• Energy Research

Abstract We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.

Abstract We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.

AbstractReconfigurable photovoltaic modules are a promising approach to improve the energy yield of partially shaded systems. So far, the feasibility of this concept has been evaluated through simulations or simplified experiments. In this work, we analyse the outdoor performance of a full-scale prototype of a series-parallel photovoltaic module with six reconfigurable blocks. Over a 4-month-long period, its performance was compared to a reference photovoltaic module with static interconnections and six bypass diodes. The results show that under partial shading, the reconfigurable module produced 10.2% more energy than the reference module. In contrast, under uniform illumination the energy yield of the reconfigurable PV module was 1.9% lower due to the additional losses introduced by its switching matrix. Finally, a modification in the reconfiguration algorithm is proposed to reduce the output current–voltage range of the module and simplify the design of module-level power converters while limiting the shading tolerance loss.

AbstractReconfigurable photovoltaic modules are a promising approach to improve the energy yield of partially shaded systems. So far, the feasibility of this concept has been evaluated through simulations or simplified experiments. In this work, we analyse the outdoor performance of a full-scale prototype of a series-parallel photovoltaic module with six reconfigurable blocks. Over a 4-month-long period, its performance was compared to a reference photovoltaic module with static interconnections and six bypass diodes. The results show that under partial shading, the reconfigurable module produced 10.2% more energy than the reference module. In contrast, under uniform illumination the energy yield of the reconfigurable PV module was 1.9% lower due to the additional losses introduced by its switching matrix. Finally, a modification in the reconfiguration algorithm is proposed to reduce the output current–voltage range of the module and simplify the design of module-level power converters while limiting the shading tolerance loss.

This package contains data and code associated with the publication "Feasibility study on photovoltaic module-integrated planar air-core inductors to facilitate embedded power electronics".The package contains:The employed COMSOL models (.mph) for simulating frequency-dependent inductance and resistance of planar inductors. The inductors are placed within a spherical air domain, with an infinite element domain at the outer boundary.The simulated inductor parameters and feasibility data reported in this study (.xlsx).

This package contains data and code associated with the publication "Feasibility study on photovoltaic module-integrated planar air-core inductors to facilitate embedded power electronics".The package contains:The employed COMSOL models (.mph) for simulating frequency-dependent inductance and resistance of planar inductors. The inductors are placed within a spherical air domain, with an infinite element domain at the outer boundary.The simulated inductor parameters and feasibility data reported in this study (.xlsx).

In many applications, the current versus voltage curve of a photovoltaic cell, module, string, or field is acquired. A high number of samples are usually acquired, but the curve contains the main information in the open- and short-circuit points, as well as where it has a strong change in the slope. In this paper, these parts are called “the fingerprint” of the photovoltaic generator. The fingerprint allows us to recognize the working conditions of the photovoltaic generator, e.g., if it is affected by a partial shadowing or not. Saving the fingerprint and discarding the other points of the original curve allows us to minimize the memory needs for storing the curve without losing the main information content. In this paper, a numerical technique for selecting, from among the samples of the acquired current versus voltage curve of any photovoltaic generator, the ones to be included in the fingerprint is proposed. The processing steps and the memory needed to achieve the result are minimized in order to allow an implementation of the algorithm also in a low-cost processor for on-field real-time applications. The technique is validated through curves generated by using analytical models as well as by means of some curves acquired experimentally in outdoor conditions.