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Solar forecasting is a rapidly evolving field that can substantially contribute to the effective integration of large amounts of solar photovoltaic (PV) capacity into the electricity system. However, newly developed solar forecasting models are rarely tested in an operational context considering the intended application and objective. Besides, models are typically evaluated considering only technical error metrics, disregarding their economic value. This paper proposes an operational bidding strategy that optimizes the participation of a PV power plant in the electricity spot markets. To this end, a novel multistage stochastic optimization method is developed that considers the day-ahead, intraday, and imbalance markets. As the developed method utilizes a scenario generation algorithm, the proposed method can be adopted for a wide variety of related applications. The performance of the developed method is assessed using technical and economic metrics and compared to a reference method. The results demonstrate the effectiveness of the proposed bidding strategy, as it substantially outperforms the reference market bidding strategy. The findings also provide insights into the value of a multistage bidding method, as extending market participation from the day-ahead to the intraday market increases revenues by 22%, while halving the total imbalance. Additionally, the study examines the relationship between the technical and economic performance of solar power forecasting models, revealing a non-linear correlation. • We develop an operational bidding strategy for PV plant operators participating in electricity spot markets. • The strategy concerns a multistage scenario-based stochastic method. • Time-independent probabilistic solar power forecasts are transformed into interdependent scenarios. • The results are thoroughly evaluated, considering technical and economic metrics. • The application of the method leads to increased revenues and reduced imbalances. [ABSTRACT FROM AUTHOR]

One of the fundamental concerns of distribution companies (DISCOs) is how to satisfy security requirements under uncertain conditions and real-time contingencies. To address this concern, it is vital to employ operational flexibility levers aimed at mitigating congestion and retaining optimal reserve capacity, particularly in critical feeders. Increased interaction between DISCOs and virtual energy storage systems (VESSs) offers an unparalleled opportunity to unlock the energy flexibility of distribution grids at the local level without increasing operational complexity. According to the state-of-the-art, models that tap the economic performance of VESSs while respecting the flexibility and security setpoints adopted by DISCOs in a distributed manner have remained scarce. This study goes beyond by developing a flexibility-oriented decision-making strategy as a single-leader multi-followers stochastic model; in which a multi-district DISCO involves the flexibility setpoints and congestion management strategy in the market participation plan of each VESS locally to enhance the energy flexibility of each district. In the upper level, DISCO aims to deal with stressful situations in real-time sessions by (i) determining the minimum reserve capacity requirements in each district to be provided by VESSs with respect to the existing uncertainty sources and the importance of each district, and (ii) alleviating the congestion in critical feeders to release the feeder capacity for deployment of the available reserve power promptly. In the lower level, each VESS makes optimal decisions on power and reserve bidding in day-ahead, reserve, and real-time markets that are affected by the flexibility services required by the DISCO. Inspired by the activity schedule of different users, it also examines how demand response programs can affect DISCO's ability to unlock consumer-side flexibility in realizing congestion management strategies. The developed bi-level scheduling model is first reformulated as single-level mathematical program with equilibrium constraints (MPEC) by employing Karush–Kuhn–Tucker (KKT) optimality conditions and then linearized into a mixed-integer linear program (MILP). The proposed strategy is validated through extensive simulations conducted on the modified IEEE 33-bus test system consisting of three districts with different users. Furthermore, DIgSILENT PowerFactory is used to verify the feasibility of the proposed model under contingency cases and to provide insights for DISCOs to control multiple VESSs and capacity-correlated uncertainties. • A local optimization strategy is proposed for coordinated operation of VESSs and multi-district DISCO. • The flexibility and security requirements of DISCO are reflected in the market-oriented strategy of VESSs. • The reserve setpoints are adopted in each district to achieve a local power balance under stressful conditions. [ABSTRACT FROM AUTHOR]