Search Results (75)

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Energy communities represent an important step towards clean energy; however, their management is a complex task due to various factors such as fluctuating demand and energy prices, variable renewable generation, and external factors such as power outages. This paper investigates the effectiveness of a Reinforcement Learning agent, based on the Proximal Policy Optimisation (PPO) algorithm, for energy management across three different energy community configurations. The performance of the PPO agent is compared against a Rule-Based Controller (RBC) and a baseline scenario using solar generation but no active management. Simulations were run in the CityLearn framework to simulate real world data. Across the three evaluated community configurations, the PPO agent achieved its greatest improvement over a single run in the scenario where all participants were prosumers (Schema 3), with a reduction of 9.2% in annual costs and carbon emissions. The main contribution of this work is demonstrating the viability of Reinforcement Learning agents in energy optimization problems, providing an alternative to traditional RBCs for energy communities. Full article

The large-scale integration of Distributed Generation (DG) poses significant challenges to the stable operation of distribution networks. It is particularly crucial to explore the power supply restoration capability of Soft Open Points with Energy Storage (E-SOP) and enhance power supply dependability. To address this issue, this paper proposes a power supply restoration method for flexible interconnected distribution networks (FIDN) considering wind–solar uncertainty. First, the control strategy and mathematical model of E-SOP are analyzed. Second, a wind–solar uncertainty model is established, with the weighted sum of maximizing restored node active load and minimizing power loss as the objective function, followed by a detailed analysis of constraints. Then, chance constraints are introduced to transform the proposed problem into a Mixed-Integer Second-Order Cone Programming (MISOCP) model. The Dung Beetle Optimization (DBO) algorithm is improved through logistic chaotic mapping, golden sine strategy, and position update coefficient to construct a distribution network power supply restoration model. Finally, simulations are conducted on the IEEE 33-node system using a hybrid optimization algorithm that combines Improved Dung Beetle Optimization (IDBO) with MISOCP. The simulation results demonstrate that the proposed method can effectively maximize power supply restoration in outage areas, further enhance the self-healing capability of distribution networks, and verify the feasibility of the method. Full article

The rural community of L’Anse au Loup in southern Labrador depends on a long-distance transmission link to Hydro-Québec for its electricity supply, with diesel generation as backup during outages. This dependence raises electricity costs, exposes the community to supply disruptions, and limits control over local energy security. This study evaluates the feasibility of a solar–wind hybrid energy system to reduce imported electricity and improve supply reliability. A detailed site assessment identified a 50-hectare area north of the community as suitable for system installation, offering adequate space and minimal land-use conflict. Using Hybrid Optimization of Multiple Energy Resources (HOMER Pro 3.18.3) software, the analysis modeled local load data, renewable resource profiles, and financial parameters to determine the optimal grid-connected configuration. The optimized design installs 19.25 MW of photovoltaic (PV) and 4.62 MW of wind capacity, supported by inverters and maximum power point tracking (MPPT) to ensure stable operation. Simulations show that the hybrid system supplies about 70% of annual demand, cuts greenhouse gas emissions by more than 95% compared with conventional generation, and lowers long-term energy costs. The results confirm that the proposed configuration can strengthen local energy security and provide a replicable framework for other remote and coastal communities in Newfoundland and Labrador pursuing decarbonization. Full article

Accelerated climate warming has led to the frequent occurrence of extreme weather events, resulting in high-frequency, large-scale, and highly destructive power outages and electricity shortages, which serve as a wake-up call for the safe and stable operation of the power system. To predict safety risks, this study constructs a baseline scenario and five power security scenarios based on the SWITCH-China model, systematically assessing the impact of external shocks on the power system’s evolution path and carbon reduction economics. The results indicate that external shocks are the key factors influencing the power system’s installed capacity structure and generation mix. The increase in demand forces the substitution of non-fossil energy. In the demand growth scenario, by 2060, wind and solar installed capacity will be 1.034 billion kilowatts higher than in the baseline scenario. Rising fuel costs will accelerate the exit of fossil fuel units. In the fuel cost increase scenario, 765 million kilowatts of coal power were reduced cumulatively across three time points. Wind and solar outages, along with transmission failures, lead to significant local economic investments while also causing inter-provincial carbon transfer. In the wind and solar outage scenario, provinces with a high proportion of wind and solar, such as Guangdong and Guizhou, see an increase in carbon emissions of 31 million tons and 8 million tons, respectively. Conversely, provinces with a lower proportion of wind and solar, such as Inner Mongolia and Xinjiang, reduce carbon emissions by 46 million tons and 39 million tons, respectively. Energy storage development supports the expansion of non-fossil energy in the power system. The study recommends accelerating wind and solar deployment, building a storage system at the scale of hundreds of billions of kilowatt-hours, and optimizing the inter-provincial transmission network to address the dual challenges of power security and carbon neutrality. Full article

Renewable energy sources play a crucial role in the urgent global pursuit of decarbonizing electricity systems. However, persistent grid congestion and lengthy planning approval processes remain the main barriers to the accelerated deployment of new green energy source capacities. Capitalizing on the synergies afforded by co-locating hybrid power plants—particularly those that harness temporally anti-correlated renewable sources such as wind and solar—behind a unified connection point presents a compelling opportunity. To this end, this paper pioneers a comprehensive planning framework for hybrid configurations, integrating transmission grid and renewable energy assets planning to include energy storage systems, wind, and solar energy capacities within a long-term planning horizon. A mixed-integer linear programming model is developed that considers both the technical and economic aspects of combined grid planning and hybrid power plant allocation. Additionally, the proposed framework incorporates the N − 1 contingency criterion, ensuring system reliability in the face of potential transmission line outages, thereby adding a layer of versatility and resilience to the approach. The model minimizes the net present value of costs, encompassing both capital and operational expenditures as well as curtailment costs. The efficacy of the proposed model is demonstrated through its implementation on the benchmark IEEE 24-bus RTS system, with findings underscoring the pivotal role of hybrid power plants in enabling cost-effective and rapid sustainable energy integration. Full article

The instability of the energy supply, growing demand and the need to reduce carbon emissions are priority challenges in developing countries such as Ecuador, where power outages affect productivity and generate economic losses. Therefore, solar energy is positioned as a sustainable alternative. The objective of this study is to evaluate a pilot photovoltaic (PV) system for residential housing in coastal areas in the Santa Elena province, Ecuador. The methodology included the following: (i) criteria for the selection of three representative residential housings; (ii) design of a distributed generation system using PVsyst software; and (iii) proposal of strategic guidelines for the design of PV systems. This proposed system proved to be environmentally friendly, achieving reductions of between 16.4 and 32 tonnes of CO₂ in the first 10 years. A return on investment (ROI) of 16 years was achieved for the low-demand (L) scenario, with 4 years for the medium-demand (M) scenario and 2 years for the high-demand (H) scenario. The sensitivity analysis showed that the Levelized Cost of Energy (LCOE) is more variable in the L scenario, requiring more efficient designs. It is proposed to diversify the Ecuadorian energy matrix through self-supply PV systems, which would reduce electricity costs by 6% of consumption (L scenario), 30% (M scenario), and 100% (H scenario). Although generation is concentrated during the day, the net metering scheme enables compensation for nighttime consumption without the need for batteries, thereby improving the system’s profitability. The high solar potential and high tariffs make the adoption of sustainable energy solutions a justifiable choice. Full article

To enable accurate evaluation of satellite laser communication terminals under solar outage interference, this paper presents the design and implementation of a solar radiation simulation system targeting the 1540–1560 nm communication band. The system reconstructs co-propagating interference conditions through standardized and continuously tunable output, based on high irradiance and spectral uniformity. A compound beam homogenization structure—combining a multimode fiber and an apodizator—achieves 85.8% far-field uniformity over a 200 mm aperture. A power–spectrum co-optimization strategy is introduced for filter design, achieving a spectral matching degree of 78%. The system supports a tunable output from 2.5 to 130 mW with a 50× dynamic range and maintains power control accuracy within ±0.9%. To suppress internal background interference, a BRDF-based optical scattering model is established to trace primary and secondary stray light paths. Simulation results show that by maintaining the surface roughness of key mirrors below 2 nm and incorporating a U-shaped reflective light trap, stray light levels can be reduced to 5.13 × 10⁻¹² W, ensuring stable detection of a 10⁻¹⁰ W signal at a 10:1 signal-to-background ratio. Experimental validation confirms that the system can faithfully reproduce solar outage conditions within a ±3° field of view, achieving consistent performance in spectrum shaping, irradiance uniformity, and background suppression. The proposed platform provides a standardized and practical testbed for ground-based anti-interference assessment of optical communication terminals. Full article

Natural disasters in the United States frequently wreak havoc on critical infrastructure, affecting energy, water, transportation, and communication systems. To address these disruptions, the use of mobile power solutions like PowerKiosk trailers is a partial solution during recovery periods. PowerKiosk is a trailer equipped with renewable energy sources such as solar panels and biogas generators, offering a promising strategy for emergency power restoration. With a daily power output of 12.1 kWh, PowerKiosk trailers can support small lift stations or a few homes, providing a temporary solution during emergencies. Their key strength lies in their mobility, allowing them to quickly reach disaster-affected areas and deliver power when and where it is most needed. This flexibility is particularly valuable in regions like Florida, where hurricanes are common, and power outages can cause widespread disruption. Although the PowerKiosk might not be suitable for long-term use because of its limited capacity, it can play a critical role in disaster recovery efforts. In a community-wide power outage, deploying the PowerKiosk to a lift station ensures essential services like wastewater management, benefiting everyone. By using this mobile power solution, community resilience can be enhanced in the face of natural disasters. Full article

In recent years, the increasing frequency of extreme weather events has led to a rise in unplanned unit outages, posing significant risks to the safe operation of power systems and underscoring the critical need for accurate prediction and effective mitigation of wind and solar power ramp events. Unlike traditional power forecasting, ramp event prediction must capture the abrupt output variations induced by short-term meteorological fluctuations. This review systematically examines recent advancements in the field, focusing on three principal areas: the definition and detection of ramp event characteristics, innovations in predictive model architectures, and strategies for precision optimization. Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. Moreover, current research highlights the necessity of developing evaluation systems tailored to the specific operational hazards of ramp events, rather than relying solely on conventional forecasting metrics. The integration of artificial intelligence has accelerated progress in both event prediction and error correction. However, significant challenges remain, particularly regarding the interpretability, generalizability, and real-time applicability of advanced models. Future research should prioritize the development of adaptive, ramp-specific evaluation frameworks, the fusion of physical and data-driven modeling techniques, and the deployment of multi-modal systems capable of leveraging heterogeneous data sources for robust, actionable ramp event forecasting. Full article

Solar-powered pumping systems using series pumps are commonly applied in the delivery of water to remote agricultural regions, particularly in hilly tropical terrain. The synchronization of these pumps typically depends on reliable communication; however, dense vegetation, elevation changes, and weather conditions often disrupt signals. To address these limitations, a fully decentralized, communication-free control system is proposed. Each pumping station operates independently while maintaining synchronized operation through emulated neighbor sensing. The system applies a discrete-time control algorithm with virtual sensing that estimates neighboring pump statuses. Each station consists of a solar photovoltaic (PV) array, variable-speed drive, variable inlet valve, reserve tank, and local control unit. The controller adjusts the valve positions and pump power based on real-time water level measurements and virtual neighbor sensing. The simulation results across four scenarios, including clear sky, cloudy conditions, temporary outage, and varied irradiance, demonstrated steady-state operation with no water overflow or shortage and a steady-state error less than 4% for 3 m³ transfer. The error decreased as the average power increased. The proposed method maintained system functionality under simulated power outage and variable irradiance, confirming its suitability for remote agricultural areas where communication infrastructure is limited. Full article

Island communities that depend on mainland grid connections face substantial risks when natural disasters sever undersea or overhead cables, often resulting in long-lasting outages. This paper presents a comprehensive and novel two-part methodological framework for enhancing the resilience of these communities through networked microgrids that interconnect local renewable energy resources and battery storage. The framework integrates techno-economic capacity optimization using HOMER Pro with agent-based simulation in AnyLogic to determine cost-effective solar and storage capacities and to model dynamic real-time dispatch under varying conditions. Six island communities across three Indonesian provinces serve as illustrative case studies, tested under best-case and worst-case disruption scenarios that reflect seasonal extremes of solar availability. Simulation results reveal that isolated expansions of PV and battery storage can ensure critical residential loads, though certain islands with limited resources continue to experience shortfalls. By contrast, networked microgrids enable surplus power transfers between islands, significantly reducing unmet demand and alleviating the need for large-scale, individual storage. These findings demonstrate the significant potential of clustered microgrid designs to improve reliability, lower operational costs, and facilitate secure energy supply even during prolonged cable outages. The proposed framework offers a scalable roadmap for deploying resilient microgrid clusters in remote regions, with direct policy implications for system planners and local stakeholders seeking to leverage renewable energy in high-risk environments. Full article

Power system imbalances pose significant challenges to maintaining grid stability and ensuring efficient market performance, particularly in the context of the Japanese electricity market. The primary drivers of these imbalances are identified as the nonlinear responses of power generation and consumer electricity demand to uncertain variables such as temperature and solar radiation, in addition to complex factors such as planned generator outages and operational constraints. Consequently, the prediction of imbalance signals using linear models is inherently challenging and requires the adaptation of more advanced methods in practice. This study comprehensively analyzes imbalance signal dynamics and develops practical forecasting tools using Machine Learning (ML) techniques. By incorporating a diverse range of features—including lagged imbalance data, weather forecast errors specific to Japan, and temporal patterns—we demonstrate that the prediction accuracy of imbalance signals is significantly improved compared to a baseline reflecting random forecasts based on class distribution observed during the initial training period. Furthermore, the proposed approach identifies the key drivers of hourly imbalance signals, while leveraging out-of-sample forecasting models. Based on these findings, we conclude that the use of multiple predictive models enhances the robustness and reliability of our forecasts, offering actionable tools for improving forecasting accuracy in real-world operations and contributing to a more stable and efficient electricity market. Full article

As global energy demand increases, ensuring a reliable electricity supply in rural or semi-remote areas remains a significant challenge. Hybrid energy systems, which integrate renewables, generators, storage, and grid connections, offer a promising solution for addressing energy reliability issues. In this context, the rural community of Aras de los Olmos, Spain, serves as the focal point because of its frequent power outages despite being connected to the main grid. This study investigates innovative solutions tailored to the community’s unique needs. It highlights critical challenges in achieving reliable energy access and bridges the gap between existing limitations and sustainable, future-oriented energy systems. This is achieved by analyzing the current energy setup and evaluating potential alternatives. Two scenarios were evaluated: one optimizing the existing configuration for economic efficiency while retaining the grid as the primary energy source, and another introducing a biomass generator to enhance reliability by partially replacing the grid. Detailed technical, financial, and environmental assessments were performed using HOMER. These assessments identified an optimal configuration. This optimal configuration improves reliability, enhances stability, reduces disruptions, and meets growing energy demands cost-effectively. As will be indicated, the first scenario can reduce total costs to approximately USD 90,000 compared to the existing setup, whereas the second scenario can lower grid dependence by approximately 70%. In addition, introducing renewable energy sources, such as solar and biomass, significantly reduces greenhouse gas emissions and reliance on fossil fuels. Additionally, these solutions create local job opportunities, promote community engagement, support energy independence, and align with broader sustainability goals. Full article

Electric power networks face critical challenges from extreme weather events and natural disasters, disrupting socioeconomic activities and jeopardizing energy security. This study presents an innovative approach incorporating virtual power plants (VPPs) within networked microgrids (MGs) to address these challenges. VPPs integrate diverse distributed energy resources such as solar- and wind-based generation, diesel generators, shunt capacitors, battery energy storage systems, and electric vehicles (EVs). These resources enhance MG autonomy during grid disruptions, ensuring uninterrupted power supply to critical services. EVs function as mobile energy storage units during emergencies, while shunt capacitors stabilize the system. Excess energy from distributed generation is stored in battery systems for future use. The seamless integration of VPPs and networked technologies enables MGs to operate independently under extreme weather conditions. Prosumers, acting as both energy producers and consumers, actively strengthen system resilience and efficiency. Energy management and VPP allocation are optimized using the jellyfish search optimization algorithm, enhancing resource scheduling during outages. This study evaluates the proposed approach’s resilience, reliability, stability, and emission reduction capabilities using real-world scenarios, including the IEEE 34-bus and Indian 52-bus radial distribution systems. Various weather conditions are analyzed, and a multi-objective function is employed to optimize system performance during disasters. The results demonstrate that networked microgrids with VPPs significantly enhance distribution grid resilience, offering a promising solution to mitigate the impacts of extreme weather events on energy infrastructure. Full article