2026· Energy Engineering· 0 citations· 19 references
Abstract
: 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.
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
With the large-scale integration of Electric Vehicles (EVs) into distribution systems, the spatiotemporal uncertainty of charging loads and the interplay between user charging behavior and network operational constraints present new challenges to the safe and economical operation of the power system. To address the insufficient coordination among flexibility characterization, distributed optimization, and user-side responses, this paper proposes a closed-loop collaborative dispatch strategy. The strategy integrates flexibility aggregation, endogenous dynamic pricing, and user charging-station selection behavior. Firstly, a three-tier collaborative architecture comprising the Distribution System Operator (DSO), Electric Vehicle Aggregators (EVAs), and EV users is established, with rolling updates implemented using Model Predictive Control (MPC). A flexible aggregation model is developed based on set operations of vehicle-level constraints, dynamically calculating power boundaries and energy feasibility domains. Furthermore, a distributed coordinated optimization model between the DSO and multiple EVAs is established and solved via the Alternating Direction Method of Multipliers (ADMM) under privacy-preserving conditions. By analyzing the correlation between ADMM dual variables and the marginal value of network constraints, a Distribution Locational Marginal Pricing (DLMP) -inspired dynamic price signal—endogenous to the optimization—is constructed to guide spatial reallocation of charging loads. Joint simulations based on an IEEE 33-node distribution network and the Sioux Falls transport network demonstrate that the proposed strategy reduces 24 h network losses from 9.05 MWh (uncoordinated) to 8.41 MWh, lowers user total costs from 22,200 yuan to 7100 yuan, and eliminates voltage limit violations (duration reduced from 1.50 h to 0), while exhibiting good distributed solution performance and closed-loop control capability.
Si-Zu Hou, Yao Sang, Xuan Zhao et al.· Energies· 0 citations
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
Next-generation green power direct-connection data centers face the dual challenges of the random nature of renewable energy output and short-term load fluctuations, which can cause significant power fluctuations at the grid connection point. This not only places enormous strain on grid operations but also poses security risks to the data centers themselves. Traditional scheduling paradigms often treat data centers as rigid loads, overlooking the inherent flexibility of computing tasks. In light of this, this paper proposes a coordinated optimization strategy for data center microgrids. First, a thermodynamic model incorporating temperature coupling is established to quantify the interactive energy consumption between IT systems and cooling systems, then a mixed-integer linear programming (MILP) model is constructed with the objective of minimizing operational costs, comprehensively considering grid power purchases, energy storage degradation, and penalty terms for workload shifting under time-of-use pricing. Simulation results indicate that, compared to traditional rigid operation modes, the proposed strategy can effectively shift peak loads, significantly reduce the curtailment rate of photovoltaic power, and demonstrate good economic performance.
Yucheng Zheng, G. I. Rashed, Xin-Fa Jiang et al.· International Conference on...· 0 citations
The rapid growth of distributed renewable generation and electric vehicles has increased the demand for flexible peak-shaving resources. Battery swapping stations (BSSs), which centrally manage standardized batteries under the battery-as-a-service model, can provide station-to-grid (S2G) services when aggregated by virtual power plants (VPPs). However, S2G adoption is influenced by contract design, market returns, subsidies, battery degradation, and heterogeneous consumer attitudes. This study develops a complex-network evolutionary diffusion model for VPP–BSS cooperation. The framework integrates a VPP profit-accounting module, a segmented Hotelling demand model, and an evolutionary game on a Newman–Watts small-world network. BSS strategies are updated through a partial asynchronous Fermi rule to reflect bounded rationality and investment inertia. Numerical simulations examine contract parameters, subsidy policies, consumer structures, exogenous variables, and network characteristics. The results show that S2G adoption follows an S-shaped trajectory but does not automatically reach full penetration. Successful diffusion requires a feasible combination of electricity prices, revenue sharing, settlement mechanisms, subsidies, consumer acceptance, and available battery capacity. The findings also reveal a trade-off between promoting BSS participation and maintaining VPP profitability, while robustness tests confirm the stability of the main conclusions.
Integrating distributed energy resources (DERs) via Virtual Power Plants (VPPs) faces challenges like renewable intermittency, communication scheduling uncertainties, and high data collection costs. While existing studies often overlook practical implementation efficiency, this paper proposes a VPP scheduling framework integrated with communication optimization. First, a communication-scheduling model is established to quantify the impact of network uncertainties on revenue. Second, an equipment pre-allocation strategy based on historical data clustering is presented to lower trial-and-error costs and algorithm complexity. Finally, a global optimization algorithm achieves time-segmented collaborative optimization of equipment access, reducing network switching frequency while balancing packet loss, transmission delay, and operational revenue. Simulation results demonstrate that the proposed strategy reduces VPP scheduling revenue loss by approximately 23.6% compared with the traditional greedy algorithm. Furthermore, when evaluated against classic metaheuristic baseline algorithms such as PSO under identical forecasting conditions, Network-Aware FA (NAFA) effectively escapes local optima and achieves the lowest revenue loss, strongly validating the economic efficiency, algorithmic superiority and scheduling reliability of the proposed framework.
