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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