A multi-agent system approach to exploit demand-side flexibility in an energy community

Reis, Inês F. G.; Gonçalves, Ivo; Lopes, Marta A. R.; Antunes, Carlos Henggeler

doi:10.1016/j.jup.2020.101114

Cited by 35 publications

(10 citation statements)

References 16 publications

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“…230-231). However, recent studies have increasingly focused on the distribution of shared energy resources in local communities [36][37][38], which supports the above-mentioned assumption of energy as a common pool resource.…”

Section: Agent-based Modelling and Simulation Of Future Energy Systemsmentioning

confidence: 72%

Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection

2020

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This paper presents an agent-based model (ABM) for residential end-users, which is part of a larger, interdisciplinary co-simulation framework that helps to investigate the performance of future power distribution grids (i.e., smart grid scenarios). Different modes of governance (strong, soft and self-organization) as well as end-users’ heterogeneous behavior represent key influential factors. Feedback was implemented as a measure to foster grid-beneficial behavior, which encompasses a range of monetary and non-monetary incentives (e.g., via social comparison). The model of frame selection (MFS) serves as theoretical background for modelling end-users’ decision-making. Additionally, we conducted an online survey to ground the end-user sub-model on empirical data. Despite these empirical and theoretical foundations, the model presented should be viewed as a conceptual framework, which requires further data collection. Using an example scenario, representing a lowly populated residential area (167 households) with a high share of photovoltaic systems (30%), different modes of governance were compared with regard to their suitability for improving system stability (measured in cumulated load). Both soft and strong control were able to decrease overall fluctuations as well as the mean cumulated load (by approx. 10%, based on weekly observation). However, we argue that soft control could be sufficient and more societally desirable.

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Section: Agent-based Modelling and Simulation Of Future Energy Systemsmentioning

confidence: 72%

Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection

2020

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“…However, only very rare contributions combine these two approaches, resulting either in purely theoretical assessments of algorithmic advances while not having proven real-world capability, or in ICT infrastructures which have not been used with real grid-related control logic. To the best knowledge of the authors, those rare contributions which are trying to combine these approaches, mainly concentrate on issues like self-consumption optimization (e.g., [66]), taking into account the better simultaneity factors in energy communities. On the other hand, flexibility issues are explored in some new research papers, yet concentrating on user convenience aspects [67] or on market participation [68] rather than effects on the associated LV grid.…”

Section: Comparison and Discussionmentioning

confidence: 99%

Toward an Effective Community Energy Management by Using a Cluster Storage

et al. 2022

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The integration of renewable local energy generation in single households -turning the household into a ''prosumer'' -is an important way to support an ecological transition of the electric power system. However, due to the volatile and distributed nature of most renewable energy sources, the power system may face stability problems when integrating a large number of renewables. The paper at hand describes an approach to overcome these shortages in a two-fold manner: First, the effects of the installed renewables shall be limited locally to a group of households -a so-called ''energy community''. To do so, all the participating households are using existing self-consumption optimization tools. However, when a household has excess energy which can not be consumed locally, this energy is shared among the other participating households by using a cluster storage device, thus enabling a community self-consumption before feeding into the low-voltage distribution grid. Second, the connected operator may request flexibility from the participating households. For that, additional loads or load sheds are triggered by the requesting grid operator, depending on the current situation in the grid. The households decide autonomously about the amount of granted flexibility, receiving respective financial incentives. This work introduces an energy management concept and a prototypical control infrastructure used for the aforementioned functionalities. In a number of simulations and field tests, the proposed approach was successfully evaluated. The article provides a comprehensive overview of the gained results and the conclusions derived from them.

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“…The supplier can also act as a community manager, providing control and optimization, just as in Strategy 2. With optimization of the site and the potential to include additional flexible loads and storage, the consumption performance and savings of the community may improve; however, not as good as with the addition of generation services, as presented in strategy 4 [51]. Examples of entities using this strategy include Megara Energía and the cooperative Energy plus people (E + P); 4.…”

Section: Energy Community Organizational Strategiesmentioning

confidence: 99%

Reviewing and Exploring the Qualitative Impacts That Different Market and Regulatory Measures Can Have on Encouraging Energy Communities Based on Their Organizational Structure

2022

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The emergence of energy communities represents a promising option to democratize the energy system by empowering consumers to take a more active role. This can aid in achieving energy and environmental goals as well as encouraging more equitable distribution of costs and revenues between all parties on the energy system. Despite this potential, energy communities are still a nascent solution, the success of which is heavily influenced by regulations. As a result, there are a wide variety of organizational structures for energy communities at this time. This paper provides a review of the policy landscape in Spain as it relates to energy communities. This work also presents a formalized method for characterizing different energy community structures and provides a qualitative assessment of the impacts of different measures to encourage energy communities with respect to their organizational structure. Findings suggest that many market-focused measures, including wholesale, local flexibility, capacity, and multisector market measures favor larger, more integrated communities, while regulatory, legal, and organizational measures, including peer-to-peer trading, aggregation, and self-consumption favor smaller, more distributed communities. Additionally, when developing policies to encourage the growth of energy communities, policymakers should be cognizant of the progression of policies in the context of the desired outcomes for energy community growth specific to the region or country and its goals.

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A multi-agent system approach to exploit demand-side flexibility in an energy community

Cited by 35 publications

References 16 publications

Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection

Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection

Toward an Effective Community Energy Management by Using a Cluster Storage

Reviewing and Exploring the Qualitative Impacts That Different Market and Regulatory Measures Can Have on Encouraging Energy Communities Based on Their Organizational Structure

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