2026· IEEE Canadian Journal of Electrical and Computer Engineering· Vol 49, pp. 495-509· 0 citations· 35 references
Abstract
Active distribution networks (ADNs) constitute a vital component of modern power systems, integrating multiple distributed generation (DG) units. Although DG integration improves efficiency by decreasing power losses, its deployment is frequently limited by technical constraints and financial considerations. Traditionally, network reconfiguration has been applied to loss optimization in distribution grids; however, in radial networks, its effectiveness is restricted because of the limited flexibility of power flow paths. Consequently, the combined application of DG placement and reconfiguration offers a more effective strategy for reducing active losses in contemporary distribution systems. During such optimization, it is crucial to include uncertainties in load and renewable generation to ensure realistic and stable results. In this context, the present study introduces a robust optimization framework designed to maintain reliable performance despite variations in generation and consumption. The proposed method ensures that optimal DG allocation and network configuration remain stable even under moderate fluctuations in system conditions. To evaluate its performance, both robust and deterministic formulations were run for a 70-bus system, and the obtained outcomes were compared with those reported in a reference study. The outcomes prove that the proposed approach significantly reduces daily energy losses under normal and uncertain operating conditions compared with the reference method.
The rapid integration of Distributed Generation (DG) units is transforming traditional passive radial distribution networks into active grids with bidirectional power flow. Determining the optimal allocation and physical hosting capacity of DG units is essential for maintaining grid stability while maximizing technical gains. This study presents a multi-objective optimization framework using empirical peak-load field data from an active medium-voltage distribution network in Türkiye. A custom Particle Swarm Optimization (PSO) algorithm was developed in MATLAB to simultaneously minimize active and reactive power losses while improving the Voltage Deviation Index (VDI). The baseline network model and load flow calculations established in DIgSILENT PowerFactory were rigorously cross-validated with MATPOWER, demonstrating complete mathematical agreement with a negligible deviation (<0.001%). Simulation results under both capacity-constrained (10 MW limit) and unconstrained Hosting Capacity Discovery scenarios demonstrate that single-objective optimization causes negative trade-offs across other operational metrics. Conversely, multi-objective hybrid approaches provide a balanced, globally common optimum operating point best suited to the grid’s natural electrical structure, consistently identifying a reliable ultimate hosting capacity threshold of 21.78 MW at Busbar 4. These findings offer a concrete, empirical decision-support framework for active distribution network planning and modernization.
: With the rapid development and widespread application of distributed energy resources (DERs), service restoration in the distribution network has become more complex and challenging. This paradigm shift, while offering unprecedented opportunities for a cleaner and more resilient energy grid, has fundamentally altered the traditional, centralized approach to managing power outages. This paper addresses the issue that intentional islanding partition and network reconfiguration are not effectively integrated during service restoration, proposing a flexible matching method for maximizing service restoration in the active distribution network. Firstly, a fault branch variable based on the basic ring matrix of the distribution network is proposed. The fault restoration strategy is then determined using the decision tree presented in this paper, and different objective functions are formulated for various fault strategies. Secondly, photovoltaic (PV) units and Wind Turbines (WTs), emergency electric vehicles (EEVs), energy storage systems (ESSs), and controllable loads are analyzed and modeled for their output characteristics in service restoration by considering the participation of DERs. Then, considering the non-convexity in controllable loads and power flow constraints, relaxation techniques are applied to transform the problem into a mixed-integer second-order cone programming (MISOCP) model for the solution. Finally, case studies are conducted on the IEEE 69-bus and IEEE 33-bus systems to verify the effectiveness of the proposed model and method.
Yi An, Litao Hong, Chun Chen et al.· Energy Engineering· 0 citations
With the increasing penetration of renewable energy sources, active distribution networks are facing severe operational challenges caused by stochastic power fluctuations, which may lead to voltage stability degradation and frequent topology adjustment requirements. Existing dynamic reconfiguration methods mainly rely on repeated power flow calculations or predefined stability evaluations, resulting in high computational burdens and limited adaptability under uncertain renewable scenarios. To address these issues, this paper proposes a stability perception-driven dynamic reconfiguration framework for active distribution networks with high renewable penetration. First, a data-driven voltage stability margin perception model is developed to rapidly characterize the security state under diverse operating conditions. By constructing the nonlinear mapping between network topology, renewable generation, load variations, and stability indicators, repetitive stability calculations during online optimization are effectively reduced. Subsequently, a multi-objective rolling reconfiguration strategy is established to coordinate voltage security, active power loss, and load distribution under stochastic renewable fluctuations. An adaptive evolutionary optimization method is employed to search feasible topology schemes, and the TOPSIS approach is adopted to determine the final compromise solution. Case studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed framework improves voltage stability performance, reduces active power losses, and decreases computational costs compared with conventional approaches. The results verify the effectiveness of the proposed method in enhancing the adaptability and operational resilience of active distribution networks under high renewable penetration conditions.
Distributed Generators (DGs) play a significant role in improving the performance and reliability of modern distribution systems and emerging microgrid environments by supplying power closer to load centers. Proper planning of DG placement becomes essential to reduce system losses, enhance voltage profiles, and improve economic benefits. This work presents a consumer-oriented DG allocation strategy applied to standard IEEE 33-bus and IEEE 69-bus distribution systems. The proposed approach considers different consumer categories such as residential, commercial, and industrial loads, and allocates DGs based on consumer load grouping. This framework is also relevant for microgrid planning, where localized generation and load-based allocation strategies enable flexible and resilient operation. In this method, one DG is allocated to each consumer category within the system. The optimal DG locations are identified using the Voltage Stability Index (VSI). Comparative analysis demonstrates a significant reduction in real power losses, improved voltage profiles, and enhanced economic performance, while supporting resilient and sustainable energy management across all test systems and multiple loading conditions. In 33 bus system, Economically, Scenario 1 provides a maximum gain of 10.63% with the PV & WIND hybrid configuration, while Scenario 2 achieves a substantially higher gain of 36.21% with WIND based DG configuration, highlighting its superior economic viability. In 69 bus system, Economically, Scenario 1 provides a maximum gain of 9.94% with the hybrid PV and WIND configuration, while Scenario 2 achieves a substantially higher gain of 38.30% with WIND-based DG, highlighting its superior economic viability.
Nithyashree Manisekaran, Alexander Aguila Téllez, Vidhya Sri Jagadeesan et al.· IEEE Access· 0 citations
Effective reduction of power losses in radial distribution systems can be achieved through strategic network reconfiguration—disconnecting existing branches and connecting alternative ones—and through optimal siting of distributed generation (DG) units, in which load characteristics play a decisive role. Since electrical loads vary with voltage levels and differ by consumer category, they can be represented by a combination of fixed-impedance, power, and current components. Identifying the proportion of these elements for each load category enables the formulation of more adaptive and realistic models for feeder reconfiguration and DG allocation. In this context, the current work introduces a mathematical framework that explicitly links load composition with consumer categories and reformulates a nonlinear polynomial load model into a tractable quadratic form suitable for linear solvers. The developed framework is evaluated on standard 16- and 118-bus distribution systems. For the 16-bus network, the flexible formulation achieved power losses of 66.4–67.08 kW, which closely match the exact nonlinear model results (66.9–67.04 kW) while reducing computation time from about 2000–2230 s to nearly 1.2 s. For the 118-bus system, losses of 410.05–470.03 kW were obtained, compared with 409.38–471.10 kW for the exact model, with processing time reduced from approximately 58 000–157 000 s to 26–68 s. According to the results, the suggested framework preserves high solution accuracy while providing orders-of-magnitude improvements in computational efficiency. The developed approach, therefore, offers a practical and flexible tool for real-time distribution network reconfiguration and DG planning under voltage-dependent and category-based load modeling.
Meisam Mahdavi, Abdullah G. Alharbi, A. BaQais et al.· IEEE Canadian Journal of Ele...· 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
