Data-driven LP electricity market enabling storage, renewables, and active network technologies

Conventional market frameworks struggle to accommodate the unique features of
novel energy and network technologies (i.e., storage, renewables, and Active Network
Technologies (ANTs)), leading to their underutilization, inappropriate remuneration,
and, as a consequence, limited integration into the networks. To reflect the current
state of technologies, this paper offers an efficient data-driven Linear Programming
(LP) market clearing formulation, which: 1. Utilizes individual historical data of
Renewable Energy Sources (RES) to ensure an accurate supply from stochastic resources and their appropriate remuneration; 2. Introduces a new storage participation model to reflect its unique features, reduce risks, and maximize its value in the market; 3. Performs network- and market-aware scheduling of ANTs to facilitate local electricity trading by accommodating infeasible transactions locked because of congestion. The extensive numerical study demonstrated that the proposed market framework can effectively accommodate stochastic offers from RES, allowing for predictable supply and reward. Notably, the maximum RES reward was observed at 1.03% of energy underdelivery by RES. Compared to the uncertainty-agnostic and chance-constrained market clearing methods, the proposed framework allows for increasing the RES reward by 18.17% and 6.46%, respectively. Finally, following the project management methodology, we provide the project scope statement and risk assessment for the implementation of the proposed market.