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Abstract 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 . [ABSTRACT FROM AUTHOR]

This paper deals with the reduction of power drawn from the supply system during generation un-certainties in D.C Micro grids using D.C Electric springs. In this work supply system is modeled using a fluctuating power source and a battery i.e. during generation uncertainties the system runs on battery support. A small micro grid system which has a critical load and non-critical load has been taken for the study and the simulations are done in MATLAB. The objective of the proposed work is to conserve power by making the non-critical loads draw less power during generation uncertainties which in turn relives the battery and renewable generator to the possible extent. D.C Electric springs are extension to A.C Electric springs used in A.C systems which make the non-critical loads draw less power by adjusting the voltage supplied to them. The D.C Electric spring presented in this work is different from the existing technologies present in the literature in terms of the circuitry used and its interpretation with mechanical spring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 . [ABSTRACT FROM AUTHOR]

This paper deals with the reduction of power drawn from the supply system during generation un-certainties in D.C Micro grids using D.C Electric springs. In this work supply system is modeled using a fluctuating power source and a battery i.e. during generation uncertainties the system runs on battery support. A small micro grid system which has a critical load and non-critical load has been taken for the study and the simulations are done in MATLAB. The objective of the proposed work is to conserve power by making the non-critical loads draw less power during generation uncertainties which in turn relives the battery and renewable generator to the possible extent. D.C Electric springs are extension to A.C Electric springs used in A.C systems which make the non-critical loads draw less power by adjusting the voltage supplied to them. The D.C Electric spring presented in this work is different from the existing technologies present in the literature in terms of the circuitry used and its interpretation with mechanical spring. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: This paper proposes a new differential evolution approach named as multiagent based differential evolution (MADE) based on multiagent systems, for solving optimal power flow problem with non-smooth and non-convex generator fuel cost curves. This method integrates multiagent systems (MAS) and differential evolution (DE) algorithm. An agent in MADE represents an individual to DE and a candidate solution to the optimization problem. All agents live in a lattice like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbors and it can also use knowledge. Making use of these agent–agent interaction and DE mechanism, MADE realizes the purpose of minimizing the value of objective function. MADE applied to optimal power flow is evaluated on 6 bus system and IEEE 30 bus system with different generator characteristics. Simulation results show that the proposed method converges to better solutions much faster than earlier reported approaches. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: Differential evolution based optimal reactive power dispatch for real power loss minimization in power system is presented in this paper. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts to be switched, for real power loss minimization in the transmission system. The problem is formulated as a mixed integer nonlinear optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling. The formulation also checks for the feasibility of the optimal control variable setting from a voltage security point of view by using a voltage collapse proximity indicator. The algorithm is tested on standard IEEE 14, IEEE 30, and IEEE 118-Bus test systems. To show the effectiveness of proposed method the results are compared with Particle Swarm Optimization and a conventional optimization technique – Sequential Quadratic Programming. [Copyright &y& Elsevier]