This paper presents a new intra-day intra-hourly local flexibility market (LFM) framework to exploit distributed energy resources' (DERs) flexibility capabilities. A technical virtual power plant (TVPP) operates the LFM in which the DER aggregators participate. The TVPP offers the provided flexibility capabilities to wholesale flexibility market (WFM) as well as compensating the intra-hourly variability in power distribution network. In the proposed framework, the TVPP clears the LFM by considering hierarchical transactions with the aggregator agents to find the market equilibrium in which the DERs' flexibility capabilities are optimally exploited while all participating agents make profit by trading flexibility capabilities in the LFM. A bilevel optimization model with multiple lower levels is considered to address different agents' preferences and transactions in the LFM. In the upper-level problem, the TVPP aims at maximizing its profit while each lower-level problem represents an aggregator agent's optimization problem. The proposed model is reformulated into a single-level mixed integer linear programming problem and is implemented on the distribution network connected to Bus 5 of the Roy Billinton test system (RBTS) as well as a 119-bus test system. The results demonstrate the effectiveness of the model to utilize DERs' flexibility and provide revenue opportunities for different agents.

This paper presents a two‐stage adaptive robust optimization framework for day‐ahead energy and intra‐day flexibility self‐scheduling of a technical virtual power plant (TVPP). The TVPP exploits diverse distributed energy resources’ (DERs) flexibility capabilities in order to offer flexibility services to wholesale flexibility market as well as preserving the distribution network's operational constraints in the presence of DER uncertainties. The TVPP aims at maximizing its profit in energy and flexibility markets considering the worst‐case uncertainty realization. In the proposed framework, the first stage models the TVPP's participation strategy in day‐ahead energy market and determines the DERs’ optimal energy dispatch. The second stage addresses the TVPP's strategy in intra‐day flexibility market to determine the DERs’ optimal flexibility capability provision by adjusting their energy dispatch for the worst‐case realization of uncertainties. The uncertainty characteristics associated with photovoltaic units, electric vehicles, heating, ventilation and air conditioning systems, and other responsive loads as well as the transmission network's flexibility capability requests are considered using an adaptive robust approach. Adopting the duality theory, the model is formulated as a mixed‐integer linear programming problem and is solved using a column‐and‐constraint generation algorithm. This model is implemented on a standard test system and the model effectiveness is demonstrated.