Economic effects of trading watts and negawatts by agile customers in hierarchic energy markets

In this paper we evaluate through simulations economic effects of active electrical energy trading for agile customers in hierarchic energy markets. The agile customers are those residential electricity customers who either have a capability to sell the so-called ”negawatts” (demand reduction), or who operate small renewable generators such as wind mills or solar panels and use storage (battery) to generate, store and trade energy. We assume that agile customers use some local energy management system, e.g. a software tool running on a residential gateway, to forecast their demand and generation and to access the local market for trading energy. Forecasting accuracy of these systems is of special interest: while lower forecasting accuracy leads to greater differences between forecasted and real consumption/generation, and hence to higher total cost of balancing energy that must be consumed from or be fed into the global grid, the higher accuracy leads to higher cost of the system. We study the impact of accuracy in an agent-based simulation environment in which we implemented a hierarchic grid and market architecture and performed experiments to discover intervals of accuracies and prices at which the agile customers achieve economic benefits.