Improving Clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy

A hybrid supervised learning model for a medium-term MV/LV transformer loading forecast with an increasing capacity of PV panels

The share of photovoltaic (PV) generation has increased quickly in the last decade. Many PV panels are connected behind-the-meter (BTM), so that they can not be identified with measurement equipment at MV/LV transformers. This poses a challenge for a medium-term MV/LV transformer loading forecast if the capacity of PV panels is increasing over time. Therefore, this paper proposes a hybrid approach for a medium-term load forecast (MTLF) of a MV/LV transformer with an increasing capacity of PV panels that are not separately measured. This approach combines a supervised learning model (data-driven approach) with a model to estimate the generation profile of the PV panels (model-based approach). The results indicate that the accuracy of the forecast improves significantly, while an accurate generation profile of the PV panels connected BTM or a disaggregation of the net load is unnecessary

Analysing the Long-Term Impact of the Energy Transition on Medium Voltage Network Assets

This paper proposes a new methodology to analyse the long-term impact of the energy transition on Medium Voltage (MV) network assets. The impact will be realized in the long term by including the impact of renewable technologies on medium to low voltage transformer loadings. To ensure future networks can be analysed, adoptions of technologies connected on low and medium voltage networks are modelled and can be altered on a neighbourhood level. Furthermore, curtailment and flexibility can be taken into account in the impact analysis. To find nodes and cables prone to voltage violations and overloading, this proposed model is validated on a typical urban, rural and industrial MV network for a scenario with peak demand (winter) -and generation (summer) during normal and redundant operations. It is found that urban networks are more likely to have a cable overload, whereas rural networks are more likely to have voltage violations. Industrial networks are highly dependent on where the large-scale installations are connected to the networks

Medium-term forecasting of active power curtailment from MV/LV transformer loadings with an increasing capacity of PV panels

Driven by the energy transition, the electricity generated by photovoltaic panels connected by customers behind-the-meter is annually increasing. This is expected to lead to congestion of medium to low voltage (MV/LV) transformers because the related capacity is unable to be reinforced fast enough to accommodate all these PV panels. Based on medium-term load forecasts of an MV/LV transformer with an annual increasing capacity of installed PV panels behind-the-meter, active power curtailment (APC) necessary to prevent congestion and related compensation costs to owners of these PV panels is forecasted. First, a month-ahead load forecast of the studied MV/LV transformer with an annual increasing capacity of installed PV panels behind-the-meter is performed multiple times based on weather conditions measured during the same month but in previous years. Second, each of these month-ahead load forecasts is used to forecast the related APC and compensation costs. Subsequently, the distribution in forecasted APC and compensation costs due to annual variation of weather conditions over a month is analyzed. In addition, APC duration curves are calculated for all these forecasts to analyze the distribution of the amount and duration of alternative solutions, such as demand-side management to reduce necessary APC and the local mismatch between supply and demand

The flexibility potential to reduce the peak load of small and medium-sized enterprises

Due to the energy transition, Electric Vehicles (EVs) and Heat Pumps (HPs) are penetrating the distribution network, which leads to an increase in (peak) demand. Additionally, the energy transition is also causing an increasing share of solar PV into the distribution network. Therefore, time-consuming and expensive network reinforcements become necessary to prevent network congestions if no alternatives are available. Two of these alternatives considered in this paper are Demand-Side Management (DSM) to unlock flexibility on the demand side and Electrical Energy Storage (EES). First, this paper introduces a method to synthesize the load profile of Small and Medium-sized Enterprises (SMEs) if EVs, HPs or solar PV are included. Finally, a theoretical method is introduced to quantify the flexibility provided by EVs and HPs to reduce the peak load and a theoretical method to quantify the impact of home batteries to reduce the peak load. The methods are used for a case study as part of the Dutch pilot project ‘Community-flex Bedrijvenpark Zuidoost’ in the city of Groningen

Interactions of Aggregating Peptides Probed by IR-UV Action Spectroscopy

Peptide aggregation, the self-assembly of peptides into structured beta-sheet fibril structures, is driven by a combination of intra- and intermolecular interactions. Here, the interplay between intramolecular and formed inter-sheet hydrogen bonds and the effect of dispersion interactions on the formation of neutral, isolated, peptide dimers is studied by infrared action spectroscopy. Therefore, four different homo- and hetereogeneous dimers formed from three different alanine-based model peptides have been studied under controlled and isolated conditions. The peptides differ from one another in the presence and location of a UV chromophore containing cap on either the C- or N-terminus. Conformations of the monomers of the peptides direct the final dimer structure: strongly hydrogen bonded or folded structures result in weakly bound dimers. Here the intramolecular hydrogen bonds are favored over new intermolecular hydrogen bond interactions. In contrast, linearly folded monomers are the ideal template to form parallel beta-sheet type structures. The weak intramolecular hydrogen bonds present in the linear monomers are replaced by the stronger inter-sheet hydrogen bond interactions. The influence of π-π disperion interactions on the structure of the dimer is minimal, the phenyl rings have the tendency to fold away from the peptide backbone to favour intermolecular hydrogen bond interactions. Quantum chemical calculations confirm our experimental observations

Interactions of aggregating peptides probed by IR-UV action spectroscopy

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