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One of the most prevalent and destructive types of cyber-attacks on power systems is the false data injection (FDI) attack. In a false data injection attack, the attacker inflicts large damages on the network by manipulating the measurements. The pivotal solution to opposing this type of cyber-attack is to use phasor measurement units (PMUs). In this paper, a new method is presented to confront the FDI attack by using the optimal placement of PMU instruments. In the proposed algorithm at the beginning, all PMUs placements that achieve network observability are determined using the tabu search (TS) algorithm. Then, from the observable placement vectors, the placements that minimize the possibility of a cyber-attack on the network is identified. For this purpose, a new attack criterion is presented, which is obtained from the adversary strategy in the attack scheme. Since the measurements obtained from the PMUs must be transferred to a phasor data concentrator (PDC) center, the PDC placement also must be determined. In this paper, the optimal placement of PDC is presented by considering the cost of communication infrastructure, because the cost of communication infrastructure between PMUs and PDC is significant. For this purpose, we have used the Kruskal algorithm. The simulations performed on the IEEE 30-bus and 118-bus test system confirm the effectiveness of the proposed method for opposing cyber-attacks and reducing the cost of communication infrastructure. [ABSTRACT FROM AUTHOR]

Nowadays, consumption of different energy carriers is increasing due to the division of community activities in various sectors such as residential, commercial, and industrial. The energy hub concept is used to meet the demands of different energy carriers in these sections. In this paper, a new method for energy management of a microgrid is presented in an intelligent network based on three types of commercial, residential, and industrial energy hubs. In the energy hubs, a wide range of components, including renewable and nonrenewable generation units, converters, storage devices, intelligent parking lots consisting of electric vehicles, P2G units, and cogeneration units, are used to supply electrical, heating, cooling, and natural gas energies. Some parameters like renewable generations, energy demands, and arriving and departure time of electric vehicles are considered to be uncertain, and a relevant method is applied to provide a near-realistic profile for them. In addition, a demand response paradigm has also been proposed for three types of electrical, heating, and cooling demands at the hub output side. Therefore, in this method, a mixed integer linear programming model is proposed based on benefit and reduction of emission caused by the activity of gas-burning units for short-term planning and obtaining an optimal solution for generation and sending loads in a distribution network containing energy hubs. To evaluate the performance of the proposed modeling and structure, the presented approach is applied on a modified 33-bus IEEE test network. According to the results of the energy management model, it is possible to significantly increase benefits and also obtain a smoother consumption pattern in consumption with financial incentives. [ABSTRACT FROM AUTHOR]

Improving network power quality through harmonic reduction requires recognition of Harmonic Sources (HSs) to drive them to compensate their harmonics. This paper proposes a new method for equitable distribution of the Harmonic Compensation Cost of the network among the HSs based on the Harmonic Contribution Matrix. Each element of the Harmonic Contribution Matrix is the harmonic contribution of a specific source to the harmonic voltage of a specific bus. The output of the proposed method is a penalty curve for each HS over time. The amount of the fine estimated for each individual HS per hour is a function of not only the contribution of that HS to the harmonic voltage of different buses, but also the contribution of the Harmonic Compensation Cost from the perspective of each bus, nominal voltage of each bus, and the sensitivity of each bus to the harmonic voltage. The proposed algorithm is evaluated on the IEEE 14‐bus network and Esfahan regional electrical power network in Iran. The simulation results demonstrate the capability of the proposed method to allocate the hourly penalty curve to the HSs. [ABSTRACT FROM AUTHOR]

By increasing the number of electric vehicles (EVs) to achieve a less carbon environment, not only they consume power from the grid to be charged, but also do they deliver power to the grid, and this can play a significant role in load frequency control. However, EVs take part in frequency regulation based on their state of charge (SoC) which will cause uncertainties. In order to manage these uncertainties, EV aggregator (EVA) concept has been introduced. The EV owner participates in the demand response program provided by the EVA arbitrarily considering her/his requirements. Accordingly, EVA calculates the up and down power reserve for the power system operator. Following any frequency deviation, EVA sends the proper commands to each EV to consume or to inject power to the system. There are also communication delays between different parts, uncertainties, and non‐linearities that existed in the power system. To overcome these issues, this study proposes a new robust load frequency controller based on feedback theory and artificial neural network which is designed through the non‐linear multi‐machine power system. Simulation results on IEEE 39‐bus power system show that the proposed controller regulates frequency more desirably in comparison with other methods. [ABSTRACT FROM AUTHOR]

Nowadays, the determination of the contribution of individual customers to the harmonic pollution of an electric power network is very essential for power quality improvement. In this study, four new indices are introduced to investigate the effect of harmonic contribution (HC) of the pollutant customers for an arbitrary time period without any access to a network model. These indices are defined according to data measured at the connection point of the suspicious loads and targeted buses under study. In this regard, a continuous HC matrix is developed where its entries are determined by using a new multi‐point and continuous HC calculation method without having to measure the phase angle, which makes this method practical and cost‐effective. The method and indices are applied to a standard power network‐based calculation example. In addition, experimental measurements, which are gathered based on the Isfahan city and Iran electric power transmission systems, are carefully analysed. The results demonstrated the capability of the proposed algorithm to evaluate the effects of harmonic sources in power networks. [ABSTRACT FROM AUTHOR]

Nowadays, the sustainable energy management of industrial environments is of great importance because of their heavy loads and behaviors. In this paper, the Virtual Power Plant (VPP) idea is commented as a collected generation to be an appropriate approach for these networks handling. Here, Technical Industrial VPP (TIVPP) is characterized as a dispatching unit contains demands and generations situated in an industrial network. A complete structure is proposed here for possible conditions for different VPPs cooperation in the power market. This structure carries out a day-ahead and intra-day generation planning by choosing the best Demand Response (DR) programs considering wind power and market prices as the uncertain parameters. A risk management study is likewise taken into account in the proposed stages for contingency conditions. So, some component changes, like, regular demand changes and single-line outage are prepared in the framework to authorize the suggested concept in the contingency situation. To determine the adequacy and productivity of the proposed strategy, the IEEE-RTS modified framework is examined to test the technique and to evaluate some reassuring perspectives too. By the proposed methodology, the delectability of DR projects is uncovered in industrial networks and the improvement level of load shedding and the lower cost will be achieved. [ABSTRACT FROM AUTHOR]

Abstract Using different types of renewable energy sources considering their uncertainties causes numerous challenges for minimizing the operation cost and maximizing the reliability of system. Hence, stochastic programming is an essential tool to consider the system uncertainties. This paper presents a day-ahead energy management system to decrease the operation cost and increase the reliability of a Microgrid considering a number of challenges for supporting electrical and thermal loads. In the proposed method, micro-CHP units, renewable energy sources, auxiliary boiler and energy storage system are all responsible for supplying the electrical and thermal loads. The problem is formulated as a multi-objective optimization problem. Moreover, the influence of considering the electrical energy storage system as a non-ideal battery with charge/discharge efficiency less than 1 is investigated. Also, demand response programs are provided based on load shifting contracts to consumers. A scenario-based approach is used to cover the uncertainties of renewable energy sources, market price and electrical load. Besides, this paper considers both islanding and grid-connected modes of Microgrid and investigates the influence of demand side management on operation cost and reliability in both modes. The capability of the proposed algorithm is analyzed by simulation results of a 3-feeder Microgrid. Highlights • Stochastic model is proposed for covering the uncertainties of variables. • The effect of micro-combined heat and power units on operation cost are considered. • The effect of storage system on operation cost and reliability index are considered. • Operation cost and reliability index are considered as objective functions. • Presenting a new mathematical method for implementing direct load control programs. [ABSTRACT FROM AUTHOR]

In the market, the aggregators offer various proposals for an electrical customer, who selects one among all of them. The proposals may include different numbers of levels of uncontracting capacity rates, demand and energy prices, measuring intervals and billing periods. To schedule the best contract between user and electricity supply provider for upcoming months, two new approaches are presented here to solve the multi‐optional multi‐level demand‐contracting problem. First, the feasible region is curtailed to the certain edged points and then, the optimal proposal and contracting demand (CD) are obtained by either direct or indirect proposed method. Moreover, a robustness analysis on the solution due to having errors in the forecasted maximum demands and changing the generations of the connected energy resource (ER) or installing a new one is presented. Some useful stability indices are also proposed. Likewise, updating the optimal solution due to having such errors is presented. Various numerical tests are taken place to analyse the influence of each parameter of the problem on the optimum and compare the proposed techniques. The results highlight the efficient capability of the proposed methods in fast obtaining the optima and stability investigation on the solution. [ABSTRACT FROM AUTHOR]

Abstract One effective way to compensate for uncertainties is the use and management of energy storage. Therefore, a new method based on stochastic programming (SP) is proposed here, for optimal bidding of a generating company (GenCo) owning a compressed air energy storage (CAES) along with wind and thermal units to maximize profits. This scheduling has been presented for the GenCo's participation in day-ahead energy and spinning reserve (SR) markets and CVaR is also considered as a risk-controlling index. Firstly, the obtained results are validated by comparing with those of two previous studies. Then, the complete results of the proposed method are presented on a real power system, which indicate the capability of SP in scheduling CAES units. Furthermore, it is observed that CAES units can gain greater profits in joint energy and reserve markets due to their high ramp rates. In addition, the value of stochastic solution (VSS) is used to quantify the advantage of the stochastic method over a deterministic one, which illustrates the advantage of SP-based optimal bidding method especially for CAES and wind units and also for risk-averse GenCos. Overall, it is concluded that the stochastic method is efficient for optimal-bidding of GenCos owning CAES and wind units. Highlights • A new stochastic programming-based optimal bidding of a GenCo including CAES is proposed. • The participation in energy and spinning reserve market is considered. • The results of the proposed method is verified by comparing with two other papers. • Simulation results using real market data show the capability of the proposed method. [ABSTRACT FROM AUTHOR]

Today, with the restructuring of the power systems, it is possible to implement a program, such as the demand response for the harmonic issues, and use customers to reduce the harmonic level of the network. In this study, a new method is presented based on harmonic pricing to control the harmonic level of the network. Given the fact that the power system is usually infected by the background harmonics, the basis for pricing is the harmonic contribution determination in order to impose a fine fair on the customers. Usually, the determination of the harmonic contribution at each harmonic order is done separately; therefore, a new index is presented, to sum up, the dominant orders in determining the harmonic contribution. This can provide an appropriate impression of the harmonic emission level at the point of common coupling. Simulation results on the standard IEEE 14‐bus network show that customers are encouraged to control their harmonic level with a fair harmonic pricing, otherwise, the independent system operator can install harmonic filters at appropriate buses, using the fines received from the customers. [ABSTRACT FROM AUTHOR]

Abstract Nowadays, renewable energy resources are increasingly used to supply electrical loads in micro grids, which these units should be scheduled coordinately. In this paper a stochastic model for coordinated scheduling of renewable and thermal units is proposed. Understudied units consists of fuel cell units with proton exchange membrane which generate heat and power simultaneously (PEMFC-CHP), wind and photovoltaic units. Moreover, the strategy of storing hydrogen is also considered for PEMFC-CHP units. Uncertainties of wind speed, solar radiation and market prices are considered using scenario based method. In the proposed stochastic programming problem, the strategy of storing hydrogen is considered by a mixed integer nonlinear programming (MINP) problem. The uncertainties of parameters convert the MINP problem to a stochastic MINP one. Moreover, optimal coordinated scheduling of renewable energy resources and thermal units in micro-grids improve the value of the objective function. To solve this problem, Modified Teaching-Learning-Based Optimization (MTLBO) algorithm is used and its performance is evaluated on a modified 33 bus distribution network. Simulation results represent that by using MTLBO method, the revenue increases more than 5 percentages in comparison with other optimization methods. In addition, considering CHP increases total profit of the system more than 15%. Highlights • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Hydrogen storage is considered for fuel cells. • Maximizing profits of micro grid is considered as objective function. • Considering the uncertainties of problem lead to profit increasing. [ABSTRACT FROM AUTHOR]

The Virtual Power Plants containing Distributed Energy Resources are classified into two main categories of Commercial Virtual Power Plant and Technical Virtual Power Plant as a suitable way to manage industrial environments. Here, Industrial Technical Virtual Power Plant is defined as a scheduling unit containing loads and generations located in an industrial grid. A comprehensive framework is proposed here for normal and contingency conditions for various Virtual Power Plants participating in a short-term market. This framework performs a day-ahead and intra-day generation scheduling by selecting the best Demand Response programs. In this framework, the wind generations and the day-ahead and intra-day electricity market prices are considered as the stochastic parameters. A risk-management aspect is noticed in the proposed stages for contingency conditions. Then, some element changes such as seasonal load change and single-line outage are trained in the system to accredit the proposed solution in the contingency condition. Hereof, an appropriate technique is defined to represent the proposed model and solution. To specify the effectiveness and efficiency of the proposed methodology, the modified Isfahan Regional Electric Power Company network in Iran is experimented to test the method and to assess some encouraging aspects as well. By the proposed approach, attractiveness of Demand Response programs is revealed in industrial grids and the lower cost will be imposed. Also the improvement percentage of load shedding can be gained by performing the proposed scheduling that is so important for industrial processes. [ABSTRACT FROM AUTHOR]

Nowadays the operation of renewable energy sources and combined heat and power (CHP) units is increased in micro grids; therefore, to reach optimal performance, optimal scheduling of these units is required. In this regard, in this paper a micro grid consisting of proton exchange membrane fuel cell-combined heat and power (PEMFC-CHP), wind turbines (WT) and photovoltaic (PV) units, is modeled to determine the optimal scheduling state of these units by considering uncertain behavior of renewable energy resources. For this purpose, a scenario-based method is used for modeling the uncertainties of electrical market price, the wind speed, and solar irradiance. It should be noted that the hydrogen storage strategy is also applied in this study for PEMFC-CHP units. Market profit, total emission production, and average energy not supplied (AENS) are the objective functions considered in this paper simultaneously. Consideration of the above-mentioned objective functions converts the proposed problem to a mixed integer nonlinear programming. To solve this problem, a multi-objective firefly algorithm is used. The uncertainties of parameters convert the mixed integer nonlinear programming problem to a stochastic mixed integer nonlinear programming problem. Moreover, optimal coordinated scheduling of renewable energy resources and thermal units in micro-grids improve the value of the objective functions. Simulation results obtained from a modified 33-bus distributed network as a micro grid illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, renewable energy sources and combined heat and power units are extremely used in micro grids, so it is necessary to schedule these units to improve the performance of the system. In this regard, a stochastic model is proposed in this paper to schedule proton exchange membrane fuel cell-combined heat and power, wind turbines, and photovoltaic units coordinately in a micro grid while considering hydrogen storage. Hydrogen storage strategy is considered for the operation of proton exchange membrane fuel cell-combined heat and power units. To consider stochastic generation of renewable energy source units in this paper, a scenario-based method is used. In this method, the uncertainties of electrical market price, the wind speed, and solar irradiance are considered. This stochastic scheduling problem is a mixed integer- nonlinear programming which considers the proposed objective function and variables of coordinated scheduling of PEMFC-CHP, wind turbines and photovoltaic units. It also considers hydrogen storage strategy and converts it to a mixed integer nonlinear problem. In this study a modified firefly algorithm is used to solve the problem. This method is examined on modified 33-bus distributed network as a MG for its performance. [ABSTRACT FROM AUTHOR]

Distributed Generation (DG) resources in industrial microgrids affect the reliability parameters in the networks. Therefore, the aim of the paper is to assess the reliability of industrial microgrids using a proposed composite index in the presence of DG and Demand Response (DR) resources. The reliability evaluation is performed on the basis of sequential Monte-Carlo method with regard to the available time load. Here, the widely used renewable generations such as wind and photovoltaic ones are used. Since the output of this type of DGs depends on random variables such as wind speed and solar radiation, a number of scenarios have been considered to determine the output amount per hour. The newly presented composite index shows the changes of conventional reliability indices (SAIFI, SAIDI, and EENS) per kW of DG installed. Due to the increase of industrial loads, a 10-year study is scheduled for both islanding and grid-connected performance conditions. In the islanding condition, the DR concept is also used. The proposed method is applied to IEEE-RBTS BUS2 standard network and real-world Mahmoud-Abad industrial zone network located in Isfahan, Iran in the presence of DG resources to show its effectiveness. The results are evaluated and compared in different conditions for elucidation. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of renewable energy resources and energy storage systems (ESS) on flow-gate marginal pricing (FMP) and locational marginal pricing (LMP) in a deregulated environment. Renewable energy resources including wind turbines and photovoltaic solar cells are modeled and discussed. Also, ESS are defined and mathematically modeled. Then, the mathematical formulation of LMP and FMP and their relationship are discussed. An IEEE six bus test system is considered as a case study. Several simulations are carried out to investigate the impacts of renewable energy resources and ESS on LPM and FMP. Furthermore, a comprehensive sensitivity analysis is conducted. It is demonstrated that renewable energy resources and their related uncertainty have a great impact on both LMP and FMP. Also, charging and discharging states of ESS significantly change LMP and FMP. It is also demonstrated that the reactive power of loads and line capacities are highly important in the FMP analysis. [ABSTRACT FROM AUTHOR]

Due to different viewpoints, procedures, limitations, and objectives, the scheduling problem of distributed energy resources (DERs) is a very important issue in power systems. This problem can be solved by considering different frameworks. Microgrids and Virtual Power Plants (VPPs) are two famous and suitable concepts by which this problem is solved within their frameworks. Each of these two solutions has its own special significance and may be employed for different purposes. Therefore, it is necessary to assess and review papers and literature in this field. In this paper, the scheduling problem of DERs is studied from various aspects such as modeling techniques, solving methods, reliability, emission, uncertainty, stability, demand response (DR), and multi-objective standpoint in the microgrid and VPP frameworks. This review enables researchers with different points of view to look for possible applications in the area of microgrid and VPP scheduling. [ABSTRACT FROM AUTHOR]

• A new strategy to counter DI attacks in electricity market operations is presented. • The preventive defense strategy is based on placing the minimum number of PMUs. • Providing two indicators, attack detectability and weakening attack effectiveness. • A trade-off mechanism between the indicators and PMU installation cost is developed. • The placement strategy is presented based on random behavior of cyber attacker. Integrity data (DI) attacks are considered malicious cyber threats to the economic performance of power markets in current power systems. A cyber attacker could mislead the system operator by implementing a DI attack, through the deviation of measured information, and causes non-optimal power distribution and erroneous participation in the electricity market (EM). This paper proposes a placement scheme of phasor measurement units (PMUs) to defend against these attacks, so that network observability is guaranteed; the possibility of detecting DI attacks by the operator is increased; and the effect of electricity price fluctuations caused by these attacks is prevented. For this purpose, we introduce two possible indices to determine the degree of attack detectability and the magnitude of system congestion variation. Accordingly, the two-objective placement model of PMUs is upgraded, in which the minimum number of PMUs and their placement must be specified to improve the proposed indices so as to minimize the possibility of financial misconduct taking place in the real time market. Using IEEE standard systems, the effectiveness of this PMU placement-based defense scheme has been confirmed. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is proposed for coordinated scheduling of combined heat and power units in micro grid considering wind turbine and photovoltaic units. Uncertainties of electrical market price; the speed of wind and solar radiation are considered using a scenario-based method. In the method, scenarios are generated using roulette wheel mechanism based on probability distribution functions of input random variables. Using this method, the probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective function, coordinated scheduling of combined heat and power, wind turbine, and photovoltaic units change this problem to a mixed integer nonlinear one. Therefore to solve this problem modified particle swarm optimization algorithm is employed. The mentioned uncertainties lead to an increase in profit. Moreover, the optimal coordinated scheduling of renewable energy resources and thermal units in micro grids increase the total profit. In order to evaluate the performance of the proposed method, its performance is executed on modified 33 bus distributed system as a micro grid. [ABSTRACT FROM AUTHOR]

Among several methods used for calculating the root mean square (RMS) of electrical signals, Fourier and wavelet transforms are the most common approaches. The latter also has the advantage of being able to analyze both stationary and nonstationary signals. However, in the wavelet-based methods, the presence of both odd and even harmonics in the input signal causes the harmonic components not to be in the center of the extracted frequency bands and this will reduce the accuracy of the RMS calculation. In order to remove this drawback, this paper proposes a new method based on wavelet and Hilbert transforms, in which the frequency of all harmonic components is increased by half of the main frequency by using a preprocessing technique. In simulation results, the RMS value of a real signal of the steel electric arc furnace of the Esfahan Mobarakeh Steel Company is calculated by using the suggested method. The results clearly show that the accuracy of the proposed approach is better than that of conventional and grouping methods. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) is combining different types of generations and interruptible loads to be able to contribute to the market as a power plant with a significant output. In other words, different power generations are combined in a complementary manner to form a defined generation and a demand profile. Because the most important elements of these operators are distributed generation (DG) units and demand response loads (DRLs), the determination of optimal location, capacity, type, as well as the installation time of DGs, DRLs participation size, and place in the distribution system are the most important challenges of operators. In this paper, the formulation of optimal placement of DG resources and DRLs is presented and solved simultaneously in a distribution system to determine the optimal VPP. For this purpose, the concepts of commercial virtual power plant and technical virtual power plant are introduced first, and then the optimal VPP, which can send its energy bids as a short-term and long-term power scheduling to the power market, is determined. Here, commercial virtual power plant and technical virtual power plant will act jointly as commercial-technical virtual power plant to extract the results of the proposed optimization procedure. In this paper, the binary particle swarm optimization algorithm is used to solve the optimization problem in the distribution system. The proposed method is applied to the IEEE 33-bus distribution test network, and the results confirm the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Following the penetration of microgrids in distribution systems, frequency deviations in contingency conditions are becoming increasingly important. Therefore, effective load shedding is necessary to regulate the frequency. This article develops a new load-shedding method for microgrids considering wind speed changes. The proposed method uses a combination of frequency and voltage data for determining load-shedding amounts in each contingency condition. For this purpose, the total required load shedding is determined first by using transient stability analysis in different contingency scenarios in microgrids. This will establish a database for an adaptive neuro-fuzzy inference system network to determine the total required load shedding. Then a fuzzy system is used to determine the load shedding in each step dynamically based on the severity of contingencies. The proposed method capability is compared with the conventional load-shedding method. Simulation results show the effectiveness of the proposed method for microgrid control in contingency conditions. [ABSTRACT FROM PUBLISHER]

One of the main classified microgrids in a power system is the industrial microgrid. Due to its behaviors and the heavy loads, its energy management is challengeable. Virtual Power Plant (VPP) can be an important concept in managing such problems in this kind of grids. Here, a transmission power system is considered as a Regional Electric Company (REC) and the VPPs comprising Distributed Generation (DG) units and Demand Response Loads (DRLs) are determined in this system. This paper focuses on Industrial VPP (IVPP) and its management. An IVPP can be determined as a management unit comprising generations and loads in an industrial microgrid. Since the scheduling procedure for these units is very important for their participation in a short-term electric market, a stochastic formulation is proposed for power scheduling in VPPs especially in IVPPs in this paper. By introducing the DRL programs and using the proposed modeling, the operator can select the best DRL program for each VPP in a scheduling procedure. In this regard, a suitable approach is presented to determine the proposed formulation and its solution in a Mixed Integer Non-Linear Programming (MINLP). To validate the performance of the proposed method, the IEEE Reliability Test System (IEEE-RTS) is considered to apply the method on it, while some challenging aspects are presented. [ABSTRACT FROM AUTHOR]

This paper deals with a coordinated generation expansion planning (GEP)–transmission expansion planning (TEP) in competitive electricity market. In the proposed method, GEP and TEP are performed at the same time, with consideration of wind farm uncertainty. The uncertainty is modeled by normal probability distribution function (PDF) and Monte-Carlo simulation (MCS) is used to include the uncertainty into the problem. The planning is managed for two master and slave levels. At slave level, all generation company (GENCO) and transmission company (TRANSCO) maximize their profit and then at master level, the system constraints are checked by independent system operator (ISO). In other words, the proposed planning aims at maximizing the expected profit of all GENCOs and TRANSCOs, while considering security and reliability constraints such as reserve margin and loss of load expectation (LOLE). The proposed problem is a constrained, nonlinear, mixed-integer optimization programming and solved by using particle swarm optimization (PSO) method. Simulation results verify the effectiveness and validity of the proposed planning for maximizing GENCOs and TRANSCOs profit in the presence of wind farm uncertainty under electricity market. [ABSTRACT FROM AUTHOR]

Distribution network expansion planning (DNEP) is one of the most important tools to deal with the demand growth in a system. DNEP is usually carried out through reinforcement or installation of new components. In this paper, a new and combined methodology is used to consider several practical aspects in DNEP such as uncertainty, distributed generation (DG), load growth, electricity market and multi stage dynamic expansion are included in the planning. So that DNEP is addressed in the presence of distributed generation (DG), considering load and price uncertainties under electricity market environment. The proposed planning aims at minimizing investment and operational costs simultaneously. Since DNEP in coordination with DG planning leads to reduce planning cost; therefore, the coordinated DNEP and DG planning are presented in this paper. The proposed planning is implemented by the particle swarm optimization (PSO) technique. Besides, the uncertainties are modeled as the probability distribution function (PDF) and Monte-Carlo simulation (MCS) is used to insert the uncertainties into the programming. The proposed planning is carried out based on the 9-bus as well as Kianpars–Ahvaz test systems (Kianpars–Ahvaz is a practical network in Ahvaz province, Iran). The simulation results demonstrate the ability and effectiveness of the proposed planning to deal with uncertainties under electricity market environment. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed for fundamental power calculation based on a wavelet transform with preprocessing by the Hilbert transform. The proposed method increases the frequencies of all harmonics by half of the main frequencies and locates both odd and even harmonics in the centres of the extracted frequency bands. Unlike the conventional method, there is no frequency interference and the fundamental power calculation is enhanced. In order to examine the proposed method, the steel electric arc furnace (EAF) of the Mobarakeh Steel Company, Esfahan, Iran is simulated in MATLAB software at the early stage of charging. Then the fundamental active, reactive and apparent power of the EAF are calculated using simulated and real data. Results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic model is proposed for planning the location and operation of Molten Carbonate Fuel Cell Power Plants (MCFCPPs) in distribution networks when used for Combined Heat, Power, and Hydrogen (CHPH) simultaneously. Uncertainties of electrical and thermal loads forecasting; the pressures of hydrogen, oxygen, and carbon dioxide imported to MCFCPPs; and the nominal temperature of MCFCPPs are considered using a scenario-based method. In the method, scenarios are generated using Roulette Wheel Mechanism (RWM) based on Probability Distribution Functions (PDF) of input random variables. Using this method, probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective functions, placement, and operation of MCFCPPs as CHPH change this problem to a mixed integer nonlinear one. So, multi-objective Modified Firefly Algorithm (MFA) and Pareto optimal method are employed for solving the multi-objective problem and for compromising between the objective functions. During the simulation process, a set of non-dominated solutions are stored in a repository. The 69-bus distribution system is used for evaluating the proper function of the proposed method. [ABSTRACT FROM AUTHOR]

Renewable resources generation scheduling is one of the newest problems of the power markets. In this paper, joint operation (JO) of wind farms (WF), pump-storage units (PSU), photo-voltaic (PV) resources, and energy storage devices (ESD) is studied in the energy and ancillary service markets. There are uncertainties in wind power generation (WPG), photovoltaic power generation (PVPG) and the market prices. To model these uncertainties, the WPG is forecasted by using ARMA model and its scenarios are generated using Weibull distribution function. Moreover, other uncertain parameters are forecasted first, and their uncertainties are modeled by using scenario generation and scenario reduction method. The proposed JO method is used to determine the optimal bidding strategy of the PSU, PV, ESD and WF of IEEE 118-bus standard system. The results for these renewable energy resources confirm that the JO of these resources increases the profit and decreases the risk of the resources in comparison with their uncoordinated operation (UO). [ABSTRACT FROM AUTHOR]

• A new attack method based on Monte Carlo is presented for an attacker participating in an electricity market. • The attack is designed taking into account that the attacker has overmuch imperfect level of the network information. • The stochastic models from cyber-attacker perspective are presented to describe the network topology and parametric uncertainties. • The contribution of each transmission line to the attacker's chances to achieve a financial profit is considered in the proposed strategy. • A robust optimization problems is designed to ensure a stealthy and profitable attack. False data injection (FDI) attacks can significantly impact on economic performance of electricity markets in modern power systems. These attacks can be stealthily accomplished by cyber-attackers for the purpose of profitability through financial arbitrage in electricity markets. In this paper, a new strategy of FDI attack based on Monte Carlo is proposed for an attacker participating in an electricity market, who has overmuch imperfect level of the network information. This piece of information, including both the connection /disconnection situation and admittance values of the transmission lines is denominated as topology and parametric uncertainties, respectively. Herein, a probable model is offered for analyzing the uncertainties by the Monte Carlo simulation (MCS). Afterwards, considering the probable errors of uncertainties, the attack strategy is designed in such a manner that the attacker obtains the most profit based on the contribution of each transmission line. The numerical results on two PJM 5-bus and IEEE 30-bus test networks could obviously demonstrate the success of such limited attackers in current electricity markets. [ABSTRACT FROM AUTHOR]

Highlights: [•] Decreasing activity cost of DISCO by reconfiguration and power generation control of DGs based on SFLA. [•] A new reconfiguration and power generation control of DGs is presented based on SFLA method. [•] In the proposed method, the problem is solved at different network load levels. [•] The proposed method is applied to the IEEE 33-bus and 69-bus test networks for evaluation. [•] The proposed SFLA method has better performance in comparison to other methods. [Copyright &y& Elsevier]

STLF (short term electric load forecasting) plays an important role in the operation of power systems. In this paper, a new model based on combination of the WT (wavelet transform) and GM (grey model) is presented for STLF and is improved by PSO (particle swarm optimization) algorithm. In the proposed model, the weather data including mean temperature, mean relative humidity, mean wind speed, and previous days load data are considered as the model inputs. Also, the wavelet transform is used to eliminate the high frequency components of the previous days load data and improve the accuracy of prediction. To improve the accuracy of STLF, the generation coefficient of GM is enhanced using PSO algorithm. To verify its efficiency, the proposed method is used for New York's and Iran's load forecasting. Simulation results confirm favourable performance of the proposed method in comparison with the previous methods studied. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new methodology for Transmission Expansion Planning (TEP) in deregulated electricity market is presented. The proposed TEP is associated with Reactive Power Planning (RPP), reliability assessment and also consideration of wind and load uncertainties. The proposed planning aims at investment cost minimization, social welfare maximization and satisfying reliability constraint at the same time with taking into account wind and load uncertainties. Expected Energy Not Supplied (EENS) is used as an index for reliability evaluation. At first, Monte-Carlo simulation is used to obtain the Probability Density Function (PDF) of Wind Turbine Generator (WTG) output. Then, the WTG and load uncertainties are considered in TEP formulation. Particle Swarm Optimization (PSO) method is considered to solve the proposed planning problem which is a constrained nonlinear mixed integer optimization programming. Simulation results on two standard test systems (Garver and RTS systems) verify the effectiveness of the proposed planning for consideration of wind and load uncertainties in TEP problem under electricity market environment. Also, the proposed method leads to reduction of the total investment cost, the reliability improvement and the social welfare maximization. [Copyright &y& Elsevier]

Highlights: [•] Generation expansion planning is presented in deregulated electricity market. [•] Wind farm uncertainty is modeled in the problem. [•] The profit of each GENCO is maximized and also the safe operation of system is satisfied. [•] Salve sector is managed as an optimization programming and solved by using PSO technique. [•] Master sector is considered in pool market and Cournot model is used to simulate it. [ABSTRACT FROM AUTHOR]

• This paper intends to provide a new data injection attack method based on Monte Carlo technique in the real-time electricity markets. • This attack strategy is for a category of cyber-attackers who have uncertainties about network topology and parametric information. • This paper presents probability distribution models for connection/disconnection status and reactance of transmission lines related to topology and parametric uncertainties, respectively. • The effect of injection shift factor on the attacker profit function is considered, so that more profit is obtained. • The attack strategy is formulated in the form of the worst case robustness optimization problem, thus ensuring a stealthy and profitable attack. The economic operations of real time (RT) electricity markets are vulnerable to false data injection (FDI) attacks, designed by cyber-attackers. Strategically, the RT locational marginal prices (LMPs) are stealthily altered by manipulating some of measurement data and it provides conditions for profitable financial misconduct in the electricity market. This paper proposes a new Monte Carlo-based FDI attack strategy for a cyber-attacker, who has very limited knowledge about the topology and parametric information of targeted network, which called an attacker with model topology-parametric uncertainties (TPUs). The main feature of the proposed attack is that despite the model errors, the attacker can guarantee the stealthy and profitable attack in advance, since the attack is designed based on an optimization problem of worst-case robust against uncertainties. Two 5-bus PJM and 30 IEEE bus systems are used to demonstrate the success of such cyber-attacks in real-time electricity markets. [ABSTRACT FROM AUTHOR]

Investment on generation system and transmission network is an important issue in power systems, and investment reversibility closely depends on performing an optimal planning. In this regard, generation expansion planning (GEP) and transmission expansion planning (TEP) have been presented by researchers to manage an optimal planning on generation and transmission systems. In recent years, a large number of research works have been carried out on GEP and TEP. These problems have been investigated with different views, methods, constraints and objectives. The evaluation of researches in these fields and categorising their different aspects are necessary to manage further works. This study presents a comprehensive review of GEP and TEP problems from different aspects and views such as modelling, solving methods, reliability, distributed generation, electricity market, uncertainties, line congestion, reactive power planning, demand‐side management and so on. The review results provide a comprehensive background to find out further ideas in these fields. [ABSTRACT FROM AUTHOR]

Highlights: [•] Joint operation (JO) of wind farms (WF) and pump-storage (PS) plants is modeled and evaluated. [•] Optimal bidding strategy of JO of WF and PS in the energy and ancillary service markets is determined. [•] Uncertainties of wind power generation and the market prices are considered. [•] JO of the plants is evaluated by the value at risk (VaR) index and their benefits. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, a large number of research works have been carried out in transmission expansion planning (TEP) field. TEP problem has been investigated with different views, methods, constraints, and objectives. Thus, it is required to evaluate and to overview the proposed works. This paper will review TEP problem from different aspects such as modeling, solving methods, reliability, distributed generation, electricity market, uncertainties, line congestion and reactive power planning. The review results provide a comprehensive background to find out the further works in this field. [Copyright &y& Elsevier]

Abstract: In the hierarchical structure of a wide area measurement system (WAMS), the measurements obtained by phasor measurement units (PMUs) in a local area are first submitted to the related phasor data concentrator (PDC), and then to the power system control center. In this paper, a new method for the optimal placement of PMUs as well as PDCs in local networks of a WAMS is proposed. This method minimizes the probability of failures in data transmission from PMUs to PDCs. In the proposed method, first it is assumed that PMUs are installed on all system buses. Then, the redundant PMUs will be omitted one after the other in a multi-stage procedure. The multi-stage elimination procedure is such that in each stage the arrangement of PMUs–PDC with the highest reliability will be kept. The IEEE 14-, 30-, as well as 118-bus test systems are used to demonstrate the proposed algorithm and to verify the results. In numerical simulations, the power system observability is treated in various conditions such as the base case, N −1 condition and the case of considering the single line outage. [Copyright &y& Elsevier]

Abstract: In this paper, Benders Decomposition method improved by Bacterial Foraging oriented by Particle Swarm Optimization method (BDI-BFPSO) is used for solving AC constrained hydro-thermal generation scheduling problem. The objective function of the proposed generation scheduling problem is to minimize the generation cost and emission cost of the power system by considering the valve point effect and prohibited operating zones of thermal units. This problem is a large scale mixed integer nonlinear programming problem with a great number of equality and inequality constraints related to generation units, hydro system and power system. In order to solve this problem using BDI-BFPSO, the optimization problem is decomposed into a master problem and a sub-problem which they can be solved iteratively. The performance of the BDI-BFPSO method is tested and evaluated on IEEE 6-bus and 118-bus standard systems. Simulation results confirm the effectiveness of the proposed BDI-BFPSO method for solving the scheduling problem in comparison with other methods. [Copyright &y& Elsevier]

One of the main methods for partial discharge (PD) source localisation in power transformers is acoustic emission measurements. This study describes a new method for detection and location of two simultaneous partial discharge sources in three‐phase power transformer. In this method, acoustic signals are detected by sensors first and are then denoised using a wavelet transform. Finally, the two PD sources are localised using the modified binary partial swarm optimisation (MBPSO) method. To prove the efficiency of the two simultaneous PD localisations, the proposed algorithm is used to localise PD sources of the arc furnace transformer at Isfahan's Mobarakeh steel company. For this purpose, the PD localisation problem converts to an optimisation problem. To prove the efficiency of the MBPSO algorithm, its performance is compared with a genetic algorithm. The PD localisation results confirm the efficiency of the proposed method for the detection and location of PD sources. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new two-step algorithm is proposed for short-term load forecasting (STLF). In the first step of the method, a wavelet transform (WT) and an artificial neural network (ANN) are used for the primary forecasting of the load over the next 24h. Inputs of this step are weather features (include the daily mean temperature, maximum temperature, mean humidity, and mean wind speed) and previous day load data. In the second step, a WT, the similar-hour method and adaptive neural fuzzy inference system (ANFIS) are used to improve the results of primary load forecasting. In this study, a WT is employed to extract low-order components of the load and weather data. Furthermore, the number of weather data inputs has been reduced by investigating the weather conditions of different cities. To evaluate the performance of the proposed method, it is applied to forecast Iran’s load and New South Wales of Australian’s load. Simulation results in four different cases show that the proposed method increases load forecasting accuracy. [Copyright &y& Elsevier]

In a daily power market, price and load forecasting are the most important signals for the market participants. In this paper, an accurate feed-forward neural network model with a genetic optimization Levenberg-Marquardt back propagation training algorithm is employed for short-term nodal congestion price forecasting in different zones of a large-scale power market. The use of genetic algorithms for neural network training optimization has a remarkable effect on the accuracy of price forecasting in a large-scale power market. The necessary data for neural network training are obtained by solving optimal power flow equations that take into account all effective constraints at any hour of the day in a single month. The structure of the neural network has 2 input signals of active and reactive powers for every load busbar in every hour of the programming model. These 2 signals are always available. In this study, an IEEE 118-bus power system is used to test the proposed method authenticity. This system is divided into 3 zones, and a neural network with genetic algorithm training optimization is employed for every zone. Performance of the proposed method is compared with ARIMA and GARCH time series for the same data. The simulation results show that the proposed algorithm is robust, efficient, and accurate. Therefore, the algorithm produces better results than the ARIMA and GARCH time series for short-term nodal congestion price forecasting. [ABSTRACT FROM AUTHOR]

Abstract: The application of phasor measurement units (PMUs) in power systems is increasing because of their advantages such as the capability for online state estimation and improvements in the speed of control, and protection systems. In this paper, we propose a new method using binary integer linear programming for the optimal placement of PMUs to guarantee full observability of a power system as well as maximizing the measurement redundancy. Moreover, the problem of the optimal placement of these units in the case of a single PMU loss or single line outage is investigated. A practical limitation is also considered on the maximum number of PMU channels, in the proposed formulation. In all of the investigations, the effect of zero-injection buses in the power system was considered. The efficiency of the proposed method was demonstrated in different conditions. The method was applied to several IEEE standard test systems, i.e., the 14-, 30-, 39-, 57-, and 118-bus systems, and in two very large-scale systems, i.e., 2383- and 2746-bus systems. The simulation results verified the acceptable performance of the proposed method. [Copyright &y& Elsevier]

Transmission Expansion Planning (TEP) is an important issue in power system studies. It involves decisions on location and number of new transmission lines. Before deregulation of the power system, the goal of TEP problem was investment cost minimization. But in the restructured power system, nodal prices, congestion management, congestion surplus and so on, have been considered too. In this paper, an AC model of TEP problem (AC-TEP) associated with Reactive Power Planning (RPP) is presented. The goals of the proposed planning problem are to minimize investment cost and maximize social benefit at the same time. In the proposed planning problem, in order to improve the reliability of the system the Expected Energy Not Supplied (EENS) index of the system is limited by a constraint. For this purpose, Monte Carlo simulation method is used to determine the EENS. Particle Swarm Optimization (PSO) method is used to solve the proposed planning problem which is a nonlinear mixed integer optimization problem. Simulation results on Garver and RTS systems verify the effectiveness of the proposed planning problem for reduction of the total investment cost, EENS index and also increasing social welfare of the system. [Copyright &y& Elsevier]

Due to increasing neutral current and power losses resulting from phase unbalancing in distribution networks, the act of phase balancing has been a matter of interest in recent years. In this paper, a new method for phase arrangement of laterals and the distribution transformers is presented based on bacterial foraging (BF) oriented by particle swarm optimization (PSO) algorithm (BF-PSO). The algorithm is proposed for radial and meshed distribution networks in the presence of unbalanced loads. The objective function of this problem includes the neutral current of the supporting feeder, the rephasing cost, the voltage drop, and the line losses. Since these objectives do not have similar units and the same variation ranges, the four objectives are fuzzified and then integrated as the fuzzy multi-objective function. In order to prove the efficiency of the BF-PSO algorithm, its performance is compared with bacterial foraging, genetic and immune algorithms. To evaluate the proposed method, it is applied to feeder No. 3062 in Ahwaz, Iran. The simulation results confirm the efficiency of the method for the reduction of system costs and network phase balancing. [ABSTRACT FROM PUBLISHER]

This paper presents a new optimal adaptive dynamic load-shedding scheme for a large steelmaking industry with cogeneration units. The proposed method is based on the initial rate of a frequency change (df0/dt) and is coordinated with tie-lines frequency protection relays. An adaptive network-based fuzzy inference system (ANFIS) with a new training algorithm is developed in order to increase the speed of the load-shedding scheme and to have an optimum response at different loading conditions. To overcome the ANFIS training difficulties, a new hybrid approach composed of particle swarm optimization and gradient decent algorithms is used. The training data set for the ANFIS is prepared by a transient stability analysis to determine the minimum load shedding for various operation scenarios without causing the tripping problem of cogeneration units. By using an accurate dynamic modeling of the Mobarakeh steelmaking company in Esfahan Regional Electrical Company network, the performance of the proposed method is compared with the traditional ANFIS learning algorithms, adaptive artificial neural network load-shedding scheme and transient stability analysis. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder–Mead (NM) method (called BF–NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF–NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system. [Copyright &y& Elsevier]

These days, the application of electronic power transformers (EPTs) is expanding in place of ordinary power transformers. These transformers can transmit power via three or four wire converters. Their dynamic performance is extremely important, due to their complex structure. In this paper, a new method is proposed for improving the dynamic performance of distribution electronic power transformers (DEPT) by using sliding mode control (SMC). Hence, to express the dynamic characteristics of a system, different factors such as the voltage unbalance, voltage sag, voltage harmonics and voltage flicker in the system primary side are considered. The four controlling aims of the improvement in dynaniic performance include: 1) maintaining the input currents so that they are in sinusoidal form and in phase with the input voltages so they have a unity power factor, 2) keeping the dc-link voltage within the reference amount, 3) keeping the output voltages at a fixed amount and 4) keeping the output voltages in sinusoidal and symmetrical forms. Simulation results indicate the potential and capability of the proposed method in improving DEPT behavior. [ABSTRACT FROM AUTHOR]

The dynamic voltage restorer (DVR) is one of the modern devices used in distribution systems to protect consumers against sudden changes in voltage amplitude. In this paper, emergency control in distribution systems is discussed by using the proposed multifunctional DVR control strategy. Also, the multiloop controller using the Posicast and P+Resonant controllers is proposed in order to improve the transient response and eliminate the steady-state error in DVR response, respectively. The proposed algorithm is applied to some disturbances in load voltage caused by induction motors starting, and a three-phase short circuit fault. Also, the capability of the proposed DVR has been tested to limit the downstream fault current. The current limitation will restore the point of common coupling (PCC) (the bus to which all feeders under study are connected) voltage and protect the DVR itself. The innovation here is that the DVR acts as a virtual impedance with the main aim of protecting the PCC voltage during downstream fault without any problem in real power injection into the DVR. Simulation results show the capability of the DVR to control the emergency conditions of the distribution systems. [ABSTRACT FROM AUTHOR]