Heat waves are increasing in severity, duration, and frequency. The Multi-Scenario Extreme Weather Simulator (MEWS) models this using historical data, climate model outputs, and heat wave multipliers. In this study, MEWS is applied for planning of a community resilience hub in Hau’ula, Hawaii. The hub will have normal operations and resilience operations modes. Both these modes were modeled using EnergyPlus. The resilience operations mode includes cutting off air conditioning for many spaces to decrease power requirements during emergencies. Results were simulated for 300 future weather files generated by MEWS for 2020, 2040, 2060, and 2080. Shared socioeconomic pathways 2–4.5, 3–7.0 and 5–8.5 were used. The resilience operations mode results show two to six times increase of hours of exceedance beyond 32.2 °C from present conditions, depending on climate scenario and future year. The resulting decrease in thermal resilience enables an average decrease of energy use intensity of 26% with little sensitivity to climate change. The decreased thermal resilience predicted in the future is undesirable, but was not severe enough to require a more energy-intensive resilience mode. Instead, planning is needed to assure vulnerable individuals are given prioritized access to air-conditioned parts of the hub if worst-case heat waves occur.
Villa, Daniel V.; Schostek, Tyler; Govertsen, Krissy; Macmillan, Madeline
Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%–90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850–1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov–Smirnov p-value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67°C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes.
Puerto Rico faced a double strike from hurricanes Irma and Maria in 2017. The resulting damage required a comprehensive rebuild of electric infrastructure. There are plans and pilot projects to rebuild with microgrids to increase resilience. This paper provides a techno-economic analysis technique and case study of a potential future community in Puerto Rico that combines probabilistic microgrid design analysis with tiered circuits in building energy modeling. Tiered circuits in buildings allow electric load reduction via remote disconnection of non-critiñl circuits during an emergency. When coupled to a microgrid, tiered circuitry can reduce the chances of a microgrid's storage and generation resources being depleted. The analysis technique is applied to show 1) Approximate cost savings due to a tiered circuit structure and 2) Approximate cost savings gained by simultaneously considering resilience and sustainability constraints in the microgrid optimization. The analysis technique uses a resistive capacitive thermal model with load profiles for four tiers (tier 1-3 and non-critical loads). Three analyses were conducted using: 1) open-source software called Tiered Energy in Buildings and 2) the Microgrid Design Toolkit. For a fossil fuel based microgrid 30% of the total microgrid costs of 1.18 million USD were calculated where the non-tiered case keeps all loads 99.9% available and the tiered case keeps tier 1 at 99.9%, tier 2 at 95%, tier 3 at 80% availability, with no requirement on non-critical loads. The same comparison for a sustainable microgrid showed 8% cost savings on a 5.10 million USD microgrid due to tiered circuits. The results also showed 6-7% cost savings when our analysis technique optimizes sustainability and resilience simultaneously in comparison to doing microgrid resilience analysis and renewables net present value analysis independently. Though highly specific to our case study, similar assessments using our analysis technique can elucidate value of tiered circuits and simultaneous consideration of sustainability and resilience in other locations.
Villa, Daniel V.; Schostek, Tyler; Bianchi, Carlo; Macmillan, Madeline; Carvallo, Juan P.
The Multi-scenario extreme weather simulator (MEWS) is a stochastic weather generation tool. The MEWS algorithm uses 50 or more years of National Oceanic and Atmospheric Association (NOAA) daily summaries [1] for maximum and minimum temperature and NOAA climate norms [2] to calculate historical heat wave and cold snap statistics. The algorithm takes these statistics and shifts them according to multiplication factors provided in the Intergovernmental Panel on Climate Change (IPCC) physical basis technical summary [3] for heat waves.
ASHRAE and IBPSA-USA Building Simulation Conference
Villa, Daniel V.; Carvallo, Juan P.; Bianchi, Carlo; Lee, Sang H.
Heat waves are increasing in severity, duration, and frequency, making historical weather patterns insufficient for assessments of building resilience. This work introduces a stochastic weather generator called the multi-scenario extreme weather simulator (MEWS) that produces credible future heat waves. MEWS calculates statistical parameters from historical weather data and then shifts them using climate projections of increasing severity and frequency. MEWS is demonstrated using the EnergyPlus medium office prototype model for climate zone 4B using five climate scenarios to 2060. The results show how changes in climate and heat waves affect electric loads, peak loads, and thermal comfort with uncertainty.
The climate crisis currently being faced by humanity is going to increase extreme weather events which are likely to make long-duration power outages for communities increase in frequency and duration. Microgrids are an important part of electrical resilience for connected communities during power outages. They also can have transactive potential to save energy on electric loads through coordinating distributed energy resources. Microgrids are expensive though. Making electric load coverage available nearly 100% of the time given known design basis threats and component failure statistics is one of the largest drivers of cost. Such high availability is non-negotiable for critical applications such as life saving equipment in a hospital but could perhaps be compromised for less critical loads.. This paper documents an analysis that used the Microgrid Design Toolkit and EnergyPlus simulation results with two energy retrofit options exercised. The results show how increasing energy efficiency and reducing availability to 90% and 80% reduced the calculated price of a photovoltaic and battery storage microgrid in a New Mexico neighborhood by 63% and 70%, respectively. A microgrid with 80% availability with 48-hour islanded run-time capability is therefore suggested as a low-cost method for accelerating microgrid infrastructure penetration into the residential sector. Such an “under-built” microgrid will significantly increase resilience even though it will not guarantee energy security for the non-critical applications in residential households. This will in turn accelerate the growth of storage potential across communities providing greater grid flexibility. The results of the study also show how increased insulation applied to the proposed residential community can be less expensive than creating a larger microgrid that carries larger electric loads. The likelihood that energy retrofits are a better investment than a larger microgrid is inversely proportional to availability. Here, availability is a metric equal to the percentage of the demand load served by the microgrid during power outages, not including the startup period.
This report provides a design study to produce 100% carbon-free electricity for Sandia NM and Kirtland Air Force Base (KAFB) using concentrating solar power (CSP). Annual electricity requirements for both Sandia and KAFB are presented, along with specific load centers that consume a significant and continuous amount of energy. CSP plant designs of 50 MW and 100 MW are then discussed to meet the needs of Sandia NM and the combined electrical needs of both Sandia NM and KAFB. Probabilistic modeling is performed to evaluate inherent uncertainties in performance and cost parameters on total construction costs and the levelized cost of electricity. Total overnight construction costs are expected to range between ~$300M - $400M for the 50 MW CSP plant and between ~$500M - $800M for the 100 MW plant. Annual operations and maintenance (O&M) costs are estimated together with potential offsets in electrical costs and CO2 emissions. Other considerations such as interconnections, land use and permitting, funding options, and potential agreements and partnerships with Public Service Company of New Mexico (PNM), Western Area Power Administration (WAPA), and other entities are also discussed.
• Shows detailed methodology for applying building energy model fleets to institutional heat wave analysis. • Demonstrates uncertainty in heat wave analysis based on meter data. • Shows how detailed building energy models used for energy retrofit analysis can be used for heat wave analyses. • The proposed methodology is much more extensible than data-driven or low-order energy models to detailed cross analyses between energy efficiency and resilience for future institutional studies. • Cross benefits between resilience analysis and energy retrofit analyses are demonstrated. Heat waves increase electric demand from buildings which can cause power outages. Modeling can help planners quantify the risk of such events. This study shows how Building Energy Modeling (BEM), meter data, and climate projections can estimate heat wave effect on energy consumption and electric peak load. The methodology assumes that a partial representation of BEM for an entire site of buildings is sufficient to represent the entire site. Two linear regression models of the BEM results are produced: 1) Energy use as a function of heat wave heat content and 2) Peak load as a function of maximum daily temperature. The uncertainty conveyed in meter data is applied to these regressions providing slope and intercept 95% confidence intervals. The methodology was applied using 97 detailed BEM, site weather data, 242 building meters, and NEX-DCP30 down-scaled climate data for an entire institution in Albuquerque, New Mexico. A series of heat waves that vary from 2019 weather to a peak increase of 5.9 °C was derived. The results of the study provided institutional planners with information needed for a site that is presently growing very rapidly. The resulting regression models are also useful for resilience analyses involving probabilistic risk assessments.