Modeling, Optimization, and Control of Energy Systems

This study aimed to optimize the incorporation of static var compensators (SVCs) into Oman’s main interconnected system (MIS) using real 2023 MIS data. Leveraging the particle swarm optimization (PSO) algorithm within MATLAB, substantial enhancements were achieved in voltage profiles, with associated losses reducing by roughly 2%. A multi-objective strategy effectively managed costs while preserving improved voltage profiles and controlled losses. Validation through DigSILENT showcased the dynamic advantages of optimal SVC placement through consistently elevating voltage profiles and mitigating losses, notably within the Muscat region. Analyses encompassing harmonics, transient stability, and load distribution indicated that harmonics remained within acceptable thresholds, and overall system stability was enhanced. Optimal SVC deployment expedited the attainment of steady-state conditions, as illustrated via the QV curve, demonstrating increased stability as the buses loaded from 18% to 96%. These findings underscore the robust and efficient nature of SVC integration as a viable solution within Oman’s MIS system, addressing voltage profile enhancement, loss minimization, and fortified system stability. Full article

The prevention and control of gas explosion accidents are important means to improving the level of coal mine safety, and risk assessment has a positive effect on eliminating the risk of gas explosions. Aiming at the shortcomings of current risk assessment methods in dynamic control, state expression and handling uncertainty, this study proposes a method combining fault tree analysis and fuzzy polymorphic Bayesian networks. The risk factors are divided into multiple states, the concept of accuracy is proposed to correct the subjectivity of fuzzy theory and Bayesian networks are relied on to calculate the risk probability and risk distribution in real time and to propose targeted prevention and control measures. The results show that the current risk probability of a gas explosion accident in Wangzhuang coal mine is as high as 35%, and among the risk factors, excessive ventilation resistance and spontaneous combustion of coal are sources of induced risk, and the sensitivity value of electric sparks is the largest, and the prevention and control of the key factors can significantly reduce the risk. This study can provide technical support to coal mine gas explosion risk management. Full article

The energy shift towards carbon-free solutions is creating an ever-growing engineering interest in electrolytic cells, i.e., devices to produce hydrogen from water-splitting reactions. Among the available technologies, Proton Exchange Membrane (PEM) electrolysis is the most promising candidate for coping with the intermittency of renewable energy sources, thanks to the short transient period granted by the solid thin electrolyte. The well-known principle of PEM electrolysers is still unsupported by advanced engineering practices, such as the use of multidimensional simulations able to elucidate the interacting fluid dynamics, electrochemistry, and heat transport. A methodology for PEM electrolysis simulation is therefore needed. In this study, a model for the multidimensional simulation of PEM electrolysers is presented and validated against a recent literature case. The study analyses the impact of temperature and gas phase distribution on the cell performance, providing valuable insights into the understanding of the physical phenomena occurring inside the cell at the basis of the formation rate of hydrogen and oxygen. The simulations regard two temperature levels (333 K and 353 K) and the complete polarization curve is numerically predicted, allowing the analysis of the overpotentials break-up and the multi-phase flow in the PEM cell. An in-house developed model for macro-homogeneous catalyst layers is applied to PEM electrolysis, allowing independent analysis of overpotentials, investigation into their dependency on temperature and analysis of the cathodic gas–liquid stratification. The study validates a comprehensive multi-dimensional model for PEM electrolysis, relevantly proposing a methodology for the ever-growing urgency for engineering optimization of such devices. Full article

The stabilization of a permanent magnet synchronous motor using digital controllers requires the design of both the feedback law and an appropriate sampling frequency. Moreover, the design approach must be robust against existing uncertainties, such as disturbances and parameter variations. In this paper, we develop a stabilizing state feedback nonlinear control scheme for the permanent magnet synchronous motor. Moreover, we consider the case where the feedback signal is transmitted over a digital platform, and we derive the stabilizing sampling frequency, such that the stability of the closed-loop system is maintained. We design the controller by emulation, where the closed-loop stability is first established in continuous time; we then take into account the effect of sampling. The feedback law consists of two parts: feedback linearization and robust linear quadratic regulator for the linearized mode. The robustness is achieved by augmenting the state space model, with additional states representing the tracking errors of the motor speed and the motor current. Then, to cope with sampling, we estimate the maximally allowable sampling interval to reduce the sampling frequency while preserving the closed-loop stability. The overall system is modeled as a hybrid dynamical system, which allows handling both the continuous-time and discrete-time dynamics. The effectiveness of the proposed technique is illustrated by simulation and verified experimentally using a hardware-in-the-loop setup. Upon implementing the proposed approach, the obtained sampling interval was around 91 ms, making it suitable for digital implementation setups. Full article

HVDC is an important part of reducing energy transmission losses and maintaining energy sustainability. Commutation failure is the most common fault in HVDC systems, but existing commutation failure analysis approaches for HVDC systems do not consider the effects of instantly increasing direct current on the turn-off angle after an asymmetric fault in the AC system. To address this problem, we developed a commutation failure analysis approach that considers instantaneous variation of the DC current and AC voltage after asymmetrical faults. Firstly, the effects of the AC voltage and the DC current on the turn-off angle and the coupling relationship between the two are analyzed. Secondly, an equivalent mathematical model of the DC line, which covers the reactance, is built in Laplace space. Combined with the phase angle offset generated by the voltage after an asymmetric fault, a single relation expression containing only the AC voltage and turn-off angle is obtained by decoupling the DC current and AC voltage. The critical instantaneous AC voltage leading to system commutation failure is then derived based on the critical turn-off angle. Lastly, based on the CIGRE HVDC model built in the PSCAD electromagnetic transient simulation software (PSCAD v46), the accuracy of the proposed commutation failure analysis method compared with the other two methods is verified via simulation experiments under different grounding impedance values, and the applicability of the proposed method is further verified using simulation experiments with different smoothing reactor parameters. Full article

To address the rising electricity demand and greenhouse gas concentration in the environment, considerable effort is being carried out across the globe on installing and operating renewable energy sources. However, the renewable energy production is affected by diurnal and seasonal variability. To ensure that the electric grid remains reliable and resilient even for the high penetration of renewables into the grid, various types of energy storage systems are being investigated. In this paper, a compressed-air energy storage (CAES) system integrated with a natural gas combined-cycle (NGCC) power plant is investigated where air is extracted from the gas turbine compressor or injected back into the gas turbine combustor when it is optimal to do so. First-principles dynamic models of the NGCC plant and CAES are developed along with the development of an economic model. The dynamic optimization of the integrated system is undertaken in the Python/Pyomo platform for maximizing the net present value (NPV). NPV optimization is undertaken for 14 regions/cases considering year-long locational marginal price (LMP) data with a 1 h interval. Design variables such as the storage capacity and storage pressure, as well as the operating variables such as the power plant load, air injection rate, and air extraction rate, are optimized. Results show that the integrated CAES system has a higher NPV than the NGCC-only system for all 14 regions, thus indicating the potential deployment of the integrated system under the assumption of the availability of caverns in close proximity to the NGCC plant. The levelized cost of storage is found to be in the range of 136–145 $/MWh. Roundtrip efficiency is found to be between 74.6–82.5%. A sensitivity study with respect to LMP shows that the LMP profile has a significant impact on the extent of air injection/extraction while capital expenditure reduction has a negligible effect. Full article

In this paper, we studied the feedback stabilization of an inductive power transfer system based on available output measurement. The proposed controller relies on a full-order state observer in order to estimate the unmeasured state. The control design problem is challenging due to the large dimension of the closed-loop system, which requires too many tuning parameters to be determined when conventional control methods are employed. To solve this issue, we propose an LQR methodology based on a genetic algorithm such that the weighing coefficients of the cost function matrices can be automatically computed in an optimized manner. The proposed approach combines the method of eigenstructure assignment and the LQR technique in order to design both the controller and the observer gain matrices. The design methodology provides a systematic way to compute the parameters of the LQR technique for a wireless power transfer system in an optimized manner, which can be a useful design tool for many other applications. The effectiveness of the approach was verified by numerical simulation on the dynamic model of the wireless power transfer system. The results show that the proposed design outperforms conventional design methods in terms of a better performance and reduced design iterations effort. Full article

A coal-rock dynamic disaster is a rapid instability and failure process with dynamic effects and huge disastrous consequences that occurs in coal-rock mass under mining disturbance. Disasters lead to catastrophic consequences, such as mine collapse, equipment damage, and casualties. Early detection can prevent the occurrence of disasters. However, due to the low accuracy of anomaly detection, disasters still occur frequently. In order to improve the accuracy of anomaly detection for coal-rock dynamic disasters, this paper proposes an anomaly detection method based on a dynamic threshold and a deep self-encoded Gaussian mixture model. First, pre-mining data were used as the initial threshold, and the subsequent continuously arriving flow data were used to dynamically update the threshold to solve the impact of artificially setting the threshold on the detection accuracy. Second, feature dimensionality reduction and reorganization of the data were carried out, and low-dimensional feature representation and feature reconstruction error modeling were used to solve the difficulty of extracting features from high-dimensional data in real time. Finally, through the end-to-end optimization calculation of the energy probability distribution between different categories for anomaly detection, the problem that key abnormal information may be lost due to dimensionality reduction was solved and accurate detection of monitoring data was realized. Experimental results showed that this method has better performance than other methods. Full article

Community energy projects have gained popularity in recent years, and encouraging citizens to form local energy communities (LEC) is considered an effective tool for raising awareness about renewable energy. Since no single universal method exists for operating LECs, this study investigated the impact that different business models and asset dispatch methods have on LECs’ economic and energy-related indicators. We carried out a case study, which included the development, modelling, and simulation of seven scenarios using mixed-integer linear programming (MILP). To measure and compare the prospective performance of the LECs in each scenario, six key metrics were evaluated and assessed. The authors find that simple, rule-based control systems might be well suited for LECs with a limited number of controllable assets that aim to provide increased levels of self-consumption of up to 3%. We also conclude that when the LEC utilises an energy cooperative business model, the selected asset dispatch method provides only minor differences in LEC performance, while for prosumer communities, the importance of selecting a suitable asset dispatch method is higher. We also conclude that LECs have the potential to significantly increase their economic performance by more than 10% by acting as aggregators and providing grid services directly to system operators. Full article

With the development of intelligent coal mine construction, China’s coal production safety has been greatly improved, but coal mine gas explosion accidents still cannot be completely avoided and the unsafe acts of miners are an important cause of the accidents. Therefore, this study firstly collected 100 coal mine gas explosion cases in China, improved the framework of human factors analysis and classification system (HFACS) and used it to identify the causes of miners’ unsafe acts in detail. A hierarchy of the impacting factors is established. Then, combining with the interpretive structural model (ISM), the correlation between the impacting factors among different levels, especially among non-adjacent levels, is qualitatively analyzed through expert judgment. Then, the correlation among the contributing factors was quantitatively tested by chi-square test and odds ratio (OR) analysis. On this basis, a Bayesian network (BN) is constructed for the impacting factors of miners’ unsafe acts. The results show that the probability of coal mine gas explosion accident is 20% and 52%, respectively. Among the leading factors, the government’s insufficient crackdown on illegal activities had the greatest impact on miners’ violations, with a sensitive value of 13.2%. This study can provide reference for evaluating the unsafe acts of miners in coal mine gas explosion accidents by the probabilistic method. Full article

The European Green Deal declares climate neutrality as a goal for the year 2050. It establishes an EU binding target to reduce greenhouse gas emissions by 55 percent by 2030 compared to 1990. The market, through the EU Emissions Trading Scheme, will determine how EU member states contribute to this target. The Effort Sharing Regulation defines binding national targets to reduce the remaining GHG emissions not covered by the EU ETS. In this paper, an energy optimization model is applied to analyze the capability of Czechia to meet the climate change targets by 2030 and 2050. We define a baseline scenario derived from the National Energy and Climate Plan and three policy scenarios to assess impacts of the extension of the EU ETS to buildings and transport (EU ETS 2) and the coal phase-out on the Czech energy system. One of the policy scenarios aims at approaching climate neutrality in 2050. In addition, another scenario does not assess the impacts of the EU ETS 2 and coal phase-out but searches for the optimal path to achieve climate neutrality in 2050. Given the high level of GHG emissions in 1990 and the significant reduction in GHG emissions in the 1990s, Czechia could achieve a 55% reduction by 2030. However, further decarbonization will be quite challenging. Full article

Even in the context of smart mines, unsafe human acts are still an important cause of coal mine gas explosion accidents, but there are few models to analyze unsafe human acts in coal mine gas explosion accidents. This study tries to solve this problem through a risk assessment method of unsafe acts in coal mine gas explosion accidents based on Human Factor Analysis and Classification system (HFACS-GE) and Bayesian networks (BN). After verifying the reliability of HFACS-GE framework, a BN model of risk factors of unsafe acts was established with the Chi-square test and odds ratios analysis. After reasoning analysis, risk paths and key risk factors of unsafe acts were obtained, and preventive measures were granted. Based on the analysis of 100 coal mine gas explosion cases, the maximum probability of five kinds of unsafe acts of employees is 38%. Among the 22 risk factors, the mental state of employees has the greatest influence on the habitual violation of regulations, and the sensitivity value is 12.7%. This study can provide technical assistance for the risk management of unsafe acts in coal mine gas explosions. Full article

Technological objects and processes are often characterized by fuzzy initial information necessary for developing their models and optimization. The purpose of the study is to develop a method for synthesizing linguistic models of fuzzy described objects and a heuristic method for solving the multicriteria optimization problem in a fuzzy environment. Based on the expert assessments and logical rules of conditional inference, a method for synthesizing linguistic models was developed for describing processes with fuzzy input and output parameters. To solve the problem of multicriteria optimization, a heuristic method based on the modification and combination of various optimality principles is proposed. Coking reactor models were developed by modifying the successive regression inclusion method and the least squares method. Linguistic models of the delayed coking process were developed in the Fuzzy Logic Toolbox, allowing to evaluate the coke quality depending on the temperature and pressure of coking reactors. Using the proposed heuristic method, the problem of two-criteria optimization of the delayed coking process with fuzzy constraints is solved. The results confirm the advantages of the proposed fuzzy approach compared with the well-known approaches. Unlike them, the proposed method allows making adequate decisions in a fuzzy environment by maximizing the use of available fuzzy information. Full article

This study presents a detailed dynamic modelling and generic simulation method of an oscillating diaphragm compressor for chemisorption energy technology applications. The geometric models of the compressor were developed step by step, including the diaphragm movement, compressor dimensions, chamber areas and volumes and so on. The detailed mathematical model representing the geometry and kinematics of the diaphragm compressor was combined with the motion equation, heat transfer equation and energy balance equation to complete the compressor modelling. This combination enables the novel compressor model to simultaneously handle the simulation of momentum and energy balance of the diagram compressor. Furthermore, an experimental apparatus was set up to investigate and validate the present modelling and the simulation method. The performance of the compressor was experimentally evaluated in terms of the mass flow rate of the compressor at various compression ratios. Additionally, the effects of different parameters such as the inlet temperature and ambient temperature at various compressor ratios on the compressor performance were investigated. It was found reducing the inlet temperature from 40 to 5 °C at a constant pressure results in the enhancement of the compressor flow rate up to 14.7%. The compressor model proposed and developed in this study is shown to be not only able to accurately deal with the complexity of the dynamic behaviour of the compressor working flow but is also capable of effectively representing diaphragm compressors for analysis and optimisation purposes in various applications. Full article

The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages. Full article

The increasing environmental concerns due to emissions from the shipping industry have accelerated the interest in developing sustainable energy sources and alternatives to traditional hydrocarbon fuel sources to reduce carbon emissions. Predominantly, a hybrid power system is used via a combination of alternative energy sources with hydrocarbon fuel due to the relatively small energy efficiency of the former as compared to the latter. For such a hybrid system to operate efficiently, the power management on the multiple power sources has to be optimised and the power requirements for different vessel types with varying loading operation profiles have to be understood. This can be achieved by using energy management systems (EMS) or power management systems (PMS) and control methods for hybrid marine power systems. This review paper focuses on the different EMSs and control strategies adopted to optimise power management as well as reduce fuel consumption and thus the carbon emission for hybrid vessel systems. This paper first presents the different commonly used hybrid propulsion systems, i.e., diesel–mechanical, diesel–electric, fully electric and other hybrid systems. Then, a comprehensive review of the different EMSs and control method strategies is carried out, followed by a comparison of the alternative energy sources to diesel power. Finally, the gaps, challenges and future works for hybrid systems are discussed. Full article

Global solar irradiation is an important variable that can be used to determine the suitability of an area to install solar systems; nevertheless, due to the limitations of requiring measurement stations around the entire world, it can be correlated with different meteorological parameters. To confront this issue, different locations in Rias Baixas (Autonomous Community of Galicia, Spain) and combinations of parameters (month and average temperature, among others) were used to develop various machine learning models (random forest -RF-, support vector machine -SVM- and artificial neural network -ANN-). These three approaches were used to model and predict (one month ahead) monthly global solar irradiation using the data from six measurement stations. Afterwards, these models were applied to seven different measurement stations to check if the knowledge acquired could be extrapolated to other locations. In general, the ANN models offered the best results for the development and testing phases of the model, as well as for the phase of knowledge extrapolation to other locations. In this sense, the selected ANNs obtained a mean absolute percentage error (MAPE) value between 3.9 and 13.8% for the model development and an overall MAPE between 4.1 and 12.5% for the other seven locations. ANNs can be a capable tool for modelling and predicting monthly global solar irradiation in areas where data are available and for extrapolating this knowledge to nearby areas. Full article

Model predictive control is considered an attractive control strategy for the operation of hydropower station systems. It is due to the operational constraints or requirements of the hydropower system for safe and eco-friendly operation. However, it is mandatory to tune the model predictive control to achieve its best and most efficient performance. This paper determines the appropriate tunning on the weight parameters and the length of the prediction horizon for implementing model predictive control on the Dalsfoss hydropower system. For that, several test sets of the weight parameter for the optimal control problem and different lengths of the prediction horizon are simulated and compared. Full article

The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61$\min$ while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time. Full article

Step-up converters are widely used in many applications, such as renewable energy generation with photovoltaic panels and fuel cell stacks. In many cases, the required voltage gain is larger for those applications than a traditional boost converter can achieve. Several large-voltage gain converters have been recently studied. This paper introduces a converter topology in which the voltage gain is larger than a traditional boost converter. The main advantages of the proposed topology are: (i) it provides a large voltage gain without the use of an extreme duty cycle; (ii) its capacitors require a smaller voltage to be sustained compared with other, similar state-of-the-art converters; (iii) the voltage among the ground input and output is not pulsating; and (iv) it can be synthesized with commercial, off-the-shelf half-bridge packed transistors. The proposed converter can be employed in different applications, such as distributed generation and microgrids. This paper presents the steady-state analysis of the proposed converter in the continuous conduction mode, a short comparison with similar topologies, and their voltage on capacitors. Computer-based simulation results are provided to verify the principle of the proposed converter in different operating conditions. Full article

This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using a system-identification method. Considering that the strong state coupling of a non-linear mechanism model, the subspace identification method is first utilized to obtain a linear state-space model, and transformed into an input–output model. By taking the inputs and outputs of the input–output model as system states, an augmented non-minimal state-space (NMSS) model of state measurable is constructed. In order to reduce the computation burden, the augmented NMSS model is further transformed into a canonical formulation by adopting a Kalman decomposition. Based on the minimal realization state-space model, the MPC controller is parameterized as a finite-horizon optimization problem. Finally, simulations are performed and evaluated the performance of the proposed method, and the simulation results show that: the linear model approximate the non-linear system accurately; the proposed MPC method can achieve a satisfactory stable control performance; and the computation time 18.388 s for the overall optimization problem also illustrates the real-time performance effectively. Full article

Run-of-river hydropower plants are a cost-efficient technology that produce a power output proportional to the instantaneous flow of water diverted from the exploited stream by exploiting several mechanical, hydraulic, and electric devices. However, as no storage is available, its design and operation is tailored according to the unpredictability of its power generation. Hence, the modelling of this type of power plants is a necessity for the promotion of its development. Accordingly, based on models from the literature, this study proposes a comprehensive methodology for optimally designed small run-of-river hydropower plants based on a levelized cost of energy (LCOE). The proposed methodology aims at facilitating a faster design for more cost-effective and energy-efficient small hydropower plants. Depending on the average daily flow rates and the gross head of a given site, the model proposed in this study calculates the diameter, thickness, and length of a penstock; it also suggests the optimal selection of a turbine, determines the admissible suction head of a turbine for its optimal implementation, and determines the optimal number of turbines, all in order to minimize the LCOE of the proposed project. The model is tested to design a small run-of-river hydropower plant with a capacity of 6.32 MW exploiting the river Nyong in Mbalmayo. The results confirm the profitability of the investment with an LCOE of around 0.05 USD/kWh, which is the lowest limit value of the LCOE range for small hydropower plants, as presented in the IPCC (Intergovernmental Panel on Climate Change) report, assuming a project lifespan of 50 years and a discount rate of 12.5%. These results also show that it may be worth to provide the energy sector with a small hydropower design tool with a graphical interface. In addition, it would be appropriate to use a similar method in an off-grid context where a hydropower plant, with or without storage, is combined with another source to meet the electrical needs of a given population. Full article

Considering the specific heat characteristics of working fluid and existence of various losses in a porous medium (PM) cycle, this paper applies finite time thermodynamic theory to study its efficient power performance with nonlinear variable specific heat model. Range of the cycle pre-expansion ratio is obtained by solving the equation, and PM cycle is converted to Otto cycle by choosing appropriate pre-expansion ratio. Influences of pre-expansion ratio, specific heat characteristics, temperature ratio, and various losses on cycle performances are investigated. Thermal efficiencies are compared at operating points of the maximum power output and efficient power. The results show that PM cycle has better performance than Otto cycle. Under certain conditions of parameters, thermal efficiencies at the maximum efficient power and maximum power output operating points are 50.45% and 47.05%, respectively, and the former is 7.22% higher than the latter. The engine designed with the maximum efficient power as the criterion can improve thermal efficiency by losing less power output. The results of this paper can guide parameters selection of actual PM heat engine. Full article

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.). Full article