Grid-connected Hybrid Renewable Energy System Research Articles

Reliability analysis of a grid-connected hybrid renewable energy system using hybrid Monte-Carlo and Newton Raphson methods

The integration of renewable energy sources into the power grid is essential for sustainable development, yet it presents significant dependability challenges, particularly in terms of reliability, stability, and robustness due to the inherent variability of these sources. This research introduces a novel hybrid methodology that combines Monte Carlo simulation with Newton-Raphson power flow analysis to enhance the reliability assessment of grid-connected hybrid renewable energy systems. This innovative approach uniquely addresses the limitations of existing methodologies by merging the probabilistic handling of uncertainties with precise deterministic power flow analysis. Our hybrid method significantly reduces the Loss of Load Expectation (LOLE) to 5 h per year and the Loss of Load Energy Expectation (LOEE) to 200 MWh per year, outperforming traditional methods which typically report LOLEs of 2020 h/year and LOEEs of 10001000 MWh/year. Additionally, the hybrid method achieves a reduction in power losses to 1.2%, showcasing its superior efficiency compared to the 2.5% losses seen with standalone Monte Carlo methods. Real-time validation using the IEEE-30 bus model further confirms the practical applicability and robustness of our approach, making it a pivotal tool for enhancing grid stability and optimizing renewable energy integration. This research not only advances the methodology for reliability assessment but also sets a new standard for balancing accuracy and computational efficiency in energy system management. The implications of this work are far-reaching, offering significant contributions to both grid reliability and the sustainable management of renewable energy resources.

Enhancing power quality in grid-connected hybrid renewable energy systems using UPQC and optimized O-FOPID

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system’s performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches.

Optimal selection and design of grid-connected hybrid renewable energy system in three selected communities of Rivers State

The pursuit of electrifying rural areas has been a multi-decade quest, emblematic of Nigeria's longstanding efforts to diversify its energy sources and reduce dependence on traditional fossil fuels. Accordingly, this study aims to evaluate the electrification status and perform a techno-economic analysis of a hybrid system to ensure electricity reliability, bill reduction and reduced grid demand for three representative rural communities in Rivers State using HOMER Pro Software. Subsequently, five hybrid system scenarios that encompassed different combinations of the grid, PV modules, wind and battery, were examined using the CRITIC-PROMETHEE II methodologies. Each community was assessed based on its own hourly electricity demand, using data obtained from the injection substation. The Aluu, Choba and Rumuekini communities experienced an average daily electricity supply of 4.6, 5.4 and 6.2 h respectively from the national grid with energy usage of 5436.4 MW for Aluu, 3375.4 MW for Choba and 1626.1 MW for Rumuekini, in 2022. The findings indicate that the grid/PV module was the optimal choice for the three communities. Moreover, the cost of energy (COE) for the proposed systems ranged from 0.0181 $/kWh to 0.0185 $/kWh, significantly lower than the grid's COE of 0.08625 $/kWh in the respective communities. This research further includes a sensitivity analysis of the optimal system, examining variations in PV cost, discount rate, inflation rate, annual scaled solar irradiance and electrical demand. The results provide a comprehensive framework to support decision-making, optimize system design and align renewable energy solutions with project goals and stakeholder priorities.

Meta-heuristic-based power quality improvement in UPQC-based grid-connected hybrid renewable energy system

Meta-heuristic-based power quality improvement in UPQC-based grid-connected hybrid renewable energy system

Enhancing Grid-Connected Hybrid Renewable Energy System with Super Lift Luo Converter for Improved Power Management and Reduced THD

Enhancing Grid-Connected Hybrid Renewable Energy System with Super Lift Luo Converter for Improved Power Management and Reduced THD

Economic predictive control-based sizing and energy management for grid-connected hybrid renewable energy systems

Hybrid renewable energy systems (HRES) comprise a group of different energy sources and storage units that feed a specific load, efficiently. This research paper aims to develop a new methodology that provides the optimal size of the proposed HRES and runs it efficiently. Bi-level mixed-integer nonlinear programming (BMINLP) optimization approach is used to combine the sizing task and the energy management strategy (EMS) established utilizing the economic model predictive control (EMPC) method. The sizing task (upper layer) is formulated as mixed integer nonlinear programming (MINLP) optimization that has been implemented by the solver: multi-objective genetic algorithm (MOGA). EMS task (lower layer) is represented as a constraint embedded within the upper layer and executed as mixed integer linear programming (MILP) per each applied solution of the sizing task. The principal findings indicate that the total cost of the system is about 114,224 $. In detail, the annual fixed operation and maintenance, investment, and operating costs are 15,090 $, 28,351 $, and 70,783 $, correspondingly. Furthermore, around 90 % of the overall produced power is imported from the grid, while the load power represents about 95 % of the total demanded power.

Control strategies for grid-connected hybrid renewable energy systems: Integrating modified direct torque control based doubly fed induction generator and ANFIS based maximum power point tracking for solar PV generation

This research article introduces advanced control strategies for grid-connected hybrid renewable energy systems, focusing on a doubly fed induction machine (DFIM) based wind power plant and a photovoltaic based solar power plant. The proposed approach integrates Direct Torque Control (DTC) with modified filters and a fractional Proportional-Integral (PI) controller for the DFIM wind power plant. The modified DTC utilizes estimated torque and flux, employing a modified filter to minimize deviations, and a fractional PI controller for improved vector selection. This results in an enhanced duty cycle for the rotor side converter, optimizing power output to the grid. Additionally, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Maximum Power Point Tracking (MPPT) algorithm is implemented for the solar power plant, dynamically adjusting the duty cycle for increased power output compared to conventional MPPT methods. Simulation results in MATLAB and experimental verification demonstrates the stability and feasibility of the proposed controllers, showcasing superior performance compared to conventional counterparts under various operating conditions.

Sizing and energy management of grid-connected hybrid renewable energy systems based on techno-economic predictive technique

This study intends to construct and optimally run a grid-connected hybrid renewable energy system (HRES) powering a residential building. A special techno-economic multi-objective optimization technique is used for sizing the HRES. In addition, an energy management strategy (EMS) is established as a distinct multi-objective optimization problem to minimize degradation and operating costs while improving system performance. A Bi-level mixed integer non-linear programming (BMINLP) is the framework used to formulate the two-optimization problems. Sizing problem is characterized by the higher level, on contrast; the nested EMS problem within the constraints of the size problem is presented on the lower level. For this investigation, the problem is executed via the MATLAB program. The global optimization toolbox is applied via the Multi-Objective Genetic Algorithm (MOGA) to solve the sizing optimization. While the EMS is executed using the "Intlinprg" mixed integer linear programming (MILP) solver. The technical and economic performance parameters such as degradation, renewable energy fraction (REF), operating, maintenance, and investment costs are evaluated in this study. The primary results reveal that the summer week encounters the lowest total cost and the highest REF, at roughly 99484 $ and 60 %, respectively. Furthermore, the investigation shows that the lowest degradation value attained in the same week with approximately 50.

An innovative hybrid controller-based combined grid-connected hybrid renewable energy system

An innovative hybrid controller-based combined grid-connected hybrid renewable energy system

Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives

Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid (ESOLD). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × 10−2 USD/kWh, the GCF is equal to 7.34 × 10−5 kWh, and the ESOLD is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development.

Salp Swarm Algorithm-Based Fractional Order PID Controller for Grid-Connected Hybrid Renewable Energy System

Salp Swarm Algorithm-Based Fractional Order PID Controller for Grid-Connected Hybrid Renewable Energy System

Optimization Model of Hybrid Renewable Energy Generation for Electric Bus Charging Stations

This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year.

Investment-based optimisation of energy storage design parameters in a grid-connected hybrid renewable energy system

Grid-connected hybrid renewable power systems with energy storage can reduce the intermittency of renewable power supply. However, emerging energy storage technologies need improvement to compete with lithium-ion batteries and reduce the cost of energy. Identifying and optimising the most valuable improvement path of these technologies is challenging due to the non-linearity of the energy system model when considering parameters as independent variables. To overcome this challenge, a novel investment-based optimisation method is proposed. The method involves linear optimisation of the hybrid renewable energy system and subsequent investment optimisation, accounting for diminishing improvements per investment. The results from applying the investment-based optimisation to thermal energy, pumped thermal energy, molten salt, and adiabatic compressed air energy storage technologies, show that improving the discharge efficiency is the most valuable for all technologies. The second most important parameters are the costs of discharge capacity and energy storage capacity, and the least important parameters are the charge capacity cost and charge efficiency. The study provides detailed improvement pathways for each technology under various operational conditions, assisting developers in resource allocation. Overall, the investment-based optimisation method and findings contribute to enhancing the competitiveness of emerging energy storage technologies and reducing reliance on batteries in renewable energy systems.

Enhancement of power quality in grid-connected HRES using UPQC with self-improved Battle Royal Optimizer in IEEE 14 bus system

The integration of hybrid renewable energy systems (HRES) in grid-connected load systems has become popular due to its ability to enhance reliability and reduce losses. However, this integration can lead to power quality (PQ) issues caused by non-linear, critical, and unbalanced load conditions. To address these PQ issues and compensate for load demand in HRES systems, a new method called Self-Improved Battle Royal Optimizer (SI-BRO) has been proposed. This method involves the use of a Unified Power Quality Conditioner (UPQC) device, which is designed to improve the system’s PQ performance. To enhance the performance of the UPQC device, an optimized fuzzy logic controller is introduced in the series and shunt active power filter, and its parameters are optimized using the SI-BRO model. The proposed method is implemented in MATLAB/Simulink, and its effectiveness is evaluated through different scenarios in IEEE 14 Bus system. These scenarios include the introduction of a nonlinear load to create PQ issues, and the comparison of Sag, Swell, and fluctuation of proposed technique i.e. SI-BRO optimizer with BRO,HHO ,EHO optimizer and calculation of THD in proposed technique.In all cases of Sag,Swell, Fluctuation , the proposed techniques gives better results in comparison to BRO,HHO & EHO.

Economic And Environmental Analysis of Grid-Connected Hybrid Renewable Energy System with Homer: Case of İzmir Industrial Region

Bu çalışma kapsamında İzmir ilinde bulunan endüstriyel bölgenin elektrik ihtiyacının karşılanması için şebeke bağlantılı hibrit enerji sistemi tasarımı yapılmıştır. Yapılan tasarımla, seçilen endüstriyel bölgenin enerji talebinin karşılanması, birim elektrik maliyetinin minimize edilmesi ve elektrik üretiminden kaynaklanan karbon emisyonunun azaltılması amaçlanmıştır. Bölgenin meteorolojik verileri incelendiğinde yıllık güneş radyasyon miktarı ve rüzgâr hızının yenilebilir enerji santralleri kurulumu açısından yeterli olduğu görülmüştür. Çalışmada enerji potansiyeli yüksek olan İzmir ilinde şebeke bağlantılı ve yenilebilir enerji sistemlerinden oluşan beş farklı senaryo tasarlanmıştır. Senaryoların oluşturduğu santrallerde kullanılan bileşenler; güneş paneli, rüzgâr türbini, dönüştürücü ve bataryadan oluşmaktadır. HOMER yazılımı kullanılarak alınan simülasyon sonuçlarında birim enerji maliyeti, net şimdiki maliyeti, başlangıç maliyeti ve yıllık emisyon değerleri karşılaştırılmıştır. Özellikle yıllık emisyon değeri ve birim elektrik maliyet kriterleri göz önüne alındığında en verimli sistemin bataryalı hibrit enerji sistem tasarımı olduğu sonucuna varılmıştır. Bu sistem tasarımında birim elektrik maliyeti 0,0730 $ seviyesine düşürülmüş ve elektrik tüketiminden kaynaklanan yıllık emisyon değerleri %82 azaltılmıştır.

Coupled thermo-electrical dispatch strategy with AI forecasting for optimal sizing of grid-connected hybrid renewable energy systems

In multi-energy systems the full utilisation of the generated energy is a challenge. Integrating heat and electricity supply at the system level design could provide an opportunity to address this challenge. In this paper we introduce and examine two coupled thermal-electrical dispatch strategies for grid-connected hybrid multi-energy systems supplying electrical and thermal demand loads. The dispatch strategy employs forecasting of energy resources and demand loads to prioritise supplying the thermal load in times of renewable surplus. Four forecasting algorithms, namely, baseline forecast, Facebook Prophet (FBP), Neural Prophet (NP), and Long Short-Term Memory model (LSTM) are implemented and used to generate annual forecast data for solar irradiance, wind speed, and thermal and electrical demand loads. To integrate forecast data within the dispatch strategy, new parameters are proposed to quantify the expected available energy within the forecast time horizon. A building complex for the Department of Education in the UK is used for conducting a system design case study. A genetic algorithm-based multi-objective optimisation with the levelised costs of electricity and heat as two objectives is conducted. The results show that the proposed dispatch algorithm produces systems with reduced levelised costs compared to the base case of using utility gas and electricity. Forecasting is particularly useful in reducing cost of heat, as it can prioritise supplying the thermal load in times of renewable surplus. LSTM proved to be the most accurate forecasting algorithm for this case, where the data has strong seasonality and trends. The main contribution of this work is to propose and demonstrate the effectiveness of tightly coupling thermo-electrical dispatch algorithms of HRES from the design stage, and how to effectively integrate forecast data within such algorithms.

Techno-economic study and the optimal hybrid renewable energy system design for a hotel building with net zero energy and net zero carbon emissions

The tourism sector is a key source of national income, and the use of renewable energy resources promotes green and sustainable tourism. In addition, ensuring a reliable supply and efficient use of clean energy is a primary objective of Egypt's Vision 2030, as it has a significant impact on the sustainable development and growth of the tourism sector in Egypt. Net-zero energy buildings (NZEBs) are a promising decarbonization effort as they reduce energy consumption while increasing the share of renewable energy. Therefore, it is worth investigatingthe opportunities for NZEBs applications in tourism sector. This paper presents an optimization analysis of a hotel’s grid-connected hybrid renewable energy system (HRES) based on its technical, economical, and environmental prospects. Seven scenarios are considered for on-site generation to match the energy requirements of the specific hotel building. The proposed hybrid systems incorporate photovoltaic (PV), wind turbine (WT), and biogas generators into a grid-connected system for supplying power to a tourist hotel. The size of the microgrid system is optimized to decrease the net present cost (NPC) and levelized cost of electricity (LCOE). The results reveal that the PV/wind grid-connected system, with renewable energy generators consisting of 143 kW of photovoltaic modules and one 20 kW wind turbine with a hub height of 18 m, has the lowest NPC and LCOE. This hybrid energy system has a net present cost of 388 k$, which is significantly lower compared to the electricity grid's cost of 873 k$. The electricity price of the proposed HRES, at 2.1 ¢/kWh, is highly competitive with Egypt's retail electricity prices of 6.9 ¢/kWh. Moreover, grid-connected HRES can lower the pressure on the electricity grid arising from the feed-in of electricity from HRES. Furthermore, the proposed HRES will help slow climate change by reducing GHG emissions and fossil fuel usage. Finally, this study will provide valuable guidance for the practical application of NZEBs in Egypt as well as similar global zones.

Techno-economic analysis of grid-connected hybrid renewable energy system adapting hybrid demand response program and novel energy management strategy

Over the last two decades, there has been a significant interest in developing solutions to improve the efficiency of power systems, such as optimal energy consumption and management. The demand response programs (DRP) have long been one of the most effective strategies to encourage consumers to change their consumption behavior. In this paper, a novel hybrid DRP has been implemented by combining the time of use (TOU) and incentive-based (IB) DRP methods. To meet the load demand, a PV-WT-BESS hybrid renewable energy system (HRES) has been proposed in this work. Further, to analyze the impact of hybrid DRP on the capacity allocation of different system components, optimal sizing has been performed in two scenarios, one with the actual load and the second with the load after considering the hybrid DRP. The optimization has been carried out by using an improved search space reduction (ISSR) algorithm. To demonstrate the effectiveness of the ISSR algorithm, the obtained results are compared with other algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), teaching-learning based algorithm (TLBO), seagull optimization algorithm (SOA), and sine cosine algorithm (SCA). Finally, a novel energy management strategy (EMS) is also proposed in this work on the supply side of the HRES, which intelligently uses the variations in the grid tariff to charge the BESS when the tariff is low. To demonstrate the proposed DRP and EMS-based sizing, a case study has been performed for a grid-connected PV-WT-BESS for the location of Kanyakumari, India. From the results obtained, it has been concluded that the hybrid DRP reduces the peak demand up to 5%, which reduces the levelized cost of energy (LCE) by 22%. Furthermore, the proposed novel EMS reduces the LCE further by 5%. The overall cost reduction of up to 25% has been achieved by using both the proposed hybrid DRP and EMS, while satisfying all necessary constraints.

Performance Analysis of a Hybrid Renewable-Energy System for Green Buildings to Improve Efficiency and Reduce GHG Emissions with Multiple Scenarios

A hybrid system, such as solar and wind, may be more successful than nonhybrid systems in accelerating the transition from conventional to renewable power sources. However, these new energy sources have several challenges, such as intermittency, storage capacity, and grid stability. This paper presents a complete analysis and study of a hybrid renewable-energy system (HRES) to convert a facility into a green building and reduce its dependence on conventional energy by generating clean energy with near-zero greenhouse-gas (GHG) emissions. The proposed system aims to reduce the energy bill of a hotel in Petra, Jordan, by considering different sustainable energy resource configurations in a grid-connected hybrid renewable energy system (GHRES). The hybrid optimization of multiple energy resources (HOMER) grid software was utilized on the hybrid systems to study ways to improve their overall efficiency and mitigate GHG emissions from an economic perspective. The hybrid system components included in the simulation were a solar photovoltaic (PV) system, a wind turbine (WT) system, a diesel generator (DG), and a converter. Five scenarios (PV–Converter–DG–Grid, PV–Converter–Battery–DG–Grid, WT–DG–Grid, PV–WT–Converter–Battery–DG–Grid, PV–WT–Converter–DG–Grid) were considered. The optimal configuration had a USD 1.16 M total net present cost, USD 0.0415/kWh cost of energy, 15.8% effective internal rate of return, and an approximately 77% reduction in carbon emissions compared to the base case.

Design and feasibility analysis of grid-connected hybrid renewable energy system: perspective of commercial buildings

Design and feasibility analysis of grid-connected hybrid renewable energy system: perspective of commercial buildings