Abstract
An effective pricing scheme that to provide the useful information to generation, transmission section and customers. These transmission pricing depends on generator, load levels and transmission line constraints. Transmission line constraints result is variations in energy prices throughout the network. The proposed approach is based on AC-DC optimal power flow model with considering of losses. Resulting optimization problem is solved by linear programming approach. Locational Marginal Pricing methodology is used to determine the energy price for transacted power and to manage the network congestion and marginal losses. Variation of LMP values with transmission constraint conditions also studied. Simulation is carried out on IEEE 57 bus system, 400/765kV MSETCL system of Maharashtra transmission line for real data bus system and the results are presented.
Highlights
By Tradition, power industry is vertically integrated, in which the generation, Transmission and distribution are arranged collectively as a single utility to serve its customers
Due to Transmission Open Access (TOA) the power flow in the lines reach the power transfer limit and so it will leads to a condition known as congestion [1,2]
The methodology has been tested on IEEE 57-Bus system and implemented on a real power system of MSETCL, Maharashtra
Summary
By Tradition, power industry is vertically integrated, in which the generation, Transmission and distribution are arranged collectively as a single utility to serve its customers. The congestion may be caused due to a mixture of reasons, such as transmission line outages, generator outages and change in energy demand. Buyers in the market pays ISO based on their price for dispatched energy. The LMP difference between two adjacent buses is the congestion cost which arises when the energy is transferred from one location to the other location[3]. LMP is the summation of the costs of marginal energy, marginal loss and congestion. Different types of optimization models are used for LMP calculations like LP and Lagrangian[8]. Among these in this paper quadrating programming is used to solve the optimization problem
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