New insights for setting up contractual options for demand side flexibility

This paper exploits the Duration-of-Use of the demand patterns as a key concept for dealing with demand side flexibility. Starting from the consideration that fine-grained energy metering is not used at the point of supply of the electricity consumers, i.e., the granularity of the energy measured (at time steps of 15 minutes, 30 minutes or one hour), the event-based energy metering (EDM) is indicated as a viable option to provides a very detailed reconstruction of the demand patterns. The use of EDM enables high-quality tracking of the demand peaks with a reduced number of data with respect to the ones needed to measure energy at regular time steps for reaching a similar peak tracking capability. From the EDM outcomes, a new class of options for setting up tariffs or contracts for flexibility, based on the demand duration curve, is envisioned

New insights for setting up contractual options for demand side flexibility

This paper exploits the Duration-of-Use of the demand patterns as a key concept for dealing with demand side flexibility. Starting from the consideration that fine-grained energy metering is not used at the point of supply of the electricity consumers, i.e., the granularity of the energy measured (at time steps of 15 minutes, 30 minutes or one hour) is not sufficient to represent the variability of the demand patterns, event-based energy metering (EDM) is indicated as a viable option to provide a very detailed reconstruction of the demand patterns. In particular, the use of EDM enables high-quality tracking of the demand peaks with a reduced number of data with respect to the ones needed to measure energy at regular time steps for reaching a similar peak tracking capability. From the EDM outcomes, a new class of options for setting up tariffs or contracts for flexibility, based on the demand duration curve, is envisioned

State of the Art and Trends Review of Smart Metering in Electricity Grids

Climate change, awareness of energy efficiency, new trends in electricity markets, the obsolescence of the actual electricity model, and the gradual conversion of consumers to prosumer profiles are the main agents of progressive change in electricity systems towards the Smart Grid paradigm. The introduction of multiple distributed generation and storage resources, with a strong involvement of renewable energies, exposes the necessity of advanced metering or Smart Metering systems, able to manage and control those distributed resources. Due to the heterogeneity of the Smart Metering systems and the specific features of each grid, it is easy to find in the related literature a wide range of solutions with different features. This work describes the key elements in a Smart Metering system and compiles the most employed technologies and standards as well as their main features. Since Smart Metering systems can perform jointly with other activities, these growing initiatives are also addressed. Finally, a revision of the main trends in Smart Metering uses and deployments worldwide is included.his work has been partially supported by the Spanish Ministry of Economy and Competitiveness (project TEC2015-67868-C3-1-R), the University of the Basque Country (UPV/EHU) within the program for the specialization of the postdoctoral researcher staff, and Microgrids with Renewable Distributed Generation (MIGEDIR) (project 713RT0468), funded by the Science and Technology for Development Iberoamerican Program (CYTED)

Real-time Event-based Energy Metering

Real-time knowledge about the energy being exchanged sustains energy efficiency applications and services. The event-based data-saving approach to the measurements of electric energy has emerged recently with conceptual and practical implications, also thanks to the manufacturing of a new technological solution. This paper explains the fundamental underlying concepts that have led to these improvements through real-case examples. This work borrows from ontology the distinction between the concepts of endurants and perdurants, associating these concepts to the quantities involved in the energy metering process. In the new event-based computational framework, energy metering is interpreted by detecting average power and accumulated energy variations, as well as highlighting the importance of the information provided by the rate of change of energy and by the rate of events gathered from meters

Data Consistency for Data-Driven Smart Energy Assessment

In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received. Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects

A Review and Synthesis of the Outcomes from Low Carbon Networks Fund Projects

The Low Carbon Networks Fund (LCNF) was established by Ofgem in 2009 with an objective to “help Distribution Network Operators (DNOs) understand how they provide security of supply at value for money and facilitate transition to the low carbon economy”. The £500m fund operated in a tiered format, funding small scale projects as Tier 1 and running a Tier 2 annual competitive process to fund a smaller number of large projects. By 31st March 2015, forty Tier 1 projects and twenty-three Tier 2 projects had been approved with project budgets totalling £29.5m and £220.3m respectively. The LCNF governance arrangements state that projects should focus on the trialling of: new equipment (more specifically, that unproven in GB), novel arrangements or applications of existing equipment, novel operational practices, or novel commercial arrangements. The requirement that learning gained from projects could be disseminated was a key feature of the LCNF. The motivation for the review reported here was a recognition that significant learning and data had been generated from a large volume of project activity but, with so many individual reports published, that it was difficult for outside observers to identify clear messages with respect to the innovations investigated under the programme. This review is therefore intended to identify, categorise and synthesise the learning outcomes published by LCNF projects up to December 2015

Systematic review of energy theft practices and autonomous detection through artificial intelligence methods

Energy theft poses a significant challenge for all parties involved in energy distribution, and its detection is crucial for maintaining stable and financially sustainable energy grids. One potential solution for detecting energy theft is through the use of artificial intelligence (AI) methods. This systematic review article provides an overview of the various methods used by malicious users to steal energy, along with a discussion of the challenges associated with implementing a generalized AI solution for energy theft detection. In this work, we analyze the benefits and limitations of AI methods, including machine learning, deep learning, and neural networks, and relate them to the specific thefts also analyzing problems arising with data collection. The article proposes key aspects of generalized AI solutions for energy theft detection, such as the use of smart meters and the integration of AI algorithms with existing utility systems. Overall, we highlight the potential of AI methods to detect various types of energy theft and emphasize the need for further research to develop more effective and generalized detection systems, providing key aspects of possible generalized solutions