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The rapid growth of the transmission networks has brought more uncertainties and new requirements in the transmission expansion planning (TEP) to the planners. The existing methods of solving TEP problem have a drawback since the DC load flow and the relaxed load flow models have been utilized to solve TEP problem. In this work, the TEP problem is solved based on mixed integer nonlinear non-convex programming model. A meta-heuristic algorithm by the means of differential evolution algorithm (DEA) is employed as an optimisation tool. An AC load flow model is used in solving the TEP problem, where accurate and realistic results can be obtained. Furthermore, the work considers the constraints checking and system violation such as real and power generation limits, possible number of lines added and bus voltage limits. The proposed technique is tested on Garver's 6 bus system and IEEE 24 bus system and has shown high capability in considering the active and reactive power in the same manner and solving the TEP problem. The method produced improved results for the test systems. In terms of minimising the cost and the solution quality, the proposed method obtained good and challenging results comparing to the previous works.

Load flow analysis is an essential tool for the reliable planning and operation of interconnected power systems. The constant increase in power demand, apart from the increased intermittency in power generation due to renewable energy sources without proportionate augmentation in transmission system infrastructure, has driven the power systems to function nearer to their limits. Though the power flow (PF) solution may exist in such circumstances, the traditional Newton–Raphson based PF techniques may fail due to computational difficulties owing to the singularity of the Jacobian Matrix during critical conditions and faces difficulties in solving ill-conditioned systems. To address these problems and to assess the impact of large-scale photovoltaic generator (PVG) integration in power systems on power flow studies, a derivative-free quasi-oppositional heap-based optimization (HBO) (QOHBO) technique is proposed in the present paper. In the proposed approach, the concept of quasi-oppositional learning is applied to HBO to enhance the convergence speed. The efficacy and effectiveness of the proposed QOHBO-PF technique are verified by applying it to the standard IEEE and ill-conditioned systems. The robustness of the algorithm is validated under the maximum loadability limits and high R/X ratios, comparing the results with other well-known methods suggested in the literature. The results thus obtained show that the proposed QOHBO-PF technique has less computation time, further enhancement of reliability in the presence of PVG, and has the ability to provide multiple PF solutions that can be utilized for voltage stability analysis.

A probabilistic load flow (PLF) method using Copula and improved Latin hypercube sampling is proposed. The stochastic dependence between input random variables is considered. Copula theory is adopted to establish the probability distribution of correlated input random variables. Based on discrete data, an improved Latin hypercube sampling is proposed. The accuracy of probability distribution of correlated input random variables established by Copula theory is evaluated by adopting the power output of wind farms located at New Jersey. The performance of the proposed PLF method is investigated using IEEE 14-bus and IEEE 118-bus test systems.

Modern power systems must deal with a greater degree of uncertainty in power flow calculation due to variations in load and generation introduced by new technologies. This scenario poses new challenges to power system operators which require new tools for an accurate assessment of the system state. This paper presents an interval load flow (ILF) approach for dealing with uncertainty in power system analysis. A probabilistic load flow (PLF), based on Monte Carlo Simulation (MCS), was also implemented for comparative purposes. The ILF and PLF are used to estimate the network states. Both methods were implemented in Python® using the IEEE 34-bus, IEEE 69-bus and 192-bus Brazilian distribution system. The results with the proposed ILF on the aforementioned benchmark test systems proved to be compatible with that of the MCS, evidencing the robustness and applicability of the proposed approach.

The ongoing study aims to establish a direct probabilistic load flow (PLF) for the analysis of wind integrated radial distribution systems. Because of the stochastic output power of wind farms, it is very important to find a method which can reduce the calculation burden significantly, without having compromising the accuracy of results. In the proposed approach, a K-means based data clustering algorithm is employed, in which all data points are bunched into desired clusters. In this regard, probable agents are selected to run the PLF algorithm. The clustered data are used to employ the Monte Carlo simulation (MCS) method. In this paper, the analysis is performed in terms of simulation run-time. Also, this research follows a two-fold aim. In the first stage, the superiority of data clustering-based MCS over the unsorted data MCS is demonstrated properly. Moreover, the impact of data clustering-based MCS and unsorted data-based MCS is investigated using an indirect probabilistic forward/backward sweep (PFBS) method. Thus, in the second stage, the simulation run-time comparison is carried out rigorously between the proposed direct PLF and the indirect PFBS method to examine the computational burden effects. Simulation results are exhibited on the IEEE 33-bus and 69-bus radial distribution systems.

Probabilistic Load Flow (PLF) calculations are important tools for analysis of the steady-state operation of electrical energy networks, especially for electrical energy distribution networks with large-scale distributed generators (DGs) and electric vehicle (EV) integration. Traditional PLF has used the Cumulant Method (CM) and Latin Hypercube Sampling (LHS) method. However, traditional CM requires that each input variable be independent of one another, and the Cholesky decomposition adopted by the traditional LHS has limitations in that it is only applicable for positive definite matrices. To solve these problems, taking into account the Q-MCS theory of LHS, this paper proposes a CM PLF algorithm based on improved LHS (ILHS-CM). The cumulants of the input variables are obtained based on sampling results. The probability distribution of the output variables is obtained according to the Gram-Charlier series expansion. Moreover, DGs, such as wind turbines, photovoltaic (PV) arrays, and EVs integrated into the electrical energy distribution networks are comprehensively considered, including correlation analysis and dynamic load flow analysis for EV-coordinated charging. Four scenarios are analyzed based on the IEEE-30 node network, including with/without DGs and EVs, error analysis and performance evaluation of the proposed algorithm, correlation analysis of DGs and EVs, and dynamic load flow analysis with EV integration. The results presented in this paper demonstrate the effectiveness, accuracy, and practicability of the proposed algorithm.

This study presents a novel adaptive management strategy for power flow in standalone hybrid power systems. The method introduces an on-line energy management by using a hierarchical controller between three energy sources: photovoltaic (PV) panels, battery storage and proton exchange membrane fuel cell. The proposed method includes a feed-forward, back-propagation neural network controller in the first layer, which is added in order to achieve the maximum power point for the different types of PV panels. In the second layer, a fuzzy logic controller has been developed to optimise performance by distributing the power inside the hybrid system and by managing the charge and discharge of the current flow. Finally, and in the third layer, local controllers are presented to regulate the fuel cell/battery set points in order to reach to best performance. Moreover, perturb and observe algorithm with two different controller techniques – the linear proportional-integral (PI) and the non-linear passivity-based controller – are provided for a comparison with the proposed maximum power point tracking controller system. The comparison revealed the robustness of the proposed PV control system for solar irradiance and load resistance changes. Real-time measured parameters and practical load profiles are used as inputs for the developed management system. The proposed model and its control strategy offer a proper tool for optimising the hybrid power system performance, such as the one used in smart-house applications.

The rapid expansion of distributed generation leads to the integration of an increasing number of energy generation sources. However, integrating these sources into electrical distribution networks presents specific challenges to ensure that the distribution networks can effectively accommodate the associated distributed energy and power. Thus, it is crucial to evaluate the electrical effects of power along the conductors, components, and loads. Power-flow analysis is a well-established numerical methodology for assessing parameters and quantities within power systems during steady-state operation. The University of São Paulo’s Cidade Universitária “Armando de Salles Oliveira” (CUASO) campus in São Paulo, Brazil, features an underground power distribution system. The Institute of Energy and Environment (IEE) leads the integration of several distributed generation (DG) sources, including a biogas plant, photovoltaic installations, and a small wind turbine, into one of the CUASO’s feeders, referred to as “USP-105”. Load-flow simulations were conducted using the PowerWorldTM Simulator v.23, considering the interconnection of these sources. This dataset provides comprehensive information and computational files utilized in the simulations. It serves as a valuable resource for reanalysis, didactic purposes, and the dissemination of technical insights related to DG implementation.

Penetration of Renewable distributed generation (RDG) units has increased in recent years due to increased environmental concerns and depleting fossil fuels. Deployment of RDG units will offer technical benefits such as loss minimization, bus voltage profile improvement, line loading reduction. Optimal allocation of RDG units is a challenging task as the generation is time-varying and uncertain in nature. In this work, optimal RDG allocation problem is formulated by considering time-varying and uncertain nature of generation and load demand using a Point estimate method (PEM)-based load flow with an objective to simultaneously minimize losses, improve voltage profile and reduce line loading. An efficient pareto front-based Multi-objective Backtracking search algorithm (PMBSA) is proposed in this work to solve optimal renewable DG placement problem. Results obtained with PEM are compared with those obtained with Monte Carlo simulation method. Efficacy of formulated approach proposed in this paper is verified on a practical 67-bus distribution system and IEEE-118 bus test system. Results show that PMBSA is superior to the standard NSGA-II algorithm in obtaining near optimal solution for optimal RDG allocation problem. It is verified that proposed approach ensures very less voltage limit violations of bus voltages.

In this study, a stochastic multi-objective framework is proposed for energy expansion planning (EEP). The proposed multiobjective framework can concurrently optimise the competing objective functions including total real energy losses, voltage deviation and the total cost of the installation equipments. Also, regarding the uncertainties of the new complicated energy systems, in this study, for the first time, system uncertainties including load uncertainty are explicitly considered in the EEP problem by the use of the probabilistic load flow technique based on the point estimate method. Since the objectives are different and incommensurable, it is difficult to solve the problem by the conventional approaches that may optimise a single objective. Hence, the metaheuristic algorithm is applied to this problem. Here, the particle swarm optimisation (PSO) algorithm as a new evolutionary optimisation algorithm is utilised. To improve the total ability of the PSO for global search and exploration, a new modification adaptive process is suggested in such a way that the algorithm will search the total search space globally. To evaluate the feasibility and the effectiveness of the proposed algorithm, three modified standard distribution systems are used as the case studies.