Probabilistic Integration of Demand Flexibilities in a Renewable Energy-Assisted Community Network

• Published in: 2023 IEEE PES GTD International Conference and Exposition (GTD) pp. 381 - 385
• Main Authors: Angizeh, Farhad, Abulibdeh, Ammar, Jafari, Mohsen A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2023
• Subjects: Adaptation models; Costs; Decision making; Distribution networks; electric vehicle (EV); Flexible load; Maximum likelihood estimation; Monte Carlo simulation (MCS); renewable energy source (RES); Simulation; two-point estimation method (2PEM); Uncertainty; uncertainty modeling
• DOI: 10.1109/GTD49768.2023.00099

This paper proposes a novel decision-making tool aiding community operators in optimally procuring their energy needs from available supply sources while incorporating potential demand-side flexibilities. The supply sources include community-operated solar plants, wind turbines, and the utility grid. Two flexible load types are modeled, that are optimally rescheduled and energized through the proposed model based on their inherent flexibilities and community conditions. The maximum likelihood estimation (MLE) method is first utilized to estimate well-fitted probability density functions (PDFs) to characterize the uncertainties of solar irradiance and wind speed. Next, a sufficiently large number of likely scenarios are generated by incorporating Monte Carlo simulation (MCS). The two-point estimation method (2PEM) is then employed to make the problem-solving tractable and construct the proposed probabilistic rescheduling model, which is a scenario-based approximated AC power flow model with distribution network constraints. Two case studies are demonstrated on the modified IEEE 33-node distribution test system. The simulation results reveal that by rescheduling potential flexibility sources, the community operator can cut its annual operation cost by ∼250,000 without sacrificing customers' comfort.