Distributed evolutionary algorithm for co-optimization of building and district systems for early community energy masterplanning

Abstract

Buildings play a significant role in climate change mitigation. In North America, energy used to construct and operate buildings accounts for some 40% of total energy use, largely originating from fossil fuels [1]. The strategic reduction of these energy demands requires knowledge of potential upgrades prior to a building's construction. Furthermore, renewable energy generation integrated into buildings faç ades and district systems can improve the resiliency of community infrastructure. However, loads that are non-coincidental with on-site generation can cause load balancing issues. This imbalance is due to solar resources peaking at noon, whereas building loads typically peak in the morning and late afternoon or evenings. Ideally, the combination of on-site generation and localized storage could remedy such load balancing issues while reducing the need for fossil fuels. In response to these issues, this paper contributes a methodology that co-optimizes building designs and district technologies as an integrated community energy system. A distributed evolutionary algorithm is proposed that can navigate over 10 154 potential community permutations. This is the first time in literature that a methodology demonstrates the co-optimization buildings and district energy systems to reduce energy use in buildings and balance loads at this scale. The proposed solution is reproducible and scalable for future

Reviewing local and integrated energy system models: insights into flexibility and robustness challenges

“…4 shows, cost minimization is the most frequent objective and is considered for more than 90% of studies while even the second most common objective, CO 2 emissions, is only present in less than a third of all studies. Other objectives such as a minimization of primary energy consumption [126,128] or non-renewable energy consumption [141] or a maximization of self-generation [142] are only encountered in individual examples.…”

Bio-inspired computation: Where we stand and what's next

“…Unsupervised classification over large-scale datasets has also been tackled by adopting dEAs, such as [221], where an island genetic algorithm is proposed for fuzzy partition problems, or [222], where dEAs are applied to improve a k-Means clustering algorithm. There has been also active research around more practical versions of dEAs, in areas such as large-scale optimization [223,224,225], Electromagnetism [226], Computational Fluid Mechanics [227], energy planning [228] or neural networks training [229].…”

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

“…Hence, the ultimate goal of sustainable development in smart buildings is to enhance energy efficiency by minimizing energy losses and environmental impacts. In particular, renewable energy generators can be built into buildings and district infrastructure to boost the sustainable community [47,66]. The discussed BEMS is also known as a nearly or net-zero energy building (nZEB) [8].…”

“…In addition, BA, which is a SI approach inspired by the properties of bats in echolocation, was used in [72], and an ensemble of GA and PSO was used in [74]. Beyond the scale of buildings, Bucking et al [66] suggested an approach to co-optimize buildings and community energy networks to minimize energy utilization and stabilize the loads. The dEA algorithm was introduced to help communities achieve net-zero energy and alleviate peaks applying a district energy system at the same time.…”