Wind Power Persistence Characterized by Superstatistics

Mitigating climate change demands a transition towards renewable electricity generation, with wind power being a particularly promising technology. Long periods either of high or of low wind therefore essentially define the necessary amount of storage to balance the power system. While the general statistics of wind velocities have been studied extensively, persistence (waiting) time statistics of wind is far from well understood. Here, we investigate the statistics of both high- and low-wind persistence. We find heavy tails and explain them as a superposition of different wind conditions, requiring q-exponential distributions instead of exponential distributions. Persistent wind conditions are not necessarily caused by stationary atmospheric circulation patterns nor by recurring individual weather types but may emerge as a combination of multiple weather types and circulation patterns. This also leads to Fréchet instead of Gumbel extreme value statistics. Understanding wind persistence statistically and synoptically may help to ensure a reliable and economically feasible future energy system, which uses a high share of wind generation

Adoption of Transactional Service in Electronic Government – A Case of Pak-Identity Service

Part 7: Digital GovernanceInternational audienceGovernments around the world are using information and communication technologies to offer both simple information portals and transactional services. A less than one-third of the electronic government (e-government) initiatives focused on the provision of transactional services and understanding on studies related to the adoption of such services using domain-specific adoption theories/models are scarce. Therefore, the objective of this study is to understand the adoption of transactional service system, i.e. ‘Pak-Identity’ by employing a domain-specific model, i.e. Unified Model of Electronic Government Adoption (UMEGA). A UMEGA model with four new constructs is validated using data gathered from 441 citizens from all over Pakistan. A survey was conducted among citizens using simple random sampling technique. The collected data were analyzed employing variance-based structure equation modelling, i.e. partial least squares technique in SmartPLS 3.0 to test the formulated hypotheses. Findings indicate that (1) facilitating conditions is the predictor of effort expectancy, (2) performance expectancy, trust, and herd behaviour are the predictors of attitude, (3) price value, grievance redressal, and attitude are the predictors of behavioural intention to use e-government service. Surprisingly, effort expectancy, facilitating conditions, social influence, and perceived risk are found to be the nonsignificant predictors of adoption of e-government service. Interestingly, new constructs and new relationships are exposed, i.e. trust and herd behaviour on attitude, and price value and grievance redressal on behavioural intention. Moreover, a 55% variance in effort expectancy, 65% variance in attitude, and 40% variance in behavioural intention to adopt e-government has been found. Implications for the academics and managers are also outlined