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This analysis investigates energy performance of the residential and commercial building sectors in the Electric Reliability Council of Texas (ERCOT) during Winter Storm Uri. ERCOT electricity demand was modeled for the ERCOT baseline building stock as well as for the baseline building stock retrofitted with an efficiency upgrade package, an electrification upgrade package, and an efficiency + electrification upgrade package. The electrification scenario that retrofitted buildings with air-source heat pumps (ASHPs) would have lowered ERCOT daily peak electricity demand relative to the baseline scenario for every day of the year, except during the week of Winter Storm Uri. As the mean outdoor temperature dropped below -5{\deg}C (23{\deg}F), diminishing ASHP efficiency would have resulted in electrification scenario demand exceeding the two distinct baseline scenario daily demand peaks on February 15th and 16th (87.3 GW and 88.7 GW) to hit 111.8 GW and 117.5 GW. The efficiency package would have lowered daily peak demand on these days to 67.0 GW and 68.0 GW. The efficiency + electrification package would have lowered peak demand on these days to 81.5 GW and 85.6 GW. When electricity shortfall profiles were produced by comparing modeled electricity demand to actual ERCOT electricity generation during the storm, the results indicate that the electrification scenario electricity shortfall (1741 GWh) would have been larger than for the baseline scenario (1225 GWh) and the electricity shortfalls for the efficiency scenario (347 GWh) and efficiency + electrification scenario (704 GWh) would have been lower than the baseline. The efficiency, electrification, and efficiency + electrification scenarios would all have lowered summer daily peak demand due to improvements in building cooling efficiency and would have lowered annual electricity consumption by 5.9%, 6.8%, and 11.9%, respectively., Comment: 35 pages, 13 figures

This analysis quantitatively compares the evolution in summer and winter peak demands in the Electric Reliability Council of Texas (ERCOT) service area from 1997 through 2021. Weather data for the days in which peak demand occurred were also compiled to investigate the relationship between peak heating and cooling loads and ambient temperature. This relationship was then applied along with population projections and a climate scenario with medium to high radiative forcing to create winter and summer peak demand growth scenarios for 2025 through 2050. This analysis informs resource planners about how ERCOT peak demand might change in the future and provides new insight into how electric load growth and non-flexible electrified heating demand could have contributed to the February 2021 ERCOT blackouts. We found that historically, summer peak demand growth has been generally stable and approximately linear with time. The stable summer peak load is likely a consequence of fairly constant temperatures observed on summer peak demand days. Conversely, the winter peak demand growth has been less consistent, varying much more around the broader trend. This phenomenon is likely a consequence of high residential electrical heating load on winter peak demand days, which saw temperatures that varied widely from the mean value. Future peak winter and summer electricity demand scenarios indicated that while average temperatures on winter peak demand days will remain fairly constant, they will be more erratic than temperatures on summer peak demand days. As a result, winter peak demand will remain more erratic and will sporadically surpass summer peak demand between 2025 and 2050. Thus, resource planners in ERCOT should place less certainty on winter peak demand projections and an increased level of winter preparedness on both the supply and demand sectors appears warranted., Comment: 28 pages, 10 figures

We analyzed data for the Electric Reliability Council of Texas (ERCOT) to assess shoulder seasons -- that is, the 45 days of lowest total energy use and peak demand in the spring and fall typically used for power plant maintenance -- and whether their occurrence has changed over time. Over the period 1996--2022, the shoulder seasons never started earlier than late March nor later than mid-October, corresponding well with the minimum of total degree days. In the temperature record 1959--2022, the minimum in degree days in the spring moved earlier, from early March to early February, and in the fall moved later, from early to mid-November. Warming temperatures might cause these minima in degree days to merge into a single annual minimum in December or January by the mid-2040s, a time when there is a non-trivial risk of 1-day record energy use and peak demand from winter storms.

Distributed energy resources (DER) as non-wires alternatives, regardless of owner, have the potential to reduce system operating costs and delay system upgrades. However, it is difficult to determine the appropriate economic signal to incentivize DER investors to install capacity that will benefit both the DER investors and the system operator. In an attempt to determine this co-optimal price signal, we present a bilevel optimization framework for determining the least cost solution to distribution system over-loads. A key output of the framework is a spatiotemporal price signal to DER owners that simultaneously guarantees the DER owners' required rate of return and minimizes the system operation costs. The framework is demonstrated with a case by which the system operator considers utility owned battery energy storage systems, traditional system upgrades, and energy purchase from DER owners. The results show that by valuing DER for non-wires alternatives the utility owned storage system sizes can be reduced, less hardware upgrades are necessary, and upfront capital costs as well as operating costs are reduced., Comment: under review

Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.