Optimal Dynamic Multi-source Multi-community Power Schedule and Trading
As peer-to-peer energy trading and local energy market are gaining momentum, a follow-up challenge is scaling up to include multi-community, multi-region power schedule and trading. This study introduces community-to-community power trading and schedules considering various generating units, including coal, gas, wind, and solar, as well as import and export prices from community transactions. These generating sources are used to fulfil the demand requirements of each community over a time horizon, as well as absorbing or trading the margin among the inter-connected communities, while fulfilling certain distribution network constraints. For a practical case, the uncertainties in wind power generations are considered. An optimality condition decomposition technique is used to decompose the overall problem into a community-based local problem. Thus, individual community solves their optimisation local problem in parallel for operational efficiency of real-time multi-commodity power schedule and trading. The initial results indicate that each community acts in its best interest to minimise its costs when there is a change in the variable. When emission costs are applied as a constraint to their generation, a reduction in power generation is observed augmented by an increase of up to 30.8% of power transferred in the inter-community transaction.