This paper proposes a coordinated strategy of a hybrid power plant (HPP) which includes a wind power aggregator (WPA) and a commercial compressed air energy storage (CAES) aggregator to participate in three electricity markets (day-ahead, intraday and balancing markets). The CAES aggregator has an extra ability which is called a simplecycle mode operation which makes it works like a gas turbine when is needed which helps the HPP to economically handle the miscalculations of the wind power and electricity price predictions. The coordinated strategy of the HPP is formulated as a three-stage stochastic optimization problem. To control the financial risks, the conditional value-at-risk model is added to the optimization problem. Moreover, the proposed offering method is capable of submitting both bidding quantity and curves to the day-ahead market. A mixed integer linear programming formulation is written for the problem which can be easily solved by commercially available software such as GAMS. The results which were tested on a realistic-based case study located in Spain show the applicability of the suggested method to increase the joint operation profit, and decrease the financial risks.
During recent years, with the advent of restructuring in power systems as well as the increase of electricity demand and global fuel energy prices, challenges related to implementing demand response programs (DRPs) have gained remarkable attention of independent system operators (ISOs) and customers, aiming at the improvement of attributes of the load curve and reduction of energy consumption as well as benefiting customers. In this paper, different types of DRPs are modeled based on price elasticity of the demand and the concept of customer benefit. Besides, the impact of implementing DRPs on the operation of grid-connected microgrid (MG) is analyzed. Moreover, several scenarios are presented in order to model uncertainties interfering MG operations including failure of generation units and random outages of transmission lines and upstream line, error in load demand forecasting, uncertainty in production of renewable energies (wind and solar) based distributed generation units, and the possibility that customers do not respond to scheduled interruptions. Simulations are conducted for two principal categories of DRP including incentive-based programs and timebased programs on an 11-bus MG over a 24-hour period and also a 14-bus MG over a period of 336 hours (two weeks). Simulation results indicate the effects of DRPs on total operation costs, customer's benefit, and load curve as well as determining optimal use of energy resources in the MG operation. In this regard, prioritizing of DRPs on the MG operation is required.
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