Optimization under economic uncertainty using a net zero energy commercial office case study

Energy modeling and optimization studies can facilitate the design of cost-effective, low-energy buildings. However, this process inevitably involves uncertainties such as predicting occupant behavior, future climate, and econometric parameters. As presently practiced, energy modelers typically do not quantify the implications of these unknowns into performance outcomes. This paper describes an energy modeling approach to quantify economic risk and better inform decision makers of the economic feasibility of a project. The proposed methodology suggests how economic uncertainty can be quantified within an optimization framework. This approach improves modeling outcomes by factoring in the effect of variability in assumptions and improves confidence in simulation results. The methodology is demonstrated using a net zero energy commercial office building case study located in London, ON, Canada

Energy Modelling Methodology for Community Masterplanning

Net-zero energy is an influential idea in guiding the building stock towards renewable energy resources. Increasingly, this target is scaled to entire communities which may include dozens of buildings in each new development phase. Although building energy modelling processes and codes have been well developed to guide decision making, there is a lack of methodologies for community integrated energy masterplanning. The problem is further complicated by the availability of district systems which better harvest and store on-site renewable energy. In response to these challenges, this paper contributes an energy modelling methodology which helps energy masterplanners determine trade-offs between building energy saving measures and district system design. Furthermore, this paper shows that it is possible to mitigate electrical and thermal peaks of a net-zero energy community using minimal district equipment. The methodology is demonstrated using a cold-climate case-study with both significant heating/ cooling loads and solar energy resources

Methodology for energy and economic modeling of net zero energy communities

Net zero energy (NZE) communities are becoming pivotal to the energy vision of developers. Communities that produce as much energy as they consume provide many benefits, such as reducing life-cycle costs and better resilience to grid outages. If deployed using smart-grid technology, NZE communities can act as a grid node and aid in balancing electrical demand. However, identifying cost-effective pathways to NZE requires detailed energy and economic models. Information required to build such models is not typically available at the early master-planning stages, where the largest energy and economic saving opportunities exist. Methodologies that expedite and streamline energy and economic modeling could facilitate early decision making. This paper describes a reproducible methodology that aids modelers in identifying energy and economic savings opportunities in the early community design stages. As additional information becomes available, models can quickly be recreated and evaluated. The proposed methodology is applied to the first-phase design of a NZE community under development in Southwestern Ontario

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

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. 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 façades 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 typically 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 10154 potential community permutations. This is the first time in literature that a methodology demonstrates the co-optimization of 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 community masterplanning studies

Multi-objective optimal design of a near net zero energy solar house

This paper presents a multi-objective redesign case study of an archetype solar house based on a near net zero energy (NZE) demonstration home located in Eastman, Quebec. Using optimization techniques, pathways are identified from the original design to both cost and energy optimal designs. An evolutionary algorithm is used to optimize trade-offs between passive solar gains and active solar generation, using two objective functions: net-energy consumption and life-cycle cost over a thirty-year life cycle. In addition, this paper explores different pathways to net zero energy based on economic incentives, such as feed-in tariffs for on-site electricity production from renewables. The main objective is to identify pathways to net zero energy that will facilitate the future systematic design of similar homes based on the concept of the archetype that combines passive solar design; energy-efficiency measures, including a geothermal heat pump; and a building-integrated photovoltaic system. Results from this paper can be utilized as follows: (1) systematic design improvements and applications of lessons learned from a proven NZE home design concept, (2) use of a methodology to understand pathways to cost and energy optimal building designs, and (3) to aid in policy development on economic incentives that can positively influence optimized home design

An information driven hybrid evolutionary algorithm for optimal design of a Net Zero Energy House

Building Performance Simulation (BPS) is a powerful tool to estimate and reduce building energy consumption at the design stage. However, the true potential of BPS remains unrealized if trial and error simulation methods are practiced to identify combinations of parameters to reduce energy use of design alternatives. Optimization algorithms coupled with BPS is a process-orientated tool which identifies optimal building configurations using conflicting performance indicators. However, the application of optimization approaches to building design is not common practice due to time and computation requirements. This paper proposes a hybrid evolutionary algorithm which uses information gained during previous simulations to expedite and improve algorithm convergence using targeted deterministic searches. This technique is applied to a net-zero energy home case study to optimize trade-offs in passive solar gains and active solar generation using a cost constraint

Energy modeling methodology for community master planning

Net zero energy is an influential goal guiding the building stock toward renewable energy resources. Increasingly, this target is scaled to entire communities, which may include dozens of buildings in each new development phase. Although building energy modeling processes and codes have been well developed to guide decision making, there is a lack of methodologies for community-integrated energy master planning. The problem is further complicated by the availability of district systems, which better harvest and store on-site renewable energy. In response to these challenges, this paper contributes an energy modeling methodology that helps energy master planners determine trade-offs between building energy saving measures and district system design. Furthermore, this paper shows it is possible to mitigate electrical and thermal peaks of a net zero energy community using minimal district equipment. The methodology is demonstrated using a cold-climate case study with both significant heating/cooling loads and solar energy resources

Planning and Implementing Low Carbon-Communities in Canada

Given the urgency to mitigate climate change, an efficient and effective approach is to reduce carbon reduction at the community level. This is more cost-effective than addressing buildings individually because it opens opportunities for both cost-effective economies of scale to deploy renewable energy and other technologies. While there are currently several low/near net zero-community pilots in existence or in the making, they usually occur in specialized circumstances such as residential subdivisions or model communities, and they tend to focus on technology fixes. To achieve climate change mitigation goals, a more holistic approach is needed that consists of a planning process for existing communities, which integrates energy and resiliency, and involves the utilities. This paper explores common barriers to an integrated process and examines the advantages of a utility-led approach

A method for extracting performance metrics using work-order data

Holistic performance metrics are necessary to understand how operational resources are used and to detect anomalous zones, floors, equipment, and work-order categories in large commercial and institutional buildings. Work-order data in computerized maintenance management systems (CMMS) represent an untapped potential to extract such performance metrics. In this paper, a method to conduct text analytics on CMMS data is developed and demonstrated through a case study in which four years’ worth of data from four large commercial buildings are used. Association rule mining technique is employed to identify building, system, and component-level recurring work-order taxonomies and common failure modes. The results highlight the potential of kernel density functions, decision trees, Sankey diagrams, survival curves and stacked line plots to effectively visualize the temporal, spatial, and categorical anomalies in the complaint patterns. It is identified that often only a few floors and complaint types account for most of the complaints in a building. The analysis of operator comments reveal that the most frequent lighting-related complaints are resolved by replacing ballasts and lights, and the thermal and air quality complaints are addressed by adjusting the temperature setpoints, airflow rates, and fan operation schedules

An optimization methodology to evaluate the effect size of incentives on energy-cost optimal curves

This paper presents a methodology to measure the effect of economic incentives on energy-cost optimal curves. A case-study using a net-zero energy home, located in Montréal, demonstrated the methodology. An EnergyPlus model evaluated the net-energy consumption objective function using 17 design variables. The life-cycle cost objective function was evaluated by post-processing energy simulation results. A multiobjective evolutionary algorithm searched the solution space for energy-cost optimal curves. The proposed methodology may be useful for policy makers seeking opportunities to reduce the initial and life-cycle costs of high-performance buildings. Copyrigh