CMES: Comprehensive Microgrid Energy Storage Designs with Guaranteed Optimality
Dr. Sanjeev Srivastava | Siemens Corporation Corporate Technology
The objectives of this project are to:
- Combine state-of-the-art convex optimization of microgrid energy storage with a novel, low-cost ZnMnO2 rechargeable battery for comprehensive microgrid energy storage designs with guaranteed optimality of long-term life-cycle costs;
- Validate the coverage probability curve, resilience, life-cycle cost, and robustness of the designs; and
- Propose practical solutions that can be implemented on commercial microgrid control products
The project will deploy holistic microgrid (MG) energy storage (CMES) solutions for five DoD sites with guaranteed optimality, using convex optimization. Based on real data and microgrid simulation models, the investigators will drive analytical models for convex optimization, which addresses a complete scope of microgrid design challenges, such as resiliency, ride-through capability, outage duration, and life-cycle cost. The performance indicators will be aggregated into a resilience metric, which will be balanced with respect to the life cycle costs. The designs will be validated using Siemens grid simulation product PSS®SINCAL. This Phase I solution will then be implemented in Phase II (demonstration). The energy management logic will be able to run on Siemens microgrid controllers (e.g., SICAM), and the solution will be compliant with DoD cybersecurity standards, such as Risk Management Framework (RMF) and DoD Information Assurance Certification (DIACAP).
DoD sites have significant potential for improvements in energy resilience, renewable energy usage, and energy bill reduction. Most of the current microgrid designs are based on ad-hoc simulation, where many optimization opportunities are lost due to the large-scale nature of the problem. The CMES method has been developed and validated over years. The project will firstly simplify the complete grid model and apply convex optimization to find the global optimal design for the simplified model. Investigators will then search for the optimal solution close to the optimal solution of the simplified system. With this two-step optimization approach, the project will explore a much wider scope than traditional simulation-based optimization methods. Due to the large scale of the DoD sites, the potential saving and improvements in 20 years can be significant.