Search

Multi-Dimensional Optimal Power Flow (MDOPF) is a fundamental task in power systems engineering aimed at optimizing the operation of electrical networks while considering various constraints such as power generation, transmission, and distribution. The mathematical model of MDOPF involves formulating it as a non-linear, non-convex optimization problem aimed at minimizing specific objective functions while adhering to equality and inequality constraints. The objective function typically includes terms representing the Fuel Cost (FC), Entire Network Losses (ENL), and Entire Emissions (EE), while the constraints encompass power balance equations, generator operating limits, and network constraints, such as line flow limits and voltage limits. This paper presents an innovative Improved Kepler Optimization Technique (IKOT) for solving MDOPF problems. The IKOT builds upon the traditional KOT and incorporates enhanced local escaping mechanisms to overcome local optima traps and improve convergence speed. The mathematical model of the IKOT algorithm involves defining a population of candidate solutions (individuals) represented as vectors in a high-dimensional search space. Each individual corresponds to a potential solution to the MDOPF problem, and the algorithm iteratively refines these solutions to converge towards the optimal solution. The key innovation of the IKOT lies in its enhanced local escaping mechanisms, which enable it to explore the search space more effectively and avoid premature convergence to suboptimal solutions. Experimental results on standard IEEE test systems demonstrate the effectiveness of the proposed IKOT in solving MDOPF problems. The proposed IKOT obtained the FC, EE, and ENL of USD 41,666.963/h, 1.039 Ton/h, and 9.087 MW, respectively, in comparison with the KOT, which achieved USD 41,677.349/h, 1.048 Ton/h, 11.277 MW, respectively. In comparison to the base scenario, the IKOT achieved a reduction percentage of 18.85%, 58.89%, and 64.13%, respectively, for the three scenarios. The IKOT consistently outperformed the original KOT and other state-of-the-art metaheuristic optimization algorithms in terms of solution quality, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]

Parameter estimation for solar photovoltaic (PV) models is a challenging issue due to the complex nonlinear multivariable of the current‐voltage and power‐voltage characteristics. In this article, an improved heap‐based algorithm (IHBA) is proposed to improve the performance of a recently published algorithm called heap‐based algorithm (HBA). The HBA's performance is enhanced by applying an effective exploitation feature to boost the searching around the leader position with the goal of enhancing its global search capabilities and avoiding becoming trapped in a local optimum. The proposed IHBA and the standard HBA are developed considering the practical limits of the electrical parameters of PV models to minimize the root mean square error (RMSE) between the experimental and simulated results. The numerical analyses for the PVM‐752GaAs PV module including single‐diode model (SDM), double‐diode model (DDM) and triple‐diode model (TDM) are investigated to estimate five, seven, and nine electrical parameters of these models, respectively. Besides, different recent optimization techniques are simulated with fair comparisons, which are forensic‐based investigation (FBI), equilibrium optimizer (EO), Jellyfish Search (JFS), HBA, Marine Predator Algorithm (MPA) and Enhanced MPA (EMPA), and are compared with the proposed IHBA. Several separate runs are illustrated for the proposed IHBA in comparison with others, whereas the whisker box plot and T‐tests are activated to evaluate their effectiveness metrics. The simulation results derive higher superiority of the proposed IHBA with the minimum RMSE objective and standard deviation of (0.000228 and 3.7 × 106), (0.000184 and 1.93 × 105) and (0.000017 and 2.11 × 105) for SDM, DDM, and TDM, respectively, with respect to the standard HBA, other recent and reported optimization techniques. Additionally, the P‐indicator is applied in this study to illustrate the evidence for the alternative hypothesis. Small values of the P‐indicator for the proposed IHBA are obtained compared to the standard HBA and other recent optimization techniques, where IHBA achieves p‐value of 1.0336 × 1017, 4.037 7× 1011 and 2.1490 × 1010 for SDM, DDM and TDM, respectively. The H‐value is always one which elucidates that the hypothesis probability is not more than 5% for all algorithms for SDM, DDM and TDM. Moreover, the proposed IHBA demonstrates higher efficiency as it provides the first ranks in the confidence interval values compared with other algorithms. Furthermore, the proposed IHBA is significantly employed for the SQ_150 PV module considering the TDM with diverse solar irradiance and temperatures, where significant closeness between the emulated and experimental P–V curves is evidently demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes an improved marine predators' optimization algorithm (IMPOA) for solving the combined heat and power (CHP) economic dispatch problem. This problem provides optimal scheduling of heat and power generation supplies and pursues to minimize the overall fuel cost (OFC) supply of cogeneration units considering their operational constraints. Four test systems are considered to check the performance of both the MPOA and the proposed IMPOA. The first test system is small sized which involve 5-unit, whereas the second system is medium sized which contains 48-unit system. The third and fourth test systems are large sized systems. The third test system includes 84-unit, which are divided into 40 power-only units, 20 heat only units, and 24 CHP units. The fourth test system includes 96-unit, which are divided into 52 power-only units, 20 heat-only units, and 24 CHP units. The obtained results clearly show the capability, efficiency, and feasibility of the IMPOA with respect to other relevant optimization techniques for optimal solutions of small, medium and large-scale systems. Additionally, the convergence characteristics of the proposed IMPOA are stable and the arrival of the optimal solution is faster than the conventional MPOA. [ABSTRACT FROM AUTHOR]

The optimal power flow is considered as a crucial tool in the power systems' operation and planning. To demonstrate, it aims at minimizing the operational costs of energy production and transmission by adjusting control variables with maintaining economic, operational, and environmental constraints. This article proposes and scrutinizes an Improved Heap-based Optimization Algorithm as a novel technique that successfully enhances the performance of a recently algorithm, namely Heap-based Optimization algorithm to address the optimal power flow problem. In the improved optimizer, an effective exploitation feature is emerged with the conventional version to improve its performance by enhancing the searching around the leader position. This enhancement can avoid being trapped in a local optimum and increase its global search capabilities. For the sake of practicality, the optimizers are developed with diverse objectives of the optimal power flow problem with minimization of fuel cost, emission amount, and transmission power losses with additional restrictions in real power systems that include valve-point effect and security constraints. A proposed multi-objective, improved heap optimization algorithm is investigated to solve multiobjective cases studied. The proposed multi-objective is developed based on the Pareto concept. Three standard systems: IEEE 57-bus system, a large scale 118-bus system, and a practical System are utilized to reveal the suitability and performance of the proposed technique in solving the optimal power flow problem. To illustrate the effectiveness of the proposed optimizer in handling non-convex and diverse scale optimization problems, a comparative analysis has been illustrated with those in the literature. • An Improved Heap-based Optimization Algorithm addresses the OPF problem. • A proposed multi-objective improved heap optimization algorithm is investigated. • Three standard systems are utilized to reveal the suitability and performance of the proposed. • The effectiveness of the proposed optimizer has been illustrated compared with others. • Significant reductions, in the technical economic point of views for single and multiobjective frameworks. [ABSTRACT FROM AUTHOR]