Search Results (588)

Concentrated Solar Power (CSP) is a strategic clean-energy technology for India, combining high-efficiency electricity generation with thermal storage to enable power supply beyond daylight hours. This review maps CSP potential across India by analysing solar resource distribution, the suitability of different CSP technologies (e.g., parabolic troughs and solar towers), and economic feasibility under current cost and policy conditions. It evaluates recent developments in deployment, technology maturity and financing mechanisms, while identifying key barriers such as land availability, grid integration and investment risk. In addition, the study identifies research gaps related to large-scale deployment, cost-reduction strategies, and long-term performance under Indian climatic conditions, and outlines future research directions and policy pathways to accelerate CSP adoption in India. By drawing on recent data and trends, the paper offers insight into how CSP can complement the ongoing expansion of renewable electricity and contribute to India’s goal of achieving 500 GW of non-fossil installed capacity by 2030 and net-zero by 2070. The analysis aims to support researchers, policymakers and industry stakeholders in making informed decisions to scale CSP deployment. Full article

The large-scale integration of wind and solar photovoltaic (PV) power is a cornerstone of low-carbon, sustainable energy systems. However, the uncertainty of the output brings great challenges to the operation and dispatching of power systems. To clearly describe the fluctuation characteristics of wind–PV power output and the spatial–temporal coupling relationship, a two-stage wind–PV scenario-generation method is proposed. This method is based on Difference-Constrained Variational Autoencoder and Mixture of Factor Analyzers (DVAE-MFA). In the first stage, a differential constraint term is added to the reconstruction loss of the Variational Autoencoder (VAE) to build the Difference-Constrained Variational Autoencoder (DVAE) model. This helps the model better learn the fluctuation characteristics of output sequences. In the second stage, to solve the problem of the posterior distribution of the DVAE latent variables deviating from the standard normal prior, the Mixture of Factor Analyzers (MFA) model is introduced for secondary probability modeling of the latent space. The simulation experiment results show that the proposed DVAE-MFA model outperforms comparison models in terms of the scenario temporal fluctuation characteristics, spatial–temporal correlations, and statistical distribution similarity. The generated output scenarios can reproduce the features of historical data, providing high-quality data support for the stochastic optimization scheduling of sustainable power systems. Full article

Driven by advances in renewable energy technologies, research on perovskite optoelectronics has advanced rapidly across material exploration, device engineering, and intelligent integrated systems. Conventional trial-and-error experiments face inherent constraints in precisely regulating perovskite chemical compositions and microstructures, as well as in mitigating degradation in perovskite solar cells (PSCs). Artificial intelligence (AI) and the Internet of Things (IoT) have emerged as powerful tools for material discovery, synthetic condition design, and the prediction of perovskite fundamental properties and device outputs. This review systematically summarizes recent advances in machine learning (ML) implementations for PSC research, covering molecular-scale material screening, synthetic parameter optimization, performance forecasting, device architecture design, and system performance evaluation. We further elaborate on key obstacles hindering ML-assisted perovskite development, including insufficient operational stability, barriers to large-scale fabrication, and limited computational efficiency. Last, we outline promising research avenues and highlight the transformative capacity of ML to advance high-performance, manufacturable perovskite optoelectronic devices. Full article

The integration of Concentrated Solar Power (CSP) and Photovoltaic (PV) technologies represents a promising strategy to enhance the reliability, flexibility, and dispatchability of solar-based electricity generation. The novelty of this work lies in the development and assessment of an integrated PV–CSP hybrid power plant in a series configuration, where the two technologies are energetically coupled and coordinated with thermal energy storage and an electrolyzer under a grid-minimization operating strategy. Unlike most previous studies, which investigate PV and CSP systems as standalone or loosely coupled technologies, the proposed approach simultaneously optimizes renewable electricity utilization, dispatchable operation, and green hydrogen production. A comprehensive simulation framework was developed using site-specific solar irradiance data, component performance models, thermal energy storage characteristics, and electrolyzer operating constraints. A seasonal operating strategy was adopted, with the CSP plant and the electrolyzer operating from 15 April to 15 October, while the PV system generated electricity throughout the entire year. Under these conditions, the electrolyzer operated for 4416 h·year⁻¹, producing 1000 t·year⁻¹ of green hydrogen and requiring an annual electricity demand of 52.4 GWh. The hybrid renewable system supplied 37.2 GWh of this demand, corresponding to a renewable penetration of approximately 71%, while the remaining 29% was covered by grid electricity purchases. Results show that the series hybridization of CSP and PV technologies improves overall plant performance compared with standalone solar systems. In particular, the integration of thermal energy storage within the CSP subsystem enabled dispatchable generation and more stable electrolyzer operation. All the electricity generated by the CSP plant was directly utilized by the electrolyzer, and approximately 17% of the renewable electricity supplied to the electrolyzer was delivered during periods when PV production was unavailable, corresponding to 12.2% of the total annual electricity demand of the electrolyzer. Furthermore, of the total annual PV generation of 33.6 GWh, 15.1 GWh were directly used for hydrogen production, while 18.5 GWh were exported to the electrical grid, resulting in a positive annual electricity balance. The analysis provides design and operational guidelines for optimizing integrated PV–CSP plants coupled with hydrogen production systems under a grid-minimization strategy. The findings confirm that hybrid solar systems integrating dispatchable CSP generation, thermal energy storage, and PV technologies can significantly increase renewable penetration, support stable, low-carbon power generation, and enable large-scale green hydrogen production with reduced dependence on grid-supplied electricity. Full article

Pumped storage hydropower (PSH), with its advantages of fast response and large-scale energy storage, has become a key means of enhancing power system flexibility. However, the time-varying nature of transmission line capacity may constrain the effective utilization of its regulating capability. Most existing studies treat transmission capacity as a fixed boundary, failing to adequately account for the impact of its dynamic variations on scheduling strategies. To address this gap, this paper constructs three typical transmission capacity scenarios: fixed high (no congestion), fixed low (persistent severe congestion), and time varying (capacity reduced during daytime and restored at night). A power system dispatch optimization model incorporating wind power, solar power, and pumped storage is established, with the objective of minimizing total system operating cost. Under different capacity scenarios, the pumping/generating behavior of PSH, unit output structure, controlled line operation status, and economic indicators are compared and analyzed. Furthermore, sensitivity analyses are conducted from three dimensions—congestion severity, congestion time window, and PSH installed power capacity—to comprehensively evaluate the marginal impacts of key factors on system performance. Results based on a modified IEEE 14-bus system indicate that under the fixed high scenario, PSH can achieve free arbitrage with the best economic performance, but does not account for capacity fluctuation risks. Under the fixed low scenario, persistent congestion leads to substantial wind and solar curtailment, significantly increased operating costs, and prolonged full loading of lines, posing the highest security risk. Under the time-varying scenario, PSH is forced to pump at full power during nighttime and generate continuously during daytime, effectively alleviating line flow pressure. Although the operating cost is slightly higher than that of the fixed high scenario, line overload is avoided, renewable energy accommodation is significantly improved, and substantial security gains are achieved at a moderate economic cost. This paper reveals the forcing mechanism of time-varying transmission capacity on PSH scheduling strategies, verifies the feasibility of the time-varying capacity strategy as an effective trade-off between economics and security, and provides a theoretical basis for optimal PSH operation and transmission capacity management in power systems with high shares of renewable energy. Full article

Perovskite solar cells (PSCs) have emerged as one of the most promising next-generation photovoltaic (PV) technologies due to their high power conversion efficiencies, low-temperature processing, and potential for low-cost manufacturing. Despite these advantages, several challenges remain that hinder their large-scale commercialization, particularly related to environmental sustainability, long-term stability, and end-of-life management (EoL). Life cycle assessment (LCA) has become an essential tool to evaluate the environmental impacts of emerging PV technologies and to identify critical hotspots across the supply chain. At the same time, concerns regarding material toxicity, particularly lead content, as well as the lack of established recycling pathways, highlight the importance of effective EoL management strategies. This review examines the current state of research on the life cycle environmental performance of perovskite solar modules (PSMs) and evaluates emerging approaches for sustainable EoL management. The study synthesizes the existing literature on manufacturing processes, environmental impact indicators, material recovery, recycling technologies, and circular economy strategies relevant to perovskite PVs. Particular attention is given to innovation-driven approaches that integrate sustainability considerations into technology development and commercialization pathways. By identifying key environmental hotspots, technological challenges, and research gaps, this review provides insights into how sustainable innovation and circular resource management can support the transition of PSMs from laboratory-scale research to economically viable and scalable commercial deployment. Full article

The large-scale integration of wind and solar power has significantly intensified peak-shaving pressure and operational risk in provincial power grids. Effectively leveraging the flexible regulation capability of hydropower to mitigate the uncertainty of wind and solar output is a promising approach to enhancing grid security and stability. To simultaneously improve the peak-shaving performance and risk resilience of hydro-wind-solar systems for a provincial power grid, this paper proposes a multi-objective short-term scheduling model that jointly minimizes the peak value of net load and the Conditional Value-at-Risk (CVaR) of flexibility shortage. Specifically, the residual peak load is used to quantify the system’s peak-shaving burden, while the average CVaR of upward/downward ramping deficits across all time periods characterizes the tail risk associated with insufficient flexibility. Historical wind and solar forecast error data are employed to generate representative uncertainty scenarios via Gaussian mixture model, and the Rockafellar–Uryasev formulation is adopted to accurately embed CVaR into a mixed-integer linear programming (MILP) framework. Furthermore, the normalized normal constraint (NNC) method is introduced to compute a well-distributed Pareto front. Numerical simulations based on a real-world hydro-wind-solar system in a provincial grid in Southwest China demonstrate that the proposed model can significantly reduce the peak load while effectively mitigating flexibility shortfall risk. The resulting Pareto front clearly reveals the trade-off between peak-shaving effectiveness and risk control, providing a scientific basis for day-ahead generation scheduling and coordinated dispatch of flexible resources. Full article

In situ detection of the tracking poses of heliostats can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power (CSP) plant, as the traditional methods normally require the heliostats to be off from sun-tracking during the calibration process. This paper presents a deep learning-based framework for in situ detection of geometric keypoints of the heliostat surface. The proposed framework is built upon YOLOv8-Pose but integrates a high-resolution P2 feature branch to recover fine-grained spatial details that are otherwise lost in deep semantic layers. Further, a geometry-consistency loss is introduced to regularize the predicted quadrilateral, enforcing strict structural integrity under dynamically changing illumination. An experimental study on a real-world heliostat image dataset shows that the proposed framework achieves an end-to-end inference speed of 25.14 FPS. The mean end-point error (EPE) of detected keypoints is around 1.22 pixels, while the stringent mAP@0.5:0.95 metric reaches 0.9823. The keypoint detection framework could be integrated with an in-field heliostat control system for further improvement of the working efficiency of heliostats in a large-scale CSP plant in future. Full article

We propose a decentralized strategy for coordinating the bidirectional charging and discharging of battery electric vehicles (BEVs) in renewable-powered parking lots. The framework combines mean-field games (MFGs) and model predictive control (MPC) to address the coupled stochastic dynamics induced by uncertain renewable generation and random vehicle arrivals and departures. Solar and wind power fluctuations are modeled using autoregressive moving-average (ARMA) processes, while the time-varying vehicle population is represented through finite Poisson processes. The coordination problem is formulated as a large-scale game, where an aggregator designs individual cost functions to maximize available energy utilization while promoting fairness through near-equal states of charge (SOCs) at departure. Scalability is achieved through MFG theory, ensuring convergence and stability even under highly volatile generation and fluctuating agent populations. Numerical simulations validate the proposed strategy against two straightforward algorithms: capacity-ordered saturation allocation (COSA) and capacity-ordered fair allocation (COFA). These centralized approaches achieve high target fulfillment in static, low-intensity environments, where available energy accommodates a stable fleet without exceeding power limits. However, their efficacy degrades significantly in dynamic, high-intensity environments, where the interplay of volatile generation, continuous fleet turnover, and strict power constraints strains the system. In contrast, the proposed MFG-MPC framework provides a decentralized response that elegantly navigates the trade-offs between energy availability, demand stochasticity, and power limits. Ultimately, this approach ensures robust energy utilization while safeguarding vehicle equity, confirming its strong suitability for real-time deployment. Full article

Due to increasing demand and consumption of electricity, as well as the need to decarbonize and mitigate climate change, solar energy is an important factor in the transition to emission-free energy sources. This study focuses on identifying the most suitable locations for the construction of large solar photovoltaic (PV) power plants while respecting environmental, economic, and technical standards. The study area covers the mainland part of Southeastern Europe (796,039 km²), including the following countries: Slovenia, Croatia, Bosnia and Herzegovina, Serbia, Montenegro, North Macedonia, Albania, Greece, Bulgaria, Romania, Moldova, and Türkiye. Using geographic information systems (GIS) and remote sensing methods, nine factors (topographic, climatic, hydrological, ecological, vegetation, and anthropogenic) were analyzed with a spatial resolution of 100 m. A fuzzy analytic hierarchy process (F-AHP) pairwise comparison matrix was constructed to quantify the relative importance of the selected criteria. The F-AHP weighting results indicate that photovoltaic output (17.9%) and land use (15.7%) are the most important among the evaluated criteria. The results show that 6.7% of Southeastern Europe is very highly suitable for installing solar PV plants, with the most suitable areas located in Moldova (14.5%) and Greece (10.5%). Through spatial analysis of the final results, 24 of the most suitable locations for large-scale solar PV power plant development were identified, with a potential to generate approximately 30.2 TWh of electricity annually. In such a scenario, the forecast indicates that 24 large-scale solar power plants would supply electricity to more than 6.7 million households, corresponding to over 17 million inhabitants. The final spatial patterns provide decision-makers at the international level with a significantly more effective basis for planning solar energy development in order to increase the share of green energy and clean technologies in this part of Europe. Full article

The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to persistently high net load and severe challenges to supply guarantee. To address this issue, this paper proposes a multi-source coordinated supply-guarantee dispatch strategy for consecutive-day renewable energy drought scenarios. First, net load is defined as the total system load minus the available wind and solar output. Based on magnitude and duration thresholds, renewable energy drought events are extracted from historical data to generate representative scarcity scenarios. Second, a multi-source coordinated optimization dispatch model is constructed, incorporating wind power, solar power, thermal units, battery energy storage, and pumped-storage hydro. The objective is to minimize the total system operating cost, which includes thermal fuel cost, start-up/shut-down costs, storage cycling cost, wind/solar curtailment penalty cost, and load shedding penalty cost. The load shedding penalty coefficient is set to a magnitude much higher than conventional costs to highlight the priority of supply guarantee. The model accounts for operational constraints such as minimum up/down times, deep regulation capability, ramping limits of thermal units, and charge/discharge power limits of storage. Taking a provincial power system in China for the year 2030 as a case study, a dispatch case covering four consecutive days (96 time periods) is designed. Based on a baseline scenario, eight groups of sensitivity analyses are conducted to comprehensively investigate the impacts of key factors on the supply-guarantee strategy, including: the minimum up/down time of thermal units, deep regulation capability, load shedding penalty cost, load level, rated energy capacity and charge/discharge efficiency of battery energy storage, rated energy capacity and pumping/generating efficiency of pumped-storage hydro, thermal fuel cost coefficient, and renewable energy capacity. Simulation results show that the proposed strategy can effectively coordinate multiple resources under consecutive-day drought conditions; reducing the minimum up/down time of thermal units improves supply flexibility but increases start-up/shut-down costs; enhancing deep regulation capability optimizes storage utilization and reduces total system cost; the load shedding penalty cost directly determines the trade-off between supply guarantee and economic efficiency; and as load level decreases by 5%, 10%, and 15%, the total system operating cost reduces by approximately 6.3%, 12.5%, and 18.8%, respectively. This study provides a quantitative method and technical support for supply-guarantee dispatch decisions and resource allocation in high-renewable power systems under persistent drought conditions. Full article

Under the background of large-scale renewable energy integration, virtual power plants (VPPs) realize unified coordination and information interaction between generation resources, user loads and power grids, which serves as a critical technical means for efficient operation in the new energy scenario. To improve energy utilization efficiency of VPPs, this paper proposes a data-driven optimal dispatch framework. First, cluster analysis is performed to clean massive operation data and remove redundant information. Then, an improved long short-term memory (LSTM) method is adopted to extract time-series features of wind–solar output and user load. On this basis, a resource aggregation and optimal control model is constructed using real-time demand response and consensus algorithm. Case studies demonstrate that all clusters reach consistent convergence after 30 iterations with a cost increase rate of 0.28 Yuan/kW. The optimal regulation powers of clusters 4, 6, 9, 10 and 13 are 7.2, 3, 1.67, 0.43 and 0.58 kW, respectively. When cluster 14 participates in regulation temporarily, convergence steps rise but the minimum-cost power optimization remains valid. Simulation of whole-day dispatch shows that demand response effectively promotes renewable energy consumption and reduces the comprehensive operating cost by 4.86%. The proposed strategy can strengthen the renewable energy accommodation capability and reduce operation costs of VPPs. Full article

With the advancement of China’s “dual carbon” targets, Desert–Gobi–Wilderness (DGW) regions have become strategic areas for large-scale renewable energy deployment. However, the intermittency and variability of wind and solar resources pose challenges to power system stability, necessitating systematic evaluation of their characteristics and complementarity. This study uses ERA5 reanalysis data (2013–2023) to assess wind and solar resources in the Badain Jaran and Kumtag Deserts. A multi-dimensional framework is developed, incorporating availability, intermittency, variability, and complementarity, and a GIS-based multi-criteria decision-making method is applied for site selection. Results show that the Badain Jaran Desert is characterised by strong wind resources (average wind power density: 235.16 W/m²) and is suitable for wind-dominated development, whereas the Kumtag Desert exhibits superior solar resources (221.08 W/m²), favouring photovoltaic deployment. Significant wind–solar complementarity is identified, particularly in the central-western Badain Jaran and northeastern Kumtag regions. Three high-suitability sites were identified, including two in the Badain Jaran Desert and one in the Kumtag Desert, all characterised by favourable topographic conditions and high engineering feasibility. This study provides a scientific basis and a methodological framework for the planning of wind–solar hybrid systems and coordinated ecological development in DGW regions. Full article

Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers using real-time solar energy data, Solcast forecasts, and battery storage records collected from Battery Management Systems (BMS), Maximum Power Point Tracking (MPPT) units, and smart inverters. An n8n AI Agent using the Ollama chat model gpt-oss:20b assesses surplus solar energy, activating selected servers to utilize otherwise wasted capacity. Workloads consistently align with solar availability, demonstrating 99% operational reliability, sub-second responsiveness, and accurate surplus-energy detection. This research demonstrates how Artificial Intelligence can repurpose surplus solar output into usable computational capacity, thereby contributing to a broader transition toward renewable-powered infrastructures. Full article

“Power-to-ammonia” is widely regarded as a viable solution for large-scale consumption of wind and solar power, as well as for deep decarbonization in the energy and chemical sectors. However, the intermittent nature of renewable energy requires ammonia synthesis systems to operate across a wide and varying range of loads, posing challenges to their economic viability. To address this, we develop a simulation and optimization methodology for ammonia reactor operation under varying loads. Firstly, a high-fidelity reactor model is developed based on the reactor’s structural characteristics by incorporating reaction kinetics and thermodynamic mechanisms. This reactor model is then integrated with compression and separation units. To ensure computational efficiency, surrogate models are developed to approximate the ammonia synthesis and flash separation units. A case study of an ammonia plant with a nominal production rate of 100,000 tons/year is conducted to demonstrate the effectiveness of the proposed method. The results indicate that the feasible operation region of the reactor narrows significantly as the system production load decreases. System operation parameters, including reactor inlet temperature, reactor pressure, and ammonia separation temperature, are optimized for the ammonia synthesis loop over a wide operating window from 30% to 100% of nominal capacity. It is recommended to increase the system inlet temperature as the production load decreases, thereby compensating for the reduced heat release per unit product resulting from the decreased system pressure. Full article