The emerging leading role of green energy in our society pushes the investigation of new economic and technological solutions. Green energies and smart communities increase efficiency with the use of digital solutions for the benefits of inhabitants and companies. The paper focuses on the development of a methodology for the energy management, combining photovoltaics and storage systems, considering as the main case study a multi-story building characterized by a high density of households, used to generate data which allow feasibility foresights. The physical model of the algorithm is composed by two main elements: the photovoltaics modules and the battery energy storage system. In addition, to gain information about the real-time consumption a machine learning module is included in our approach to generate predictions about the near future demand. The benefits provided by the method are evaluated with an economic analysis, which computes the return of the investment using the real consumptions of a Boarding School, located in Turin (Italy). The case study analyzed in this article showed an increase in purchased energy at the minimum price from 25% to 91% and a 55% reduction in the electricity bill compared to most solutions on the market, with no additional costs and a stabilizing effect on the grid. Finally, the economic analysis shows that the proposed method is a profitable investment, with a breakeven point of thirteen years, due to the very simple implementation and the zero additional cost requested.

The role of ocean energy is expected to grow rapidly in the coming years, and techno-economic analysis will play a crucial role. Nowadays, despite strong assumptions, the vast majority of studies model costs using a top-down approach (the TdA) that leads to an unrepresentative economic model. WEC developers usually go through the the TdA approach because more detailed cost data are not available at an earlier design stage. At a very advanced design stage, some studies have also proposed techno-economic optimisation based on the bottom-up approach (BuA). This entails that the detailed cost metrics presented in the literature are very specific to the WEC type (hence not applicable to other cases) or unrepresentative. This lack of easily accessible detailed cost functions in the current state of the art leads to ineffective optimisations at an earlier stage of WEC development. In this paper, a BuA for WECs is proposed that can be used for techno-economic optimisation at the early design stage. To achieve this goal, cost functions of most common components in the WEC field are retrieved from the literature, exposed, and critically compared. The large number of components considered allows the results of this work to be applied to a vast pool of WECs. The novelty of the presented cost functions is their parameterization with respect to the technological specifications, which already enables their adoption in the design optimisation phase. With the goal of quantifying the results and critically discuss the differences between the TdA and the BuA, the developed methodology and cost functions are applied to a case study and specifically adopted for the calculation of the capital cost of PeWEC (pendulum wave energy converter). In addition, a hybrid approach (HyA) is presented and discussed as an intermediate approach between the TdA and the BdA. Results are compared in terms of capital expenditure (CapEx) and pie cost distribution: the impact of adopting different cost metrics is discussed, highlighting the role that reliable cost functions can have on early stage technology development. This paper proposes more than 50 cost functions for WEC components. Referring to the case study, it is shown that while the total cost differs only slightly (11%), the pie distribution changes by up to 22%. Mooring system and power take-off are the cost items where the TdA and the HyA differ more from the BuA cost estimate.

The increasing penetration of variable renewable energy sources is progressively eroding the ability of conventional power plants to ensure grid stability. Alternative assets, including storage and control systems, are required to fill the gap and maintain the system stable, but all contributions should be previously analysed and studied by means of energy models. However, the modelling of operational constraints such as reserve requirements leads to increased computational burden, especially when employing long-term planning methods with unit-commitment needs. Therefore, planning tools often approximate or neglect reserve needs, leading to major sizing errors. This paper presents a novel model and policy recommendations for integrating short-term aspects and power reserve requirements in the planning of off-grid microgrids. The developed framework includes the formulation of an accurate cost and unit commitment model for fuel-fired generators, the formalisation of power reserve requirements and the representation of the contribution of storage and non-dispatchable technologies to power reserve. The mathematical formulation of the different assets is explored, also investigating the mutual influence of the modeled phenomena. The approach is implemented on a Mediterranean non-interconnected island. The results show acceptable trade-off between computation time and accuracy in one-year hourly simulations. Evidence demonstrates that ignoring the reserve requirements may lead to a 30% misjudgment of costs and an underestimation of the necessary reserve requirements. Enabling storage to provide reserve significantly reduces overall system costs (up to -20%), decreases fuel consumption (-35%), and improves resilience, hence suggesting the pivotal role of storage in providing reserve.

In order to achieve climate goals and limit the global temperature rise, an increasing share of renewable-energy sources (RESs) is required. However, technologies for the use of RESs need to be integrated into the landscape and ecological heritage to ensure a fully sustainable energy transition. This work aims to develop a scalable technique for integrating the estimation of rooftop PV and wind potential into spatial planning, providing a framework to support decision-makers in developing energy policies. The methodology is applied to the minor Sicilian islands, which are characterised by significant environmental and landscape constraints. The methodology is used to identify the areas eligible for the installation of onshore wind turbines and the usable roof surfaces for the installation of PV systems. It is shown that the available technical potential of rooftop PV installations could ensure a higher production than the actual consumption on 13 of the 14 islands studied. Nevertheless, efforts must be made to improve the legal framework, which currently places major limits on the use of wind energy.

The need for a clean and affordable energy supply is a major challenge of the current century. The tough shift toward a sustainable energy mix becomes even more problematic when facing realities that lack infrastructures and financing, such as small islands. Energy modeling and planning is crucial at this early stage of the ecological transition. For this reason, this article aims to improve an established long-run energy model framework, known as “OSeMOSYS,” with an add-on tool able to estimate different types of Levelized Cost Of Electricity (LCOE): a real and theoretical LCOE of each technology and a real and theoretical system LCOE. This tool fills a gap in most modeling frameworks characterized by a lack of information when evaluating energy costs and aims at guiding policymakers to the most appropriate solution. The model is then used to predict future energy scenarios for the island of San Pietro, in Sardinia, which was chosen as a case study. Four energy scenarios with a time horizon from 2020 to 2050—the Business-As-Usual (BAU) scenario, the Current Policy Projection (CPP) scenario, the Sustainable Growth (SG) scenario, and the Self-Sufficient-Renewable (SSR) scenario—are explored and ranked according to the efforts made in them to achieve an energy transition. Results demonstrates the validity of the tool, showing that, in the long run, the average LCOE of the system benefits from the installation of RES plants, passing from 49.1 €/MWh in 2050 in the BAU scenario to 48.8 €/MWh in the ambitious SG scenario. On the other hand, achieving carbon neutrality and the island’s energy independence brings the LCOE to 531.5 €/MWh, questioning the convenience of large storage infrastructures in San Pietro and opening up future work on the exploration of different storage systems.