This paper presents a general description of local flexibility markets as a marketbased management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids' hosting capacity.

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.

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