PRESENTED BY
ANGIARASA BABU CHIGURUPATI

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MICRO GENETIC ALGORIRHM BASED OPTIMAL POWER DISPATCH IN MULTINODE ELECTRICITY MARKET
INTRODUCTION
• The electricity industries in number of countries have recently been deregulated to introduce competition.
• Deregulation is defined as the process of removing restrictions and regulations to achieve competitive wholesale prices without compromising adequacy, system reliability and security.
• In a centralised power industry the planning is done to minimise the production cost.
• In a competitive electricity market, generation resources are, scheduled based on offers and bids of the suppliers and consumers.
• One of the competitive electricity markets is the auction market model, in which participants place their bids to sell or buy electricity.
• In an electricity market the two main participants are distribution companies and generation companies.
• These participants will submit their bids to an Independent System Operation (ISO) company.
• A supply bid is given as a cost per MW and a quantity in MW which a generating company is willing to generate in a particular period.
• A demand bid is given as a cost per MW and a quantity in MW which a distribution company is willing to consume in a particular period.
• In this thesis optimal power dispatch problem in multi node electricity market for the maximization of the total participants benefit will be solved using Genetic algorithm and Micro genetic algorithm. The above methods will be tested on 17-node, 26-line system and compared to demonstrate their performance.
• VIEU
• De Regulated power system
SINGLE NODE ELECTRICITY MARKET
• For a single node market, the supply and demand curves at each single node can be illustrated as shown in Fig. 1.
• The supply curve is obtained by ordering selling bids in increasing order of price where as the demand curve is obtained by ordering buying bids in decreasing order of price.
• The spot price at a single node is the price which matches the supply and demand bids, i.e. the point at which the supply and demand curves intersect each other.
• At the spot price, the benefit of participants is maximised and this is illustrated by the shaded area in Fig. 1.
• The mathematical form for participants benefit in a single node market is given by the following eqn.
MULTI NODE ELECTRICITY MARKET
• For a multi node electricity market, there are transmission lines connected between bidding nodes.
• The connections result in real power and reactive power injection to the network at each node.
• As an example Fig. 2. illustrates the dispatch of the bids when the real power injection is considered.
• In the above Fig.2(a). The injection of Pk to the node is supplied by the partly dispatched generator bid. The spot quantity has increased and the price has not changed.
• If the injected power is greater than the undispatched amount of the partly dispatched supply bid then the additional amount cannot be supplied at the same price.
• Therefore, the spot price will increase as shown in Fig. 2(b).
• The mathematical form for participants benefit at node k is given by the following eqn.
• The optimisation problem had nonlinear constraints which is difficult to solve using linear programming technique.
• A genetic algorithm is proposed to solve the above problem.
TEST SYSTEM
GENETIC ALGORITHM
• Methodology
• Initialization
• Selection
• Reproduction
• Termination
Conceptual Algorithm
• Simple generational genetic algorithm Pseudocode
• Choose the initial population of individuals
• Evaluate the fitness of each individual in that population
• Repeat on this generation until termination: (time limit, sufficient fitness achieved, etc.)
– Select the best-fit individuals for reproduction
– Breed new individuals through crossover and mutation operations to give birth to offspring
– Evaluate the individual fitness of new individuals
– Replace least-fit population with new individuals
• Basic principles 1
• Random generation
– String with all parameters
• Fitness function
– Parent selection
• Reproduction
– Crossover
– Mutation
• Convergence
– When to stop
NEED FOR INTRODUCTION OF MICRO GENETIC ALGORITHM
• The disadvantage of Gas is the high processing time associated. That is due to their evolutionary concept, based on random processes that make the algorithm quite slow.
• One of the alternative methods is micro genetic algorithms.