Mahmoud Abbas El-Dabah

The rapid expansion of microgrids (MGs) is driven by the need to meet growing electricity demand sustainably through high penetration of renewable energy resources (RERs). However, the intermittency of RERs introduces significant operational uncertainty, making energy storage systems (ESSs) essential for reliable MG operation. The ESS planning problem is formulated as a constrained optimization model that incorporates power balance, battery capacity limits, and technical and operational constraints of MGs. Because it offers a viable solution, this study investigates the optimal method to allocate ESSs (batteries) using metaheuristic optimization techniques. In this work, the newly proposed Walrus Optimizer (WO) approach, which shows promising results, is used. A modified IEEE 33-bus distribution test system is employed as the benchmark. The performance of the WO is compared with five established metaheuristics: Detective Behavior Algorithm (DBA), Stochastic Social Learning Optimization (SSLO), Improved Grey Wolf Optimizer (I-GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). All of these algorithms aspire to reduce power losses, boost voltage profiles, and enhance overall efficiency, reliability, stability, and operational cost reduction. Simulation results show that the WO-based framework yields a 53.56% reduction in total system power losses. Moreover, it strengthens the weakest bus voltage to 0.9803 p.u., improves the voltage stability index (VSI) to 1.0201 p.u., and accomplish a cost reduction of 10.74%, outperforming other competitive algorithms. The results demonstrate the originality and effectiveness of the WO for optimal ESS integration in RER-dominated MGs, providing an encouraging tool for enhancing voltage stability, reducing losses, and supporting reliable, sustainable, and cost-effective MG operation.

Stability of power systems is crucial for maintaining a consistent and dependable supply of electricity to users during both normal and disrupted operating conditions. Networks may encounter blackouts when frequency instability prevents power generation and load demands from being matched. To get around this problem, the interconnected power system must be managed to achieve two main functions: damp out frequency deviation and maintain tie-line power at its predetermined level. As a component of Automatic Generation Control (AGC), the Load-Frequency Control (LFC) mechanism can accomplish these two goals. In this study, a secondary controller called the Linear Quadratic Gaussian (LQG) is introduced, and its weighting matrices are tuned using the Multi-Verse Optimizer (MVO) algorithm. Under the different levels of load disturbances along with penetration of solar (PV) modules or installation of electric vehicles (EVS), 3-island interconnected power system will be analyzed as a case study. According to simulation findings performed with MATLAB/SIMULINK software, the LQG controller can push both frequency and tie-line power oscillations to zero under any challenging operating scenario.

Power systems are currently undergoing a transition from centralized synchronous generators to decentralized non-synchronous generators that rely on renewable energy sources. This shift poses a challenge to system operators, as the high penetration levels of renewable energy introduce variability and changes in the physics of power systems. Load-frequency control is one of the biggest challenges faced by electrical grids, especially with increased wind energy penetration in recent years. The inertial controller is one of the methods used to support system frequency in variable-speed wind turbines. In this study, a proportional resonant (PR) controller was added to an inertial controller to achieve better frequency regulation by controlling the active power of the doubly fed induction generator (DFIG). First, the impact of the PR controller parameters on the frequency deviation, overshoot, settling time, and system stability was investigated to identify the optimal values that achieved the lowest frequency deviation while maintaining system stability. Second, the performance of the proposed method was compared that of the traditional method under different load perturbations. The results prove that improperly determining the proportional gain of the PR controller negatively affects system stability and frequency deviation. In addition, the results validate the hypothesis that the proposed method would provide fast frequency support for all the studied cases. The analysis and simulation of these scenarios were performed using the MATLAB/SIMULINK program.

Decentralized energy systems can significantly enhance living circumstances in remote areas without a grid connection. This paper formulates a hybrid renewable energy model for a rural Egyptian village. The system includes PV, wind, biomass generator, battery storage, and power converters. The reliability and efficiency of the system were conducted, with HOMER Pro attaining a loss of power supply probability (LPSP) of 0.0000% and a surplus energy percentage of 29.3827%. In comparison, CO and FVIM exhibited LPSPs of 1.4877% and 2.5724%, along with excess energy percentages of 5.9408% and 7.3600%, whilst SBOA demonstrated an LPSP of 2.6733% and an excess energy percentage of 7.3229%.SBOA reported a net present cost of $3,560,416.88, a cost of energy of $0.121 per kilowatt-hour, and annual greenhouse gas emissions of 688.122 tons. CO and FVIM achieved NPCs of $3,572,813.22 and $3,587,924.56, COEs of $0.123/kWh and $0.124/kWh, and emissions of 690.315 and 692.874 tons/year, respectively. HOMER Pro conducted $3,671,711 NPC, $0.1/kWh COE, and 11.016 tons/year GHG emissions, demonstrating the advantages of biomass-based systems for sustainable rural development.

Enhancement of Distribution System using Improved Real-Coded Genetic Algorithm

High levels of series compensation in transmission lines, particularly those integrated with wind farms, exacerbate the risk of sub-synchronous oscillation (SSO), a phenomenon detrimental to system stability. Gate-controlled series capacitor (GCSC) is one of the used means to mitigate the SSO and enhance power transfer capability. In the previous studies, the combined proportional-integral controller with supplementary damping controller was an effective approach for controlling the GCSC and mitigating the SSO. In this paper, the ability to cascade a proportional-resonant controller with a PI controller for damping the SSO is investigated. The proposed method’s performance is evaluated through extensive time-domain simulations using the modified IEEE First benchmark model. The simulations incorporate a diverse range of wind speeds, compensation levels, as well as sub-synchronous control interactions. Furthermore, a comparative analysis with recent SSO damping techniques is conducted to assess the strategy’s performance under varying operating conditions. The comparative results overwhelmingly demonstrate the superiority and effectiveness of the proposed strategy in alleviating SSO, especially at low wind speeds and high compensation levels.

Researchers tackled the challenge of finding the right parameters for a transformer-equivalent circuit. They achieved this by minimizing the difference between actual measurements (currents, powers, secondary voltage) during a transformer load test and the values predicted by the model using different parameter settings. This process considers limitations on what values the parameters can have. This research introduces the application of a new and effective optimization algorithm called the dandelion algorithm (DA) to determine these transformer parameters. Information from real-time tests (single- and three-phase transformers) is fed into a computer program that uses the DA to find the best parameters by minimizing the aforementioned difference. Tests confirm that the DA is a reliable and accurate tool for estimating the transformer parameters. It achieves excellent performance and stability in finding the optimal values that precisely reflect how a transformer behaves. The DA achieved a significantly lower best fitness function value of 0.0136101 for the three-phase transformer case, while for the single-phase case it reached 0.601764. This indicates a substantially improved match between estimated and measured electrical parameters for the three-phase transformer model. By comparing DA with six competitive algorithms to prove how well each method minimized the difference between measurements and predictions, it could be shown that the DA outperforms these other techniques.