Abstract
This paper presents how the InterConnect project is enhancing the relationship between smart buildings, energy communities and grids, enabling the potential of interoperable flexibility mechanisms and the offer of new energy and non-energy services. Within this framework DSO will leverage its role of neutral market facilitator acting as key enabler for new business models. The paper presents the first technical definition of the DSO Interface of the H2020 InterConnect project that will ensure interoperable integration of flexibility services between DSOs and the different market parties to support the grid operation towards an increasingly decentralized, digitalized and decarbonized electric system. © 2021 The Institution of Engineering and Technology.

Abstract
Renewable energy communities (REC) are bound to play a crucial role in the energy transition, as their role, activities, and legal forms become clearer, and their dissemination becomes larger. Even though their mass grid integration, is regarded with high expectations, their diffusion, however, has not been an easy task. Its legal form and success, entail responsibilities, prospects, trust, and synergies to be explored between its members, whose collective dynamics should aim for optimal operation. In this regard, the pairing methodology of potential participants ahead of asset dimensioning seems to have been overlooked. This article presents a methodology for pairing consumers, based on their georeferenced load consumptions. A case study in an area of Porto (Asprela) was used to test the methodology. QGIS is used as a geo-representation tool and its PlanHeat plugin for district characterization support. A supervised statistical learning approach is used to identify the feature importance of an overall district energy consumption profile. With the main variables identified, the methodology applies standard K-means and Dynamic Time Warping clustering, from which, users from different clusters should be paired to explore PV as the main generation asset. To validate the assumption that this complementarity of load diagrams could decrease the total surplus of a typical PV generation, 18 pairings were tested. Results show that, even though it is not true that all pairings from different clusters lead to lower surplus, on average, this seems to be the trend. From the sample analyzed a maximum of 36% and an average of 12% less PV surplus generation is observed.

Abstract
Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (baseline) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, and lessons from use cases. In particular, the article describes and focuses on the different baseline methods applied in some European H2020 projects, showing the results achieved in terms of measurement accuracy and costs in real test cases. The most suitable methodology choice among the several available depends on many factors. Some of them can be the function of the Demand Response (DR) service in the system, the broader regulatory framework for DR participation in wholesale markets, or the DR providers characteristics, and this list is not exclusive. The evaluation shows that the baseline methodology choice presents a trade-off among complexity, accuracy, and cost.

Abstract
The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively.

Abstract

Abstract