Energy Resources Research Articles

Modelling the availability of water, energy, and food resources in transboundary river basins to achieve Sustainable Development Goals 2, 6, and 7

The Water-Energy-Food Nexus (WEFN) has gained international attention as an approach for managing water more holistically, building on the progress made by the Integrated Water Resources Management (IWRM) approach. However, modelling the availability of water, energy, and food and their interconnections is complex, with major barriers that include the availability of both data and modelling platforms capable of assessing the interconnected systems of the WEFN. There have been attempts to model resource availability in each sector generally, but few attempts to consider the WEFN in complex transboundary basins, especially considering Sustainable Development Goals (SDGs). Here, a water, energy, and food availability model was developed using a combination of remotely sensed, national government reported, and global datasets. The model explores the complex interactions between the physical availability and production of water, energy, and food for several subbasins representing different states. Results showed the potential impacts of socioeconomic development, population changes and transboundary cooperation on resources. Transboundary cooperation in the case study Ma Basin in Southeast Asia, under existing resources and operations could improve monthly food security and reduce coal power operations using excess hydropower in the basin during the dry season. These findings show that transboundary cooperation improves access to energy and food, whilst potentially reducing demands for water. Furthermore, the research has highlighted ways in which transboundary cooperation can further assist in attaining SDGs 12 and 13 on responsible production and climate action, respectively. The modelling framework is easily transferable and can support similar analysis in other basins, contributing to global attainment of several SDGs.

The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Bayesian-optimized LSTM-DWT approach for reliable fault detection in MMC-based HVDC systems

As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Solid oxide fuel cell (SOFC) control strategy enhancement by adaptive neuro-fuzzy inference system (ANFIS)

Global warming is a vital problem that many researchers tried to solve with so many solutions such as: reducing electricity production with conventional generators by using renewable energy resources and using hydrogen as an alternative to fossil fuels. The universal Economic crisis came across to cut off certain quantities at scheduled times. This, directly, affects renewable energy sources connected to the grid due to voltage and frequency variations. To solve this dilemma, in this paper, the grid-connected solid oxide fuel cell (SOFC) model which is fed by green hydrogen to produce AC power, is developed by an Adaptive neuro-fuzzy inference system (ANFIS). It is one of the artificial intelligence applications to improve grid-connected SOFC dynamic response. ANFIS is mathematically presented and simulated using MATLAB/SIMULINK. The results show that the ANFIS controller has succeeded in enhancing most of the desired control and operation signals.

Automation of transportation of vapor of atmospheric deaerator in boiler installations

At present, when energy prices are unstable and periodically rise, enhancing the energy efficiency of thermal power plants, which implies the production of the same or larger amounts of energy with a lower consumption of energy resources, remains an urgent problem in the field of thermal power engineering. Such tasks aimed at reducing the consumption of energy resources for their own needs are quite urgent for manufacturing enterprises that use energyconsuming equipment, in particular steam boiler plants. Steam boiler houses contain a sufficient amount of energyintensive equipment, be it the boilers themselves or atmospheric deaerators, which are used to purify the boiler feedwater from corrosive gases. The article discusses the option of automating the process of closing the vapor during atmospheric deaeration in boiler plants by installing valves with automatic drives on the vapor removal pipelines. The article also analyzes the annual replenishment flow rate of the deaerator, assesses the quality of the condensate supplied to the deaerator, describes the conditions for opening and closing valves with automatic drives, and provides a calculation of the time delay required to remove vapor from the deaerator before closing the valves. A functional block diagram for controlling automatically driven valves is presented and its components are described. Thus, when implementing the described technology, significant savings in production steam spent on the deaerator will be achieved due to the closing of the deaerator vapor, with no chemically purified water supplied to the deaerator. This solution helps to increase the operating efficiency of the entire boiler installation.

Assessment of natural resources for energy transition in Rivers state, Nigeria

The increasing problems associated with global climate change due to the use of fossil fuels and greenhouse gases in the atmosphere call for serious concerns. This scenario has necessitated the need for a shift from the use of fossil fuels to renewable energy to curtail the impact of climate change on the environment. The study was conducted to assess the potential and variability of climatic and other natural resources for renewable energy and evaluation of public perception of the energy transition process in Rivers State Nigeria. The climate data was obtained from the Nigeria Meteorological Agency (NIMET) from 2000 and 2023 and analyzed using the Linear Regression Model while data on public perception was generated from structured questionnaires and analyzed using descriptive statistics. The outcome of the Regression and ANOVA analysis were generally significant for both the natural resources and residents of the study area. Also, 65.7% of the respondents affirmed that the available natural resources are viable and suitable for energy transition. The significant and positive relationship of the different climatic elements in the study signposts huge potential for the renewable energy transition in the study area. The perception of residents was positive and showed excellent knowledge of the abundance of natural resources for the energy transition. It is therefore recommended that the Rivers State government should partner with the private sector to increase investment in energy infrastructure for improved environmental sustainability.

Metabolite comparative variation related lipid metabolisms among fruit, leaf, and stem of Jatropha curcas

The issue of non-renewable energy scarcity has persisted over an extended period, primarily due to the depletion of fossil fuel reserves and the adverse effects of their utilization. This scarcity stems from the finite nature of fossil energy resources. The development of oil energy or biofuels aims to utilize oil-producing plants such as Jatropha curcas to develop alternative energy resources. However, metabolomic studies in Jatropha curcas are limited and need more investigations. Therefore, this research was essential to find biomarkers of metabolites among the fruit, leaf, and stem of Jatropha curcas using the GC-MS technique. We tested the metabolite profile with the R program, especially the metaboanalystR package, to determine fold change metabolite and pathway analysis. We found that 54 metabolites were detected in both fruit, leaf, and stem tissues of Jatropha curcas L, of which 19 metabolites were upregulated in the fruit, 20 metabolites in the leaf, and 15 up-regulated metabolites in the stem. The metabolites found formed three clusters based on correlation and networking metabolites analysis. The three clusters showed a relationship with the lipid biosynthesis pathway. In this study, provisional information was obtained that there was a different pattern of expression of metabolites between fruit, leaf, and stem tissues in Jatropha curcas, which was thought to be related to the critical metabolites of oleic acid and methylcyclohexane carboxylate in the biosynthetic pathway of fatty acids and unsaturated fatty acids. This information is essential as an initial reference for genetic engineering Jatropha curcas so that it can be used to transform plants, especially lipid-producing plants, as a source of oil.

Energy Efficient Task Offloading and Resource Allocation in Air-Ground Integrated MEC Systems: A Distributed Online Approach

Energy Efficient Task Offloading and Resource Allocation in Air-Ground Integrated MEC Systems: A Distributed Online Approach

Impact of Globalization and Energy Consumption on CO2 Emissions in China: Implications for Energy Transition

Despite the importance of understanding the impact of economic globalization on CO2 emissions through renewable and non-renewable energy consumption, previous studies have yielded contradictory results. This study addresses this gap by examining the relationship between trade openness, foreign direct investment, renewable energy consumption, non-renewable energy consumption, and CO2 emissions in China using wavelet analysis. The methodology allows for simultaneous evaluation of the commonality between variables in both time and frequency domains. The results reveal that the causality between variables varies with frequency, with notable reversals in lead-lag relationships. Economic globalization affects CO2 emissions differently through renewable and non-renewable energy consumption, with distinct short-term and long-term dynamics. The findings highlight the need for credible energy transition programs, increased renewable energy utilization, and strengthened global cooperation to balance economic globalization and environmental quality in China. Future research could explore heterogeneity among provinces, nonlinear and asymmetric effects, and expand the scope to other developing economies.

A hybrid physical and co-simulation modern adaptive power protection testbed for testing the resilience of smart grids under cyber-physical threats

Power protection systems play a critical role in ensuring the safe and reliable operation of modern power grids. With the increasing complexity of grid topologies and the integration of Distributed Energy Resources (DERs), traditional protection schemes face challenges in maintaining effective coordination among relay systems. This paper presents a new adaptive Overcurrent Relay (OCR) protection and investigates the resilience of different traditional and new adaptive OCR protection schemes against various cyber-physical threats in a smart grid environment. Key contributions include the implementation of a novel adaptive protection scheme designed to dynamically adjust OCR settings based on real-time grid conditions, improving flexibility and responsiveness in managing grid variations induced by DER integration and operational changes. The proposed scheme is validated and implemented using the Multifunction Protection Relay SIEMENS 7SJ62, following to the programming standards of IEC 61131–3, ensuring the reliability and practical applicability of the adaptive OCR scheme in real-world scenarios. Additionally, a dedicated testbed is developed to simulate real-time cyber-physical interactions, incorporating hardware components such as the SIEMENS 7SJ62 and OMICRON-256 test device, along with high-performance computers and communication networks to facilitate comprehensive evaluations of adaptive OCR schemes under varying operational conditions. The study also examines the implications of real-time cyber-attacks on power system operations and the effectiveness of adaptive OCR protection schemes, addressing cybersecurity concerns and proposing mitigation strategies to the system resilience.

Optimization and improvement method for complementary power generation capacity of wind solar storage in distributed photovoltaic power stations

Abstract With the increasing energy demand, distributed photovoltaic power generation and wind energy are used as new energy sources for sustainable development. To solve this problem, this paper optimizes and improves the distributed photovoltaic power station. This project will fully consider the complementary relationship between photovoltaic, wind and energy storage, and optimize the charging and discharging strategy of energy storage batteries. An optimal scheduling method based on fuzzy C-mean clustering is proposed to improve the power supply reliability and energy utilization of distributed photovoltaic power generation systems. The test results show that the output of the wind turbine and solar cell designed in this paper is quite high, and has good tracking ability. The research results of this project will provide an effective way to efficiently utilize wind energy and wind energy resources in distributed photovoltaic power stations.

Achieving carbon neutrality in an emerging oil-producing country: Renewable energy transition and international trade flows in Ghana

Over the past decade, climate variabilities have emerged as shaping the dynamics of international trade. The adverse externalities posed by climate change require a corresponding improvement in the production of food, trade and the adoption of abundant renewable energy resources. Our study focused on evaluating the impact of renewable energy transition on international trade flows in Ghana. We employed the dynamics ARDL simulation model to analyse our results using data from various institutions. We found that energy transition using both low carbon energy and renewable energy in electricity generation by 1% and 5% significantly increases the balance of trade to the 4th year after which energy transition decreases the balance of trade to the 9th year. After the 9th year, energy transition by both percentages increases the trade balance of Ghana. We observe that the energy transition by 5% has a higher impact than the energy transition by 1%. Additionally, it is observed that using the share of renewable energy in electricity generation has a decreasing rate relative to the first four years. Drafting renewable energy plans such as allocation of funds, supportive policy frameworks and incentives such as green financing, subsidies, and tax incentives can enhance and develop renewable energy infrastructure to increase renewable energy efficiency and reliability for international trade.

Ce-substituted P2-type layered cathode with interfacial/ bulk stability for sodium ion batteries

Addressing a changing energy environment, sodium-ion batteries (SIBs) have emerged as one of the most potentially applicable energy resources in the world. Nevertheless, its further application is hindered owing to high degree of interfacial degradation and structural changes, which caused rapid capacity fading. Herein, a novel Ce modified P2-type layered structure cathode material Na0.67Mn0.8Co0.17Ce0.03O2 @ CeO2 is introduced, the stability of cell is improved by simultaneous bulk doping and surface coating in one scalable step. The doping of Ce4+ increases sodium ion transport rate by shrinking transition metal layer ions and expanding Na layer spacing. Irreversible phase transition can be mitigated from the stronger the bond energy between TM and O, which is conducive to the decrease in the Mn3+/Mn4+ ratio, and suppression of the Jahn−Teller distortion. Besides, the doping of larger radius Ce4+ extends the internal paths of crystal structure, which decreases the barrier of Na+, correspondingly, the Na+ diffusion rate was increased. Meanwhile, the CeO2 coating layer effectively inhibits parasitic reactions on the surface and improves the stability of the interface. which display excellent capacity with a remarkable retention of 86.9% after 100 cycles at 0.5 C and outstanding discharge rate performance of 87 mA h g−1 at 20 C. This study provides a simple process to concurrently stabilize the bulk structure and interface for the future application for sodium-ion batteries.

A fuzzy analytic hierarchy process model to enhance energy security: the case of Morocco

Energy security has become increasingly important because of its impact on all sectors of the economy, and even more because of the scarcity of primary energy resources and price fluctuations caused by geopolitical tensions. This has prompted all countries to assess their energy situation and take strategic measures to mitigate energy risks. Accordingly, the aim of this study is to identify the dimensions of energy security with the greatest impact on Morocco's energy security, as well as the priority strategic measures for mitigating energy risk. To this end, we consider the fuzzy analytic hierarchy process (F-AHP) method, which is a multiple criteria decision analysis method, which has been used to obtain results based on a structured evaluation process. The obtained results, following the considered comparison matrix, underscore that availability and resilience dimensions have the greatest impact on the Moroccan energy security, with respective weights of 48% and 28%. Besides, mitigating energy risk in Morocco primarily involves the development of renewable energies compared with seven other proposed measures, along with their degree of priority.

Theoretical and experimental analyses and optimizations of direct coupling photovoltaic-thermoelectric systems

A photovoltaic-thermoelectric (PV-TE) system is modeled theoretically and demonstrated experimentally. Mathematical formulations of the system are proposed according to energy conservation and finite-time thermodynamics, which can be applicable to analyze and optimize the system that consists of commercial photovoltaic (PV) cells and thermoelectric generators (TEGs). The genetic algorithm is introduced to maximize the system's power by optimizing multiple parameters. In Yan'an City, which is located in the northern Shaanxi Province in northwest China, the actual solar irradiance is recorded for almost a full year and the proposed model is verified. Theoretical and experimental results are analyzed and compared. It is found that there are several independent structure parameters in the PV-TE systems and they should be optimized simultaneously to maximize the performance. The PV-TE system’s maximum theoretical average power of 0.343 W is 2.69 % greater than the solo PV cell’s maximum theoretical power of 0.334 W. The measured average power of the system of 0.317 W is 0.32 % more than that of a solo PV cell of 0.316 W. The duration of strong lighting in Yan'an City reaches 8 hours every day for nearly one year and the irradiation intensity can approach 1000 W/m2. The solar radiance is weakly affected by the seasonal changes of four seasons but is greatly affected by rainy days and cloud thickness. The research findings can serve as a helpful guide for the specific implementation of the PV-TE system and exploitation of solar energy resources in northwest China.

Multi-temporal assessment of wind, solar, and hydropower resources for off-grid microgrid

For a proposed multi-source all-renewable microgrid in Nigeria’s Middle-belt region, this paper presents a multi-temporal approach to the investigation of the uncertainty in the potential of renewable energy resources. The wind, solar, and hydropower resources for a proposed multi-source all-renewable off-grid community microgrid are considered using an array of probabilistic techniques. The peculiar variances in the location’s climate throughout the year make the more common method of annual models of renewable resources unsuitable for power system planning. Consequently, a more granular model of its renewable resources over time is needed. Therefore, for the chosen location, for each renewable resource, a composite multitemporal maximum-likelihood estimation-based (MLE) probabilistic model for characterization is developed. A total of 39 probabilistic models are developed. Up to 40% improvement in the accuracy of the statistical measures for renewable resource uncertainty was observed. Multi-temporal approach provides more accurate information for power system planning over time than the conventional approach of single aggregate models, especially for hydropower, which is strongly affected by the relatively sporadic occurrence of rainfall. The study shows that solar energy is promising, hydropower potential is seasonal and complementary, and wind potential is low at the location considered in this study.

Unlocking Responsive Flexibility within Local Energy Communities in the Presence of Grid-Scale Batteries

The transition towards a decentralized, decarbonized, and distributed energy infrastructure necessitates techno-economic initiatives to empower local energy communities (LECs) to achieve self-reliance and evolve into self-sustained electricity networks. It is crucial to underscore the significance of network resilience, especially in the context of local power generation, battery storage, and the radial topology of low-voltage (LV) networks. While contemporary LV networks have made significant attempts to integrate distributed energy resources (DERs), the notable deficiency lies in their lack of network redundancy, posing a substantial challenge in the occurrence of high-impact, low-probability (HILP) events. Therefore, to enhance LV network resilience and leverage its capability to withstand unexpected disruptions, the network operator needs to unlock the potential contributions of end-users within the active distribution networks (ADNs). In this paper, a comprehensive model is developed based on multi-temporal optimal power flow (MTOPF) for unbalanced LV networks addressing the technical issues in islanded microgrid operational planning. The contributions of the grid-scale batteries in forming islanded microgrids and the flexibility that can be provided by the end-users in the LEC have been considered in this paper. To demonstrate the performance of the proposed model, the simulation studies have been carried out on a part of medium and low voltage networks, consisting of network reconfiguration and load transferring capability to reduce the service interruptions during HILP events. The energy-not-served (ENS) is chosen as one of the key performance indicators (KPIs) in this study. With the unlocking flexibility potentials and contribution of the DERs, including grid-scale energy storage (GES) units and Photovoltaic (PV) panels, the ENS has been reduced from 700.8 kWh to 447.5 kWh by activating the local resources, proper switching action, and contribution of the flexible loads, for one of the severe HILP events, i.e., the main grid outage. In this case, the full load curtailment index is reduced from 180 to 106 client hours.

Environmental Protection and Sustainable Waste-to-Energy Scheme through Plastic Waste Gasification in a Combined Heat and Power System

By utilizing plastic waste as a feedstock for gasification and integrating a solid oxide fuel cell, this system has the potential to not only generate electricity and heat efficiently but also reduce environmental pollution by converting waste materials into valuable energy resources. This research explores a combined heat and power system that integrates polystyrene waste gasification with a solid oxide fuel cell technology. The study thoroughly examines and optimizes the system's performance through the application of response surface methodology. Higher steam to plastic waste ratio improves the power production (from 244.1 to 246kW) and mitigates the emission (from 785.4 to 761.3kg/MWs). Enhanced power (from 86.25 to 337kW, representing an impressive improvement of approximately 291%) and heating (from 172.6 to 275.3kW, marking a substantial improvement of approximately 59.5%) are achieved at higher current densities. At a current density of 3200-3600A/m² and a temperature of 1020-1040K, the system can achieve 200-240kW power, 390-450g/s heating value, and 840-860kg/MWs emissions. Further optimization at a steam to plastic ratio of 0.95-1.1 and 1030-1040K temperature can improve performance to 280-300kW power, 580-600g/s heating value, and 840-860kg/MWs emissions. The study provides valuable insights into the potential benefits of integrating plastic waste gasification with solid oxide fuel cell technology, highlighting the importance of sustainable energy solutions in addressing environmental challenges. Moving forward, further research in this area could lead to the development of more efficient and environmentally friendly energy systems.

A New Methodology for Reducing Carbon Emissions Using Multi-Renewable Energy Systems and Artificial Intelligence

Microgrid cost management is a significant difficulty because the energy generated by microgrids is typically derived from a variety of renewable and non-renewable sources. Furthermore, in order to meet the requirements of freed energy markets and secure load demand, a link between the microgrid and the national grid is always preferred. For all of these reasons, in order to minimize operating expenses, it is imperative to design a smart energy management unit to regulate various energy resources inside the microgrid. In this study, a smart unit idea for multi-source microgrid operation and cost management is presented. The proposed unit utilizes the Improved Artificial Rabbits Optimization Algorithm (IAROA) which is used to optimize the cost of operation based on current load demand, energy prices and generation capacities. Also, a comparison between the optimization outcomes obtained results is implemented using Honey Badger Algorithm (HBA), and Whale Optimization Algorithm (WOA). The results prove the applicability and feasibility of the proposed method for the demand management system in SMG. The price after applying HBA is 6244.5783 (ID). But after applying the Whale Optimization Algorithm, the cost is found 4283.9755 (ID), and after applying the Artificial Rabbits Optimization Algorithm, the cost is found 1227.4482 (ID). By comparing the proposed method with conventional method, the whale optimization algorithm saved 31.396% per day, and the proposed artificial rabbit's optimization algorithm saved 80.3437% per day. From the obtained results the proposed algorithm gives superior performance.

A Stochastic Iterative Peer-to-Peer Energy Market Clearing in Smart Energy Communities Considering Participation Priorities of Prosumers

The penetration of distributed energy resources (DERs) has changed the role of a consumer to a prosumer, i.e., producer and consumer. This new role provides the opportunity for peer-to-peer (P2P) energy trading. In this paper, a three-stage iterative framework is proposed to clear the price and quantity of trading in P2P markets while addressing price and DER uncertainties by the Monte Carlo simulation (MCS) method. Initially, bids and offers of customers are determined by implementing an advanced satisfaction-based home energy management system (HEMS) at each home. Subsequently, the market operator prioritizes bids and offers according to the amount of customers’ participation in the market. Finally, the P2P market is cleared by application of the alternating direction method of multipliers (ADMM), and the market clearing prices (MCPs) are determined. MCPs are used as a parameter to repeat the three stages, and the procedure is redone until the stopping rule is met. The proposed method's effectiveness has been investigated in communities with 8, 50, and 100 prosumers. Results indicate a 69.51% cost reduction in a smart energy community with 50 homes through P2P energy market participation. The proposed market clearing method is compared with the common mid-market rate (MMR) and Stackelberg game methods and demonstrates over 25% reduction in community costs.