cloud coordinator - stanford universityforum.stanford.edu/events/posterslides/powernet...r....
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Cloud Coordinator:Cost minimization with batteries in distribution grid
Problem
Solution Concept
Algorithm Examples
R. Rajagopal1, J. Qin1, M. Kiener1, T. Navidi1
1S3L – Stanford University, Stanford, CA, USA
Objective:
• Minimize expected
daily cost of energy
Constraints:
• AC PF and voltage
constraints
• Battery constraints
• Stochastic net load
• Limited
communication
Challenging tradeoffs
Coupled system
via networkLocal info
and control
• HH’s communicate information to CC
• CC performs global optimization and sends results to HH’s
Timeline:
• Periodic CC global optimization promotes system
coordination
• Real time HH optimization utilizes updated information
• As global update period increases, local controllers still save cost
• Comparison of arbitrage profits vs. solar penetration support
• NSC uses local control for profit, but little solar support
• LFLC uses local control for solar support, but little profit
• DSC has no local control and underperforms in both categories
Next steps• Add ancillary services support (ramping, regulation)
• Add Volt/Var control through network and inverters
Global Local
Direct storage: choose same battery charging over scenarios
Execute battery
charging directly
Net load following: choose same net load over scenarios
• Ensures network constraints satisfied if followed
Nominal
net load
Local tracking
Results
Local cost opt
within bounds
Nodal slack: obtain net load bounds at each bus
• For a specific bus with others fixed at nominal, find within network constraints
Promote reliability in satisfying voltage constraints
Allow some local flexibility for cost savings
(max for upper bound)
Lower
bound
Upper
bound