Adoption Of Renewable Energy Sources Research Articles

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

A Strategic Framework for Sustainable Business Model of Renewable Energy Services

Purpose: The main objective of this research is to investigate the ongoing scientific investigations into sustainable business models framework in the energy sector, with a particular focus on the shift towards renewable energy sources. This entails comprehending how firms may adjust to and propel the transition towards sustainability in accordance with the Sustainable Development Goals (SDGs).   Theoretical Framework: The theoretical foundation of this study is anchored in the concept of the triple bottom line, which emphasizes the need for businesses to focus on three key areas: economic, social, and environmental dimensions. This approach is essential for achieving sustainable development, as it ensures that business models are designed to contribute positively to society and the environment, in addition to being economically viable. Methodology: A comparative literature review serves as the methodology for this research. This method involves systematically analyzing existing literature on sustainable business models in the energy sector to identify trends, gaps, and theoretical underpinnings. The comparative aspect allows for the evaluation of different approaches and the identification of best practices and areas for future research.   Findings: The research identifies that while there is a growing trend towards the adoption of renewable energy sources, significant challenges remain. These include technological limitations, regulatory barriers, and financial constraints. However, by refining the business model canvas to incorporate elements of sustainability, businesses can create adaptable frameworks that address these challenges. This revised canvas includes components that emphasize economic viability, socio-cultural values, and ecological integrity.   Research & Practical Implications: The conceptualization of a sustainable business model canvas is a key practical implication of this research. It offers businesses a clear and systematic way to integrate sustainable practices into their operations. This tool is designed to promote holistic sustainable development and informed decision-making by incorporating economic, socio-cultural, and ecological values into the business planning process. By doing so, it has the potential to facilitate the transition towards more sustainable business practices in the energy sector and beyond, thereby contributing to the achievement of the SDGs.   Originality: The originality of this study is grounded in its comprehensive approach to integrating sustainability into business models, its adaptation of existing frameworks to better suit sustainable development goals, and its forward-looking perspective that seeks to guide future research and practical applications in the energy sector and beyond.

Assessing the Role of Hydrogen in Sustainable Energy Futures: A Comprehensive Bibliometric Analysis of Research and International Collaborations in Energy and Environmental Engineering

The main results highlighted in this article underline the critical significance of hydrogen technologies in the move towards carbon neutrality. This research focuses on several key areas including the production, storage, safety, and usage of hydrogen, alongside innovative approaches for assessing hydrogen purity and production-related technologies. This study emphasizes the vital role of hydrogen storage technology for the future utilization of hydrogen as an energy carrier and the advancement of technologies that facilitate effective, safe, and cost-efficient hydrogen storage. Furthermore, bibliometric analysis has been instrumental in identifying primary research fields such as hydrogen storage, hydrogen production, efficient electrocatalysts, rotary engines utilizing hydrogen as fuel, and underground hydrogen storage. Each domain is essential for realizing a sustainable hydrogen economy, reflecting the significant research and development efforts in hydrogen technologies. Recent trends have shown an increased interest in underground hydrogen storage as a method to enhance energy security and assist in the transition towards sustainable energy systems. This research delves into the technical, economic, and environmental facets of employing geological formations for large-scale, seasonal, and long-term hydrogen storage. Ultimately, the development of hydrogen technologies is deemed crucial for meeting sustainable development goals, particularly in terms of addressing climate change and reducing greenhouse gas emissions. Hydrogen serves as an energy carrier that could substantially lessen reliance on fossil fuels while encouraging the adoption of renewable energy sources, aiding in the decarbonization of transport, industry, and energy production sectors. This, in turn, supports worldwide efforts to curb global warming and achieve carbon neutrality.

Multi-Time-Scale Energy Storage Optimization Configuration for Power Balance in Distribution Systems

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the “dual carbon” objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years.

Optimizing Building Energy Management with Deep Reinforcement Learning for Smart and Sustainable Infrastructure

This study develops a new technique for optimising Energy Consumption (EC) and occupant satisfaction in business centres using Building Energy Management Systems (BEMS) that implement Deep Reinforcement Learning (DRL). Energy Management Models (EMM) are growing increasingly advanced and vital for intelligent power systems due to the growing demand for energy efficiency and the adoption of Renewable Energy Sources (RES), which are subject to variability. Flawed energy Consumption (EC) and problems are typical effects of traditional BEMS due to their unpredictability and failure to adapt to new environments. In this intended investigation, a DRL framework is demonstrated that may evolve its decision-making in real-time to control energy savings, electricity, and HVAC through input from the environment in which it operates. A pair of significant metrics, namely the cost of energy and room temperature stability, are employed to assess the effectiveness of the model compared to that provided by conventional rule-driven and predictive control systems. As investigated with different baseline models, the experimental findings proved that the DRL approach significantly reduced the cost of electricity while maintaining stable levels of comfort.

Innovative Compact Twin Whirly Bird-based Wind Turbine: A Sustainable Solution for EV Charging Infrastructure

The global adoption of whirly bird vent roofs across various industries underscores their utility in harnessing wind energy for cooling purposes within warehouses and similar environments. The innovation proposed in the current study builds upon this established technology by integrating semi-perpetual motion principles into a compact twin whirly bird-inspired wind turbine design. This novel approach aims to enhance power generation efficiency, thereby offering a sustainable solution for energy production. The compact twin whirly bird turbine concept represents a significant advancement, particularly in the context of powering electric vehicle (EV) chargers. As the demand for EVs continues to surge, concerns persist regarding the availability of charging infrastructure. By integrating the proposed innovative wind turbine technology into the EV charging ecosystem, we address this critical issue while simultaneously promoting the adoption of renewable energy sources. The key to our proposal is the deployment of small-scale wind turbines in diverse public locations, thereby democratizing access to clean energy generation. Unlike conventional large-scale wind turbines, our whirly bird-inspired turbines boast versatility in installation, akin to streetlamps, facilitating widespread deployment across urban landscapes. While these turbines may yield comparatively modest power outputs relative to megawatt-scale counterparts, their strategic placement ensures a decentralized network capable of powering EV charging stations. In summary, our research presents a compelling solution that bridges the gap between EV charging infrastructure availability and the utilization of renewable energy sources. By leveraging the inherent advantages of whirly bird-inspired wind turbines, we offer a practical pathway towards sustainable energy provision in public spaces, thereby facilitating the widespread adoption of electric vehicles.

Determinants of Green Energy Use: Household Level Comparative Analysis of Southern Khyber Pakhtunkhwa

In recent times, the utilization of renewable energy sources has gained significant traction and garnered widespread attention across diverse social segments. The prominent driver behind this surge in popularity is its cost-effectiveness and long-term durability. Notably, even individuals with limited financial means are increasingly drawn toward various forms of renewable energy. This research endeavor aims to draw comparisons between users and non-users of green energy and their corresponding household behaviors. Upon conducting a rigorous data analysis, the study will present a comprehensive and well-founded conclusion, along with offering pertinent recommendations. This study is slated to take place in the rural regions of Southern Khyber Pakhtunkhwa, Pakistan. The study employed random sampling to select a sample from the population and focused on examining the relationship between the utilization of green energy, its characteristics, conventional green energy use, and the quality of solar energy. The primary objective of the study was to explore the choices made by individual households concerning green energy consumption and the factors that influence this choice. The analysis was carried out using Ordinary Least Squares (OLS) regression. This research gap presents a significant opportunity for advancing the promotion and adoption of renewable energy sources.

Multi-scenario flexibility assessment of power systems considering renewable energy output uncertainty

The widespread adoption of renewable energy sources presents a significant challenge to the flexibility of power system. To assess the flexibility of the power system in scenarios with uncertain renewable energy output, it is crucial to quantify it quantitatively. This evaluation plays a vital role in planning flexible regulatory resources and dispatching resources for both the energy source and load. This study introduces a novel flexibility assessment model tailored for power grids with high renewable energy penetration, specifically addressing uncertainty associated with wind and PV. By analyzing the impact of wind and PV uncertainty on system flexibility, the paper proposes an improved cohesive hierarchical cluster analysis method, incorporating reliability considerations based on the Davies-Bouldin classification reliability index. Additionally, the study develops models for flexibility resources and demands within high renewable energy power systems, along with quantitative assessment indicators across three key aspects. Through a structured flexibility assessment process accounting for wind and PV uncertainty, the effectiveness of the proposed approach is validated using real-world data from a renewable energy power grid in Shandong province. A set of typical renewable energy output scenarios with uncertainty is constructed using the improved hierarchical cluster analysis method. The study then analyses the impact of different wind and PV penetration rates and the proportion of energy storage units on system flexibility by the flexibility assessment model to validate the proposed method's effectiveness.

Opportunities and Challenges in AI Based Modern Power System

Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.

Employing advanced control, energy storage, and renewable technologies to enhance power system stability

As the world witnesses a surge in the adoption of renewable energy sources to meet the surging global power demands, the dynamic and intermittent nature of these sources emerges as a significant hurdle. This article extensively explores the potential of advanced control systems, energy storage technologies, and renewable resources to fortify stability within power systems. Advanced control methodologies are strategically amalgamated with energy storage deployment and the utilization of renewable energy, to advance the reliability, predictability, and sustainability of power systems. The stability analysis, with a dedicated focus on Input-to-input-to-state stability (ISS), is conducted meticulously by applying the Lyapunov function and the Reciprocally Convex Approach, resulting in an impressive stability rate of 23.6%. Additionally, Positive Realness is substantiated by extracting Linear Matrix Inequalities (LMI) in the context of Enhancing Grid Stability with Wind Power. The study places particular emphasis on evaluating ISS and Passivity in both delayed and non-delayed systems, with a specific focus on neutral time-delay systems. This evaluation involves the selection of an appropriate Lyapunov-Krasovskii Functional (LKF) and its derivation, coupled with the integration of reciprocally convex methods, descriptive approach, and Jensen inequality. The outcomes of these analyses shed light on the causes of excess energy and its effective storage, along with highlighting the synergistic impact of integrating renewable sources and controlling grid frequency, voltage, and power in real time. The study also accentuates the robustness achieved through Enhanced Stability and the mitigation of Reduced Fluctuations, especially in the context of renewable energy sources.

Photovoltaic cooling techniques’ effect on the average monthly performance

Nowadays, mitigating climate-altering emissions resulting from air conditioning and mechanical ventilation of indoor spaces is of utmost importance. Encourage the adoption of renewable energy sources for power generation is a critical approach in this regard. Among the available technologies, photovoltaic technology stands as the most mature option. However, it does have limitations, such as reduced efficiency and performance degradation at elevated temperatures. To enhance the efficiency of photovoltaic systems, various solutions have been proposed over time, with significant research focusing on the exploration of new materials. One of the most promising solutions involves panel cooling through the utilization of external fluids, either in a forced or natural manner. Furthermore, the extracted heat from this cooling process can be effectively reused in other industrial processes, adding to its appeal. Nonetheless, despite its potential, the application of panel cooling technology is relatively recent, and assessing its suitability in specific scenarios at an early stage can be challenging. Currently, there is a lack of clear and straightforward methodologies to evaluate the performance gains achievable through the implementation of panel cooling. The primary objective of this research is to present an innovative methodology that can effectively assess panel cooling efficiency on an average daily-monthly basis. Specifically, we propose corrective parameters that modify the widely used Siegel method, which determines the monthly average daily efficiency of uncooled panels. Throughout the study, it has become evident that the input values derived from the UNI standard do not fully represent the real-world conditions. This finding may indicate the necessity for regulatory updates to accurately account for the practical operational environment.

Decentralized Energy Trading for Grids Using Blockchain for Sustainable Smart Cities

Blockchain technology can be used to create a decentralized energy trading system for grids that rely on renewable energy sources. This system would allow individuals and organizations that generate renewable energy, such as solar or wind power, to sell excess energy to others on the grid through a peer-to-peer network. Blockchain has smart contracts that facilitate the transactions and ensure that the energy is exchanged securely and transparently. This type of system can increase the efficiency and flexibility of the grid, and help to improve the acquisition of sustainable energy resources. The system allows organizations and smart homes that generate sustainable energy to trade excess energy to others on the grid through a peer-to-peer network, facilitated by smart contracts. The use of blockchain in this manner can increase the efficiency and flexibility of grids and promote the adoption of renewable energy sources. Blockchain technology possesses the potential to transform the energy trading system by creating a decentralized, secure, and transparent P2P energy trading facility for grids that rely on renewable energy sources. This facility allows the trading of energy that is produced in excess by any entity through smart contracts. By using blockchain technology, the energy trading process is more efficient, secure, and transparent, and it can facilitate the adoption of sustainable energy sources. The implementation of such a system, however, requires collaboration between various stakeholders and compliance with existing regulations and standards of the energy sector. Despite the challenges, the potential benefits of a blockchain-based smart decentralized energy trading system make it a promising solution for the future of the energy sector.

Optimization of Stable Energy PV Systems Using the Internet of Things (IoT)

The modern power grid faces rapid growth in load demand due to industrialization, leading to an unregulated environment and increasing adoption of renewable energy sources, which presents technical challenges, particularly in terms of stability. Hydrogen conversion technology revolutionizes clean electricity storage with renewable energy, and solar hydrogen is now available in autonomous solar systems. The efficiency of solar photovoltaic systems is closely related to using digital electronic maximum peak power tracking (MPPT) technology. The Internet of Things (IoT) is crucial for performance monitoring and real-time control of PV systems, enhancing the understanding of real-time operating parameters. IoT and wireless sensor networks for distributed solar energy devices and joint building design are essential for developing the photovoltaic construction industry. In this paper, the monitoring system that has been proposed offers a potentially effective solution for the intelligent remote and real-time monitoring of solar photovoltaic (PV) systems. It demonstrated a high level of accuracy, reaching 98.49%, and can transmit graphical representations to a smartphone application within a time frame of 52.34 seconds. Consequently, the battery's longevity was extended, energy consumption was diminished, and the quality of service (QoS) for real-time applications inside the Internet of Things (IoT) was enhanced.

The future of tire energy: a novel one-end cap structure for sustainable energy harvesting

AbstractPiezoelectric energy harvesting is gaining popularity as an eco-friendly solution to harvest energy from tire deformation for tire condition monitoring systems in vehicles. Traditional piezoelectric harvesters, such as cymbal and bridge structures, cannot be used inside tires due to their design limitations. The wider adoption of renewable energy sources into the energy system is increasing rapidly, reflecting a global attraction toward the utilization of sustainable power sources (Aljendy et al. in Int J Power Energy Convers 12(4): 314–337, 2021; Yesner et al. in Evaluation of a novel piezoelectric bridge transducer. In: 2017 Joint IEEE International Symposium on the Applications of Ferroelectric (ISAF)/International Workshop on Acoustic Transduction Materials and Devices (IWATMD)/Piezoresponse Force Microscopy (PFM). IEEE, 2017). The growing interest in capturing energy from tire deformation for Tire Pressure Monitoring Systems (TPMS) aligns with this trend, providing a promising and self-sustaining alternative to traditional battery-powered systems. This study presents a novel one-end cap tire strain piezoelectric energy harvester (TSPEH) that can be used efficiently and reliably inside a tire. The interaction between the tire and energy harvester was analyzed using a decoupled modeling approach, which showed that stress concentration occurred along the edge of the end cap. The TSPEH generated a maximum voltage of 768 V under 2 MPa of load, resulting in an energy output of 32.645 J/rev under 1 MPa. The computational findings of this study were consistent with previous experimental investigations, confirming the reliability of the numerical simulations. The results suggest that the one-end cap structure can be an effective energy harvester inside vehicle tires, providing a valuable solution for utilizing one-end cap structures in high-deformation environments such as vehicle tires.

ENERGY TRANSITION POLICIES: A GLOBAL REVIEW OF SHIFTS TOWARDS RENEWABLE SOURCES

The global landscape of energy production is undergoing a profound transformation as nations grapple with the imperatives of sustainable development and climate change mitigation. This paper encapsulates a comprehensive review of Energy Transition Policies, specifically focusing on the global shift towards renewable energy sources. The paper delves into the myriad policy initiatives adopted by countries worldwide to transition from conventional fossil fuel-based energy systems to cleaner and more sustainable alternatives. The analysis explores the diverse motivations driving these energy transitions, ranging from environmental concerns and climate change imperatives to economic competitiveness and energy security. By examining the unique policy frameworks of different regions, the research provides valuable insights into the challenges and opportunities associated with the adoption of renewable energy sources. It investigates the regulatory mechanisms, financial incentives, and technological advancements that shape the trajectory of energy transition policies on a global scale. Furthermore, the paper sheds light on the impact of energy transition policies on various stakeholders, including governments, industries, and local communities. It examines the socio-economic implications of renewable energy integration, job creation, and the role of international collaborations in fostering sustainable energy practices. The study adopts a comparative approach, drawing on case studies from diverse geographical contexts to distill common trends and best practices in energy transition policy formulation and implementation. This paper provides a nuanced understanding of the complex interplay between policy frameworks and the global transition towards renewable energy. By synthesizing insights from different regions, it contributes to a holistic perspective on the challenges and opportunities inherent in shaping a sustainable and resilient energy future. The research is an invaluable resource for policymakers, researchers, and industry stakeholders seeking to navigate the intricate landscape of energy transition on a global scale.
 Keywords: Energy, Renewable Energy, Energy Innovation, Energy Transition, Review. 

Strategic drivers for the deployment of energy economics principles in the developing construction industry: A Nigerian perspective

AbstractIn developing countries, there is a scarcity of research focusing on the factors that drive the adoption and utilization of energy economics principles (EEPs). EEPs encompass a diverse set of strategies and practices designed to encourage energy efficiency, promote the adoption of renewable energy sources, and foster sustainable energy practices among construction stakeholders. Despite their potential benefits, such as reducing energy consumption and greenhouse gas emissions, there remains a knowledge gap regarding the specific drivers influencing the implementation of EEPs in developing countries. Therefore, this study addresses this knowledge gap by examining the primary drivers for utilizing EEPs in the Nigerian construction industry. To achieve this objective, a quantitative research approach was adopted. Close‐ended questionnaires were developed and distributed to professionals in the construction industry, encompassing architects, builders, engineers, and quantity surveyors. The normality of the data was confirmed through univariate skewness and kurtosis analysis. Based on the relative importance index (RII) and mean scores, the highest‐ranked driver was government policies and regulations, followed by economic growth and job creation. Environmental sustainability ranked third, while energy cost savings and return on investment occupied the fourth and fifth positions respectively. Through exploratory factor analysis and fuzzy synthetic evaluation, six key criteria that drive the adoption of EEPs were identified: financial‐related drivers, environmental‐related drivers, social‐related drivers, government‐related drivers, technological‐related drivers, and behavioral‐related drivers. By identifying these six distinct clusters of drivers, this study enhances the understanding of the drivers for implementing EEPs, ultimately facilitating the transition to more energy‐efficient and sustainable practices in the construction sector and beyond.

Hour-Ahead Photovoltaic Power Prediction Combining BiLSTM and Bayesian Optimization Algorithm, with Bootstrap Resampling for Interval Predictions

Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from a small PV installation located at the Polytechnic School of the University of Alcala. To improve the quality of historical data and optimize model performance, a robust data preprocessing algorithm is implemented. The BiLSTM model is synergistically combined with a Bayesian optimization algorithm (BOA) to fine-tune its primary hyperparameters, thereby enhancing its predictive efficacy. The performance of the proposed model is evaluated across diverse meteorological and seasonal conditions. In deterministic forecasting, the findings indicate its superiority over alternative models employed in this research domain, specifically a multilayer perceptron (MLP) neural network model and a random forest (RF) ensemble model. Compared with the MLP and RF reference models, the proposed model achieves reductions in the normalized mean absolute error (nMAE) of 75.03% and 77.01%, respectively, demonstrating its effectiveness in this type of prediction. Moreover, interval prediction utilizing the bootstrap resampling method is conducted, with the acquired prediction intervals carefully adjusted to meet the desired confidence levels, thereby enhancing the robustness and flexibility of the predictions.

Balancing the Water-Carbon Trade-Off: Development of a Bi-Level Source-Grid-Load Synergistic Optimization Model for Multi-Regional Electric Power System

This study introduces the Bi-Level Source-Grid-Load Synergistic Optimization (BL_SGLSO) model, which effectively balances the competing objectives of water conservation and carbon emission reduction in the power industry. The model aims to establish a clean and low-carbon electric power system by harmoniously reconciling these two critical goals. Through the application of bi-level programming, the BL_SGLSO model adeptly manages the preferences and conflicts of decision makers at various levels while capturing regional interactions and the intricacies of electricity transmission. Key findings reveal that non-fossil energy conversion technologies are poised to become the dominant force in electricity generation, accounting for an impressive 89.34% share by 2050. To mitigate the spatial mismatch between power load and resource allocation, the development of new transmission pathways and the expansion of the “power transmission from west to east” initiative are paramount. Furthermore, the implementation of a carbon-reducing power system offers significant potential for conserving water resources and alleviating water stress. These insights provide invaluable guidance for decision makers seeking to optimize multi-regional electric power systems for both water efficiency and low-carbon outcomes while simultaneously promoting the adoption of renewable energy sources and fostering synergistic development across regions.

Do energy transition and environmental taxation contribute to sustainable development? Evidence from OECD countries

PurposeIn this study, the author intend to investigate the impacts of renewable energy use and environmental taxation on sustainable development measured by the adjusted net savings (ANS).Design/methodology/approachThis study employs the quantile regression (QR) for a set of 24 Organization for Cooperation and Economic Development (OECD) countries over the period 1994–2018.FindingsThe main empirical findings of estimates show that access to renewable energy and environmental taxation generate positive and significant effects in increasing the ANS for most quantiles. Hence, they are practical tools for achieving sustainable development goals (SDGs).Practical implicationsThis study has important implications for governments and policymakers of the OECD countries. Therefore, governments can use subsidies and incentives to promote the adoption of renewable energy sources, energy-efficient technologies and sustainable practices. Similarly, by imposing taxes on pollution and resource use, governments can encourage the adoption of cleaner technologies and practices toward more sustainable behavior.Originality/valueThis paper is based on a novel measure of sustainable development (ANS) and a novel econometric method (QR).

Techno-economic comparative analysis of PV third–party ownership between customer and developer sides in Japan

The adoption of renewable energy sources (RES) offers a significant opportunity for nations across the world to achieve regional decarbonization. Nevertheless, the substantial penetration levels of RES can cause grid overcapacity and operational challenges. In Japan, photovoltaic (PV) third-party ownership (TPO) has emerged as the primary implementation approach aimed at bolstering collective energy self-consumption. However, economic uncertainties raise concerns about the financial feasibility of TPO for both developers and customers. This work presents a techno-economic analysis of rooftop PV-Only and PV-Battery system implementation, assessing the economic feasibility of TPO based on compensation mechanisms for project developers and five customer types at the regional scale. The results indicate that the TPO model is economically feasible, as the levelized cost of energy (LCOE) of developers is from 8.98 to 9.90 ¢/kWh. Additionally, the power purchase agreement (PPA) strategy provides up to 19.12 % higher benefits than the lease strategy (commercial building scenario). On the other hand, the economic performance of the energy storage strategy is inferior to that of the net energy metering strategy, as it relies on fluctuations in market electricity tariffs. This study provides valuable insights for regional decarbonization policies and helps different stakeholders make informed investment decisions.