Backup Source Research Articles

Using geothermal energy in enhancing all-air HVAC system performance - Case study, thermal analysis and economic insights

The background is the same for addressing the twin challenges of attaining thermal comfort and concurrently reducing energy use and CO2 emissions. As a safeguard against worsening contamination, combining geothermal energy with an All-Air Heating, Ventilating, and Air Conditioning system (HVAC) with 100 % fresh air intake is also crucial in the context of the lessons learned from the COVID-19 pandemic. As a result, this study suggests using geothermal energy as a backup source to run a typical All-Air centralized HVAC system. The resulting system is referred to as a combined system from now on, and its primary goals are to lower the amount of operating energy needed while being environmentally and financially sound. In this context, the creative way of combining the suggested geothermal duct with the All-Air HVAC system possesses various insights, as demonstrated by the thorough thermal analysis and related design. Strong conclusions were derived from a thorough case study focused on Lebanon: adding 100 % fresh air intake leads to an astounding yearly energy savings of 67 %, whereas configurations with 10 % and 30 % fresh air produced energy savings of 52 % and 36 %, respectively. In the study, a geothermal multi-duct system was also suggested and looked at. The research revealed that the 311-m length of the single geothermal duct connected to a pressure of 7000 Pa was reduced to 210 m and the pressure drop was lowered to 140 Pa when a geothermal multi-duct system was employed. Furthermore, as the computed payback period makes clear, the integrated system presents an appealing chance to drastically cut resource usage during energy consumption, lower CO2 emissions, and provide 100 % fresh air circulation.

Model of the non-stationary thermal processes in the ground heat pump system

One of the main directions for improving heat supply systems is the tendency to switch to low-temperature heating systems by using heat pump units is the tendency of implementation the low-temperature heating systems based on the use of heat pump units. In the work, the main attention is focused on the improvement of thermal parameters and circuit design features of elements of the heat pump system of heat supply using soil energy. For this purpose, the toolkit of mathematical modelling of processes in ground heat pumps is applied, taking into account the climatic conditions of Ukraine. The paper proves the technical possibility of creating an autonomous system of alternative heat supply for energy-saving technologies with the possibility of using ground energy, taking into account environmental requirements. It is substantiated that the use of the proposed systems is a promising direction for Ukraine, which is experiencing a shortage of its own energy resources, because it provides an opportunity to increase the share of organic fuel substitution and reduce the volume of heat emissions into the environment. A mathematical model of heat processes in the elements of the heat exchange equipment of the heat pump system based on renewable energy (soil energy) and secondary energy (wastewater) was developed, taking into account environmental requirements (preventing hypothermia of the soil during the operation of soil pipes and damage to vegetation, respectively), which allows choosing a rational mode of operation of the system under seasonal climatic changes and technological conditions. Numerical modelling of heat processes in elements of the heat exchange equipment of the heat pump system using groundwater and waste water was performed, and the energy and environmental efficiency of the system under variable climatic and technological factors was determined. Analysis and generalization of the calculation results of the heat pump heat supply systems using alternative energy based on the proposed mathematical model, taking into account environmental requirements (preventing thermal relaxation of the soil) allows to take into account the change in the temperature gradient around the soil heat exchanger and more accurately determine the heat flow from each section of the heat exchanger. It has been established that for heat pumps with soil heat exchangers, the maximum substitution ratio of the traditional fuel is 75 %, and the economically feasible quantity of soil heat exchangers is 9 units. Heat pumps using waste water can completely replace traditional heat supply systems. For individual consumers, it is possible to recommend heat pump units with soil heat exchangers, at the same time, it is necessary to provide a backup source of energy to increase the reliability of heat supply.

Solar PV focused LFC studies utilizing an SFS-optimized PID with fractional derivative (PIDDμ), and incorporating BESS and FESS applications

This study investigates the application of a PID controller with a fractional derivative component (PIDDμ), optimized using the stochastic fractional search (SFS) technique, for load frequency control (LFC) in power systems. Additionally, solar PV and battery energy storage systems (BESS) and flywheel energy storage systems (FESS) are integrated as backup sources to enhance the LFC performance. To assess the effectiveness of the proposed approach, three error metrics—Integral Time Absolute Error (ITAE), Integral Time Multiplied Square Error (ITSE), and Integral of Absolute Error (IAE)—are evaluated. The results are compared with recent optimization-based LFC techniques across various scenarios. It is found that the SFS-PIDDμ achieves IAE=0.006786, ITSE=1.07e-05, and ITAE=0.01853, which are significantly lower than the error values produced by other LFC techniques. Furthermore, integrating solar PV, along with BESS and FESS, further reduces these error values. The graphical LFC responses of the SFS-PIDDμ outperform other optimization-based LFC techniques. The robustness of the approach is also validated against random load changes of varying magnitudes and for broader parametric variations.

Implementation of a multistage predictive energy management strategy considering electric vehicles using a novel hybrid optimization technique

In this paper, a novel artificial intelligence (AI)-based energy management strategy across three levels is designed for isolated microgrids. During the initial phase, AI is not employed. A precise and rapid-response microcontroller, namely the FPGA, is utilized. The performance of the FPGA is experimentally investigated under various operation conditions. In the second phase, AI plays a crucial role in enhancing both the reliability and economic effectiveness of the system. The multi-objective snake optimization (SO) algorithm is employed to attain high technical and economic performance within predefined constraints. Three objective functions are involved in this phase, focusing on the minimization of operational costs, loss of power supply probability (LPSP), and electrical energy losses in the dummy load. In the third level, a novel control strategy-based coordinated model predictive control is presented as part of the proposed AI-embedded energy management strategy. A hybrid optimization algorithm combining the multilayer feed-forward neural networks (MFFNN) and SO algorithms is proposed to predict the output power of backup sources. The microgrid under consideration is supported by a hybrid backup system comprising battery energy storage systems (BESS), electric vehicle (EV) batteries, and fuel cells (FCs). The effectiveness of this hybrid algorithm is evaluated through comparison with many other algorithms, aiming to assess its performance in accurately predicting the output power of backup sources. The results show the feasibility of using AI to achieve efficient operation and energy management in microgrids. The proposed MFFNN-SO algorithm achieves the best performance, as the normalized root mean square error (NRMSE) for FCs, BESS, and EV is about 0.1296 %, 0.0094 %, and 0.1304 %, respectively. The SO algorithm achieves a notable 6.3361% reduction in operating costs, resulting in a final operational cost of 166.4811 $.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

A high-efficiency poly-input boost DC–DC converter for energy storage and electric vehicle applications

This research paper introduces an avant-garde poly-input DC–DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources—solar energy and fuel cells—with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85–90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91–95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC’s adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC–DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

Reservation of the heat supply system with biofuel heat energy sources

The implementation of the reservation of the heat supply system to a populated area was analyzed. It is noted that increasing the reliability of heat supply to consumers is a fundamental task, especially given the current military situation in Ukraine. The solution to this difficulty is considered when reserving heat supply by constructing a backup source of thermal energy using alternative types of fuel. A combined thermal scheme for connecting a hot water heat generator using biofuel to the existing city heat supply system is proposed to increase its reliability and survivability, which ensures the efficiency and reliability of operation of the heat supply system during the heating and summer periods. It is shown the possibility of operating a solid fuel heat generator with its power significantly less than the total heat load of the city's heating supply in several operating modes: during the heating period - for heating the return coolant at the inlet to the existing gas boiler house; in the summer period - for the needs of hot water supply in full; in the event of an emergency shutdown of a gas boiler house in winter - to maintain the viability of the heat supply system. The results of a calculation analysis of the operation of a 5,0 MW biofuel heat generator under different operating modes are presented. Heating the return coolant by 4...5 °C in winter, in addition to saving natural gas, prevents low-temperature corrosion of existing gas boilers. In the event of an emergency lack of natural gas supply, the combustion of wood chips ensures the production of thermal energy at the level of thermal losses of the heating network while maintaining the coolant temperature at least 3 °C at the calculated external air temperature. Thanks to the accumulation of thermal energy at night in the summer period in the volume of heating network pipes as a buffer tank, compensation for peak hot water consumption is provided with the available power of the solid fuel boiler house equal to the average load of the hot water supply system. When implementing a project to back up the heat supply system in Lutsk using a combined thermal scheme for connecting a solid fuel boiler house, an increase in the reliability and viability of the system and natural gas savings of 40,5% during a year of operation were achieved

Investigation on NO and N2O emissions characteristics in deep peak regulation circulating fluidized bed boilers

Abstract To adapt to the increasing proportion of new energy power generation capacity, coal power must transition from its traditional role as the primary power source to serving as a fundamental backup and system regulation energy source. The circulating fluidized bed technology is known for its wide range of load regulation capabilities; however, emissions of pollutants during load regulation can exhibit significant variability. This study utilized Aspen Plus software to develop a circulating fluidized bed combustion model based on a gas-solid fluid dynamics model, an equivalent coal pyrolysis model, and a multi-phase macroscopic combustion reaction dynamics model of pyrolysis products. This model was used to predict both the temperature distribution within the furnace chamber and the distribution of pollutant concentrations. Predictions of pollutant emissions from 100 % load to 30 % load of the circulating fluidized bed were explored under the original combustion condition and 5 % proportion of recirculated flue gas. Under the primary combustion condition, the emission concentration of NO x showed a decreasing and then increasing trend with decreasing load, while the concentration of nitrous oxide, in contrast to NO x , showed an increasing and then decreasing trend. The effect of recirculated flue gas on pollutant emissions was not significant at reduced loads. This study aims to provide technical support and theoretical guidance for the management of pollutant emissions from the deep peak regulation of actual circulating fluidized beds.

Maximizing hybrid microgrid system performance: A comparative analysis and optimization using a gradient pelican algorithm

The development of hybrid systems that combine different energy sources has become increasingly important to meet the growing demand for clean and sustainable energy. These systems can offer a reliable and efficient solution to reduce dependence on conventional energy sources and address the issues of climate change and environmental degradation. This study focuses on two types of hybrid systems, namely PV/biomass and PV/diesel, which are commonly used in off-grid areas or places with limited access to electricity. The proposed optimization framework aims to determine the optimal sizing of each component in the system, including PV panels, batteries, and backup sources, to maximize the performance and efficiency of the hybrid system. The Gradient Pelican Optimization Algorithm (GPOA), which is an advanced optimization technique, is proposed to solve the optimization problem. The algorithm is modified to include a local escaping operator, which allows the algorithm to escape local optima and achieve better solutions. The outcomes of the simulation show that the GPOA algorithm outperforms other well-known algorithms in terms of accuracy and computational efficiency. The simulation results also demonstrate that the PV/biomass hybrid system outperforms the PV/diesel hybrid system in terms of cost and environmental impact. The proposed framework and optimization methodology can be useful for designing and optimizing other hybrid systems and can be applied to various applications, including off-grid and remote areas, distributed power systems, and microgrids. The findings of this study can also contribute to the development and promotion of renewable energy and sustainable development.

Improving Performance of Solar Array Fed BLDC Drive Using Firefly Algorithm Based High Gain Converter

The improved transient response of PID controller for improved Luo converter supplied Brushless DC (BLDC) motor using solar energy source is extensively investigated. The BLDC drive uses a hybrid PV system and battery-based power generation as a power source to assure continuous power supply to pump water regardless of solar insolation. The PV system serves as the main energy source, and the battery providing as a backup source that discharged its power only during inclement weather or at night if the photovoltaic panel is insufficient to operate the centrifugal pump. The solar panel feeds power into the battery, whenever water to the field is not required, thus there is no need for an extra power source to charge the batteries. The high output voltage gain DC-DC converter does not make any discomfit to the system performance in regards to irradiance variation, switching loss, or power loss by converter or motor side respectively. Moreover, current sensor, voltage sensor, and control circuits are completed excluded which enhance performance and the cost of the system get reduced effectively. A bidirectional charging control allows a bi-directional converter to alter the battery operation mode automatically. The overall system performance is validated using simulation and experimental setup.

Optimal resilient operation and sustainable power management within an autonomous residential microgrid using African vultures optimization algorithm

This paper introduces an adaptive artificial intelligence (AI)-based home energy management system (HEMS) to mitigate the discomfort of customers and reduce operational costs. A techno-economic smart HEMS is proposed for home microgrid management, employing two control approaches. The initial approach involves employing an FPGA as a central control unit due to its high-speed processing capabilities and efficient handling of rapid changes within desired limits. The other approach aims to optimize the performance of the microgrid and obtain an optimal operating plan for home microgrids by integrating power limitations using a developed coordinated strategy. A multi-objective optimization problem is formulated that involves the coordinated operation of backup sources. The study utilizes the African Vultures Optimization Algorithm (AVOA) and compares it with newly introduced algorithms, with a specific emphasis on three techno-economic objectives. The simulation and experimental results indicate that the AI-embedded FPGA-based HEMS not only enhances performance and extends Battery Energy Storage Systems (BESSs) life but also reduces peak times resulting from random electric vehicle charging. Through comparative analysis, the superiority of the AVOA is evident, attaining the lowest operating cost of 1308.85$ by the end of the day, reflecting a 3.77% reduction in operating costs compared to the previous study.

PERENCANAAN INSTALASI LISTRIK GEDUNG WAREHOUSE PT. XYZ SEMARANG JAWA TENGAH

Warehouse Building PT. XYZ at Semarang Central Java is a building place for keeping logistics owned by the company with a large area. There is also buildings' support in it so that required system installation suitable electricity with applicable standards such as PUIL 2011 and other standards in the field of electricity. The main electricity supply uses a source from PLN, and the generator is set as a backup source when electricity from PLN is dead. Plan installation at PT. XYZ Warehouse building, namely the installation of lighting, socket installation, air conditioning installation, electronics installation, installation of rolling door, installation of local government fire pump, and installation of clean water transfer pump. There are LVMDP panels, SDP panels, and subpanels as supply power for the installation that will be installed. Breaker rating selection power and dimensions cable delivery person who will use will affect on reliability network installation. Calculation of CRC will influence election breaker power, cable diameter selection delivery, and drop the voltage to happen on the network installation. The calculation was conducted manually. With more formerly collected load data equipment electricity at PT. XYZ Semarang and classification distribution system split panel installation, SDP panel, and LVMDP panel. The total load installed on the PT. XYZ Semarang Warehouse building is known as 318,406 VA or 318 kVA so the needed splicing power from PLN using customer of 329 kVA with use transformer with 400 kVA capacity and required generator set capacity of 400 kVA. The load on the LVMDP panel is 254,725 watts with KHA 569 A, so using MCCB with a rating of 630 A, using cable 7 x NYY 1c x 185 mm 2 with drop voltage 7.47 Volts or by 1.96% on the network 3 phase system. By PUIL 2011 standard decline voltage maximum of 4% so that the drop in the voltage on the LVMDP panel is still by the required standard.

Revitalizing lead-acid battery technology: a comprehensive review on material and operation-based interventions with a novel sound-assisted charging method

This comprehensive review examines the enduring relevance and technological advancements in lead-acid battery (LAB) systems despite competition from lithium-ion batteries. LABs, characterized by their extensive commercial application since the 19th century, boast a high recycling rate. They are commonly used in large-scale energy storage and as backup sources in various applications. This study delves into the primary challenges facing LABs, notably their short cycle life, and the mechanisms underlying capacity decline, such as sulfation, grid corrosion, and positive active material (PAM) degradation. We present an in-depth analysis of various material-based interventions, including active material expanders, grid alloying, and electrolyte additives, designed to mitigate these aging mechanisms. These interventions include using barium sulfate and carbon additives to reduce sulfation, implementing lead-calcium-tin alloys for grid stability, and incorporating boric and phosphoric acids in electrolytes for enhanced performance. In contrast, operation-based strategies focus on optimizing battery management during operation. These include modifying charging algorithms, employing desulfation techniques, and integrating novel approaches such as reflex and electroacoustic charging. The latter, a promising technique, involves using sound waves to enhance the electrochemical processes and potentially prolong the cycle life of LABs. Initial findings suggest that electroacoustic charging could revitalize interest in LAB technology, offering a sustainable and economically viable option for renewable energy storage. The review evaluates the techno-economic implications of improved LAB cycle life, particularly in renewable energy storage. It underscores the potential of extending LAB cycle life through material and operation-based strategies, including the innovative application of electroacoustic charging, to enhance the competitiveness of LABs in the evolving energy storage market.

Optimal selection and analysis of microgrid energy system using Markov process

This study investigated the reliability of a microgrid (MG) energy system using the Markov process reliability method. Twenty possible MG configurations based on photovoltaic (PV), wind turbine generator (WTG), biomass generator (BMG), battery bank (BB), diesel generator (DG) and an electric vehicle (EVs) have been considered. This study also selects an optimal configuration based on reliability indices (failure rate, repair time, unavailability, expected energy not supplied (EENS) and loss of load probability (LOLP)), annually load demand, availability of the system and its components. The results show that the hybrid PV/WTG/BMG/BB/EVs/DG microgrid system has the lowest system failure rate (0.01012 /year), repair time (1.1858 hrs), unavailability (0.0120 hrs/year), EENS (4.5 kWh/year) and LOLP (1.36986E-06 /year). This MG configuration has an availability of 99%. The PV array (97.55%) and the changeover switch (99.5%) are the parts of the MG system that are the most reliable. The least stable elements of an MG system are battery (63.64%), diesel generator (80%) and biomass generator (83.89%). PV (62%) and WTG (28% of total) are the main contributors to electricity generation, with the battery, electric vehicles, and diesel generator acting as backup sources. Under different weather and load conditions, the hybrid PV/WTG/BMG/EVs/BB/DG microgrid was found to be a reliable and efficient system for remote areas. This research carries a significant impact for policymakers, government, energy planners, and researchers working on stand-alone microgrid systems as it provides valuable insights into optimizing system configuration for reliability and efficiency.

Design and Development of Hybrid Microgrid Control System for Renewable Energy Generation

Renewable energy sources are widely acknowledged as the optimal substitutes for conventional energy outlets globally. Countries are endowed with diverse renewable resources, including solar, wind, biomass, hydro, and tidal energy. Despite this abundance, there exists a substantial disparity between the demand and supply of electrical energy, with numerous regions still facing insufficient access to power. The integration of various renewable energy sources in remote and isolated locations forms a Microgrid (MG), catering adequately to local energy requirements. These microgrids have the capability to function seamlessly alongside conventional grids. Hybrid MG system, incorporating Photovoltaic (PV) with battery storage and a Wind Turbine (WT), emerges as a practical solution for electrifying remote areas in islanded mode. The WT’s installed capacity is chosen to meet critical load requirements during periods of non-availability of renewable sources. The battery's capacity is selected to cover the transition period between renewable sources or as a backup source, ensuring cost-effectiveness over WTs. The application of Adaptive Particle Swarm Optimization (APSO) has demonstrated favorable outcomes compared to existing PSO algorithms, enhancing control efficiency. In a grid-connected MG system, the integration of distributed PV generation has notably improved the voltage profile, reducing overall losses. Consequently, the presented MG systems provide both technically and economically viable solutions for ensuringcontinuous electricity supply in isolated and grid-connected systems. This approach not only mitigates pollution but also allows for the possibility of capacity addition, fostering sustainable growth in these regions.

Hybrid model for microgrid short term load forecasting based on machine learning

Short-term load forecasting (STLF) is crucial for microgrid (MG) operators to optimize energy generation and storage schedules based on anticipated load variations. Accurately predicting peak demand periods enables operators to ensure sufficient power supply while minimizing reliance on expensive backup sources, resulting in cost savings, and improved overall system efficiency. Residential MG power demand is highly dynamic due to external factors like residents’ lifestyles, behaviors, and weather responses, leading to significant irregularity and management challenges. To deal with these challenges, we propose a hybrid STLF model that combines Artificial Neural Networks (ANN) and Fuzzy Logic (FL), referred to as the Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was trained and tested using real power consumption data. We evaluated the performance of the ANFIS model by comparing it with another ANN model using three evaluation metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The trained ANFIS model achieved an MAPE of 8.7528% and an RMSE of 0.4752kw, while the ANN model achieved an MAPE of 8.8123% and an RMSE of 0.4816kw. These results confirm the accuracy of the hybrid ANFIS model compared to ANN. The ANFIS model demonstrated its ability to capture complex and nonlinear relationships between various factors affecting load demand, making it suitable for handling the dynamic nature of MG load.

Криминологические риски и проблемы квалификации публичной демонстрации порнографических материалов с использованием сети интернет

During the analysis of the phenomenon of “webcams” within the context of the criminal legislation of the Russian Federation, its social danger was investigated. Judicial practice was analyzed, and the author provided comments regarding its interpretation and understanding. Directions for further research and study of “webcams” from a legal perspective are proposed. The problem of illegal distribution of pornographic materials, especially involving minors, in the context of the development of digital and information-telecommunication technologies was identified. The author notes that such websites often have short lifespans and use additional or backup sources to avoid being blocked by law enforcement agencies. These crimes are difficult to detect and prosecute due to the anonymity provided by the Internet. Methods of combating illegal distribution of pornographic content in the information sphere were identified, including responding to user complaints, reacting to signals from Roskomnadzor (Russian Federal Service for Supervision of Communications, Information Technology and Mass Media), and independent monitoring and removal of unlawful content. However, the author emphasizes that social networks are merely information intermediaries, and the content is created by users. The development of digital technologies, such as online broadcasts and webcam shows, has contributed to the expansion of the porn industry and facilitated users’ access to pornographic websites.

PLANNING AND FEASIBILITY STUDY OF A HYBRID SOLAR POWER PLANT WITH AN ADDED AUTOMATIC TRANSFER SWITCH (ATS) FOR AN OFFICE BUILDING

The Office of the Regent of Sidenreng Rappang (Sidrap), situated on Harapan Baru Street, Batu Lappa, Watang Pulu District, Sidrap Regency, South Sulawesi, consumes 200 kWh of electricity daily for lighting, resulting in substantial energy costs. Recognizing the potential for renewable energy, especially with a daily solar radiation potential of 5.8 kWh/m2, this study proposes the implementation of a hybrid solar power plant system. The system incorporates Photovoltaic (PV) as the primary energy source, with the Grid and Generator serving as backup sources through an AC Coupling configuration utilizing Automatic Transfer Switch (ATS). The research employs a simulation approach using HOMER Pro software for system modeling, SketchUp software for solar panel layout, AutoCAD software for ATS circuit modeling, and theoretical calculations for financial analysis. The results indicate a solar power plant capacity of 39.6 kW, producing 75,701 kWh/year with an impressive 83.3% renewable penetration. From an economic standpoint, the project requires an investment of IDR 642,714,960, with a net present cost of IDR 1,573,177,823, and a cost of energy value of IDR 1,401.38/kWh. In terms of feasibility, the project demonstrates a net present value exceeding zero (IDR 216,680,041), a profitability index greater than one (1.33), an internal rate of return surpassing the credit interest rate (12.488%), and a payback period of 7 years and 7 months. These findings affirm the feasibility of the hybrid solar power plant planning project for the Sidrap Regent's Office, showcasing its economic viability and potential for sustainable energy solutions.

Techno-economic configuration of a hybrid backup system within a microgrid considering vehicle-to-grid technology: A case study of a remote area

This paper assesses the feasibility of the hybrid backup system within the microgrid that incorporates vehicle-to-grid (V2G) technology. The proposed strategy is verified through a real case study in a remote area of Egypt. Several operating configurations for the hybrid backup system are studied. In this study, the proposed backup sources are the battery energy storage system (BESS), the hydrogen energy storage system (HESS), and the electric vehicle battery (EVB). To improve the robustness and reliability of the system, an enhanced power management strategy for the hybrid backup system is implemented. The main objectives are to minimize the operational costs, the wasted energy in the dummy load, and the loss of power supply probability (LPSP). The optimization problem is performed using the african vultures optimization algorithm (AVOA). The results clearly indicate that the proposed AVOA outperforms other recent algorithms, specifically the reptile search algorithm (RSA) and the snake optimizer (SO). The AVOA shows a 14.377 % reduction in computational time compared to the RSA, while it is nearly equal to the computational time of SO. Based on the technical and economic analysis, the configuration that combines the three backup sources has superiority over the other configurations. When using AVOA, the cumulative operating cost throughout the day for this configuration is approximately 13066.21$. The BESS/EVB backup system has an operating cost of 13120.66 $, while the BESS/HESS and BESS backup systems have operating costs of 13084.27 $ and 13,085 $, respectively. Regarding accuracy, the three algorithms achieve close results.

PLANNING STUDY OF HYBRID POWER PLANT SOLAR PV-DIESEL GENERATOR ON KODINGARE ISLAND, SINJAI REGENCY

Kodingare Island is located in Pulau Sembilan District, Sinjai Regency, one of nine islands. Currently, most people still rely on conventional energy from diesel power plants. The reason is that this island does not yet receive an electricity supply from the electricity grid due to the geographical limitations of the archipelago. It is known that the most potential renewable energy source on Kodingare Island is solar energy, with a potential for solar radiation reaching 5.86 kWh/m2/day. This research aims to analyze an innovation that combines PV and solar, where PV acts as the main electricity generator, while solar functions as a backup and additional energy source. The method used in this research uses simulation methods, layout modeling, and financial analysis using HOMER Pro simulation software to determine the potential and performance of hybrid power plants and SketchUp Pro software to produce three-dimensional layouts and economic and feasibility values obtained through financial analysis. Technical aspects include producing an electrical energy system of 37,029 Wh/year, consisting of PV of 32,981 Wh/year and solar of 4,048 Wh/year with energy consumption of 33,850 Wh/year. The required fuel consumption is 2,086 L/year, with excess electricity of 931 kWh/year and renewable energy penetration of 89.1%. From an economic perspective, planning this hybrid power system requires an investment of 258.290.000 IDR, O&M costs of 19.350.600 IDR, and the cost of energy value of 1,352/kWh IDR. In contrast, from the feasibility aspect of planning a hybrid electric power system, it is said to be feasible because it produces a Net Present Value of 9,870,151 IDR, is more significant than zero, the Profitability Index is 1.03 greater than one, the Internal Rate of Return is 8.90% greater than the credit interest rate of 8.43% and the Payback Period required for return of capital is nine years nine months.