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The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Ancillary services and other interconnected services are important especially in a competitive operation environment. Since these services are costs related, they should be allocated in order to send the correct price signal to market participants. An average sensitivity method is proposed for allocating services to simultaneous transactions. It can provide results close to power flow-based methods, while requiring as little computational time as traditional sensitivity-based approaches. Except for rare cases, it satisfies the requirement of allocation quantity, allocation ratio, and application feasibility. Test results indicate that the average sensitivity method is a successful allocation algorithm.

Control methods based on global positioning systems (GPS) are reported in multiple literatures recently, which achieve a fixed frequency operation of the microgrid and therefore has the tremendous benefit that any problems related to frequency instability are eliminated. However, the possible interruption of GPS timing signals may cause current circulating and finally leads to instability of the microgrid, yet it is largely neglected in literatures. This paper presents an angle synchronizing mechanism which utilizes the timing signal from GPS satellites and an auxiliary frequency droop loop to ensure synchronization during GPS offline events. Smooth transfer is guaranteed between primary and auxiliary control loops with discrete control architecture. Also, to allow microgrid using GPS-based control to work in tandem with the bulk power system or another frequency droop microgrid, an extra synchronization algorithm is proposed. The viability and performance of the proposed control structure is validated by case studies.

Abstract District heating (DH) enables the utilisation and distribution of heating from sources unfeasible for stand-alone applications and combined with cogeneration of heat and power (CHP), has been the cornerstone of Denmark’s realisation of a steady national primary energy supply over the last four decades. However, progressively more energy-efficient houses and a steadily improving heat pump (HP) performance for individual dwellings is straining the competitive advantage of the CHP–DH combination as DH grid losses are growing in relative terms due to decreasing heating demands of buildings and relatively high DH supply temperatures. A main driver for the DH water temperature is the requirements for domestic hot water (DHW) production. This article investigates two alternatives for DHW supply: (a) DH based on central HPs combined with a heat exchanger, and (b) a combination of DH based on central HPs and a small booster HP using DH water as low-temperature source for DHW production. The analyses are conducted using the energyPRO simulation model and are conducted with hourly varying factors; heating demands, DH grid losses, HP coefficient of performance (COP) and spot market prices in order to be able to analyse the relative performance of the two options and their performance over the year. Results are also compared to individual boilers and individual HPs. The results indicate that applying booster HPs enables the DH system to operate at substantially lower temperature levels, improving the COP of central DH HPs while simultaneously lowering DH grid losses significantly. Thus, DH performance is increased significantly. Additionally, performance for the DH HP with booster combination is considerably better than individual boiler or HP solutions.

The paper comprises a systematic experimental investigation into the influence of AC test source parameters on the leakage current pulses, voltage fluctuations and flashover voltages of polluted insulators. The test techniques include both salt-and clean-fog methods, while the test insulators comprise long-rod and cap and pin strings of different lengths as well as support columns.

A fundamental objective of rangeland managers all over the world is to take measures to ensure that the rangeland being managed stays away from irreversible states in which it provides little or no consumptive and non-consumptive services to humans. Nevertheless, despite a manager's best attempts, the rangeland under study may still hit an irreversible state. Accordingly, given a particular management regime, it is useful to know the length of time taken to hit an irreversible state. In this paper, we conduct a theoretical analysis of this hitherto unstudied question. We first provide a probabilistic description of two range management regimes. Next, we calculate the mean time until an irreversible state is hit for both these regimes. Finally, we provide a numerical example to demonstrate the working of our model and then we discuss the ramifications of our findings for rangeland management in general.

Understanding the system of connections between societal contexts and policy outcomes in municipal governments provides important insights into how community sustainability happens, and why it happens differently in various communities. A growing body of research in recent years has focused on understanding the socio-economic characteristics of communities and cities that are recognized as policy leaders in sustainability. In this paper, we expand the focus beyond the leaders in sustainability as we apply a selection of socio-demographic influences of community sustainability to a large sample of U.S. communities using community classification analytics to identity a range of community types and levels of engagement with sustainability. Our typology presents an integrated and comprehensive perspective on the structure of community sustainability in the United States, highlighting key points of comparison between human capital factors such as population size and density, affluence, home ownership, and adoption of sustainability policy. The analysis provides new insights not only about community leaders in sustainability, but also communities with the civic and social capacity to do more, and the challenges that may inhibit sustainability efforts in others.

The classical approach to load modeling is to divide the load by customer class and then identify and model the load components corresponding to each customer class. This work explores an alternative way to break down the load using readily available data. Namely, using historical usage data and historical weather data the load can be first divided into temperature-dependent and temperature-independent portions. A simple thermal model of the structure(s) served by the electrical circuit forms the theoretical and mathematical basis of this separation. During summer peaking conditions, the temperature-dependent load is generally composed of air conditioners, which must be modeled properly in order to analyze delayed voltage recovery or other dynamic phenomenon. Once the temperature-dependent fraction of the load has been characterized, the size of compressor, fan, and pump motors can be inferred by the customer class.