Towards energy transition: A novel day-ahead operation scheduling strategy for demand response and hybrid energy storage systems in smart grid - Elsir, Mohamed; Al-Sumaiti, Ameena Saad; El Moursi, Mohamed Shawky (Energy (Oxford), 2024)
Fossil fuel power plants continue to contribute significantly to carbon emissions, necessitating a transition towards cleaner energy sources. Despite the growing presence of renewables within the power systems, the incorporation of carbon capture technologies into the traditional thermal power plants holds great potential in emissions reduction. In this paper, the integration of renewable energy sources (RES) and coal-fired power generation units outfitted with carbon capture schemes is addressed. Multiple demand response (DR) programs and hydropower plants are strategically utilized to increase the power system flexibility. To effectively plan the day-ahead (DA) operation of the power system, a presumed market-clearing framework is adopted and modelled as a risk-constrained two-objective stochastic mixed-integer linear programming problem. The proposed framework helps to tackle the uncertainties related to RES and demand variations by employing a hidden Markovian process (HMP) technique. To simultaneously minimize the system’s operational costs and CO2 emissions, an enhanced version of the augmented ɛ-constraint method is employed. To prove its value, the proposed framework is devoted to the 24-bus IEEE reliability test system (IEEE-RTS). The system features substantial penetration of RES (exceeding 87% of peak load) and standard DR options capacities (less than 25% of peak load). The results show a 24% reduction in load peaks, an over 63% decrease in emissions, and a 17% reduction in the overall operation costs. •Proposing a comprehensive linearized mathematical framework accelerating the energy transition.•Coordinating the responsive power systems with carbon capture and hybrid ESS (BESS, PHS, HES).•Enhancing an effective augmented ɛ-constraint technique to simultaneously optimize cost and CO2.•Presenting robust decision-making with two-stage SP, hidden Markovian process, and CVaR strategy.•Integrated approach: 87% of RES, cost & CO2 reductions, fewer load peaks, enhanced flexibility.