Search Results (40)

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article

To address the issue of significant perturbations caused by the limited flexibility of clean energy grid integration, along with the combined effects of electric vehicle charging demand and the uncertainty of high-penetration intermittent energy in the integrated energy system (IES), a capacity optimization method for the IES subsystem of a hydrogen-containing chemical park, accounting for strong perturbations, is proposed in the context of the park’s energy usage. Firstly, a typical scenario involving source-load disturbances is characterized using Latin hypercube sampling and Euclidean distance reduction techniques. An energy management strategy for subsystem coordination is then developed. Building on this, a capacity optimization model is established, with the objective of minimizing daily integrated costs, carbon emissions, and system load variance. The Pareto optimal solution set is derived using a non-dominated genetic algorithm, and the optimal allocation case is selected through a combination of ideal solution similarity ranking and a subjective–objective weighting method. The results demonstrate that the proposed approach effectively balances economic efficiency, carbon reduction, and system stability while managing strong perturbations. When compared to relying solely on external hydrogen procurement, the integration of hydrogen storage in chemical production can offset high investment costs and deliver substantial environmental benefits. Full article

The islanded line-commutated-converter-based high-voltage direct-current (LCC-HVDC) transmission system is becoming a key solution for delivering multiple types of clean energy from large-scale renewable energy bases, including wind power, photovoltaic power, hydropower, and energy storage. However, the high penetration of renewable sources significantly increases the risks of frequency fluctuations and voltage violations due to their inherent volatility and uncertainty, posing serious challenges to system stability. To enhance the integration capacity of clean energy and ensure the stable operation of islanded systems, this paper proposes a maximum capacity optimization method tailored for islanded DC transmission involving multiple energy types. A K-medoids clustering algorithm is applied to historical data to extract typical wind and photovoltaic output scenarios, and a virtual balancing node is introduced. Subsequently, an active power droop control strategy and reactive power regulation are applied to enhance system frequency and voltage stability. Finally, the capacities of wind, photovoltaic, and energy storage systems are jointly optimized using particle swarm optimization. Simulation results demonstrate that the proposed approach can accurately determine the maximum allowable integration of wind and photovoltaic power while satisfying system operational constraints, and effectively reduce the required energy storage capacity. Full article

With the increasing popularity of low-cost, clean, and environmentally friendly new energy sources, the proportion of grid-connected new energy units has increased significantly. However, since these units are frequency decoupled from the grid through a power electronic interface, they are unable to provide inertia support during active power perturbations, which leads to a decrease in system inertia and reduced frequency stability. In this study, the urgent need to accurately assess inertia is addressed by developing an energy-function-based inertia identification technique that eliminates the effect of damping terms. By integrating vibration mechanics, the proposed method calculates the inertia value after a perturbation using port measurements (active power, voltage phase, and frequency). Simulation results of the Western System Coordinating Council (WSCC) 9-bus system show that the inertia estimation error of the method is less than 1%, which is superior to conventional methods such as rate-of-change-of-frequency (RoCoF) and least squares methods. Notably, the technique accurately evaluates the inertia of synchronous generators and doubly fed induction generators (DFIGs) under virtual inertia control, providing a robust inertia evaluation framework for low-inertia power systems with high renewable energy penetration. This research deepens the understanding of inertial dynamics and contributes to practical applications in grid stability analysis and control strategy optimalization. Full article

Enhanced or engineered geothermal systems (EGSs), or non-hydrothermal resources, are highly notable among sustainable energy resources because of their abundance and cleanness. The EGS concept has received worldwide attention and undergone intensive studies in the last decade in the US and around the world. In comparison, hydrothermal reservoir resources, the ‘low-hanging fruit’ of geothermal energy, are very limited in amount or availability, while EGSs are extensive and have great potential to supply the entire world with the needed energy almost permanently. The EGS, in essence, is an engineered subsurface heat mining concept, where water or another suitable heat exchange fluid is injected into hot formations to extract heat from the hot dry rock (HDR). Specifically, the EGS relies on the principle that injected water, or another working fluid, penetrates deep into reservoirs through fractures or high-permeability channels to absorb large quantities of thermal energy by contact with the host hot rock. Finally, the heated fluid is produced through production wells for electricity generation or other usages. Heat mining from fractured EGS reservoirs is subject to complex interactions within the reservoir rock, involving high-temperature heat exchange, multi-phase flow, rock deformation, and chemical reactions under thermal-hydrological-mechanical (THM) processes or thermal-hydrological-mechanical-chemical (THMC) interactions. In this paper, we will present a THM model and reservoir simulator and its application for simulation of hydrothermal geothermal systems and EGS reservoirs as well as a methodology of coupling thermal, hydrological, and mechanical processes. A numerical approach, based on discretizing the thermo-poro-elastic Navier equation using an integral finite difference method, is discussed. This method provides a rigorous, accurate, and efficient fully coupled methodology for the three (THM) strongly interacted processes. Several programs based on this methodology are demonstrated in the simulation cases of geothermal reservoirs, including fracture aperture enhancement, thermal stress impact, and tracer transport in a field-scale reservoir. Results are displayed to show geomechanics’ impact on fluid and heat flow in geothermal reservoirs. Full article

To facilitate power system decarbonization, optimizing clean energy integration has emerged as a critical pathway for establishing sustainable power infrastructure. This study addresses the multi-timescale operational challenges inherent in power networks with high renewable penetration, proposing a novel stochastic dynamic programming framework that synergizes intraday microgrid dispatch with a multi-phase carbon cost calculation mechanism. A probabilistic carbon flux quantification model is developed, incorporating source–load carbon flow tracing and nonconvex carbon pricing dynamics to enhance environmental–economic co-optimization constraints. The spatiotemporally coupled multi-microgrid (MMG) coordination paradigm is reformulated as a continuous state-action Markov game process governed by stochastic differential Stackelberg game principles. A communication mechanism-enabled multi-agent twin-delayed deep deterministic policy gradient (CMMA-TD3) algorithm is implemented to achieve Pareto-optimal solutions through cyber–physical collaboration. Results of the measurements in the MMG containing three microgrids show that the proposed approach reduces operation costs by 61.59% and carbon emissions by 27.95% compared to the least effective benchmark solution. Full article

With the growing integration of renewable sources on a large scale into modern power systems, the operation of distribution networks faces significant challenges under fluctuating renewable energy outputs. Therefore, achieving multi-objective optimization over multiple time periods, including minimizing energy losses and maximizing renewable energy utilization, has become a pressing issue. This paper proposes a Collaborative Intelligent Optimization Reconfiguration Strategy (CIORS) based on a dual-agent framework to achieve a global collaborative optimization of distribution networks in a multi-time period environment. CIORS addresses goal conflicts in multi-objective optimization by designing a collaborative reward mechanism. The discrete agent and continuous agent are responsible for optimizing the switch states within the distribution grid while coordinating the control of both active and reactive power flows through Soft Open Points (SOPs), respectively. To respond to the dynamic fluctuations of loads and renewable energy outputs, CIORS incorporates a dynamic weighting mechanism into the comprehensive reward function, allowing the flexible adjustment of the priority of each optimization objective. Furthermore, CIORS introduces a prioritized experience replay (PER) mechanism, which improves sample utilization efficiency and accelerates model convergence. Simulation results based on an actual distribution network in a specific area demonstrate that CIORS is effective under high-penetration clean energy scenarios. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

This paper examines how the slope of the merit-order curve and the share of non-zero-dollar dispatched energy affect photovoltaic (PV) price cannibalization and the declining market value of all generation types. Using historical merit-order data from Alberta, Canada—during its coal-to-gas transition—we simulated the introduction of zero-marginal-cost PV offers. The increased PV penetration rapidly suppresses midday electricity prices, forming a “duck curve” that challenges solar project economics. Emission reductions improve with rising carbon prices, indicating environmental benefits despite declining market revenues. Years with steeper merit-order slopes and lower non-zero-dollar dispatch shares show intensified price cannibalization and a reduced PV market value. The integration of battery storage alongside PV significantly flattened daily price profiles—raising the trough prices during charging and lowering the highest prices during discharging. While this reduces price volatility, it also diminishes the market value of all generation types, as batteries discharge at zero marginal cost during high-price hours. Battery arbitrage remains limited in low- and moderate-price regimes but becomes more profitable under high-price regimes. Overall, these dynamics underscore the challenges of integrating large-scale PV in energy-only markets, where price cannibalization erodes long-term investment signals for clean energy technologies. These insights inform sustainable energy policy design aimed at supporting decarbonization, and investment viability in liberalized electricity markets. Full article

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). Full article

Hydrogen, as a clean energy source, has gradually become an important choice for the energy transformation in the world. Utilizing existing natural gas pipelines for hydrogen-blended transportation is one of the most economical and effective ways to achieve large-scale hydrogen transportation. However, hydrogen can easily penetrate into the pipe material during the hydrogen-blended transportation process, causing damage to the properties of the pipe. The heat-affected zone (HAZ) of the weld, being the weakest part of the pipeline, is highly sensitive to hydrogen embrittlement. The microstructure and properties of the grains in the heat-affected zone undergoes changes during the welding process. Therefore, this paper divides the HAZ of X80 welded pipes into three sub-HAZ, namely the coarse-grained HAZ, fine-grained HAZ, and intercritical HAZ, to study the hydrogen behavior. The results show that the degree of hydrogen damage in each sub-HAZ varies significantly at different strain rates. The coarse-grained HAZ has the highest hydrogen embrittlement sensitivity at low strain rates, while the intercritical HAZ experiences the greatest hydrogen damage at high strain rates. By combining the microstructural differences within each sub-HAZ, the plastic damage mechanism of hydrogen in each sub-HAZ is analyzed, with the aim of providing a scientific basis for the feasibility of using X80 welded pipes in hydrogen-blended transportation. Full article

Optimizing the drilling process is critical for the exploration of natural resources. However, there are several mechanic parameters that continuously interact with formation properties, hindering the optimization process. Rate of penetration (ROP) and mechanical specific energy (MSE) are considered two key performance indicators that allow the identification of ideal conditions to enhance the drilling process. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a 2D analysis of parameters as a function of depth, response surface methodology (RSM), and multi-objective optimization. The results show that the RSM method and multi-objective optimization provide better results when compared with 2D analysis of parameters as a function of depth. The RSM method can be used as a tool to analyze the effects of the independent drilling mechanical parameters (WOB, RPM, FLOW, and TOR) on the response variables (ROP and MSE) with a 95% confidence level. Through multi-objective optimization, it was possible to concomitantly achieve an ROP of approximately 22 ft/h and MSE of nearly 11 kpsi using the values of WOB, RPM, FLOW, and TOR of about 11 klb, 109 rev/min, 803 gpm, and 3 klb-ft, respectively. Using high WOB values, i.e., from the mean value up to the maximum value of approximately 43 klb, reflects a low ROP and most likely indicates an operation beyond the foundering point. High FLOW promotes a more efficient hole cleaning and higher rates of cuttings transport, thus preventing eventual in situ drill-bit sticking. Flow adjustment also ensures an adequate balance of dynamic bottom hole pressure, in addition to controlling the force impact force of the drilling fluid in contact with the rock being drilled, expressing importance in terms of efficiency and rock penetration. Finally, it is important to mention that the results of this research are not only applicable to hydrocarbon exploration but also to geothermal and natural hydrogen exploration. Values analyzed and presented with decimal precision should be logically focused as integers when in industrial application. Full article

Offshore wind power is a new type of clean energy with broad development prospects. Accurate analysis of the uplift capacity of offshore wind turbine foundations is a crucial prerequisite for ensuring the safe operation of wind turbines under complex hydrodynamic conditions. However, current research on the uplift capacity of suction caissons often neglects the high-sensitivity characteristics of marine soils. Therefore, this paper first employs the freeze–thaw cycling procedure to prepare high-sensitivity saturated clay. Subsequently, a single−tube foundation for wind turbines is constructed within a centrifuge through a penetration approach. Ten sets of centrifuge model tests with vertical cyclic pullout are conducted. Through comparative analysis, this study explores the pullout capacity and its variation patterns of suction caisson foundations in clay with different sensitivities under cyclic loading. This research indicates the following: (1) The preparation of high-sensitivity soil through the freeze−thaw procedure is reliable; (2) the uplift capacity of suction caissons in high−sensitivity soil rapidly decreases with increasing numbers of cyclic loads and then tends to stabilize. The cumulative displacement rate of suction caissons in high-sensitivity soil is fast, and the total number of pressure–pullout cycles required to reach non-cumulative displacement is significantly smaller than that in low-sensitivity soil; (3) the vertical cyclic loading times and stiffness evolution patterns of single-tube foundations, considering the influence of sensitivity, have been analyzed. It was found that the secant stiffness exhibits a logarithmic function relationship with both the number of cycles and sensitivity. The findings of this study provide assistance and support for the design of suction caissons in high-sensitivity soils. Full article

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz. Full article

Heat is involved in many processes in the food industry: drying, dissolving, centrifugation, extraction, cleaning, washing, and cooling. Heat generation encompasses nearly all processes. This review first presents two representative case studies in order to identify which processes rely on the major energy consumption and greenhouse gas (GHG) emissions. Energy-saving and decarbonating potential solutions are explored through a thorough review of technologies employed in refrigeration, heat generation, waste heat recovery, and thermal energy storage. Information from industrial plants is collected to show their performance under real conditions. The replacement of high-GWP (global warming potential) refrigerants by natural fluids in the refrigeration sector acts to lower GHG emissions. Being the greatest consumers, the heat generation technologies are compared using the levelized cost of heat (LCOH). This analysis shows that absorption heat transformers and high-temperature heat pumps are the most interesting technologies from the economic and decarbonation points of view, while waste heat recovery technologies present the shortest payback periods. In all sectors, energy efficiency improvements on components, storage technologies, polygeneration systems, the concept of smart industry, and the penetration of renewable energy sources appear as valuable pathways. Full article