Chance-constrained CAES and DRP scheduling to maximize wind power harvesting in congested transmission systems considering operational flexibility

As enlarging share of renewables brings up a promising future for clean power generation, nonetheless, it imposes new challenges into the secure operation of power systems such occurrence of distasteful congestion, discriminatory locational marginal pricing (LMP) and also increasing uncertainty and inflexibility. To address these issues, a novel chance constrained two-stage programming is developed, where in the first stage social welfare of system is maximised while in the second stage a stochastic security constrained unit commitment problem is executed along with compressed air energy storage (CAES) and demand response program (DRP) to minimize both operation costs and wind curtailment. Both DRP and CAES are cooperatively applied to maximize wind proliferation and social welfare, alleviate the congestion of network, smooth LMP at different nodes, and improve technical characterizations of system. The problem is formulated as an exact mixed integer non-linear programming (MINLP) considering operational flexibility by means of power capacity for up/down power regulation and then is solved using primal-dual interior point solver. Finally, a case study based on modified IEEE 30-bus transmission system with three zones is performed and the results are duly expressed and analysed to corroborate the pertinence of the proposed model.