Restructuring in the power industry is followed by splitting different parts and creating a competition between purchasing and selling sections. As a consequence, through an active participation in the energy market, the service provider companies and large consumers create a context for overcoming the problems resulted from lack of demand side participation in the market. The most prominent ch...

nowadays generation capacity in traditional grid depends on fossil fuels and contributes significantly to the increase of pollution emission. in deregulated grids in addition to using demand response programs (drps) to reducing the cost of electricity production, peak clipping and improve reliability use of green plants such as hydro plant, wind plant become widespread. in a smart grid, end use...

In this article we report an experiment that examines how demand-side bidding can discipline generators in a market for electric power. First we develop a treatment without demand-side bidding; two large firms are allocated baseload and intermediate cost generators such that either firm might unilaterally withhold the capacity of its intermediate cost generators from the market to benefit from ...

In this paper, particle swarm optimization algorithm is proposed to determine the optimal bidding strategy in competitive auction market. The market includes generating companies (Gencos), large consumers who participate in demand side bidding, and small consumers whose demand is present in aggregate form. By using previous bidding data and multi-round auction process, the optimal bidding strat...

The stringent requirement of the demand-supply equilibrium for delivering electricity has traditionally been dealt with a supply-side perspective, assuming that the demand is not alterable. With the promises of the Smart Grid, demandside management techniques are increasingly becoming more feasible. A demand-side management technique, called Demand Response, aims at inducing changes in electric...

This paper presents a novel approach for evaluating impacts of price-sensitive loads on electricity price and market power. To accomplish this aim an analytical method along with agent-based computational economics are used. At first, Nash equilibrium is achieved by computational approach of Q-learning then based on the optimal bidding strategies of GenCos, which are figured out by Q-learning, ...