A novel feature set for low-voltage consumers, based on the temporal dependence of consumption and peak demands

This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands

A low-voltage DC backbone with aggregated RES and BESS : benefits compared to a traditional low-voltage AC system

The increasing penetration of PV into the distribution grid leads to congestion, causing detrimental power quality issues. Moreover, the multiple small photovoltaic (PV) systems and battery energy storage systems (BESSs) result in increasing conversion losses. A low-voltage DC (LVDC) backbone to interconnect these assets would decrease the conversion losses and is a promising solution for a more optimal integration of PV systems. The multiple small PV systems can be replaced by shared assets with large common PV installations and a large BESS. Sharing renewable energy and aggregation are activities that are stimulated by the European Commission and lead to a substantial benefit in terms of self-consumption index (SCI) and self-sufficiency index (SSI). In this study, the benefit of an LVDC backbone is investigated compared to using a low-voltage AC (LVAC) system. It is found that the cable losses increase by 0.9 percent points and the conversion losses decrease by 12 percent points compared to the traditional low-voltage AC (LVAC) system. The SCI increases by 2 percent points and the SSI increases by 6 percent points compared to using an LVAC system with shared meter. It is shown that an LVDC backbone is only beneficial with a PV penetration level of 65% and that the BESS can be reduced by 22% for the same SSI

Assessing the influence of the aggregation level of residential consumers through load duration curves

Residential electricity consumers at present pose a major challenge to simulate due to their inherent stochastic nature. However, as this sector represents approximately one fourth of the total electricity consumption across the European Union, it is necessary to gain more insight in their consumption, both on an individual as well as on an aggregated level. In this work, we propose the use of load duration curves (LDCs) to characterize residential consumers. We construct LDCs for individual as well as artificially aggregated groups of residential consumers, and present how these LDCs can lead to a better understanding of what influence the aggregation level has on key characteristics of the consumption profile, how aggregation affects the height and duration of the peak demand, and what inherent trade-offs are present at higher aggregation levels

Peak demand dynamics of low-voltage consumers under aggregation and its impact on upstream PV injection

Renewable Energy Communities will allow consumers on the low-voltage grid to actively participate in the energy market. This work explores the dynamics of the daily peak demands of these communities under aggregation, as multiple countries are introducing capacity-based grid tariffs for residential consumers. Both the aggregation level and yearly consumption of the households comprising it are shown to have a significant impact on the timing of the daily peak demands. While considering both the size of the PV installation and its orientation as variables, it is subsequently shown that this effect of the aggregation level is extended to the PV self-consumption and self-sufficiency indices

A Data-Driven Approach to Assessing and Improving Stochastic Residential Load Modeling for District-Level Simulations and PV Integration

This paper presents an assessment and improvement of stochastic load modeling for district-level analyses with integration of photovoltaic panels (PV), by comparison with measurement data. Stochastic load profiles for individual households were produced using the bottom-up ‘Stochastic Residential Occupancy Behavior’ (StROBe) model. The self-consumption of households with PV installations and the district-level peak demand are examined as properties relevant for the estimation of PV hosting capacity and accompanying grid-related problems. The comparison shows that while the synthetic profiles produce reasonable estimates of simultaneity and summer peak demand, they insufficiently represent the seasonal variations. In addition, self-consumption is overestimated by the model. The observed discrepancies can be traced back to inaccurate modeling of the peak timing and seasonal variation in individual peak load and simultaneity. Furthermore, vacant homes in the measured data are found to contribute significantly to discrepancies in holiday periods. Adjusting the stochastic modeling to account for these vacant homes results in improved performance of the model. This research demonstrates that harvesting the full potential of bottom-up stochastic load modeling would require more up-to-date information on residential electricity use patterns.status: accepte

Stochastic generation of residential load profiles with realistic variability based on wavelet-decomposed smart meter data

Residential smart meter data with high time resolution are integral to many data-driven applications, ranging from hosting capacity studies to R&D activities of private enterprises. However, privacy legislation restricts public availability of large-scale datasets. Furthermore, existing datasets may suffer from imbalances in terms of underrepresented classes. To address these concerns, this study presents a novel decomposition–recombination approach for generating synthetic load profiles that exhibit realistic variability and demand peaks. High-frequency load profiles are decomposed into a low-frequency base load and high-frequency variability at the daily level through a discrete wavelet transformation. Components from different households are subsequently rescaled, shifted and recombined in a stochastic load profile generator to obtain new daily load profiles with high-fidelity behavior. The performance of this generator is evaluated through benchmarking, resulting in a mean average error of 0.09 kW on an average value of less than 3 kW for the daily peaks, whilst preserving their seasonality. The introduced load profile generator is validated as an alternative to privacy-sensitive residential smart meter data in a hosting capacity case study. The analysis focuses on the voltage drop caused by residential electric vehicle charging, considering both real and synthetic data. The synthetic data demonstrated voltage drops with a mean average error less than 0.2 V for the 10th and 90th percentile when benchmarked with respect to the real voltage level distribution. The introduced decomposition–recombination method is shown to accurately capture the high-frequency variability and peak behavior, and is suitable for practical applications at the daily level

A data-driven approach to assessing and improving stochastic residential load modeling for district-Level simulations and PV integration

This paper presents an assessment and improvement of stochastic load modeling for district-level analyses with integration of photovoltaic panels (PV), by comparison with measurement data. Stochastic load profiles for individual households were produced using the bottom-up ‘Stochastic Residential Occupancy Behavior’ (StROBe) model. The self-consumption of households with PV installations and the district-level peak demand are examined as properties relevant for the estimation of PV hosting capacity and accompanying grid-related problems. The comparison shows that while the synthetic profiles produce reasonable estimates of simultaneity and summer peak demand, they insufficiently represent the seasonal variations. In addition, self-consumption is overestimated by the model. The observed discrepancies can be traced back to inaccurate modeling of the peak timing and seasonal variation in individual peak load and simultaneity. Furthermore, vacant homes in the measured data are found to contribute significantly to discrepancies in holiday periods. Adjusting the stochastic modeling to account for these vacant homes results in improved performance of the model. This research demonstrates that harvesting the full potential of bottom-up stochastic load modeling would require more up-to-date information on residential electricity use patterns

Sizing BESS for a peak shaving and valley filling control strategy for residential consumers based on their load-duration curves

Driven by the renewable energy transition and the increasing penetration of distributed generation on the distribution grid, many countries are rethinking their electricity tariff structures. The focus is shifting towards capacity-based grid tariffs, with users being charged more for their peak demands in order to make the tariff structure more cost-reflective. However, a group of residential consumers will be subjected to changing grid tariffs without established technologies, e.g. PV. To lower their invoice, they can only change their consumption behaviour via e.g. load shifting, as not all rooftops are suitable for PV installations. Consequently, this work investigates the sizing of battery storage systems for peak shaving purposes at the level of the individual household in the absence of local generation. We propose using the analytic form of the load-duration curve to determine the peak shaving threshold. The results show that on average under the proposed sizing methodology, the battery remains idle for more than 95% of the time while realising a mean monthly peak reduction of 50%. Therefore, the battery could and should provide additional services through aggregators at the low-voltage level, which would provide necessary complementary revenue streams that make such a system economically viable

Self-sufficiency and lifetime improvement of community BESS on an LVDC backbone compared to individual BESS

Previous analyses on the benefits of LVDC backbones have exposed the huge potential in urban environment with high PV penetration. However, the energy efficiency and lifetime improvement by integrating a community battery energy storage systems (BESS) on an LVDC backbone have not been thoroughly investigated yet. In this contribution multiple comparisons are made between individual and virtual community BESS on LVAC and real community BESS on LVDC. It has been shown that the reduced conversion loss and the slight lifetime improvement are the main advantages of community BESS on LVDC compared to individual BESS on LVAC