B. Saravanan

A comparative study on transmission network cost allocation methodologies

In the deregulated electricity market, it has become very important to determine the complete information about the participants who are utilizing the transmission network. Transmission line usage computation requires information of generator to load contributions and the path used by various generators to meet loads and losses. In the present restructured electricity market, it is necessary to develop appropriate pricing techniques that can provide the useful economic information to market participants. In this paper, the three existing techniques namely Zbus method, Zbusavg method and Relative Electrical Distance (RED) method for the network cost allocation is compared. It has been successfully applied on an IEEE 24 bus-Reliability Test System (RTS) and the results obtained are compared. The results obtained are quite encouraging and useful for the deregulated electricity market related issues. The simulation is carried out using MATLAB 7.8.0 (R 2009a).

Unit commitment using DP — An exhaustive working of both classical and stochastic approach

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 unit commitment problem. The use of augmented Lagrangian technique by convergence of decomposition method was proposed in 1994, and in 2007 chance constrained optimization was used for providing a solution to the stochastic unit commitment problem. Dynamic Programming is a conventional algorithm used to solve deterministic problem. In this paper DP is used to solve the stochastic model. The stochastic modeling for generation side has been formulated using an approximate state decision approach. The programs were developed in MATLAB environment and were extensively tested for 4 unit 8 hour system. The results obtained from these techniques were validated with the available literature and outcome was satisfactory. The commitment is in such a way that the total cost is minimal.

A new control algorithm for energy conservation from main grid during generation intermittence in the micro grids using A.C electric springs

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.

Day ahead scheduling of generation and storage sources in a microgrid using artificial fish swarm algorithm

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.

A solution to unit commitment problem by considering load to be a probabilistic one

This paper gives a solution for the unit commitment problem (UCP) for an power system to minimize the total production cost over a period of time by considering the load as a random(Probabilistic) variable instead of a deterministic one. The UCP is a mixed integer large scale non linear program. If the number of unit is more and the time horizon is large, then the analysis become more complex. That is the reason still researches are active in finding the solution for unit commitment problem. In this paper the proposed methodology by considering the load as a probabilistic variable instead of regular deterministic one. By considering the load in this way we can solve the problem of UCP as a real time problem. In the proposed methodology LR method is used to separate the problem in to a primal and dual problem and each sub-problem is solved by using dynamic programming. The economic dispatch problem is solved by particle swarm optimization. By comparing the results with conventional method the proposed methodology gives the schedule of generator with minimum production cost.