Hybrid Renewable Energy Sources Research Articles

VSC-STATCOM Performance Under Different Fault Sensing using PSO Tuned Hybrid SMC

In this paper, we investigate the PSO-tuned hybrid SMC performance based VSC-STATCOM under different conditions of fault using hybrid renewable energy sources (HRES). A hybrid renewable energy resource system (HRES) consists of PV, wind power, and batteries. Here the Irradiance is the PV input and the wind energy is Wind Input. The storage of energy is used for battery. The battery is used for changing weather condition or the changing the condition of the environment. Hybrid VSC-STATCOM controller based on SMC to reduce power quality issues like sag, swell, harmonics etc. associated with HRES system mainly due to non-linear load conditions. The novelty of our proposed PSO-tuned hybrid Sliding Mode Controller (SMC) method lies in its integration of Particle Swarm Optimization (PSO) as a tuning mechanism within the SMC framework. The Harmonic reduction and efficiency improvement is verified by using the Simulation. Therefore, the proposed system performance is simulated and to optimize the terms are real and reactive power, Total Harmonic Distortion (THD) and Voltage Sag.

A Voltage Source Inverter-Based Hybrid Renewable Energy Source for Improving Power Quality

The power demand is quickly rising day by day and however, the available industrial sector needs the demand for power high. Therefore, different loads and different output sources can be employed between supply and demand. This approach intends to assess the current condition need for power quality, without distortion, and Bus interface management with fully integrated PV-based MPPT (Maximum power point tracking) and Wind-based rotor winding induction generator. Solar energy is directly converted into electrical energy using a Photovoltaic (PV) system. The PV cell is the fundamental component of a PV array and can be created by grouping cells and the voltage and current that are accessible at a PV device's contacts. In solar PV, the MPPT technique and boost converter are used to maximize the buck or boost and feed it to the single-phase Pulse Width Modulation (PWM) to the inverter. The rectifier circuit rectifies the AC input source and is connected through the controller; simultaneously, the DC-DC converter converts the DC input and bucks or boosts up the from the input sources. The voltage source inverter is combined with shunt active power filter functionality, which is further used to interface the DC-shunted wind-solar hybrid model to the grid. A voltage source inverter has more efficiency and both different input sources are connected via a DC bus. In this method, both DC and AC output loads are connected, managing the desired output voltage with efficient and better output results, and analyzing the total harmonics distortion method using Mat-lab simulation.

Techno-economic assessment of hybrid renewable energy system with multi energy storage system using HOMER

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE.

Modeling Approach for Hybrid Integration of Renewable Energy Sources with Vehicle-to-Grid Technology

<div>This article presents a technical study on the integration of hybrid renewable energy sources (RES) with vehicle-to-grid (V2G) technology, aiming to enhance energy efficiency, grid stability, and mitigating power imbalances. The growing adoption of RES and electric vehicles (EV) necessitates innovative solutions to mitigate intermittency and optimize resource utilization. The study’s primary objective is to design and analyze a hybrid distribution generation system encompassing solar photovoltaic (PV) and wind power stations, along with a conventional diesel generator, connected to the utility grid. A V2G system is strategically embedded within the microgrid to facilitate bidirectional power exchange between EV and the grid. Methodologically, MATLAB/Simulink<sup>®</sup> 2021a is employed to simulate the system’s performance over one day. This research addresses a critical research gap in comprehensively evaluating the synergy between hybrid RES and V2G technology within a microgrid context. The study contributes by demonstrating the potential of EVs as dynamic energy storage units, effectively mitigating the intermittency of renewable energy (RE) and supporting grid stability. This is achieved by injecting or absorbing energy to address frequency deviation events and improve power flow based on demand needs and generated power from the source. The results highlight the capability of the V2G system to optimize energy flow, regulate grid frequency, and alleviate power imbalances. Main findings underscore the significant role of V2G in enhancing grid resilience and flexibility, especially during RE fluctuations and unexpected events. Moreover, the study underscores the feasibility of achieving sustainable energy goals through the coordinated operation of hybrid RES and V2G systems.</div>

Optimal design of hybrid renewable energy sources with battery storage using an efficient weighted mean of vectors algorithm

An improved algorithm called mINFO is developed, based on the modification of the original weIghted meaN oF vectOrs Optimizer (INFO). In this article, to enhance the original INFO method, an improved version called mINFO is presented by incorporating the sine-cosine function and balancing exploration/exploitation strategy through the control randomization parameter. This suggested mINFO algorithm is applied to obtain the optimal size design of two hybrid renewable power sources (HRPS), the first configuration consists of the photovoltaic (PV)/biomass system (BS)/fuel cells (FC) and the second configuration consists of the PV/BS/battery storage units. This HRPS are designed to generate the power needed to meet the loads supply on the campus of Aswan University in the Sahary region of Aswan city, Egypt. The proposed mINFO approach is developed to avoid the weaknesses of the original INFO method by enhancing the balance between the exploration and exploitation phases and preventing the conventional INFO method from getting stuck in local minima. In order to prove the efficiency and robust search capabilities of the developed mINFO algorithm, the mINFO algorithm is tested by using 10 IEEE CEC’20 test suites and its results are compared with the original INFO method and other well-known optimization algorithms. The proposed mINFO algorithm achieved the first rank in terms of the Friedman mean rank-sum test over F2, F3, F4, F6, F9, and F10, and the second rank after the original INFO algorithm over F1 and F7. Boxplot analysis is also used to show the features of data distribution. For the majority of functions, the boxplots generated by the mINFO algorithm have the narrowest ranges of values out of all comparison algorithms. As a result, for the majority of test functions, the mINFO algorithm shows more promise than its competitors. The main objectives of the suggested HRPS are to minimize energy cost (EC), excess energy (EXE), and maximize the power system's reliability (LPSP). By applying the mINFO algorithm to find the optimal sizing for the two configurations proposed in this study and by comparing the mINFO results with other algorithms such as the original INFO, Particle Swarm optimization (PSO), Runge Kutta optimization (RUN), Sooty Tern Optimization Algorithm (STOA), and Gray Wolf optimization (GWO). The proposed mINFO method shows the best results for the two suggested configurations compared to the other algorithms used by studying the systems for 100 iterations and 50 times run. For the PV/BS/battery units, the best optimal solution has achieved after 36 iterations with the lowest EC with 0.11676275 $/kWh, net present cost (NPC) with 3,420,036 $, EXE with 0.090282 and LPSP with 0.0419687649, while for the PV/BS/FC units, the optimal solution has achieved after 11 iterations with the minimal EC with 0.17996701 $/kWh, NPC with 5,271,318 $, EXE with 0.150022 and LPSP with 0.07598679.

Techno-Economic Performance and Sensitivity Analysis of an Off-Grid Renewable Energy-Based Hybrid System: A Case Study of Kuakata, Bangladesh

Hybrid renewable energy sources (HRES) are increasingly being utilized to meet global energy demands, particularly in rural areas that rely on diesel generators and are disconnected from the utility grid, due to their environmental and human health benefits. This study investigates the performance of an off-grid, hybrid PV/diesel generator/battery system for a decentralized power plant in Kuakata, Bangladesh, meeting a load demand of 3000 kWh/day with a 501.61 kW peak load demand. HOMER Pro (hybrid optimization model for electric renewable) software (version 3.11) was used to simulate and optimize system operations utilizing real-time solar radiation and load profile data from that location. This study also includes a sensitivity analysis of the off-grid HRES system under different electrical load demands, project longevity, and derating variables. The results reveal that CO2 emissions have potentially decreased by more than 30% and over 10 tons per year, respectively, when compared to traditional power plants. The optimized system’s net present cost (NPC) was determined to be around USD 5.19 million, with a cost of energy (COE) of USD 0.367 per kWh per unit with a 100% renewable component. Furthermore, the current study’s findings are compared to previous research that has resulted in an economical hybrid renewable energy system with an affordable COE. The hybrid energy system under consideration might also be applicable to other parts of the world with comparable climate conditions.

Enhanced Proportional Resonant – Second-Order General Integrators (EPR-SOGI) With Fuzzy Logic Control in Hybrid Renewable Energy Source-Based STATCOM

In this paper, the fuzzy-logic controllers (FLC)-based enhanced proportional resonant – second-order general integrators (SOGI) method for Static Compensator is provided to improve the quality of power. The Hybrid Renewable Energy Sources provide support for the quasi-Z- source inverters (qZSI) that is known as HRES-STATCOM. In the usual approach, the actual dc-link voltages of the inverters are controlled by the PI controllers using a fixed reference DC-link voltage, which may result in excessive switching loss and switching noise. As system or load characteristics vary in real-world applications, a low DC-link voltage may be required, resulting in less switching losses and noise. As a result, an FLC-based EPR-SOGI method has been suggested. This approach gives the optimal reference DC-link voltage, which reduces the switching losses and hence offers improved compensation in varying-parameter settings as well. In the framework of Matlab/Simulink, the EPR-SOGI algorithm is designed and then simulated for three distinct cases. According to the findings of the research, it is abundantly clear that the suggested algorithm is better than the conventional algorithm in terms of its adaptability and its robustness. A laboratory-developed experimental setup is also used to verify the superiority of the suggested algorithm over the traditional method.

Deep Learning-Based Equivalent Modelling of Hybrid RES Plant for Efficient, Repetitive Power System Transient Stability Studies

The paper presents the methodology for dynamic equivalent modelling of hybrid renewable energy source (HRES) plant with the aim of obtaining highly accurate time domain HRES plant power responses at any time during the year. The focus is on equivalent modelling for transient stability studies. Historical HRES plant production dataset and transmission network short-circuit fault statistical data, along with unsupervised data mining and deep learning techniques, represent a basis of the modelling procedure. Dynamic equivalent model (DEM) is developed in the form of deep Long Short Term Memory network. Real and imaginary parts of voltage at the point of common coupling (PCC) are model inputs, while real and reactive power at the PCC are model outputs. The paper also proposes a practical approach for selecting the adequate DEM at any time in a year based on the forecasted HRES plant operating scenario only. Model performance is evaluated on a large number of case studies using the HRES plant consisting of three renewable energy sources. The results have shown that only a few DEMs are required for representing the annual HRES plant dynamic performance and obtaining reliable transient system stability results.

A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimization

A Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems (EMSs) and ESSs, which further enhanced the operational performance of NGs. It was seen that EMS acts as the distributed energy source in the NG setup and assists in power generation, usage, dissemination, and differential pricing. Hence this study employed the MATLAB Simulink software for modelling the grid-connected NG that included HRES; such as wind and PV; in addition to 3 Battery Storage Devices (BSDs) to design an effective EMS for the NG system and decrease its overall costs. For this purpose, a Rule-Based EMS (RB-EMS) that employs State Flow (SF) to guarantee a safe and reliable operating power flow to the NG has been developed. In addition to that, a Genetic Algorithm (GA)-based optimization system and Simulated Annealing optimization Algorithm (SAA) were proposed to determine an economical solution for decreasing the cost of the NG system depending on its operational constraints. Lastly, comparison about the cost between RB-EMS, GA and SAA has been presented. According to the simulation results, the proposed GA displayed an economical performance since it could achieve a 40% cost saving whereas the SAA system showed a 19.3% cost saving compared to the RB-EMS. It can be concluded from the findings that the GA-based optimization technique was very cost-effective displays many important features, like rapid convergence, simple design, and very few controlling factors.

Enhancing Power Quality with PDO-FOPID Controller in Unified Power Quality Conditioner for Grid-Connected Hybrid Renewables

Addressing the pressing research problem of power fluctuations and grid harmonics in the integration of renewable energy, our  proposed control strategy utilizes the Prairie Dog Optimization Fractional Order Proportional Integral Derivative (PDO-FOPID) controller  within a Unified Power Quality Conditioner (UPQC) system. This innovative approach is tailored to mitigate harmonics and meet load  requirements in grid-connected hybrid renewables. The UPQC system is instrumental in regulating coupling point voltage, countering  voltage and current harmonics to enhance overall power quality. The PDO-FOPID controller dynamically adapts control parameters to system dynamics and load changes, ensuring a stable power supply despite the variability of renewable sources. Simulations in MATLAB/ Simulink confirm its superiority over traditional control strategies, such as PI, sliding mode, and fuzzy control, in harmonics mitigation,  load fulfilment, and power stability. By effectively addressing these challenges, our proposed solution not only contributes to resolving a  critical research problem but also advances the seamless integration of Hybrid Renewable Energy Sources (HRES) into power systems,  thereby enhancing overall grid performance and the efficacy of renewable energy integration. 

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid—integrating multiple Microgrids with a battery management system—can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results, Voltage variation of PI, Fuzzy, and LSTM-ANN is 370V,180V,and 70V . The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty

Microgrids can assist in managing power supply and demand, increase grid resilience to adverse weather, increase the deployment of zero-emission energy sources, utilise waste heat, and reduce energy wasted through transmission lines. To ensure that the full benefits of microgrid use are realised, hybrid renewable energy-based microgrids must operate at peak efficiency. To offer an optimal solution for managing microgrids with hybrid renewable energy sources (HRESs) while taking microgrid reserve margins into account, the particle swarm optimisation (PSO) method is suggested. The suggested approach demonstrated good performance in terms of charging and discharging BESS and maintaining the necessary reserve margins to supply critical loads if the grid and renewable energy sources are unavailable. On a clear day, the amount of electricity sold to the grid increased by 58%, while on a partially overcast day, it increased by 153%. Microgrids provide a good return on investment for their operators when they are run at peak efficiency. This is because the BESS is largely charged during off-peak hours or with excess renewable energy, and power is only purchased during less expensive off-peak hours.

Techno-economic feasibility analysis of hybrid renewable energy sources for a health clinic: A case study

Techno-economic feasibility analysis of hybrid renewable energy sources for a health clinic: A case study

Novel design and adaptive coordinated energy management of hybrid fuel‐cells/tidal/wind/PV array energy systems with battery storage for microgrids

AbstractThis paper proposes a comprehensive solution to the challenges of managing a hybrid microgrid that generates electricity from multiple sustainable energy sources by proposing a coordinated energy management strategy and storage system. As renewable energy generation becomes increasingly popular, it introduces greater intermittency and stochasticity in energy management. To address this issue, a coordinated energy management strategy and storage system is proposed, which includes a fuzzy logic modified super twisting algorithm (MSTA). The objectives are to optimize the design and operation of microgrid including electrical based energy conversion systems such as photovoltaic and wind turbines, fuel cells, tidal energy, electric vehicle charging stations, and main grid. The second objective is to develop an energy management system for hybrid energy storage systems (HESS) and renewable energy sources (RESs) to maximize power production and ensure service continuity and smooth output energy of the microgrid, while also providing optimal benefits. To maintain cost‐effectiveness, an On/Off maximum power point tracking (MPPT) algorithm is also proposed. The contribution of this paper is to provide a solution to the intermittent and stochastic nature of renewable energy management and to improve the efficiency and durability of the energy conversion systems. The proposed management unit provides consistent output power and long‐term service.

Mitigation of Power Quality Issue in Hybrid Renewable Energy Source by UPQC with Optimization Techniques

This paper presents a comprehensive approach to mitigate power quality issues in hybrid renewable energy systems through the utilization of Unified Power Quality Conditioner (UPQC) integrated with optimization techniques. As renewable energy sources like wind and solar play an increasingly significant role in power generation, ensuring high-quality power supply becomes imperative. The UPQC, comprising series and shunt active power filters, is strategically deployed to address voltage fluctuations, harmonics, and other disturbances. To enhance its efficacy, advanced optimization techniques such as control algorithm optimization and parameter tuning are employed. These techniques enable the UPQC to adapt to varying operating conditions and optimize its performance in real-time. Integration with hybrid renewable energy sources is seamless, facilitating coordinated operation for optimal power quality enhancement. The proposed approach emphasizes real-time monitoring and control, enabling proactive response to power quality fluctuations and ensuring stable and reliable electricity supply. Through rigorous testing, validation, and maintenance protocols, the effectiveness and reliability of the integrated system are demonstrated. This holistic approach contribute to the stability and sustainability of modern power grids, fostering the widespread adoption of hybrid renewable energy systems.

IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells

As the world seeks sustainable energy solutions, Internet of Things (IoT) applications demand consistent and efficient power sources. This paper presents an innovative hybrid renewable energy system, seamlessly integrating solar photovoltaic panels, wind turbines, and hydrogen fuel cells, tailored for IoT applications. Through machine learning algorithms, our proposed system not only optimizes energy generation in real-time but also ensures uninterrupted energy supply to IoT devices and consumers, even in fluctuating environmental conditions. This universal approach markedly diminishes the dependence on non-renewable energy sources, promoting a greener and more resilient energy infrastructure. The incorporation of hydrogen fuel cells uniquely positions our system as a reservoir for excess energy, ensuring consistent power even when solar or wind outputs diminish. Moreover, by synchronizing IoT devices with our energy system, we have procured real-time data on energy dynamics, facilitating unparalleled optimization and reduced wastage. The presented system shows the way for a sustainable future through the efficient green energy generation with the ever-evolving landscape of IoT applications and machine learning techniques.

Hybrid renewable energy source optimization using black widow optimization techniques with uncertainty constraints

To encourage the growth and use of renewable energy such as PV Solar Energy, Wind Energy, India is speeding up the creation of a renewable portfolio standard. Scheduling practises for thermal power and other renewable energy producers in the electric power system will shift as a result of the adoption of the standard and accompanying marketable green credits. In this study, we develop a dynamic economic emission dispatch model for wind, solar, and hydro power under tradable green certificates, taking into account many objectives simultaneously. The drive to transition towards cleaner and more sustainable energy sources has underscored the significance of optimizing hybrid renewable energy systems. This study presents a novel approach that leverages the Black Widow Optimization (BWO) algorithm to address the challenges of optimizing such systems while accounting for uncertainty constraints. The core problem revolves around the effective integration and management of multiple renewable energy sources, including solar, wind, and hydro, within a framework that ensures robustness and adaptability. In the face of variable weather conditions and equipment reliability uncertainties, the BWO algorithm excels in balancing exploration and exploitation to yield optimal solutions. Through rigorous modelling and sensitivity analysis, this study provides quantitative insights into the system's performance, offering a clear understanding of the trade-offs between cost-effectiveness and reliability. Real-world case studies validate the practicality of the proposed approach, underlining its importance in achieving sustainable and resilient energy systems. This study's goal is to minimise the yearly cost of operating a hybrid wind-solar renewable energy system to meet a specified load. The objective was to minimise the yearly cost of the system by selecting the optimal level of system components to meet the required load in an efficient and cost-effective manner. Several computational strategies inspired by nature, such as Black Widow Optimization (BWO), were used to maximise fitness. The fitness function and the techniques used to create it where both coded in MATLAB.

Application of generalized hopfield neural network for the steady state analysis of self-excited induction generators

This study proposes a generalized Hopfield neural network (GHNN) for solving the non-linear equations involved in the steady state analysis of Self-Excited Induction Generators (SEIGs). Unlike other neural network architectures, the generalized Hopfield neural network can convert a non-linear equation solving problem into an optimization problem. Moreover, the weights and bias of GHNN can easily be obtained from the coefficients of the non-linear equations to be solved without additional training dataset. Motivated by its simplicity, faster convergence, and global stability properties, this study proposes a GHNN for solving the non-linear equations involved in the steady state analysis of Self-Excited Induction Generators (SEIGs). Moreover, unlike the heuristic algorithms, this approach does not require the boundary values for the unknown parameters (per unit frequency, magnetizing reactance Xm and core loss resistance Rm) of the SEIG to be given as inputs, for initiating the optimization process and it ensures guaranteed convergence. Symbolic programming technique has been employed to form the non-linear equations required for the analysis, thereby eliminating the lengthy derivations involved. The formulated equations are then used to form an energy function and a set of differential equations describing the dynamic behavior of the Hopfield network is obtained from this function. These equations are then solved until the energy function minimizes to zero to arrive at the unknown parameters of the SEIG. Using the energy function equation, the stability analysis of the proposed network has been carried out according to the Lyapunov’s second method of stability, which assures the convergence of the proposed method leading to the solutions. The proposed method has been found to be more efficient than other conventional optimization techniques, in terms of accuracy, number of iterations required and computation time. Experiments have been conducted on a 3.5 kW, 415 V SEIG and the results are shown to be in close agreement with the values predetermined using GHNN. It is also concluded that wind driven SEIGS, will find increased deployment in hybrid renewable energy sources, being applied in several emerging areas.

Optimizing renewable energy utilization with high gain converters

This research proposes a novel control technique for optimizing hybrid renewable energy sources (HRES) by using a high-gain DC-to-DC converter topology. The Hybrid RES system comprises a battery components, wind turbine (WT), and photovoltaic (PV). The proposed control methodology utilizes an enhanced jellyfish search (EJS) controller, which enhances the search behavior of the jellyfish search algorithm through the incorporation of mutation and crossover operators. The converter is designed to maximize efficiency, minimize switching losses, and effectively utilize renewable energy. To achieve optimal performance, a dataset with optimum gain values is constructed using a minimal error target function and proportional-integral (PI) controller gain parameters. The EJS algorithm determines the PI controller gain based on the error in reactive and active power. Randomly generated gain values are used as inputs to the system, and the algorithm adjusts the PI settings by minimizing system error and optimizing power flow management. The inclusion of batteries helps balance renewable power and enhances output consistency. The proposed system is implemented in MATLAB and is compared with existing systems. The novelty of this research lies in the combination of the EJS algorithm with converter efficiency, switching losses, and hybrid renewable energy sources. By utilizing the EJS algorithm, the proposed strategy has the potential to achieve superior outcomes based on cost savings, energy efficiency, and grid stability compared to conventional methods. Furthermore, the integration of this method contributes to the advancement of optimization algorithms and neural network applications.

A hybrid technique for optimal sizing and performance analysis of hybrid renewable energy sources

A hybrid technique for optimal sizing and performance analysis of hybrid renewable energy sources