Search Results (176)

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

This study explores practical approaches to improving the reliability of power supply systems through the expansion and optimization of substation power lines. As electricity demand steadily increases, ensuring a stable and efficient power delivery network has become essential to support industrial growth and socio-economic development. This study focuses on challenges such as vulnerability to single-line faults, limited transmission capacity, and complex coordination in system operation. To address these issues, the proposed strategy includes building redundant transmission lines, improving network configuration, and applying modern transmission technologies to enhance operational flexibility. Notably, a Deep Q-Learning algorithm is introduced during the planning and optimization process. Its ability to accelerate convergence and streamline decision making significantly reduces computation time while maintaining solution accuracy, thereby increasing overall efficiency in evaluating large-scale network configurations. Simulation results and case studies confirm that such improvements lead to shorter outage durations, enhanced fault tolerance, and better adaptability to future load demands. The findings highlight strong practical value for industrial applications, offering a scalable and cost-conscious solution for strengthening the reliability of modern power systems. Full article

Frequent extreme natural disasters can lead to large-scale power outages, significantly compromising the reliability and sustainability of urban power supply, as well as the sustainability of urban development. To address this issue, this paper proposes a two-layer resilience optimization method for distribution networks aimed at improving voltage quality during post-disaster power restoration, enhancing the resilience of the power grid, and thus improving the sustainability of urban development. Specifically, the upper-layer model determines the topology of the urban distribution network and dispatches emergency resources to restore power and reconstruct the original topology. Based on this restoration, the lower-layer model further enhances voltage quality by prioritizing the dispatch of flexible resources according to voltage sensitivity coefficients derived from power flow calculations. A larger voltage sensitivity coefficient indicates a stronger voltage optimization effect. Thus, the proposed method enables comparable voltage regulation performance with lower operational cost. Simulation findings on the IEEE-33 bus test system revealed that the proposed strategy minimized the impact of voltage fluctuations by 10.92 percent and cut the cost related to restoration by 31.25 percent, as compared to traditional post-disaster restoration plans, which do not entail optimization of system voltages. Full article

As the final stage of electrical energy delivery, distribution networks play a vital role in ensuring reliable power supply to end users. In regions with limited distribution automation, reliance on operator experience for fault handling often prolongs outage durations, undermining energy sustainability through increased economic losses and carbon-intensive backup generation. Remote-controlled switches (RCSs), as fundamental components of distribution automation, enable remote operation, rapid fault isolation, and load transfer, thereby significantly enhancing system reliability. In the process of intelligent distribution network upgrading, this study targets scenarios with sufficient line capacity and constructs a reliability-oriented analytical model for optimal RCS allocation by traversing all possible faulted lines. The resulting model is essentially a mixed-integer linear programming formulation. To address bilinearities, the McCormick envelope method is applied. Multi-binary products are decomposed into bilinear terms using intermediate variables, which are then linearized in a stepwise manner. Consequently, the model is transformed into a computationally efficient mixed-integer linear programming problem. Finally, the proposed method is validated on a 53-node and a 33-bus test system, with an approximately 30 to 40 times speedup compared to an existing mixed-integer nonlinear programming formulation. By minimizing outage durations, this approach strengthens energy sustainability through reduced socioeconomic disruption, lower emissions from backup generation, and enhanced support for renewable energy integration. Full article

High-performance UPS inverters prevent IoT devices from power outages, thus protecting critical data. This paper suggests an intelligent, robust control technique with closed-loop voltage sensing for UPS (uninterruptible power supply) inverters in IoT (internet of things) devices. The suggested control technique synthesizes a modified gray fast variable structure sliding mode control (MGFVSSMC) together with a neural network (NN). The MGFVSSMC allows system states to speedily converge towards the equilibrium within a shorter time while eliminating the problems of chattering and steady-state errors. The MGFVSSMC may experience state prediction errors when the UPS inverter is subjected to external highly nonlinear loads or internal parameters changing drastically. This results in high harmonic distortion and inferior dynamic response of the inverter output, affecting the guarding of the IoT device. An NN by means of a learning mechanism is employed to properly compensate for the prediction error of the MGFVSSMC, achieving a high-performance UPS inverter. The suggested control technique operates with one voltage sensing, which can yield fast transience and low inverter output-voltage distortion. Both simulations and digital signal processing (DSP) implementation results demonstrate the effectiveness of the suggested control technique under a variety of load conditions. 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

In recent years, extreme natural disasters, such as typhoons, have become increasingly frequent, leading to persistent power outages in urban distribution grids. These outages pose significant challenges to the stability of urban power supply systems. With the growing number of electric vehicle (EV) users and the expanding EV industry, and considering the potential of EVs as flexible load storage resources, this paper proposes a post-disaster power supply restoration strategy that takes into account the potential of coordinated scheduling of EVs and mobile energy storage. First, a compression method based on the Minkowski addition is proposed for the EV cluster model in charging stations, which establishes an EV dispatchable model. Second, the spatiotemporal matrix of failure rates for distribution network elements is calculated using the Batts wind field model, enabling the generation of distribution network failure scenarios under typhoon conditions. Finally, the power supply restoration strategy of multi-source coordination with the participation of EV cluster and mobile storage is formulated with the objective of minimizing the loss of the distribution network side. Simulation results demonstrate that the proposed strategy effectively utilizes the load storage potential of EVs and mobile energy storage, enhances recovery performance, ensures cost-effectiveness, and explicitly solves the islanding operation stability problem. Full article

This paper proposes an optimization strategy for Feeder Terminal Unit (FTU) configuration in distribution networks, accounting for the influence of Distributed Generation (DG). Firstly, the impact of different FTU configurations on load interruption duration was analyzed. Regions were divided based on the planned installation locations of FTUs, and a model for calculating load interruption losses in different regions was established. Secondly, an all-probability model was introduced to calculate the probability of DG disconnection during faults. The importance weight of DG was determined based on its capacity, and a loss model for photovoltaic disconnection was constructed accordingly. Then, an optimization configuration model was established with the objective of minimizing the weighted sum of FTU installation costs, load interruption losses, and DG disconnection losses, while constraining the solution by supply reliability. Finally, the accuracy of the proposed optimization model was validated using Particle Swarm Optimization (PSO) through the IEEE 33-node distribution network model. Full article

The existing literature on power grid reliability extensively examines the effects of individual automation technologies, such as Smart Grids, IoT, and AI, on reducing SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index) indices. However, previous studies have largely focused on partial analyses, often limited to specific aspects of grid operation or isolated case studies. As a result, there is a lack of a comprehensive and integrated theoretical approach that considers the interdependencies between different automation technologies, their impact on various levels of grid management and the economic consequences of their deployment. This study presents a novel theoretical framework aimed at providing a holistic perspective on power grid automation and its impact on energy supply reliability. The key elements of this approach include developing a multidimensional mathematical model that integrates the impact of key automation technologies on SAIDI and SAIFI, allowing for a quantitative assessment of different implementation strategies and applying a probabilistic approach to predict the likelihood of power outages based on the level of automation and real-time grid conditions. This proposed framework offers a holistic view of power grid automation, integrating technical, economic and operational dimensions. It serves as a foundation for further empirical research and the implementation of intelligent grid modernisation strategies, aiming to enhance power supply stability and increase the resilience of distribution networks against outages. The introduced concept aligns with the current challenges of the energy transition, providing utilities and policymakers with analytical tools for making optimal decisions regarding the adoption of digitalisation and automation technologies in the power sector. Full article

When power system equipment fails due to disasters, resulting in the isolation of parts of the network, the loads within the isolated system cannot be guaranteed a continuous power supply. However, for critical loads—such as hospitals or data centers—continuous power supply is of utmost importance. While distributed energy resources (DERs) within the network can supply power to some loads, outages may lead to compensation and fairness issues regarding the unsupplied loads. In response, this study proposes a methodology to determine the appropriate power contract price for key customers by estimating the unsupplied power demand for critical loads in isolated networks and incorporating both outage compensation costs and voltage stability penalties. The microgrid under consideration comprises DERs—including electric vehicles (EVs), fuel cell electric vehicles (FCEVs), photovoltaic (PV) plants, and wind turbine (WT) plants—as well as controllable resources such as battery energy storage systems (BESS) and hydrogen energy storage systems (HESS). It serves both residential load clusters and critical loads associated with social infrastructure. The proposed methodology is structured in two stages. In normal operating conditions, optimal scheduling is simulated using second-order conic programming (SOCP). In the event of a fault, mixed-integer SOCP (MISOCP) is employed to determine the optimal load shedding strategy. A case study is conducted using the IEEE 123 bus test node system to simulate the outage compensation cost calculation and voltage penalty assessment processes. Based on this analysis, a contract price for key customers that considers both disaster-induced outages and voltage impacts is presented. Full article

With the increasing demand for power supply reliability, efficient load restoration in large-scale distribution networks post-outage scenarios has become a critical challenge. However, traditional methods become computationally prohibitive as network expansion leads to exponential growth of decision variables. This study proposes a multi-agent reinforcement learning (MARL) framework enhanced by distribution network partitioning to address this challenge. Firstly, an improved Girvan–Newman algorithm is employed to achieve balanced partitioning of the network, defining the state space of each agent and action boundaries within the multi-agent system (MAS). Subsequently, a counterfactual reasoning framework solved by the QTRAN-alt algorithm is incorporated to refine action selection during training, thereby accelerating convergence and enhancing decision-making efficiency during execution. Experimental validation using a 27-bus system and a 70-bus system demonstrates that the proposed QTRAN-alt with the Girvan–Newman method achieves fast convergence and high returns compared to typical MARL approaches. Furthermore, the proposed methodology significantly improves the success rate of full system restoration without violating constraints. Full article

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article

In the transition to a sustainable energy system, the integration of electric vehicles into residential energy systems is an innovative solution for increasing energy resilience and optimizing electricity consumption. This article presents a bidirectional AC/DC converter capable of charging the electric vehicle battery under normal conditions, while providing power to a critical consumer in the event of a power grid outage. The simulations performed show us the functionality of this converter, demonstrating its efficiency in ensuring the continuity of supply. Full article

Reducing damage caused by power outages is important against the background of severe natural disasters. Estimating the value of lost load (VoLL) is key to making an optimal investment plan for power systems. This paper aims to estimate the recent residential VoLL in Japan by using a survey. The contingent valuation method quantifies the residential willingness to pay (WTP) and its distribution in a 2 h outage during summer. When combining actual demand data, the VoLL is estimated at 501.1 JPY/kWh for a predictable outage and 559.9 JPY/kWh for a sudden one. In addition, the random utility model reveals the effect of people’s attributes on WTP. Larger annual incomes and electricity bills significantly increase WTP. Evacuation experiences and stockpiles also affect WTP in a sudden outage. Finally, 80% of respondents answered that refrigerators, air conditioners, and water supplies are important during outages. Full article