Compensation Admittance Load Flow: A Computational Tool for the Sustainability of the Electrical Grid

Compensation Admittance Load Flow (CALF) is a power flow analysis method that was developed to enhance the sustainability of the power grid. This method has been widely used in power system planning and operation, as it provides an accurate representation of the power system and its behavior under different operating conditions. By providing a more accurate representation of the power system, it can help identify potential problems and improve the overall performance of the grid. This paper proposes a new approach to the load flow (LF) problem by introducing a linear and iterative method of solving LF equations. The aim is to obtain fast results for calculating nodal voltages while maintaining high accuracy. The proposed CALF method is fast and accurate and is suitable for the iterative calculations required by large energy utilities to solve the problem of quantifying the maximum grid acceptance capacity of new energy from renewable sources and new loads, known as hosting capacity (HC) and load capacity (LC), respectively. Speed and accuracy are achieved through a properly designed linearization of the optimization problem, which introduces the concept of compensation admittance at the node. The proposed method was validated by comparing the results obtained with those coming from state-of-the-art methods

Softcomputing for the Identification of the Jiles-Atherton Model Parameters

This paper presents a Jiles-Atherton hysteresis model identification method based on a partnership of heuristic techniques and fuzzy logic (softcomputing). Two different soft-computing approaches are proposed and analyzed: a partnership between genetic algorithms (GAs) and fuzzy logic (FL) and one between GAs and simulated annealing (SA). Validations of both symmetric (saturated or minor loops) and asymmetric loops are described

Comparative Analysis between Modern Heuristics and Hybrid Algorithms

Purpose – The purpose of the present paper is to show a comparative analysis of classical and modern heuristics such as genetic algorithms, simulated annealing, particle swarm optimizationand bacterial chemotaxis, when they are applied to electrical engineering problems. Design/methodology/approach – Hybrid algorithms (HAs) obtained by a synergy between the previous listed heuristics, with the eventual addiction of the Tabu Search, have also been compared with the single heuristic performances. Findings – Empirically, a different sensitivity for initial values has been observed by changing type of heuristics. The comparative analysis has then been performed for two kind of problems depending on the dimension of the solution space to be inspected. All the proposed comparative analyses are referred to two corresponding different cases: Preisach hysteresis model identification (high dimension solution space) and load-flow optimization in power systems (low dimension solution space). Originality/value – The originality of the paper is to verify the performances of classical, modern and hybrid heuristics for electrical engineering applications by varying the heuristic typology and by varying the typology of the optimization problem. An original procedure to design a HA is also presented

Genetic Algorithms and Neural Networks Generalizing the Jiles-Atherton Model of Static Hysteresis for Dynamic Loops

This paper presents a method based on genetic algorithms and neural networks suitable for finding the five parameters of the Jiles-Atherton (JA) model for generalization to dynamic hysteresis loops. The aim is to obtain an equivalent static model for dynamic loops by updating its parameters varying the frequency of the imposed magnetic field H(t). Validations of the present approach compared to other numerical approaches, based on adding frequency-dependent losses to the static model, and versus experimental tests will be shown

Modern Techniques for the Optimal Power Flow Problem: State of the Art

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the state of the art (according to the authors, the most pertinent studies). The presented optimization techniques are grouped according to their sources of inspiration, including human-inspired algorithms (harmony search, teaching learning-based optimization, tabu search, etc.), evolutionary-inspired algorithms (differential evolution, genetic algorithms, etc.), and physics-inspired methods (particle swarm optimization, cuckoo search algorithm, firefly algorithm, ant colony optimization algorithm, etc.)