A two-stage stochastic model for security-constrained market clearing with wind power plants, storage systems and elastic demands

In electricity markets, there is a significant time gap between clearing of the market and operation of power system, so there is a significant probability of contingency for generators and transmission lines. In security-constrained market clearing (SCMC), the possible contingencies are taken into account. Review of the literature indicates that the optimal participation of storage systems has only been found from the perspective of their independent investor and the participation of storage systems as a network asset in SCMC has not been investigated. Therefore, in this research, a two-stage stochastic model is proposed for SCMC in a power system with storage systems and wind units as network assets. In the proposed model, system operator is able to purchase reserve from generators and demands; Up-going or down-going spinning and non-spinning reserves are offered in the market. Social cost of the market participants in pre-contingency state plus the expected cost of reserve deployment and shed is minimised in the model. The results show that storage systems which are being charged at hours with low demand hours and being discharged at high demand hours significantly increase the social welfare; the increase in daily expected social welfare is as high as $60. The results indicate that the schedule of market participants does not only rely on the price and quantity of offers and bids, but also relies on possible failure scenarios, possible failure times and their probabilities. The results imply that the consideration of the elasticity of demands in market clearing affects the market clearing outcomes. The results also show that the specifications of storage systems, i.e., their size, charging and discharging efficiencies as well as their storage loss factor significantly affect the social welfare. • A two-stage stochastic model has been developed for security-constrained market clearing. • Wind plants and storage systems as network assets and elastic demands were used. • An accurate model has been used for storage systems. • Effect of storage systems on social welfare was investigated. • The effect of specifications of storage systems on social welfare has been investigated.