Congestion Management of Power System With Integration of Renewable Resources Considering Demand Response Based on Improved Ecosystem-Based Optimization Method
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.
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.
Jayesh G. Priolkar, G. Kunkolienkar· International Journal of Ele...· 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
Transmission congestion has become a major challenge in deregulated power systems (DPS). Although flexible AC transmission system (FACTS) devices are effective for congestion management, their high installation cost and complex control requirements limit widespread deployment. This paper proposes a battery energy storage system (BESS)-based congestion mitigation approach for DPS. The optimal locations of BESS units are identified using bus sensitivity factors (BSFs), while a weighted-sum scalarized single-objective optimization problem involving active power loss, voltage deviation, and system security margin is solved using salp swarm optimization (SSO). The main contribution of this work is the integrated BSF–SSO framework, which combines sensitivity-based placement with an efficient metaheuristic optimization technique. The proposed methodology is validated on the IEEE 30-bus test system. Results show that the optimally placed BESS units reduce active power losses by 31.7% relative to the base case, while it is reduced by 39.1% when using the post contingency of 25 MW as the baseline, voltage deviation by approximately 30.26%, and the congestion security index by 25.14%, thereby significantly alleviating transmission congestion. In addition, reactive power flow distribution and 24 h state-of-charge (SoC) dynamics are analyzed to provide a more comprehensive assessment of system performance. Compared with FACTS-based solutions, the proposed approach offers superior technical and economic benefits. A parametric sensitivity analysis further shows that SSO attains near-optimal convergence with as few as 15 agents, offering a favorable accuracy-versus-runtime trade-off for real-time DPS operation; the main results reported in this study, however, were generated using the more conservative configuration of n = 30 agents to maximize solution robustness.
Future power grids will be characterized by the extensive integration of distributed generation (DG) from renewable energy sources, introducing new challenges for system operation and management. This study addresses the underutilized reactive power potential of distributed generation (DG) in power systems through a comprehensive five-objective optimization framework. The framework simultaneously minimizes transmission losses, voltage deviations, generation costs, voltage stability indices, and DG reactive power requirements. Four metaheuristic algorithms (SPEA2, MOPSO, NSGA-II, and MOEA/D) are comparatively evaluated on a modified IEEE 57-bus system with solar DG capacity across five operational scenarios over 24 hours. Results show that coordinated DG reactive support reduces transmission losses compared to active-power-only operation. SPEA2 and MOPSO provide better Pareto front quality with higher hypervolume values and DG units can provide 20-40 MVAr of reactive support, representing significant economic value under emerging reactive power markets. These findings provide practical guidelines for system operators designing reactive power strategies in networks with high renewable penetration, showing that proper DG coordination improves technical performance while creating new revenue opportunities through ancillary services.
The prompt adoption of Electric Vehicles (EVs) offers substantial challenges to modern power distribution systems, incorporating enlarged power demand, voltage variability, and elevated energy losses. To solve such problems, this paper proposes an integrated optimization scheme for the simultaneous allocation of EV Charging Stations (EVCSs) and Distributed Generators (DGs) within a microgrid. Using the IEEE 33-bus radial distribution network as a test case, an objective problem is expressed for reducing active power loss and voltage variation while increasing the Voltage Stability Index (VSI). The proposed framework is solved using three metaheuristic algorithms: the novel Walrus Optimization Algorithm (WaOA), alongside the well-established Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). Simulations across various scenarios reveal that uncoordinated EVCS integration severely degrades system performance, whereas the optimal co-placement of EVCSs and DGs dramatically enhances operational efficiency. The WaOA consistently demonstrated superior performance, notably in a scenario with three optimally placed DGs, achieving a 53.79% reduction in active power losses (from 202.53 kW to 93.59 kW) and a 50.44% reduction in reactive power losses. In addition, it significantly enhanced the voltage profile, boosted the minimum VSI from 0.6956 to 0.92721, and reduced the lowest voltage deviation to 0.000128 p.u. Comparative study confirms that WaOA outperforms PSO and WOA in both convergence speed and solution quality. This study underscores the critical importance of coordinated planning for EVCS and DG integration, providing a robust strategy to enhance grid reliability and support the sustainable transition to electric mobility.
Ahmed I. Omar, Mahmoud M. Elbaz, Mahmoud N. Ali et al.· Scientific Reports· 0 citations
This study proposes a comprehensive multi-objective optimization framework for demand-side management of a hybrid microgrid comprising photovoltaic (PV) panels, wind turbines (WT), a battery energy storage system (BESS), a fuel cell (FC), and a grid connection. The framework simultaneously minimizes the Peak-to-Average Ratio (PAR) and total operating cost through dynamic load scheduling under real-time pricing (RTP). A renewable energy utilization strategy prioritizes clean energy dispatch, while an intelligent battery management scheme optimizes charging and discharging decisions according to renewable generation availability, load demand, and electricity price signals. To address the limitations of conventional weighted-sum optimization approaches, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to generate a diverse and well-distributed Pareto front without requiring predefined objective weights. The proposed framework is evaluated under three energy system configurations: (i) grid-only operation, (ii) grid-integrated renewable energy and battery storage, and (iii) grid-integrated renewable energy, battery storage, and fuel-cell support. The results demonstrate that hybrid renewable energy configurations significantly improve both economic and operational performance compared with conventional grid-dependent operation. The proposed framework generated multiple Pareto-optimal operating strategies with different trade-offs between operating cost and PAR. The minimum-cost solution achieved an operating cost of 131.73 Cents, while a representative compromise solution achieved 155.98 Cents with improved demand-side management performance. Comparative evaluation against NSGA-II, MOPSO, SPEA2, and the Weighted Sum Method reveals that NSGA-III consistently achieves superior Pareto-front quality, convergence characteristics, solution diversity, and robustness across 30 independent trials. The findings demonstrate the effectiveness of NSGA-III for multi-objective energy management and provide a scalable optimization framework for enhancing the economic efficiency, operational flexibility, and sustainability of future smart microgrid systems.
Mohd Bilal, Arshad Mohammad, Imdadullah et al.· Scientific Reports· 1 citation
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