Cleaning of Floating Photovoltaic Systems: A Critical Review on Approaches from Technical and Economic Perspectives

There are some environmental factors, such as ambient temperature, dust, etc., which cause a reduction in the efficiency of Photovoltaic (PV) systems. Installation of PV panels on the water surface, commonly known as Floating Photovoltaic (FPV) systems, is one solution to employ PV panels in a cooler environment, achieve higher efficiency, and reduce water evaporation. FPV systems open up new opportunities for scaling up solar generating capacity, especially in countries with high population density and valuable lands, as well as countries with high evaporation rates and water resources deficiency. Since the FPV system is an almost new concept, its cleaning techniques have not been comprehensively studied. While FPV systems are located on the surface of water resources and reservoirs, the water quality can limit the application of different cleaning techniques. Therefore, this paper investigates different techniques of FPV systems cleaning and categorizes them into water-based and water-free approaches. In addition, their cleaning frequencies, as well as economic aspects, are presented and discussed to determine their merits and demerits for using them in FPV system

Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method

The two-way communication of electricity and information in smart homes facilitates the optimal management of devices with the ability to charge and discharge, such as electric vehicles and electrical storage systems. These devices can be scheduled considering domestic renewable energy units, the energy consumption of householders, the electricity tariff of the grid, and other predetermined parameters in order to improve their efficiency and also the technical and economic indices of the smart home. In this paper, a novel framework based on decision vectors and the analytical hierarchy process method is investigated to find the optimal operation schedule of these devices for the day-ahead performance of smart homes. The initial data of the electric vehicle and the electrical storage system are modeled stochastically. The aim of this work is to minimize the electricity cost and the peak demand of the smart home by optimal operation of the electric vehicle and the electrical storage system. Firstly, the different decision vectors for charging and discharging these devices are introduced based on the market price, the produce power of the domestic photovoltaic panel, and the electricity demand of the smart home. Secondly, the analytical hierarchy process method is utilized to implement the various priorities of decision criteria and calculate the ultimate decision vectors. Finally, the operation schedule of the electric vehicle and the electrical storage system is selected based on the ultimate decision vectors considering the operational constraints of these devices and the constraints of charging and discharging priorities. The proposed method is applied to a sample smart home considering different priorities of decision criteria. Numerical results present that although the combination of decision criteria with a high rank of electricity demand has the highest improvement of technical and economic indices of the smart home by about 12 and 26%, the proposed method has appropriate performance in all scenarios for selecting the optimal operation schedule of the electric vehicles and the electrical storage system

Aggregation ready flexibility management methods for mechanical ventilation systems in buildings

| openaire: EC/H2020/856602/EU//FINEST TWINS Funding Information: This work has been supported by the European Commission through the H2020 project Finest Twins grant No. 856602; and by the Estonian Ministry of Education and Research and European Regional Fund grant 2014-2020.4.01.20-0289. Additional support was acquired from the Estonian Research Council grant PSG739. Publisher Copyright: © 2023Increasing use of volatile renewable energy sources causes challenges in balancing supply and demand. Therefore, demand-side flexibility has rising importance for system operators and balancing authorities. Flexibility management methods are needed to integrate loads like ventilation systems of different buildings (e.g., residential and commercial) into flexibility service. However, the available methods described in research papers require further development for implementation in practice. Heating and cooling systems have received much attention from researchers, but the potential of ventilation systems has been left out of focus. Therefore, this paper provides a complete set of novel flexibility management methods for ventilation systems created from an aggregator's viewpoint. The flexibility is quantified through capacity (e.i. the amount of power consumption that can be altered), forced ventilation rate duration, and the tendered price for the service. The proposed methods were tested on a building modelconstructed and simulated in IDA ICE. The data processing and flexibility management methods were applied in MATLAB. Two types of ventilation systems with different sensor configurations were considered: constant and variable air volume. Forced ventilation rate duration is calculated using energy and mass balance analysis where the root means squared error was 10 to 33 min, depending on the system type, measured parameter, and sensor location. The flexibility service pricing model was tested on the 2022 years' manual frequency restoration reserve (mFRR) activation and balance energy market data.Peer reviewe

Clustering-based Penalty Signal Design for Flexibility Utilization

As the penetration level of renewable energy sources (RES) increases, the associated technical challenges in the power systems rise. Enhancing the utilization of energy flexibility is known to be the main key to overcome the load-supply balance challenge caused by RES. In this regard, the trend is toward the utilization of demand-side flexibility. Meanwhile, individual penalty signals positively affect the utilization of available flexibility from the demand-side. Previous studies in this field are based on designing penalty signals according to electricity price and regardless of the demand situation. However, designing and implementing a proper penalty signal with finite amplitude requires analyzing large datasets of load, storage and generation. Therefore, to fill this gap in designing a proper penalty signal we have proposed a novel approach in which, clustering is used to overcome the complexity of analyzing large datasets. The main goal of the proposed method is to utilize energy flexibility from responsive batteries according to a request from the aggregator without violating the consumers' privacy and comfort level. Therefore, aggregator's attainable load and generation datasets are used in the case studies to maintain the practicality of the proposed method. Simulation results show the proposed penalty signal designing method effectively increases the available flexibility of microgrids.This work was supported in part by the H2020 Project Finest Twins under Grant 856602, in part by the European Economic Area (EEA) and Norway financial Mechanism Baltic Research Program in Estonia under Grant EMP474, in part by the Estonian Research Council under Grant PRG675, and in part by the Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts ZEBE under Grant 2014-2020.4.01.15-001