A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.

Buildings are responsible for over 30% of global final energy consumption and nearly 40% of total CO₂ emissions. Thus, rapid penetration of renewable energy technologies (RETs) in this sector is required. Integration of renewable energy sources (RESs) into residential buildings should not only guarantee an overall neutral energy balance over long term horizon (nZEB concept), but also provide a higher flexibility, a real-time monitoring and a real time interaction with end-users (smart-building concept). Thus, increasing interest is being given to the concepts of Hybrid Renewable Energy Systems (HRES) and Multi-Energy Buildings, in which several renewable and nonrenewable energy systems, the energy networks and the energy demand optimally interact with each other at various levels, exploring all possible interactions between systems and vectors (electricity, heat, cooling, fuels, transport) without them being treated separately. In this context, the present paper gives an overview of functional integration of HRES in Multi-Energy Buildings evidencing the numerous problems and potentialities related to the application of HRESs in the residential building sector. Building-integrated HRESs with at least two RESs (i.e., wind–solar, solar–geothermal and solar–biomass) are considered. The most applied HRES solutions in the residential sector are presented, and integration of HRES with thermal and electrical loads in residential buildings connected to external multiple energy grids is investigated. Attention is focused on the potentialities that functional integration can offer in terms of flexibility services to the energy grids. New holistic approaches to the management problems and more complex architectures for the optimal control are described.

This article addresses the problem of optimizing voltage profiles in distribution networks. The voltage optimization is split into two stages; the former is performed offline and the latter online. First, the network is partitioned into several weakly coupled voltage control zones (VCZs) with pilot nodes (PNs). Then, the partitioning is used to optimize the voltage profiles of the distribution systems on the frame of a two time-scale-based coordinated approach. At the first level, a centralized voltage optimization problem (VOP), minimizing the distance of bus voltages at the PNs from their reference values and subject to linearized power flow equations, is solved to fix the positions of the on-load tap changer and of step-voltage regulators, and the reactive powers provided by capacitor banks. At the second level, the VOP is implemented according to a decentralized approach, in which the solution is obtained by applying a distributed algorithm based on the alternating direction method of multipliers. It optimizes in each VCZ the voltage at the PN by acting on the active and reactive powers provided by the distributed energy resources present in the VCZ; the VCZ solutions are driven to the global optimum of the whole distribution system by a limited data exchange between the PNs. The proposed approach reduces the complexity and computational burden typical when solving the VOP on a large scale system. The proposed strategy is tested on the modified IEEE 123-bus system; various load and generation scenarios are analyzed proving the effectiveness of the proposed approach in achieving the objective of voltage regulation.

This paper deals with the problem of the voltage profile optimization in a distribution system including distributed energy resources. Adopting a centralized approach, the voltage optimization is a non-linear programming problem with large number of variables requiring a continuous remote monitoring and data transmission from/to loads and distributed energy resources. In this study, a recently-proposed Jacobian-based linear method is used to model the steady-state operation of the distribution network and to divide the network into voltage control zones so as to reformulate the centralized optimization as a quadratic programming of reduced dimension. New clustering methods for the voltage control zone definition are proposed to consider the dependence of the nodal voltages on both active and reactive powers. Zoning methodologies are firstly tested on a 24-nodes low voltage network and, then, applied to the voltage optimization problem with the aim of analyzing the impact of the R/X ratios on the zone evaluation and on the voltage optimization solution.

A key issue in Low Voltage(LV) distribution systems is to identify strategies for the optimal management and control in the presence of Distributed Energy Resources (DERs). To reduce the number of variables to be monitored and controlled, virtual levels of aggregation, called Virtual Microgrids (VMs), are introduced and identified by using new models of the distribution system. To this aim, this paper, revisiting and improving the approach outlined in a conference paper, presents a sensitivity-based model of an LV distribution system, supplied by a Medium/Low Voltage (MV/LV) substation and composed by several feeders, which is suitable for the optimal management and control of the grid and for VM definition. The main features of the proposed method are: it evaluates the sensitivity coefficients in a closed form; it provides an overview of the sensitivity of the network to the variations of each DER connected to the grid; and it presents a limited computational burden. A comparison of the proposed method with both the exact load flow solutions and a perturb-and-observe method is discussed in a case study. Finally, the method is used to evaluate the impact of the DERs on the nodal voltages of the network.