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In deregulated power systems, reactive power ancillary service through electricity market is becoming relevant where private generation companies participate in maintaining system wide bus voltage within the permissible limits. Marginal cost price (MCP) based real time reactive power ancillary service market faces several challenges due to the localized nature of reactive power. In this paper, a market mechanism for real time reactive power ancillary service market based on Stackelberg game model is proposed considering voltage-apparent power coupled subsystems. In the proposed Stackelberg game model, Independent System Operator (ISO) is considered as the leader, and GENCOs as followers. In the formulation, each GENCO is associated with a relevance factor in the partitioned subsystem so as to consider the real time voltage support requirement in the system. The market is then formulated as Mathematical Program with Equilibrium Constraints problem (MPEC). Existence of equilibrium, incentive compatibility, and individual rationality of the proposed market mechanism is then analysed in this work. The numerical examples are illustrated in PJM 5-bus system, and tested on IEEE 30- bus system, and Nordic 32 Bus-system. The mechanism induces truth-telling behavior of GENCOs, yields a non-negative profit, and the system wide bus voltage is improved.

The optimal protection coordination (OPC) problem of directional overcurrent relays (OcR) in power system networks is a complex, highly constrained nonconvex optimization problem because of the non...

Most of the countries in the world have a futuristic vision of generating at least 33% of total installed generation capacity from the renewable sources like wind, solar etc. by 2020. As part of the vision, one of the key steps that are being taken is to integrate residential micro grids in large scale at low voltage distribution level. In this paper, we propose a novel and unique algorithm to effectively monitor and control the residential micro grids (MG) connected to low voltage radial distribution systems. An Advanced Metering Infrastructure (AMI) & Geographical Information System (GIS) based near real-time load flow method is developed for this purpose. Proposed method along with weighted graph representation of each 1-phase network connected to a 3-phase distribution transformer (DT) helps in real-time monitoring & control of residential micro grids at individual DT level. A case study is performed on standard IEEE 33 bus radial system demonstrating the working method of proposed algorithm and its major applications for distribution system automation & control are discussed.

This work proposes a novel relative electrical distance measure that provides information of coupling between voltage and apparent power between two buses in power systems. Relative electrical distance measure is derived from the bus admittance matrix which can be obtained in real time using Phasor Measurement Units. Based on the relative electrical distance measure, in this work, an isoperimetric clustering based algorithm for partitioning power systems into voltage–apparent power coupled areas is proposed. The advantage of the partitioning algorithm proposed in this work is that large networks can be represented as a weighted graph with number of vertices equal to number of generators in the system which is much lesser than the size of system, thereby reducing the computational effort for partitioning. Isoperimetric clustering technique along with k-means is then applied to the graph to obtain voltage–apparent power coupled areas. Simulations carried out on New England 39-bus system and IEEE 118-bus system demonstrate the effectiveness of the proposed methodology for partitioning the system into voltage–apparent power coupled areas, subject to changes in the operating condition of the system. The quality of clustering is analysed and compared with Cheeger inequality bounds, which ensures that power system is well partitioned.

This paper proposes a robust fully distributed consensus based algorithm (RFDCA) for optimal dispatch of generating units in power systems with a practical communication network, which considers noise, communication delay and a dynamically switching communication topology. The proposed approach is based on double consensus $- \lambda$ consensus achieved through first-order consensus protocol and $\Delta P$ consensus through average consensus protocol. The adverse effects of noise and communication delay can be lessened through a monotonically decreasing consensus gain function. The main advantage of this method is that the generating units need not share their cost characteristics to anyone, thereby preserving their private information. Another advantage of the proposed approach is that it can deal with networks of different sizes and topology, and by choosing the proper convergence coefficient, a faster rate of convergence can be achieved. Simulations are carried out on standard IEEE test systems to illustrate the efficacy of the RFDCA approach on both smaller and larger systems.

The model predictive controller exhibits excellent constraint and non-linearity handling capabilities. But still, it is less preferred as a centralized controller in the nonlinear standalone micro-grids for two reasons. One is the computational effort involved in predicting the micro-grid response with a nonlinear micro-grid model. The other one is the involvement of more decision variables in the MPC problem. This paper uses the approximated linear model for the forced response prediction to address the first drawback and solves the nonlinear micro-grid model for natural response prediction. Continuous-time Kautz functions are used in the formulation of the MPC problem to address the second drawback. The Kautz functions approximate the control trajectory of each of the inputs within the control horizon. The approximation decreases the decision variables in the MPC control problem. While doing so, the performance capabilities of the controller can still be conserved.

Dynamic Line Ratings based on thermal limit increases utilization of existing transmission line network. The excess capacity can be used to alleviate congestion, which has positive ramification on system. The computational complexity of dispatch problem increases as temperature and current are related through set of differential equations. Hence, a temporal decomposition based multi-interval ACOPF incorporating dynamic line ratings is proposed to manage strict computational requirements in real time dispatch. The method is tested on 4 bus system and application based on congestion management during real time dispatch and improvement in utilization of wind power based system is proposed.

Proper investments for expansion of generation, transmission and distribution systems in an electric grid is a very important issue that rely on optimal expansion planning of the grid resources. Investments on transmission network influence those in generation and distribution side which motivates a co-optimization of all these different resources of a grid. The co-optimization based Generation - Transmission Expansion planning is a large scale, constrained, hard bound optimization problem. This research article proposes an Information exchange based Clustered Differential Evolution algorithm (IE-CDE) for solving the problem of expansion planning of generation and transmission resources in an electric grid. The proposed algorithm is first tested extensively on the CEC 2017 constrained optimization benchmark problems and the results are compared with those obtained by state-of-the art algorithms to investigate the efficiency of the proposed algorithm in solving challenging constrained optimization problems. Then the proposed algorithm IE-CDE is used to solve the challenging Generation-Transmission expansion planning problem (GT) on a test system called Garver system. The implementation is also extended to incorporate the expansion planning of demand management resources along with generation and transmission resources (GTD) on the same test system mentioned before as well as an additional one called IEEE 24 bus system. The results obtained by proposed IE-CDE on the GT and GTD expansion planning problems are compared with state of the art algorithms in the literature and the comparison reveal that the proposed method is able to find better solutions than the other algorithms yielding lower cost of expansion for the electric grid. The claim for superiority of the proposed method over others is also substantiated by statistical significance tests on the obtained results.

This paper is a review of uncertainty modeling techniques used in smart grid studies. The literature dealing with uncertainty from various sources in smart grid is analyzed and presented. In a modern power grid, the risk may arise due to different reasons; in-termittent renewable energy sources, uncertain consumer reactions on demand response, driving patterns of electric vehicles, etc. The paper has two objectives. First is to bring out the trends in uncertainty handling techniques used in electrical power system problems, and second to introduce the scope of new risk processing techniques with the perspective of recent smart grid issues.

One of the critical requirements of smart distribution grid is to incorporate self-healing mechanisms which help in effective operation and better management of distribution system. Distribution system has grown in a multi-fold fashion during recent times not only in terms of increased load but also with the addition of advanced infrastructure like communication, smart meters & sensors, automation and information technology. Digitization of distribution grid led to the development of new technologies and solutions like residential micro-grids, electric vehicle charging, demand response, net metering, demand-side management and outage management system for effective load management and control in Low Voltage (LV) distribution networks. At the same time, this has been posing tremendous challenges in effective operation of Medium Voltage (MV) distribution networks due to the lack of real-time and additional information from LV networks. Distribution Automation (DA) applications at MV level has limited access or visibility outside distribution substation and reaching down to the LV network level. In this paper we propose a method of distribution load modelling in DA system based on Advanced Metering Infrastructure (AMI) system feedback which helps in effective operation of DA system along with suitable case study analysis.

The work presented in this paper discusses about the usage of DC electric springs in the microgrid system consisting of more than one renewable energy source and one main grid connection point. The objective of this work is to reduce the dependability of the microgrids on the main grid by coordinating the power supplied by renewable energy sources during generation intermittence of the renewable generators. In order to achieve the said objective, a control algorithm is proposed in this work which makes the DC noncritical loads consume lesser power during generation deficits in the microgrid. The proposed control algorithm is simple to implement and can be used for complex microgrid structures also. A microgrid system consisting of two renewable generators, one main grid connection point and DC loads fed through a rectifier are considered for the study, and the models of the same are developed in MATLAB/Simulink.

This paper analyses the False Data Injection Attack (FDIA) on tie-line sensor measurements of a Load Frequency Control (LFC) System from an attacker’s perspective. The attack vector has been modeled considering an advanced LFC system, wherein, the measurements undergo State Estimation and bad data detection algorithms. Two different attack vector modeling scenarios have been considered: in the first model, an arbitrary attack vector modeling is performed to study its effect on the system operation; The later model is built systematically to induce sequential outages. The significant advantage of the proposed method is that the number of measurements to be attacked and system knowledge like topology data, line parameter values, transfer function parameters that are required is very low. The results are studied for an IEEE 39 bus, 3 area system.

Among the ancillary service markets for smooth real time operation of the system, reactive power support ancillary service market is regarded as one of the most important services. In this work, the total reactive power required in the system is formulated and based on the bids received from the market participants, value for reactive power is determined. Due to localised nature of reactive power, an assessment of local reactive power requirement is more effective and hence in this work, a localised reactive power market is formulated. The objective of system operator is to minimise value of reactive power in each local market such that optimal scheduling of reactive power generation is achieved subject to system constraints and voltage support. The value function is formulated from the operation cost bids and lost opportunity cost bids (LOC) received from Generation Companies (GENCOs). From the Lagrange function, locational marginal price (LMP) is then determined. The proposed localised reactive power market is tested on IEEE 24-Bus system. It is found that the localised reactive power market maintains sufficient reserve and provide better voltage support. The advantage of proposed pricing scheme is that, the generators are paid based on the value of reactive power requirement in the system determined locally, thereby reducing volatility of price and chance of exercising market power.

Reactive power service is considered as an important ancillary service, due to its contribution towards maintaining system wide bus voltage. In this work, a value based reactive power pricing model suitable for real time market is proposed. In the proposed reactive power market model, bids(operation cost and lost opportunity cost(LOC) of reactive power) are received from the Generating Companies (GENCOs) participating in the real time market. From the bids received, a three component reactive power value function is formulated. The components of value function being load serving component, voltage support component and reserve component of reactive power requirement in the system. The objective of the Independent System Operator (ISO) is to minimise the value function of reactive power requirement in the system subject to generator limits, bus voltage limits and transmission line limits. From the Lagrange function of the optimisation problem, the marginal value (MV) of reactive power is derived to calculate the marginal price (MP) of reactive power. The NLP formulation of the market model is solved using DICOPT solver in GAMS and anlaysed on IEEE 24-Bus system. The simulation results prove that the proposed algorithm is efficient in clearing the reactive power market in such a way that the system wide bus voltage deviation is minimal and sufficient reactive power reserve is maintained in the network.

Modern power system is moving towards a smart and competitive grid, with competing generating companies, power retailers, and strategically behaving consumers playing a crucial role in the daily operation of the system. Independent System Operator (ISO) monitors these daily operations and procure required services through market operations. Active and reactive power pricing in real-time at the wholesale and retail level is considered an efficient energy management method. However, procurement of reactive power through a market mechanism in real-time is not being implemented, despite its crucial role in maintaining system parameters within the permissible limits. This paper attempts to detail the challenges faced in implementing such reactive power markets and presents a review of existing reactive power market mechanisms to address these challenges. A framework suitable for reactive power ancillary service in a smart grid is also detailed in this paper.

Advances in microgrid enabling technologies and utilization of Renewable Energy Sources are prompting more and more number of smaller investors to invest in Renewable energy generation and distribution at microgrid level. The increased competition requires the energy producers to offer energy at minimum possible cost to gain the confidence of consumers, which needs efficient methods to schedule the energy generation among the available Renewable Energy Sources. Optimal scheduling of generation is one of the methods used to reduce the cost of generation. Out of many types of algorithms used effectively to solve the problem, evolutionary program techniques are proven and time tested to be one of the best solutions. A stochastic based search algorithm, called Artificial Fish Swarm Algorithm is used in this article to solve the problem of optimal scheduling of energy generation among the available Renewable Energy Sources. The effectiveness of the algorithm is validated by implementing to schedule generation in a microgrid scenario. The results are validated by comparing to an already tested Additive Increase Multiplicative Decrease algorithm.

A two stage algorithm is proposed in this paper to optimize cost of generation with application to a virtual power plant. First stage of the algorithm presents a methodology to draw a hierarchy for the choice of distributed generators based on the cost of generation. Second stage of the algorithm optimizes generation to minimize cost. An Additive Increase and Multiplicative Decrease algorithm, which is already used for optimization in microgrids is improved further and is presented as Modified Additive Increase Multiplicative Decrease algorithm and is applied in the second stage of the algorithm for optimization. The Modified Additive Increase Multiplicative Decrease algorithm is validated by implementing to schedule generation of distributed generators with intermittent power availability in a Virtual Power Plant in grid connected mode to optimize the cost of generation. The Modified AIMD algorithm is proved to be much more effective than the original AIMD algorithm.

Electric vehicles are an integral part of futuristic smart grids. Electric vehicles give rise to a new player in the retail market called as aggregator. This paper proposes an intelligent charging scheduling problem for an Electric Vehicle (EV) aggregator considering vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capabilities with an objective to minimize the total charging cost. Since electricity price at the charging node may be subject to uncertainties, Information Gap Decision Theory (IGDT) is proposed in this paper to handle uncertainties in the price. The original intelligent charging scheduling problem is non-linear. The paper proposes a modified Mixed Integer Linear Programming (MILP) based reformulation and solves with CPLEX using GAMS as an aggregator.

An iterative distributed algorithm is proposed for existing power system optimization problems. Here the centralized optimal dispatch problem is explained in a decentralised manner. The line flow constraints along with generation limits are distributed into different nodes of the given network. In order to achieve a globally optimal solution, the incremental cost and the penalty factor for congestion are varying iteratively. The distributed algorithms provide flexibility, robustness, and scalability. The algorithm is tested for optimal power dispatch in an IEEE 39-bus system and welfare maximization problem in a 3-bus system. The results are validated with the standard centralised approach.

Under deregulation, Automatic Generation Control (AGC) scheme may consider economic dispatch of Generating Companies (GENCOs) as well in addition to minimising the system frequency deviation. This will result in increased participation from GENCOs in AGC. GENCOs being private utilities, will have the private information like cost function etc which will not be shared with the system operator. This imposes a challenge on implementing classical economic dispatch problem in AGC scheme. The contribution of the paper is a game theoretic based model of AGC scheme that will ensure an optimal dispatch to competing GENCOs. This is achieved by defining an optimal participation factor for GENCOs that will minimize their cost of production. A game theoretic approach towards AGC as a dynamic game is presented and GENCOs are considered as Reinforcement learning agents. A Single Agent Q-Learning method along with pursuit algorithm is used at each GENCO agent to achieve the equilibrium point in AGC. The algorithm was studied with three competing GENCOs. Results show the suitability of the proposed AGC model and its ability to minimize the deviation in system frequency while ensuring economic operation of the system.

The work proposed in this article proposes a different energy management scheme for residential consumers having higher penetration of solar power and heating loads. The presented methodology assumes the heating loads present in the residence as non-critical loads and tries to operate them in power saving mode, whenever necessary. In the considered scheme non-critical loads are connected in series with AC electric springs which in turn schedule them as per the requirement. The major scope of this work is to make the non-critical loads consume lesser power by pressing springs into action in the presence of solar power. It is expected that the springs compliment the efforts of solar panels in reduction of power consumed from utility grid, daily peaks and energy purchase from the utility grid. In order to implement the proposed methodology a completely customer driven home energy management algorithm is proposed and the robustness of the same is tested on a residential consumer, considering a 24 load curve. The simulation studies for the projected methodology are performed in MATLAB.

This paper analyses the benefit of a multi-stage transmission expansion planning (TEP) over single stage TEP. The paper establishes the conditions (load growth and interest rate) under which the two formulations are equivalent, and when a multi-stage TEP formulation could lead to a better solution. The paper answers an important question of how the planning span shall be divided into stages when going for a multi-stage TEP. The paper further analyses the effect of high peak to average load ratio (PAR) in a multi-stage TEP. The single-stage and multi-stage TEP problems are formulated to minimize the total investment and operation cost over the planning period. The operating cost is calculated by dispatching the generators and loads in a double sided auction market where the supply and demand bids are assumed to be fixed over the planning period. The TEP problem has a mixed integer linear programming (MILP) formulation and is tested on 6 bus Garver System implemented in GAMS.

The increased integration of Distributed Energy Resources (DER) and intelligent control technologies to distribution networks have succeeded in framing foundation for smart power grids. Design of protection systems and voltage regulation methodologies for an interactive smart grid will face new challenges with the introduction of new grid codes, active network management schemes and islanded operation. The active network management and optimal sizing of distributed sources shall change the operating point of the smart inverter-based DER in the islanded electric power system (EPS) continuously. This paper assesses the impact of such schemes on fault current magnitudes for various fault cases and voltage regulation at various nodes of an islanded EPS with distributed energy resources.

Power restoration schemes incorporated into Distribution Automation (DA) system helps in quickly restoring the power during emergency situations like faults. Due to the numerous advancements made in the distribution system operation in recent times, power restoration analysis needs to be done in a look-ahead manner based on system wide operational & monitored data apart from just fault data processing during the outages. With the evolution of Advanced Metering Infrastructure (AMI) during recent times, it is now possible to design the look-ahead restoration schemes by integrating the operation of DA with AMI system using internet protocol based communications, preferably. In this paper, we propose a novel method for power restoration analysis which execute all pre-defined restoration schemes in a look-ahead manner by representing distribution system as a weighted graph and computing graph weights based on representation of DA & AMI system’s current operational/measured/constraint data from each distribution load segment. Computed graph weights are converted to restoration indices with associated ranking based mechanism to indicate criticality & considerations for each power restoration scheme in case of fault occurrence in real-time. A case study on the standard RBTS test system has been demonstrated for the proposed method.

The work presented in this paper proposes a new Demand side management (DSM) algorithm for residential users having higher penetration of heating/resistive loads. The proposed algorithm uses electric springs to operate the non-critical loads in power saving mode, so as to reduce the daily peak, ramping and energy bill paid by the consumers. In order to implement the suggested methodology it is assumed that all the non-critical loads present in the residential complex are fitted with electric springs. The quantum of the power to be saved is completely left to the consumers and the customers are given full independence to choose the required percentage saving in power, which in turn reduces the electricity bill paid by them. Further it should be noted that the proposed energy management scheme re-shapes the entire load curve and achieves energy saving without shifting the non-critical appliances to off-peak timings, which is something new to the literature. The robustness of the suggested algorithm is tested by considering a 24 hour load curve of a residential user and the simulation studies of the same are performed in MATLAB.

In the present electricity market, where renewable energy power plants have been included in the power systems, there is a lot of unpredictability in the demand and generation. There are many conventional and evolutionary programming techniques used for solving the unit commitment (UC) problem. Dynamic programming (DP) is a conventional algorithm used to solve the deterministic problem. In this paper DP is used to solve the stochastic model of UC problem. The stochastic modeling for load and generation side has been formulated using an approximate state decision approach. The programs were developed in a MATLAB environment and were extensively tested for a four-unit eight-hour system. The results obtained from these techniques were validated with the available literature and outcome was good. The commitment is in such a way that the total cost is minimal. The novelty of this paper lies in the fact that DP is used for solving the stochastic UC problem.

 Abstract—Security assessment is a major concern in real time operation of electric power systems. Traditional method of security assessment performed by continuous load flow analysis involves long computer time and generates voluminous results. This paper presents a practical and feasible Support Vector Machine Based Pattern Classification (SVMBPC) approach for static security assessment in power systems. The proposed approach classifies the security status of any given operating condition in one of the four classes - Secure, Critically Secure, Insecure and Highly Insecure based on the computation of a numeric value called security index. The feature selection stage uses a simple and straightforward forward sequential method to select the best feature set from a large set of variables. The static security classifier is designed by a multi-class SVM with different parameter tuning methods. The proposed approach is implemented in New England 39 bus and IEEE 118 bus systems and the results are validated. techniques and expert systems like fuzzy set theory has been proposed for security assessment problem, overcoming the pitfalls of traditional method. Literatures have reported the use of Artificial Neural Network techniques (4), (5), fuzzy logic combined with neural network (6), genetic based neural network (7) for static security assessment process. The performance of all these existing techniques are highly problem dependent and hence its suitability cannot be generalized. Nowadays, pattern classification is gaining more importance in solving many power system problems. In this approach, main bulk of work is done off-line to generate sufficient dataset. The classification function, designed based on the train set, helps to access the system security level in a short period of time. This paper addresses security assessment as a pattern classification problem with the classifier function designed by Support Vector Machine (SVM). SVM is a new and promising tool for learning separating functions in PR system with the capability of handling non-linear separability. The SVM classifier is designed for multi-classification based on the calculation of a term called Static Security Index (SSI), for each specified contingency. In this paper, four-class logic is used for the definition of system security viz., secure, critically secure, insecure, highly insecure. An operator likes to know exactly the severity level of disturbances for a given system operating condition. On-line security assessment allows the operator to know the security status and helps to determine the corrective actions. This paper also addresses different heuristic optimization techniques like Particle Swarm Optimization (8), Real Coded Genetic Algorithm (9) and Differential Evolution (10) used in the selection of SVM parameters globally. The classification approach is implemented in New England (NE) 39 bus system and IEEE 118 bus system and the results are compared.

Reactive power plays a prime role in power system operation. But contingencies due to reactive power shortages are rarely considered while evaluating the reliability of a system. This paper takes into account the failure of reactive power sources also while evaluating reliability of a system and calculates a separate set of reliability indices due to reactive power shortage. Demand Side Management (DSM)is a promising attempt in improving the system reliability. Here we consider the novel concept of including the effect of reactive power failures in DSM reliability studies. Reliability indices are evaluated for the modified IEEE 30 bus system and also for the same one where DSM is already in operation, which makes it a smart grid. We see that the reliability is improved in the latter case even on considering reactive power source failures also.

It is important to handle the transmission network expansion planning problem under restructured power systems to allow fair competition in the system. A scenario-based two-stage Transmission Network Expansion Planning (TNEP) is presented in the paper and solved using a meta-heuristic algorithm. Various scenarios, for market-driven power flow patterns is considered in a 6-bus Garver Test System, and Differential Evolution is employed to solve the traditional Transmission Network Expansion planning problems in each scenario. A decision making scheme based on risk analysis is employed to select a plan with minimum risk. In this paper it is assumed that the market players trade only via pool market model, where bid pricing affects the generator loading.

Non-consistency of energy availability from Renewable Energy Sources needs estimation and scheduling in advance so that the other certain sources of energy like fuel cells, diesel generators, storage devices etc., can be scheduled appropriately to maintain load-generation balance in real time. Evolutionary program techniques are proving handy and reliable in the process. This article uses an Artificial Fish Swarm algorithm to solve the problem of day-ahead scheduling of generation in a mix of Renewable Energy Sources, despatchable sources and storage. The utility function of hourly generation cost is considered for optimization along with various microgrid operational constraints. The performance of the algorithm is validated by applying to schedule generation in a microgrid in grid connected mode consisting of one wind turbine and one PV source as Renewable energy sources, one diesel generator and fuel cell as despatchable generators and a battery for storage. The scheduled generation of each generator, power exchange of storage source along with its state of charge are evaluated for optimum cost of generation.

The work presented in this paper addresses the problem of main grid dependability in micro grids using Electric springs. The micro grids which have more penetration of renewable sources are bound to depend on main grids during generation intermittence of the micro sources. In order to reduce the dependability of micro grids on main grid an efficient energy management algorithm has been presented. The proposed algorithm schedules the non-critical loads present in the micro grid using A.C Electric springs and the simulation studies are performed in MATLAB. The reduction in energy consumption from the main grid is achieved by making the non-critical loads consume lesser power during generation intermittence in the micro grid.

Power system is a large-scale network with a number of components and interconnections for which centralized control becomes cumbersome. For multi-area computations, decentralization is necessary. For implementation of this approach network decomposition becomes an essential task. In this paper the network decomposition problem is solved as an optimization problem using the harmony search (HS) algorithm. To improve the performance of the HS algorithm, a widely used graph bi-partitioning method called Kernighan-Lin (KL) strategy is used in the improvisation process. KL strategy is used in the partitioning of digital and VLSI circuits and is suitable for bi-partitioning networks. The connectivity of the partitioned clusters are checked by means of graph traversal techniques. Simulation are carried out on IEEE Standard systems and found to be very effective in decomposition of the system hierarchically. � 2012 Elsevier B.V. All rights reserved.

Power system has a highly interconnected network that requires intense computational effort and resources for centralized control. Distributed computing needs the systems to be partitioned optimally into clusters. The network partitioning is an optimization problem whose objective is to minimize the number of nodes in a cluster and the tie lines between the clusters. Harmony Search(HS) Algorithm is one of the recently developed meta heuristic algorithms that can be applied to optimization problems. In this work, the HS algorithm is applied to the network partitioning problem and power flow based equivalencing is done to represent the external system. Simulation is done on IEEE Standard Test Systems. The algorithm is found to be very effective in partitioning the system hierarchically and the equivalencing method gives accurate results in comparison to the centralized control. � 2012 Elsevier Inc. All rights reserved.

This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques.

The paper proposes a multi-objective biogeography based optimization (MO-BBO) algorithm to design optimal placement of phasor measurement units (PMU) which makes the power system network completely observable. The simultaneous optimization of the two conflicting objectives such as minimization of the number of PMUs and maximization of measurement redundancy are performed. The Pareto optimal solution is obtained using the non-dominated sorting and crowding distance. The compromised solution is chosen using a fuzzy based mechanism from the Pareto optimal solution. Simulation results are compared with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Non-dominated Sorting Differential Evolution (NSDE). Developed PMU placement method is illustrated using IEEE standard systems to demonstrate the effectiveness of the proposed algorithm.

This paper presents a method for the use of Supervisory Control and Data Acquisition (SCADA) and synchronized measurements for complete observability of a power system. Under normal operation, both Node Phasor Measurement Unit (NPMU) and SCADA measurements are optimally placed using integer programming and Genetic Algorithm (GA) respectively. The minimum condition number of the Jacobian matrix is used as a criteria in conjunction with GA to obtain a completely determined condition. Next, a triangular factorization approach is used to search for the necessary candidates for single branch outage and single/multiple measurement loss. These candidate measurements are optimized by the binary integer programming method. Numerical results on the IEEE test systems are demonstrated. The results clearly show the robustness of the method to obtain reliable measurements under both normal and contingency conditions.

This paper presents a new multi-objective harmony search (MOHS) algorithm for environmental/economic dispatch (EED) problem. The EED problem is formulated as a non linear and constrained optimization problem with competing and non-commensurable objectives. The two competing objectives, fuel cost and emission, were optimized simultaneously using the proposed MOHS algorithm. The MOHS algorithm uses a non dominated sorting and ranking procedure with dynamic crowding distance to develop and maintain a well distributed Pareto-optimal set. The proposed algorithm has been tested on the standard IEEE 30 bus and 118 bus systems. Simulation results are compared with the fast non dominated sorting genetic algorithm (NSGA-II) method. The results clearly show that the proposed method is able to produce a well distributed Pareto-optimal solutions than the NSGA-II method. � 2011 Elsevier B.V. All rights reserved.

The paper presents a biogeography based optimization algorithm to design an optimal meter placement scheme which makes the power system network observable. The procedure consists of initially determining the optimal meter set under normal conditions, followed by the optimal reliable meter locations that are obtained under two types of contingencies termed as single meter failure or loss and single branch outages. This is achieved by modifying the derived scheme from the normal condition using penalty functions. The effectiveness of the meter placement algorithm is demonstrated for IEEE standard systems. � 2011 Elsevier B.V. All rights reserved.

The paper proposes a novel hybrid algorithm connecting interior point method (IPM) and differential evolution (DE) for solving economic load dispatch problem with valve point effect. The algorithm involves two stages. The first stage employs IPM to minimize the cost function without considering the valve point effect. The second stage considers valve point effect and minimizes the cost function using DE. The initial population for DE is generated in a narrow range of 2?/f around the solution obtained in the first stage. The effectiveness of the algorithm is validated by carrying out extensive tests on three different systems involving 13 and 40 thermal generating units. The proposed method outperforms other existing techniques for economic load dispatch considering valve-point effects. � 2010 IEEE.

This paper presents an implementable distributed state estimation method for online analysis of power systems having multiple, geographically separated areas. Distributed state estimation apart from giving a faster solution, also improves the condition number of the resultant gain matrices. The method proposed here uses the conventional WLS estimator (Gauss-Newton method) with equality constraints forcing the tie line flows as calculated by the adjacent areas to be equal. Based on the topology of the network, the system is partitioned into multiple areas and a processor is assigned to each of these areas for solving the local state estimation problem. Simulations carried out on the IEEE 14, 30 and 118 bus systems show good convergence properties and improvement on the condition number of the gain matrices when compared with centralized algorithms. � 2010 Elsevier Ltd. All rights reserved.

Security is recognized as an important problem in planning, design and operation stages of electric power systems. Power system security assessment deals with the system's ability to continue to provide service in the event of an unforeseen contingency. This paper proposes a particle swarm optimization (PSO) based classification for static security evaluation in power systems. A straightforward and quick procedure is used to select a small number of variables as features from a large set of variables which are normally available in power systems. A simple first order security function is designed using the selected features for classification. The training of weights in the classifier function (security function) is carried out by PSO technique. The PSO algorithm has minimized the error rate in classification. The procedure to determine the security function (classifier) is discussed. The performance of the algorithm is tested on IEEE 14 Bus, IEEE 57 Bus and IEEE 118 Bus systems. Simulation results show that the PSO classifier gives a fairly high classification accuracy and less misclassification rate. � 2010 Elsevier B.V. All rights reserved.

Modeling and simulation of nonlinear systems under chaotic behavior is presented. Nonlinear systems and their relation to chaos as a result of nonlinear interaction of different elements in the system are presented. Application of chaotic theory for power systems is discussed through simulation results. Simulation of some mathematical equations, e.g. Vander Pol's equation, Lorenz's equation, Duffing's equation and double scroll equations are presented. Theoretical aspects of dynamical systems, the existence of chaos in power system and their dependency on system parameters and initial conditions using computer simulations are discussed. From the results one can easily understand the strange attractor and transient stages to voltage collapse, angle instability or voltage collapse and angle divergence simultaneously. Important simulation results of chaos for a model three bus system are presented and discussed. � 2010 Elsevier B.V. All rights reserved.