Simulation modeling for energy consumption of residential consumers in response to demand side management.

Energy efficiency in the electricity distribution system continues to gain importance as demand for electricity keeps rising and resources keep diminishing. Achieving higher energy efficiency by implementing control strategies and demand response (DR) programs has always been a topic of interest in the electric utility industry. The advent of smart grids with enhanced data communication capabilities propels DR to be an essential part of the next generation power distribution system. Fundamentally, DR has the ability to charge a customer the true price of electricity at the time of use, and the general perception is that consumers would shift their load to a cheaper off-peak period. Consequently, when designing incentives most DR literature assumes consumers always minimize total electricity cost when facing energy consumption decisions. However, in practice, it has been shown that customers often override financial incentives if they feel strongly about the inconvenience of load-shifting arrangements. In this dissertation, an energy consumption model based on consumers‟ response to both cost and convenience/comfort is proposed in studying the effects of differential pricing mechanisms. We use multi-attribute utility functions and a model predictive control mechanism to simulate consumer behavior of using non-thermostatic loads vi (prototypical home appliances) and thermostatically controlled load (HVAC). The distributed behavior patterns caused by risk nature, thermal preferences, household size, etc. are all incorporated using an object-oriented simulation model to represent a typical residential population. The simulation based optimization platform thus developed is used to study various types of pricing mechanisms including static and dynamic variable pricing. There are many electric utilities that have applied differential pricing structures to influence consumer behavior. However, majority of current DR practices include static variable pricings, since consumer response to dynamic prices is very difficult to predict. We also study a novel pricing method using demand charge on coincident load. Such a pricing model is based on consumers‟ individual contribution to the monthly system peak, which is highly stochastic. We propose to use the conditional Markov chain to calculate the probability that the system will reach a peak, and subsequently simulate consumers‟ behavior in response to that peak. Sensitivity analysis and comparisons of various rate structures are done using simulation. Overall, this dissertation provides a simulation model to study electricity consumers‟ response to DR programs and various rate structures, and thus can be used to guide the design of optimal pricing mechanism in demand side management

A Few-shot Learning Model based on a Triplet Network for the Prediction of Energy Coincident Peak Days

In an electricity system, a coincident peak (CP) is defined as the highest daily power demand in a year, which plays an important role in keeping the balance between power supply and its demand. Advanced information about the time of coincident peaks would be helpful for both utility companies and their customers. This work addresses the prediction of the five coincident peak days (5CP) in a year. We present a few-shot learning model to classify a day as a 5CP day or a non-5CP day 24-hours ahead. A triplet network is implemented for the 2-way-5-shot classifications on six different historical datasets. The prediction results have an average (across the six datasets) mean recall of 0.933, mean precision of 0.603, and mean F1 score of 0.733

Harnessing Flexible and Reliable Demand Response Under Customer Uncertainties

Demand response (DR) is a cost-effective and environmentally friendly approach for mitigating the uncertainties in renewable energy integration by taking advantage of the flexibility of customers' demands. However, existing DR programs suffer from either low participation due to strict commitment requirements or not being reliable in voluntary programs. In addition, the capacity planning for energy storage/reserves is traditionally done separately from the demand response program design, which incurs inefficiencies. Moreover, customers often face high uncertainties in their costs in providing demand response, which is not well studied in literature. This paper first models the problem of joint capacity planning and demand response program design by a stochastic optimization problem, which incorporates the uncertainties from renewable energy generation, customer power demands, as well as the customers' costs in providing DR. We propose online DR control policies based on the optimal structures of the offline solution. A distributed algorithm is then developed for implementing the control policies without efficiency loss. We further offer enhanced policy design by allowing flexibilities into the commitment level. We perform real world trace based numerical simulations. Results demonstrate that the proposed algorithms can achieve near optimal social costs, and significant social cost savings compared to baseline methods

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

The building sector consumes 75% of US electricity, offering substantial energy, cost, and CO2 emissions savings potential. New technologies enable buildings to flexibly manage electric loads across different times of day and season in support of a low-cost, low-carbon electric grid. Assessing the value of such technologies requires an understanding of building electric load variability at a higher temporal resolution than is demonstrated in previous studies of US building efficiency potential. We adapt Scout, an open-access model of US building energy use, to characterize sub-annual variations in baseline building electricity use, costs, and emissions at the national scale. We apply this baseline in time-sensitive analyses of the energy, cost, and CO2 emissions savings potential of various degrees of energy efficiency and flexibility, finding that efficiency continues to have strong value in a time-sensitive assessment framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency

Long Range Forecast Possibilities for X-Band Radar Construction on Shemya

Current moisture initialization sources lack the spatial and temporal resolution required for mesoscale moisture forecast accuracy critical for military operations. The Global Positioning System (GPS) satellite constellation provides an opportunity to extract accurate moisture observations based on the refraction of the GPS signal through the troposphere. GPS-derived precipitable water (PW) from two different research areas was independently compared with the Air Force Weather Agency s (AFWA\u27s) MM5 PW model output. Results were concurrent with similar studies comparing GPS-derived PW with numerical weather models. The mean correlation in CONUS was 92.5%, while in Alaska it was 72.8%. Mean model biases were 1.22 mm in CONUS and 0.69 mm in Alaska. Mean RMSEs were 4.36 mm in CONUS and 2.76 mm in Alaska. In addition, comparisons were made between moist and dry locations, showing a 21.5% difference in correlation and a 17.8% difference in RMSE. The GPS network s superior temporal resolution captured the diurnal variations in PW, while the model consistently failed to take such variations into account as its forecast progressed. This seems it could be the largest source of error between the two data sets. A number of non-meteorological error sources exist that could impact use of GPS-derived PW in operational applications, such as terrain differences between the GPS receiver sites and the model interpolated heights. These error sources need to be further addressed prior to operational assimilation of this data into military weather models

Modelling of building performance under the UK climate change projections and the prediction of future heating and cooling design loads in building spaces

New climate change projections for the UK were published by the United Kingdom Climate Impacts Programme in 2009. They form the 5th and most comprehensive set of predictions of climate change developed for the UK to date. As one of main products of UK Climate Projections 2009 (UKCP09),the Weather Generator, can generate a set of daily and hourly future weather variables at different time periods (2020s to 2080s) and carbon emission scenarios (low, medium, high) on a 5 km grid scale. In a radical departure from previous methods, the 2009 Projections are statistical- probabilistic in nature. A tool has been developed in Matlab to generate future Test Reference Year (TRY) and Design Reference Years (DRY) weather files from these Projections and the results were verified against results from alternative tools produced by Manchester University and Exeter University as well as with CIBSE's Future Weather Years (FWYs) which are based on earlier (4th generation) climate change scenarios and are currently used by practitioners. The Northumbria tool is computationally efficient and can extract a single Test Reference Year and 2 Design Reference Years from 3000 years of raw data in less than 6 minutes on a typical modern PC. It uses an established ISO method for generating Test Reference Year data and an alternative method of constructing future Design Reference data is proposed

Seasonal variation in household electricity demand: A comparison of monitored and synthetic daily load profiles

Abstract This paper examines seasonal variation in household electricity demand through analysis of two sets of half-hourly electricity demand data: a monitored dataset gathered from 58 English households between July and December 2011; and a synthetic dataset generated using a time-of-use-based load modelling tool. Analysis of variance (ANOVA) tests were used to identify statistically significant between-months differences in four metrics describing the shape of household-level daily load profiles: mean electrical load; peak load; load factor; and timing of peak load. For the monitored dataset, all four metrics exhibited significant monthly variation. With the exception of peak load time, significant between-months differences were also present for all metrics calculated for the synthetic dataset. However, monthly variability was generally under-represented in the synthetic data, and the predicted between-months differences in load factors and peak load timing were inconsistent with those exhibited by the monitored data. The study demonstrates that the shapes of household daily electrical load profiles can vary significantly between months, and that limited treatment of seasonal variation in load modelling can lead to inaccurate predictions of its effects

NDM-552: COMBINED PROBABILITIES OF PEAK WIND AND SNOW LOAD EVENTS

The National Building Code of Canada 2010 (NBCC) defines several loading combination scenarios for use in structural design. Appropriate combination factors are provided based on the probability of failure due to the simultaneous occurrence of the specified loads. Load Combination Cases 3 and 4 of Table 4.1.3.2.A include the combination of wind and snow loads, which are transient in nature. The recommended combination factors are intended to provide a uniform degree of reliability for design. However, in reality, the probability of the simultaneous loading due to wind and snow depends on the local meteorological climate. This probability can be more accurately simulated through the Finite Area Element (FAE) process, which studies the hour-by-hour accumulation and depletion of snow based on historical meteorological records. It takes into account variables such as wind speed and direction, temperature, humidity, water retention in a snow pack and many others. In the present work, the accumulation and depletion of snow on a modelled ground patch and the corresponding wind speeds were computed on an hourly basis to determine the correlation of wind and snow loads. Using this process, this paper investigates the interaction between wind and snow loads for 25 distinct regions in Canada, for both ground and roof snow loads