Further Optimized Scheduling of Micro Grids via Dispatching Virtual Electricity Storage Offered by Deferrable Power-Driven Demands

• Published in: IEEE transactions on power systems Vol. 35; no. 5; pp. 3494 - 3505
• Main Authors: Nguyen, Hoach The, Al-Sumaiti, Ameena Saad, Turitsyn, Konstantin, Li, Qifeng, El Moursi, Mohamed Shawky
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregator; Comparative studies; demand response; Distributed generation; distributed storage; Dynamic scheduling; Electric energy storage; Electrical loads; Energy storage; generalized battery model; Load; Load modeling; Mixed integer; optimal power flow; Optimization; Power systems; Renewable energy sources; Scheduling; Spikes; Storage facilities; Stress concentration; Thermal analysis; Thermal loading; Thermal storage; water distribution network; Water resources; Water storage
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2020.2979032

Deferrable power-driven demands such as water and thermal ones possess a capability of energy storage which can be exploited to further optimize electric systems. This paper proposes a new optimized scheduling framework for electric grids by simultaneously exploiting the high-inertia thermal dynamics and lossless water storage. Unlike previous ideas in the literature, both water and thermal demands are managed to realize the long-term and short-term load-shifting strategies, respectively. First, distributed water/thermal loads are aggregated where the virtual storage can be represented by battery models. Then, a further optimized scheduling framework is proposed with a mixed-integer nonlinear programing problem. Comparative studies show that the water storage offers outstanding flexibility for electric system via pumps scheduling, especially for the electric load-shifting strategy in a long time-horizon thanks to the lossless water-storage process. Meanwhile, the thermal storage can directly support short-term electric load-shifting to avoid price spikes of electricity. Numerical results show that the proposed method can reduce the total cost of micro-grids by maximizing the usage of renewable energy sources, avoiding price spikes, and reducing dependence on high-cost centralized energy-storage facilities provided that the critical water-energy demands are preserved.