Chandrasekhar Yammani

Optimal placement and sizing of the DER in distribution systems using Shuffled Frog Leap Algorithm

Electrical power consumption is increasing day by day, complicating the operation of distribution systems. Distributed Energy Resource (DER) integration in distribution system is one of the options which give benefits like loss minimization, peak shaving, over load relieving and improved reliability. This paper presents an algorithm for optimal placement and size of the DER considering system loss minimization and voltage profile improvement as objective functions. This work is tested on IEEE 15, 33, 69 and 85 bus distribution systems. For all cases studied, a new heuristic optimization technique Shuffled Frog Leaping Algorithm (SFLA) is applied and current injection based distribution load flow method is employed. Further the results are compared with those results obtained by Particle swarm optimization (PSO) method and found to be encouraging.

Optimal placement and sizing of DER's with load models using BAT algorithm

This paper presents an algorithm for optimal placement and size of the Distributed energy resources (DERs) considering system loss minimization and voltage profile improvement as objective functions. DERs are the energy resources which contain renewable energy resources such as wind, solar and fuel cell and some artificial models like micro turbines, gas turbines, diesel engines, sterling engines, and internal combustion reciprocating engines. Combinations of DER studies and for every combination, indices, active and reactive losses and voltage profiles are studied. To optimize the objective function, new optimization technique called Bat algorithm(BA) is proposed. The Bat algorithm is tested on 37-bus distribution system with different load models like residential, Industrial, Commercial and Mixed loads. For all cases current injection based distribution load flow method is used.

Optimal placement and sizing of DER's with load models using a modified teaching learning based optimization algorithm

In this paper, a method which employs Modified Teaching-Learning Based Optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Energy Resources (DERs) units in distribution systems. DERs are commonly connected near the load in electric power distribution systems and include renewable energy sources such as wind and solar, fossil-fuel-based generation such as micro turbines, and other distributed energy storage elements. Loss minimization and voltage profile improvement as objective function and for every combination of DERs, impact indices, active and reactive losses and voltage profiles is studied on different load models. For all cases current injection distribution load flow method is used and tested on 84-bus Taiwan Power Company distribution system using MTLBO algorithm.

Optimal placement and sizing of multi Distributed generations with renewable bus available limits using Shuffled Bat algorithm

In this paper a new optimization algorithm is proposed for optimal planning of the Distributed generation (DGs) with renewable bus available limit constraint. Distribution system objectives considered for optimization are Active and reactive power losses minimization, bus voltage profile improvement, and line flow capacity limits. Power system modeled Distributed generations such as wind, solar and fuel cell and some artificial models like micro turbines are used to study the proposed algorithm. To optimize the objective function with voltage limits and renewable DG bus available limit constraints, Shuffled Bat algorithm (ShBAT) is proposed and compared with Genetic Algorithm (GA) and Bat Algorithm (Bat). 84-bus distribution system testing with proposed algorithm is presented with results.

Fuel cost minimization with reserve capacity and inter-area flow limit for reliable and cost effective operation of multi microgrids

Power system is undergoing profound changes such as Distributed Generation, microgrid and then Smart Grid. One of the biggest challenges in it is integration renewable energy sources with conventional DGs for reliable operation of microgrid. An optimization algorithm for reducing total cost considering inter-area power flow limit and reserve capacities is developed and implemented. Power reference points were obtained for each DG with mixed power flow control for cost effective and reliable operation of microgrid. Numerical experiment was performed in MATLAB 2012 using Direct Search Technique.

A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of DGs with load variations

A new hybrid Multi-objective Shuffled Bat optimization algorithm is proposed in this paper for Distributed generations (DGs) optimal placement and sizing. Multiple objectives like system power losses, cost of DG and system voltage profiles are considered to evaluate the impact of DG placement and sizing for an optimal development of the distribution system with load variations. Furthermore, the study is demonstrated with different % loading such as 80,100 and 120% of base load condition. The proposed technique is tested in 33 bus distribution network, and compared against Non-dominated Sorting Genetic Algorithm II (NSGA-II).

Optimal placement and sizing of DGs at various load conditions using Shuffled Bat algorithm

In this paper a new and efficient hybrid optimization algorithm is proposed for optimal placement and sizing of the Distributed Generations (DGs). Bus voltage profile improvement, line flow capacity, active and reactive power loss minimization are considered as multi-objectives to optimize under various distribution load conditions. Renewable energy resources such as wind, solar, fuel cell and micro turbines are considered in power system modeling for finding the optimal placement and sizing. Current injection based distribution load flow is considered in DGs modeling in power systems. To optimize the objective function, a new optimization technique called Shuffled Bat algorithm (ShBAT) is proposed. The proposed methodology is tested on 84-bus Taiwan power company distribution systems with 90%, 100% and 120% of base load conditions to demonstrate its performance and effectiveness. Results show that the planned methodology is superior to existing strategies in terms of multi-objectives considered.

Optimal placement of PMU's considering sensitivity analysis

This paper presents various aspects of optimal Phasor measurement unit (PMU) placement problem with load sensitivity analysis. Binary integer linear programming based methodology for optimal placement of PMU in a given power network for full observability of that network is presented in this paper. First, complete observability of the given network is formulated conventionally and then, zero injection bus constraints are added in conventional formulation. Load sensitivity analysis is done using Newton-Rapson Load flow and most sensitive buses based on load sensitivity analysis, are sorted out. Minimum number of PMUs, less than the optimal number (without considering load sensitivity) are placed such that it covers most sensitive buses and also most of the buses are observed. In this paper optimal PMU placement problem considering sensitivity analysis is presented for IEEE-14 bus and IEEE-30 bus systems.