Roni Zakaria Raung, Wahyudi Sutopo, Muhammad Hisjam
et al.
With over 130 million motorcycles, Indonesia faces a critical challenge in transitioning to electric mobility to meet its carbon emission reduction commitments under the Paris Agreement. Despite the existing government incentives and subsidies, the adoption of electric motorcycles (EM) remains critically low, only 0.18% of the 13 million units targeted by 2030. This study aims to evaluate the effectiveness of current EM subsidy and incentive policies and to determine the suitable strategies for achieving the 2030 target. It adapts PTTMAM model, a system dynamics (SD) model that captures the complex interactions among four market agents: users, manufacturers, infrastructure providers, and government, to the context of EM in Indonesia. It also enriches the willingness to consider (WTC) framework within the model by incorporating behavioral variables such as lifestyle and awareness of future trends. The model is calibrated and validated using historical data (2013–2023) through sensitivity and extreme case analysis. A total of 72 subsidy and incentive policy scenarios involving the market agents were constructed to assess the achievement of current policies and identify optimal strategies to reach government’s target. Simulation results of the scenarios reveal that current policies are insufficient, projecting only 15.9% achievement of the 2030 target. More aggressive interventions, including extended subsidies, carbon taxes, and electricity incentives, could enable reaching the target by 2033. Hence, the existing 2030 goal appears overly ambitious without strategic adjustments. This study underscores the need for policy redesign and offers a robust, behaviorally informed SD framework to guide Indonesia’s electric mobility transition.
Navid Mahdavi, Animesh Dutta, Syeda Humaira Tasnim
et al.
The integration of solar greenhouses into smart energy systems (SESs) remains largely unexplored, despite their potential to enhance energy sharing and hydrogen production. This review investigates the role of solar greenhouses as active energy contributors within SESs, emphasizing their biomass waste gasification for hydrogen production and their integration into district heating and cooling (DHC) networks. A structured classification of machine learning (ML) and deep learning (DL) techniques applied in forecasting and optimizing these processes is provided. Additionally, the evolution of DHC systems is analyzed, with a focus on fifth-generation DHC (5GDHC) networks, which facilitate bidirectional energy exchange at near-ambient temperatures. The review highlights that existing studies have predominantly addressed SES advancements and ML-driven energy management without considering the contributions of solar greenhouses. A novel framework is proposed, illustrating their role as prosumers capable of exchanging electricity, hydrogen, and thermal energy within SESs. Key findings reveal that integrating solar greenhouses with SESs can enhance energy efficiency, reduce carbon emissions, and improve system resilience. Furthermore, ML-driven predictive control strategies, particularly model predictive control (MPC), are identified as essential for optimizing real-time energy flows and biomass gasification processes. This study provides a foundation for future research on the technical, economic, and environmental feasibility of integrating greenhouses into SESs. The insights presented offer a pathway toward more sustainable, AI-driven energy-sharing networks, supporting policymakers and industry stakeholders in the transition toward low-carbon energy solutions.
Abstract The penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances.
Control engineering systems. Automatic machinery (General)
This paper presents a comprehensive review of super-resolution methods for smart meter data analysis. Smart meters provide valuable insights into household electricity consumption, but their low-frequency data limits the ability to capture detailed patterns. Super-resolution techniques address this challenge through the reconstruction of high-resolution data from low-resolution measurements. The review covers both non-machine learning-based methods (interpolation, signal processing, and statistics) and machine learning-based methods (CNNs, GANs). Four selected methods are discussed in detail, highlighting their principles, advantages, and limitations. These methods demonstrate superior accuracy in enhancing data completeness, capturing complex relationships, and improving resolution. The review contributes to the advancement of super-resolution techniques for smart meter data analysis, providing researchers and practitioners with valuable insights for efficient energy management and forecasting.
Mateusz Hämmerling, Natalia Walczak, Tomasz Kałuża
The operation of water structures causes various problems. They are related, for example, to the material carried by the water, hydrological conditions, range of operation of hydroelectric turbines, or water elevations at the lower position of the hydroelectric power plant. Among the various operational problems, this article focuses mainly on the impact of the backwater of Gwda river on the water level elevations at the lower station of the Stary Młyn hydropower plant in Dobrzyca. The power plant is located on Głomia river. The analysis was carried out for different flow variants in both the Gwda and Głomia rivers. The effect of characteristic flows on the water surface level at the lower station of the hydropower plant was examined. It was found that the water surface level at the lower station of the hydropower plant is strongly influenced by flows higher than the average high flow on Gwda river. Due to the extent of the backwater in current operating conditions, the hydroelectric power plant is shut down from flows on Gwda river of 30–28 m<sup>3</sup>/s (flows that are not much higher than the multi-year average SSQ). The modeling results were confirmed by an analysis of power plant shutdowns of normal operation especially in wet years, when the plant did not operate for almost half of the year (188 days), with losses of 203 MWh. It was also shown that even a small additional damming of water, e.g., of the order of 0.2 m, can extend the operating time of a power plant up to 249 days even under unfavorable hydrological conditions. Factors related to climate change are beginning to play an increasingly important role in the current operating conditions of small lowland hydroelectric power plants. They can contribute to a reduction in electricity production. The proposed solution related to the possibility of greater water retention on dammed-up water barrages allows one to partially offset these problems as well.
In the period of 2005 to 2016, multiple air pollution control regulations have entered into effect in the United States at both the Federal and state level. In addition, economic changes have also occurred primarily in the electricity generation sector that substantially changed the emissions from this sector. This combination of policy implementations and economics has led to substantial reductions in PM<sub>2.5</sub>, its major constituents, and source specific PM<sub>2.5</sub> concentrations across the New York State, particularly those of sulfate, nitrate, and primary organic carbon. However, secondary organic carbon and spark-ignition vehicular emission contributions have increased. Related studies of changes in health outcomes, the excess rates of emergency department visits and hospitalizations for a variety of cardiovascular and respiratory diseases and respiratory infections have increased per unit mass of PM<sub>2.5</sub>. It appears that the increased toxicity per unit mass was due to the reduction in low toxicity constituents such that the remaining mass had greater impacts on public health.
The rapid development of sensing technology has created an urgent need for chemical sensor systems that can be rationally integrated into efficient, sustainable, and wearable electronic systems. In this case, the triboelectric nanogenerator (TENG) is expected to be a major impetus to such innovation because it can not only power the sensor by scavenging mechanical energies and transforming them into electricity but also act as the chemical sensor itself due to its intrinsic sensitivity towards the chemical reaction that occurs at the triboelectric interface. In this review, recent research achievements of chemical sensors that are based on TENGs are comprehensively reviewed according to the role of TENGs in the system, that is, pure power supplies or self-powered active chemical sensors. Focus is put on discussing the design criteria and practical applications of the TENG-based active sensors in different fields, which is unfolded with a classification that includes biosensors, gas sensors, and ion sensors. The materials selection, working mechanism, and design strategies of TENG-based active chemical sensor systems (CSSs) are also discussed, ending with a concise illustration of the key challenges and possible corresponding solutions. We hope this review will bring inspiration for the creation and development of TENG-based chemical sensors with higher sensitivity, simpler structure, and enhanced reliability.
Flexible thermoelectrics, including flexible thermoelectric materials and devices, can directly convert the heat from human body into useful electricity, providing a promising solution for uninterrupted power to wearables. In the past decade, flexible thermoelectrics has achieved notable progress. Various kinds of flexible thermoelectric materials have been developed and some of them have been fabricated into flexible thermoelectric devices, showing the ability to generate nW‐level or even μW‐level electricity. Herein, the basic design principles and typical configurations of flexible thermoelectric devices, as well as the requirements on thermoelectric materials to achieve high performance flexible thermoelectric devices, are first introduced. Then, the recent progress achieved in flexible thermoelectric devices based on organics materials, traditional inorganic materials, other organic/inorganic composites/hybrids, and plastic deformable inorganic semiconductors, respectively, are summarized. Finally, an outlook on the future development of flexible thermoelectrics is briefly given. This study sheds light on the further development of flexible thermoelectrics.
Materials of engineering and construction. Mechanics of materials
Abbas Q. Mohammed, Kassim A. Al-Anbarri, Rafid M. Hannun
This paper explores the sizing optimization of stand -alone hybrid energy system (HES) in southern Iraq (Thi Qar province) for supply stand-alone households by the electricity. HES consist of three components (solar cell (PV), diesel generator (DG) and battery storage (BS)). Particle swarm optimization (PSO) used in this study for find optimal sizing of the HES to minimizing multi-objective, first objective is to minimizing the total system cost (TSC) that lead to minimizing cost of energy (COE). Second objective is to minimizing total emission CO2 (TECO2). The constraint of the optimization is the reliability (100 %) mean continuous provide the load demand by the electricity. The results of the optimization show the ability the algorithm to minimizing the multi-objective with continuous supply the load by the electricity through life cycle of the project (25) years.
Keifa Vamba Konneh, Hasan Masrur, Mohammad Lutfi Othman
et al.
The concept of introducing hybrid off-grid systems has made electricity accessible to areas that are far or have no access to grid network. This paper evaluates the techno-economic and environmental characteristics of a hybrid renewable energy system considering three different scheduling approaches, four different solar tracking systems, two different PV modules and eight scheduling scenarios to supply sustainable electricity to a rural community in Sierra Leone. Each scenario consists of a solar tracking system, a specific type of PV module and a scheduling approach. The aim is to find the most efficient and cost-effective scenario that meets the electrical demands of the village. Results revealed that the ‘Two axis tracking system’ generated the highest PV power, 28.8% additional power compared to the ‘No tracking system’ confirming the superiority of using a tracking system though it comes with initial cost repercussions. Also, systems that employed the use of Canadiasolar Dymond CS6K-285M-FG PV module tend to be more efficient and cost-effective than those that employed Sharp ND-250QCS PV module even with the same solar tracking technology and scheduling approach. From the best scheduling approach (third scheduling), Scenario 7 (SC#7) gives the lowest net present cost (NPC) of <inline-formula> <tex-math notation="LaTeX">$ \$ $ </tex-math></inline-formula>1.53M with <inline-formula> <tex-math notation="LaTeX">$ \$ $ </tex-math></inline-formula>0.173/kWh cost of energy (COE) and CO<sub>2</sub> emission of 8.54 kg/yr making it the optimum scenario. A daily operation of the optimum scenario on both a sunny and rainy day confirms that the system is capable of supplying the required electricity for both rainy and dry seasons. Sensitivity analyses explain the high reliance of the system cost on the erratic inflation rate, discount rate and PV derating factor. Maintaining a healthy and sustainable environment depends on the minimum load ratio of both the biogas and diesel generators.
Permanent access to energy is an essential pillar of economic development. However, there is a growing evidence that contemporary energy systems are not able to provide energy to the entire population on a sustainable basis and at affordable prices. In the face of these challenges, renewable energy can play an important role, especially in rural areas where access to centralized electricity grids is difficult. This paper aims to examine the access gaps of enterprises to renewable energy between rural and urban areas in Cameroon. The analysis is based on a sample of 209,482 enterprises, taken from the Second General Census of Enterprises in Cameroon (RGE-2) carried out by the National Institute of Statistics (NIS). The econometric estimations, obtained using the Blinder-Oaxaca decomposition, reveal that access rate to renewable energy for firms in rural areas is lower than that of firms located in urban areas. An increase in the level of education of the promoter of an enterprise, obtaining credit from banks, microfinance and savings, and the formalization of enterprises in rural areas can also contribute to reducing the gap in rural areas in terms of accessing to renewable energy. The discrimination suffered by rural enterprises related to the gender of entrepreneurs, the sector of activity, the business environment and professional experience tend to increase this gap. To reduce this gap, there is a need to promote access to finance for rural enterprises and their migration from the informal to the formal sector.