Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.

Summary In this survey, the objective is to identify current trends in the smart grid research by exploring the work carried out in numerous smart grid labs worldwide. For this purpose, a large number of smart grid labs are identified, and a short description of their activities is given. Fifty‐eight out of the 75 identified labs are located in Europe. Smart grid research is divided into categories, which represent popular topics of research in the field. The predominant category of research is identified to be generation and distributed energy resources (Gen & DER) with 91% of the labs conducting research in this field. Aggregated information is presented regarding the labs, providing a clear idea of the topics of research carried out. Connections between different topics of research are presented, which reveal synergies or collaboration gaps among various smart grid topics. Grid management and Gen & DER and energy storage and Gen & DER have been found to be popular combinations of topics with 55 labs active in both, respectively. In addition, we provide insights on the entities at which research is targeted and consider the evolution of publications produced by the labs on the different categories. An overall increase in publications was observed over the past 11 years in virtually all categories of smart grid research with the most published scientific papers in Gen & DER and electromobility. Collaborations between research institutes have been analyzed, pointing out existing joint research conducted and the huge potential to explore synergies between institutes further. Our work is useful in order to identify the smart grid areas where research is focusing on. This gives a clear picture of potential synergies between labs for knowledge sharing and enhancing their research efforts.

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The power system is undergoing significant changes so as to accommodate an increasing amount of renewably generated electricity. In order to facilitate these changes, a shift from the currently employed zonal pricing to nodal pricing is a topic that is receiving increasing interest. To explore alternative pricing mechanisms for the European electricity market, one needs to solve large-scale nodal optimization problems. These are computationally intensive to solve, and a parallelization or sequencing of the models can become necessary. The seasonality of hydro inflows and the issue of myopic foresight that does not display the value in storing water today and utilizing it in the future is a known problem in power system modeling. This work proposes a heuristic step-wise methodology to obtain state of charge profiles for hydro storage units for large-scale nodal and zonal models. Profiles obtained from solving an aggregated model serve as guidance for a nodal model with high spatial and temporal resolution that is solved in sequences. The sequenced problem is guided through soft constraints that are enforced with different sets of penalty factors. The proposed methodology allows for adjustments to congestions on short timescales and proves to perform well in comparison to other approaches to this issue suggested in the literature. Following the input profile closely on a long timescale renders good results for the nodal model.

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