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Project management often overlooks the consideration of long-term effects that may impact sustainability transition and innovation. This paper addresses this gap by presenting an analysis that extends the traditional project life cycle model through the incorporation of a new phase, labelled “system impact”, which integrates innovation and sustainability into project management using a system dynamics methodology. To explore this extension, a simulation model is developed to analyse a solar photovoltaic (PV) power project, providing valuable insights into the systemic and dynamic impacts required for successful project outcomes, including effective benefits management and value creation. The results provide a sustainability-focused assessment of project success. Process innovation efficiency reaches its peak at 140 completed tasks, shortening the project duration from 18 to 13.25 months. This study highlights CO₂ emission avoidance over 25 years compared to fossil fuel generators. Economically, despite an initial cost three times higher, the solar PV alternative proves more cost-effective in the long run, amounting to only 19% of the total cost of the fossil fuel option. Full article

In order to reduce the economic costs, enhance the efficiency, and improve the structural stability of microgrids, this paper proposes a novel AC/DC hybrid microgrid structure. This structure, based on Silicon Controlled Converters (SCCs) and Polarity Reversal Switches (PRSs), enables bidirectional power flow and provides a low-cost and straightforward control solution. This paper elaborates on the overall control strategy of the microgrid under different states of the PRS and introduces the control logic of the Current Reversible Chopper (CRC) circuit. For typical daily scenarios across the four seasons, where wind and photovoltaic (PV) power generation outputs and load demands vary, this study combines sampled data to investigate the coordinated configuration scheme of wind energy, PV energy, and energy storage within the microgrid, and analyzes the state changes in the PRS. Furthermore, this paper conducts simulation analysis of the microgrid under different states of the PRS and during the switching process of the PRS, verifying the feasibility of the proposed new structure. Finally, this paper compares the proposed structure with traditional microgrid structures in terms of economics, system efficiency, and structural stability, and analyzes the impact of this structure on the frequency, inertia, and multi-energy interaction of the system. Full article

A coordinated control for the volt-var optimization (VVO) problem is presented using load tap changer transformers, voltage regulators, and capacitor banks with the integration of a PV-based microgrid. The harmony search (HS) algorithm, which is a metaheuristic-based optimization algorithm, was used to determine global optimum settings of related devices to operate efficiently under changing conditions. The major objectives of volt-var optimization were to reduce power losses, peak power demands, and voltage variations in the distribution circuit while maintaining voltages within the permitted range at all nodes and under all loading conditions. The problem was a mixed integer nonlinear problem with discrete integer variables; binary variables for the capacitor status on/off, voltage regulator taps as integers, and continuous variables; the current output of the microgrid; and nonlinear electric circuit equations. The simulations were verified using the IEEE 13-node test circuit. Daily load profiles of the main power system grid and the microgrid’s PV were used with a 15 min resolution. Power flow solutions were produced using the OpenDSS (version 9.5.1.1, year 2022) power distribution system solver. It can be applied to operational and planning purposes. The results showed that active power loss, peak power demand, and voltage fluctuation were significantly reduced by the coordinated control of the volt-var problem. Full article

The integration of Internet of Things (IoT) technologies into solar energy systems has transformed them into smart solar energy systems, enabling advanced real-time monitoring, control, and optimization. However, this connectivity also expands the attack surface, exposing critical components to cybersecurity threats that could compromise system reliability and long-term sustainability. This study presents a comprehensive cybersecurity threat modeling analysis for IoT-based smart solar energy systems using the STRIDE threat model to systematically identify, categorize, and assess potential security risks. These risks, if unmitigated, could disrupt operations and hinder large-scale adoption of solar energy. The methodology begins with a system use case outlining the architecture and key components, including sensors, PV modules, IoT nodes, gateways, cloud infrastructure, and remote-access interfaces. A Data Flow Diagram (DFD) was developed to visualize the data flow and identify the critical trust boundaries. The STRIDE model was applied to classify threats, such as spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege across components and their interactions. The DREAD risk assessment model was then used to prioritize threats based on the Damage Potential, Reproducibility, Exploitability, Affected Users, and Disability. The results indicate that most threats fall into the high-risk category, with scores ranging from 2.6 to 2.8, emphasizing the need for targeted mitigation. This study proposes security recommendations to address the identified threats and enhance the resilience of IoT-enabled solar energy systems. By securing these infrastructures, this research supports the transition to sustainable energy by ensuring system integrity and protection against cyber threats. The combined use of STRIDE and DREAD provides a robust framework for identifying, categorizing, and prioritizing risks, enabling effective resource allocation and targeted security measures. These findings offer critical insights into safeguarding renewable energy systems against evolving cyber threats, contributing to global energy sustainability goals in an increasingly interconnected world. Full article

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT) controllers to optimize the efficiency of PV systems by extracting accessible maximum power. This research investigates the performance and comparison of various MPPT control algorithms for a standalone PV system. Several cases involving individual MPPT controllers, as well as hybrid combinations using two and three controllers, have been simulated in MATLAB/SIMULINK. The sensed parameters, i.e., output power, voltage, and current, specify that though individual controllers effectively track the maximum power point, hybrid controllers achieve superior performance by utilizing the combined strengths of each algorithm. The results indicate that individual MPPT controllers, such as perturb and observe (P&O), particle swarm optimization (PSO), and fuzzy logic (FL), achieved tracking efficiencies of 97.6%, 90.3%, and 90.1%, respectively. In contrast, hybrid dual controllers such as P&O-PSO, PSO-FL, and P&O-FL demonstrated improved performance, with tracking efficiencies of 96.8%, 96.4%, and 96.5%, respectively. This research also proposes a new hybrid triple-MPPT controller combining P&O-PSO-FL, which surpassed both individual and dual-hybrid controllers, achieving an impressive efficiency of 99.5%. Finally, a comparison of all seven cases of MPPT control algorithms is presented, highlighting the advantages and disadvantages of individual as well as hybrid approaches. Full article

Due to the integration of distributed photovoltaic (PV) into distribution networks, significant challenges have affected voltage regulation and power quality maintenance. To improve the flexibility and stability of system operation, a synergetic optimization operation method based on PV and a flexible interconnection device (FID) is proposed. Both PV and FID hold the capability of controlling active power and reactive power. Besides the active power output of PV, three reactive power output schemes of power factor controlling, direct reactive power output, and night static var generator scheme are defined and analyzed. By adopting different schemes during the day or night, five reactive power output modes were built. FID with four-quadrant power control ability was used to coordinate with PV in system power balance. Different port numbers of FIDs are discussed. An optimization model with the aim of reducing voltage deviation, network loss, and the ratio of PV abandonment was constructed. Three algorithms were used for solving the multi-objective optimization model. Simulation results verify that the proposed synergetic optimization method can obviously improve power quality and decrease network loss. The optimal performance is obtained when PV operates in mode 5 and FID holds four ports. The proposed method shows potential in the coordinated operation of various resources and the flexible interconnection of the distribution network. Full article

The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization framework to design an HRES in Kern County, USA. The proposed system integrates wind turbines (WTS), photovoltaic (PV) panels, Biomass Gasifiers (BMGs), batteries, electrolyzers (ELs), and fuel cells (FCs), aiming to minimize Annual System Cost (ASC), minimize Loss of Power Supply Probability (LPSP), and maximize renewable energy fraction (REF). Results demonstrate that the PSO-optimized system achieves an ASC of USD6,336,303, an LPSP of 0.01%, and a REF of 90.01%, all of which are reached after 25 iterations. When compared to the Genetic Algorithm (GA) and hybrid GA-PSO, PSO improved cost-effectiveness by 3.4% over GA and reduced ASC by 1.09% compared to GAPSO. In terms of REF, PSO outperformed GA by 1.22% and GAPSO by 0.99%. The PSO-optimized configuration includes WT (4669 kW), solar PV (10,623 kW), BMG (2174 kW), battery (8000 kWh), FC (2305 kW), and EL (6806 kW). Sensitivity analysis highlights the flexibility of the optimization framework under varying weight distributions. These results highlight the dependability, cost-effectiveness, and sustainability for the proposed system, offering valuable insights for policymakers and practitioners transitioning to renewable energy systems. Full article

Cucumber mosaic virus (CMV) is one of the most dangerous viral diseases threatening Solanaceae crops, in particular Capsicum sp. This study aims to develop double haploid (DH) pepper lines from germplasm resistant to CMV in order to speed up the breeding process. For this purpose, six genotypes previously tested for CMV resistance were used. Two induction mediums (17-2 and 17-3) with different concentrations of 2,4-Dichlorophenoxyacetic acid (2,4-D) for anther incubation and further plant regeneration were applied. L10 was the most responsive genotype, exhibiting the highest direct embryogenesis and the most plant regenerants on both mediums. Medium-specific response was observed in genotype L9 where regenerants were observed only on 17-2. Further, eight DH lines were evaluated with two CMV isolates (L-BG and PV-0418) and checked for local and systemic presence of the pathogen in leaves and fruits for a period of 60 days by DAS-ELISA. Of the tested DH lines, four (DH2, DH6, DH7 and DH9) were resistant to both strains, two (DH5 and DH14) were resistant to L-BG, and two (DH19 and DH21) were susceptible to both isolates. Field evaluation of DH7, DH9, and DH14 for some agronomic and morphological traits divided them into two groups according to the original genotypes. Full article

This paper presents findings from the LEOPARD project, part of the LEAP-RE program, a joint European Union (EU) and African Union initiative to advance renewable energy solutions. The study employs a simulation-based approach to optimize solar-integrated microgrid configurations for rural electrification. The project deployed a solar-integrated pilot microgrid at the Songhai agroecological center in Benin to address key challenges, including load profile estimation, energy balancing, and diesel dependency reduction. A hybrid methodology integrating predictive modeling, real-time solar and weather data analysis, and performance simulations was employed, leading to a 65% reduction in diesel reliance and an LCOE of EUR 0.47/kWh. Quality control measures, including compliance with IEC 61215 and IEC 62485-2 standards, ensured system reliability under extreme conditions. Over 150 days, the system consistently supplied energy, preventing 10.16 tons of ${CO}_2$ emissions. Beyond the Benin pilot, the project conducted feasibility assessments in Senegal to evaluate microgrid replicability across different socio-economic and environmental conditions. These analyses highlight the scalability potential and the economic viability of expanding solar microgrids in rural areas. Additionally, this research explores innovative business models and real-time diagnostics to enhance microgrid sustainability. By providing a replicable framework, it promotes long-term energy access and regional adaptability. With a focus on community involvement and capacity building, this study supports efforts to reduce energy poverty, strengthen European–African collaboration, and advance the global clean energy agenda. Full article

The increasing penetration of photovoltaic (PV) systems and the need for reliable backup power solutions have led to the development of hybrid uninterruptible power supply (UPS) systems. These systems integrate PV energy storage with battery backup and grid power to optimize real-time energy management. This paper proposes an advanced energy management strategy and an artificial neural network (ANN)-based control method for PV-integrated hybrid UPS systems. The proposed strategy dynamically determines the optimal power-sharing ratio between battery storage and the grid based on real-time economic parameters, load demand, and battery state of charge (SoC). A centralized ANN-based controller ensures precise control of the LLC converter and rectifier, achieving stable and efficient power distribution. Additionally, a genetic algorithm is implemented to optimize the power sharing ratio, minimizing the LCOE under varying load and electricity pricing conditions. The proposed approach is validated through simulations, demonstrating significant improvements in cost-effectiveness, system stability, and dynamic adaptability compared to conventional control methods. These findings suggest that integrating ANN-based control with optimized energy management can enhance the efficiency and sustainability of hybrid UPS systems, particularly in fluctuating energy markets. Full article

The large-scale integration of wind, solar, and battery energy storage is a key feature of the new power system based on renewable energy sources. The optimization results of wind turbine (WT)–photovoltaic (PV)–battery energy storage (BES) hybrid energy systems (HESs) can influence the economic performance and stability of the electric power system (EPS). However, most existing studies have overlooked the effect of power electronic converter (PEC) efficiency on capacity configuration optimization, leading to a significant difference between theoretical optimal and actual results. This paper introduces an accurate efficiency model applicable to different types of PECs, and establishes an enhanced mathematical model along with constraint conditions for WT–PV–BES–grid–load systems, based on precise converter efficiency models. In two typical application scenarios, the capacity configurations of WT–PV–BES are optimized with optimal cost as the objective function. The different configuration results among ignoring PEC loss, using fixed PEC efficiency models, and using accurate PEC efficiency models are compared. The results show that in the DC system, the total efficiency of the system with the precise converter efficiency model is approximately 96.63%, and the cost increases by CNY 49,420, about 8.56%, compared to the system with 100% efficiency. In the AC system, the total efficiency with the precise converter efficiency model is approximately 97.64%, and the cost increases by CNY 4517, about 2.02%, compared to the system with 100% efficiency. The analysis clearly reveals that the lack of an accurate efficiency model for PECs will greatly affect the precision and effectiveness of configuration optimization. Full article

With power demands continuously growing, the penetration of renewable energy resources, particularly solar photovoltaic (PV) systems, across the residential sector has been extensive. A voltage source inverter (VSI) is the key element for efficiently processing energy conversion and connecting PV systems to home loads or utility grids. The operation of this inverter relies heavily on power-switching devices, which suffer from larger power losses due to the conventional semiconductors used based on silicon (Si) material. The new materials of wide bandgap (WBG) semiconductors, for example, gallium nitride (GaN) and silicon carbide (SiC), provide remarkably distinct characteristics of semiconductor devices to minimize power loss and boost the inverter’s operational capabilities. This research paper assesses the effects of integrating SiC-MOSFET devices into VSIs in order to improve the switching behavior and efficiency level. An experimental double-pulse testing (DPT) circuit was configured and set up for investigating the switching characterization of SiC-MOSFETs compared to the widely used Si-IGBTs. Under various operating circumstances, the switching behavior of two different types of power transistors was tested while their turning-on and turning-off losses were measured. The VSI based on SiC and Si transistors was simulated to examine the performance of the inverter. The results reveal that incorporating SiC-MOSFETs into the VSI substantially enhances the switching operation and reduces total power losses while increasing the efficiency compared to the inverter based on Si-IGBTs. Full article

With the fast-growing implementation of renewable energy projects, Morocco is positioned as a pioneer in green and sustainable development, aiming to achieve 52% of its electricity production from renewable sources by 2030. This ambitious target faces challenges due to the intermittent nature of renewable energy, which impacts grid stability. Hydrogen offers a promising solution, but identifying the most cost-effective production configurations is critical due to high investment costs. Despite the growing interest in renewable energy systems, the techno-economic analysis of (Concentrating Solar Power-Photovoltaic) CSP-PV hybrid configurations remain insufficiently explored. Addressing this gap is critical for optimizing hybrid systems to ensure cost-effective and scalable hydrogen production. This study advances the field by conducting a detailed techno-economic assessment of CSP-PV hybrid systems for hydrogen production at selected locations in Morocco, leveraging high-precision meteorological data to enhance the accuracy and reliability of the analysis. Three configurations are analyzed: (i) a standalone 10 MW PV plant, (ii) a standalone 10 MW Stirling dish CSP plant, and (iii) a 10 MW hybrid system combining 5 MW from each technology. Results reveal that hybrid CSP-PV systems with single-axis PV tracking achieve the lowest levelized cost of hydrogen (LCO_H2), reducing costs by up to 11.19% and increasing hydrogen output by approximately 10% compared to non-tracking systems. Additionally, the hybrid configuration boosts annual hydrogen production by 2.5–11.2% compared to PV-only setups and reduces production costs by ~25% compared to standalone CSP systems. These findings demonstrate the potential of hybrid solar systems for cost-efficient hydrogen production in regions with abundant solar resources. Full article

The rapid acceptance of solar photovoltaic (PV) energy across various countries has created a pressing need for more coordinated approaches to the sustainable monitoring and maintenance of these widely distributed installations. To address this challenge, several digitization architectures have been proposed, with one of the most recently applied being the digital twin (DT) system architecture. DTs have proven effective in predictive maintenance, rapid prototyping, efficient manufacturing, and reliable system monitoring. However, while the DT concept is well established in fields like wind energy conversion and monitoring, its scope of implementation in PV remains quite limited. Additionally, the recent increased adoption of autonomous platforms, particularly robotics, has expanded the scope of PV management and revealed gaps in real-time monitoring needs. DT platforms can be redesigned to ease such applications and enable integration into the broader energy network. This work provides a system-level overview of current trends, challenges, and future opportunities for DTs within renewable energy systems, focusing on PV systems. It also highlights how advances in artificial intelligence (AI), the internet-of-Things (IoT), and autonomous systems can be leveraged to create a digitally connected energy infrastructure that supports sustainable energy supply and maintenance. Full article

With the high proportion of wind and photovoltaic (PV) power connection in the new electricity system, the system output power volatility is enhanced. When the output fluctuation of the system is suppressed, the pumped storage condition is changed frequently, which leads to the vibration enhancement of the unit and a decrease in the system safety. This paper proposes a pump turbine health evaluation model based on the combination of a weighting method and cloud model in a high proportion wind and PV power connection scenario. The wind–PV output characteristics of the complementary system in a year (8760 h) and a typical week in four seasons (168 h) are analyzed, and the characteristics of frequent working condition transitions of pumped storage units are studied against this background. A five-level health classification system including multi-dimensional evaluation indicators is established, and a multi-level health evaluation based on cloud membership quantification is realized by combining the weighting method and cloud model method. The case analysis of a pumped storage power station within a new electricity system shows that the system as a whole presents typical cloud characteristics (Ex = 76.411, En = 12.071, He = 4.014), and the membership degree in the “good” state reaches 0.772. However, the draft tube index (Ex = 62.476) and the water guide index (Ex = 50.333) have shown a deterioration trend. The results verify the applicability and reliability of the evaluation model. This study provides strong support for the safe and stable operation of pumped storage units in the context of the high-proportion wind and PV power connection, which is of great significance for the smooth operation of the new electricity system. Full article