Renewable energy communities are catalysts of social innovation, the citizens’ engagement in energy actions, and the exploitation of local resources. Thus, this paper defines a model for analyzing and optimally sizing energy systems serving renewable energy communities. Then, the proposed and replicable model was tailored to the economic feasibility analysis of a renewable energy community in the municipality of Tirano (Northern Italy). An energy audit was carried out to identify the electricity production and consumption within the perimeter of the primary substation and the thermal energy demand of the existing district heating network. The technical features of the energy conversion systems serving the renewable energy community were determined: an organic Rankine cycle biomass-based cogeneration plant, a mini-hydro plant, and a distributed photovoltaic system. Moreover, several different scenarios have been identified, in terms of cogeneration operating mode, photovoltaic penetration, and thermal energy economic value. The results show that, moving from 4.22 MW to 5.22 MW of photovoltaic peak power, the annual renewable electricity production increases by 10.1%. In particular, the simple pay back ranges between 4.90 and 4.98 years and the net present value between EUR 12.4 and 13.3 M for CHP operating at full power mode, considering that thermal energy available from the cogeneration unit is sold at EUR 49.2/MWh. These outcomes demonstrate the economic feasibility of wood-biomass-based renewable energy communities, which may help to enlarge the contribution of renewable technologies other than photovoltaic.

The increasing penetration of variable distributed generation causes the transmission lines to operate closer to their design thermal limits. In this context, Dynamic Thermal Rating is a very promising technique, since it permits a better exploitation of the real capability margins of the infrastructures and eliminate network congestions. In this vein, the paper proposes a novel control strategy that allows maintaining the conductor temperature of a given line within its thermal limit through the real-time curtailment of distributed energy resources in the network. The impact of weather volatility and measurement uncertainty on the dynamic response of the controller is evaluated. A comprehensive case study, based on a real-world Italian sub-transmission system and measurement data serve to illustrate the dynamic behavior of the proposed controller. The effect of measurement noise and delays is also discussed. Finally, the performance of the proposed control strategy is compared with a conventional robust optimal power flow approach.

Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the impacts induced by extreme weather events has been considered as one of the most critical issues to address, since they can induce multiple, and large-scale system contingencies. In this context, the development of new computing paradigms for resilience analysis has been recognized as a very promising research direction. To address this issue, this paper proposes two methodologies, which are based on Time Varying Markov Chain and Dynamic Bayesian Network, for assessing the system resilience against extreme wind gusts. The main difference between the proposed methodologies and the traditional solution techniques is the improved capability in modelling the occurrence of multiple component faults and repairing, which cannot be neglected in the presence of extreme events, as experienced worldwide by several Transmission System Operators. Several cases studies and benchmark comparisons are presented and discussed in order to demonstrate the effectiveness of the proposed methods in the task of assessing the power system resilience in realistic operation scenarios.