Vasco Medici

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

We consider the problem of estimating the unobserved amount of photovoltaic (PV) generation and demand in a power distribution network starting from measurements of the aggregated power flow at the point of common coupling and local global horizontal irradiance (GHI). The estimation principle relies on modeling the PV generation as a function of the measured GHI, enabling the identification of PV production patterns in the aggregated power flow measurements. Four estimation algorithms are proposed: the first assumes that variability in the aggregated PV generation is given by variations of PV generation, the next two use a model of the demand to improve estimation performance, and the fourth assumes that, in a certain frequency range, the aggregated power flow is dominated by PV generation dynamics. These algorithms leverage irradiance transposition models to explore several azimuth/tilt configurations and explain PV generation patterns from multiple plants with nonuniform installation characteristics. Their estimation performance is compared and validated with measurements from a real-life setup including four houses with rooftop PV installations and battery systems for PV self-consumption.

Modeling and Simulation of a Residential Neighborhood with Photovoltaic Systems Coupled to Energy Storage Systems

The diffusion of the photovoltaic installations is reaching significant levels in the low voltage electric grid, therefore the need of effective energy management strategies increases. Within the Swiss2Grid (S2G) pilot project we are designing and testing a tool that allows the decentralized management of the distributed generation and consumption by controlling local energy storage systems and shifting the activation of households loads. In order to assess the effects of higher levels of diffusion of photovoltaic and storage system into the low voltage grid, we modeled the pilot residential neighborhood in Modelica, a multi-domain, open-source modeling language. In this paper we describe the components and the selected penetration scenarios. We finally present the results of the simulations, analyzing the effects of the chosen scenarios on neighborhood overall power, voltage instabilities and self-consumption.

Distributed demand side management using electric boilers

Demand side management is a promising approach towards the integration of renewable energy sources in the electric grid, which does not require massive infrastructural investments. In this paper, we report the analysis of the performance of a demand side management algorithm for the control of electric boilers, developed within the context of the GridSense project. GridSense is a multi-objective energy management system that aims at decreasing both the end user energy costs and the congestions on the local feeder. The latter objective is minimized exploiting the existent correlation between the voltage measured at the connection point to the grid and the power flow measured at the low voltage transformer. The algorithm behavior has been firstly investigated by means of simulation, using typical water consumption profiles and a simplified thermodynamic model of the electric boiler. The simulation results show that the algorithm can effectively shift the boiler’s electric consumption based on voltage and price profiles. In the second phase, we analyzed the results from a pilot project, in which the GridSense units were controlling the boilers of four households, located in the same low voltage grid.

Inferring power fluctuations at a low voltage transformer from local voltage measurements in the underlying distribution grid

The incremental deployment of small scale stochastic generators has a significant impact low voltage grids. We investigated the applicability of local voltage measurements at household sockets as predictors of the power at the low voltage branch of the transformer. The general goal is to evaluate the feasibility of a decentralized demand-side control algorithm using local voltage as the regulation input. In this paper we introduce the approach adopted by our study and describe the experimental results, which demonstrate the possibility of using a local voltage measurement as an input signal for decentralized control.

HAC: Hardware design for a Smart-Home and Smart-Grid decentralised load management system

The growth of energy demand and decentralised renewable energy generation (e.g. photovoltaic, eolic) can lead to electric grid imbalances requiring extra investments in the electric grid infrastructure. One of the goals of Smart-Home and Smart-Grid solutions is to solve this issue. The majority of the solutions are focused on centralised load management. Furthermore most of the Smart-Home and Smart-Grid publications analyse the topics from the application point of view so that there is a lack of specific descriptions of the infrastructure and of the used technology. SUPSI has proposed and verified a decentralised and innovative Smart-Grid approach. To demonstrate the feasibility of this approach a custom HAC (Household Appliance Controller) and a communication infrastructure has been developed. This paper describes this design, and its use in several pilot projects performed in Switzerland (Swiss2Grid, RiParTi 2.0, HCD 2.0) which in their turn have demonstrate the feasibility and the benefits of a decentralised Smart-Grid management.