Hybrid Energy Sources Research Articles

Optimal Allocation of Hybrid Renewable Distributed Generation with Battery Energy Storage System Using MOEA/D-DRA Algorithm

The need for energy and environmental sustainability drives the adoption of renewable energy systems to reduce greenhouse gas emissions. Efficient planning of energy sources is crucial for a reliable distribution network. This article solves the optimal allocation of hybrid energy sources (i.e., Renewable Distribution Generators (RDGs) and Battery Energy Storage System (BESS)) problem, aiming to minimize the annual energy loss and investment costs simultaneously. The proposed formulation includes the uncertainties such as forced outages of RDG’s, intermittence in RDG power generation, Expected Energy Not Served (EENS) by DG units, and time-varying demand altogether in the existing formulation. To evaluate the effectiveness of the proposed formulation, a Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamic Resource Allocation (MOEA/D-DRA) is used. Simulation studies are conducted on IEEE 33-node and TNEB 84-node Radial Distribution Systems (RDSs), comparing results with the Rider Optimization Algorithm (ROA) and Hybrid Nelder Mead-Particle Swarm Optimization (HNMPSO) respectively. The proposed formulation for a Multi-objective Optimization of Hybrid Energy Sources allocation problem solved by the MOEA/D-DRA algorithm provides improved benefits like minimum annual energy loss, investment cost, CO2 emission, EENS by the DG units, and enhanced system voltage stability and voltage profile.

Optimal energy management and feasibility analysis of hybrid renewable energy sources with BESS and impact of electric vehicle load with demand response program

Microgrid hybrid renewable energy sources with green energy have become a need for the environment. Electric vehicle (EV) contribution is another revolution in clean energy. The Micro-grid with hybrid renewable energy (HREs) is a cost-effective, eco-friendly solution.The main contribution of the proposed work is; (i) to analyze the impact of the operating reserve power, battery energy storage system (BESS), renewable penetration percentage, and demand response program (DRP) for the EVs load with HREs integration. (ii) minimization of the net present cost (NPC) and Levelized cost of energy (LCOE) of the system with zero unmet HREs, (iii) obtaining the techno-economic analysis of the different combinations of hybrid energy sources as Photovoltaic/Wind-Turbin/Diesel Generator and BESS, with EV load growth. The annual energy purchased and sold from the grid has been reduced by 15.35% and increased by 42.98%, respectively, considering DRP. The NPC of the system has been reduced by 62.62%, considering the BESS. The Hybrid Optimization Model for Electric Renewable (HOMER) software with MATLAB R21a interface has been used for the analysis. This paper represents the feasibility of energy storage with the HREs management study for EV load considering techno-economic, cost-effective, and economic dispatch for feasibility analysis of Hawai Island, USA.

Variable Coupling Coefficient Integrated Inductor for Hybrid Energy Source Systems

Variable Coupling Coefficient Integrated Inductor for Hybrid Energy Source Systems

Hybrid Power System

Abstract: Energy is one of the most important factors in the country's development and advancement. Several developed countries are facing issues in the production of energy. When we come to electricity, it is the most important source for running all kinds of machinery and industries. Across the world thermal power production is the most commonly used method. But it causes a lot of pollution and many resources are consumed. To avoid all these issues, the world is looking for the best renewable energy sources like wind, solar power productions. This project mainly focusses on design of an integrated setup of wind turbine and solar panel to form a hybrid energy source which is installed over the pole. When it's sunny outside, wind energy can be used to illuminate solar panels, and wind energy can also be used at night and on cloudy or rainy days. Together, the two processes produce a significant amount of electric energy, which solar cells store for use as nighttime lighting . We considered the mono crystalline solar panels(The required no. of cells would be based on the application part) such that the capacity lies between 260-280W. We made sure that the solar inverter capacity would be around 200W. We have chosen the wind turbine generator of 60W capacity. Accordingly, we have considered the solar battery of 12V. And finally, a converter which converts DC to AC.

Evolution of Micro-Nano Energy Harvesting Technology—Scavenging Energy from Diverse Sources towards Self-Sustained Micro/Nano Systems

Facing the energy consumption of a huge number of distributed wireless Internet of Things (IoT) sensor nodes, scavenging energy from the ambient environment to power these devices is considered to be a promising method. Moreover, abundant energy sources of various types are widely distributed in the surrounding environment, which can be converted into electrical energy by micro-nano energy harvesters based on different mechanisms. In this review paper, we briefly introduce the development of different energy harvesters according to the classification of target energy sources, including microscale and nanoscale energy harvesters for vibrational energy sources, microscale energy harvesters for non-vibrational energy sources, and micro-nano energy harvesters for hybrid energy sources. Furthermore, the current advances and future prospects of the applications of micro-nano energy harvesters in event-based IoT systems and self-sustained systems are discussed.

Design and analysis of a dual input quasi SB-SC cell high gain converter

ABSTRACT A DC-DC converter that can deliver high voltage gain and isolation with dual input is presented. The proposed converter incorporates a quasi-switched boost topology with an inductor in series with the main source. A switched capacitor cell and an auxiliary input source are connected before a push-pull switching stage to improve voltage boosting and the utilisation of dual input source. The high-frequency transformer provides galvanic isolation and improved voltage gain. The voltage doubler circuit (VDR) preceding the load has the ability to convert the transformer output to a DC supply and double the boost factor. Detailed operating waveform, theoretical and simulation results of the converter are presented with component selection and comparison with relevant converter topologies. A 250 W converter is implemented for an output of 400 V, when fed from two energy sources of 24 V and 12 V. The experimental results obtained satisfy the performance of the proposed converter. The benefits of the converter are (i) utilisation of hybrid energy sources, (ii) near to continuous input current, (iii) galvanic isolation and (iv) very high boost voltage.

Cost Analysis for DC Microgrid Incorporating Renewable Energy Resources using Search- and Rescue-Based Emperor Penguin Optimization Algorithm

The increase in the usage of fossil fuels for energy production has led to the extermination of natural minerals, which gives rise to the hybridization of non-renewable resources with renewable resources. The energy generation system’s integration has confiscated a new problem in load dispatch to the consumers with economic satisfaction. In this research work, hybrid optimization is proposed for the economic load dispatch formation by allocating the power in the hybrid energy sources. The PV-WT-diesel generator is used for power generation, and the search- and rescue-based emperor penguin optimization algorithm is used to allocate the power to the corresponding generation unit. The implementation is done by considering the ramp rate limit and the valve point effect. The implementation is done on the MATLAB platform for modeling the hybrid energy source and the optimization process. As a result, the combination of tri-generators has proved for a better result, costing 27248.88$/hr. At 400 MW load, inducing the emission cost of 0.012$/hr. In contrast to other methods, the fuel and emission cost is reduced to 2.66%.

A hybrid energy storage system using GA and PSO for an islanded microgrid applications

AbstractThe renewable‐based hybrid energy storage systems have gained significant attention in recent times, due to their increased power extraction efficiency, cost‐effectiveness, and eco‐friendly nature. But, the power management, optimal sizing of components, economic cost of energy, and system reliability are considered as the major problems of hybrid energy storage systems. For this purpose, the different types of optimization methodologies are developed in the conventional works for optimizing the size and cost of hybrid energy systems. The main contribution of this work is to design an efficient and reliable hybrid energy storage system based on the combination of solar, wind, biomass, batteries, and generators with optimal sizing of components, and reduced system cost. Hence, two different types of meta‐heuristics optimization techniques such as genetic algorithm (GA), and particle swarm optimization (PSO) are validated and compared with select the most suitable one for the reliable hybrid energy systems. Here, the mathematical modeling of a hybrid energy sources are presented for generating the electricity. It also discussed about the operating principles, working nature, flow of modeling, advantages, and disadvantages of GA and PSO techniques. During simulation, the performance of these algorithms is evaluated and compared by using various measures. Due to the increased convergence rate, reduced overfitting, and local optimum the performance of PSO is highly improved, when compared with the GA. Also, the PSO is discovered to perform better than the GA because it concurrently executes global and local searches, whereas the GA focuses primarily on the global search. The total excess energy for the entire year is estimated to be 5139 kWh/yr based on the findings. The PSO algorithm accurately predicts solar PV, wind turbines, batteries, and biomass gasifier with an ASC of 63 006$/yr and an LCOE of 0.173$/kWh. Hence, the results of PSO are superior than the standard GA mechanism. Moreover, the obtained results indicate that the PSO algorithm provides the better results compared with GA and HOMER.

Development of fuzzy logic-based demand-side energy management system for hybrid energy sources

Demand-side energy management techniques, such as load shielding, shifting, and delaying appliance operation during peak periods, are typically used to reduce electricity costs at the expense of users’ comfort. To address these challenges, especially where operational delays are unacceptable, distributed generation (DG) is frequently incorporated into the grid system to improve power balance and total energy costs. However, dynamic load demands and varying outputs from renewable DG sources such as solar photovoltaic (PV) systems make energy management in microgrids (MGs) extremely challenging. Moreover, most of the existing studies in this domain focus on objective functions that are geared on optimising the economic balance between cost and value of MG operation over a certain time period. Nevertheless, research that took into consideration the stochastic behaviour of DG’s subsystems in addition to cost and benefit of MG operation are still limited. This current study proposed a fuzzy logic control (FLC) integrated energy management system (EMS) for commercial loads with hybrid grid-solar PV/battery energy system. The proposed technique intelligently selects energy sources using the grid energy cost and the state of charge (SoC) of the solar PV/battery at any time of the day. The EMS operate the loads at a reduced cost without any operational delay or shifting. The system was implemented in the MATLAB/Simulink environment, and the techno-economic feasibility of energy cost savings was investigated by comparing the developed scheme with the Homer hybrid energy system model for a hotel building. The developed EMS reduced energy costs by an average of 11.87 % per day and 7.94 % over a 20-year lifetime.

Optimal energy scheduling of a standalone rural microgrid for reliable power generation using renewable energy resources

ABSTRACT Rural villages can be electrified using renewable power sources for the area to grow effectively. Microgrids powered by renewable energy is more environmentally friendly and practical choices for electrifying rural areas. This study attempts to ascertain the technological and financial viability of an autonomous hybrid energy source for a remote Indian area in order to provide a reliable power supply. The region’s load requirements can be met with the help of a solar energy system, biogas, wind energy, diesel generators, and batteries. Differential evaluation (DE) is used to optimize the proposed microgrid for grid-independent operation. The system’s ultimate objective is to reduce energy costs and recommend workable component configurations. Particle swarm optimization (PSO) and the genetic algorithm (GA) are used to compare the DE findings. The finding suggested the optimal energy cost of 0.22 $/kWh, a net current cost of $5,18,656, and a capital cost of $8,50,774. DE’s total net present cost is 4.3% less than PSO and 5.2% less than GA. However, it has been determined that the recommended approach is more practical and affordable for electrifying rural areas.

Adaptive intelligent hybrid energy management strategy for electric vehicles

AbstractElectric vehicles (EVs) utilizing hybrid energy sources is a significant step toward a sustainable future in the transportation industry. The electric three‐wheeler (3W) considered in the proposed work includes three sources—battery, supercapacitor (SC), and photo‐voltaic (PV) panels. In battery electric vehicles (BEV), battery life cycle, energy efficiency, and performance are affected by variations in driving conditions that inhibit their wider adoption. The main focus of the proposed intelligent hybrid energy management strategy (IHEMS) is to enable the vehicle to adaptively manage and diminish the effects of load fluctuations due to varying conditions. IHEMS diverts the load fluctuations to the SC bank by ensuring an effective absolute energy sharing among the sources with a fuzzy logic control algorithm. PV energy is utilized to assist the battery during sunny days. Performance of the EMS in hybrid source EV is analyzed in MATLAB/SIMULINK environment with a combination of three different standard real‐time driving profiles (NYCC, Artemis Urban, WLTP class‐1). Proposed EMS reduces peak battery power by 20% and 14.35% and improves battery life by 16.4% and 11.4% compared to BEV and conventional EMS, respectively. This proves that the proposed EMS exhibits adaptive energy management irrespective of the driving conditions and ensures improved battery performance and longevity.

Global sensitivity analysis of a generator-absorber heat exchange (GAX) system’s thermal performance with a hybrid energy source: An approach using artificial intelligence models

• Temperature, heat and flow measurements of an experimental GAX cycle. • Evaluation of artificial intelligence (AI) techniques for multivariate modeling. • Artificial neural networks represent the GAX system with the best accuracy. • AI model coupled to the PAWN method for a global sensitivity analysis of GAX cycle. • COP is affected the most by the temperature measured at the generator inlet. Generator-absorber heat exchange (GAX) systems represent a promising alternative to substitute environmentally harmful refrigeration devices based on conventional vapor compression, as long as a proper analysis of thermal performance and the complex interactions of heat transfer that occur into GAX cycle is taken in consideration. In this research, a cooling process based on a GAX system that uses ammonia-water working fluid and a hybrid source (natural gas-solar) is studied to analyze the variables that affect the system’s thermal performance. The work’s novelty is the hybridization between artificial intelligence (AI) modeling and the global sensitivity analysis (GSA) developed with the PAWN method. Experimental data was obtained from a system with a cooling capacity of 10.5 kW (3 Ton), designed to work at heat source temperatures of 200 °C. The measured variables were the temperatures at generator, heat at evaporator, and working fluid volumetric flow. Three AI techniques (artificial neural networks, genetic programming, and support vector machines) were evaluated for modeling the thermodynamic cycle. Results obtained from the PAWN method applied to the artificial neural network, since it was the best AI model, indicates that the operational parameters with a greater impact in the system’s performance are the inlet temperature at the generator (30.7 %) and the heat measured at the evaporator for NH 3 (27.4 %), for the first output COP N H 3 . For the second output COP H 2 O , the inlet temperature at the generator (32.5 %) and the and heat measured at the evaporator for H 2 O (26.7 %), have a greater impact for such output. The proposed IA-GSA methodology contributes to the development of operational decision-making related to instrumentation, operation performance, and corrective and/or preventive maintenance actions of GAX systems. The developed thermal performance model has potential for implementation in embedded systems (smart sensors) as a critical element in control and optimization strategies to improve the performance of these cycles.

RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

The diesel generators sets (DGs) and battery storage systems (BSS) are the essential energy sources in a modern high-rise buildings. In this paper DG, BSS and Photovoltaic system (PV) has been considered to minimize the grid power injection using a centralized Energy Management System (EMS). Machine Learning (ML) techniques are used to predict the performance of various regression models by comparing grid power and load curves. It includes Artificial Neural Network (ANN), Wide Neural Network (WNN), Linear Regression (LR), Linear Regression Interaction (LR-I), Linear Regression Stepwise (LR-S), Regression Fine Tree (RF-T), Regression Coarse Tree (RC-T) and Gaussian Process Regression (GPR) based techniques. The Demand Side Management (DSM) techniques such as peak shaving and valley filling is integrated with ML technique in a Hybrid energy source (HS) system.The comparative analysis of results depicts the effective reshaping of the grid profile without scheduling or disconnecting the loads. Matlab simulation software is used to validate the results.

Power Quality Diagnosis Due to Integration of Renewable Energy Source at Utility Grid

Article outlines hybrid energy sources for fulfil supply demand through proposed approach. The hybrid renewable energy sources generation capacity of 6 kW as solar power and 50 kW power as solid oxide fuel cell plant with storage system are interfaced to the DC bus in proposed paper for performance diagnosis on the basis of power demand as per utility load application by conversion of DC power to AC power and power quality of grid/load parameters collected on PCC, which affected by RE source integration at utility bus in terms of power quality parameters as per event ON/OFF grid mode. For this study design a two-bus test system, first bus is DC bus for integration of solar PV plant and solid oxide fuel cell power plant to generate hybrid DC power which convert to AC power by using inverter technology and deliver to the second bus of utility load bus in proposed test system for application of utility load as well as utility grid as per event proposed. A low pass LC filter is modeled to remove high frequency harmonics from the inverter output which is fed to grid as well as test system of utility network.

Voltage control of hybrid energy sources for microgrid employing biomass-solar-wind

Voltage control of hybrid energy sources for microgrid employing biomass-solar-wind

Speed control of hybrid energy sources fed BLDC motor drive with FOPID controllerusing various optimization techniques

This article suggests the control of current and speed approach to reduce the torque ripple in BLDC motor. Initially, the renewable energy hybrid power system (REHPS) is composed of a generation system of PV, fuel cell (FC), and the storage system of battery bank. This REHPS uses solar power as their main source of electricity during the day. It uses the fuel cell as a secondary source for maintenance at night or during periods of shaded conditions. The novelty of the proposed method is to achieve torque ripple minimization and to control the speed of the BLDC motor. The speed and error torque of the BLDC motor is optimized by mayfly optimization algorithm (MOA). The MOA provides gain parameters of the fractional order proportional–integral–derivative (FOPID) controller. The advantage of the proposed method is to improve the level of dependability and provide flexibility in solving the system error. The proposed model is implemented in MATLAB/Simulink and experimental setup. The results of the proposed method are compared with the existing research techniques such as particle swarm optimization (PSO) and moth flame algorithm (MFA).

An Effective Load Management for Grid Connected Hybrid Energy Sources

This paper introduces to control a standalone hybrid renewable system, involving PV cell and wind turbines as the primary energy sources along with fuel cell and battery energy source as an emotionally supportive system. While trying to work on the solidness and security of the hybrid renewable system sustainable framework, a battery bank, is incorporated as supporting units, due to the discontinuous and fluctuation in primary energy sources commitment. In this paper we model an independent sustainable source micro grid with various sources, which are wind energy with PMSG, PV panel, Fuel cell and battery storage system. Analysis of each source is done under variation conditions and variation of source parameters, such as wind speed of wind turbine, illumination, temperature of PV cell and state of charge of the battery. If main power sources of PV panel and wind turbines is not available, the battery storage device act as backup supply for load. This storage source (battery) can be charged when abundance power is produced from the PV panel and wind generation system. Investigation on each sources with dynamic changes of boundaries are studied with siumulation results analysis is studied by utilizing MATLAB/ SIMULINK software.

Implementasi Sistem Kontrol Manajemen Energi Hibrid Berbasis Mikrokontroller

Currently, world researchers are competing to find the most effective and efficient way to use renewable energy sources, namely energy from sunlight and solar and wind heat or it can be called hybrid energy. The use of this hybrid energy technology can certainly save conventional fuel and reduce the risk of ecosystem damage caused. This study aims to see the energy value resulting from the implementation of a hybrid energy management control system that has been created based on a microcontroller. With the experimental method, the results were obtained that the solar cell voltage value was highest at 11 pm with 28.87 Vdc, then for the voltage of the wind energy was the highest at 27.85 at the 3rd minute at 4 pm. For energy testing of batteries, the results were obtained that if there is only one energy source to charge the battery, it takes 9 hours while if it is a hybrid energy source, it only takes 2.5 hours. This proves that hybrid energy is very effective rather than relying solely on one of the energy sources.

Development of High Voltage DC Power Supply from Tesla Coil Using Hybrid Energy Sources

Background: High Voltage Direct Current (HVDC) is a well-proven technology used to transmit electricity over long distances by overhead transmission lines or submarine cables. It is also used to interconnect separate power systems, where traditional Alternating Current (AC) connections cannot be used. Objective: In this paper, an HVDC supply has been proposed with the Tesla coil using hybrid energy sources such as solar panels, batteries, and AC main supply with an AC-DC converter. Method: DC voltage is obtained from various hybrid sources and is supplied to the input of the Cockcroft-Walton (C-W) voltage multiplier circuit through DC/AC inversion. Result: The produced HVDC is provided to the Tesla coil to obtain a stable HVDC output. Fractional Order PID (FOPID) controller is used in the system for control purposes to stabilize the output voltage due to load variations. The model was designed in Matlab / Simulink environment. The simulation results show that the proposed design successfully achieved a stable 15 kV HVDC. Conclusion: The advantage of the proposed system is the availability of variable output voltage as per load requirement compared to the fixed limited output voltage generated by conventional HVDC systems.

Integration of Renewable Energies in Mobile Employment Promotion Units for Rural Populations

The article aims to analyze, evaluate, and improve solutions for the integration of hybrid energy sources (Solar Photovoltaic PV/Batteries/Diesel Generator (DG)) in mobile service units (MSU), designed to provide services to rural populations (drug delivery, vaccination, training, employment promotion, bank, laboratory, etc.). The first objective is to evaluate the performance of two already deployed photovoltaic systems installed on the roofs of trucks, with respective powers of 2.12 and 3.54 kWp. Solar production, consumption, and SOC (State of Charge) of batteries are collected and analyzed. We modelled the energy conversion chain and simulated its behavior on all days of the year. Simulated results are then compared to the on-site measurements. Several association scenarios (PV/batteries) are then studied to propose the optimal combination, taking into account the surface offered for the installation of the PV modules (roof of the truck), the weight, and the lifespan of the batteries. The developed and deployed solution proposes more advantageous association scenarios (PV/Storage), and reduces the time of recourse to the DG. From this perspective, we simulated the operation of the hybrid system for the three battery capacities: 40,000, 31,680, and 19,200 Wh (~1667, 1320, and 800 Ah). The results reveal that the uncaptured energy for a 3540 Wp field is five times greater than that of a 2120 Wp field. On the other hand, the number of battery charge/discharge cycles is divided by ten. Doi: 10.28991/CEJ-2022-08-07-07 Full Text: PDF