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• Importance and challenges of relay coordination in Microgrid. • Overview of different adaptive schemes opted for relay coordination. • Different optimization techniques which have been used for the calculation of relay settings. • Evolvement of algorithms from conventional to Hybrid methods which combines two different metaheuristic methods to solve a complex, non- linear optimization problem in an efficient way for optimized relay coordination. • Future directions of research for further modifications possible in Algorithms are stated at the end. Concept of microgrids (MGs) have gained significant attention in order to enhance the resilience and efficiency of modern Power Systems. MG offers bi-directional power flow so an effective protection and control becomes a vital requirement to ensure the reliable operation of MGs. Relay coordination being an integral part of the protection system, plays a critical role in prevention of power disturbances and equipment damage due to the presence of bi-directional power flow. This review paper focuses on the optimization of relay coordination in MGs, and highlights the importance of adaptive and intelligent approaches involved for the efficacy of MG operation. This paper overviews different schemes used to adapt while determining the DOCRs settings with different contingencies considered. The paper depicts the key challenges associated with relay coordination in MGs; varying fault currents, and bidirectional power flows. To overcome these challenges, recent researches have explored advanced techniques that includes intelligent algorithms to operate on collected real-time data. Additionally, the impact of communication technologies for relay coordination in real-time has been discussed for Grid connected and Islanded mode of operation. This review covers a wide range of optimization methods such as; Traditional, Meta-Heuristic, and Hybrid algorithms, which allows adaptive and reliable protection schemes during different modes of MG operations. [ABSTRACT FROM AUTHOR]

The hosting capacity region defines a joint feasible region in the operational space involving various installations. By facilitating their coordinated operation, this region succeeds in tapping into the grid's power delivery potential. Meanwhile, when approximating this region using an expansive polytope, facet selection should be carefully considered. Random facet selection will cause the Matthew effect, indicating that a facet which was more frequently selected owns a higher likelihood of being selected again. Such effect eventually harms the region expansion efficiency. Beyond static facet selection measures, this paper proposes adaptive measures for further improved assessment performances. The adaptive scheme is engineered to cyclically use original measures, thereby merging and leveraging their potentials on efficient facet selection. Relevant 3-dimensional region assessment experiments are conducted on a 10.5 kV Dutch grid case, which is modeled on Pandapower toolkit. The results demonstrate that, compared to static alternative measures, adaptive measures contribute to larger region space consistently, with potential region space gain being up to 62.2%. This validates the superior efficacy of adaptive mechanism in facet selection for region assessment. • HCR defines a feasible region in the combined operational space of energy units. • HCR is beneficial for coordination operation to unleash grid power potential. • Analyze the Matthew effect in facet selection problem during HCR computation. • Integrate metaheuristics and RL to proposed adaptive facet selection algorithms. • Validate the methods by comparative numerical tests with state-of-art ones. [ABSTRACT FROM AUTHOR]

Faults are an inherent part of power distribution systems, and fault indicators play a crucial role in self-healing smart distribution systems by assisting in fault detection and location. However, the effectiveness of fault indicators depends on their number and location in the distribution system. This paper addresses the optimal decisions on the allocation of fault indicators in distribution networks to minimize the expected fault location time. To build upon the existing knowledge, a comprehensive review of the literature on the allocation of fault indicators is provided. Additionally, a novel approach is given by formulating the fault location time as a quadratic function of the network's section sizes. From a relationship established between the fault indicator allocation problem and connected edge-partitions, a mathematical model and a metaheuristic are developed. To evaluate the proposed methodologies, a benchmark comprising 19 networks is employed, including the largest network solved for this problem to date. The results demonstrate the superiority of the new methodologies over previous approaches, as evidenced by the quality of the solutions and their optimality gaps. • A quadratic programming model is proposed for the fault indicator allocation problem. • A scalable BRKGA metaheuristic was proposed to tackle large instances. • Results are given for 19 networks, among which the largest being solved to date. • Significant improvements were attained w.r.t. solution quality and optimality gaps. • Source codes and data are made public to encourage further investigations. [ABSTRACT FROM AUTHOR]

· Consideration of reactive power tariffs in optimizing the sizing and siting of distributed generation systems (DGs) for improvement of efficiency. · Applying water flow optimization (WFO) method on the problem of optimal sizing and siting of DGs for the first time. · Taking into account power factor limitations for DGs capable of injecting both active and reactive power. · Examination of multi-objective functions, including DG allocation to minimize economic losses and annual active/reactive power loss. Distributed generation (DG) systems are gaining popularity because they generate electricity closer to the point of consumption. This reduces transmission and distribution losses and enhances system reliability. For the efficient and cost-effective use of these systems, optimal sizing and siting of DGs is crucial. This paper introduces a Water Flow Optimization (WFO)-based method for the optimal placement and sizing of DG systems capable of injecting both active and reactive power simultaneously. This study assesses both the technical and economic facets of the system, taking into account power losses and the comprehensive annual economic costs, including capital investment, deployment, operation, and maintenance. A significant aspect of this research is the DG units' capability to enhance voltage and minimize losses, factoring in the reactive power tariff. The findings show that the WFO method surpasses other optimization techniques, such as particle swarm optimization, whale optimization algorithm, genetic algorithm and gray wolf optimization. Furthermore, the study emphasizes the cost advantages of injecting reactive power. The minimum annual economic losses (AEL) recorded in this study are $19,207 and $13,852 for IEEE-33 and IEEE-69 bus systems, respectively. In comparison, for DGs only injecting active power, the AEL increases to $24,749 and $41,381 for the corresponding systems. [ABSTRACT FROM AUTHOR]

• A time based two types of dynamic pricing strategies are employed for microgrid-based CS. • The microgrid-based CS's cumulative profit is maximized, peak to valley ratio of CS and electricity purchase cost for the EV owner are reduced. • The uncertainties associated with the sources and EV SoC variation are effectively handled. • The EVs are used as a source of flexibility through V2G/G2V operation to offer ancillary services to the utility grid. • Risk management is included to cope with the uncertainties and risks in the electricity market. The FHO algorithm is applied to solve the proposed complex problem on the IEEE 33 bus modified system for two operational cases. This paper presents a risk-based dynamic pricing strategy for the microgrid based electric vehicle charging station. The microgrid is composed of renewable resources such as solar energy, wind energy and fuel cells. The proposed microgrid-based charging station operates off-grid and on-grid depending on the charging demand and trades the electricity with the main grid when needed. In this work, to increase the reliability and to decrease the detrimental impact of electric vehicle load on the distribution network electric vehicles act as a source of flexibility through grid to vehicle and vehicle to grid provisions. The uncertainty of solar and wind sources is a key issue that is efficiently addressed in this work along with one of the electric vehicles load uncertain factors such as state of charge variation using a probabilistic method. Two types of competent time-based dynamic pricing strategies are employed in this work to enhance the collective profit and to curtail the peak-to-valley ratio of charging station and electricity purchase cost of the electric vehicle owner. To deal with the uncertainty in the electricity market and its associated risk, the average value-at-risk is applied. The proposed multi-objective problem is tested on an IEEE 33 bus-modified system using MATLAB software. A metaheuristic fire hawk optimization technique is employed to implement dynamic pricing on charging station for two cases. To observe the efficiency of the applied algorithm, the findings of this work are compared with the other techniques described in the literature. [ABSTRACT FROM AUTHOR]

• The slow convergence rate and lack of positive semi-definiteness issues of UKF are surmounted. • Whale Optimization Algorithm (WOA) and Tunicate Swarm Algorithm (TSA) are used to develop HW-TO-UKF. • The proposed HW-TO-UKF is used to optimize scaling parameters that are involved in computing the weights of the sigma points of UKF. • WSCC-3 machine and NPCC_48 machine systems are used for simulations. • The efficacy of proposed HW-TO-UKF is compared with various heuristic-based existing methods. The Energy Management Systems (EMS) utilizes Dynamic State Estimation to monitor the operating condition of the power grid. For a synchronous generator, the speed and angle of a rotor are the most important dynamic states. These states are responsible for producing uncertainty in the power system models. Extended Kalman Filter (EKF) and Unscented Kalman Filters (UKF) are utilized to estimate the dynamic states of the power system when it perceives significant disturbances or noise. The conventional Unscented Kalman Filter (UKF) has limitations in terms of slow convergence speed and its lack of positive semi-definiteness. This prohibits its ability to accurately track the transients of the system. The scaling parameters of UKF (alpha and beta) are optimally tuned to overcome these issues. In this paper, a novel Hybrid Whale-Tunicate-based UKF (HW-TO-UKF) is proposed for tuning the scaling parameters of UKF for reducing the mean difference between the real state and the measured state. During the validation of the proposed method, better robustness and statistical efficiency are achieved by defining the objective function as the minimization of the error difference between the actual and measured states. The simulation is carried out on the WSCC-3 and NPCC-48 machine systems to demonstrate the performance of the proposed HW-TO-UKF over the existing models. [ABSTRACT FROM AUTHOR]

This paper presents a review on common sources of inaccuracy related to solving the non-convex economic dispatch problem. The inaccuracies originate from using invalidated cost function, inaccurate calculation of transmission losses, violating equality or inequality constraints, comparing the performance of different algorithms based on different modeling equations, and comparing the results with different variants of benchmark systems. Numerous observations of unintended inaccuracies reported in previous published research work are used to illustrate the each argument; however, only few variants of benchmark test systems were sufficient to demonstrate and explain the existence of inaccuracies. The observed inaccuracies presented in this paper are explained, and the corresponding accurate results are presented to be used as standards in the future research. [ABSTRACT FROM AUTHOR]

• Keeping in mind the challenges in the practical implementation of the computational intelligence-based optimization methods, a conventional optimization-based topology error detection method is proposed in this paper. • Proposing a conventional optimization-based topology error detection method for systems with hybrid measurements, i.e., measurement set consisting of both supervisory control and data acquisition (SCADA) and PMU measurements. • Designing a computationally efficient optimization-based topology error detection method by application of the matrix inverse lemma. The proposed optimization-based topology error detection method is implemented in TOMLAB optimization platform using glcDirect solver. The paper presents an optimization-based method for topology error detection in power systems. The method utilizes the residual analysis in state estimation and minimization of normalized measurement residual, with the application of matrix inverse lemma. The work considers a hybrid measurement configuration, i.e., both SCADA and PMU measurements, for the test systems studied. The proposed method is implemented on the TOMLAB optimization platform under the mixed integer nonlinear programming category. The proposed method has been applied and tested on standard IEEE 14-bus and IEEE 118-bus test systems. The method is designed to be computationally efficient and produces accurate results for single topology error detection. The results from the IEEE 14-bus and IEEE 118-bus test systems have shown that the proposed method produces 100% and 94% accurate results for single topology error detection, respectively. The proposed method performs robustly with the increased measurement uncertainties and inclusion of bad data or gross errors in the measurements. The method has superiority in practical implementation over the meta-heuristics-based optimization methods. The proposed method can be easily implemented and could have potential application in the energy management systems of the power system control center. [ABSTRACT FROM AUTHOR]

Highlights • Exploits the parallel architecture of graphics processing units in DG allocation. • OpenCL implementation enables application on GPU and on multicore CPU. • Results show that MCS can be integrated in metaheuristic optimization approaches. Abstract Monte-Carlo simulation (MCS) is the most accurate technique for considering the stochastic nature of renewable energy resources in power system analysis and planning. However, due to its heavy computational burden, MCS is rarely utilized when solving the multi-objective renewable distributed generation (DG) allocation problem. In order to address this problem, this paper proposes a novel methodology to exploit the massively parallel architecture of graphics processing units (GPU) in a way that enables the use of MCS when solving the multi-objective renewable DG allocation problem. First, the renewable DG allocation problem is formulated as a multi-objective optimization problem to minimize the lines losses and the costs pertaining to installing renewable DG units in the distribution network. Then, a parallelized implementation of NSGA-II using OpenCL is described in details to solve the formulated multi-objective renewable DG planning problem. The feasibility and effectiveness of the proposed methodology are validated using the IEEE 32-bus test system and two real distribution test systems. The results show that the proposed parallelized implementation can enable the use of MCS for modelling the generation and demand uncertainties when solving the multi-objective renewable DG allocation problem using a metaheuristic approach. [ABSTRACT FROM AUTHOR]

In this paper, an improved single-phase grounding fault (SPGF) nature identification strategy based on machine learning is proposed. To solve the problem of conventional single-phase automatic reclosure cannot distinguishing the nature of SPGF in AC-DC hybrid lines, the fault phase voltage characteristics of AC lines with DC feed are analyzed, and the voltage harmonic energy feature vector is extracted. The whale optimization algorithm (WOA) designed by orthogonal experiment design (OED) is used to select the model parameters of the least square support vector machine (LSSVM), and a discriminative model for the fault properties of the hybrid transmission line is obtained. The simulation experiments based on PSCAD/EMTDC 4.5.0 platform are conducted for model training and fault identification performance testing, and the results verifies the effectiveness and feasibility of the proposed method. Compared with LSSVM, the fault identification strategy based on OED-WOA-LSSVM has a higher accuracy in fault identification. In addition, the accuracy of the model identification strategy under different fault locations, parallel compensation degree, transition resistance, and power system noise effects is also verified. [ABSTRACT FROM AUTHOR]

• An improved metaheuristic algorithm named the Elite Marine Predators Algorithm (EMPA) is designed. • The proposed algorithm is used to solve the directional overcurrent relay coordination problem. • The EMPA algorithm is used to find the optimal relays settings and operating times in four different power systems (3-bus, 8-bus, 9-bus and 15-bus). • The results using EMPA algorithm were compared against those of well-regarded and recent published algorithms and proved to have superior performance. The integration of renewable energy sources in the distribution network (DN) has had a significant influence by reducing power loss and enhancing network dependability. Aside from that, the protection system has met coordination issues as a result of bidirectional power flow and variations in fault levels. Therefore, an optimal coordination strategy is required to deal with relays coordination problem. The coordination problem of the directional overcurrent relays (DOCRs) is a restricted optimization issue that involves determining appropriate time dial settings (TDS) and plug setting (PS) to reduce relays operating time. Currently, a various nontraditional optimization strategies have been presented to overcome this challenge. In this paper, a modified version of the marine predators algorithm (MPA) referred to as Elite marine predator (EMPA) is developed for the optimal coordination of DOCRs. Therefore, the EMPA method is used to find out the optimal settings for the DOCRs problem. The suggested algorithm's performance is evaluated using standard test systems, including 3-bus, 8-bus, 9-bus, and 15-bus. The findings are compared with the traditional MPA and with other recent optimization methods presented in the literature to prove the efficiency and superiority of the proposed EMPA in reducing relay operation time for optimal DOCRs coordination. [ABSTRACT FROM AUTHOR]

The Transmission Network Expansion Planning problem (TNEP) can be modeled either as a static, a pseudo-dynamic, or a dynamic problem. Most of the existing formulations do not include reactive power planning within the TNEP problem, leading to sub-optimal designs leading to higher system costs in reality. This paper proposes a dynamic (multi-stage) non-convex formulation that optimizes the addition of transmission circuits and reactive power compensation devices, accounting for operational costs including losses. The planning is done considering (N − 1) security constraints using an AC model. As there are no similar research works to benchmark the outcomes, the results were compared with those obtained from the static and pseudo-dynamic approaches, showing that the proposed approach provides more economical solutions. It is also shown that better solutions are obtained when Reactive Power Planning (RPP) is considered in the problem formulation. An improved Differential Evolution (DE) and Continuous Population Based Incremental Learning (PBILc) hybrid solution method (IDE-PBILc) is proposed which drastically improves calculation time and robustness. Comparisons with two different state-of-the-art metaheuristics are performed for validation. The results were obtained for the Garver 6-bus, IEEE 24-bus, and the IEEE 118-bus systems. Even though in this work uncertainties are not considered, the proposed approach could be of particular use when studying systems with high renewable energy penetration scenarios, due to its computational efficiency. • Dynamic AC transmission expansion planning. • Full AC non-convex modeling. • Reactive power planning. • (N − 1) Security constraints and operational costs. • Improved DE-PBILc solution method. [ABSTRACT FROM AUTHOR]

Optimal reactive power dispatch (ORPD) is a particular case of the optimal power flow. ORPD was usually considered as the minimization of an objective function representing the total active power losses in the electrical networks. The constraints involved are the generator voltages, tap regulating transformers ratios and the amount of reactive shunt compensators. The goal of this study is to find the best vector of control variables, so that the power loss decreasing can be realized. In this paper, a new metaheuristic technique inspired from the bubble-net hunting technique of humpback whales, namely whale optimization algorithm (WOA), has been applied to solve the ORPD problem. The WOA method has been examined and confirmed on the IEEE 14-bus, IEEE 30-bus, in addition to a practical and large scale Algerian electric 114-bus test system. The obtained outcomes have been compared with two own developed methods, namely, particle swarm optimization (PSO) and particle swarm optimization with time varying acceleration coefficients (PSO-TVAC). Afterwards, an analysis of variance (One-way ANOVA test) has been implemented in order to verify the performance of our proposed algorithm in solving the ORPD problem. In summary, the comparison study shows the potential of this recent optimization method, and proves its robustness and effectiveness in solving the ORPD problem. [ABSTRACT FROM AUTHOR]

The most common damped filters (DFs) are the second-order, third-order, C -type, and double-tuned filters. Other DFs such as the first-order and band-pass filters exist, but their high operating losses considerably diminish their usage. In this paper, firstly, for the third-order damped filter with equal and unequal capacitors, the relations among their circuit parameters are derived. Secondly, the optimal design problem of the two third-order high-pass filters is formulated by regarding these expressions to minimize the filter cost taking into account both the investment and operating expenses. The total and individual harmonic distortion indices, power factor and the harmonic voltage amplification ratios which measure the filter’s resonance damping capability, are considered as constraints. A recent metaheuristic optimization technique based on the intelligent behavior of crows, known as the Crow Search Algorithm (CSA), is employed for the solution of the formulated design problem. Further, a comparative analysis of the two designs of the third-order high-pass filters and a third-order C -type filter is presented. The results reveal that all the proposed filters guarantee no electrical resonance hazards while maintaining the allowable limits for the various performance indices of the system, load, and filter. Besides, the comparative analysis validates that the C -type filter provides higher power factor, system efficiency and transmission loss improvement than the other two filters, and that the proposed filters achieve almost the same voltage and current harmonic mitigation levels. The solution of the cost minimization problem reveals that the C -type filter and the third-order high-pass filter with equal capacitors have the worst and best resonance damping capabilities respectively, under the worst case conditions. Additionally, the filters with the lowest and highest cost are found as the third-order filter with unequal capacitors and the C -type one, respectively. Besides, the CSA is compared to the genetic algorithm (GA), and particle swarm optimization (PSO) techniques and the results show the fast convergence capability and the effectiveness of the proposed algorithm in solving the problem of optimal design of third-order resonance-free passive filters in distribution networks. [ABSTRACT FROM AUTHOR]

A powerful metaheuristic technique is proposed that uses a mathematical concept called Stochastic Fractal Search (SFS) to ensure fast convergence along with accuracy. This paper intends to determine the optimal set of multilayer perceptron neural network (MLP) parameters (weights and thresholds) to improve the performance of MLP by using the SFS technique. The SFS is used because of its effective search in finding the global minima and therefore, it avoids the MLP neural network trapped in local minima. The hybrid approach (MLP-SFS) is applied to solve the dynamic state estimation (DSE) problem at the filtering stage. DSE amalgamates forecasting procedure with measurement data to precisely assess the system state. The approach classifies the process into three stages. In the first stage, a short term hourly load forecasting is applied using support vector machine (STLF-SVM) for time series to forecast the unavailable load data due to communication failure from the previous hourly historical data load. The second stage constitutes an optimal power flow (OPF) that is used to determine the minimum cost generation dispatch to serve the given load and convert the obtained loads, and generations into measurement data. The third stage has a filtering process, which uses SFS technique to optimize the MLP neural network parameters (weights and thresholds) to estimate the system state. The hybrid MLP-SFS is used to find the optimal connection weights and thresholds for the MLP neural network. Following this, a simple backpropagation neural network (BPN) will adjust the final parameters. The approach is tested on IEEE 14-and 118-bus systems using realistic load patterns from the New York Independent System Operator (NYISO) under several scenarios of measurement error and communication failure. The mean absolute percentage error index of the system state (phase and magnitude voltage) is used to determine the accuracy of the approach (MLP-SFS). Results of the proposed approach (MLP-SFS) are compared with non-optimized MLP (random weights and thresholds) and other methods, such as, optimized MLP based on genetic algorithm (MLP-GA) and Particle Swarm Optimization (MLP-PSO) individually. The results indicate that the hybrid (MLP-SFS) increases the precision by about 20%–50% and reduces the computational time around by 30%–50%, which is good for real-time applications, such as, security assessment and contingency evaluation. Details of the models of generation and distribution level are not part of the state estimation in the high voltage transmission problems. [ABSTRACT FROM AUTHOR]

In this paper, an efficient metaheuristic technique is proposed for solving the static transmission expansion planning problem. This technique is known as the social spider algorithm. A suitable methodology for handling the problem constraints is also proposed. In the proposed methodology, a DC power flow sub-problem is solved for each network resulting from adding a potential solution developed by the social spider algorithm to the base network. The proposed algorithm and the accompanying constraints handling methodology have been tested through solving the transmission expansion planning problem of three benchmark systems having 6-buses, 46 buses, and 87 buses. Excellent performance and new solutions are demonstrated in the results section. The new solutions achieve a remarkable reduction in the total investment cost. [ABSTRACT FROM AUTHOR]

This paper proposes three metaheuristic optimization techniques to solve the plug-in electric vehicle (PEV) charging coordination problem in electrical distribution systems (EDSs). Optimization algorithms based on tabu search, greedy randomized adaptive search procedure, and a novel hybrid optimization algorithm are developed with the objective of minimizing the total operational costs of the EDS, considering the impact of charging the electric vehicle batteries during a specific time period. The proposed methodologies determine a charging schedule for the electric vehicle batteries considering priorities according to the PEV owners charging preferences. A 449-node system with two distributed generation units was used to demonstrate the efficiency of the proposed methodologies, taking into account different PEV penetration levels. The results show that the charging schedule found makes the economic operation of the EDS possible, while satisfying operational and priority constraints. [ABSTRACT FROM AUTHOR]

The smart-grid brings new challenges to the optimal dispatch of power. Current research aims to develop optimization techniques capable of handling large networks using accurate models and realistic constraints, all in the shortest possible execution time. For this purpose, this paper presents a metaheuristic-based parallel optimal power flow algorithm for graphics processing units (GPUs). Metaheuristics have the advantage of handling discrete variables and being resilient to premature convergence towards local optima. However, they require significant computing power which limits their use in on-line applications. The proposed implementation addresses this limitation and significantly accelerates the calculation by exploiting the massively parallel architecture of GPUs. The developed software uses a particle swarm optimizer and runs a full ac Newton–Raphson power flow analysis to evaluate the candidate solutions. The algorithm is tested on the IEEE 30-bus, 118-bus and 300-bus networks and provides a maximum speedup of 17.2×. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of determining the optimal location and operation of distribution static synchronous compensators (D-STATCOMs) in power distribution networks. Metaheuristic algorithms have been conventionally used to solve this mixed-integer nonlinear programming problem. In contrast, we propose a mixed-integer second-order cone programming (MI-SOCP) model that guarantees global optimum and fast convergence of the algorithms in optimization solvers. The model seeks to minimize the annual installation cost and the operating cost, subject to active and reactive power balance constraints, voltage regulation bounds, devices capabilities, and the number of D-STATCOMs available to be connected. The multiobjective nature of the problem is also analyzed and solved using the proposed MI-SOCP model. An extensive set of simulations on the IEEE-33 nodes test system demonstrate the advantages of this approach compared to conventional heuristic algorithms and with solutions given by algebraic modeling software. • D-STACOMs permit annual operative costs reduction in distribution grids. • MI-SOCP formulation allows finding the global optimal solution of the exact MINLP model through solution space convexification. • The proposed optimization model does not require statistical evaluation to validate its efficiency since it is mixed-integer convex in nature. [ABSTRACT FROM AUTHOR]

Service restoration problem in distribution systems emerges after the faulted areas have been identified and isolated. A solution is obtained by determining the minimal number of switching operations that results in a configuration with a minimal number of healthy out-of-service areas without violating the operational and radiality constraints. Recently a practical and efficient methodology was developed and demonstrated through tests performed on the real and large-scale distribution system of Londrina city (Brazil). This methodology, named MEAN-MH, combines a M ulti-objective E volutionary A lgorithm with Node-Depth Encoding, M ultiple criteria tables and an alarming H euristic. As any methodology based on meta-heuristics, the MEAN-MH does not guarantee to find the optimal solution of the service restoration problem, even when the optimal solution requires operations only in normally open switches incident to the healthy out-of-service areas (named as Tier 1 normally open switches). This paper proposes an extension of MEAN-MH that incorporates an Exhaustive Search (ES) procedure as a previous stage before MEAN-MH. The proposed ES guarantees the generation and analysis of all possible radial configurations that restore the service to all healthy out-of-service areas requiring operations only in Tier 1 normally open switches. Therefore, when the optimal solution of the service restoration problem requires operations only in Tier 1 NO switches, the proposed methodology, named MEAN-MH+ES, guarantees the optimum. However, when the optimal solution requires operations also in other switches, the MEAN-MH+ES searches by a feasible solution minimizing both the number of switching operations and the number of healthy out-of-service areas. To demonstrate the effectiveness of the proposal, both MEAN-MH and MEAN-MH+ES are applied to two real and large-scale distribution systems of Brazil. Moreover, the results obtained by MEAN-MH+ES are compared with those found in another published work. [ABSTRACT FROM AUTHOR]

Transmission Expansion Planning (TEP), the problem of deciding the new transmission lines that should be added to an existing transmission network in order to satisfy system objectives efficiently, is one of the main strategic decisions in power systems and has a deep, long-lasting impact on the operation of the system. Relatively recent developments in power systems, such as renewable integration or regional planning, have increased considerably the complexity and relevance of this problem. This is particularly true in the case of the European Union. These issues have motivated the appearance of a vast array of projects that propose specific development plans for the required transmission, together with academic literature that deals with the different theoretical aspects of the problem. It seems, therefore, pertinent to review these recent works and put them into context. This paper performs a critical review on TEP focusing on its most recent developments. It analyzes the current challenges to transmission planning and illustrates them with some instances of TEP in a European context. Then, it proposes a taxonomy of modeling decisions and solution methods for this problem, linking them to some of their main representative works in the literature with an emphasis on the most recent advances. These alternatives are critically compared, providing with insights that can guide researchers or practitioners when undertaking this kind of studies. [ABSTRACT FROM AUTHOR]

In this paper, the problem of secondary distribution system (SDS) design considering plug-in electric vehicles (PEVs) charging demand is addressed using meta-heuristic optimization to minimize the overall secondary system costs. The objective function and the necessary constraints are mathematically formulated and presented. The effectiveness of the proposed SDS design approach is evaluated in terms of the overall SDS costs and transformer loss of life (LOL). The proposed design approach represents an effective tool for electric utilities to accommodate PEVs during the planning stage of SDS and also when retrofitting existing SDS. [ABSTRACT FROM AUTHOR]

In this paper, a comparative study of different meta-heuristic optimization approaches, which had been proposed in the literature for directional overcurrent relay coordination (DOCRs), is presented. Towards this goal, five most effective meta-heuristic optimization approaches such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), harmony search (HS) and seeker optimization algorithm (SOA) have been considered. The performances of these optmization methods have been investigated on several power system networks of different sizes. The comparative performances of these methods have been studied by executing each method 100 times with the same initial conditions and based on the obtained results, the best meta-heuristic optimization method for solving the coordination problem of directional overcurrent relays is identified. [ABSTRACT FROM AUTHOR]

• Reconfiguration is one of the most effective ways to reduce ohmic losses. • The hybrid methodology explores each set of problem variables with different techniques. • Particle swarm optimization considers distribution network reconfiguration and optimal power flow adjusts the value of PV inverters and maintenance network operating limits. • Photovoltaic systems can reduce the apparent power in the grid and reduce electrical losses. • The proposed solutions can avoid excess reactive power charges for PV purposes. This paper presents a hybrid approach, which associates a mathematical programming technique with a metaheuristic, to optimize the electricity distribution network with penetration of photovoltaic (PV) generation. This optimization considers the distribution network reconfiguration (DNR) and the adjustment of the power factor of photovoltaic (PV) inverters about the reactive power, controlling the voltage profile and minimizing network losses. The methodology uses particle swarm optimization (PSO) for DNR and an optimal power flow (OPF) to adjust the value of the PV inverters and maintenance of network operating limits, this being a great difference from other methodologies. The objective of the hybrid approach is to obtain the best result for the problem, taking advantage of the best characteristics of both techniques. The hybrid approach was compared with the PSO, highlighting its superiority in terms of the solutions found. The tests were performed using the AMPL Modeling Language in the 33-node system with the addition of PV generation. The approach considers the variations in the powers generated by the PV systems and the variations in the loads over 24 h. The results indicate that the combination of PV inverter control and DNR can reduce the active power losses by more than 37% in 24 h. [Display omitted] [ABSTRACT FROM AUTHOR]

• Fuel cell • Optimization techniques • Fractional-order PI Fractional-order PI (FOPI) is considered a possible efficient alternative controller to classical PI controller for fuel cell (FC) on-grid systems applications. This paper aims to enhance the dynamic performance of grid-connected FC systems while employing different metaheuristic optimization techniques to FOPI controllers and consequently improve the power quality and stability of the system. The proposed metaheuristic techniques are modified flower pollination algorithm (MFPA), harmony search (HS), and electromagnetic field optimization (EFO). In the study, two FOPI-based regulators are suggested for controlling the current and the power flow between the FC and the network. For this purpose, the inverter's pulse width modulation (PWM) regulation is performed while integrating FC systems into the network. The three metaheuristic optimizers are used for the optimal gain scheduling of the FOPI controller parameters while minimizing the absolute value of the error as the cost function. To demonstrate how efficient the FOPI controllers are, fair comparisons for the on-grid FC-based system performance while proposing the different optimized advanced FOPI and conventional PI controllers are analyzed. According to the simulation results, the most efficient metaheuristic technique is identified based on an impartial defined criterion regarding the dynamic behaviors' steady-state error, overshoot besides both setting and execution time. Through the suggested criterion, the EFO is considered the best optimization approach. Through the numerical analysis, the power quality assessment of the optimally controlled overall FC on-grid systems is illustrated through voltage sag/swell studies. [ABSTRACT FROM AUTHOR]

• A novel adaptive (dynamic and flexible) protection approach for earth and phase OCR coordination in microgrid system. • An new Optimal protection scheme of phase and earth OCRs in DN. • New optimization methods to minimize the total tripping time of OCRs in microgrid system. Microgrids are increasingly paying attention to the emerging of distributed energy resources. However, microgrid protection has challenged engineers because of the varying nature of fault current distribution during grid-connected and islanded operations. This paper suggests a novel approach of coordination between phase and earth fault overcurrent relays to ensure stability and security for the performance of protection schemes in active distribution networks. In this study, a combination of phase and earth overcurrent relays is coordinated to avoid mis-coordination in the overcurrent protection schemes. In the proposed approach, phase overcurrent function was employed as backup protection for earth fault function locally in the same relay instead of the traditional remote backup earth fault protection coordination approach. The suggested coordination approach provides a fast and reliable protection performance in different earth faults scenarios. The coordination was tested on the IEC Microgrid Benchmark network. Also, the objective function was modified to reduce the overall operational time using two recently developed metaheuristic optimization algorithms known as the charged system search (CSS) and Teaching-Learning-Based Optimization Algorithm (TLBO) in the MATLAB package. Finally, the time multipler setting values were adapted and compared to verify the speed and reliability of the proposed coordination approach using ETAP. [ABSTRACT FROM AUTHOR]

Highlights: [•] Improvement for the optimal siting and sizing of distributed generation. [•] This paper presents a probabilistic three phase load flow in radial distribution networks. [•] The model takes into account the random nature of lower heat value of biomass. [•] A shuffled frog-leaping algorithm and probabilistic load flow are used. [•] Acceptable solutions are reached in a small number of iterations. [ABSTRACT FROM AUTHOR]

Abstract: The availability of intelligence at substation level, combined with the adoption of pervasive communication networks, offers technologies and opportunities to decentralize voltage regulation in active power distribution systems. Armed with such a vision, this paper proposes the employment of meta-heuristic optimizer agents aimed at addressing voltage regulation in a distributed scenario. In particular, it is demonstrated that the cost functions describing the voltage regulation objectives can be obtained by solving distributed consensus problems over a network of cooperative and dynamic agents. In addition, all the basic operations required to solve the voltage regulation problem can be computed by the distributed meta-heuristic optimizer agents according to a totally decentralized/non-hierarchical paradigm. Definitively, in a very simple way, based on the global grid conditions each voltage controller may decide if and when a reactive power flow injection into the network is most useful. [Copyright &y& Elsevier]

Abstract: In this paper, a novel metaheuristic optimization methodology is proposed to solve large scale nonconvex economic dispatch problem. The proposed approach is based on a hybrid shuffled differential evolution (SDE) algorithm which combines the benefits of shuffled frog leaping algorithm and differential evolution. The proposed algorithm integrates a novel differential mutation operator specifically designed to effectively address the problem under study. In order to validate the SDE methodology, detailed simulation results obtained on three standard test systems 13, 40, and 140-unit test system are presented and discussed. Transmission losses are considered along with valve point loading effects for 13 and 40-unit test systems and calculated using B-coefficient matrix. A comparative analysis with other settled nature-inspired solution algorithms demonstrates the superior performance of the proposed methodology in terms of both solution accuracy and convergence performances. [Copyright &y& Elsevier]

• Adaptive setting of directional overcurrent protection based on fuzzy system. • Consideration of system stability constraint in protection setting. • Teleprotection model for increased performance. • Time-domain simulation model for performance analysis. Based on the expected features of modern relays, such as adaptive and self-adjustment functions, this paper presents a model and time-domain simulation of a directional overcurrent relay (DOR) with adaptive pick-up current (I p) provided by a fuzzy system, where the teleprotection scheme is incorporated as a performance enhancer. A comparison of the operation time of conventional relays optimally coordinated with the evolutionary particle swarm optimisation (EPSO) meta-heuristic algorithm, considering stability constraint, with the proposed adaptive method has been carried out. For the calculation of the critical stability time, the bisection and potential energy boundary surface (PEBS) methods were considered. The results of the comparison have shown the good performance of a fuzzy-based setting on the DOR with the reduction in the average operation time while ensuring the system operation in the stable region. The role of the teleprotection and the stability analysis are also shown with time-domain simulations using the Alternative Transients Program – The Electromagnetic Transients Program (ATP-EMTP). [ABSTRACT FROM AUTHOR]

• Development of probabilistic and deterministic optimization problem to tune power system stabilizers. • Use of the new directional bat algorithm. • Detailed comparison of probabilistic and deterministic tuning method. • Testing of controller performance under different critical scenarios. The modern power system consists of a large mixture of renewable energy sources (RES), varying and flexible loads, and is also experiencing a situation where a significant number of conventional generators are being replaced by power electronics based sources. All of these factors may lead to a decrease in the small-signal stability (SSS) margin of the system which in turn can cause power system instability. Power system stabilizers (PSSs) are widely used to improve the low-frequency oscillatory stability of power systems. Currently, there are two popular methods to coordinate multiple PSSs to improve SSS: deterministic and probabilistic method. This paper first formulates the problem of coordinating multiple PSSs to improve SSS as a deterministic and a probabilistic optimization problem which is then solved using the new directional bat algorithm. A detailed comparison between the two design methods under different scenarios based on obtained results is carried out afterward. All of the analysis was carried out on a modified IEEE 68 bus system. The obtained results provide key insights as well as the advantages and limitations of the probabilistic and deterministic approach to optimize the PSS parameters. [ABSTRACT FROM AUTHOR]

• A novel hybrid optimization approach based on meta-heuristic algorithms to solve the non-convex economic dispatch (ED) problem in large-scale power grids is proposed by an optimal and adaptive formulation. • This proposal optimizes the solution of the ED problem, reducing the generation costs and demonstrating a better performance during its convergence characteristics. • The adaptive hybrid optimization offers flexibility and soft computing for large-scale power system analysis. • The proposed approach demonstrates the superiority of the contribution to show the new advantages regarding other algorithms via numerical results in large-scale power grids. This paper proposes a robust adaptive hybrid optimization approach taking advance of two meta-heuristic algorithms to solve the non-convex economic dispatch (ED) problem in large-scale power grids. The demanded load is continuously increasing, leading to the operators to cope with new challenges associated with the reduction of the generation costs. As a consequence of this unmeasured increasing, algorithms with flexible and soft computing capabilities for analysing large-scale power systems are required. Thus, the key idea behind the proposed hybrid optimization framework is based on the abilities of the adaptive simulated annealing (ASA) and genetic operators for evaluating the economic dispatch problem, since these accelerate the convergence and reduce the total number of evaluations. This proposal optimizes the solution of the non-convex ED problem, decreasing the generation costs and emissions, and demonstrating a better performance during its convergence characteristics. Numerical tests are carried out in two power grids, where a reduced version of the Mexican interconnected power system is considered with a 24-h wind profile, aiming to demonstrate the superiority of this work, and show the new advantages regarding other algorithms. [ABSTRACT FROM AUTHOR]