Two-time-scale dispatch strategy for networked micro-grids considering uncertainties and demand response

Under the vision of Carbon Neutrality, multi-energy networked micro-grids (MGs) becomes a reliable and clean solution to achieve this goal. High penetration of renewable energy resources (RERs) brings great uncertainties to stable operation. Uncertainties in day-ahead scheduling must not be ignored. Also, demand response (DR) is getting increasing attentions in modern electricity market. In this regard, this study proposes a two-time-scale dispatch strategy for networked MGs considering multiple uncertainties and DR events. Both electric and thermal demands are taken into account. This strategy encompasses two stages, uncertainties considered day-ahead stage and DR considered intraday stage. The former stage combines scenarios analysis and budgeted robust methods to provide a day-ahead scheduling plan with robustness. The latter stage is an online model to deal with the newly updated forecast data and DR events. Adjustment cost is employed to measure the rescheduling cost. Emission governance cost has already been considered in the both stages, which contributes to realisation of carbon-neutral society. Four cases are simulated to verify performances of the two-time-scale dispatch strategy. Results show this strategy can effectively provide stable energy supply in networked mode. There are 12.0 MWh electricity and 11.8 MWh heat transmitted among MGs to guarantee reliability. Compared with nominal dispatch, 4.34% in average surplus electricity will be generated for robustness. And 19, 13 and 9 times constraint violation for each MG can be avoided in the robust day-ahead scheduling plan. Moreover, the purchased electricity from the grid also has a distinct reduction. It can be seen from the numerical simulation results that one MG can reduce 15.1% and 11.1% of the agreed electricity in the DR intervals. This proposed strategy does not only help MGs decrease the operation cost but also aid utility company relieving the peak demands.