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A Data-Driven Process for Optimal Incentive Sharing in Collective Self-Consumption Groups of Residential Users
Abstract
With the widespread adoption of renewable energy systems in residential buildings, particularly in the context of collective self-consumption groups (CSC) and Renewable Energy Communities (REC), understanding user behavior becomes pivotal for enhancing energy efficiency and increasing the energy share among participants for an optimal use of renewable resources. Regardless of which configuration is adopted (CSC or REC), a key aspect is how to share the generated economic benefits from the self-produced energy and identify the fairest way to distribute the incentive derived from the shared energy among users.
In this context, the aim of this work is to introduce a data-driven energy benchmarking process that leverages the analysis of long-term monitoring data of residential buildings to i) characterize energy consumption patterns of users over time, ii) support the development of an optimal incentive sharing mechanism among users involved in such legal entities. The proposed approach is tested on a monitored residential building, located in Northern Italy, which includes 13 flats and is equipped with a centralized photovoltaic system.
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With the proliferation of Internet of Things (IoT) sensors and metering infrastructures in buildings, external energy benchmarking, driven by time series analytics, has assumed a pivotal role in supporting different stakeholders (e.g., policymakers, grid operators, and energy managers) who seek rapid and automated insights into building energy performance over time. This study presents a holistic and generalizable methodology to conduct external benchmarking analysis on electrical energy consumption time series of public and commercial buildings. Differently from conventional approaches that merely identify peer buildings based on their Primary Space Usage (PSU) category, this methodology takes into account distinctive features of building electrical energy consumption time series including thermal sensitivity, shape, magnitude, and introduces KPIs encompassing aspects related to the electrical load volatility, the rate of anomalous patterns, and the building operational schedule. Each KPI value is then associated with a performance score to rank the energy performance of a building according to its peers. The proposed methodology is tested using the open dataset Building Data Genome Project 2 (BDGP2) and in particular 622 buildings belonging to Office and Education category. The results highlight that, considering the performance scores built upon the set of proposed KPIs, this innovative approach significantly enhances the accuracy of the benchmarking process when it is compared with a conventional approach only based on the comparison with the buildings belonging to the same PSU. As a matter of fact, an average variation of about 14% for the calculated performance scores is observed for a testing set of buildings.
Renewable Energy Communities are aggregations of final users/prosumers whose energy comes from renewable power generators. This work systematically assesses the rules of the current and new approaches to sharing the generated benefits obtainable from renewable energy production and its consumption among members of a virtual net-metering energy community. In this work, three sharing mechanism algorithms are proposed to distribute the energy community net profit among the members, utilizing a Token currency, in proportion to the service they provide: financing service, self-consumption service, and both financing and self-consumption services. An energy community, comprising of 100 households and supplied by a 100 kWp photovoltaic system, has been simulated. Six scenarios were designed to determine how profits could be shared between users and prosumers. We focused on the impact that the adoption of a specific sharing mechanism could have on the different categories of members (e.g., low-energy users, high-energy users with ownership, etc.).
This paper proposes an energy sharing method for collective self-consumption called virtual net-billing. The proposed method enables the members of an energy community to fairly share energy in real-time, based on their individual contribution. To achieve this, a mathematical model is developed, which separates the total self-consumption of the community into a portion caused by behind-the-meter self-consumption and a portion caused by the energy sharing. The properties of the proposed method are explored analytically, using cooperative game theory, and through simulations of 600 hypothetical energy communities, developed using real-world data. To assess the fairness of the proposed method, a theoretical framework is developed, which consists of three mutually non-exclusive definitions and three numerical indicators. Using this framework, the method is compared to five existing energy sharing methods from the scientific literature. The results indicate that virtual net-billing provides the lowest trade-off between fairness and computation complexity compared to the existing methods, which makes it suitable for large energy communities and easy implementation in real-world settings.
The demand side is increasingly expected to provide energy flexibility for power grid economy and reliability. Buildings have various flexibility sources that can be effectively utilized for such purposes. According to different requirements of demand responses to power grid on response duration, response direction and response speed (within seconds, minutes, or even longer timescales), building energy flexibility is categorized as fast regulation, moderate regulation, load shedding, load shifting and load covering. In this paper, a comprehensive method is proposed to quantify building energy flexibility based on these categories. Two sets of flexibility indexes (flexibility capacities and flexibility ratios) for the above five energy flexibilities are proposed. An implementation case study is conducted to illustrate the use of these indexes and to validate the effectiveness of using them in flexibility performance assessment of buildings in particular. The impacts of different system design and control parameters on flexibility performance are also investigated quantitatively. The potential economic benefits of utilizing those energy flexibilities are analyzed in a real electricity market with an optimized use of different flexibility sources. Results show that electricity costs can be reduced by up to 21% if the market is available for such grid-responsive buildings.