Optimizing the integration of renewable energy in existing buildings
Introduction
The Energy Information Administration (EIA) reported in 2016 that the US produces the highest CO2 emission per capita, and more than 80% of these CO2 emissions are related to energy consumption [1]. To address this negative environmental impact, an increasing number of private and public owners of existing buildings require significant reductions in energy consumption in their buildings. For example, a number of federal laws and regulations were enacted to reduce the energy consumption of federal buildings and their related CO2 emissions. In 2007, the United States Congress enacted the Energy Independence and Security Act (EISA2007) to promote green building and sustainability. EISA requires federal buildings to reduce their reliance on fossil fuels by 80% by 2020 and 100% by 2030 compared to the 2003 baseline [2]. Moreover, it requires a saving to investment ratio of more than 1.0 for all energy retrofit projects and a cost-effective use of solar water heating for at least 30% of building needs without disturbing key design features or functional requirement.
To comply with the aforementioned laws and regulations, there is a pressing need to expand the use of onsite renewable energy sources in federal buildings [3]. Expanding the use of onsite renewable energy presents buildings designers and decision-makers with a number of challenges, including how to (a) satisfy the requirements of integrating renewable energy in existing buildings with the least possible upgrade cost; (b) identify an optimal combination of onsite renewable energy upgrades from a large set of feasible alternatives; and (c) consider practical constraints that affect the use of renewable energy measures in existing buildings including and functional requirements and constructability constraints.
To address the aforementioned challenges, several studies were conducted to investigate and analyze the integration of onsite renewable energy in existing buildings. These studies used many techniques in optimizing: (1) selection of energy efficient strategies and renewable energy measures; (2) offsite hybrid renewable energy supply system for distributed generation in off-grid and grid-connected systems; and (3) selection and integration of onsite renewable energy in existing buildings.
The first group of studies utilized a holistic approach to reduce building energy dependence on the grid. Several studies focused on passive design strategies pertained to building architecture and envelop materials to improve the buildings energy efficiency and occupants thermal comfort by: (1) improving the thermal behavior of the building envelope [4], [5], [6], [7], [8]; (2) utilizing passive cooling and natural ventilation to reduce the building cooling load [9], [10], [11], [12]; and (3) passive solar heating to mitigate buildings heating load [13], [14], [15]. For example, Pelaz et al. 2017 [16] explored the impact of different wood cladding samples on building thermal behavior to reduce energy demand. Other studies focused on the integration of energy efficiency and renewable energy measures in buildings. For example, Bucking et al. [17] used an evolutionary algorithm coupled with building simulation to generate and analyze optimal trade-offs between passive and active solar design in houses. The developed optimization model was designed to minimize net energy consumption and life-cycle cost while accounting for available economic incentives for renewable energy. Coakley et al. [18] conducted a review of several approaches for the development and calibration of Building Energy Performance Simulation models and provided a detailed assessment of their merits and limitations. Ascione et al. [19] proposed a three-stage optimization model that was designed to perform (1) sensitivity analysis to determine the proper energy retrofit measures that can enhance the building thermal performance; (2) bi-objective optimization to find optimal combinations of energy retrofit measures that minimize heating and cooling loads; and (3) tri-objective optimization to minimize investment cost, energy consumption, and life-cycle cost. Hamdy et al. [20] proposed three consecutive stages to optimize the selection of (a) building envelope material, (b) heating and cooling systems, and (c) renewable energy measures. Other studies focused on green and low energy buildings aiming to achieve zero or plus energy status [21], [22], [23]. The studies proposed decision support systems to assist designers in selecting an optimal combination of energy efficiency measures and renewable energy sources.
The second group of studies focused on optimizing the size and configuration of hybrid small-scale power generation systems. For example, Ashok [24] minimized the total life-cycle cost of a PV/Wind/micro-hydro system in a remote city in India using an iterative algorithm. Khatod et al. [25] focused on minimizing production cost energy generated by wind and solar panels for small autonomous power systems. The study considers the fluctuation of renewable energy using Monte Carlo Simulation. Shi et al. [26] utilized non-dominated sorting genetic algorithm to optimize the total cost, autonomy, and wasted energy performance of a PV/Wind hybrid system. Gangwar et al. [27] developed an optimization model to minimize the levelized cost of energy for PV/wind/battery supply systems taking into consideration the growth of energy requirements in the future. Sharafi et al. [28] used a particle swarm optimization algorithm to minimize total net present cost, CO2 emission for required system and maximize the renewable energy ratio in hybrid renewable energy systems for residential buildings. Other studies used a probabilistic distribution approach to account for uncertainties in wind speed and solar radiation and improve system dependability on the grid in rural areas [29], [30], [31].
The third group of studies focused on optimizing the selection of onsite renewable energy measures from a set of feasible alternatives. For example, Ascione et al. [32] developed an optimization model to generate optimal tradeoffs between the initial cost of energy generated by a combination of PV, ventilation air preheats systems, solar water heaters, and heat pumps. Lu et al. [33] and González-Mahecha et al. [34] focused on optimizing the integration of renewable energy technologies in low and zero energy buildings. Milan et al. [35] developed a linear programming model to minimize the initial investment cost of utilizing renewable energy technologies in residential buildings. Zheng et al. [36] developed an optimization model to determine the optimal size of a hybrid renewable energy system in a typical residential house that minimizes installation costs. Magrassi et al. [37] developed a decision support system to minimize the carbon footprint of a renewable energy system that utilizes PV, small wind turbines, natural gas micro-turbine in buildings. Other studies focused on integrating renewable energy in building facades. For example, Youssef et al. developed a model for optimizing the integration of building integrated PV in building envelopes to maximize the exposure of PVs to solar energy [38], [39]. Other studies focused on retrofitting building envelope utilizing modular facades and building integrates PV systems [40], [41].
Despite the significant contribution of the aforementioned studies, they are incapable of: (1) minimizing the building upgrade cost needed to fully comply with the earlier described federal laws and regulations requiring the use of renewable energy in existing buildings; (2) accounting for all practical constraints that affect the integration of renewable energy measures in existing buildings including structural capacity, and functional requirements; (3) considering the impact of complex building façade and roof design features on the selection of renewable energy measures; and (4) providing practical visualization of the optimization results.
Section snippets
Objectives
The objective of this research study is to develop a novel model for optimizing the integration of onsite renewable energy measures in existing buildings that overcomes the aforementioned limitations of existing studies. The model is designed to minimize the required upgrade cost of existing buildings to achieve full compliance with owner-specified reductions in building energy consumption levels, such as those provided by the EISA2007 energy requirements for federal buildings. As shown in Fig.
Building segmentation phase
The purpose of this phase is to subdivide the building into a number of segments to enable the model to identify all feasible RE measures that can be integrated in each building segment. For example, a segment of a building façade that has a design-required transparency level for natural lighting limits the types of RE technologies that can be integrated in this building segment. Similarly, the location of a building landscape segment affects its feasibility to utilize RE technologies such as
Model formulation
This phase focuses on the formulation of a novel model to minimize the required upgrade cost for integrating onsite renewable energy measures in existing federal buildings. The developed model is formulated in three steps that focus on: (1) defining the model decision variables; (2) formulating its objective function; and (3) identifying all its practical constraints.
Model implementation
The model is designed to assist decision-makers in finding the optimal solution that corresponds to the minimum upgrade cost and complies with owner-specified energy consumption reduction targets. The model is implemented in three stages: (1) input data; (2) optimization computations; and (3) model output, as shown in Fig. 10.
Case study
The performance of the optimization model is analyzed using a real-life case study to demonstrate its novel capabilities. The case study analyses an educational building with contemporary design and architectural features (see Fig. 11). The building was constructed in 2015 in Urbana, Illinois, and includes more than 20 lab spaces, classrooms, offices, and other service spaces such as restrooms and cafeterias with a total area of 20,717 m2 (223,000 ft2), as shown in Fig. 12. The building was
Conclusion
A novel model was developed for optimizing the integration of onsite renewable energy measures in existing buildings. The model was designed to minimize the required building upgrade cost to achieve full compliance with owner-specified reductions in building energy consumption levels, such as those provided by the EISA2007 energy requirements for federal buildings. The model was developed in four phases: building segmentation, formulation, implementation, and performance evaluation phases.
CRediT authorship contribution statement
Ahmed A. Hassan: Conceptualization, Methodology, Resources, Software, Investigation, Data curation, Formal analysis, Writing - original draft, Visualization. Khaled El-Rayes: Supervision, Methodology, Validation, Writing - review & editing, Funding acquisition.
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.
Acknowledgement
The authors wish to acknowledge William E. O’Neil award for its financial support. The authors also would like to thank the Facilities and Services Department at the University of Illinois at Urbana-Champaign for providing the data required for the case study.
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