Aug 2026· Mathematics· Vol 14, pp. 2926· 0 citations· 27 references
Abstract
The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems.
This paper addresses the complex scheduling optimization problem in multi-UAV collaborative power line inspection by proposing an Adaptive Ant Colony Optimization Algorithm with Elite Strategy (AACOES). The study comprehensively considers multiple practical constraints, including UAV flight characteristics, battery endurance, and external wind conditions, to construct a scheduling optimization model closely aligned with real-world inspection operations. To overcome limitations in convergence speed and global search capability inherent in traditional ant colony algorithms, the proposed method incorporates an elite strategy and adaptive adjustment factors. It optimizes pheromone update rules, effectively enhancing colony diversity and accelerating convergence. This enables efficient identification of near-optimal solutions under multiple constraints. Simulation experiments comparing AACOES with particle swarm optimization, genetic algorithms, and traditional ant colony algorithms demonstrate its significant advantages in optimizing both single-unit performance and total flight distance, coupled with more stable convergence. This validates its effectiveness and practicality for multi-UAV collaborative inspection scheduling in complex environments, providing an efficient and reliable technical approach for real-world applications such as power line inspections.
Xiangdong Zu, Jiaxing Fu, Hai Zhao et al.· Scientific Reports· 0 citations
The increasing penetration of renewable energy sources and storage technologies is driving the transformation of conventional power distribution systems toward decentralized microgrid-based architectures. A major challenge lies in jointly determining the optimal segmentation of existing networks into microgrids and the strategic allocation of distributed energy resources, while balancing investment, operational efficiency, and reliability. This paper proposes a novel single-stage optimization methodology that simultaneously determines microgrid segmentation and the optimal placement of batteries, solar, and wind generators. The formulation minimizes annual operating costs by considering investment and operational expenses, energy losses, and the cost of energy not supplied due to service interruptions. Starting from a conventional radial topology, the optimization is performed using simulated annealing to explore a large combinatorial solution space efficiently. The methodology is applied to the IEEE 69-node distribution system, achieving a 15% reduction in total annual costs and enhanced reliability, with System Average Interruption Frequency Index and System Average Interruption Duration Index reduced by 35% and 47%, respectively. These results highlight the effectiveness and flexibility of the proposed approach as a medium-term planning tool for the design of self-sufficient and resilient distribution networks.
C. Bonetti, G. D. Puccini, J. Rodríguez-García et al.· Green Energy and Environment...· 0 citations
To solve the problems of low renewable energy utilization and high grid dependence in multi-park hybrid microgrids (MPHMs), this paper aims to develop a centralized optimization framework for the wind-solar-storage resource configuration to realize the coordinated operation of new energy suppliers, integrated energy service providers and end-users. An multi-objective economic dispatch model considering source-storage-load-grid coordination, time-of-use pricing and demand response incentive, as well as technical constraints of energy storage is established. An improved hybrid intelligent algorithm combining NSGA-III with enhanced solution selection based on clustering is designed to efficiently solve the optimization problem and obtain practical configurations. The proposed method is applied to a system including industrial, commercial and residential parks with different loads and renewable generation capacity. Simulation results indicate that the optimal energy storage configuration can eliminate the renewable curtailment in all parks and reduce both daily electricity purchasing cost from main grid and total daily power supply cost. These results demonstrate that coordinated planning can improve both sustainability and economy of MPHMs. Sensitivity analyses validate that the solution is robust to the variations of storage cost, incentive policy and renewable generation capacity. The proposed method performs better than non-storage scenario even in unfavorable cost conditions. The proposed scalable and practical method provides a valuable reference for optimizing multi-area microgrids and integrated energy systems.
Tsz Sen Zhu· Mathematical Modeling and Al...· 0 citations
To address the high dependence on diesel generation, renewable energy variability, and limited demand-side flexibility of remote islanded microgrids, this study develops a low-carbon economic dispatch framework for an islanded photovoltaic–wind–diesel–battery energy storage system with coordinated demand response. The proposed model minimizes the operating cost, pollutant treatment cost, and load-loss penalty cost while satisfying generation-output, battery state-of-charge, charging and discharging, demand-response, and islanded power-balance constraints. To solve the resulting high-dimensional, nonlinear, and strongly constrained optimization problem, a rotation quantum particle swarm optimization algorithm (RQPSO) is proposed. In contrast to the conventional velocity–position update, RQPSO independently encodes each decision variable using a full-dimensional quantum phase representation and performs the search through a shortest-path rotation-guided phase-updating mechanism. Adaptive angular mutation, elite local refinement, and stagnation-aware restart are further incorporated to balance global exploration, local exploitation, and convergence stability. The algorithm is evaluated using nine 30-dimensional benchmark functions and representative 24 h forecasted load and renewable-generation profiles for Island data. Under the reliability-priority scheduling scheme, RQPSO achieves a total scheduling cost of 69,017.69 CNY, diesel fuel consumption of 6636.20 kg, and estimated CO2 emissions of 18,332.49 kg. Compared with conventional PSO, these three indicators are reduced by 9.34%, 12.25%, and 12.25%, respectively. RQPSO also reduces the total cost by 6.16–27.36% relative to six comparison algorithms. The results demonstrate that the coordination of demand response and battery storage can improve peak–valley regulation, reduce diesel dependence and emissions, and maintain feasible and economical operation under different renewable-generation conditions.
Guanting Zhu, Weimin Yu, Fei Long et al.· Processes· 0 citations
Recently, transmission congestion remains a critical challenge in power systems, especially in deregulated markets. While Generation Rescheduling (GR) is the conventional approach for Congestion Management (CM), integrating Demand Response (DR) and Distributed Generation (DG) has also proven to offer system operational benefits. However, coordinating these three elements (GR, DR, and DG) within an AC model imposes a computational burden, making the problem highly challenging for standard optimization techniques. To address this problem, this paper proposes an Improved Artificial Ecosystem-Based Optimization (IAEO) algorithm. The proposed IAEO incorporates stochastic search and random crossover mechanisms to significantly enhance the exploration and exploitation capabilities of the original AEO, preventing premature convergence in non-convex search spaces. The proposed framework is validated on the IEEE 30-bus and IEEE 118-bus systems and benchmarked against standard and recent algorithms (AEO, EEFO, SPO, PSO, and DE). Simulation results indicate two major findings. First, incorporating DR reduces CM costs by 2.8% and 32.3%, while the fully coordinated GR, DR, and DG strategy achieves remarkable cost reductions of 53.8% and 35.9% for the respectively considered systems compared to the conventional GR approach. Second, the optimality of the proposed method is improved up to 9.85% and 21.2% in the IEEE 30-bus and IEEE 118-bus systems, respectively. Furthermore, statistical evaluations using the Wilcoxon signed-rank test confirm that the performance improvements achieved by the IAEO are statistically significant compared to others.
Van Tuan Duong, Thanh Long Duong· IEEE Access· 0 citations
Electric vehicles (EVs) are increasingly considered a significant challenge to the stability of smart grids as they are integrated into urban distribution systems. Stochastic load variations are introduced by uncoordinated EV charging, leading to voltage distortion, transformer overloading, and increased power losses. Existing optimization methods, such as Particle Swarm Optimization (PSO) and Model Predictive Control (MPC), are characterized by limited real-time adaptability, high computational cost, and insufficient scalability under uncertain operating conditions. To address these limitations, the EvoGrid-Optimizer is proposed as a novel evolutionary algorithm for the dynamic multi-objective optimization of EV charging schedules in smart grids. The proposed framework simultaneously optimizes operational cost, voltage deviation, and peak load demand using a weighted fitness formulation, while grid operational constraints are satisfied, and is evaluated on a simulated IEEE 33-bus distribution network with stochastic EVs load and renewable energy sources. The simulation results demonstrate that superior performance is achieved by the EvoGrid-Optimizer across all key performance indicators when compared to baseline uncoordinated charging, PSO, and MPC. Specifically, a reduction in operational cost of 22.4%, a voltage deviation of 0.037 p.u., a peak load of 1480 kW, and a load balancing efficiency of 93.8% are achieved.
Safwan Nadweh, Mohamad Abed, Nabil Mohammed et al.· 2026 6th International Confe...· 0 citations