Rural Power Research Articles

Optimal power management of a stand-alone hybrid energy management system: Hydro-photovoltaic-fuel cell

The field of electrical engineering with renewable energy systems has witnessed a remarkable improvement due to an increase in the world's growing population as available resources become limited. This study proposes an optimal and efficient hybrid energy management system that combines photovoltaic, hydro, and fuel cell renewable energy resources that can solve the drawbacks of current energy setups and offer a dependable power source for stand-alone purposes. This novel AC-linked hybrid energy system features a comprehensive power management strategy with actual weather data and a realistic load demand profile. The HOMER Pro technique is employed for calculating the emission of toxic gases, system economic benefits, sensitivity analysis and cost minimization of the system while providing sustainability. The numerical outcomes show optimal results as compared to the existing systems in the literature. The system consists of 55 kW of PV modules, a 30 kW FC generator, and 40 kW of hydropower, suggesting optimal and excellent economic and environmental sustainability. This system appeared to be an especially cost-effective solution, with a net present cost (NPC) of $248,773 and a cost of energy (COE) of $0.0546/kWh. The results show that the system can meet energy needs while utilising the available resources efficiently in different weather conditions. The proposed system has the potential to increase the sustainability and dependability of power generation in a variety of applications and aid in the design and implementation of alternative energy systems, particularly in off-grid or remote areas. The proposed system can be extended to other such locations that will provide a scalable and flexible solution for electricity generation, making it suitable for a variety of applications, from small rural power plants to large industrial power supplies. Overall, the proposed study provides an important resource for development in hybrid energy system design and operation, with far-reaching implications for the transition to a sustainable and low-carbon future.

Identifying Key Sources for Air Pollution and CO2 Emission Co-control in China.

China is confronting the dual challenges of air pollution and climate change, mandating the co-control of air pollutants and CO2 emissions from their shared sources. Here we identify key sources for co-control that prioritize the mitigation of PM2.5-related health burdens, given the homogeneous impacts of CO2 emissions from various sources. By applying an integrated analysis framework that consists of a detailed emission inventory, a chemical transport model, a multisource fused dataset, and epidemiological concentration-response functions, we systematically evaluate the contribution of emissions from 390 sources (30 provinces and 13 socioeconomic sectors) to PM2.5-related health impacts and CO2 emissions, as well as the marginal health benefits of CO2 abatement across China. The estimated source-specific contributions exhibit substantial disparities, with the marginal benefits varying by 3 orders of magnitude. The rural residential, transportation, metal, and power and heating sectors emerge as pivotal sources for co-control, with regard to their relatively large marginal benefits or the sectoral total benefits. In addition, populous and heavily industrialized provinces such as Shandong and Henan are identified as the key regions for co-control. Our study highlights the significance of incorporating health benefits into formulating air pollution and carbon co-control strategies for improving the overall social welfare.

Determinants of Peri-urban Land Use Management Practices in Case of Assosa City, Western Ethiopia

Peri-urban areas are today's metaphors for several rapidly changing and competing socioeconomic aspects of urban and rural power. Suburban areas are facing the challenge of active, illegal, and unauthorized spatial transformations. The division between the institutional and legal frameworks also exacerbates the issue. Robust and holistic land management are prospects for today's peri-urban areas to become tomorrow's huge cities. This study aims to examine determining factors of peri-urban land management practice in Assosa city peri-urban areas to support the design of effective land management systems. A mixed method study was used to collect primary and secondary data sources. The sample of peri-urban households was selected using simple random sampling techniques. Both descriptive and inferential statistical data analysis methods were utilized. The findings demonstrated that the bifurcated and ineffective legal and institutional frameworks, limited public-private organizational support and participation influence awareness and motivations of peri-urban landholders land management practice in suburban settings. Following this, the majority of peri-urban landholders consequently lack access to the land management practice. The estimated results of the binary logit model show the following variables: awareness, motivation, participation, institutional setup and land disputes were found to be factors determining peri-urban land management practice. The study recommends an all-in-one robust institutional framework to promote sustainable land management.

Advances in the Design of Renewable Energy Power Supply for Rural Health Clinics, Case Studies, and Future Directions

Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally.

Optimization method for capacity of BESS considering charge‐discharge cycle and renewable energy penetration rate

AbstractIn order to achieve the “carbon peaking and carbon neutrality” goals, we must vigorously develop renewable energy power generation. As the penetration of renewables progressively escalates, the corresponding demand for battery energy storage systems (BESS) within the power grid rises concomitantly. This paper presents an innovative optimization approach for configuring BESS, taking into account the incremental variations in renewable energy penetration levels and BESS charge‐discharge cycles. Employing incremental analytical techniques and pivotal metrics such as capacity elasticity, the proposed method determines the optimal penetration rate and corresponding BESS capacity outcomes for deploying energy storage systems. An example analysis of a rural power distribution benchmark is carried out by using the method in this paper, which proves the effectiveness of the method in this paper. This methodology was substantiated through its application to a case study of a rural power distribution benchmark, thereby validating its efficacy. Furthermore, it was compared with the particle swarm optimization, providing a comparative assessment of their relative performance.

Short-Term Prediction of Rural Photovoltaic Power Generation Based on Improved Dung Beetle Optimization Algorithm

Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for the sustainable development of rural energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) and an improved dung beetle optimization algorithm (IDBO) with the kernel extreme learning machine (KELM). Initially, a Gaussian mixture model (GMM) is used to categorize PV power data, separating analogous samples during different weather conditions. Afterwards, VMD is applied to stabilize the initial power sequence and extract numerous consistent subsequences. These subsequences are then employed to develop individual KELM prediction models, with their nuclear and regularization parameters optimized by IDBO. Finally, the predictions from the various subsequences are aggregated to produce the overall forecast. Empirical evidence via a case study indicates that the proposed VMD-IDBO-KELM model achieves commendable prediction accuracy across diverse weather conditions, surpassing existing models and affirming its efficacy and superiority. Compared with traditional VMD-DBO-KELM algorithms, the mean absolute percentage error of the VMD-IDBO-KELM model forecasting on sunny days, cloudy days and rainy days is reduced by 2.66%, 1.98% and 6.46%, respectively.

More Snakes Than Ladders: Mass Schooling, Social Closure, and the Pursuit of Taraqqi (Social Mobility) in Rural Pakistan☆

AbstractThe international educational agenda that drives the educational expansion in the Global South makes universalist assumptions about the role of education in reducing poverty and promoting social mobility. Challenging these assumptions, this paper asks whether educational expansion in such contexts reduces social inequality across generations by offering individuals, from whatever background, educational opportunities and access to higher status livelihoods. By using Weberian and Bourdieusian theoretical frameworks, and drawing upon the perspectives of the families in rural Punjab, the paper contributes to the literature by revealing how local rural power relations in landownership, caste systems, religious identity, and political patronage, and their manifestation in the cultural realm, enact a form of social closure that closes off resources and opportunities for the disadvantaged to pursue social mobility through schooling. These power relations not only place limits on the assumed equalizing role of education in rural Punjab, but they also tend to perpetuate inequality in and through education. The paper argues that a sociological understanding of the role that education plays in the everyday rural life is crucial for designing a nuanced educational agenda capable of engaging with, rather than assuming away social and economic inequality in hierarchal societies.

Modeling load distribution for rural photovoltaic grid areas using image recognition

Expanding photovoltaic (PV) resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape, aligning with the “carbon peaking and carbon neutrality” objectives. However, rural power grids often lack digitalization; thus, the load distribution within these areas is not fully known. This hinders the calculation of the available PV capacity and deduction of node voltages. This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas. First, houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model. The distribution of the houses is then used to estimate the load distribution in the grid area. Next, equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines. Finally, by calculating the connectivity matrix of the nodes, a minimum spanning tree is extracted, the topology of the network is constructed, and the node parameters of the load-distribution model are calculated. The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas. The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters, thereby offering vital support for determining PV access capability.

Energy assessment in rural regions of China with various scenarios: Historical–to–futuristic

Approximately 172 million rural households in China in 2022, will still rely on traditional biofuels instead of electricity. This study explores the feasibility of achieving rural electrification in China utilization various Energy origins and assesses its potential impact on climate change mitigation. To do so, we employ the Regional Energy Model (REM) to formulate hypotheses for rural power distribution. Covering the era from 2020 until 2050. We evaluate consequences in terms of greenhouse gas discharges, principal energy consumption, associated expenses by comparing these possibilities with a Business-as-Usual (BU) scenario. Our findings reveal that diesel-based arrangements tend to produce ‘the maximum Carbon Dioxide (CO2) emissions, trailed by network-based solutions. Electrification focused on renewable energy-driven applications has the potential to reduce total CO2 emissions by up to 97 % and primary energy consumption by 43 % in 2050 compared to the BU scenario. We also find that electrification through decentralized diesel systems tends to be the priciest choice. Rural electrification utilizing renewable energy emerges as a cost-effective choice when the majority of end-use applications rely on renewable energy sources. However, it becomes less economical than network expansions when electric end-user appliances are predominant. In summary, this study investigates the viability of renewable energy for rural power distribution and its role in climate change abatement in rural China, offering policy suggestions.

Collaborative scheduling method of active-reactive power for rural distribution systems with a high proportion of renewable energy

Large-scale distributed renewable energy connected to the rural distribution network has given birth to a new rural distribution system with a high proportion of new energy typical characteristics, and the optimal scheduling of the new rural distribution system has become an important issue to ensure the safe and stable operation of the power grid. This paper proposes a method of active-reactive power collaborative optimization scheduling for rural power distribution system with a high proportion of renewable energy. Firstly, the active support capability evaluation model is established, and the active power support capability and reactive power support capability of rural power distribution system are quantitatively evaluated, which provides data basis and boundary conditions for the scheduling part. Then, considering power-loss cost, distribution network operation cost, and penalty cost, a method of active-reactive power collaborative optimization scheduling for rural power distribution systems with a high proportion of renewable energy is proposed. Finally, the active support capability evaluation and regulation platform of the rural power distribution system is built to provide technical support services for the safe and stable operation of the rural power distribution system. Given the problems of overload and overvoltage faced by rural power distribution systems with a high proportion of renewable energy, this paper aims to solve the key technical challenges of optimization and regulation of new rural power distribution systems. The results show that the optimized control method proposed in this paper has better security and economy, and is conducive to promoting the construction and operation of the new rural power distribution system.

Artificial Intelligence-Driven Multi-Energy Optimization: Promoting Green Transition of Rural Energy Planning and Sustainable Energy Economy

This research contributes to the overarching objectives of achieving carbon neutrality and enhancing environmental governance by examining the role of artificial intelligence-enhanced multi-energy optimization in rural energy planning within the broader context of a sustainable energy economy. By proposing an innovative planning framework that accounts for geographical and economic disparities across rural regions, this study specifically targets the optimization of energy systems in X County of Yantai City, Y County of Luoyang City, and Z County of Lanzhou City. Furthermore, it establishes a foundation for integrating these localized approaches into broader national carbon-neutral efforts and assessments of green total factor productivity. The comparative analysis of energy demand, conservation, efficiency, and economic metrics among these counties underscores the potential of tailored solutions to significantly advance low-carbon practices in agriculture, urban development, and industry. Additionally, the insights derived from this study offer a deeper understanding of the dynamics between government and enterprise in environmental governance, empirically supporting the Porter hypothesis, which postulates that stringent environmental policies can foster innovation and competitiveness. The rural coal-coupled biomass power generation model introduced in this work represents the convergence of green economy principles and financial systems, serving as a valuable guide for decision-making in decisions aimed at sustainable consumption and production. Moreover, this research underscores the importance of resilient and adaptable energy systems, proposing a pathway for evaluating emission trading markets and promoting sustainable economic recovery strategies that align with environmental sustainability goals.

Performance Optimization Design Study of Box-Type Substations Subjected to the Combined Effects of Wind, Snow, and Seismic Loads

As a pivotal node in both urban and rural power grids, the box-type substation not only serves the functions of power conversion and distribution but also need to provide structural support and environmental adaptability. However, deficiencies in strength, stiffness, or vibration characteristics may lead to vibration and noise issues, and extreme environmental changes can pose risks of structural damage. This study aims to verify and optimize the seismic resistance and environmental adaptability of box-type substations through finite element simulation methods. Using SOLIDWORKS, a three-dimensional model of the box-type substation was constructed, and static and dynamic analyses were conducted using Ansys Workbench to comprehensively evaluate the dynamic response of the box-type substation under wind, snow loads, and seismic action. Through iterative simulations and a comparison of multiple design solutions, the structural optimization of the substation was achieved. The optimized structure balances strength and stiffness, significantly reducing the weight of the substation body, with the wall thickness reduced by 60%. Additionally, the phenomenon of stress concentration on the side walls was eliminated, ensuring that the equivalent stress is below the material yield strength. This research provides methods and empirical results for enhancing the performance and reliability of box-type substations under seismic conditions, confirming the feasibility of a lightweight design, while ensuring structural safety.

TS fuzzy control of PV assisted single phase three phase induction motor drive for rural pumping applications

The motor drives for aqua farms and large-scale irrigation system needs a reliable electric drive, which requires the continuous power supply and efficient control. However, the rural single phase power supply is frequently interrupted. Renewable assistance would improve the availability of supply and heuristic control approach improves robustness in control. This paper presents a three phase induction motor drive fed from single phase electric grid with assistance from PV and battery energy storage. TS- fuzzy based direct torque control is employed for robust control during load changes, and the topology, component modelling, front-end converter control, PV interface DC–DC converter control, and inverter control are presented. MATLAB/Simulink is used to simulate the proposed drive system. The performance of the proposed system is validated using simulation data for both steady-state and transient states.

Direct load control-based optimal scheduling strategy for demand response of air-conditioning systems in rural building complex

The abrupt surge in electricity consumption for air-conditioning (AC) in rural areas has exacerbated the rural power supply and demand imbalance, presenting a significant challenge to the reliability and stability of power provision in rural low-voltage grids. Implementing power demand response (DR) in buildings is recognized as an effective solution to address the grid's power imbalance and reliability issues. Among various building DR strategies, direct load control (DLC) stands out as a proficient approach to rapidly curtail building AC loads. However, applying this method solely to individual rural buildings proves less effective in peak regulation (PR). To fully exploit the DR potential of AC systems in rural building complex, it is essential to scale up the application of DLC. Consequently, a DLC-based DR optimal scheduling strategy is proposed for rural building complex AC systems. This strategy aims to meet PR demand and maximize the benefits for load aggregator. It establishes four DLC methods and differential compensation mechanisms, taking into account the varying thermal comfort requirements of AC users. The DR optimization model is solved using a genetic algorithm (GA), with PR demand derived from long and short-term memory neural network predictions. To validate the proposed strategy, a simulation study is conducted on a 200-unit farmhouse AC system, utilizing a typical rural soil source heat pump system in Xi'an, Shaanxi Province. The results demonstrate that the strategy surpasses the PR target by approximately 6.65 % during the DR period, while ensuring a minimum level of thermal comfort for all user categories. Additionally, it yields a PR benefit of 88.73 yuan per day for all users, thereby promoting the adoption of clean energy in rural areas.

Consumption as the catalyst: Analyzing rural power infrastructure and agricultural growth through panel threshold regression and data-driven prediction

With ongoing improvements in infrastructure, extensive data consistently demonstrate that the development of rural power infrastructure plays a significant role in driving agricultural growth. However, the existing research has only scratched the surface in comprehensively assessing the multifaceted impact of different aspects of power infrastructure, primarily owing to the narrow scope of performance indicators. To address this gap, this study employs principal component analysis (PCA) to develop panel threshold models and data-driven predictive models based on an indicator framework. Using Fujian Province as a case study, this research systematically analyzes the impact of rural power infrastructure development on agriculture across four stages, i.e., generation, transmission, distribution, and consumption. The findings highlight the crucial role of the consumption stage in nurturing agricultural growth through rural power infrastructure construction. Notably, the investigation reveals that residential electricity consumption and the number of service users have a significantly positive impact on rural agricultural development, while the influence of agricultural electricity consumption remains comparatively limited when compared with the secondary and tertiary sectors. Furthermore, the data-driven predictive model introduced in this study provides a more precise forecast of future trends in rural agricultural development. This research underscores the critical importance of conducting in-depth explorations to uncover the actual role of power infrastructure development in the context of rural agriculture. With regards to rural power infrastructure construction, it is advisable to invest prudently, formulate relevant policies, with a specific focus on the consumption stage, and implement targeted technology upgrades to enhance productivity and ultimately promote agricultural development.

Capacity Demand Analysis of Rural Biogas Power Generation System with Independent Operation Considering Source-Load Uncertainty

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene’s data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness.

Data-driven two-stage robust optimization dispatching model and benefit allocation strategy for a novel virtual power plant considering carbon-green certificate equivalence conversion mechanism

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure.

Affordability of electricity to rural consumers in Tanzania: An elephant in the room?

Affordability of electricity in rural areas has received negligible concern, yet with equivocal information. Thus, this paper extrapolates this disregarded aspect by raising empirical debates on the affordability of electricity connection and consumption while also predicting the factors of domestic electrical appliances among rural consumers. Affordability of electricity connection was measured by a catastrophic approach through the index of utility Price Income Ratio (PIR) at a threshold of 10%. Consumption affordability was estimated through PIR at a 5% share of household income, coupled with a monthly basic electricity consumption of 30 kWh per household. A multiple regression model was adopted to determine electrical appliance ownership in the household. The paper reveals that electricity connection was not affordable to consumers in the lowest income quintile (61.76%), who spent up to 33% of their income. On consumption, electricity was affordable as consumers spent no more than 5% of their household income. Moreover, the paper explains that electricity affordability and the desire for wellbeing motivate consumers to use domestic electrical appliances. Measuring the affordability of energy has a paucity in the energy literature. Thus, the paper provides insight into the present debate regarding affordability measures. Although the paper uniquely uncovers the affordability of electricity connection and consumption as prime factors for policy consideration, energy policy should also consider that affordability does not end at connection; rather, it extends to consumption. Energy policy must prioritise free technical services for rural power connection and subsidise consumption costs for the economically disadvantaged.

Whether rural rooftop photovoltaics can effectively fight the power consumption conflicts at the regional scale – A case study of Jiangsu Province

To fight the power consumption conflicts at the regional scale, rooftop solar photovoltaics (RTSPV) in rural areas is considered as a critical way. In this study, we constructed a sophisticated framework for evaluating the regional RTSPV power generation potential of rural areas. Focusing on Jiangsu Province, the rural RTSPV power generation potential was evaluated. Then, the generation potential was compared with electricity demand to describe the spatial heterogeneity of the supply and demand situation, and proposed the pathways to alleviate the regional consumption conflicts. The results showed that the total RTSPV power generation potential of rural areas in Jiangsu Province is 202.91 TWh, which can meet the electricity demand of rural areas in this province. From the perspective of supply and demand, the conflicts between rural RTSPV power generation and electricity demand will be aggravated, especially in areas of rapid economic development. The rural RTSPV power generation of Suzhou, Wuxi, and Changzhou is far low the actual demand for electricity. While for the city with significant disparities between urban and rural areas, the development of rural RTSPV can effectively alleviate the power consumption conflicts. In particular, the rural RTSPV power generation in Xuzhou can meet the demand of total electricity.

The Impact of Relocation Policy on the Quality of Life of Residents in the Yellow River Beach Area

Background and Aim: The Relocation Plan for Residents of the Yellow River Beach Area in Shandong Province has yielded positive outcomes, but the relocation process also entails significant changes in agricultural production methods, adjustments in family economic structures, reconstruction of rural political power relations, conflicts, and integration of cultural values, as well as disintegration and reconstruction of social relations. These issues can potentially impact the quality of life of the residents. Materials and Methods: This qualitative research investigates the issues arising from the relocation policy, assesses the effects of the policy, and provides recommendations on enhancing the living standards of residents in the Yellow River beach area. This research involved conducting on-site interviews with 10 villagers and 5 members of a village committee from a village in Caiyuanji Township and a relevant literature review. Results: It was found that the relocation policy for the Yellow River beach area is a positive livelihood policy, as villagers have relocated from dilapidated cottages to modern new communities. However, there remain some challenges, including the high cost of living for farmers, issues related to the division of rights among rural village committees, and conflicts in farmers' values. Conclusion: Thus, this paper offers personal recommendations for the Yellow River Mudflat Relocation Project, including increasing investment in supporting industries for agriculture, promoting correct values among villagers, targeting poverty alleviation efforts, and adjusting the power structure of village committees.