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Optimal cost-based model for sizing grid-connected PV and battery energy system

Authors : Yara Khawaja, Damian Giaouris, Haris Patsios, and Mohamed Dahidah

Abstract : Photovoltaic-battery energy systems (PV-BESs) have recently emerged as a promising alternative energy solution for electricity consumers. Due to the high level of unpredictability and intermittency associated with solar energy, the optimal sizing and intermittency mitigation of PV-BESs is necessary while integrating them into the grid. This paper presents a technical and economical model for the optimal sizing of a grid-connected PV-BES system for different battery technologies. An iterative analytical approach is utilized to determine the battery capacity, generate multiple combinations of PV-BES over a defined range of PV rated power, and apply a proper energy management strategy to control the energy flow through the system. This is followed by an economic model to calculate the system levelized cost of energy (LCOE) for all possible PVBES sizes. The optimal PV size and best BES coupled with the PV system is chosen depending on the minimum LCOE. In this context, an improved formula of LCOE is proposed which includes new parameters reflecting the impact of surplus PV output and the energy purchased from the grid. Additionally, the proposed model uses the levelized cost of delivery (LCOD) for BES and compares it with system LCOE. Data over one year of hourly solar irradiation, temperature and load demand are used for system sizing. The results show that the minimum system LCOE is observed when the PV rated power is 710 KW, and the most suitable BES in conjunction with the PV system is redox flow battery with 1 MWh capacity. A cost reduction of 18% obtained compared to the grid electricity price. Moreover, the proposed model allows 75% of self-consumed energy by the PV-BES compared to 48% when using the PV system alone.

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An integrated framework for sizing and energy management of hybrid energy systems using finite automata

Authors : Yara Khawaja, Adib Allahham, Damian Giaouris, Charalampos Patsios, Sara Walker, Issa Qiqieh

Abstract : Due to the significance of hybrid energy systems, selecting the appropriate sizing-energy management strategy combination represents a key factor to ensure the efficient operation while offering a competitive operating cost. This paper presents an integrated framework for finding the best size-energy management strategy combination for a hybrid energy system. Fundamental to this framework is utilizing finite automata to develop multiple energy management strategies that fully take into consideration the dynamic relationship between all the assets in the hybrid energy system. The proposed integrated framework consists of three main steps. First, an analytical and economic sizing approach is performed to find the initial sizes of the hybrid energy system assets based on an initial energy management strategy; second, using finite automata to implement the initial energy management strategy and instantiate different energy management strategies; and third, an evaluation model is developed to assess the instantiated energy management strategies and extract the featured conditions to create new-improved one. This new energy management strategy is used to re-exercise the analytical and economic sizing to obtain the best size-energy management strategy combination. The novelty in this work can be summarized as taking the impact of selecting the right energy management strategy on the sizing of a hybrid energy management. This can lead to better performance and can be explained in our integrated framework by reducing the cost, reducing the diesel generator and fuel cell working hours and increasing the photovoltaic utilization. Moreover, using finite automata in implementing and instantiating multiple energy management strategies to attain an improved one has not been reported. A comparison between the results of the proposed framework and the results of the analytical and economic sizing approach is carried out. The size of the photovoltaic is reduced from 140 kW to 60 kW when using the integrated framework and therefore the size of the electrolyzer and the hydrogen tank reduced to the half. Moreover, a reduction in the diesel generator working hours by 35% and in the levelized cost of energy by 40% are achieved

Keywords : Hybrid energy systems, Energy management strategies, Finite Automata, Analytical and Economical Sizing, Integrated Framework

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Optimal cost based model for sizing grid-connected PV and battery energy system

Authors : Yara Khawaja, Damian Giaouris, Haris Patsios, and Mohammed Dahidah

Keywords : PV Sizing, Storage Sizing, Energy Management Strategy, Levelized Cost of Energy, Levelized Cost of Delivery

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An integrated framework for sizing and energy management of hybrid energy systems using finite automata

Authors : Yara Khawaja, Adib Allahham, Damian Giaouris, Charalampos Patsios, Sara Walker, Issa Qiqieh

Keywords : Hybrid energy systems, Energy management strategies, Finite Automata, Analytical and Economical Sizing, Integrated Framework.

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Design of cost-based sizing and energy management framework for standalone microgrid using reinforcement learning

Authors :Yara Khawaja, Issa Qiqieh, Jafar Alzubi, Omar Alzubi, Adib Allahham, Damian Giaouris

Abstract :The standalone photovoltaic-battery energy storage (PVBES) microgrid has gained substantial interest recently due to its ability to provide uninterrupted power to consumers in remote areas. In such microgrids, components must be precisely sized and energy must be supplied most cost effectively at all times. This paper presents a cost-based framework for determining the optimal size and energy management of standalone microgrids using reinforcement learning. Fundamental to this framework is two essential phases; the first is finding the best size of PV-BES using an analytical and economic sizing (AES) model based on minimum levelized cost of energy (LCOE). The AES phase is then followed by optimizing the energy management strategy (EMS) of the microgrid using reinforcement learning to provide optimum cost savings. The novelty in this work can be outlined as optimizing both the size and EMS of a standalone PV-BES microgrid using the AES model and Q-learning in an integrated framework. This can lead to improved performance demonstrated in reducing the LCOE, decreasing diesel generator working hours, and enhancing PV utilization and system efficiency. The results show an advantageous reduction in total cost while meeting load requirements. Additionally, the proposed framework is evaluated using several metrics to measure the impact of employing Q-learning against the AES-finite automata model. For instance, a decrease of 22% in diesel generator working hours and an increase of 6% in PV utilization while a reduction of 11% in the LCOE is accomplished. On the other hand, the proposed framework is examined against two rulebased EMSs, load following strategy (LFS) and cycle charging strategy (CCS), and outperforms these two EMSs in terms of LCOE, PV utilization, and system efficiency

Keywords : Sizing, Energy Management, Energy Storage, Levelized Cost of Energy, Reinforcement Learning, Q-learning.

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Battery management solutions for li-ion batteries based on artificial intelligence

Authors :Yara Khawaja, Nathan Shankar, Issa Qiqieh, Jafar Alzubi, Omar Alzubi, MK Nallakaruppan, Sanjeevikumar Padmanaban

Abstract :The automobile industry is currently undergoing a paradigm change from conventional, diesel, and gasoline-powered vehicles to hybrid and electric vehicles of the second generation. Lithium-ion (Li-ion) batteries have sparked the automotive industry’s interest for quite some time. One of the most crucial components of an electric car is the battery management system (BMS). Since the battery pack is an electric vehicle's most significant and expensive component, it must be carefully monitored and controlled. The precise measurement and calculation of the many states of a Li-ion battery's cells, such as the State of Health (SOH) and State of Charge (SOC) is a difficult procedure as they cannot be monitored directly. This paper examines various methodologies and approaches for estimating the SOC and SOH of Li-ion batteries using Artificial Intelligent methods. Six machine learning algorithms are intensively utilized to investigate the Li-ion battery state estimation. The employed methods are linear, random forest, gradient boost, light gradient boosting (light-GBM), extreme gradient boosting (XGB), and support vector machine (SVM) regressors. In comparison to all other models employed in this study, the discharge prediction made using random forest exhibits significantly greater performance at a low loss of accuracy. For instance, with the highest R2-score of 0.999, the random forest regressor achieves only 0.0035, 0.0013, and 0.0097 for mean and median absolute error, and root means squared error (RMSE), respectively. We showed that the state estimation of Li-ion batteries can be precisely predicted using AI methods, which can be combined with a battery management system to improve electric vehicle performance.

Keywords :Artificial Intelligence, Battery Management System, Lithium-ion Batteries, Neural Network, State of Charge, State of Health

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Recognizing Traffic Lights of Interest for Vehicle Driver without Prior Information

Authors : Reem Shadid, Waseem Shadid, Yara Khawaja

Abstract : This paper presents a novel automatic system to estimate the traffic light signal of interest for a vehicle driver in colored images of multiple lanes street from a camera mounted on car. The system analyzes the image to determine the candidate regions for traffic light signals. These regions are categorized based on their color and shape. Then spatial constraints are applied to generate an estimate of the traffic location. Existing solutions attempt to detect all traffic light signals in an image. Further solutions require prior information to specify the traffic light of interest with GPS information. But there is no work that estimate the traffic light signal of interest for the driver when multiple traffic light signals present in an image without prior information. There are two key contributions for this work: Building the first prototypical system that generates an estimate for the traffic light signal, and using a unique combination of the normalized RGB and the HSV color spaces. The proposed system was evaluated on LISA Traffic Light Dataset with maximum accuracy 97.6%. Moreover, the proposed system has been tested on private data sets from real-world environments. This work helps to improve the performance of advanced driver assistance systems.

Keywords : Color thresholding; Image processing; Normalized RGB and HSV; Traffic light.

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Investigation of weather conditions on the output power of various photovoltaic systems

Authors :Reem Shadid, Yara Khawaja, Abdullah Bani-Abdullah, Maryam Akho-Zahieh, Adib Allahham

Abstract : The quantity of solar radiation received by photovoltaic panel surfaces and their efficiency are influenced by environmental factors, including dust buildup and weather changes. This study presents an experimental analysis to determine how dust and rain affected the output of photovoltaic power for five different types and orientations of solar module systems of 5 kW each. All systems are installed at Applied Science Private University in Jordan, and attached to sunny portal system to monitor the PV output power for each type in real time. After one–four weeks of exposure, the effects of dust buildup and weather conditions on the power output of the module systems were examined. The results showed that four weeks of dust buildup resulted in 16.7%, 28%, 16.6%, 26.7%, and 21% reduction in PV power production for Monocrystalline (c-Si)/South, Monocrystalline (c-Si) East/West, Polycrystalline (p-Si)/South, Polycrystalline (p-Si)/East/West, and Thin-film (a-Si, microcrystalline)/South system respectively. In addition, a 20% average decrease and fluctuation in PV output power under wet conditions is observed. Moreover, the results showed that the power sold to the grid would be lower than predicted by 21% after one month of dust accumulation, which should be included in energy management planning.

Keywords :Photovoltaic modules, Dust accumulation, Photovoltaic efficiency, Output power

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