snippets

Problem definition

This work presents a generic framework to optimize the trade-offs in terms of the energy-water-land nexus for the selection, design and allocation of renewable energy systems. To define the system under consideration the set of energy sources, energy conversion technologies, and energy storage technologies locally available, along with energy demand profiles to be satisfied, need to be specified. This is represented in Fig. 1 where an example of different technological options is illustrated.

Framework for the optimal allocation of renewable energy systems under energy-water-land nexus considerations

Fig. 2 summarizes the developed framework that includes two main parts: (i) the modeling of the key components of the energy system; and (ii) their integration into a holistic energy system model.

The first step involves the collection of local resource data, including historic weather data (solar irradiance, wind speeds, etc.), land prices, and infrastructure costs. Detailed models for each energy generation technology are then built based on the input data. The generated data is used in the

Case study - renewable energy system design in Amarillo, TX

To demonstrate the model, a case study was performed for the design of an energy system in Amarillo, Texas. The population in Texas is rapidly increasing, and with that comes an increased demand for energy. The current electrical grid in Texas relies heavily on fossil fuels, which create harmful emissions. Therefore, fossil fuels alone will not be a desirable energy option in the future. Renewable energy resources must be integrated into the grid in order to meet the energy demands for a

Conclusion

A literature gap has been identified regarding comprehensive studies holistically addressing energy-water-land nexus considerations for the design of renewable energy systems.

The proposed framework can address this gap by analyzing renewable energy data in terms of wind speeds, solar irradiance and biomass resource consumption, along with regional factors and perspectives in a combined MILP model. Local restrictions regarding water and land use can be taken into consideration to incorporate the

Abbreviations

    CAES

    compressed air energy storage

    GHG

    global greenhouse gas

    MILP

    mixed-integer linear program

    MINLP

    mixed-integer nonlinear program

    PHS

    pumped hydropower storage

    SAT

    single axis tracking

Parameters

    νn, t

    wind speed at day n and time t at hub height of wind turbine

    νn, t0

    measured wind speed at day n and time t

    νci

    cut in speed of wind turbine

    νco

    cut out speed of wind turbine

    νn

    nominal speed of wind turbine

    νr, n, t

    wind speed at day n and time t at hub height of wind turbine in row r

    Asol, fixed

    area required per energy output of fixed solar panels (ha/kWh/year)

    Asol, SAT

    area required per energy output of fixed solar panels (ha/kWh/year)

    Apanel

    surface area os a single solar panel

    Arot

    rotor area of

Sets

    C

    column index for wind turbine placing

    GB

    gearbox options of wind turbine, GB = {Three − Stage Planetary/Helical, Single − Stage Drive with Medium − Speed Generator, Multi − Path Drive with Multiple Generators, Direct Drive}

    GEN

    generator options of wind turbine, GEN = {Three − Stage Drive with High − Speed Generator, Single − Stage Drive with Medium − Speed and Permanent − Magnet Generator, Multi − Path Drive with Permanent − Magnet Generator, Direct Drive}

    G

    generating technologies, G = {solar

Variables

    κk

    capacity of storage technology k

    κplant

    capacity of solar farm

    ϕ

    azimuth angle of solar panel

    θ

    tilt angle of solar panel

    A

    area

    Adife

    effective area difference between wind and maize

    Adif

    area difference between wind and maize

    Amaize

    area required for biomass farming

    Asolar

    area required for fixed and SAT solar panel systems

    Atotal

    total area of all generating technologies

    Awind

    area required for wind turbines

    Cemp

    overall cost of a single wind turbine (empirical correlations)

    Cinv

    investment cost

    Cland

    cost of land

    C

CRediT authorship contribution statement

Julie Cook: Methodology, Software, Validation, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Marcello Di Martino: Validation, Writing – original draft, Writing – review & editing. R. Cory Allen: Methodology, Software, Validation, Writing – review & editing, Supervision. Efstratios N. Pistikopoulos: Formal analysis, Writing – review & editing, Supervision, Project administration, Funding acquisition. Styliani Avraamidou: Conceptualization,

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

This work was supported by the National Science Foundation (Grant no. 1739977 [INFEWS]). The authors also gratefully acknowledge financial support from the University of Wisconsin-Madison, Texas A&M University, and Texas A&M Energy Institute.

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