  The recent deployment of distributed battery units in prosumer premises offer
new opportunities for providing aggregated flexibility services to both
distribution system operators and balance responsible parties. The optimization
problem presented in this paper is formulated with an objective of cost
minimization which includes energy and battery degradation cost to provide
flexibility services. A decomposed solution approach with the alternating
direction method of multipliers (ADMM) is used instead of commonly adopted
centralised optimization to reduce the computational burden and time, and then
reduce scalability limitations. In this work we apply a modified version of
ADMM that includes two new features with respect to the original algorithm:
first, the primal variables are updated concurrently, which reduces
significantly the computational cost when we have a large number of involved
prosumers; second, it includes a regularization term named Proximal Jacobian
(PJ) that ensures the stability of the solution. A case study is presented for
optimal battery operation of 100 prosumer sites with real-life data. The
proposed method finds a solution which is equivalent to the centralised
optimization problem and is computed between 5 and 12 times faster. Thus,
aggregators or large-scale energy communities can use this scalable algorithm
to provide flexibility services.
