Aug 2026· International Journal of Electrical and Computer Engineering (IJECE)· Vol 16, pp. 1688· 0 citations
Abstract
The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
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
: Large-scale integration of customer-side flexible resources and distributed resources can aggravate line congestion and voltage violations in active distribution networks, particularly under power supply guarantee scenarios. This paper develops a bi-level congestion management method that coordinates heterogeneous flexible resources through a Stackelberg game framework. Distributed energy storage, electric vehicles, interruptible loads, and time-shiftable loads are scheduled, and vehicle-to-grid capability is explicitly incorporated to enhance operational flexibility during critical supply periods. The model captures the interaction between the load aggregator (LA) and the distribution system operator (DSO): the LA optimizes the dispatch of aggregated flexible resources in response to price signals, while the DSO seeks to maximize social welfare subject to network security constraints. To solve the nested bi-level problem, an improved grey wolf optimizer (IGWO) with Tent chaotic initialization and nonlinear convergence control is employed. Simulations on a modified IEEE 33-bus system show that the proposed method can relieve line overloading, keep nodal voltages within allowable limits, smooth net-load fluctuations, and improve peak-shaving and valley-filling performance, thereby reducing social welfare losses. The results indicate that the method provides practical support for the secure and economic operation of active distribution networks and facilitates the effective integration of renewable generation and distributed storage.
C. Yuan, Zhu Liang, Ke Xu et al.· Energy Engineering· 0 citations
This paper presents an introduction to a multi-objective optimization framework that has been specifically designed to enhance the short-term operational scheduling of energy systems within smart parking lots. The innovative framework integrates the Improved Seagull Algorithm (ISA) with an Adaptive approach, which is crucial for effectively balancing the dual aspects of global exploration and local exploitation, particularly in the context of dynamic and uncertain environments that characterize modern energy systems. To address the complexities involved, a multi-objective formulation has been meticulously developed to take into account the stochastic behavior associated with wind generation, the fluctuations in real-time electricity prices, and the varying demands of electric vehicles (EVs). The primary goal of this model is to jointly optimize several critical factors, including operation costs, voltage deviations, and the dependency on power drawn from the grid. Through extensive simulation studies conducted across a variety of case studies and test systems, it has been demonstrated that the proposed algorithm significantly outperforms established benchmark techniques as well as other competing algorithms in the field. Notably, this method achieves an impressive 18.1% reduction in total operational costs, alongside a remarkable 23.8% decrease in dependency on the grid for power. These compelling findings highlight the proposed framework as not only a practical solution but also a computationally efficient approach for managing uncertainty-aware, multi-objective energy scheduling, paving the way for advancements in the next generation of smart grids.
M. Mohammadi, Khashimova Naima, Khodjaeva Nodirakhon et al.· Discover Sustainability· 0 citations
With the growing integration of distributed energy resources and the increasing operational complexity of modern distribution networks, ensuring high reliability and maintaining voltage stability during fault recovery have become critical challenges for utilities. To address these challenges, this paper proposes a novel optimization framework for the coordinated planning of sectionalizing switches, tie lines, and mobile energy storage systems (MESSs). The framework employs a bilevel optimization model to achieve coordinated asset placement and operational scheduling. Specifically, the upper-level model aims to achieve a balanced trade-off between economic investment and reliability performance by minimizing both the installation costs and the outage penalties quantified by the expected energy not supplied (EENS). To efficiently solve the upper-level multiobjective problem, an improved multiobjective gold rush optimizer is employed, generating the Pareto-optimal planning solutions. During the iterative optimization process, an enhanced fault incidence matrix–based reliability assessment method is integrated, in which the contribution of MESSs is quantified through a sparse matrix
R
dis
derived from the lower-level operational model, enabling more accurate analytical evaluation. Complementarily, the lower-level model focuses on operational quality during fault recovery by optimizing MESS dispatch using a mixed-integer second-order cone programming formulation. This model minimizes voltage deviations at isolated load nodes, thereby ensuring stable and effective power supply restoration. Additionally, the ZIP load model is also embedded to precisely capture load reduction behavior under fault conditions, enabling a more realistic evaluation of energy curtailment. Case studies conducted on a 41-node distribution network demonstrate that the proposed bilevel optimization approach effectively reduces EENS by up to 21% and reduces the number of unqualified voltage nodes with a moderate investment increase, highlighting the importance of the proposed method.
Shuai Huang, Yuzheng Lv, Ye Xiong et al.· Journal of Energy Engineerin...· 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
With the increasing penetration of renewable energy and the diversification of load patterns, power and energy balance analysis is facing coupled challenges involving multiple periods, multiple scenarios, and uncertainties on both the supply and demand sides. Data centers possess the potential for spatial and temporal load shifting as well as aggregated dispatch, enabling them to participate in system balancing and dispatch optimization as demand response resources. This paper constructs a data center aggregator framework, develops a load demand response model that accounts for spatial-temporal shifting, and formulates an optimization scheduling model applicable to multi-period power balance analysis, with the objectives of minimizing data center operating costs and maximizing aggregator benefits. The Karush-Kuhn-Tucker conditions and the big-M method are employed to transform the bilevel model into a single-level mixed-integer linear programming model for solution. The results show that the proposed strategy can enhance the workload flexibility of data centers, reduce operating costs, and support power balance analysis and dispatch strategy optimization over annual, seasonal, monthly, and weekly time scales.
Qian Ma, Aoyu Lei, Na-Min Hou et al.· 2026 5th International Confe...· 0 citations