Renewable Energy Sources Power Research Articles

Concept and Advantages of Carbon/Air Secondary Battery System as a Fixed Compact Power Storage with Large Capacity

Introduction One of the critical technological isuues to constract carbon neutral society is the suppression of the impact of the volatility in power generation from inexpensive variable renewable energy power sources, such as solar cells and wind powers. It is necessary to maintain a balance between power generation, transmission, and consumption, and to maintain three types of stability in the power system: frequency stability, synchronization stability, and voltage stability. For this reason, energy storage technology is one of the most important issues for making up carbon neutral energy system. Among energy storage technologies, lithium-ion secondary batteries are superior in terms of both output density and energy density, but they present a cost challenge for large-capacity energy storage. Pumped storage power generation is the mainstay of long-term large-capacity energy storage in the current power system, but significant pumped storage expansion is subject to geopolitical constraints. Therefore, a compact, large-capacity energy storage system that can be installed in the power grid is required to expand the introduction of variable renewable energy power sources.In this research, we first describe the operating mechanism and advantages of the "Carbon/Air Secondary Battery (CASB) system" [1,2], a compact power storage system that integrates the functions of a solid oxide fuel cell that generates electricity by producing CO2 through the oxidation reaction of carbon and a solid oxide electrolytic cell that produces carbon through the electrolysis of CO2. The operating mechanism and features of the CASB system are introduced. In addition, we report the calculation results of feasible system efficiency based on thermodynamic considerations compared to power storage system using H2/H2O-solid oxide fuel cells/electrolysis cells. In addition, the advantages/disadvantages of the two types of CASBs, integrated type of CASB between carbon deposition /electrochemical reaction, and the separated type of CASBs, as well as their representative charge/discharge characteristics will be presented. Theoreticl advantage of CASB as a fixed compact power storage with large capacity The theoretical charge/discharge efficiency of CASB, which is the ΔG/ΔH of reaction “C+O2→CO2” is 100%. This means that there is no heat input and output ideally in both carbon generation and CO2 electrolysis (in charge/discharge). Therefore, temperature control is simple and a heat exchanger can be minimized, which may allow the system to be compact. The feasible charge/discharge system efficiency of CASB including possible heat exchange were estimated as 92% based on thermodynamics, which is much higher than that of power storage system using H2/H2O-solid oxide fuel cells/electrolysis cells (63%). In addition, since CO2 can be liquefied at about 6.4 MPa at 25°C, its volumetric energy density to 1.62 × 103 Wh L-1, about four times higher than the volumetric energy density of hydrogen compressed at 20 MPa at 25°C, 3.79 × 102 Wh L-1. A key technological point of CASB at its discharging , which is combination between electrochemical reaction (CO2→ CO+O2-) and thermochemical reaction (2CO→ C+CO2) by achiving at closed system The discharge reaction of the CASB is the same as that of the Rechargeable Direct Carbon Fuel Cell [3,4,5]. The reason for the first successful demonstration of the CASB is that the combining electrochemical reaction with low overpotential (CO2 →CO+O2-) with the thermochemical reaction, the Boudouard equilibrium reaction (2CO → C+CO2) were achieved at the charging by making the system a closed system. Two types of CASBs, (1) Integrated type of CASB between carbon deposition /electrochemical reaction, and (2) the separated type of CASBs Since generally, the carbon deposition on the electrode cause its degradation [6], two type of CASBs can be designed and demonstrated (as shown in Fig.1). Both types of CASBs could be demonstrated.

The Potential for the Use of Hydrogen Storage in Energy Cooperatives

According to the European Hydrogen Strategy, hydrogen will solve many of the problems with energy storage for balancing variable renewable energy sources (RES) supply and demand. At the same time, we can see increasing popularity of the so-called energy communities (e.g., cooperatives) which (i) enable groups of entities to invest in, manage, and benefit from shared RES energy infrastructure; (ii) are expected to increase the energy independence of local communities from large energy corporations and increase the share of RES. Analyses were conducted on 2000 randomly selected energy cooperatives and four energy cooperatives formed on the basis of actual data. The hypotheses assumed in the research and positively verified in this paper are as follows: (i) there is a relationship between hydrogen storage capacity and the power of RES, which allows an energy community to build energy independence; (ii) the type of RES generating source is meaningful when optimizing hydrogen storage capacity. The paper proves it is possible to build “island energy independence” at the local level using hydrogen storage and the efficiency of the power-to-power chain. The results presented are based on simulations carried out using a dedicated optimization model implemented by mixed integer programming. The authors’ next research projects will focus on optimizing capital expenditures and operating costs using the Levelized Cost of Electricity and Levelized Cost of Hydrogen methodologies.

Distinguishing Electrochemical Processes in PEM Electrolysis By Impedance Modeling – Distribution of Relaxation Times

Proton exchange membrane (PEM) water electrolysis is a key technology in hydrogen production to provide capacity and flexibility for renewable energy power sources. Various research topics for electrolyzer development focus on cost reduction, performance optimization and lifetime extension with minimum platinum group metal (PGM) loading required [1]. Understanding degradation mechanisms in PEM electrolysis cells becomes necessary for developing mitigation strategies in dynamic durability tests, which facilitates the importance of advanced electrochemical characterization methods.Electrochemical impedance spectroscopy (EIS) is a commonly used diagnostic technique in electrochemical systems. For impedance interpretation, equivalent circuit model (ECM) can be an option with prior knowledge about the system and appropriate circuit elements to prevent overfitting. Alternatively, distribution of relaxation times (DRT) provides a model-free pathway to address electrochemical mechanisms with different time constants and it has been applied to fuel cells, batteries and capacitors in the past years [2, 3]. Impedance deconvolution by DRT, however, has not been well adapted for PEMECs. In this study, electrochemical processes are successfully identified from the DRT profile by varying membrane electrode assembly (MEA) compositions and different operational conditions. In a single cell testing, four main peaks in DRT results associated with (1) mass transport losses (2) reaction kinetic (3) ionic conductivity within the catalyst layer and (4) ohmic properties at the interface of membrane and catalyst layers can be separated and quantified. Examination of electron microscopy provides further explanation of different MEA characteristics in performance analysis. This study verifies DRT is a promising tool to better understand PEM electrolysis processes and will be applied to degradation mitigation in the future.

Lifetime (Bx) improvement of PV inverter using Si-SiC H-IGBT/Diode: a reliability approach

Technological advancements have made it possible to harness the power of renewable energy sources. The efficiency of power electronic devices has increased to almost 98%. In order to reduce the risks of failure and maintain the operation of photovoltaic (PV)-based energy converters, reliable devices are needed. Due to the increasing number of wide-bandgap silicon in electronic converters, the need for more efficient and reliable devices has become more prevalent. However, the cost of these devices is a major issue. Hence, in this work extensive analysis of hybrid silicon (Si)-IGBT and silicon carbide (SiC) antiparallel Diode (H-IGBT/Diode) based PV inverter is proposed to improve the lifetime (Bx). A reliability oriented lifetime assessment is performed on a test case of single stage three kilowatt photovoltaic inverter with 600 V/30 A hybrid switch. Long term mission profile for one year is considered for evaluation at B. V. Raju Institute of Technology (BVRIT), Telangana, India. Finally, B10 lifetime is calculated, comparison analysis is presented between conventional Si-IGBT and proposed Si-SiC H-IGBT/Diode. The results of the study revealed that the H-IGBT exhibited a significant increase in PV inverter reliability.

Feasibility analysis of virtual clean hydrogen plant platform: A case study on energy policy in S. Korea using stochastic programming

In S. Korea, high-density renewable and high-density load areas are separate. This separation has led to increased transmission constraints. As both Renewable Energy Source (RES) penetration and power demand simultaneously increase, RES saturation occurs. This phenomenon reduces both the benefits of power transactions and the acceptance of RES. Addressing this issue requires actions from Independent Power Producers (IPP) and policymakers. This paper suggests a Virtual Clean Hydrogen Plant (VCHP) platform design that consolidates the Photovoltaic (PV) energy from high-density renewable areas into a single power plant. The platform converts excess energy, generated by PV due to transmission constraints, into hydrogen and supplies it to the high-density load areas. The South Korean government plans to introduce a Clean Hydrogen Portfolio Standard (CHPS), which offers a fixed premium (FP) to private power generators that provide power through clean hydrogen. Consequently, the proposed benefit structure of the VCHP platform includes an FP, settled through CHPS. This paper's design and operation algorithm for the VCHP platform incorporates stochastic programming to analyze PV operation and deterministic optimization for the optimal scheduling of Fuel Cells (FC) that provide power and heat to high-density load areas. The simulation results demonstrate that the minimum subsidy required for CHPS benefits varies depending on how PV-coupled P2G technology is utilized. Specifically, engaging in net metering with VCHP over the course of one month can establish a cost-effective benefit structure, verified to require a subsidy ranging from a minimum of 0.2036 $/kWh to a maximum of 0.2416 $/kWh.

Performance evaluation of the Fe–Cl and Mg–Cl cycle for hydrogen production of the minor actinide fuelled PACER fusion blanket

Thermochemical processes used to produce clean hydrogen (H2) on a large scale by utilizing renewable energy and nuclear power sources are considered one of the most environmentally friendly strategies. In this study, the neutronic performance and hydrogen production potential of the PACER fusion blanket have been investigated during the operation period. Firstly, neutronic calculations have been performed to produce an electrical energy of 1000 MW from fusion explosions of 8.36 TJ to be repeated every 40 min with Monte Carlo Calculation Method (MCNP). Minor actinide flouride (MA) fuel out of the nuclear waste of LWRs with volume fractions of 2% have been mixed homogenously in the Flibe coolant. Tritium breeding ratio (TBR) at startup is calculated as 1.27. TBR and energy multiplication factor (M) decrease gradually. TBR >1.05 can be kept over 7 years for a self-sustained reactor. The M due to higher fissionable fuel content in the molten salt computes as 3.3 at initial. The burn up exceeds ∼800 GWd/tM at the end of operation period. Secondly, the potential of hydrogen production for four step iron chloride (Fe–Cl) and three step magnesium chloride (Mg–Cl) thermochemical cycles of the hydrogen unit PACER fusion blanket. The ratio of thermal power (1 − ψ), total thermal power (Phpf) and rate of mass flow of hydrogen (m˙H2) depends on M for Fe–Cl, Mg–Cl (option I) and Mg–Cl (option II) thermochemical cycle have been presented. The ratio of thermal power (1 − ψ) for Fe–Cl, Mg–Cl (option I) and Mg–Cl (option II) have been obtained as 0.6494, 0.131 and 0.095 whereas, total thermal power (Phpf) have been computed as 5298.8, 3608.43 and 3488.925 MW, respectively, at start up. The produced of hydrogen amount for Fe–Cl, Mg–Cl (option I) and Mg–Cl (option II) have been calculated as ∼ 534.65; ∼1236.82 and ∼806.03 kg/year, respectively, at startup.

Integration of RES and AI for Grid Distribution Network Energy Management

In an era where sustainability and efficient resource utilization are paramount, optimizing energy management in grid distribution networks is a top priority. This research introduces a cutting-edge approach that harnesses the power of Renewable Energy Sources (RES), Artificial Intelligence (AI), Long Short-Term Memory (LSTM) networks, Quadratic Regression, and Demand Response mechanisms for Grid Distribution Network Energy Management. By fuzing these state-of-the-art technologies, we unlock the potential to revolutionize energy load forecasting with unprecedented precision and foresight. LSTM models, enriched with historical load data, weather conditions, and demand-response patterns, empower us to anticipate grid requirements with exceptional accuracy. Our approach consistently achieves an average Mean Absolute Percentage Error (MAPE) below 5% and a Root Mean Square Error (RMSE) under 2% for load predictions with an overall grid distribution efficiency is 98%, surpassing conventional forecasting methodologies. Furthermore, the integration of demand response strategies results in a remarkable 20% peak shaving ratio, contributing to a 15% reduction in energy demand during high-demand periods. This research not only enhances smart grid technologies but also ushers in a more resilient, adaptive, and eco-friendly energy infrastructure for the future.

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles

This study proposed an intelligent energy management strategy for islanded networked microgrids (NMGs) in smart cities considering the renewable energy sources uncertainties and power fluctuations. Energy management of active power and frequency control approach is based on the intelligent probabilistic wavelet fuzzy neural network-deep reinforcement learning algorithm (IPWFNN-DRLA). The control strategy is formulated with deep reinforcement learning approach based on the Markov decision process and solved by the soft actor-critic algorithm. The NMG local controller (NMGLC) provides information such as the frequency, active power, power generation data, and status of the electric vehicle's battery energy storage system to the NMG central controller (NMGCC). Then the NMGCC calculates active power and frequency support based on the IPWFNN-DRLA approach and sends the results to the NMGLC. The proposed model is developed in a continuous problem-solving space with two structures of offline training and decentralized distributed operation. For this purpose, each NMG has a control agent (NMGCA) based on the IPWFNN algorithm, and the NMGCA learning model is formulated based the online back-propagation learning algorithm. The proposed approach demonstrates a computation accuracy exceeding 98%, along with a 7.82% reduction in computational burden and a 61.1% reduction in computation time compared to alternative methods.

Multiparameter Bifurcation Analysis of Power Systems Integrating Large-Scale Solar Photovoltaic and Wind Farms Power Plants

In this paper, we propose a novel codimension-three-parameter bifurcation analysis of equilibria and limit cycles when integrating Renewable Energy Sources (RESs) power plants with an exponential static load model. The study investigates the effect of solar photovoltaic generation margin, wind power generation margin, and loading factor on the local bifurcation of the modified IEEE nine-bus system. The proposed technique considers the real case of the West System Coordination Council (WSCC), the western states of the USA, by using specific models of RES power plants and static loads. The proposed technique helps to create a set of linearly varying parameters for critical operating points of nonlinear systems. The study explores detailed voltage stability analysis through the examination of bifurcation diagrams. The Hopf, limit-induced, and saddle-node bifurcation branches are identified, defining the parameter space’s stable and unstable operational regions. Additionally, the stability regions surrounding the equilibrium points are diligently explored, clarifying the consequences that various bifurcations may exert on these regions. The study offered in this proposed work aids in determining the best ways to monitor and improve these margins while considering system variables and load design.

Novel Curtailment Control Strategy for Wind Power Plants Based on the SQP Optimization

As the penetration level of renewable energy sources (RESs) increases, the output power of RESs needs to be curtailed to balance the power supply and load demand. Nevertheless, depending on the curtailment control strategy for wind power plants (WPPs), while the total amount of output power curtailment remains the same, the overall stored inertial energy within WPPs may vary. Furthermore, this stored inertial energy within WPPs can be used during disturbances to enhance frequency stability. This paper proposes a novel curtailment control strategy based on the sequential quadratic programming (SQP) optimization algorithm to effectively curtail WPPs and increase the overall stored inertial energy within WPPs. Then, the proposed solution can enhance frequency stability by providing a more inertial response from WPPs during disturbances. To verify the effectiveness of this novel curtailment control strategy, several case studies are conducted using the IEEE 39-bus system. The results show that the proposed curtailment control strategy effectively increases the stored inertial energy within WPPs while satisfying the required output power curtailment.

Experimental Research of Piezoelectricity as Renewable Energy Power Source for Conveyor Electric Motor on Garbage Collection Vessel

Experimental Research of Piezoelectricity as Renewable Energy Power Source for Conveyor Electric Motor on Garbage Collection Vessel

A Coordinated Consensus Control Strategy for Distributed Battery Energy Storages Considering Different Frequency Control Demands

With the increasing penetration of renewable energy sources (RESs), ensuring frequency stability is becoming urgent for the power system. The distributed battery energy storage systems (DBESs) is one of the promising solutions to this problem. In this paper, a consensus control-based strategy is proposed for coordinating the DBESs to participate in the frequency regulation. Firstly, the system frequency response (SFR) model with the DBESs is introduced, and different frequency control demands are analyzed in detail, based on which the designing principles of the strategy are elaborated. Then, a revised dead band of frequency response is proposed to improve the frequency control performance under severer and more frequent RESs power fluctuation, reduce the possibility of frequency variation crossing the thresholds, and fully utilizes the DBESs' frequency regulation capability. Further, a developed consensus strategy derived from droop control is proposed, which enables the DBESs to participate in different frequency control demands simultaneously. The proposed strategy can prolong the service time when DBESs involved in frequency control as an aggregator, avoid unexpected energy exhaustion during frequency regulation, and reduce the frequency variation. The performance of the proposed control strategy is validated by the simulation cases with different operating scenarios.

Achieving energy independence in urban microgrids: Strategies for domestic resource utilization and environmental sustainability

The quest for energy independence within urban microgrids (MGs) has become increasingly crucial for ensuring domestic resource utilization and environmental sustainability. One of the pivotal challenges lies in the clustering of MGs, a complex task aimed at enhancing their robustness and economic performance during events. However, limited research has been conducted on this intricate clustering problem. This study addresses this gap by proposing an artificial intelligence-based method tailored for MG clustering. In this paper, we introduce a novel multi-objective optimization approach to determine the optimal clustering of standalone MGs. Our methodology considers a range of critical parameters, including distributed generation (DG) droop settings, DG locations and capacities, renewable energy resources from solar units and wind turbines, capacitors, and power transmission lines. By minimizing power losses and enhancing voltage stability, we identify a virtual cut-set line, often referred to as the "virtual line," characterized by minimal power exchange between clustered MGs. To achieve optimal and cost-effective performance, we develop an enhanced differential genetic evolution algorithm designed to address multi-objective problems within stringent constraints. Additionally, we employ Bayesian theory within artificial intelligence to estimate the optimal output power of renewable energy sources, specifically wind turbines and solar panels. To validate the effectiveness of our approach, we apply it to a 69-bus MG across various scenarios. Our findings demonstrate the proficiency of our method in achieving energy independence, domestic resource utilization, and environmental sustainability within urban microgrids.

Reviewing the Situation and Prospects for Developing Small Renewable Energy Systems in Poland

The objective of this study was to evaluate changes in the number of small renewable energy sources (RES) power plants and the volume of generated energy in the years 2016–2020, with an outlook to year 2025. The study covered the area of Poland, including the division into provinces and different sources of renewable energy. Absolute values of electric power production and sale were presented, in addition to calculated structure indices. Moreover, the number and structure of small power plants using different renewable energy sources was determined for every Polish province. A classification of the provinces was made, where four classes were distinguished depending on the number of RES plants operating in the provinces. The research results allowed us to diagnose the current situation and make a prognosis for the future, which may translate into support for the development of particular types of installations, depending on the natural and economic characteristics of each area. The added value of the study stems from the fact that previous reports focused mainly on micro or large power plants and the time span covered data before and during the pandemic. This made it possible to assess the impact of the pandemic on the development of small renewable energy sources.

Stochastic uncertainty management in electricity markets with high renewable energy penetration

Abstract The high penetration of renewable energy sources (RESs) in modern power systems poses two conflicting issues. First one is the reduction in the operation costs resulted from RESs utilization instead of the expensive fossil fuel thermal generating units. However, the RESs are characterized by its uncertain and intermittent behavior that their output power is not dispatchable and not known exactly due to forecast errors. Therefore, reserves are scheduled in the day-ahead market to meet the uncertain supply from RESs which add some costs to the system. The decision maker should be aware of those two conflicting objectives in order to operate the system in the optimal way with minimum operation costs. The problem of the market clearing is formulated as Mixed Integer Linear Programming (MILP) problem using GAMS software. It is considered a two stage stochastic programming with the objective of minimizing the expected total operation energy and reserve costs while satisfying the various operational constraints. The results show the effectiveness of the RESs integration in different cases with the consideration of load shedding, RESs curtailment and transmission congestion. As shown from the results, when the network is congested, the operation costs are increased due to the load shedding and RESs curtailment events occur. Moreover, scheduled reserves are increased to face the uncertainty of RESs. As the RESs power penetration increases with no network congestion, a decreasing trend in the operation cost can be seen. However, this trend is less marked as the uncertainty of RESs generation increases. For the network congested case, the reduction rate of the operation cost is decreased for increasing RESs power penetration levels.

A Vehicle-to-Grid System for Controlling Parameters of Microgrid System.

The power system for large-scale adoption of hybrid electric vehicles can benefit from a distributed reserve provided by the vehicle-to-grid (V2G) concept. This study suggests a V2G technology that can effectively control frequency on a microgrid throughout a 24-h cycle. When usage is at its lowest in the spring or fall, a microgrid is intended to be large enough to simulate a community of 2000 households. A 1:5 ratio of cars to households is realized by modelling 400 electric vehicles (EVs) as a basic model, indicating a typical case in the future. An in-depth analysis of the voltage, current, reactive, and active power is carried out for a microgrid. By coordinating control of diesel generation, renewable energy source (RES) generation, power exchange, and EV generation, the system frequency of a microgrid can be managed by regulating load demand with V2G devices. The proposed microgrid with V2G effectively manages energy and reduces the uncertain and variable nature of RES power generation with enhanced performance. System parameter variations have been investigated for various operating scenarios, and it has been discovered that error is confined to less than 5%.

Adaptive real-time protection scheme for distribution networks with high penetration levels of renewable energy resources

The increasing demands of electrical power have imposed a necessity to integrate renewable energy power sources (REPS) into electrical power networks. The presence of REPS within electrical distribution networks mandated an update in the settings of the overcurrent protection relays. In this paper, a real-time adaptive protection scheme is proposed to redefine the settings of the overcurrent protection relays by replacing the traditional time multiplier setting (TMS) with a newly proposed real-time adaptive time multiplier setting (ATMS). The proposed ATMS is formulated as the product of two factors namely; the acceleration factor (AF) and the tuning factor (TF). The AF is set as the ratio of the impedance seen by the relay during a fault to the pre-fault impedance. The TF is a multiplication constant that is determined offline prior to operation in order to optimize ATMS. The scheme was tested upon different radial and meshed bench-mark testing systems; the IEEE 34 system and the IEEE 30 bus system employing MATLAB software package. The proposed scheme proved its capability of optimizing the response of overcurrent relays and minimizing total operating time. In addition to its adaptability to different operational and loading conditions during the day.

A Market Framework for a 100% Renewable Energy Penetration Spot Market

The global installed capacity of renewable energy sources (RESs) is increasing rapidly. The 100% RESs power supply is being pursued. When renewable energy penetration tends to be 100%, the source-load coordination problem becomes extreme. The problems of market failure caused by the near-zero marginal cost of RESs and the scarcity pricing of flexible resources (FRs) participating in the rapid energy deviation balance need to be solved. Accordingly, a spot market framework including the day-ahead renewable energy temporal capacity market and the real-time energy sharing market is proposed in this paper. The RESs and FRs are decoupled to ensure that the homogenous commodities compete together. In the day-ahead stage, to obtain the locational marginal price with the guiding role of resource allocation, the fixed cost of the RESs is apportioned to obtain the spatial-temporal capacity price. In the real-time stage, a price-driven distributed energy sharing mechanism is proposed. It is proven that the sharing mechanism can achieve optimum social welfare and satisfy incentive compatibility. The simulation verifies that the new framework can solve market failure. The real value of FRs is discovered when a price consensus is reached.

Research on failure prediction of wind turbines in offshore wind clusters

Abstract Offshore wind is a very promising renewable energy source and offshore wind power has been widely used. However, the maintenance of offshore wind power is a very inconvenient task, which is not only difficult to operate, but also can be very costly, and it is essential to anticipate offshore wind power failures in advance. In this paper, we focus on wind turbine faults for analysis, we use a high-performance radial basis function (RBF) neural network, and to obtain the center of its radial basis function, the normalization constant, and the weighting coefficients from the hidden layer to the output layer for better performance, we use an optimized particle swarm optimization (PSO) algorithm to obtain more accurate parameters to seek better performance. We focus on two very high failure rates of wind turbines for testing, and the results show that our proposed method can predict the occurrence of failures 10h-20h in advance and have a good performance.

Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage

At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm.