Renewable Energy Communities and Smart Grids: Building Loads and Renewables, Development of Optimization and Management Algorithms

Topic Information

Dear Colleagues,

The concept of a Renewable Energy Community (REC) was introduced by the Renewable Energy Directive (RED II), and it is being transposed into national legislations by means of different regulatory actions. The aim of an REC is to increase the share of renewable energy in energy use among the community and improve the energy shared between members. It also aims to achieve a better balance between load demands and energy supply to optimize the welfare of the community by reducing energy consumption, emissions, and costs. In this context, the present Topic aims to collect research on the evaluation of the technical and economic viability of RECs. We are interested in proposals for innovative tools and techniques and articles with a focus on energy sources, the types of renewables used by a community, the kinds of citizens involved, primary energy demand, electric grid features, services, and the related policies of various countries. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Renewable energy community planning;
• Smart Grids;
• Local energy markets and trading mechanisms;
• Machine/deep learning applied to the study and optimization of RECs;
• Business and energy models;
• Load management;
• Renewables;
• Storage;
• Photovoltaics;
• Distributed generation;
• Sustainable and efficient buildings;
• Electric power systems.

Dr. Elisa Belloni
Dr. Cristina Moscatiello
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Batteries batteries	4.8	6.6	2015	18.5 Days	CHF 2700	Submit
Buildings buildings	3.1	4.4	2011	14.9 Days	CHF 2600	Submit
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Smart Cities smartcities	5.5	14.7	2018	26.8 Days	CHF 2000	Submit
Sustainability sustainability	3.3	7.7	2009	19.3 Days	CHF 2400	Submit

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Published Papers (2 papers)

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All Batteries Buildings Energies Smart Cities Sustainability

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15 pages, 835 KB  

Open AccessArticle

Dynamic Knowledge Guided Transfer Optimal Scheduling for Home Energy Management System Considering User Preference

by Xi Zhang

Sustainability 2025, 17(23), 10844; https://doi.org/10.3390/su172310844 - 3 Dec 2025

Viewed by 225

Abstract

Home energy management systems (HEMSs) have attracted considerable research interest in residential appliance management. Although optimal scheduling of home appliances has been extensively studied, these problems are fundamentally dynamic multi-objective optimization problems. This paper proposes a dynamic appliance scheduling model under time-of-use electricity [...] Read more.

Home energy management systems (HEMSs) have attracted considerable research interest in residential appliance management. Although optimal scheduling of home appliances has been extensively studied, these problems are fundamentally dynamic multi-objective optimization problems. This paper proposes a dynamic appliance scheduling model under time-of-use electricity pricing based on user’s preferences, to minimize energy costs and user dissatisfaction. A knee point-based manifold transfer algorithm (KPMT-DMOEA) is proposed to solve the scheduling problem. This approach leverages high-quality knee points from previous environments to generate optimized initial populations in response to environmental changes, thereby improving solution quality and convergence speed. The experimental results validate the effectiveness and feasibility of the proposed scheduling framework. By making a comparison with state-of-the-art algorithms, the experimental results demonstrate that the proposed method outperforms others and is able to efficiently generate optimal schedules for each appliance under different environments. Full article

(This article belongs to the Topic Renewable Energy Communities and Smart Grids: Building Loads and Renewables, Development of Optimization and Management Algorithms)

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37 pages, 2229 KB  

Open AccessReview

Energy Communities, Renewables, and Electric Mobility in the Italian Scenario: Opportunities and Limitations in Historic Town Centers

by Muhammad Jawad Ul Hassan, Elisa Belloni, Antonio Faba and Ermanno Cardelli

Energies 2025, 18(22), 5999; https://doi.org/10.3390/en18225999 - 15 Nov 2025

Viewed by 344

Abstract

The emergence of energy communities in the energy transition world could be beneficial for sustainable development, particularly in ancient town centers. The interaction between energy groups, renewable energy sources, and electric vehicles in Italy’s historic cities is the primary concern of this work. [...] Read more.

The emergence of energy communities in the energy transition world could be beneficial for sustainable development, particularly in ancient town centers. The interaction between energy groups, renewable energy sources, and electric vehicles in Italy’s historic cities is the primary concern of this work. It examines the potential for these interconnected components to collaborate to revitalize Italian historical sites and ensure their sustainable management. This study focuses on the overall potential of energy communities to boost democracy and energy security, and decrease negative environmental impacts. It is studied by analyzing rules and regulation along with new technologies and changes in society and economy that are affecting the energy sector. This paper focuses on approaches to the application of renewable energy resources and examines electric mobility and its role in realizing ecologically sustainable transportation in cities. It also demonstrates the needs to occur with infrastructures, use rates and policies that must be implemented to get a person to drive electric cars around historic districts. This improves the management’s capacity to implement an easy transition to low carbon because, related to energy production and consumption, techniques of comprehensive planning should be adopted. Full article

(This article belongs to the Topic Renewable Energy Communities and Smart Grids: Building Loads and Renewables, Development of Optimization and Management Algorithms)

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