• Energy Research

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environ-mental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simula-tions. Lastly, an economic evaluation of the considered system is presented.

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environ-mental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simula-tions. Lastly, an economic evaluation of the considered system is presented.

Background Energy communities facilitate several advantages, including energy autonomy, reduced greenhouse gas emissions, poverty mitigation, and regional economic development. They also empower citizens with decision-making and co-ownership prospects in community renewable projects. Integrating renewable energy sources and sector coupling is a crucial strategy for flexible energy systems. However, demonstrating clean energy transition scenarios in these communities presents challenges, including technology integration, flexibility activation, load reduction, grid resilience, and business case development. Methods Based on the system of systems approach, this paper introduces a 4-step funnel approach and a 4-step reverse funnel approach to systematically specify and detail demonstration scenarios for energy community projects. The funnel approach involves four steps. First, it selects demonstration scenarios promoting energy-efficient state-of-the-art renewable technologies and storage systems, flexibility through demand side management techniques, reduced grid dependence, and economic viability. Second, it lists all existing and planned project technologies, analysing energy flows. Third, it plans actions at different levels to implement the demonstration scenarios. Fourth, it validates the strategies using key performance indicators (KPI) to quantify the effectiveness of the planned measures. Furthermore, the reverse funnel approach delves deeper into the demonstration scenarios. The four steps involve identifying stakeholder perspectives, describing scenario scopes, listing conditions for realisation, and outlining business models, including value chains and economic assumptions. Results This approach provides a detailed analysis of the demonstration scenarios, considering actors, objectives, boundary conditions, and business assumptions. The methodologies are exemplified in three diverse European energy communities extending across residential, commercial, tertiary, and industrial establishments, allowing power-to-x and sector coupling opportunities. The paper also suggested thirteen KPIs for validating renewable-focused energy community projects. Conclusions Finally, the paper recommends increased collaboration between energy communities, knowledge sharing, stakeholder engagement, transparent data collection and analysis, continuous feedback, and method improvement to mitigate policy, technology, business, and market uncertainties.

Background Energy communities facilitate several advantages, including energy autonomy, reduced greenhouse gas emissions, poverty mitigation, and regional economic development. They also empower citizens with decision-making and co-ownership prospects in community renewable projects. Integrating renewable energy sources and sector coupling is a crucial strategy for flexible energy systems. However, demonstrating clean energy transition scenarios in these communities presents challenges, including technology integration, flexibility activation, load reduction, grid resilience, and business case development. Methods Based on the system of systems approach, this paper introduces a 4-step funnel approach and a 4-step reverse funnel approach to systematically specify and detail demonstration scenarios for energy community projects. The funnel approach involves four steps. First, it selects demonstration scenarios promoting energy-efficient state-of-the-art renewable technologies and storage systems, flexibility through demand side management techniques, reduced grid dependence, and economic viability. Second, it lists all existing and planned project technologies, analysing energy flows. Third, it plans actions at different levels to implement the demonstration scenarios. Fourth, it validates the strategies using key performance indicators (KPI) to quantify the effectiveness of the planned measures. Furthermore, the reverse funnel approach delves deeper into the demonstration scenarios. The four steps involve identifying stakeholder perspectives, describing scenario scopes, listing conditions for realisation, and outlining business models, including value chains and economic assumptions. Results This approach provides a detailed analysis of the demonstration scenarios, considering actors, objectives, boundary conditions, and business assumptions. The methodologies are exemplified in three diverse European energy communities extending across residential, commercial, tertiary, and industrial establishments, allowing power-to-x and sector coupling opportunities. The paper also suggested thirteen KPIs for validating renewable-focused energy community projects. Conclusions Finally, the paper recommends increased collaboration between energy communities, knowledge sharing, stakeholder engagement, transparent data collection and analysis, continuous feedback, and method improvement to mitigate policy, technology, business, and market uncertainties.

Electricity access in developing countries, where the availability of public distribution grids is still poor, is considered a key factor for improvement of people life conditions. In these situations, the lack of a reliable grid can be mitigated by the introduction of stand-alone DC microgrids, including small Photovoltaic (PV) generators and storage devices. This paper focuses on optimal energy management and power supply reliability of such a microgrid. In particular, a Model-Predictive-Control (MPC) - based control system is introduced to optimally manage storage devices and coordinate load shedding actions. Additionally, an Artificial-Neural-Network (ANN) - based predictor is introduced to manage unpredictable solar irradiance and temperature variations. The availability of reliable adaptive forecasts provided by the ANN-based predictor increases the efficiency of the optimization performed by the MPC-based control over the prediction horizon, avoiding the well-known issues related to optimization performed on unreliable forecast. In this paper, the proposed control approach is detailed for a specific case study and its advantages with respect to traditional controller algorithms are highlighted by comprehensive numerical simulations. The presented results highlight that the proposed MPC controller provides a substantial increment in power supply reliability with respect to standard controls, especially for priority loads. This is obtained at the expense of an increased battery stress, which is acceptable for electricity access applications where power supply reliability is usually the foremost need.

Electricity access in developing countries, where the availability of public distribution grids is still poor, is considered a key factor for improvement of people life conditions. In these situations, the lack of a reliable grid can be mitigated by the introduction of stand-alone DC microgrids, including small Photovoltaic (PV) generators and storage devices. This paper focuses on optimal energy management and power supply reliability of such a microgrid. In particular, a Model-Predictive-Control (MPC) - based control system is introduced to optimally manage storage devices and coordinate load shedding actions. Additionally, an Artificial-Neural-Network (ANN) - based predictor is introduced to manage unpredictable solar irradiance and temperature variations. The availability of reliable adaptive forecasts provided by the ANN-based predictor increases the efficiency of the optimization performed by the MPC-based control over the prediction horizon, avoiding the well-known issues related to optimization performed on unreliable forecast. In this paper, the proposed control approach is detailed for a specific case study and its advantages with respect to traditional controller algorithms are highlighted by comprehensive numerical simulations. The presented results highlight that the proposed MPC controller provides a substantial increment in power supply reliability with respect to standard controls, especially for priority loads. This is obtained at the expense of an increased battery stress, which is acceptable for electricity access applications where power supply reliability is usually the foremost need.