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Highlights • Analysis of electricity demand in neighbourhoods, districts and municipalities. • Data-driven classification of electricity demand profiles of nearly 15000 urban areas. • Three statistically different urban area types found: residential, business and mixed. • Urban area types differ in terms of demand profiles and energy user composition. • Often-used residential-type demand represents only a minority of analysed areas. Abstract Cities and communities worldwide are seeking to become more sustainable by transitioning to renewable energy resources, and by introducing electric transportation and heating. The impact and suitability of such technologies for a given area heavily depend on local conditions, such as characteristics of local demand. In particular, the shape of a local demand profile is an important determinant for how much renewable energy can be used directly, and how charging of electric vehicles and use of electric heating affect a local grid. Unfortunately, a systematic understanding of local demand characteristics on different urban scales (neighbourhoods, districts and municipalities) is currently lacking in literature. Most energy transition studies simplify local demand to household demand only. This paper addresses this knowledge gap by providing a novel data-driven classification and analysis of demand profiles and energy user compositions in nearly 15000 neighbourhoods, districts and municipalities, based on data from the Netherlands. The results show that on all urban scales, three types of areas can be distinguished. In this paper, these area types are termed "residential", "business" and "mixed", based on the most prevalent energy users in each. Statistic analysis of the results shows that area types are pairwise significantly different, both in terms of their profiles and in terms of their energy user composition. Moreover, residential-type demand profiles are found only in a small number of areas. These results emphasise the importance of using local detailed spatio-temporal demand profiles to support the transition of urban areas to sustainable energy generation, transportation and heating. To facilitate the implementation of the obtained insights in other models, a spreadsheet modelling tool is provided in an addendum to this paper. [ABSTRACT FROM AUTHOR]

Demand side management is perceived as a tool to support a secure and reliable energy system operation amid growing integration of renewable energy resources. However, the lack of scalable modeling and control procedures hinders the practical implementation. To address this challenge, this paper proposes a novel signal matrix model predictive control algorithm. Compared to existing data-driven methods, this approach explicitly provides stochastic predictions considering both disturbance and measurement errors with few tuning parameters, ensuring reliability by high-probability constraint satisfaction. The performance is extensively compared with three state-of-the-art algorithms in a space heating case study using a high-fidelity simulator. The results are further validated with physical experiments using the same system that the simulator is based on. To assess transferability, the algorithm is further implemented on diverse controlled systems, including a domestic hot water heating system and a stationary electric battery. The simulation results show that, compared to existing data-driven methods, the proposed approach improves constraint satisfaction and energy savings by up to 90 % and 8 %, respectively. The experimental results further confirm that the algorithm is applicable to multiple tasks of demand side management, with reasonable control performance observed in all case studies. • A novel signal matrix model predictive control algorithm. • Extensive comparison of multiple data-driven algorithms on a high-fidelity simulator. • Improvement of constraint satisfaction and energy savings by up to 90% and 8%. • Experimental validation of transferability on various controlled systems. • Data-driven predictive control can be reliable with a stochastic control framework. [ABSTRACT FROM AUTHOR]

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to the retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users' types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users. The performance of the IRMAC is studied and compared with various state-of-the-art methods. The proposed method reached an accuracy of 96% in identifying rooftop PV users and 94.4% in identifying electric heating users, which is comparable to the best solution based on deep learning, while the speed of the inference model is a thousand times faster. Last but not least, the proposed method is a transparent algorithm, which can tackle the concerns regarding the agnostic decision-making process when policies prohibit some machine learning methods. [ABSTRACT FROM AUTHOR]

This paper analyze the benefits of the use of bioenergy, solar thermal and wind energy in a flexible energy system to increase the share of renewable sources (RES) in primary energy supply, reduce primary energy consumption (PEC) and ensure power supply security in Inland Norway, and Norway at large. Firstly, the Inland reference energy system was built and validated using the EnergyPLAN system analysis tool based on the year 2009. Two alternative systems (scenarios), mainly of bio-heat and heat pumps in individual and district heating systems were then constructed and compared with the reference system using EnergyPLAN. The quality of a given energy system can be best described by its PEC, RES, emission levels and socio-economic costs. The result shows that it is plausible to improve the quality of the Inland energy system by optimal resource assortment in the energy mix. Integrated use of bio-heat and heat pumps in individual and district heating systems, as a replacement for direct electric heaters would reduce PEC and socio-economic costs considerably more than intensive bio-heating deployment alone, thereby increasing total domestic green electricity generation. The ability to integrate wind power and its value in the Inland energy system is more reflected by reducing imports of electricity during peak demand periods in winter, as wind power availability in the region is significant as opposed to the low precipitation during these periods. In addition, increasing wind energy penetration helps to limit biomass consumption in a district heating system built on heat pumps and bio-heat boilers. [ABSTRACT FROM AUTHOR]

Abstract: An ejector refrigeration system has been designed and developed to operate with a simulated (electric) heat source, which can be realized in practical applications by renewable energy sources like solar energy, geothermal energy, etc., or waste heat. In this paper, an experimental study on an ejector refrigeration system working with ammonia is presented. The influence of the generator, condenser, and evaporator temperatures on the ejector refrigeration system performance is presented. The entrainment ratio and COP of the system increase with increasing generator and evaporator temperatures and decrease with increasing condenser temperature. [Copyright &y& Elsevier]