Probabilistic optimal power flow analysis for optimal deployment of Unified power flow controller in renewable integrated hydro power systems using marine predators algorithm

AI-augmented residential PV system with battery storage for enhanced energy harvesting

Fast security-constrained optimal power flow under topology variations via heterogeneous graph neural network learning

A day ahead scheduling model of a smart hydrogen-based microgrid taking into account PV production and electrical load demand forecasting errors

The increasing penetration of photovoltaic (PV) generation in low-voltage distribution networks presents operational challenges, with overvoltages being among the most critical. This study introduces a tool based on Unbalanced Optimal Power Flow (UBOPF) to assess cost-effective local inverter control strategies specifically aimed at mitigating overvoltage issues. Two approaches are examined: dynamic active power curtailment and combined active and reactive power control. These strategies are tested on a residential low-voltage network with high PV penetration, where the UBOPF model with voltage-magnitude constraints was implemented in Julia using the JuMP optimization package. The results demonstrate that both methods are effective in maintaining voltage levels within regulatory limits, with the latter leading to lower PV curtailment. The analysis highlights the need to consider these control actions as ancillary services to the grid, which should be properly compensated given their effect on generator revenues.

Real-time ANN-based multi-indicator voltage stability assessment for mesh distribution systems with high SPV-EV penetration

Wastewater treatment plants (WWTPs) are major energy consumers, accounting for 3–4 % of total energy consumption in the United States. Aeration tanks are particularly energy-intensive, constituting 45–75 % of the energy footprint. This study introduces a groundbreaking approach by merging Photovoltaic (PV) technology with WWTP infrastructure. It provides a detailed model that simulates and validates the integration of PV systems within WWTPs using real-world data, aiming to enhance system efficiency and reduce operational expenses. The research modeled this integration in WWTPs across four scenarios, focusing on improving efficiency and reducing operational costs. In the first scenario, assessing the baseline power flow without PV systems, a peak summer power demand of approximately 1600 kW was observed, emphasizing the plant's significant seasonal energy consumption variation. The second scenario, examining the power system's response to a sudden generation loss, revealed a spike in voltage to 1.10 p.u. and frequency fluctuations up to 80 Hz during a short circuit, highlighting the system's vulnerability to disturbances. The third scenario, introducing PV systems, showed a balanced voltage level across all busbars, approximately one p.u., indicating improved power distribution and efficiency with PV system integration. The fourth scenario demonstrated that voltage levels remained stable at about 1.02 p.u during a fault event with PV integration. Furthermore, the frequency was maintained at 60 Hz, showcasing the ability of the PV system to enhance grid resilience and operational reliability.

Optimal UPFC deployment for voltage stability enhancement: Application of weighted voltage stability indices and hybrid optimization

Optimizing static interphase power controllers (SIPC) placement: A comprehensive multi-objective strategy for enhanced performance of the electric transmission networks

Survey on power flow calculation in distribution networks: Intelligent algorithms driven by data-physics fusion

Resilience enhancement for distribution networks: Co-deployment of mobile energy storage systems and EV charging stations in strong wind events

A holomorphic embedding power flow algorithm for cost-based droop microgrids

Modern power systems face severe dynamic stability and operational scheduling challenges due to the rapid penetration of renewable energy sources (RES). Aiming at the problems of insufficient dynamic modeling accuracy, low efficiency of stability analysis, and the difficulty in balancing operational safety and economy under high RES integration, this study proposes an integrated theoretical and methodological framework for large-scale power system dynamic analysis and optimal scheduling. First, a sixth-order nonlinear differential equation model is established by integrating the electromechanical transients of synchronous generators, excitation regulation dynamics, load characteristics, and network power flow balance, which makes up for the deficiency of the traditional second-order swing equation in ignoring multi-subsystem coupling. Based on Taylor expansion at the equilibrium point, a linearized state-space representation is derived, and the analytical expressions of three key transient stability indices (the maximum rate of change of power angle, steady-state power angle deviation, and power angle oscillation amplitude) are obtained, which quantitatively reveal the influence of key parameters such as excitation gain and synchronous torque coefficient on system stability. Second, an improved ADMM-based distributed optimization algorithm with variable splitting and asynchronous iteration mechanisms is developed, which embeds dynamic security constraints into a multi-timescale scheduling framework. A two-layer control structure combining ADMM distributed global optimization and MPC centralized local control is constructed to solve the inefficiency of traditional centralized algorithms in large-scale system scheduling. Finally, the effectiveness of the proposed model and algorithm is verified on the IEEE 10-machine 39-bus system and extended to the 100-machine 300-bus system for scalability analysis.

Difficult Optimization problems can be efficiently solved through nature-inspired optimization algorithms, which remain problem-independent and are computational models at a conceptual level. In this work, a Jaya algorithm will be offered for tackling the Optimal Power Flow (OPF) problem. The basic idea of the Jaya algorithm for optimization problems is that the candidate solutions should move closer to the global optimum solution without converging at suboptimal solutions. Like other nature-inspired optimization techniques, the Jaya method is parameter-free and does not require any algorithm-specific control factors, such as learning or mutant parameters. The parameter freedom of an optimization algorithm not only improves its simplicity, but it also overcomes the challenge of adjusting optimization algorithm parameters, which affects performance as indicated in the literature, and can be expensive for some computations. The OPF problem aims to optimize the different objective functions using the control variables of the power systems. These include improving the voltage stability, decreasing the cost and emissions, and reducing power loss. For comparison purposes, the Jaya optimization technique for the OPF problem has been tested on the IEEE 30-Bus, 57-Bus and IEEE 118 Bus systems. The results were compared to previously reported outcomes of optimization approaches. The result of the computational study reveals the effectiveness of the Jaya optimization technique when compared with the other reported techniques. For instance, when the Jaya algorithm was applied to the IEEE 30-bus system, the fuel cost was reduced by approximately 11.31% compared to the initial operating condition.

ABSTRACT In order to standardize the conversion between Chinese and English in the process of wind power and photovoltaic power generation and improve the efficiency of microgrid management, this paper proposes a Chinese–English translation method across microgrids. Firstly, the current, power flow and voltage indicators in the voltage devices are classified by the dichotomy method, and the initial microgrid equipment management set is constructed by mapping. Finally, the whale search method is used to match the device name, and the matching results are deeply mined to improve the management effect of voltage devices across the microgrid. The results show that the translation model can effectively identify the name of voltage equipment, with a recognition rate of 90%, and the effect of Chinese–English conversion on the management of cross-microgrid equipment is improved, with an improvement of 30%. At the same time, the Chinese–English translation method can judge the wrong equipment across microgrids in multiple dimensions, with a judgment rate of 85%, which can meet the stable needs of microgrids. Therefore, the Sino-British conversion effect can optimize the microgrid, improve the management level of voltage equipment, and expand and deepen the development of new energy.

This study explores sustainable energy management approaches for a smart distribution network that combines multiple infrastructures, such as electric vehicle charging stations, hydrogen refueling facilities for fuel cell vehicles, and renewable energy systems integrated with hydrogen storage. These components are managed in a coordinated manner to satisfy both operational requirements and security criteria defined by the distribution system operator. A key feature of the hydrogen storage unit is its dual functionality, as it not only stores electrical energy but also supplies hydrogen to end users. The primary objective is to reduce overall energy losses within the distribution system. To accomplish this, the research considers several important factors, including AC power flow modeling, grid voltage operational and security constraints, system flexibility, environmental restrictions, operational characteristics of electric vehicles charging and hydrogen stations, and performance models of renewable energy systems coupled with hydrogen storage. Furthermore, the proposed framework accounts for uncertainties related to load demand, renewable generation, and variations in the number of electric vehicles by applying a scenario-based stochastic optimization technique. The findings demonstrate significant enhancements in both system performance and security. In particular, the proposed method decreases voltage deviations, power losses, and peak load capacity by approximately 24.4%, 32.8%, and 38.3%, respectively, compared to conventional load flow analyses. Moreover, voltage security within the network is improved by nearly 10.2%, confirming the efficiency of the proposed integrated energy management strategy.

Abstract The efficiency and selectivity of gas-phase synthesis of few-layer graphene (FLG) are governed by the balance between carbon growth species and competing reaction pathways in microwave plasma environment. Acetylene decomposition in an atmospheric-pressure dual-channel microwave plasma torch in Ar was used to systematically examine how hydrogen, nitrogen, and oxygen admixtures modify these pathways. Under highly stable plasma formed by dual-channel configuration, the structural transition of solid state material from disordered carbon nanoparticles to FLG could be observed for a wide range of C 2 H 2 (13 sccm)/ (H 2 , O 2 , N 2 - 6.5 to 26 sccm) conditions in dependence on delivered microwave power, 130 to 350 W. Selective synthesis of FLG was achieved for all types of gas admixture, as determined by electron microscopy and Raman spectroscopy, and required power was decreasing with increasing flow rate of reactive gas. Oxygen strongly suppressed solid carbon formation, whereas hydrogen and nitrogen preserve carbon availability, however, using nitrogen required substantially higher power to obtain FLG. The yield of the synthesis increased monotonically with increasing delivered microwave power, up to 68 % at 350 W, and slightly decreased with decreasing C/H(N) ratio. Experiments using reduced flow rate of pure C 2 H 2 , 6.5 sccm, demonstrated that selective FLG synthesis can be achieved even without reactive gas admixtures at high power of 350 W. Complementary experiments using methane, 13-38 sccm, achieved best selectivity and lowest structural disorder of FLG at low flow rate of CH 4 in the whole range of delivered microwave power. This work defines a unified process window for selective high yield FLG synthesis governed by the interplay between delivered microwave power and the C 2 H 2 /reactive gas ratio, identifying carbon availability as necessary but not sufficient controlling parameter. Selective FLG synthesis with pure C 2 H 2 , and analogous power–flow dependencies observed for CH 4 , confirm the generality of the process framework.

The proliferation of vehicle-to-grid (V2G) charging stations in distribution networks introduces both voltage regulation challenges and untapped reactive power resources. This paper proposes a reactive–active power coordination control strategy for grid-forming (GFM) V2G charging stations to achieve voltage regulation in radial distribution networks. First, a voltage–reactive power sensitivity matrix is analytically derived from the linearized DistFlow equations, quantifying the voltage influence of each V2G station. The sensitivity matrix is computed from the network topology and line parameters, and its accuracy under varying operating conditions is validated against nonlinear power flow solutions. Second, a dynamic residual reactive capacity model exploits the inverter apparent power margin without curtailing active power, and a sensitivity-weighted proportional allocation distributes the reactive power references among stations. Third, a two-timescale hierarchical control architecture is designed: the upper layer solves a quadratic programming problem every 60 s to determine optimal set-points, while the lower layer employs GFM droop control with a 1 ms response to track references and provide inertia support. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method reduces the maximum voltage deviation by 62% compared with active-power-only control, while maintaining a frequency nadir of 49.73 Hz, confirming negligible frequency performance degradation. Extended simulations covering a 24 h period with stochastic EV arrival and departure patterns as well as varying load conditions further confirm the robustness of the proposed strategy.

This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster.

As electric vehicle systems grow, so does the need for more advanced energy control tools. This paper suggests a new DC/DC power link that works both ways for electric cars with more than one battery. This makes it easier for energy to move from the batteries to the goods inside the vehicle. The suggested system makes it easier to recover, charge, and release renewable energy in both directions. By keeping an eye on voltage, temperature, and charge levels, the complex control system improves battery performance and power flow. This makes sure that each battery works well with the others, uses less power, and is more reliable and efficient. This method gives us a flexible and scalable way to make fast and efficient electric cars (EVs).