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- Real-Time Scheduling of Demand Response Options Considering the Volatility of Wind Power GenerationPublication . Talari, Saber; Shafie-khah, Miadreza; Chen, Yue; Wei, Wei; Gaspar, Pedro Dinis; Catalão, J. P. S.In this paper, a new methodology to unleash the potential of demand response (DR) in real-time is presented. Customers may tend to apply their DR potential in the real-time market in addition to their scheduled potential in the day-ahead stage. Thus, the proposed method facilitates balancing the realtime market via DR aggregators (DRAs). It can be vital once the stochastic variables of the network such as production of wind power generators (WPG) do not follow the forecasted production in real-time and have some distortions. Two-stage stochastic programming is employed to schedule some DR options in both day-ahead and real-time markets. DR options in real-time are scheduled based on possible scenarios that reflect the behaviors of wind power generation and are generated through Monte-Carlo simulation method. The merits of the method are demonstrated in a 6-bus case study and in the IEEE RTS-96, which shows a reduction in total operation cost.
- Demand Response-Based Operation Model in Electricity Markets With High Wind Power PenetrationPublication . Hajibandeh, Neda; Shafie-khah, Miadreza; Talari, Saber; Dehghan, Shahab; Amjady, Nima; Mariano, S.; Catalão, JoãoThe issue of climate change has received considerable attention in recent decades. Therefore, renewable energies and especially wind units have become a central point of attention. To cope with the uncertainties of wind power generation, resulting from the intermittent nature of this kind of energy, this paper proposes a Demand Response (DR) based operation approach. In other words, unlike the previous models in the literature that considered a supplementary role for the DR, this paper introduces the main role for the DR in the operation of future electricity markets. This approach focuses on a comprehensive modeling of the Demand Response Programs (DRPs) for the operational scheduling of electricity markets, considering the uncertainties of the generation of wind turbines, aiming at increasing the network security and decreasing the operation cost. The incorporation of market-based DRPs such as Demand Bidding (DB) and Ancillary Service Demand Response (ASDR) is also considered. Two novel quantitative indices are introduced to analyze the success of DRPs regarding efficiency and wind integration. Numerical results obtained on two IEEE test systems indicate the effectiveness of the proposed model.
- Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term SchedulingPublication . Talari, Saber; Catalão, João Paulo da Silva; Gaspar, Pedro Miguel de Figueiredo Dinis Oliveira; Shafie-khah, MiadrezaCurrent technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.