Abstract This study explores the evolution of wind resources and the effects of these parameters on the wind energy potential in Côte d’Ivoire. An assessment of the climatology of wind resources in Côte d’Ivoire was conducted using the meridional and zonal components of wind speed data at altitudes of 100 and 20 m from the ERA5 reanalysis data for 1980–2022. The results revealed two main areas with high wind speeds, namely the north and the south of Côte d’Ivoire. The wind speed in these two areas exceeded 4 and 3 m.s-1 at altitudes of 20 and 100 m, respectively. High wind speeds were observed from February to August. Subsequently, at the altitude of 100 m, the Maximum number of Consecutive hours of Wind per day (CWM) for average wind speeds (3–6 m.s-1) varied between 5 and 20 h depending on the region and month. At an altitude of 20 m, the CWM was around 5 h for most months in the north and exceeded 10 h for most months in the south. Although such conditions may not be ideal for large-scale wind farms, they are well-suited for low-cost and small-to-medium-scale applications for transitioning to clean energy sources.
Residential buildings in Nigeria increasingly face an unreliable electricity supply due to grid instability, making rooftop solar photovoltaic (PV) systems a necessary, cost-effective, and sustainable energy alternative. This study presents a techno-economic evaluation and investment decision framework for residential rooftop PV systems in Nigeria, using lifecycle cost (LCC) and levelized cost of electricity (LCOE) analyses. A real discounting approach was used to estimate the LCC and LCOE for 17 rooftop PV systems over a 25-year service life in Ibadan, Nigeria. Primary data were collected using structured questionnaires administered to PV system users through purposive and snowball sampling. Given the dataset size, mean, correlation, and regression analyses are used to explore the relationships among system capacity, lifecycle cost, and unit electricity cost. A quadrant-based investment framework was developed to classify PV systems into cost–performance categories using sample-mean thresholds for LCC and LCOE. The LCOE values are based on estimated lifetime energy generation. Results show that initial capital costs account for approximately 45–50
To address the heavy workload of wire disconnection and reconnection, the measurement errors caused by external-circuit shunting, and the difficulty of obtaining specific component parameters using the comprehensive impedance method in field testing of damping and voltage-balancing components in HVDC converter valve thyristor levels, this paper proposes a parameter measurement method for thyristor-level components in converter valves without wire disconnection based on the Broyden quasi-Newton method. In the proposed method, two adjacent thyristor levels are selected as the measurement object, and the equivalent parameters of the external circuit are incorporated into the solving model. Parameter equations for the DC voltage-balancing resistors, damping resistors, and damping capacitors are established using DC and AC measurement data. The resulting nonlinear equations are then solved iteratively using the Broyden quasi-Newton method. MATLAB/Simulink simulation results show that the proposed method can accurately identify key thyristor-level component parameters while maintaining the intact wiring state of the converter valve. When compared with the direct measurement method that ignores the external circuit, the proposed method effectively reduces the influence of external-circuit shunting and provides higher measurement accuracy and better engineering applicability.
This study suggests a voltage stabilization control technique for wind power generation systems based on an enhanced maximum power tracking algorithm in order to address the issues of maximum power tracking hysteresis and DC-bus voltage instability in wind power generation systems under wind speed fluctuations. The study first develops an adaptive perturbation search-based maximum power tracking technique. By dynamically adjusting the speed search step size and direction, it effectively overcomes the tracking lag and power oscillation problems of traditional methods under variable wind speeds. Then, the study proposes a sliding mode control strategy for integrated supercapacitor energy storage. By designing a sliding mode surface containing an integral term and an improved reaching law, the charging and discharging of the energy storage are coordinated to quickly compensate for the power difference and suppress voltage fluctuations. The simulation outcomes revealed that the tracking accuracy of the suggested improved maximum power tracking algorithm reached 98.7
As the global energy structure undergoes a transition toward cleaner sources, the increase in the proportion of wind power poses severe challenges to the stable operation of the power grid due to its random and fluctuating output. Traditional prediction models tend to get stuck in local optima and have insufficient generalization ability, making them unable to meet the high-precision requirements for wind power prediction. Therefore, this study proposes an improved seagull optimization algorithm (LSC-SOA) based on the Logistic-Sine-Cosine composite chaotic mapping, which generates a uniformly distributed initial population through the Logistic-Sine-Cosine chaotic mapping to enhance the global search ability. It is combined with the backpropagation neural network (BP) to construct the LSC-SOA-BP wind power prediction model, which is used to optimize the initial weights and thresholds of the network. Experimental results show that the improved algorithm converges faster and has lower fitness; the built model has the lowest RMSE of 0.11 on the test set, MAE of 0.08, R2 of 0.97, and an annual RMSE of 0.115 in seasonal and variable wind speed scenarios, which is at least 30.3
Fine-grained urban carbon stock management and decision-making for land-use regulation require adequate, effective and theoretically supported methods. Based on multi-temporal, high-resolution land cover data, temporal structural differences in the carbon stocks of different functional zones were revealed by calculating and analyzing the carbon storage at multiple times. Carbon storage changes and the key transition pathways affecting it were then analyzed using the Patch-generating Land-Use Simulation (PLUS) model. GeoDetector was employed to analyze the explanatory power of different factors affecting the carbon storage changes, and the corresponding driver was ranked. By incorporating the ranked drivers into scenario parameter settings to construct closed-loop consistency validation indicators, a reasonable and practical assessment was conducted. The results showed that total carbon stock in Shenyang City decreased from 92.47 × 106 t (corresponding to 13.61 MgC·rm⁻2) in 2000 to 86.18 × 106 t (13.08 MgC·rm⁻2) in 2015. The area of high-carbon zones (˃5 MgC·ha⁻1) accounted for 914.5 km2, or 3.35
This systematic literature review aggregates current research on agrivoltaics (published between 2015 and mid-2024) with a focus on the integration of the three dimensions of sustainability, namely the environment, economics, and society, with the aid of decision science approaches. In line with the PRISMA 2020 guidelines, 66 peer-reviewed articles were examined. The study suggests that agrivoltaic technology improves land-use efficiency, conserves water, and offers socio-economic co-benefits, including enhanced socio-economic acceptability. Nonetheless, the trade-off between the optimal achievement of sustainable photovoltaic and agricultural production has persisted. MCDA methods, including AHP and GIS-based approaches, have been applied to address trade-offs between competing objectives. Nevertheless, there are a few areas where research has not been sufficiently conducted, namely the limited consideration of the socio-ecological implications of agrivoltaics, the lack of sufficient consideration of fairness and justice, and the limited consideration of interdisciplinary approaches to policy innovation and stakeholder governance. This review spotlights potential for alignment between global sustainable objectives through agrivoltaics and calls for more effective modeling of energy–agricultural trade-off conflicts, participatory planning tools, as well as equitable policy innovations for broader adoption and systemic sustainability.
By introducing methane oxidation into the anode of solid oxide electrolysis cell (SOEC), the power consumption can greatly be reduced during water electrolysis. In this study, the non-noble metal-loaded and zirconia-stabilized cerium oxides were used as anode catalysts to achieve methane oxidation at the anode, resulting in a reduced cell voltage of water electrolysis. The results show that the catalyst prepared by the sol–gel method with excellent electrochemical performance exhibits the lowest electrolysis voltage, while the performances under methane and Ar anode atmosphere are similar due to its low methane oxidation activity. The high methane oxidation activity of the anode catalyst is crucial to reducing the cell voltage for water electrolysis under a methane anode atmosphere through lowering the oxygen partial pressure. Both good electrochemical performance and methane oxidation activity are required to reduce the electrolysis voltages under a methane anode atmosphere, and Ni is the most suitable active metal for this system.
Abstract This study investigates the feasibility of integrating a micro-hydropower unit within the inlet pipe of a clean water storage tank to enable energy recovery in urban water supply networks. The Swaya water pumping centre operated by the Mbeya Urban Water and Sanitation Authority (Mbeya-UWSA), Tanzania, was used as a case study. Field measurements, hydraulic analysis, electrical load assessment, and dynamic modelling in MATLAB/Simulink were employed to evaluate system performance under realistic operating conditions. The available flow and head conditions indicate sufficient hydraulic potential to support low-head power generation suitable for auxiliary loads such as lighting, control systems, and small treatment motors. Simulation results demonstrate stable voltage and frequency regulation, effective transient response, and reliable power delivery without compromising water supply performance. Annual energy recovery analysis indicates substantial electricity cost savings, highlighting the economic viability of the proposed system. The findings indicate that integrating micro-hydropower turbines into clean water tank inlet pipes may be technically feasible and economically promising. However, field implementation and experimental validation are required before wider replication in similar urban water supply networks can be confirmed.
This paper examines the steady-state and dynamic analysis of injecting solar PV into the southern interconnected grid (SIG) of Cameroon. This is crucial as Cameroon is making reasonable progress to increase the quantity of renewables in the electricity generation mix. To perform the analysis, preconceived solar PV injection are executed on selected busbars in the grid, while the transient and steady-state conditions of the network are monitored. The PV injections were modeled at 10
Urban logistics is facing the dilemma of concentrated carbon emissions caused by high-density transportation networks and inefficient end of pipe distribution exacerbating pollution. With the popularization of electric vehicles and the promotion of green supply chain policies, emission reduction needs to shift toward systematic optimization. This study aims to achieve systematic emission reduction in infrastructure layout and transportation efficiency through collaborative optimization of logistics distribution center location and route. It divides the distribution area through the “elbow method–K-means clustering”, combines the center of gravity method to determine the location of the distribution center, and then constructs a "total cost minimization" model covering fixed, transportation, punishment, cargo damage, and carbon emission costs. This model is solved using an I-ACO that combines variable neighborhood search and dynamic pheromone updating. The results showed that the I-ACO reduced the total cost by 10.2
The global transition toward renewable energy is essential for mitigating climate change, enhancing energy security, and achieving sustainable development. However, the large-scale integration of renewable energy sources, particularly solar and wind, introduces significant operational challenges due to their inherent variability, uncertainty, and decentralized characteristics. These challenges affect forecasting accuracy, grid stability, maintenance planning, and overall system efficiency, necessitating advanced analytical and control strategies. This review critically examines the role of Artificial Intelligence (AI) as a system-level enabler for enhancing the efficiency, reliability, and resilience of smart renewable energy systems. Unlike existing domain-specific reviews, this study provides a cross-domain synthesis of AI applications across key functional areas, including renewable energy forecasting, smart grid optimization, predictive maintenance, energy storage management, and operational decision-making. The analysis integrates recent advances in machine learning, deep learning, neural networks, and reinforcement learning, highlighting their capability to model complex nonlinear relationships and support adaptive system control. Empirical case studies in solar power forecasting and wind farm operation are evaluated to demonstrate measurable performance improvements, including reductions in forecasting error, enhanced energy capture, and improved operational efficiency. However, the findings also reveal that these improvements are highly dependent on data quality, model generalization, and system integration, and may not be directly transferable across different environments. The review further identifies critical barriers to large-scale deployment, including limitations in data availability, cybersecurity risks, computational complexity, and evolving regulatory frameworks. Emerging research directions, such as Edge AI, hybrid physics-based and data-driven models, AI-enabled microgrids, and advanced cybersecurity architectures, are examined as potential solutions to these challenges. Particular emphasis is placed on unresolved technical bottlenecks, including model interpretability, transferability, real-time deployment constraints, and the need for large-scale field validation. Overall, this review demonstrates that AI has significant potential to transform renewable energy systems, but its effectiveness depends on the integration of data-driven intelligence with physical system constraints, robust infrastructure, and supportive policy frameworks. The insights presented provide a structured foundation for researchers, industry practitioners, and policymakers seeking to develop scalable and reliable AI-driven solutions for next-generation smart renewable energy systems.
Agrivoltaics presents a transformative opportunity for Nigeria by integrating renewable energy with sustainable agriculture. However, its adoption involves complex governance challenges that require coordinated policy responses to balance environmental sustainability with economic growth. The study employed interviewer-administered questionnaires to collect data from 390 purposively selected civil servants across relevant federal and state ministries in energy, agriculture, and environmental governance. Data were analyzed using descriptive statistics and ordinary least squares (OLS) regression to examine factors influencing developmental challenges in agrivoltaics adoption. Our results show strong support for agrivoltaics adoption (87.69
Abstract The European Union’s accelerated decarbonisation agenda emphasises citizen-led pathways, such as energy communities. However, the roles of vulnerable groups within energy communities, and the policy conditions that enable their active participation, remain unclear. This study develops a policy scenario for Norway and Denmark that foregrounds inclusion and energy vulnerability. Building on empirical work of 54 interviews with project leaders, policy stakeholders and community members in Norway and Denmark, as well as a literature review, social, economic and political descriptors were used to define scenarios for Norway and Denmark. Using the Cross-Impact Balance analysis and Scenario Wizard software, the mutual consistency among descriptor states was assessed to derive internally coherent futures for 2030. Through the lens of social capital theory, a plausible scenario is identified to reduce energy vulnerability and develop inclusive energy communities. Institutionalising participatory practices in energy communities, community ownership and representation of vulnerable groups, expanding equitable finance (e.g. low-interest loans, targeted subsidies and pay-later options), investing in lifelong energy literacy and inclusive communication, digital tools for behavioural change and strengthening Nordic/EU cooperation have been recommended to reach an inclusive energy community.
Short-term photovoltaic (PV) power forecasts are essential for storage dispatch, reserve scheduling, and grid safety, yet remain challenging under rapid irradiance ramps and seasonal regime shifts. We present a compact, causal CNN–LSTM architecture that couples local temporal pattern extraction with long-range sequence memory, augmented by physics-aware features (solar geometry, plane-of-array irradiance, clear-sky indices) and strict leakage safeguards. Using a one-hour-ahead task, we evaluate on a 2023 Accra, Ghana simulation study built with PVWatts v8 driven by NSRDB PSM v3.2 (60 kWp DC, 55 kW AC). Metrics are reported in kW and normalized to DC capacity, with daylight/overall splits for fairness. The proposed model achieves RMSE = 0.127 kW, MAE = 0.092 kW, and R^2 = 0.956 on the test split, reducing RMSE by 21.6 k=5 , m=32 ) with d=128 LSTM units is near-Pareto-optimal (about 0.093 M parameters and 2.20 M MACs per step). Baselines (persistence, clear-sky-scaled smart persistence, and GBRT) are included to contextualize deterministic accuracy and skill. We also provide error anatomy by hour and season to highlight residual risks at dawn/dusk and during fast cloud transients. While results are strong, they reflect a simulation (plain PVWatts; no row-to-row shading or sensor noise). We outline a path to operational validation on measured plant AC data across seasons/sites and discuss extensions to probabilistic forecasting with calibrated intervals.
As one of the major sources of global carbon emissions, the transportation sector faces significant challenges in its decarbonization process. Against this backdrop, China—with the world’s largest new energy vehicle (NEV) market—serves as an important case for systematically evaluating the carbon reduction mechanisms and potential of vehicle electrification. By integrating life-cycle assessment (LCA) with provincial panel econometric models, this study examines the dual-path carbon mitigation mechanisms of NEVs in China during the 2016–2022 period. Results indicate that a 1 percentage point increase in NEV market share reduces annual carbon emissions by 0.358
The rapid expansion of renewable energy sources and their integration into modern power grids has intensified the need for accurate, reliable, and interpretable forecasting methods. While numerous review articles have examined machine learning (ML) applications in renewable energy forecasting, a comprehensive synthesis that systematically maps the evolution from classical models to emerging paradigms—while explicitly analyzing their trade-offs, deployment constraints, and domain-specific suitability—remains lacking. This review addresses this gap by providing a structured and up-to-date analysis of ML techniques for solar, wind, and hydropower forecasting, covering developments from 2018 to 2024. Unlike prior reviews that focus narrowly on model taxonomies or single energy sources, this work uniquely integrates three analytical dimensions: (i) a quantitative comparison of model families (regression, tree-based ensembles, deep learning, and hybrid frameworks) with respect to accuracy, interpretability, and computational efficiency; (ii) a critical evaluation of data processing strategies, feature engineering, and uncertainty quantification methods; and (iii) a forward-looking discussion of emerging paradigms—including physics-informed ML, federated learning, edge computing, and explainable AI—that address persistent challenges such as data scarcity, overfitting, and real-time deployment. By synthesizing findings from over 90 studies and presenting a comparative framework that links methodological choices to operational requirements, this review offers actionable insights for researchers and practitioners. Key challenges and future research directions are outlined to guide the development of more resilient, scalable, and cost-effective forecasting systems for next-generation renewable energy grids.
The industrial sector is the cornerstone of the global economy and remains the largest consumer of energy, accounting for 30.4
The rising share of renewable energy has amplified electricity price volatility, underscoring the need for accurate forecasting and robust risk management. This study proposes an integrated machine learning framework that combines advanced forecasting models (Random Forest, XGBoost, LSTM) with feature engineering and probabilistic risk assessment. Value at risk (VaR) and conditional VaR (CVaR) are derived from predictive distributions to guide hedging strategies using futures and options. Empirical tests on 3 years of hourly data show that the approach reduces RMSE by up to 18
Energy consumption rights trading, as a market-based environmental regulation measure for source governance, is an important policy to help achieve the “dual carbon” goals. Based on the panel data of 260 cities in China from 2007 to 2022, the difference-in-differences method was used to explore the impact effect and mechanism of the implementation of the energy consumption rights trading pilot policy (ECRTP) on carbon emission intensity (CEI). The research findings are as follows: (1) the pilot policy of energy consumption rights trading has effectively reduced CEI in the pilot areas and played a positive role in market-based environmental regulation. This result remains valid after a series of robustness tests such as endogeneity test, parallel trend test, PSM–DID test, and placebo test. (2) The results of the mediating effect test first show that ECRTP mainly achieves CEI through two channels: promoting industrial structure upgrading and optimizing the energy structure. (3) Heterogeneity analysis reveals that the impact of ECRTP on CEI is more significant in mature and growing resource cities as well as in the central and western regions. The above conclusions provide important empirical evidence for further promoting the stable development of the national carbon trading market, giving full play to the positive role of market-based environmental regulations, and advancing the realization of the “dual carbon” goals.
