In the future power network, power consumers as well as power generators participate in electricity market trading as selfish market players. Dynamic pricing is one of the useful tools to manage such networks in a distributed manner by changing electricity prices appropriately. In this chapter, we show distributed price decision procedures regarding the dynamic pricing to maximize social welfare in a power grid with information privacy of market players. Specifically, we first deal with an electricity market that covers multiple regional areas in a power grid and propose a market trading algorithm to derive the optimal regional electricity prices based on alternating decision making of market players. Subsequently, we deal with an electricity market in one regional area where an aggregator participates in the market trading as a mediator between the market operator and consumers. For this market, we propose a trading algorithm to adjust power demand of consumers depending on their lifestyles in a day-ahead electricity market. This chapter also shows the convergence properties of the proposed market trading algorithms and illustrates that these methods enable us not only to derive the optimal electricity prices but also to improve their convergence speed through numerical simulation.
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