North Macedonian crisis spurs move to renewable energy

Significance Electricity companies wanted a near-38% rise amid soaring international market prices, but the ERC wanted to avoid a price shock. In November, the government declared an ‘energy crisis’ at the ERC’s request, thanks to reduced domestic electricity supply and the global market situation, and extended it in December for six months. Impacts Investment in infrastructure and technologies should contribute to economic growth and create jobs. Care will have to be taken that closing established mines and power plants do not depress economies locally and raise unemployment. Rising domestic utility prices will inflict political damage on a fragile government. Phasing out coal will improve air quality and population health and well-being, with knock-ons for healthcare priorities and spending.

Exploring domestic energy consumption feedback through interactive annotation

We report on a three-week field study in which participants from nine households were asked to annotate their domestic electricity consumption data using a prototype interactive visualisation. Through an analysis of the annotations and semi-structured interviews, our findings suggest that the intervention helped participants to develop a detailed and accurate understanding of their electricity consumption data. Our results suggest that energy data visualisations can be improved by having users actively manipulate and annotate their data, as doing so encourages reflection on how energy is being used, facilitating insights on how consumption can be reduced. One of the key findings from our thematic analysis was that participants went beyond the data in their reflections, talking about generational issues, upbringing, financial matters, socio-economic comparisons, environmental concern, mistrust towards utilities, convenience, comfort and self-reported waste. Reading beyond the data illustrates the importance of social practices in the context of energy feedback, embedding eco-feedback research into the relevant context of sociology and psychology research.

The Influence of Policy Risk on Swiss Wind Power Investment

Wind energy is one of the most affordable and fastest-growing sources of electricity worldwide. As a large share of wind power generation occurs in the winter season, it could make an important contribution to seasonal diversification of domestic electricity supply. However, the development of wind energy projects in Switzerland has been characterized by long and complex administrative processes, with the planning phase taking up to a decade, more than twice as long as the European average. The objective of this chapter is to quantify the risk premium that lengthy permitting processes imply for wind energy investors in Switzerland and to suggest ways to reduce policy risk. The data have been gathered through 22 confidential interviews with project developers and several cantonal permitting agencies as well as a review of federal and cantonal regulatory documents. Furthermore, a discounted cash flow model was built to compare the profitability indicators (IRR, NPV) and the levelized cost of electricity (LCOE) of a reference case to scenarios with various risks—for example, delays in the permitting process, downsizing the project, or changes in the regulatory environment such as phasing out feed-in tariffs. The model shows that the highest profitability risks are related to the availability of a feed-in tariff, but other changes in the permitting process can also have a critical impact on the project’s bottom line. The findings illustrate a significant policy risk premium in the pre-construction stage faced by wind energy project developers in Switzerland.

Spatial analysis of public residential housing's electricity consumption in relation to urban landscape and building characteristics: A case study in Singapore

In a highly populated country like Singapore, a significant percentage of our gross annual electricity consumption stems from our domestic electricity usage in our residential houses. Analyzing and understanding factors that could influence such patterns is thus essential in order to derive effective measures to reduce usage. In this research, 16 identified variables were calculated and considered in the spatial analyses based on various buffer sizes. Both multilinear regression (MLR) and geographically weighted regression (GWR) based analyses were conducted using each residential housing's Energy Unit Intensity (EUI) as the dependent variable. The analyzed results have shown that building characteristics variables have more significant influences towards energy consumption patterns as compared to urban landscape variables. Although little difference was observed across different buffer sizes, more reliable results were obtained from a smaller buffer size of 50 m, suggesting its suitability in using these obtained values for further prediction model analysis and development. Results obtained from the GWR-based analysis have shown a significant improvement in the goodness-of-fit value compared to the MLR-based analysis, effectively indicating that GWR performs better in this context, apart from its better explanation on the contribution of these identified variables to the EUI in this case study.

DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany

Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.

Synthetic Domestic Electricity Demand in Thailand using A Modified High Resolution Modelling Tool by CREST

A residential electricity demand profile is one of the key roles for investigating the impacts of high penetration of low carbon technologies, such as photovoltaic systems and electric vehicles, on distribution networks. However, it is difficult to identify the true daily electricity consumption of Thailand household, caused by the lack of routine real time demand monitoring and residential electricity meter is normally on monthly which is a low time resolution. In this paper, the CREST Demand Model is employed to simulate a high resolution domestic electricity demand in Thailand, without installing new monitoring devices and customer interruption, through a stochastic process which is a combination of patterns of active occupancy, the outdoor ambient light characteristic and daily activity profiles. Due to the model is based on time use survey data in UK, the outdoor irradiance and appliance configuration are adapted to fit for the Thailand case study. In order to verify the model, the synthetic load profiles by CREST Demand Model is compared against measured data from the actual monitoring in a real low voltage network in Thailand. The results show that it is promising to apply the high resolution demand model by CREST to simulate the domestic electricity demand profiles in Thailand.