Performance analysis of photovoltaic thermal (Pvt) panels considering thermal parameters

Solar PVT panels are getting popular for wider spectrum of applications for concurrent heat and power generation (CHP). These panels can provide the heating demand of buildings while generating electricity which becomes ideal for building applications of urban energy systems. Energy flow analysis of such panels and performance analysis of such systems becomes essential to design PVT systems matching with the operating conditions. A number of studies have used both theoretical and experimental methods to optimize PVT. However, this task is challenging due to interrelation of CHP production based on two different phenomena where classical optimization methods cannot be applied directly. Hence basic performance analysis considering primary design parameters plays a major role. In this study, a computational model is developed to evaluate sensitivity of design, operating and climatic parameters for a hybrid PVT system and to analyze the performances of PVT for five different design configurations. Five main configurations of the PVT system are considered based on the heat transfer fluid and the arrangements of glass and tedlar layers of PVT collector. This study presents comprehensive performance analysis conducted to evaluate the sensitivity of mass flow rate and working fluid temperature for the five different design configurations of PVT panels. Results show that glass-tedlar water collector performs better when compared to other configurations. Subsequently, the sensitivity of wind speed and solar irradiation is evaluated. The behavior of thermal and electrical efficiencies is analyzed at different wind speed and solar irradiation levels for a range of mass flow rates and working fluid temperatures. Important conclusions on the performance of PVT panels are given based on this detailed analysis.

Introducing reinforcement learning to the energy system design process

Vahid Moussavi Nik, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

Design optimization of distributed energy systems has become an interest of a wider group of researchers due the capability of these systems to integrate non-dispatchable renewable energy technologies such as solar PV and wind. White box models, using linear and mixed integer linear programing techniques, are often used in their design. However, the increased complexity of energy flow (especially due to cyber-physical interactions) and uncertainties challenge the application of white box models. This is where data driven methodologies become effective, as they demonstrate higher flexibility to adapt to different environments, which enables their use for energy planning at regional and national scale. This study introduces a data driven approach based on reinforcement learning to design distributed energy systems. Two different neural network architectures are used in this work, i.e. a fully connected neural network and a convolutional neural network (CNN). The novel approach introduced is benchmarked using a grey box model based on fuzzy logic. The grey box model showed a better performance when optimizing simplified energy systems, however it fails to handle complex energy flows within the energy system. Reinforcement learning based on fully connected architecture outperformed the grey box model by improving the objective function values by 60%. Reinforcement learning based on CNN improved the objective function values further (by up to 20% when compared to a fully connected architecture). The results reveal that data-driven models are capable to conduct design optimization of complex energy systems. This opens a new pathway in designing distributed energy systems.

2020

IntegrCiTy - D4.2 Report - Method towards key bottlenecks identification

Luc Girardin, Pablo Puerto

The increasing use of renewable energy is a deep going trend [1] mainly supported by the sustained annual growth rate of solar photovoltaic (+45.5%), wind power (+24.0%) and Biogases (+12.8%) (Figure 1). In this context, decentralized power generation and heat pumps technologies are expected to play an increasing role [2]. This evolution is going to increase the stress on the electricity and gas distribution network while pushing the development of district heating and cooling (DHC) networks. At the grid level, the uncertainty in energy generation from renewable energy, the trend towards decentralisation and the emergence of new energy prosumers are going to increase bi‐directional energy interconnections [3], therefore challenging the energy networks to balance supply and demand. Therefore, there is a need for a method to identify actions and opportunities for the design and retrofit of future resilient urban energy systems. The IntegrCiTy project demonstrates the use of co‐simulation techniques to support the growing penetration of renewables systems through the interconnection of energy conversion technologies with multi‐energy urban networks supplying natural gas, electricity, heating and cooling. This report presents a method using co‐simulation models to identify and characterize the key bottlenecks (such as pipe sizes, heat exchangers or limiting transformers) of urban energy system from a multi‐energy perspective. Bottlenecks are defined and characterized at the level of the electricity, gas and heating/cooling distribution networks in Chapter 2. In Chapter 3, the selection of extreme operating conditions are presented to identify the bottleneck with a minimum set of operating points. Based on the definition of the bottlenecks, a stochastic hosting capacity approach is proposed in Chapter 4 to evaluate the penetration of renewable energy technologies resulting from the capacity limit of the existing infrastructure. Finally, the approach is demonstrated with the identification of the bottlenecks in the photovoltaic (PV) conversion of solar energy on the roofs of a district.

2018

Tools for multi time horizon PV point forecasting and prediction intervals

Mario Paolone, Enrica Scolari, Fabrizio Sossan

The massive integration of stochastic renewable generation in modern power systems requires decentralized control schemes that cope with the uncertainties of the distributed energy resources (DERs). Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future power generation capacity. Therefore, solar resource assessment and the associated forecasting at various time scales are of fundamental importance for power systems operators. The activity here summarized focuses on developing forecasting methods based on the integrated use of time series, all-sky camera images and generation models of PV systems, considering short-term temporal horizons (below one hour) and fine spatial resolution (single site installations). We aim to improve the efficiency of forecasting methods developed by the DESL through prediction of clouds movement and associated cover of the sun disk. For this, we developed and validated different machine-learning techniques relying on the integrated use of Global Horizonal Irradiance (GHI) time series, all-sky image processing and cloud motion identification. Furthermore, a methodology to estimate the irradiance from all-sky images is proposed, investigating the possibility of using all-sky cameras as irradiance sensors.

SCCER-FURIES2020

IntegrCiTy - D4.2 Report - Method towards key bottlenecks identification

Luc Girardin, Pablo Puerto

2018