We study how to embed dynamic agents that transform gas to power, or power to gas in the different energy grids, such as the gas, power, industry and heat grids in a distributed and optimal manner. Integrated in a community, the agents aim at controlling their supply and demand levels thereby achieving the community goal and maximizing their utility by selling their energy to the energy grids.
Using the dual decomposition approach, the associated optimal control problems are studied for the exchange of energy flow among the agents in the community and between the agents and the energy grid operators. At the level of the grids, capacity issues play a role. Consequently, another level of optimization which is not fully distributed is added to the problem setting, intuitively playing the role of grid operators. In other words, agents make decisions based on local information, yet still coordinate their bids to the grid operators and to their neighboring agents within the community in order to avoid overloading the grids and to globally achieve the community goal, respectively, with the help of shadow prices.
In addition, we study the optimality of the problem when the information exchange is done asynchronously. Simulations in which the proposed asynchronous distributed coordination is applied to micro-CHP devices and Power-to-Gas facilities illustrate some potentials of the approach.
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