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Market clearing in a Day Ahead Electricity Market is an important task in any Electric Power Exchange (PX) around the world. Market clearing requires searching of market equilibrium point i.e. Market Clearing Volume (MCV) and Market Clearing Price (MCP) in a hourly Day Ahead Electricity Market. It plays a crucial role in determining social welfare for all players participating in PX. Therefore this paper presents an efficient common cumulative power method for market clearing in Day Ahead Electricity market and compared its results with that obtained by Linear Programming (LP) method and Intermediate Value method in terms of three different aspects. First, the capability of the proposed method for accurately obtaining MCV & MCP is tested under four different possible intersection cases. The results clearly shows that the proposed method is efficient enough to accurately generate MCV & MCP for all cases. Second, the performance of proposed method is compared with two other method in terms of percentage error in obtained MCV & and MCP. Results clearly shows that the proposed method is capable of generating MCV & MCP with zero percentage error. Finally, performance of the proposed method is compared in terms of simulation time required to obtain market equilibrium with LP method by increasing the number of supply offers / demand bids and the results clearly shows proposed algorithm takes much less time upto 10000 number of Supply offers / demand bids.

Electric power supplier using thermal generator submits block bid in a Day-Ahead Electricity Market to compensate for high startup and shut down cost. Flexi type Block Bid is a special kind of Block Bid, currently used in Nord Pool, have flexible execution time set by Power Exchange (PX) based on Social Welfare maximization. The optimal placing of block bid execution time is an NP-hard combinatorial optimization problem whose solution is difficult to obtain using repeated application of Mixed Integer Linear Programming algorithm in polynomial time. Therefore this work formulates the optimal placement of suppliers block bid in a Day-Ahead Electricity Market as an optimization problem and solve using Genetic Algorithm. Social welfare for 24 hours of next day called social welfare volume has been used as an objective function to maximize. Computer simulation was performed using MATLAB with hourly electric power demand randomly generated as per the typical hourly power demand curve in Indian power system. Two types of Block Bid are considered for simulation, the first one in which length of the block is fixed and second one in which length of block bid can vary from fixed minimum to fixed maximum. Simulation result clearly shows that there is a significant increase in social welfare volume and hourly Market Clearing Volume and a significant decrease in Hourly Market Clearing Price for both the cases.