Search Results (37)

In this paper, an emission- and reliability-aware stochastic optimization model is proposed for the economic planning of electric bus parking lots (EBPLs) with photovoltaic (PV) and wind-turbine (WT) resources in an 85-bus radial distribution network. The model simultaneously minimizes operating, emission, and energy-loss costs while increasing system reliability, measured by energy not supplied (ENS), and uses a fuzzy decision-making approach to determine the final solution. To address optimization challenges, a new multi-objective entropy-guided Sinh–Cosh Optimizer (MO-ESCHO) is proposed to efficiently mitigate premature convergence and produce a well-distributed Pareto front. Also, a hybrid forecasting architecture that combines MO-ESCHO and artificial neural networks (ANN) is proposed for accurate prediction of PV and WT power and network loading. The framework is tested across five cases, progressively incorporating EBPL, demand response (DR), forecast information, and stochastic simulation of uncertainties using a new hybrid Unscented Transformation–Cubature Quadrature Rule (UT-CQR) method. Comparative analyses against conventional methods confirm superior performance in achieving better objective values and ensuring computational efficiency. The outcomes indicate that the combination of EBPL with RES reduces operating costs by 5.23%, emission costs by 27.39%, and ENS by 11.48% compared with the base case with RES alone. Moreover, incorporating the stochastic model increases operating costs by 6.03%, emission costs by 5.05%, and ENS by 7.94% over the deterministic forecast case, reflecting the added complexity of uncertainty. The main contributions lie in coupling EBPLs and RES under uncertainty and proposing UT-CQR, which exhibits robust system performance with reduced variance and lower computational effort compared with Monte Carlo and cloud-model approaches. Full article

This study introduces a health-aware fuzzy logic (FL) energy management strategy (EMS) for fuel cell electric commercial vehicles (FCECVs) that aimed to improve energy efficiency and extending fuel cell system (FCS) lifespan. The FL-based EMS was developed using vehicle power demand and battery state of charge (SOC) as inputs, with the FCS power change rate as the output, aiming to mitigate degradation induced by abrupt load transitions. A multi-objective optimization framework was established to optimize the fuzzy logic controller (FLC) parameters, achieving a balanced trade-off between fuel economy and FCS longevity. The non-dominated sorting genetic algorithm-II (NSGA-II) was utilized for optimization across various driving cycles, with average Pareto-optimal solutions employed for real-time application. Performance evaluation under standard and stochastic driving cycles benchmarked the proposed strategy against dynamic programming (DP), charge-depletion charge-sustaining (CD-CS), conventional FL strategies, and a non-optimized baseline. Results demonstrated an approximately $38\%$ reduction in hydrogen consumption (HC) relative to CD-CS and over $75\%$ improvement in degradation mitigation, with performance superior to that of DP. Although the strategy exhibits an average $17.39\%$ increase in computation time compared to CD-CS, the average single-step computation time is only $2.1$ ms, confirming its practical feasibility for real-time applications. Full article

Distribution network operators face the complex challenge of maintaining stable electricity access for diverse consumers while balancing economic constraints, user comfort, and the impact of stochastic events, particularly the increasing integration of renewable energy sources and electric vehicles. To address these challenges, this paper introduces a novel decision-making system for energy management within smart energy meters, leveraging a specifically designed fuzzy inference system. This fuzzy inference system autonomously interprets real-time energy consumption patterns and responds to control commands from distribution network operators, optimizing energy flow at the consumer level. Unlike generic energy management approaches, this study provides a detailed mathematical model of the proposed low-cost fuzzy inference system-based system, explicitly outlining its rule base and inference mechanisms. Simulation studies conducted under varying load conditions and renewable generation profiles demonstrate the system’s effectiveness in achieving a balanced response to grid demands and user needs, yielding a quantifiable reduction in peak demand during simulated stress scenarios. Furthermore, experimental validation on resource-constrained embedded platforms confirms the practical feasibility and real-time performance of the proposed system on low-cost smart energy meter hardware. The differential contribution of this work lies in its provision of a computationally efficient and readily implementable fuzzy logic-based solution tailored for the limitations of low-cost smart energy meters, offering a viable alternative to more complex artificial intelligence algorithms. The findings underscore the necessity and justification for optimizing algorithm code for resource-constrained smart energy meter deployments to facilitate widespread adoption of advanced energy management functionalities. Full article

In this study, we conceptualize the demands imposed on emergency supply chains during extraordinary emergency events as “stress” and develop a scenario-based stress evolution (SE) analytical approach in emergency mobilization decision-making. First, we characterize emergency supply chain stress by uncertainty, abruptness, urgency, massiveness of scale, and latency. Leveraging lifecycle theory and aligning it with the event’s natural lifecycle progression, we construct a dual-cycle model—the emergency event-stress dual-cycle curve model—to intuitively conceptualize the SE process. Second, taking China’s emergency medical supply chain as an illustrative example, we employ set theory to achieve a structured representation of emergency supply chain stress evolution (ESCSE). Third, we propose a novel ESCSE modeling methodology based on stochastic Petri nets and establish both an ESCSE model and a corresponding isomorphic Markov chain model. To address parameter uncertainties inherent in the modeling process, the fuzzy theory is integrated for parameter optimization, enabling realistic simulation of emergency supply chain stress evolution dynamics. Finally, the SE of the ibuprofen supply chain in Beijing during the COVID-19 pandemic is presented as a case study to demonstrate the working principle of the model. The results indicate that the ESCSE model effectively simulates the SE process, identifies critical states, and triggers actions. It also reveals the evolution trends of key scenario elements, thereby assisting decision-makers in deploying more targeted mobilization strategies in dynamic and changing environments. Full article

This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges. Full article

With the growing complexity of logistics and the demand for sustainability, the vehicle routing problem (VRP) has become a key research area. Classical VRPs now incorporate practical challenges such as time window constraints and carbon emissions. In uncertain environments, where many factors are stochastic or fuzzy, optimization models based on uncertainty theory have gained increasing attention. A single-objective optimization model is proposed in this paper to minimize the total cost of VRP in uncertain environments, including fixed costs, transportation costs, and carbon emission costs. Practical constraints like time windows and load capacity are incorporated, and uncertain variables, such as carbon emission factors, are modeled using normal distributions. Two uncertainty models, based on the expected value and chance-constrained criteria, are developed, and their deterministic forms are derived using the inverse distribution method. To solve the problem effectively, a hybrid ant colony–zebra optimization algorithm is proposed, integrating ant colony optimization, zebra optimization, and the 3-opt algorithm to enhance global search and local optimization. Numerical experiments demonstrate the superior performance of the hybrid algorithm, achieving lower total costs compared to standalone ant colony, zebra optimization, genetic algorithm, and particle swarm optimization algorithms. The results highlight its robustness and efficiency in addressing complex constraints. Full article

DC microgrids play a crucial role in both industrial and residential applications. This study focuses on minimizing output voltage ripple in a DC microgrid, including power supply resources, a stochastic load, a ballast load, and a stabilizer. The solar cell serves as the power supply, and the stochastic load represents customer demand, whereas the ballast load includes a load to safeguard the boost circuits against the overvoltage in no-load periods. The stabilizer integrates components such as electrical vehicle batteries for energy storage and controlling long-time ripples, supercapacitors for controlling transient ripples, and an over-voltage discharge mechanism to prevent overcharging in the storage. To optimize the charging and discharging for batteries and supercapacitors, a multi-objective cost function is defined, consisting of two parts—one for ripple minimization and the other for reducing battery usage. The battery charge and discharge are considered in the objective function to limit its usage during transient periods, providing a mechanism to rely on the supercapacitor and protect the battery. Particle swarm optimization is employed to fine-tune the fuzzy membership function. Various operational scenarios are designed to showcase the DC microgrid’s functionality under different conditions, including scenarios where production exceeds and falls below consumption. The study demonstrates the improved performance and efficiency achieved by integrating a PSO-based fuzzy controller to minimize voltage ripple in a DC microgrid and reduce battery wear. Results indicate a 42% enhancement in the integral of absolute error of battery current with our proposed PSO-based fuzzy controller compared to a conventional fuzzy controller and a 78% improvement compared to a PI controller. This translates to a respective reduction in battery activity by 42% and 78%. Full article

In the field of logistics and transportation (L&T), this paper reviews the utilization of simheuristic algorithms to address NP-hard optimization problems under stochastic uncertainty. Then, the paper explores an extension of the simheuristics concept by introducing a fuzzy layer to tackle complex optimization problems involving both stochastic and fuzzy uncertainties. The hybrid approach combines simulation, metaheuristics, and fuzzy logic, offering a feasible methodology to solve large-scale NP-hard problems under general uncertainty scenarios. These scenarios are commonly encountered in L&T optimization challenges, such as the vehicle routing problem or the team orienteering problem, among many others. The proposed methodology allows for modeling various problem components—including travel times, service times, customers’ demands, or the duration of electric batteries—as deterministic, stochastic, or fuzzy items. A cross-problem analysis of several computational experiments is conducted to validate the effectiveness of the fuzzy simheuristic methodology. Being a flexible methodology that allows us to tackle NP-hard challenges under general uncertainty scenarios, fuzzy simheuristics can also be applied in fields other than L&T. Full article

Photovoltaic (PV) arrays have a considerably lower output when exposed to partial shadowing (PS). Whilst adding bypass diodes to the output reduces PS’s impact, this adjustment causes many output power peaks. Because of their tendency to converge to local maxima, traditional algorithms like perturb and observe and hill-climbing should not be used to track the optimal peak. The tracking of the optimal peak is achieved by employing a range of artificial intelligence methodologies, such as utilizing an artificial neural network and implementing control based on fuzzy logic principles. These algorithms perform satisfactorily under PS conditions but their training method necessitates a sizable quantity of data which result in placing an unnecessary demand on CPU memory. In order to achieve maximum power point tracking (MPPT) with fast convergence, minimal power fluctuations, and excellent stability, this paper introduces a novel optimization algorithm named PSO-AWDV (particle swarm optimization–adaptive weighted delay velocity). This algorithm employs a stochastic search approach, which involves the random exploration of the search space, to accomplish these goals. The efficacy of the proposed algorithm is demonstrated by conducting experiments on a series-connected configuration of four modules, under different levels of solar radiation. The algorithm successfully gets rid of the problems brought on by current traditional and AI-based methods. The PSO-AWDV algorithm stands out for its simplicity and reduced computational complexity when compared to traditional PSO and its variant PSO-VC, while excelling in locating the maximum power point (MPP) even in intricate shading scenarios, encompassing partial shading conditions and notable insolation fluctuations. Furthermore, its tracking efficiency surpasses that of both conventional PSO and PSO-VC. To further validate our results, we conducted a real-time hardware-in-the-loop (HIL) emulation, which confirmed the superiority of the PSO-AWDV algorithm over traditional and AI-based methods. Overall, the proposed algorithm offers a practical solution to the challenges of MPPT under PS conditions, with promising outcomes for real-world PV applications. Full article

One of the constant concerns in public and private organizations is choosing a project from among the multitude of potential projects to be implemented. Due to the limited resources in different sectors, projects should be prioritized in order to obtain the maximum benefit. In national and government projects, it is not necessarily important to pay attention to financial components, and more dimensions should be considered. Sustainability is a component that considers various economic, environmental, and social aspects in the evaluation of projects. In this regard, in this study, the main goal is to evaluate and select rail transportation projects according to sustainability criteria. In general, 15 indicators were identified in three economic, environmental, and social sectors, which were weighted using the best–worst fuzzy method (FBWM). The most important indicators in the evaluation of projects are the investment cost, the rate of internal return from a national perspective, and the lesser impact of the plan on environmental destruction. According to the weighted indicators, the stochastic VIKOR approach is developed for the first time in this article, which was evaluated according to two scenarios of demand changes and cost changes of candidate projects. In the stochastic VIKOR approach, to deal with uncertainty, different scenarios are defined, through which it is possible to respond to different conditions and evaluate projects more realistically. Validation of this method is compared to other multi-criteria decision-making methods. The main contribution of this study is presenting the stochastic VIKOR approach for the first time and considering the uncertainty in project evaluation. The findings show that the projects that have the most economic gains from the national and environmental aspects are selected as the best projects. Full article

Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts. Full article

Unpredictable natural disasters brought by extreme climate change compound difficulties and cause a variety of systemic risks. It is thus critical to provide possibilistic scheduling schemes that simultaneously involve emergency evacuation and relief allocation. But the existing literature seldom takes emergency evacuation and relief supplies as a joint consideration, nor do they explore the impact of an unpredictable flood disaster on the scheduling scheme. A multi-stage stochastic programming model with updating information is constructed in this study, which considers the uncertainty of supply and demand, road network, and multiple types of emergency reliefs and vehicles. In addition, a fuzzy algorithm based on the objective weighting of two-dimensional Euclidean distance is introduced, through moderating an effect analysis of the fuzzy number. Computational results show that humanitarian equity for allocating medical supplies in the fourth period under the medium and heavy flood is about 100%, which has the same as the value of daily and medical supplies within the first and third period in the heavy scenarios. Based on verifying the applicability and rationality of the model and method, the result also presents that the severity of the flood and the fairness of resources is not a simple cause-and-effect relationship, and the consideration of survivor is not the only factor for humanitarian rescue with multi-period. Specifically, paying more attention to a trade-off analysis between the survival probability, the timeliness, and the fairness of humanitarian service is essential. The work provides a reasonable scheme for updating information and responding to sudden natural disasters flexibly and efficiently. Full article

This paper discusses the optimization of emergency resource scheduling for major railway emergencies under multiple uncertainties while considering the uncertainties in demand, reserve, and transportation costs of resources. We introduce a novel approach that integrates stochastic mathematical programming, interval parameter programming, and fuzzy mathematical programming to study uncertain parameter interactions and coupling. A two-stage interval fuzzy credibility-constrained model is established and solved using an interval interactive algorithm. Finally, through a case study on China Railway Nanchang Group Co., Ltd., the novelty and effectiveness of the proposed method for optimizing emergency resource scheduling strategies under multiple uncertainties are demonstrated. Full article

The estimation of the efficiency of road transport vehicles remains a significant problem for contemporary transport companies, as numerous stochastic impacts, such as demand stochasticity, road conditions uncertainty, transport market fluctuations, etc., influence the technological process. A fuzzy-logic approach is proposed to consider the uncertainty relating to estimating vehicle fleet efficiency. According to the developed approach, vehicle efficiency is described based on a membership function, whereas the efficiency of the whole vehicle fleet is evaluated as a fuzzy set. To demonstrate the developed approach, a case study is depicted for using cargo vehicles to deliver agricultural products in the Republic of Kazakhstan. The numeric results are presented for the selected models of vehicles that a transport company uses to service a set of clients located in Northern Kazakhstan: the transport services provided for each of the clients are characterized by numeric demand parameters—the consignment weight and the delivery distance. The completed calculations allowed us to obtain the membership functions for the alternative vehicle models and to present the transport company’s vehicle fleet as a fuzzy set. Full article

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor—driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system’s overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs. Full article