Cost Of Electricity Research Articles

Green Hydrogen Cogeneration Through Solid-Particle Concentrated Solar Power System Integrated With Proton Exchange Membrane Stacks

This paper presents a techno-economic analysis of third-generation (Gen3) Concentrated Solar Power (CSP) systems using solid particles and Proton Exchange Membrane (PEM) stacks for green hydrogen production. The study assesses the Levelized Cost of Hydrogen (LCOH2) as a key metric. A 100 MWe CSP plant can achieve a LCOE of 55-60 $/MWh, with a Solar Multiple (SM) of 3 and Thermal Energy Storage (TES) capacity between 7 h and 16 h. Results show that a 1:1 ratio between PEM and CSP capacities is not needed to optimize hydrogen production, enabling hybrid schemes for electricity and hydrogen co-generation. However, the achieved LCOH2 does not meet IEA’s 2030 target of below 4 $/kg-H2. Key challenges include reducing PEM costs for large-scale applications and ensuring a cost of electricity below 55 $/MWh. Addressing these issues will be crucial for the economic viability of Gen3 CSP+PEM systems in the transition to sustainable hydrogen production.

Towards Zero-Energy Buildings: A Comparative Techno-Economic and Environmental Analysis of Rooftop PV and BIPV Systems

The integration of photovoltaic (PV) systems in buildings is crucial for reducing reliance on conventional energy sources while promoting sustainability. This study evaluates and compares three energy generation systems: rooftop PV, building-integrated photovoltaics (BIPV), and a hybrid combination of both. The analysis covers energy production, economic feasibility through the levelized cost of electricity (LCOE), and environmental impact by assessing unreleased carbon dioxide (UCD). A residential building in Kerman, Iran, serves as the case study. The results indicate that rooftop PV exhibits the lowest LCOE at USD 0.023/kWh, while BIPV has a higher LCOE of USD 0.077/kWh due to installation complexities. The hybrid system, combining both technologies, achieves a balance with an LCOE of USD 0.05/kWh while maximizing energy generation at 16.2 MWh annually. Additionally, the hybrid system reduces CO2 emissions by 9.7 tons per year, surpassing the standalone rooftop PV (5.0 tons) and BIPV (4.7 tons). The findings highlight the synergistic benefits of integrating both PV systems, ensuring higher self-sufficiency and enhanced environmental impact. This research underscores the necessity of comprehensive urban energy planning to optimize renewable energy utilization and accelerate the transition toward zero-energy buildings.

Economic Analysis of Heat Exchanger for a Simple Cycle Gas Turbine Power Plant

This study undertakes a comprehensive economic analysis of integrating a heat exchanger with a simple cycle gas turbine power plant, with a specific focus on the Siemens SGT5-2000E gas turbine model. The analysis leverages on key parameters such as Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP) and Levelized Cost of Electricity (LCOE), to evaluate the viability and profitability of the investment. The results of the analysis show that integrating a heat exchanger with the gas turbine power plant yields a positive NPV of N166,031,518.3, signifying that the investment is profitable. Furthermore, the IRR is approximately 12.22 %, which is the discount rate at which the NPV becomes zero. The payback period is determined to be 7.7 years, indicating that the investment will break-even within a reasonable timeframe. Additionally, the levelized cost of electricity is calculated to be N65.23/kWh, which corresponds to a discount rate of 55 %. The findings of this study provide valuable insights for investors, policymakers, and plant operators seeking to optimize the efficiency and competitiveness of gas turbine power plants. Overall, the results suggest that integrating a heat exchanger with a simple cycle gas turbine power plant is a viable and profitable investment opportunity.

Contracting Matters: Hedging Producers and Consumers With a Renewable Energy Pool

Renewable energy installations are rapidly gaining market share due to falling technology costs and supportive policies. Meanwhile, the energy price crisis in 2022 shifted the energy policy debate toward the question of how consumers can better benefit from the low and stable generation costs of renewable electricity. Long-term contracts for renewable energy to link producers and consumers are an option to address these concerns. Various market failures limit the potential for bilateral contract structures between power producers and consumers. Hence, we assess the option of a government-backed Renewable Energy Pool which tenders long-term contracts with new renewable projects and passes the pooled contracts on to consumers who thereby benefit from reliably lower-cost electricity supply. We assess the effect of the measure on producers and consumers of clean electricity, as well as the incentives for investing in flexibility options. JEL Classification: D44, D47, G32, L94

Impact of Green Energy Transition on Healthcare Infrastructure: A Case Study of MRI Systems

Aim: To discuss green energy transition impact on healthcare infrastructure using MRI systems as a case study. Problem Statement: The emissions generated in the course of health care provision have greatly contributed to climate change, air pollution and environmental degradation. A major section is the magnetic resonance imaging which is one of the largest contributors of GHG emissions as a result of its huge energy consumption. About 5% global climate change has been linked to healthcare sector responsible for approximately 8.5% of GHG emissions. Significance of Study: The discussion of procedures to adopt in tackling the GHG emissions from the MRI image alongside its high energy consumption is essential. A prominent technique is transitioning into green energy in the healthcare sector. Methodology: Previous literatures, chapters in book and relevant journals that present information on the influence of green energy transition on healthcare infrastructure with reference to MRI systems were consulted to compile this article. An up-to-date systematic review was conducted using published articles between 2018 to 2024. Discussion: The significance of environmental sustainability in radiology departments using MRI as a case study is becoming alarming due to its potential for optimization of cost, reduction of carbon footprint and substantial energy savings. These can be achieved in MRI operations through implementation of power management informatics systems that turn off automatically and lower the energy consumption when the equipment is idle. The newest type of MRI is the power save mode which is designed to further minimize the energy consumption during non-productive period. With this, about 35–47 MWh consumed on yearly basis can be reduced and the previous electricity cost ($8050–10,800) can be lowered thereby, reducing the GHG emissions. Conclusion: This review article is insightful for policymakers, healthcare providers and researchers in developing initiatives and strategies targeted at environmental sustainability promotion. Information on green energy transition impact on healthcare infrastructure using MRI as a case study is presented. The adaption plans development techniques are recommended to prepare for climate change impact and GHG emissions reduction because of their cost savings and GHG emissions reduction attributes.

Providing the transport sector in Europe with fossil free energy - a model-based analysis under consideration of the MENA region

For reaching the European greenhouse gas emission targets, the phase-in of alternative technologies and energy carriers is crucial for all sectors. For the transport sector, synthetic fuels are–next to electromobility–a promising option, especially for long-distance shipping and air transport. Within this context, the import of synthetic fuels from the Middle East and Northern Africa (MENA) region seems attractive due to low costs for renewable electricity in this region and low transport costs of synthetic fuels at the same time. Against this background, this paper analyzes the role of the MENA region in meeting the future synthetic fuel demand in Europe using a cost-optimizing energy supply model. In this model, the production, storage and transport of electricity, hydrogen and synthetic fuels by various technologies in both European and MENA countries in the period up to 2050 are explicitly modeled. Thereby, different scenarios are analyzed to depict regional differences in investment risks: a base scenario that does not take into account regional differences in investments risks and three risk scenarios with different developments of regional investment risks. Sensitivity analyses are also carried out to derive conclusions about the robustness of results. Results show that meeting the future synthetic fuel demand in Europe to a large extent by imports from the MENA region can be an attractive option from an economic point of view. If investment risks are incorporated, however, lower import quotas of synthetic fuels are economically attractive for Europe: the higher generation costs are outweighed by the lower investments risks in Europe to a certain extent. Thereby, investment risks outweigh other factors such as transport distance or renewable electricity generation costs in terms of exporting MENA regions and a synthetic fuel import is especially attractive from MENA countries with low investment risks. Concluding, within this paper, detailed export relations between MENA and EU considering investment risks were modeled for the first time. These model results should be complemented by a more in-depth analysis of the MENA countries, including evaluating opportunities for local value chain development, sustainability concerns (including social factors), and optimal site selection.

A Win-Win Coordinated Scheduling Strategy Between Flexible Load Resource Operators and Smart Grid in 5G Era

With the rapid expansion of 5G base stations, the increasing energy consumption and fluctuations in power grid loads pose significant challenges to both network operators and grid stability. This paper proposes a coordinated scheduling strategy designed to address these pressing issues by leveraging the flexible load management capabilities of 5G base stations and their potential for inter-regional power demand response within the smart grid framework. This study begins by quantifying the dispatch potential of 5G base stations through a detailed analysis of their load dynamics, particularly under tidal fluctuations, which are critical for understanding the temporal variability of energy consumption. Building on this foundation, dormancy and load transfer strategies are introduced to model the scheduling potential for regional energy storage, enabling more efficient utilization of available resources. To further enhance the optimization of energy distribution, a many-to-many proportional energy-sharing algorithm is developed, which facilitates the aggregation of scheduling capacities across multiple regions. Finally, a comprehensive multi-objective, two-layer collaborative dispatching strategy is proposed, aiming to mitigate grid load volatility and reduce electricity procurement costs for 5G operators. Extensive simulation results demonstrate the effectiveness of this strategy, showing a significant reduction in grid load variance by 37.88% and a notable decrease in operational electricity costs for 5G base stations from CNY 4616.0 to 3024.1. These outcomes highlight the potential of the proposed approach to achieve a win-win scenario, benefiting both base station operators and the smart grid by enhancing energy efficiency and grid stability.

Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors

The purpose of this paper is to suggest short-term Seasonal forecasting for hourly electricity demand in the New England Control Area (ISO-NE-CA). Precision improvements are also considered when creating a model. Where the whole database is split into four seasons based on demand patterns. This article’s integrated model is built on techniques for machine and deep learning methods: Adaptive Neural-based Fuzzy Inference System, Long Short-Term Memory, Gated Recurrent Units, and Artificial Neural Networks. The linear relationship between temperature and electricity consumption makes the relationship noteworthy. Comparing the temperature effect in a working day and a temperature effect on a weekend day where at night, the marginal effects of temperature on the demand in a working day for power are likewise at their highest. However, there are significant effects of temperature on the demand for a holiday, even a weekend or special holiday. Two scenarios are used to get the results by using machine and deep learning techniques in four seasons. The first scenario is to forecast a working day, and the second scenario is to forecast a holiday (weekend or special holiday) under the effect of the temperature in each of the four seasons and the cost of electricity. To clarify the four techniques’ performance and effectiveness, the results were compared using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Normalized Root Mean Squared Error (NRMSE), and Mean Absolute Percentage Error (MAPE) values. The forecasting model shows that the four highlighted algorithms perform well with minimal inaccuracy. Where the highest and the lowest accuracy for the first scenario are (99.90%) in the winter by simulating an Adaptive Neural-based Fuzzy Inference System and (70.20%) in the autumn by simulating Artificial Neural Network. For the second scenario, the highest and the lowest accuracy are (96.50%) in the autumn by simulating Adaptive Neural-based Fuzzy Inference System and (68.40%) in the spring by simulating Long Short-Term Memory. In addition, the highest and the lowest values of Mean Absolute Error (MAE) for the first scenario are (46.6514, and 24.759 MWh) in the spring, and the summer by simulating Artificial Neural Networks. The highest and the lowest values of Mean Absolute Error (MAE) for the second scenario are (190.880, and 45.945 MWh) in the winter, and the autumn by simulating Long Short-Term Memory, and Adaptive Neural-based Fuzzy Inference System.

The Impact of Uranium Resource Constraints on China’s Nuclear Power Development

As a low-carbon and efficient energy source, nuclear power plays an indispensable role in China’s pursuit of carbon neutrality. However, existing studies on China’s nuclear energy development often overlook the constraints posed by uranium resources, limiting a comprehensive assessment of the pathways to carbon neutrality. This study incorporates uranium resource constraints into the China Global Energy Model (C-GEM) to analyze in detail the impact of uranium scarcity on China’s nuclear power development, electricity costs, and carbon emissions. Under a scenario of severe uranium resource constraints, the results indicate that nuclear power generation in 2060 is projected to be 35–50% lower than in a high-resource scenario, with nuclear power costs increasing by over 12% and carbon prices rising by approximately 2%. Without robust management of the uranium supply, the development of nuclear power may be constrained, hindering China’s ability to achieve its carbon neutrality targets. Therefore, this study suggests that China’s nuclear energy policies should focus on strengthening uranium resource security through enhanced domestic and international exploration, investing in advanced fuel recycling and high-efficiency reactor technologies, and integrating these measures into the broader low-carbon energy framework to ensure the sustainable development of nuclear power.

Virtual Power Purchase Agreements and Their VAT Treatment: A Complex Legal and Tax Framework

Power Purchase Agreements (PPAs) have become an increasingly important instrument in the energy market, particularly as companies seek to manage their electricity costs and sustainability commitments. While these agreements have evolved into various forms, virtual PPAs present unique challenges from a tax perspective, particularly with respect to their VAT treatment. In this article, the authors examine the complex legal and tax framework surrounding virtual PPAs, with a specific focus on recent developments in the Italian context.

Design of a hybrid renewable energy system and green hydrogen production for smart cities: A carbon emission reduction approach

The rapid growth of population and industrial development worldwide has significantly increased energy demand. With the limitations and environmental impacts of conventional energy resources, renewable energy sources are essential for sustainable development. This study presents a renewable hybrid energy system designed to meet a city's electricity needs while generating green hydrogen for hydrogen-powered vehicles, a rising trend in transportation. The goal is to create a self-sufficient and environmentally friendly smart city. The proposed system integrates photovoltaic panels, wind turbines and a biomass generator, supported by lithium-ion batteries for energy storage and green hydrogen generation. Optimized through numerical analysis of experimental load data, the system is designed to handle an average annual electrical load of 14,946,686.40 kWh and produce 58.5 kg of hydrogen daily. A total of 14,550 simulations were conducted, yielding a levelized cost of electricity (LCOE) of $0.3959/kWh. The system is projected to reduce 9,398 tons of CO2 emissions annually. Additionally, the use of 150 hydrogen-powered vehicles in the smart city is estimated to prevent further emissions of 325,215 tons/year, 295,650 tons/year, and 204,491 tons/year under different scenarios. This research highlights the transformative potential of hybrid renewable energy systems and hydrogen-powered vehicles for urban sustainability. By substantially reducing carbon emissions, it supports the development of greener, smarter cities and opens avenues for future innovations in renewable energy integration and sustainable urban planning.

Energy Development Assessment of Biomass Power Plant with Rice Husk Fuel Source in Thailand: Analysis of the Performance, LCOE and Carbon Emissions Reduction

This study was conducted from the technical and financial from two rice husk power plants in Thailand. A proposed rice husk power plants use Rankine cycle power plant as a combustion configuration for electricity generation. The simulation in this research uses the System Advisor Model (SAM) to study the plant performance and financial analysis. This research investigates by using input technical plant data, and the financial variable assumptions. The results in this research can be concluded that the LCOE (the Levelized cost of electricity) of the electricity generation from the rice husk power plants at 6.62-6.63 ¢/kWh. The potentials of CO2 reduction of rice husk plants in this study are 38,319 tons/year for plant A, and 53,923 tons/year for plant B. These results can be used as a useful tool for developing strategic plans for biomass power plants in Thailand.

MANAGEMENT OF ELECTRICAL LOAD OF PRODUCTION FACILITIES WITH THE HELP OF CONSUMER - REGULATORS

It is shown that in the existing energy sector of Ukraine, basic power generating capacities significantly outnumber shunting capacities, which are essential for efficiently covering electricity needs, especially during periods of peak demand for electricity. The creation of shunting power capacities requires significant funds and, just as importantly, considerable time. Another way to solve this problem is to manage electricity consumption. Reducing electricity consumption by industrial enterprises during peak hours can help to level the load schedules of power systems, which will reduce the required number of shunting power sources to cover the deficit during peak hours and, accordingly, reduce the cost of electricity. At enterprises, load reduction can be achieved at the expense of regulated consumers. To rank and optimize the work of consumer-regulators, a genetic algorithm is proposed - a heuristic search method used to solve optimization and modeling problems by randomly selecting a combination and variation of the desired parameters. A genetic algorithm for selecting consumers-regulators, which will be used to regulate the load, has been studied and built. This model was tested for a chemical industry enterprise producing ammonia.

Performance analysis of integrated solar and natural gas combined cycle power plants in high solar potential regions

This study offers a comprehensive techno-economic and environmental evaluation of a hybrid solar-natural gas combined cycle power plant designed for the Kirkuk region, taking advantage of its high solar irradiance. The proposed system incorporates advanced technologies to maximize efficiency and sustainability, including absorption refrigeration systems, steam Rankine cycles, and organic Rankine cycles. Two configurations were analyzed: Model 1 integrates a conventional gas turbine with a steam Rankine cycle driven by exhaust gases and solar energy collectors and an organic Rankine cycle; Model 2 combines all the components of Model 1 with the absorption refrigeration system (ARS) to enhance turbine efficiency through compressor inlet air cooling. The results indicate that Model 2 delivers a net power output between 235 MW and 245 MW, exceeding Model 1 by up to 12.7 MW. It offers significant 5–10% reductions, with electricity costs ranging from $70/MWh to $76.5/MWh, while also cutting CO₂ emissions by 0.7 to 2 kg CO2/MWh, particularly during hotter periods. In June, Model 2 achieved the lowest power cost of $70/MWh and a peak output of 245 MW, compared to $72/MWh and 235 MW for Model (1) During December, however, Model 1 shows slightly better performance due to cooler conditions, with costs of $78/MWh versus $76.5/MWh for Model (2) Exergy analysis highlights the combustion chamber as the main contributor to system losses, accounting for 46.07% of total exergy destruction. Nevertheless, Model 2 integrates solar energy and ARS effectively, achieving energy and exergy efficiencies of 59.25% and 57.21%, respectively, demonstrating its superior overall performance. These findings demonstrate that integrating gas turbines with renewable energy and advanced cooling technologies provides a scalable, economically viable solution to Iraq’s energy challenges. Additionally, this research establishes a replicable framework for regions with high solar potential, emphasizing the transformative potential of hybrid energy systems in achieving sustainable energy security while mitigating environmental impacts.

Design of Solar Panel Based Powerbank with Control System BMS (Battery Management System)

Electrical energy is the main energy for humans. Electrical energy has a very important role in the development of modern technology in all sectors of households and industries. Electrical energy must be sustainable for human survival. Therefore, the problem of saving electrical power needs to be raised in research. This study aims to design a solar panel-based power bank to meet household electricity needs such as fans. This is done to reduce the use of electricity costs sourced from PLN. The study's results showed that by using 1 sheet of GH Solar 50 Wp solar panel, 4 units of LI-P0 12 Volt 22 AH batteries using a series circuit and an inverter. Each installed component meets the specifications, so this PLTS system can turn on 1 fan unit with a load power of 42.0 Watts. Furthermore, for charging HP of 8 watts and Laptop of 40.3 watts for 2 hours 45 minutes for 1 day.

Analysis and optimization of hybrid renewable energy systems for remote community applications

ABSTRACT Hybrid renewable energy systems (HRESs) can provide an effective approach to replacing diesel power in remote communities in Canada where people live off-grid. This paper deals with analyzing and optimizing HERSs that consist of solar photovoltaic (PV) panels, wind turbines, a biomass power generator, and batteries with different combinations for remote community applications. A model is developed to design, simulate, and optimize the HRESs, aiming at minimizing the net present cost (NPC) and levelized cost of electricity (LCOE) of the systems for Canada’s remote and northern communities. Economic assessment of the HRES with different configurations is conducted, and the amount of electricity produced by each subsystem is calculated. The NPC ranges from $4.17 M to $8.68 M and the LCOE ranges from $0.33.9/kWh to $0.693/kWh for the five optimized HRES configurations in a selected remote community. It is shown that in Configuration A, the HRES generates 824,152 kWh/year of which the biomass electricity accounts for 484,632 kWh/year, the solar electricity accounts for 72,939 kWh/year, and the electricity generated by wind turbines accounts for 266,581 kWh/year. Through the present research, HRES is shown to be an effective option to supply green electricity in remote communities.

Energy Conservation in Palm Oil Mill by Installing Inverters for Motors

Thailand is the third largest palm oil producer, accounting for 3.9% of global palm oil production, approximately 84.6%. Energy Efficiency Plan 2018 (EEP2018) aims to reduce energy consumption (Energy intensity) by 30% in 2037. This study investigates the conversational energy of palm oil mills (POM) by installing motor inverters to save energy and reduce the cost of electricity. The experiment was an energy consumption estimate pre- and post-installation of a motor inverter for Thongmongkol Palm Oil Industry Co., Ltd. That analysis used energy consumption and a payback period. The result was that Thongmongkol Palm Oil Industry Co., Ltd. used energy consumption of 4,923.09-6,364.54 MWh. The factory can generate energy for approximately 99% of the factory and purchases from the Electricity Generating Authority of Thailand (EGAT) approximately 1%. These are several motors of 229 units, a Power of 2,785.44 kW, and a horsepower of 3,742.97 HP. Station 4 is the primary process of the oil palm mill. Installing the motor inverter decreases the electricity power consumption by 10.43%. It can save energy costs of 51,091.35 Baht/year. Specific energy consumption (SEC) of 0.013 MWh/ton from 0.015-0.20 MWh/ton. It reduced SEC by 13.33-35.00%. The payback period for installing the motor inverter is 3.16 years.

Development of an Energy-Efficient Electrical Load Model for Optimizing Electricity Consumption in the Headquarters Building of PT Semen Padang Using the PDCA Approach

The increasing electricity consumption in the public building sector has a significant impact on greenhouse gas (CO2) emissions, which is proportional to the volume of energy used. This study aims to develop an energy-efficient electrical load model to optimize electricity consumption in the headquarters building of PT Semen Padang using the Plan-Do-Check-Action (PDCA) approach. In the Plan stage, problem identification was conducted through a Pareto diagram and root cause analysis using a fishbone diagram. The dominant issue identified was energy waste in the lighting system. The solution implemented included the installation of motion sensor-based switch control devices to regulate lighting use automatically. In the Do phase, the devices were installed and tested, while in the Check phase, the evaluation showed a reduction in electricity consumption from over 85,000 kWh per month to below 80,000 kWh per month, resulting in an electricity cost savings of IDR 31 million per month. The Action phase established standards for inputs, processes, and outputs to ensure the sustainability of the improvements made. This study not only provides financial benefits but also supports the company's Energy Management System policy. With the PDCA approach, improvement processes can be carried out systematically and continuously, making a tangible contribution to energy efficiency in the public building sector.

Enhancing Demand Response Scheduling in Smart Grids With Integrated Renewable Energy Sources PV and Wind Systems Using Hybrid Epistemic Neural Networks—Clouded Leopard Optimization Algorithm

Abstract Demand response (DR) improves grid stability by enabling communication between the grid and consumers. However, managing residential load variability during DR events is challenging, especially in smart grids with renewable energy sources like wind and photovoltaic systems. This study aims to develop an advanced demand response scheduling strategy that optimizes electricity costs, reduces peak loads, and maintains user comfort. The primary goal is to enhance load demand prediction accuracy and optimize cost‐efficient energy consumption in residential smart grids. A hybrid approach, the Epistemic Neural Network‐Clouded Leopard Optimization Algorithm (ENN‐CLOA) technique, is proposed. ENN is used for precise load demand forecasting, while CLOA optimizes electricity costs by dynamically adjusting energy consumption patterns. The method is implemented in MATLAB and compared with existing approaches, including artificial neural networks (ANN), deep neural networks (DNN), and recurrent neural networks (RNN). The ENN‐CLOA technique achieves superior cost efficiency, with a minimum electricity cost of ¥10580, outperforming ANN (¥10870), RNN (¥10780), and DNN (¥10670). The proposed method also demonstrates lower error rates in load prediction and improves peak load management. The proposed technique enhances demand response performance by reducing electricity costs, mitigating peak loads, and ensuring better energy efficiency in smart grids.

Small-Scale Incinerator as Thermoelectric Power Generator

Waste management is a critical priority that requires attention from society, as electricity is essential for every household. The challenge of high electricity costs is exacerbated by a persistent electrical shortage stemming from limited energy sources. This study aimed to evaluate the acceptability of Small-Scale Incinerators as Thermoelectric Power Generators. Utilizing a descriptive and experimental research approach, the researchers developed small-scale incinerators designed to convert heat energy into electricity through the Seebeck effect. A descriptive research methodology was employed, wherein the researchers surveyed ninety respondents to assess the Small-Scale Incinerator based on factors such as affordability, appearance, durability, functionality, and safety. The findings indicated that 1 kilogram of biodegradable waste produced an output voltage of 0.010 volts with a recorded heat of 545°C, fully charging a twenty-four-volt battery in 685.71 minutes. In contrast, 3 kilograms yielded an output voltage of 0.030 volts at a heat of 650°C, fully charging a twenty-four-volt battery within 228.57 minutes, while 5 kilograms produced an output voltage of 0.050 volts at a heat of 820°C, fully charging a twenty-four-volt battery in 137.14 minutes. Overall, based on the performance of the Small-Scale Incinerator as a Thermoelectric Power Generator, respondents found it acceptable in terms of affordability, appearance, durability, functionality, and safety.