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Demand-side response (DSR), the incentivised time-shifting of energy use by consumers away from peak times, is regarded as a potentially effective measure to balance electricity supply and demand. This will be even more important in the low-carbon energy system of the future, with a high share of non-dispatchable power, such as variable renewable energy and nuclear power. Most DSR programmes require consumers’ active engagement in shifting end-use activities. Previous studies have, however, rarely revealed socio-demographic factors influential for consumers’ willingness-to-shift specific end-use activities. This study thus aims to fill this research gap and, using a multinomial logistic model to analyse a nationwide survey, identify factors influential for DSR-related decisions. The nationwide survey for 1004 respondents was carried out to collect data about consumers’ willingness-to-shift their daily activities. We focused on the activities that constitute the major part of domestic energy consumption, i.e. cooking, dish-washing, entertainment, heating, laundry and showering. According to the results, consumers’ original timing of the end-use activities, socio-demographic factors, ownership of specific appliances and level of concern for energy-saving are influential for their willingness-to-shift activities. These findings can not only help policymakers make more targeted DSR promotion plans but also help to improve broader modelling tools to better consider consumers’ willingness-to-shift their demand.

The design of cost-effective power systems with high shares of variable renewable energy (VRE) technologies requires a modelling approach that simultaneously represents the whole energy system combined with the spatiotemporal and inter-annual variability of VRE. Here, we soft-link a long-term energy system model, which explores new energy system configurations from years to decades, with a high spatial and temporal resolution power system model that captures VRE variability from hours to years. Applying this methodology to Great Britain for 2050, we find that VRE-focused power system design is highly sensitive to the inter-annual variability of weather and that planning based on a single year can lead to operational inadequacy and failure to meet long-term decarbonization objectives. However, some insights do emerge that are relatively stable to weather-year. Reinforcement of the transmission system consistently leads to a decrease in system costs while electricity storage and flexible generation, needed to integrate VRE into the system, are generally deployed close to demand centres. The rising share of variable renewable energy sources in the grid makes planning future power systems more complex. Zeyringer et al. present an approach that uses multiple weather-years of data and highlights the need to incorporate inter-annual weather variability into planning decisions.

To reduce the amount of water wastage caused by leakage, the utilities have to monitor and detect leakage of water distribution networks periodically. In order to identify leaking pipelines efficiently when limited resources are available, a cluster identification method (CIM) is proposed to establish a priority for leakage detection and to assess whether spatial clusters of high failure-prone areas exist. The proposed CIM evaluates the difference between the observed data and simulated trials to determine the statistical significance of each cluster; a method previously applied only in epidemiology studies to assess the occurrence probabilities of rare diseases for spatial clusters. The CIM suggested in this study is the overlapping local case proportions (OLCP) that uses grids to scope the entire area and then to simulate the number of failures in the neighborhood of each grid. The simulated failure ratios are then compared with the existing records to determine the statistical significance. The statistical significance represents the potential of the grid requiring further leakage detection. Three failure probability estimation methods, including local average, global average, and empirical equation, are utilized to analyze the suitability of the OLCP for use with various probability inputs. A case study in the central region of Taiwan was implemented to demonstrate the applicability of the proposed method. The results indicate that the rate of failure in the following year found within the spatial clusters determined by the OLCP was twice the average amount and thus provided valuable information used to prioritize the pipelines for further inspection.