This paper presents a real-word implementation of a TSO-DSO-customer coordination framework for the use of flexibility to support system operation. First, we describe the general requirements for TSO-DSO-customer coordination, including potential coordination schemes, actors and roles and the required architecture. Then, we particularise those general requirements for a real-world demonstration in Sweden, aiming to avoid congestions in the grid during the high-demand winter season. In the light of current congestion management rules and existing markets in Sweden, we describe an integration path to newly defined flexibility markets in support of new tools that we developed for this application. The results show that the use of flexibility can reduce the congestion costs while enhancing the secure operation of the system. Additionally, we discuss challenges and lessons learned from the demonstration, including the importance of the engagement between stakeholders, the role of availability remuneration, and the paramount importance of defining appropriate technical requirements and market timings.

Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load forecasting problem. First, we provide a classification of load forecasting cases in two dimensions: temporal and hierarchical. Then, we identify typical features and models for forecasting and compare their applicability in a structured manner depending on six previously defined cases. These models are compared against real data in terms of their computational effort and accuracy during development and testing. From this comparative analysis, we derive a generic guide for the selection of the best prediction models and features per case.

Forecast developers predominantly assess residuals and error statistics when tuning the targeted model’s quality. With that, eventual cost or rewards of the underlying business application are typically not considered in the model development phase. The analysis of the power system wholesale market allows us to translate a time series forecast method’s quality to its respective business value. For instance, near real-time capacity procurement takes place in the wholesale market, which is subject to complex interrelations of system operators’ grid activities and balancing parties’ scheduling behavior. Such forecasting tasks can hardly be solved with model-driven approaches because of the large solution space and non-convexity of the optimization problem. Thus, we generate load forecasts by means of a data-driven based forecasting tool ProLoaF, which we benchmark with state-of-the-art baseline models and the auto-machine learning models auto.arima and Facebook Prophet.