Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets

Highlights: • A general OBS model in the day-ahead and real-time markets is proposed. • Uncertainties in the load and renewable generation are considered. • The global solutions are guaranteed by the use of BC optimization method. • The risk of the market participation is bounded via CVaR criteria. • The effectiveness of the proposed model is verified using VSS index. -- Abstract: A comprehensive optimal bidding strategy model has been developed for renewable micro-grids to take part in day-ahead (energy and reserve) and real-time markets considering uncertainties. A two-stage stochastic programming method has been employed to integrate the uncertainties into the problem. Moreover, the Latin hypercube sampling method has been proposed to generate the wind speed, solar irradiance, and load realizations via Weibull, Beta, and normal probability density functions, respectively. In addition, a hybrid fast forward/backward scenario reduction technique has been applied to reduce the large number of scenarios. Furthermore, the risk of participation in the markets has been investigated by the use of "conditional value at risk" criteria, and the efficiency of the stochastic approach has been evaluated via "value of stochastic solution". The case study micro-grid involves three wind turbines, two photovoltaics, two microturbines, two fuel cells, one energy storage system, and 100 kw volunteer loads for curtailment. The accurate modeling of the components and constraints has led to a mixed integer nonlinear programming problem which has a lot of binary variables. Lindogloabal/AlphaECP solvers in GAMS have been applied to guarantee the global solutions.