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In recent years, there has been an increasing interest in using renewable energy resources. With the high penetration of these uncertain resources in power system studies, continuity and stability have become a principal issue for consumers. Therefore, the flexibility concept has been introduced, which has a pivotal role in the power grid to ensure energy consumption security. In this paper, an exploratory framework is presented for investigating the value of flexibility in the power system. Hence, a new index is introduced for flexibility assessment based upon maximum available capacity as well as reaction time of generating units. The results of this study indicate that the uncertain supply-side resources cause to decrease the flexibility level. Under the smart grid environment, virtual demand response resources (DRRs) can be called for compensating the uncertainty of renewable energy resources. Here, a new formulation of flexibility-based unit commitment associated with negawatt demand response resources (FBUCDRRs), is suggested. The uncertainty of DRRs is modeled using Z-number as a possibilistic-probabilistic method. The uncertainty of supply-side resources is also intended with Monte Carlo procedure. The point of FBUCDRRs is determining the optimum value of flexibility as a crucial issue, while the total operation cost accomplishes at the minimum possible level. The results indicate the improvement in the flexibility index without increasing the operation cost. Several analyses are carried out on a modified 10-unit test system and Kerman province power generation system as a practical power system to trace the capability of the proposed structure.

The resilient smart distribution system is intended to cope with low probability, high-risk extreme events, including extreme natural disasters. In this regard, the flexible partitioning distribution system into supply-sufficient microgrids can be considered an interesting subject. This paper introduces a novel technique for partitioning a smart distribution system into supply-sufficient microgrids. In the presented model, the impact of demand response programs and energy storage has been considered in the proposed framework. Furthermore, besides the electricity aspects, the effects of gas network infrastructure on flexible portioning are also investigated to improve system resiliency facing natural disasters. The proposed model aims to maximize served load, which has been structured as a mixed integer linear programming (MILP) problem. The IEEE 34 bus standard test system is used to investigate the effectiveness of the proposed structure. The results of the study show that the proposed structure can increase the served load and the supply-sufficiently of the smart grid power system.

Among different subjects related to the natural gas and electricity market, this paper is particularly focused on the analysis of different scenarios for exporting natural gas. Iran has the second largest reservoirs of natural gas in the world and exports it to different countries. On the other hand, in the restructured power system, the role of natural gas is growing in the electricity generation due to its lower pollution. This paper analyzes different scenarios for exporting the natural gas including direct transfer at forward and hub prices, exporting at citygate price, conversion into electricity for exporting at forward price, and transferring via the power market. In this regard, system dynamics is applied for long-term analysis of the considered scenarios. The natural gas price is modeled by Markov chain Monte Carlo for a long-term period, and the models are analyzed using the data published by the energy information administration. The results show that exporting the natural gas or the electricity in suitable forward price are the most profitable scenarios. While exporting the natural gas at the demand price is profitable, transferring the electricity via the power market cannot meet the economic goals, even by expanding the renewable resources.

Energy efficiency programs as an energy source with multiple advantages (low cost, more reliable and better clean) are considered as one of the most effective solutions to meet future energy needs. In this paper, energy efficiency programs are modelled as virtual sources of power generation to investigate their impacts on expanding transmission network and reactive power planning. A multi-stage model based on AC power flow has been used to indicate the possibility of postponing the investment actions as the investment deferral in transmission expansion problems. Two groups of regulatory support schemes are considered as investor income to promote participation in energy efficiency programs, namely purchasing certified emission reductions and shared saving model. In addition, two standard case studies (Garver & 46-bus Brazilian) are evaluated to demonstrate the promising potential of the proposed model in handling the planning problems of practical power systems. The simulation results show the effectiveness of the proposed model to solve such planning problems in the presence of energy efficiency programs.

Responsive loads, according to their increasing penetration, short response rate and flexibility are important sources of reserve in the smart power systems. Although in recent years demand response (DR) contribution in the (reserve) electricity market has been widely accepted with an important role in the reliable and economic operation of power systems, due to uncertain availability, sheddability, and controllability of these sources their uncertain behavior is worth to be investigated. For this purpose, at first, a reliability-based unit commitment is solved and then the results are used to clear reserve market in the presence of uncertain responsive loads based on information gap decision theory (IGDT) concept. Responsive loads are considered as reserve providers which participate in the market by offering their price-quantity capacity bids to the reserve market. The proposed method does not minimize the reserve market clearing cost, but with regard to the minimum cost, assesses the risk aversion or risk-taking nature of different strategies and also measures the related risk/immunity cost. Using this method, the system operator can select the best strategy according to the desired risk level, taking into account demand side uncertainty. Proposed method has been simulated over the 24 bus IEEE Reliability Test System.

Nowadays, the energy hub as one of the most interesting aspects of smart grid power system concept has been emerged. The heat energy market is a novel concept in the energy hub scheduling problem. Furthermore, the demand side Energy Efficiency Programs (EEPs) and Demand Response Programs (DRPs) as energy democracy policy plays important role in the smart grid power system. It is should be noted that although the EEPs are mid-term programs but, effects of EEPs on the short term programs such as energy hub scheduling programs are impressive. Therefore, in this paper a new model of the energy hub scheduling is provided associated with EEPs and DRPs. Also, the heat energy market has been considered to increase the efficiency of the proposed energy hub framework. In this regard, the various type of demand such as electrical, thermal and gas is considered in the provided structure. The proposed Mixed Integer Linear Programing (MILP) Model is implemented with CPLEX solver in GAMS Environment. The results of the study shows that the EEPs and DRPs has a significant impact of the operation cost on the energy hub system.

Power systems should operate in reliable, stable, and efficient conditions. Addition of new generation units or loads to the power systems may change their performance. Therefore, appropriate decisions should be made to manage these elements to improve the power system performance. In this study, optimal placement and sizing of photovoltaic systems and electric parking lots (EPLs) are studied considering the reactive power capability of the inverters and load variation in a 24-h period. For the EPL, a proper charge/discharge scheme (CDS) is initially proposed to flatten the daily load profile; then the EPL with the associated CDS is considered to find its optimal location. Voltage profile, energy losses, bus, and line voltage stability are considered as the objectives of the problem. Genetic algorithm and backward–forward power flow method are utilised to solve the problem considering the IEEE 33-bus system. The results show that all objectives are improved utilising the proposed method.

In recent years, Energy Efficiency Programs (EEPs) have been greatly expanded in multifarious aspects. Here, EEPs is considered as a virtual resource affecting operating expenditures of the power system. On the other hand, under the smart environment, Demand Response (DR) programs are also contemplated as virtual power plants in energy policy decisions ranging from short term to long term. Therefore, in this paper, a novel integrated model of demand in presence of EEPs as well as DR programs has been nominated. The presented model is handled in a Two-Stage Structure to Coordinate the EEPs and DR programs, namely TSSC DR EEP . In the first stage, the level of Energy Efficiency Investment (EEI) is determined over the midterm horizon time. In the next stage, regarding the rate of EEI, a unit commitment problem incorporating EEPs as well as DR programs is handled while the interactions between EEPs and DR have been scrutinized. The proposed problem is implemented in GAMS. To evaluate the capability of EEPs and DR programs coordination, several analysis are carried out on the IEEE 10-unit to confirm the capability of the proposed framework from economic aspect.

Transmission expansion planning (TEP) is a critical issue, particularly coping with the new challenges of smart grids. Under the smart grid environment, demand response resources (DRRs) are contemplated as virtual power plants in energy policy decisions. DRRs affect the controllability of power systems, ranging from short-term to long-term scheduling. In this paper, a nonlinear economic model of responsive loads is presented based on price elasticity of demand and customers benefit function. In order to investigate the impacts of DRRs on TEP, a probabilistic multiobjective TEP incorporating demand response programs (DRPs) is introduced (PMO-TEP $^\mathrm{DRPs}$ ). IC, congestion costs, risk costs, and total incentive costs for participating in DRPs are considered different objectives of PMO-TEP $^\mathrm{DRPs}$ . Here, a probabilistic analysis technique, the so-called two-point estimation method, is applied to handle the uncertainty of wind-farm generation in the TEP problem. Furthermore, the maximum achievable potential and realistic achievable potential for participating customers in DRPs are derived. Due to the problem's nonconvex formulation, a nondominated sorting genetic algorithm II is utilized to evaluate PMO-TEP $^\mathrm{DRPs}$ . Several analyses are carried out on the IEEE Reliability Test System and Iran 400-kV transmission grid to confirm the capability of the proposed framework.

This paper scrutinizes the impacts of demand-side resources (DSRs) on the power system reliability via a novel multi-target generation maintenance (GM) model. The nominated model uses the Lexicographic preferences to hierarchically consider the environmental issues, economics, and reliability of power systems. In this regard, the produced emission, which reflects the per unit produced pollutant values in different locations, is minimized. Taking into account the environmental constraints, the total incurred expenditures, including operating and maintenance costs, reserves costs, and total incentives are also minimized. Subsequently, the GM problem considering the correlation constraints is handled while the reliability index, the average net reserve value (ANRV), is maximized over the scheduling horizon. The DSRs improve the system reliability such that the total costs, and emission level, do not exceed the situation in which DSRs are not available. The GM scheduling is a highly complicated problem and considering DSRs makes it even more complicated. To handle this problem more efficiently, appropriate linearization technique is used, while the proposed model is formulated in GAMS modeling language. To evaluate the capability of DSRs in system reliability improvement, the modified 24-bus IEEE-RTS is conducted. Results indicate that by selecting proper location and incentives, significant improvement is obtained.

In recent years, examining the ruinous consequence of extreme weather events on the power system is one of the most challenging issues that researchers have confronted to. Considerable extreme conditions are generally the missing part of a realistic self-scheduling problem. Considering high-impact low-probability (HILP) events into the model have at least two benefits: first, generation companies (GenCos) can elude from financial disadvantages of upcoming HILP events and then the ISO can better clear energy and reserve markets with a preventive-oriented process to enhance power system resilience. This paper provides a pre-extreme condition self-scheduling for a price-taker generation company with renewable generation units which participates in the day-ahead energy and spinning reserve markets. Uncertainties associated with electricity prices and wind power production are characterized by multiple stochastic scenarios. The stochastic behavior of wind power is presented by using the Beta probability density function (PDF). In order to model the uncertainty of forced outages of generating units due to HILP events and the probability of being called for reserve deployment, a possibilistic approach is proposed. By comparing the generation scheduling under different risk factors and according to the financial disadvantages of HILP events, the conditional value-at-risk (CVaR) risk-averse strategy is considered into the model.

The optimal, resilient and flexible operation of the power system is one of the most important challenges of the power grid. Fast resources as a backup capacity to provide power are one of the ways the power system to be more flexible and resilient. In this paper, a new joint power and reserve scheduling is done considering scheduling based on power instead of scheduling based on energy, the correct management of the thermal units’ ramp, the use of responsive loads includes a collection of commercial customers and plug-in electric vehicles (PEVs) in the secondary (15-minute) and tertiary (30-minute) reserves and discharge management of PEVs. A part of the power demand of these responsive loads is provided as free by renewable resources in times when there are no bad weather conditions. The use of responsive loads, correct management of the ramp of thermal units and a part of the discharge of PEVs can help restore the network quickly. The mixed-integer linear programming (MILP) method has been used to solve the joint power and reserve scheduling problem. The results show that the proposed model can lead to more flexibility and resiliency of the system and reduce the cost of the system.

The purpose of load management and energy management in power networks is reducing economic costs and increasing the reliability of the power grid. Achieving this goal is possible in management both the demand and supply side of electric power system. In this article, two methods of forecasting for electricity demand have been presented. Gray Model (GM) and trigonometric gray prediction approach. These two methods are used to forecast total electricity demand of Kerman province for a period of 2012–2031.then electric power supply system of Kerman province for base year 2012 has been modeled using LEAP software. Then the electric power supply system has been developed for the target year 2031 based on two different scenarios, namely, status quo scenario and cooling loads management scenario (CLM). These scenarios have been evaluated in three sections viz. power generation, cost analysis, and environmental emissions. results indicate that, assuming the implementation of CLM scenarios from 2020, capacity requirement of power plants will be reduced up to 1000 MW in comparison with status quo scenario by the year 2031 which results in a significant reduction in the capital costs, operation and maintenance costs, fuel costs, electricity generation, greenhouse gas emissions from fossil fuel consumption in power plants, and peak load of the electric power system by the year 2031. © 2016 American Institute of Chemical Engineers Environ Prog, 2016

Generators maintenance scheduling is addressed as a crucial issue that may affect both economy and reliability of power systems. System reserve procurement may facilitate preventive maintenance scheduling in such way to guarantee system reliability as well as security. In this paper, a new deterministic criterion for determining operating reserve capacity is introduced. In the proposed model, the unit reserve provision is handled based upon unit responsibility criterion (URC) that depends on unit capacities as well as number of committed units. Therefore, a new formulation for reserve assessment based upon URC incorporating preventive maintenance scheduling (PMSURC) is developed. The proposed model is structured as a mixed integer programming (MIP) and is solved using CPLEX solver. Several analyses are conducted to investigate the impact of unit responsibility criterion on the reserve assessment expenditure. An IEEE Reliability Test System (RTS) is employed to demonstrate the effectiveness of the proposed methodology and simulation results are promising.

In recent years, load management (LM) programs have been contemplated as a crucial option in all energy policy decisions. Under deregulation, the scope of LM programs has been considerably expanded to include demand response (DR) programs. Here, the market-based DR programs are regarded as a virtual resource for reserve provision. Basically, demand-side reserve affects handling and controlling of power systems ranging from short-term to long-term scheduling. Preventive maintenance scheduling (PMS) of generating units is addressed as a long-term scheduling in power system studies, which is affected by DR programs. In this paper, a new structure for security-constrained PMS associated with DR programs is suggested. In order to scrutinize the economic- and environmental-driven measures of DR programs, a new linearized formulation of cost-and-emission-based preventive maintenance problem is presented. Here, the proposed framework is structured as a mixed-integer programming problem and solved using a CPLEX solver. This model would schedule reserves provided by DR providers, maintenance scheme, and commitment status of generating units. Values of energy and reserves over the scheduling time horizon are also simultaneously determined in this paper. The IEEE Reliability Test System is utilized to demonstrate the effectiveness of the proposed structure.

Nowadays, Energy Efficiency as a strategic energy policy has been extended to handle the power system either in demand-side or supply-side affecting the proficiency of the grid. On the other hand, the penetration rates of green resources as PhotoVoltaic (PV) system and Vehicle to Grid (V2G) technologies and; Demand Response (DR) resources as virtual resources have been extremely expanded in the smart power system. Accordingly, in this paper, a novel concept of energy efficiency form economic aspect in presence of Green Virtual Resources (GVRs) will be presented while an economic model for Energy Efficiency Programs (EEPs) is nominated. Therefore, an economic efficiency maximization model of energy management in a Micro Grid (MG) is suggested based upon the difference of revenues and financial burden; whereas the optimal sitting and sizing of GVRs are determined. Furthermore, three multifarious techno economic coefficients including Economic Efficiency Index (EEI), Technical Efficiency Index (TEI) and Total Technical Efficiency Index (TTEI) are nominated to evaluate the impact of GVRs in efficiency improvement of the MG. To evaluate the capability of GVRs, the standard 33-bus IEEE distribution system is conducted. Results indicate that by selecting proper location and size of GVRs, significant improvement is obtained.

This paper presents a novel approach to generation maintenance scheduling (GMS) problem. The GMS problem is one of the main issues in the restructured power system, due to its effect on the security risks and the profit of the generation company (Genco). In order to consider the risk of GMS for producers, the GMS problem from the perspective of producers is modeled using non-cooperative game theory, and to determine the Genco's optimal strategy profile, the Nash equilibrium is used. In this paper, the coordinating procedure to satisfy reliability criterion and greenhouse gases (GHGs) reduction is done by the independent system operator (ISO). In recent years, extensive researches have been fulfilled on the implementation of energy efficiency programs in the restructured electricity industry. Energy efficiency programs play a significant role in meeting our energy requirements, energy security risks, and GHGs problems. In this paper, an economic-environmental model of efficiency power plants (EPP) has been applied to analyze impacts of the energy efficiency programs in the generation maintenance scheduling (GMSEPP). Also, the impact of energy efficiency on emission production in GMSEPP is considered. The expansion of the EPP has been facilitated by regulatory support schemes. In this work, the energy efficiency feed-in tariff is considered as a regulatory support system to encourage the investors. The Implementation of the energy efficiency programs on generation maintenance scheduling can lead to the improvement in the reliability and reduction in GHGs in the modified IEEE RTS 24 bus.

In recent years, and with rapid rise of climate change, many countries have established multifield policies to drive more renewable resources into the power system. The increasing electricity generation by renewable resources gives rise to various structural issues like uncertainty and variability in modern power systems. Here, facing the abovementioned issues will be one of the most pressing problems in future energy systems and leads to define a new feature namely as power system flexibility. A novel framework for considering the enhancement of flexibility in the power system is presented in this paper. So, an innovative structure is suggested for flexibility improvement based upon unit commitment problem as well as construction of flexible fast ramp generating units. Here, an innovative formulation for uncertainty based flexible unit commitment and construction in combination with demand response resources i.e. (UBFUCCDRRs) is suggested. There are several uncertain resources affecting the UBFUCCDRRs. Here, one of the foremost imperative issues is the uncertainty of demand side resources. This paper considered, the price-elasticity of electrical consumption, as an uncertain, unavailable and hard to estimate parameter which is modelled using Z numbers as a possibilistic-probabilistic methodology. Moreover, the supply side resources uncertainty considered with Monte Carlo simulation methodology. In the modern smart grids, DRRs, fast ramp resources and energy storages can be considered for compensating the variability and uncertainty of renewable energy resources. Actually, with an increasing thrust towards renewable integration across the globe, energy storage as one of the most important flexibility resources has the potential to manage demand and supply dynamics. The energy storage is mostly used to adjust the timing of electricity supply by storing electricity at the lowest price level and discharging at an expensive time. So the energy storage plays an important role in reducing costs and increasing flexibility at the same time. Therefore, this paper sheds light on the various issues of future flexible power systems with significant amounts of non-dispatchable and uncertain renewable energy capacities. UBFUCCDRRs 's primary goal is to determine the optimum flexibility value as a key problem while achieving the operation and investment costs at the lowest possible level. The results indicate improvement of the flexibility index with cost adjustment. In order to trace the capability of the suggested framework, several cases are modeled on a modified standard 10-unit system.

One of the main challenges and essentials of the power system is the flexibility of generation scheduling. The flexibility of a system can be enhanced by using a smart grid comprising demand response, hybrid/diesel generation units and energy storage system. In this paper, an improved flexibility index is defined with the concept of fast reserve supply. The uncertainties of wind/solar power plants and required reserve of thermal units are considered using Latin Hypercube Sampling (LHS). The smart grid supplies a part of load profile of commercial consumers and a part of charge profile of plug-in hybrid electric vehicles (PHEVs) through wind and solar virtual power plants (VPPs), responsive loads, distributed generators (DGs) and the energy storage system. Moreover, the PHEVs considered in this paper provide a system with more flexibility. This paper has solved the unit commitment problem in a single-node system that has no transmission constraints. The mixed integer linear programming (MILP) and the mixed integer non-linear programming (MINLP) methods have been used in order to solve the unit commitment problem and the smart grid scheduling, respectively. The results show that the presented model can optimize the costs of the system and causes the system to become more flexible.

Hastening the power industry reregulation juxtaposed with the unprecedented utilization of uncertain renewable energies (REs), faces power system operation with sever uncertainties. Consequently, uncertainty assessment of system performance is an obligation. This paper reviews the probabilistic techniques used for probabilistic optimal power flow (P-OPF) studies and proposes a novel and powerful approach using the unscented transformation (UT) method. The heart of the proposed method lies in how to produce the sampling points. Appropriate sampling points are chosen to perform the P-OPF with a high degree of accuracy and less computational burden compared with features of other existing methods. The proposed method can take into account the correlation between uncertain input variables. In order to examine performance of the suggested method, two case studies are conducted and obtained results are compared with those of Monte Carlo simulation (MCS) and two point estimation method (2PEM). Comparison of the results justifies the effectiveness of the proposed method with regards to both accuracy and execution time criteria.

Smartening of contemporaneous power delivery systems in conjunction with the increased penetration of renewable energies (REs), change the way to energize consumers who are willing to maximize their utility from energy consumption. However, there is a high degree of uncertainty in the electricity markets of such systems. Moreover, the unprecedented ascending penetration of distributed energy resources (DERs) mainly harvesting REs is a direct consequence of environmentally friendly concerns. This type of energy resources brings about more uncertainties into power system operation resulting in, necessitates probabilistic analysis of the system performance. In the smarter power markets, encountered the restructuring and deregulation, the online studies of system performance is of huge interest. This paper proposes a new methodology for real time state estimation, e.g. energy pricing by probabilistic optimal power flow (P-OPF) studies using the concept of hybrid artificial neural networks (ANN) and differential evolutionary (DE) method. In order to examine the effectiveness and applicability of the proposed method, two case studies are conducted and the obtained results are compared against those of Monte Carlo simulation (MCS) technique. Comparison of the results reveals the impressiveness of the method regards to both accuracy and execution time criteria.

This paper presents a method to solve spatial network planning (SNP) in power systems expansion. SNP is an integration of transmission expansion planning (TEP) and optimal line routing problems. In fact, SNP considers the impacts of lines routing on the network power flow. Therefore, it can exploit such impactsto attain a signi cant techno-economic solution. In this work, a preprocess stage is employed to reduce the computational e ort demanded by line routing. Furthermore, a meta-heuristic algorithm is engaged to solve such a complicated problem. Applying these modi cations made it possible to solve SNP for large-scale systems with high-resolution rasterized maps. The proposed method is applied to a 3-bus system, an 18-bus test system as well as the original and modi ed versions of the IRAN southeast high-voltage transmission network to demonstrate the capability of the proposed methodology.

Carbon emission reduction is one of the most challenging issues in our emission-constrained world. However, a reduction may come into reality when enough information is available. Although the emissions are produced on the generation side as a result of fuel combustion, this generation is driven by demand, and consequently, the consumers are the prime responsible for carbon emissions in the electric power sector. On the generation side, providing enough information is relatively easy. It only requires economic-oriented tools such as economic dispatch and optimal power flow (OPF). However, on the demand side, providing consumers such useful information, to show them their role in emission production, requires more complicated tools and procedures. To explain and remedy this shortcoming, in this paper, we allocate the carbon footprint among the consumers as well as the transmission loss via a tracing method. To show the effectiveness and applicability of the proposed approach, two small-scale systems, 5- and IEEE 30-bus, and a very large-scale power system, the Polish 2383-bus system, are conducted. This proposal can be applied to smart demandside management aiming at carbon emission reduction.

As energy resource planning associated with environmental consideration are getting more and more challenging all around the world, the penetration of distributed energy resources (DERs) mainly those harvesting renewable energies (REs) ascend with an unprecedented rate. This fact causes new uncertainties to the power system context; ergo, the uncertainty analysis of the system performance seems necessary. In general, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, while they must be represented in another manner i.e. possibilistically. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution methodology is needed. This paper proposes a new analytical probabilistic- possibilistic tool for the power flow uncertainty assessment. The proposed methodology is based upon the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. This possibilistic–probabilistic formulation is solved in an uncertain power flow (UPF) study problem.

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies such as wind and solar, is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system context, consequently, necessitates probabilistic analysis of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognised as an appropriate probabilistic method. This study proposes a new methodology for probabilistic power flow studies for such a problem by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables, but the proposed method has been equipped with this ability. To justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the IEEE118-bus test systems are examined using the proposed method, then the obtained results are compared against the Monte Carlo simulation results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.

Incorporating plug in electric vehicles (PEVs) to power systems may address both additional demand as well as mobile storage to support electric grid spatially. Better utilization of such potential depends on the optimal scheduling of charging and discharging PEVs. Charging management malfunction of PEVs may increase the peak load which leads to additional generation. Therefore, charging and discharging of PEVs must be scheduled intelligently to prevent overloading of the network at peak hours, take advantages of off peak charging benefits and delaying any load shedding. A charging and discharging schedule of PEVs with respect to load curve variations is proposed in this paper. The proposed methodology incorporates integrated PEVs; the so-called parking lots; into the unit commitment problem. An IEEE 10-unit test system is employed to investigate the impacts of PEVs on generation scheduling. The results obtained from simulation analysis show a significant techno-economic saving.

As a matter of course, the unprecedented ascending penetration of distributed energy resources (DERs), mainly harvesting renewable energies (REs), is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system, consequently, necessitates probabilistic analyses of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognized as an appropriate probabilistic method in small scale or even medium scale problems. This paper develops a new methodology for probabilistic optimal power flow (P-OPF) studies for such problems by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables but the proposed method has been equipped with this ability. In order to justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the Mathpower 30-bus test systems are examined using the proposed method, then, the obtained results are compared against the Monte Carlo simulation (MCS) results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.

Load shedding is a crucial issue in power systems especially under restructured electricity environment. Market-driven load shedding in reregulated power systems associated with security as well as reliability is investigated in this paper. A technoeconomic multi-objective function is introduced to reveal an optimal load shedding scheme considering maximum social welfare. The proposed optimization problem includes maximum GENCO s and loads' profits as well as maximum loadability limit under normal and contingency conditions. Particle swarm optimization (PSO) as a heuristic optimization technique, is utilized to find an optimal load shedding scheme. In a market-driven structure, generators offer their bidding blocks while the dispatchable loads will bid their price-responsive demands. An independent system operator (ISO) derives a market clearing price (MCP) while rescheduling the amount of generating power in both pre-contingency and post-contingency conditions. The proposed methodology is developed on a 3-bus system and then is applied to a modified IEEE 30-bus test system. The obtained results show the effectiveness of the proposed methodology in implementing the optimal load shedding satisfying social welfare by maintaining voltage stability margin (VSM) through technoeconomic analyses.

A Hybrid Mutation Particle Swarm Optimisation (HMPSO) technique for improved estimation of Available Transfer Capability (ATC) as a decision criterion is proposed in this paper. First, this is achieved by comparing a typical application of the Particle Swarm Optimisation (PSO) technique with conventional Genetic Algorithm (GA) methods. Next, a multi-objective optimisation problem concerning optimal installation and capacity allocation of Flexible AC Transmission Systems (FACTSs) devices is presented and demonstrated. Modern heuristic techniques such as PSO have been demonstrated to be suitable approaches in solving non-linear power system problems. The outcome of this research further demonstrates that with better utilisation of FACTS devices, it is possible to improve transmission capabilities. The motivation of this research is a direct consequence of the deregulation of electricity industries and power markets worldwide. The current deregulated environment provides transmission systems operators (TSOs) with more options when procuring transmission services. The effectiveness of the proposed algorithm is demonstrated across a range of case studies, and the results are validated through analyses conducted on IEEE 30-bus and 57-bus test systems.

One of the most important concerns in the smart grid environment is congestion management which is performed by independent system operator. In this paper, in addition to generation rescheduling, demand response resources (DRRs) as a consequence of demand response programs are utilized in smart grid environment in order to relieve the transmission congestion. Previously, congestion management was implemented without considering the uncertainty of power system. Herein, the stochastic congestion management with taking into account the outage of generation units and transmission lines is presented. The power system is presented by the DC model for power flow calculation which uses Transferred Power Distribution Factors (TPDFs) to describe the relation between generators/DRRs and line power flows. The proposed framework is applied to the 24-bus Reliability Test System (RTS) so that the results of stochastic congestion management and deterministic congestion management with existence and no existence of DRRs are compared with each other.

This paper proposes a novel mechanism for maintenance scheduling of generating units in the smart environment concentrating on Demand Response Resources (DRRs). DRRs as virtual power plants in energy policy decisions affect the controllability of power system ranging from short-term to long-term. Here, an economic model of responsive loads based upon price elasticity of demand and customers' benefit function is utilized. The nominated multi objective security constrained Preventive Maintenance (PM) scheduling incorporating Demand Response Programs (DRPs), aims to minimizing total system expenditures and generated emissions over the scheduling time horizon. Total system expenditures include operation and maintenance costs, reserve cost, and total incentive due to participating in DRPs. Utilizing Lexicographic method as one of the Multi Objective Decision Making (MODM) techniques, the maintenance scheme, commitment status, energy and reserve scheduling are determined along the scheduling horizon. Furthermore, the actual potential for customers' participation in DRPs and, multifarious optimal incentive in per bus are also derived. Several analyses are carried out on the IEEE-RTS to confirm the capability of the proposed framework.

SUMMARY Transmission network expansion planning (TNEP) is one of the most important parts of power system studies. An optimal expansion of a transmission system should also capable of facilitating forecasted load patterns as well as future generation. Mathematically, TNEP is a mixed integer nonlinear programming as a non convex problem that can be solved by either conventional or heuristic optimization algorithms. This paper proposes a methodology for choosing the optimal transmission expansion plan considering less computational effort. The proposed methodology is composed from combination of Modified Scatter Search Algorithm and Constructive Heuristic Algorithm (CHA) considering New Load Shedding Index (NLSI). The proposed NLSI is implemented without generation rescheduling in order to decrease the number of linear programming obtaining an optimal solution. The algorithm is applied to different cases: Garver, 24-Bus IEEE systems and Southern Brazilian System of 46 Buses system. The simulation result shows a significant performance of the proposed method in comparison with some studies addressed in literature. Copyright © 2012 John Wiley & Sons, Ltd.

Various technical limitations of generating units and ancillary services requirements have more complicated the power generation control and operation. This paper introduces a novel methodology for day-ahead unit commitment, which is a crucial challenge in restructured power systems, considering primary frequency control reserve. In addition, also spinning reserve uncertainty as a practical constraint has been taken into account. In this paper, we formulate and solve simultaneous scheduling of energy and primary reserve as a mixed integer non linear programming problem that simultaneously accounts aforementioned constraints. In proposed formulation, system frequency is allowed to fall from nominal value to a critical specified limit. Finally, the proposed approach is implemented to the scheduling of a 17-unit isolated power system over 24-hours. Case studies and numerical results present significant outcomes and verify the robustness of the proposed method, while it creates a schedule consistent with the primary frequency control and spinning reserve uncertainty.

Under the smart grid environments, Demand Response Resources (DRR) as a power system resources can effectively participate in and improve performance of electric systems. Congestion management is one of the technical challenges in which DRRs can play significant role. Previously, congestion management is applied without considering the power system uncertainties. Therefore, a stochastic congestion management by means of a trade-off between DRRs and load shedding is proposed so that the outage of transmission lines and generating units are considered. In order to investigate the proposed framework, two types of Monte Carlo simulation methods, namely 1) ordinary and 2) lattice rank-1, are utilized and compared. Hence, Independent System Operator (ISO) can be able to relieve the existed transmission line congestion considering the uncertain network configuration. The proposed model is applied to the 24-bus Reliability Test System (RTS) and simulation studies are performed to examine the effectiveness and capability of the proposed framework.

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time.

Congestion management is one of the most important functions of independent system operator (ISO) in the restructured power system. This paper presents two new methodologies for optimal sitting and sizing of distributed generations (DGs) in the restructured power systems for congestion management. The proposed methodologies are based upon locational marginal price (LMP) and congestion rent that forms a priority list of candidate buses to reduce the solution space. The proposed priority list facilitates the optimal placement as well as the level of output power of DGs. The proposed methods are implemented on the IEEE 14-bus and IEEE 57-bus test systems to illustrate their effectiveness. An economic consideration of DG placement and its operation is also studied. Simulation studies and results analysis show that the proposed methodologies are capable of finding the best location and optimal size for DGs, which can alleviate congestion in transmission systems.

This is the post-print version of the final paper published in International Journal of Electrical Power & Energy Systems. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V. This paper proposes a new algorithm for estimating voltage security margin. The algorithm is based on the perturbation method and has significant computational efficiency. The proposed algorithm can be used for on-line voltage security evaluation. It has been validated using IEEE-14, IEEE-30 and IEEE-57 bus systems. Results from the tests show higher efficiency and smaller error margins compared to continuation power flow (CPF) method. Voltage collapse is a serious threat to the security of stressed power systems; therefore, voltage security (VS) has become a major challenge for management of power systems. The motivation for this research is a direct consequence of the deregulation of electricity industries and markets worldwide.

Procurement of optimum available transfer capability (ATC) in the restructured electricity industry is a crucial challenge with regards to open access to transmission network. This paper presents an approach to determine the optimum location and optimum capacity of TCSC in order to improve ATC as well as voltage profile. Real genetic algorithm (RGA) associated with analytical hierarchy process (AHP) and fuzzy sets are implemented as a hybrid heuristic technique in this paper to optimize such a complicated problem. The effectiveness of the proposed methodology is examined through different case studies.

Procurement and pricing of electric power and ancillary services is one of the most important issues of electricity market manager under new restructured environment. Ancillary services are necessary to provide security and reliability constraints applied to the dispatching and scheduling problem while they are essential for commercial functions. In a market payments should be based on energy and ancillary services capacities. Spinning Reserve (SR) is an ancillary service that is coupled with energy capacity, should be procured and priced via a joint economic dispatch mechanism to provide a right signal for all electricity customers. This paper presents a new approach for joint economic dispatch as an optimal criterion for spinning reserve allocation and pricing. Case study shows the significance of the proposed methodology.

In spite of the widespread applications of probabilistic criteria in large systems there is considerable reluctance to apply probabilistic techniques in small isolated power systems. This reluctance dictates a need to create a bridge between the conventional deterministic methods and the prevalent probabilistic teclmiques. This paper presents a well-being approach to illustrate the adequacy assessment of small isolated power systems. A well-being index designated as LOHE and the conventional LOLE index are utilized to determine the maximum load carrying capability in two isolated systems.

The future of power systems known as smart grids is expected to involve an increasing level ofintelligence and incorporation of new information and communication technologies in everyaspect of the power grid. Demand response resources and gridable vehicle are two interestingprograms which can be utilized in the smart grid environment. Demand response resources canbe used as a demand side virtual power plant (resource) to enhance the security and reliabilityof utility and have the potential to offer substantial benefits in the form of improved economicefficiency in wholesale electricity markets. An economic model of incentive responsive loadsis modelled based on price elasticity of demand and customers’ benefit function. On the otherhand, a gridable vehicle can be used as a small portable power plant to improve the reliabilityas well as security of the power system.This paper formulates a mixed-integer programming approach to solve the unit commitmentproblem with demand response resources and gridable vehicles. The objective function of theunit commitment problem has been modified to incorporate demand response resources andgridable vehicles. The proposed method is conducted on the conventional 10-unit test systemto illustrate the impacts of smart grid environment on the unit commitment problem. Moreoverthe benefits of implementing demand response resources and gridable vehicle in electricitymarkets are demonstrated.

Preventive maintenance scheduling (PMS) of generating units is addressed as a long-term scheduling in power system studies which ensures a reliable supply of demand and economical operation of power systems. In this paper, a new structure of security-constrained PMS associated with a novel cost reduction index (CRI) is developed. CRI encourages generating units to be placed in a desirable situation while determining the most appropriate maintenance scheme and generation pattern. In this paper, a new linearized formulation for cost-based preventive maintenance scheduling associated with the CRI is presented. Here, the proposed framework is structured as mixed integer programming (MIP) problem and solved using CPLEX solver. This model would schedule maintenance scheme and commitment status of generating units while the system total cost including maintenance, operation and reserve expenditures is minimized. Values of energy and reserves over the scheduling time horizon are also determined simultaneously in this paper. The IEEE reliability test system (RTS) is utilized to demonstrate the effectiveness of the proposed structure.

As the share of wind energy in power system increases, consideration of these resources in power system planning, particularly in Transmission Expansion Planning (TEP) is becoming more and more important. In this paper, a Probabilistic Multi Objective Transmission Expansion Planning (PMO-TEP) is presented to find the optimal node for connecting the large-scale distant wind farm to the network from viewpoint of market competition. Furthermore, to investigate the uncertainty associated with wind speed variation and load prediction, a probabilistic analysis tool namely Two-Point Estimate Method (2PEM) is applied. The investment cost of new lines and congestion cost for enhancement of market competition are considered as multifarious objectives in PMO-TEP. Moreover, an overload index is evaluated to ensure that all optimal plans are satisfying the security constraint (n-1 criterion). Here, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to handle the multi-objective optimization. Finally, a fuzzy decision-making analysis is used to obtain the optimal node for connecting the wind farm to the network among all optimal Pareto solutions. Different scenarios are demonstrated on the modified IEEE-RTS to trace the capability of proposed model.

A stable and reliable power distribution system is an inevitable pre-requisite for the technological and smarter power system. Furthermore, the traditional methodology for improving reliability of distribution system under smart grid environment will be unavailing. For this purposed, a novel approach is presented for multi objective placement and number of the Fault Indicator (Fl) in the smart distribution systems. The reliability index and investment cost are considered as multifarious objectives in the multi objective problem. Due to its good handling of non-convex and mixed integer problem, a Fuzzy Clustering-based Genetic Algorithm (FCGA) is utilized for optimization problem. The simulation is conducted on a 50 node real system to trace the capability of proposed algorithm.

Transmission and generation expansion planning (TEP and GEP) considering loadability limit of power system is studied in this paper. Artificial neural network (ANN) technique is used to evaluate loadability limit of the power system because of its sensitivity characteristic. Power system restructuring and separation of decision-making organizations of transmission and generation expansion, make coordination between generation and transmission companies more crucial. On the other hand, voltage stability is one of the indicators of power system security level. In this paper, first the load pattern of a six-bus power system is improved and then the best bus for load increment is determined using sensitivity characteristic of ANN. Afterwards the strategic interaction between transmission company (TransCo) and generation company (GenCo) for TEP and GEP in a competitive electricity market is proposed using Game Theory (GT). The proposed algorithm comprises three optimization levels to determine Nash equilibrium such that the most profitable strategy for both sides of the game can be found out in an expansion planning game.

Power systems may not capable of utilizing full transmission capacity. The process of reregulation causes more challenges with regards to technical and trading issues. Restructuring of electricity industry may need some management criteria in order to improve technical as well as economical efficiency. Under the new scheme of power markets, congestion management is a crucial problem that is needed to be considered. Congestion relief can be handled using FACTS devices, where transmission capability may be improved. In this paper, real genetic algorithm (RGA) is used for optimization process while analytical hierarchy process (AHP) with fuzzy sets is implemented to evaluate RGA fitness function. The effectiveness of the proposed methodology is shown through a modified IEEE 30-bus system.

This paper proposes a method to determine the output of all online units with minimum total cost when the amount of emission is reasonable. A joint economic and emission dispatch is proposed in order to get a significant compromise between costs and emission such that real power supply-demand equilibrium is satisfied. In order to have a meaningful compromise between costs and emission in the problem formulation, two variables are used, weighting factor and price penalty factor. A case study comprising of a 3-unit power system is employed, where various demand is used. Results for the test system indicate the fastness and effectiveness of proposed method.

A metaheuristic technique for solving the short-term transmission network expansion and reactive power planning problems, at the same time, in regulated power systems using the AC model is presented. The problem is solved using a real genetic algorithm (RGA). For each topology proposed by RGA an indicator is employed to identify the weak buses for new reactive power sources allocation. The fitness function is calculated using the cost of each configuration as well as constraints deviation of an AC optimal power flow (OPF) in which the minimum reactive generation of new reactive sources and the active power losses are objectives. With allocation of reactive power sources at load buses, the circuit capacity increases and the cost of installation could be decreased. The method is tested in a well known test system, presenting good results when compared with other approaches.