Energy Systems Division
Renewables and Grid Modernization
Advanced Grid Modeling
Stochastic Sizing and Operation of Grid-Level Energy Storage Systems under Intermittent Renewable Generation and Increasing Load Forecasting Uncertainties
Completed on July 31, 2021.
Energy storage systems (ESSs) offer a possible means of maintaining a healthy level of grid inertial response where renewable generation penetration levels are high. ESSs possess response speeds that are superior to conventional generators and can be beneficial to operations by providing much needed energy, ramping as well as reserve services, especially under high penetration of intermittent renewables. The major challenges that need to be addressed with ESS deployment are (1) what are the required capacities of ESSs, i.e., the sizing of ESSs, to achieve the desired inertial response to different types of disturbances; and (2) how to efficiently operate the grid with ESSs to ensure efficiency, security and reliability upon their deployment.
As part of our energy storage research, BNL developed revolutionary new probabilistic sizing techniques for ESSs based on grid-inertia responses under high penetration of renewables, along with stochastic operation optimization techniques for ESSs that account for the intermittence of renewable generation. The grid inertial response-based ESS sizing approach ensures the frequency stability of the grid, while the stochastic operation optimization approach ensures efficient and reliable operation of ESSs together with other units on the grid.
- Developed a Markovian approach-based model and random field theory (RFT)-based approach for a more realistic representation of wind generation related uncertainties that are suitable for ESS operation and planning studies by considering both spatial and temporal evolutions of wind speed information
- Performed a systematic constraint tightening approach that significantly reduces the computational requirements to solve MILP problems
- Refined a scalable Surrogate Absolute-Value Lagrangian Relaxation (SAVLR) method that can be easily scaled up for large systems via a decomposition and coordination approach
- Developed a rolling horizon-based concept that can be used together with the SAVLR to create the “SAVLRseq” approach that enables practical solutions to long-term planning problems without requiring high performance computing (HPC) facilities.
- Introduced an ordinal-optimization (OO) concept to approximately solve a Markovian subproblem in ESS operation via simplified models with much reduced complexity while maintaining the quality of the overall solution
- Implemented the SAVLR and SAVLRseq in an open-source, modular, and flexible tool that is readily used for solving utility-scale UC problems. The tool is available by contacting Meng Yue at BNL (firstname.lastname@example.org )
- Developed an innovative integrated mathematical optimization and machine learning (ML) method, i.e., a ML-assisted SAVLR or ML-SAVLR, to address the complexity of the optimal planning and operation.
Publications, Manuscripts, and Final Report
- Zhan, M. Yue, and L. Fan, “Reliability-Based Stochastic Transmission Expansion Planning Considering Uncertainties of Dynamic Thermal Rating and Wind Power,” IEEE PES GM 2019.
- Luh et al, “Frequency Dynamics Constrained Unit Commitment for Reliable Power System Operation,” Presentation in Panel Session of Improving the efficiency of large-scale Unit Commitment towards future resource Integration, IEEE PES GM, August 2020.
- Zhao et al, “A scalable stochastic optimal planning of Energy Storage Systems Considering Frequency Dynamics,” manuscript completed and to be submitted
- Raghunathan et al, “Frequency Dynamics Constrained Unit Commitment with Energy Storage Systems," manuscript completed and to be submitted.
- Raghunathan et al, "Markovian Frequency Dynamics Constrained Unit Commitment with Energy Storage Systems," manuscript completed and to be submitted.
- Yue, T. Zhao, N. Raghunathan, P. Luh, B. Yan, and M. Bragin, “Stochastic Sizing and Operation of Grid-Level Energy Storage Systems under Intermittent Renewable Generation and Increasing Load Forecasting Uncertainties,” Technical report submitted to DOE AGM, July 31, 2021.