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The aim is to demonstrate the reliability of a population at consecutive points in time, where a sample at each current point must prove that at least 100p$$ p $$% of the devices function until the next point with a probability of at least 1−ω$$ 1-\omega $$. To test the reliability of the population, we flexibilise standard lifetime models by allowing the unknown parameter(s) of the corresponding counting process to vary in time. At the same time, we assign a prior distribution that assumes the parameters to be constant within a certain interval. This flexibilisation has several advantages: it can be applied for all parametric lifetimes; its Markov property allows the efficient derivation of the number of defective devices, even for a large number of testing times; and the inference is less certain and hence more realistic and leads to less frequent acceptance of poor quality populations. On the other hand, the inference is stabilised by the informative prior. Based on the flexibilisation of the homogeneous Poisson process (HPP), we derive acceptance sampling plans to test the future reliability of a population. Applying the zero failure sampling plans on simulations of Weibull processes shows their good frequentist properties and their robustness. In the case of utility meters subject to German regulations (Mess‐ und Eichverordnung (MessEV). 2014: 2010–2073.), application of the derived sequential sampling plans when the conditions of these plans are met can lead to an extension of the verification validity period. These sampling plans protect the consumer better than those from an HPP and are still cost efficient. [ABSTRACT FROM AUTHOR]

This study endeavored to enhance the efficiency and utility of microcomputer meters. In the past, their role was predominantly confined to remote meter reading, entailing high construction and communication transmission costs, coupled with subsequent maintenance and operational expenditures. These factors collectively impacted the enthusiasm of various stakeholders to invest in this realm. Hence, in alignment with the smart city development initiative, the natural gas industry has pioneered the establishment of an advanced metering infrastructure with heterogeneous wireless sensor networks (HWSNs) at its core. This visionary leap incorporates global machine-to-machine connectivity (G-M2MC) technology, interconnecting all facets of its operations, thereby positioning itself as a trailblazer within the industry. While advancing this endeavor, the project's scheduling aligns with the enterprise's sustainability goals in the early stages of digital transformation. This strategic allocation of resources is responsive to government policies and aspires to cultivate a digitally connected smart green energy hub, thereby expediting the transformation of the living environment. The objective is to provide a stable, secure, cost-effective, and reliable system that can be shared among peers. Furthermore, this study delved into the analysis of congestion avoidance in intelligent Zigbee satellite transport networks based on the HWSNs-GM2MC of non-synchronous satellite orbit system (NGSO) pivotal technologies, utilizing them to integrate the smart LNGas management system (SGMS). Concurrently, it developed application services through the smart meter application interface (SMAPI), distinct from conventional microcomputer meters. However, it is imperative to acknowledge that cloud computing, while processing sensitive data, grapples with issues of latency, privacy, efficiency, power consumption, and zero-trust security risk information management and ethical authority management capabilities in the defense of disaster relief responses. [ABSTRACT FROM AUTHOR]

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by discussing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies and above-average electric utility rates. [ABSTRACT FROM AUTHOR]

• Actual operational data from nine commercial-scale solar PV systems in Virginia over single- to multi-year periods are used. • Facilities analyzed encompass four different end-use categories with summer- and daytime-peaking (SDP) electricity demand. • Granular performance analyses demonstrate a consistent potential to reduce peak demand for individual SDP customers and coincident peak demand for regional grids. • Properly sized systems (large enough but not oversized) can maximize financial savings for commercial solar customers. • With proper scale and policy support, solar PV can provide financial savings to solar customers, utilities, and utility ratepayers alike. The conventional approach to developing commercial behind-the-meter solar discounts peak demand savings and focuses on maximizing the number of installed panels with little consideration for optimizing economic benefits to solar customers, much less for utility ratepayers. While a select number of studies similarly point to the peak demand reduction benefits of distributed solar, they typically rely on simulations and theoretical models to support their conclusions. In contrast, this study draws on a wealth of empirical data from a commercial solar development company, demonstrating consistent peak demand savings for various solar customers as well as grid-scale benefits to utilities and their ratepayers. Using 5- to 30-minute interval data from solar arrays and utility meters, we analyze the impact of solar PV on operational energy demand for nine commercial facilities in Virginia, USA. All facilities analyzed exhibited a mid-day- and summer-peaking demand profile, making them ideal for solar integration. Through analyzing both sets of interval data, we find that solar reduced monthly peak demand by an average of 22.9%, consistently shifted peak load towards off-peak hours, and reduced demand during coincident peak periods for utilities in PJM, the mid-Atlantic regional transmission organization, by an average of 42.3%. In addition to demonstrating substantial peak demand reduction, these findings emphasize the importance of systematically optimizing system capacity according to facility-level energy consumption and utility rate tariffs. This paper debunks conventional dogma that distributed solar burdens non-participants with cross-subsidization, rather highlighting the need for policies which promote distributed solar access and more equitable energy grids. [ABSTRACT FROM AUTHOR]