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Due to the high penetration of electric vehicles (EVs) to the power grid in recent years, the electricity operators have been encouraged to use the advantages of this new equipment in the peak flattening, voltage profile improvement, reactive power control and fast frequency regulation. Electric vehicles are introduced as an appropriate alternative to small-scale power plants through distributed profits for owners. Since the electric vehicles which are connected to the grids at large-scale, they confer a number of challenges and benefits, while the operation scheduling of traditional distribution networks in their presence will be more complicated. Truthfully, the electric vehicles could be recognized as a demand supplier for a part of the network, however, the intelligent battery charging control should be carefully achieved to keep the network appropriately balanced in power procurement. In this paper, an applicable framework for charging/discharging control of EVs is provided that not only maintains the large scale of vehicles connecting to the grid, but also can improve the voltage profile of buses in each section of the power system. The proposed formulations describe the principles of electric vehicles operation and the state of charge (SOC) control regarding to the owners’ profit optimization.

The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.

Microgrids need control strategies to achieve maximum performance. An appropriate control strategy should have high performance in unbalanced conditions in addition to normal conditions. Today, the use of microgrids, with a variety of power sources, including solar, wind, diesel, energy storage sources to increase the capability of distribution grid has made significant progress. However, controlling and managing their energy in the event of a fault is a challenge that researchers are faced. In this article, two microgrids connected to the grid are studied using a back-to-back (BTB)-voltage source converters (VSC) converter. The results of this research showed that by the usage of above-mentioned convertor first, with power management between microgrids the frequency remains constant in island mode second, they are isolated from each other in terms of faults. The results showed that the use of the proposed method controlled the frequency of two microgrids simultaneously.

This paper presents an innovative instantaneous pricing scheme for optimal operation and improved reliability for distribution systems (DS). The purpose of the proposed program is to maximize the operator’s expected profit under various risk-taking conditions, such that the customers pay the minimum cost to supply energy. Using the previous information of the energy consumption for each customer, a customer baseline load (CBL) is defined; the energy price for consumption costs higher and lower than this level would be different. The proposed scheme calculates the difference between the baseline load and the consumption curve with the electricity market price instead of calculating the total consumption of the customers with the unstable price of the electricity market, which is uncertain. In the proposed tariff, the developed cost and load models are included in the distribution system operation problem, and the objective function is modeled as a mixed integer linear programming (MILP) problem. Also, the effect of demand response (DR) and elasticity on the load curve, the final profit of the distribution system operator, and payment risk and operation costs are examined. Since there are various uncertainties in the smart distribution grid, the calculations being time-consuming and volumetric is important in the evaluation of reliability indices. Thus, when computation volume can be decreased and computation speed can be increased, analytical reliability analysis methods can be used, as they were in the present work. Finally, the changes in the reliability indices were calculated for the ratio of the customers’ sensitivity to the price and the customers’ participation in the proposed tariff using an analytical method based on Monte Carlo simulation (MCS). The results showed the efficiency of the proposed method in increasing the operator profit, reducing the operation costs, and enhancing the reliability indices.

The penetration level of renewable and sustainable energy resources such as geothermal, wind, solar energies and ocean in power generation systems has been increasing. One of the important renewabl...

Tidal as renewable energy is increasingly utilised for power generation in many countries. Integration of tidal plants into the power system requires considering the intermittent nature of the generated power caused by variable tide levels. Thus, the reliability studies of power systems including high tidal generation affected by the uncertain nature resulting from the variable water levels are investigated. The reliability model of a tidal power plant on a barrage is proposed. In the proposed model, the failure rates of the composed components (especially the hydro pump and gate) and the effect of tidal height variation on the components failure rate are considered. Also, the reliability model with numerous states resulting from the variability of the generated power through the fuzzy C-means clustering technique and Xie-Beni index is reduced to a multi-state reliability model. The resulting reliability model is utilised for the generation adequacy studies of a power system containing large-scale tidal power plants. Moreover, different reliability indices are calculated for future generation expansion planning. The proposed technique is applied to the two-test systems, and sensitivity analysis is performed. Besides, the effects of the hydro pump, maintenance, ageing of the components and peak load are investigated.

Multi-carrier energy systems (MCESs) provide collaboration between various kinds of energy carriers to supply the electricity, heating, and cooling demands. With the widespread use of MCESs in recent years, the security assessment of energy systems has attracted the attention of many contemporary researchers. However, the complexity of an MCES, including electrical, natural gas, and district heating networks, and different uncertainties imposes vast challenges to keep a safe operation energy supply. In this paper, a systematic methodology for the security analysis of MCESs is presented. For this purpose, considering electrical, natural gas, and district heating networks, an integrated model of energy systems is introduced. The security analysis of this framework is evaluated using some indices. In this approach, two well-known performance indices, including power performance index (PIP) and voltage performance index (PIV), are used to analyze the electrical networks’ security. Besides, the concept of Energy not supplied (ENS) is used for natural gas and district heating networks. In this regard, security analysis of a typical MCES including the IEEE 14-bus electrical network, the IEEE 30-bus electrical network, 20-node Belgian natural gas network, and 14-node district heating network is examined. The applicability of the proposed technique will be proven using comprehensive simulation analysis.

Distribution Static Synchronous Compensator (D-STATCOM) is a shunt compensator in the distribution systems that one of the most important tasks is to improve the imbalance, reduce and eliminate the nonlinear loads harmonics. Distributed generation resources such as photovoltaic (PV) array can be used as the DC input of D-STATCOM. In this paper, PV array and DC/DC boost convertor are used to stabilize the DC link voltage of D-STATCOM. The main advantage of the proposed method is that for all time provides continuous compensation. Another power quality issues are neutral current in four-wire systems that are created due to the harmonics and system imbalance. Zig-Zag transformer is one of the ways for compensating neutral current and providing isolation between the convertor and flow of the fundamental zero sequence component which contains harmonic neutral current. Role and performance of the distribution shunt compensator in the improvement of power quality indices depends on the performance of its control system. In this paper, the control scheme of synchronous reference frame theory based on fuzzy controller is used for D-STATCOM based on PV and Zig-Zag transformer. Performance and behaviour of the proposed system examined using Matlab/Simulink software and the results will be presented.

In this paper, a new type of single-level power-conditioning system for photovoltaic arrays connected to the single-phase grid is presented in which transformer-based impedance source inverter (Trans-ZSI) is employed to reduce cost and volume, and meanwhile to improve the efficiency. As a buck–boost inverter, the Trans-Z-source inverter includes just an inverter bridge and a passive network, which itself consists of a pulse transformer and a capacitor. This inverter can be an appropriate alternative for the common structures consisting of inverters and DC/DC converters. In this article, presenting an efficient controlling method, Trans-Z-source inverter is used to connect the photovoltaic system to a single-phase grid. In the controlling structure, the improved INC method is proposed to track the maximum power point, while the proportional–resonance and proportional–integral controllers are employed to inject power to the grid and stabilize the Trans-ZSI capacitor voltage, respectively. Simulations show the functionality of the proposed system.

The issue of environmental emissions has forced the power systems to use cleaner energy sources such as renewable and hydroelectric technologies. However, during recent decades due to the limitations on the available water in many regions, the optimal water reservoir usage has been highlighted. In this regard, this paper proposes a multi-objective model for short-term hydrothermal scheduling problem in the presence of the pumped-storage technology. It is noted that the framework well models the cascaded configuration of hydro reservoirs. Besides, in order to more accurately model the mentioned problem, a Mixed-Integer Non-Linear Programming (MINLP) optimization framework is presented. In this respect, the valve-loading effects occurred in thermal power generation technologies have been taken into account which turns the existing convex optimization problem into a non-convex one. In order to solve the mentioned problem, the Normal Boundary Intersection (NBI) method has been used while the VIKOR decision maker is employed to choose the most compromise solution amongst the Pareto optimal solutions obtained by NBI method. Finally, the efficiency of the proposed model has been verified through implementing four case studies and comparing the obtained results with those obtained by different methods.

The presence of gas-to-power (G2P) and power-to-gas (P2G) systems in the power system has led to the interdependency of gas and electricity energies. Thus, to increase energy efficiency and achieve desirable financial profit to the mentioned elements, an energy market and ancillary services combined by gas and electricity energies are required so that interdependency of electrical and gas energies and vice versa and the dependency of price to energy can be observable. Hence, this paper presents the strategic offering method for gas and electricity producers (GPs and EPs) in the coupled gas and electricity market as the day-ahead (DA) energy and reserve model. This strategy includes a two-stage problem, where the first stage contains a bi-level model that minimizes the difference between the expected operation cost and expected revenue of all strategic EPs (SEPs) in DA electricity energy and reserve markets subjects to the SEPs model in the upper-level. The lower-level problem minimizes the expected non-SEPs operation cost limited to DC optimal power flow equations, reserve, and non-SEPs formulation. The same problem with the first stage formulation is repeated for GPs in the second stage. Strategic GPs (SGPs) participation model in the DA gas energy and reserve markets presents in the upper-level problem but the market clearing price (MCP) method in the gas network expresses in the lower-level problem. This scheme includes uncertainties of demand and renewable power that are modeled by stochastic programming. The Karush-Kuhn-Tucker (KKT) approach obtains a single-level formulation in each stage, and thus, the master/salve decomposition algorithm solves the proposed strategy to achieve the optimal solution. Finally, this method is simulated on a standard test system to examine its capabilities. The numerical results obtained by the algorithm highlights that it can find the optimal solution in the least possible time considering the least computational errors. Furthermore, units with inexpensive fuel generally participate in the energy market following the proposed strategy; however, all units participate in the reserve market at all operating hours, which aims to achieve high optimal financial profit for the units. Following this, SEPs and SGPs in the mentioned market succeeded to obtain a profit of 37.5% and 13.6%, respectively, in their operating cost.

Contingency ranking is one of the most important stages in the analysis of power system security. In this paper, an integrated algorithm has been proposed to address this issue. This algorithm employs neural networks method to quickly estimate the power system parameters and Stochastic Frontier Analysis (SFA) in order to calculate the e ciency of each contingency. Network security indices (voltage violation and line flow violation) and economic indices (locational marginal price and congestion cost) have been simultaneously considered to rank the contingencies. The e ciency of each contingency shows its severity, and indicates that it a ects network security and economic indices concurrently. The proposed algorithm has been tested on IEEE 14-bus and 30-bus test power systems. Simulation results show the high e ciency of the algorithm. Test resultsindicate that predicted quantities are estimated accurately and quickly. The proposed method is capable of producing fast and accurate network security and economic indices, so that it can be used for online ranking.

A reward and penalty scheme (RPS) is used for setting up the service quality which exposes the distribution companies to financial benefits caused by demand for the reliability of customers. In this paper, an algorithm for the optimal switch number and placement in distribution networks in the presence of RPS is presented. The primary objective is reliability improvement and minimization of the cost of sectionalizing switches (SS) and tie switches (TS) for a given regulatory period considering acceptable financial risk. In this algorithm, the uncertainty in the reliability is appeared as financial risk. A genetic algorithm is adopted to solve the optimization problem. The number and location of SS and TS is found while financial incentives of RPS, capital investment and annual operation and maintenance costs are considered. The performance of the proposed approach is assessed and illustrated on a real distribution network. The results show the efficiency of the proposed algorithm.

In this paper, a hybrid approach for tuning and placement of power system stabilizers (PSS) in multi-machine power systems is provided with the aim of reducing low-frequency oscillations (LFO) and improving power system dynamic stability with wide range of changes in system parameters and operating point of the system. PSS parameters are adjusted using Particle Swarm Optimization algorithm (PSO), and using Takagi–Sugeno (TS) fuzzy, optimal location for the PSS is determined. The employed fuzzy system has two inputs, the real part and the damping coefficient of network eigenvalues. The results of test on a grid four machine-two area sample, show optimal performance of the proposed method in improving system stability and reducing low-frequency oscillations of local and inter-area modes.

In this paper, an algorithm is presented to obtain the parameters of reward and penalty schemes (RPSs) for each electric company using Data Envelopment Analysis (DEA). This algorithm is based on reliability indices and customers’ value of interruptions. The companies’ efficiency scores are calculated using a DEA methodology. The companies’ efficiency scores are based on their performance in quality and the best performer is utilized as a benchmark for other companies. DEA efficiency score and historical quality level are used to set a quality target for each electric distribution company. In this algorithm more than one reliability index can be included in RPS and weighting value for each reliability index is calculated based on their impacts on the customers. The performance of the proposed algorithm is demonstrated in a case study to design RPS for Iranian electricity distribution companies. The results of the algorithm include DEA efficiency score, parameters of RPS and weighting value for each reliability index.

Distribution networks are often very wide and are comprised of vast number of feeders. Always feeders are considerably broadened, and for them lots of maneuver points placements are predicted for load relocation and reducing the power outage period of customers. In this paper, locating the switching equipment in a radial distribution feeder is investigated. Given to budget restrictions and looking for technical and economic considerations and importance of load points, a combinatorial objective function is defined that pursues three main goals. First goal is minimizing installation and maintenance costs and power outages by optimal switches placement so that maximum budget is considered. For the second goal, consuming available and financial resources in distribution network is investigated in order to optimizing reliability. As the third goal, reaching a particular reliability level with the lowest price is considered. For optimization problem single objective and double objective genetic algorithm are employed. Experiments on a paradigm network and on a real Iranian power distribution network has validate the efficiency of the proposed method.

In this paper, an integrated algorithm is presented to assess the efficiency of electric companies using data envelopment analysis (DEA) combined with fuzzy c-means clustering (FCM) and principle component analysis (PCA). FCM algorithm is applied to find similar distribution companies and cluster similar companies into different groups. One of the important steps in the design of the benchmarking model is selecting input and output variables. PCA is used to reduce the number of input and output variables under study. DEA efficiency is highly sensitive to errors in the data, so a bootstrap method is used to assess this uncertainty by estimating bias and confidence intervals. The results of benchmarking include DEA efficiency scores (overall, technical, and scale efficiency), bootstrap technique, slacks in inputs and outputs of inefficient companies, and sensitivity analysis.

In this article, a smart stochastic framework is developed to use optimal switching to support increasing the role of renewable energy sources and electric vehicles in microgrid operation. The proposed method uses the point estimate method to model the uncertainties of electric vehicle charge and discharge demand, wind turbine, photovoltaics, load demand, and market price. The problem formulation is constructed in the form of a constrained single-objective optimization problem minimizing the total microgrid cost when meeting the practical operation constraints. Due to the high nonlinearity and the multi-modal characteristics of the proposed problem, a new optimization method based on the cuckoo search algorithm is proposed to solve the problem optimally. Also, a new three-stage modification approach is developed to increase the algorithm population and avoid the premature convergence. The feasibility and appropriate performance of the proposed method are examined on an IEEE test system. It is shown in the simulation results that using electric vehicle and optimal switching will result in better power dispatch amongst the distributed generations in the microgrid with lower power losses and costs.

After the introduction of distributed generators, regarding the challenges these networks face, employing AC/DC Hybrid microgrids would be an undeniable issue due to its many advantages and it will certainly find a significant position. Thus, discussing the power quality in this type of microgrids and clean power for feeding the load via this microgrid is a drastic challenge. This paper proposing a hybrid microgrid with 2 interface converters, one placed in series and the other placed in parallel, tries to accomplish these control objectives in an AC microgrid in order to improve power quality: input sine current with Cosϕ=1 and output sine voltage with normal value when source voltage is abnormal and non-sinusoidal, besides, non-linear loads’ presence. Simulation results verify the favorable performance of converters and effective functionality of control systems.

During recent years, Iranian authorities have tried to make competitive condition and to grant distribution companies to private sectors. In this paper, a new algorithm for assessment of the efficiency of Iranian electric companies using data envelopment analysis (DEA) with k-means clustering and principle component analysis (PCA) is presented. K-means algorithm is applied for finding similar distribution companies and clustering similar companies into different groups also PCA is used to reduce the number of input and output variables under study. The results of benchmarking are DEA efficiency score (overall, technical and scale efficiency), slacks in inputs and outputs of inefficient companies.

During recent years, Iranian authorities have tried to make competitive condition and to grant distribution companies to private sectors. The aim of Iranian authorities is not only to prevent the misuse of the monopoly position, but also to provide companies with efficiency incentives. In this paper, a new algorithm is presented to assess the efficiency of Iranian electric companies using data envelopment analysis (DEA) with principle component analysis (PCA). One of the important steps in the design of the benchmarking model is selecting input and output variables. PCA is used to reduce the number of input and output variables. DEA efficiency is highly sensitive to errors in the data, so a bootstrap method is used to assess this uncertainty by estimating bias and confidence intervals. The results of benchmarking include DEA efficiency score (overall, technical and scale efficiency) and bootstrap technique.

A reward and penalty scheme (RPS) in performance-based regulation (PBR) penalises companies for providing poor reliability and rewards them for providing good reliability. In this study, an algorithm is presented to obtain the parameters of RPS for each electric company by using data envelopment analysis (DEA) and fuzzy c-means clustering (FCM). This algorithm is based on system average interruption duration index (SAIDI) and customers' value of interruptions. FCM algorithm is applied to find similar distribution companies and cluster companies into different clusters. The best performer in each cluster is utilised as a benchmark for other companies and DEA is used to set a quality target for each electric distribution company. The performance of the proposed algorithm is demonstrated in a case study to design RPS for Iranian electricity distribution companies. The results of the algorithm include DEA efficiency score, parameters of RPS and financial risk assessment.