Jean-François Toubeau

Infliximab Trough Levels at Induction to Predict Treatment Failure During Maintenance

Background: Infliximab (IFX) is indicated for the treatment of inflammatory bowel diseases (IBD). Nevertheless, loss of response (LOR) to IFX is reported in up to 10% to 30% of patients within the first year of treatment. Our objective was to evaluate the impact of the pharmacokinetics of IFX at induction on treatment failure. Methods: This is a longitudinal cohort study on 269 patients with IBD treated with IFX in a single center. A total of 2331 blood samples were prospectively collected from 2007 until March 2015 with a retrospective analysis of clinical data. IFX trough levels (TLs) were measured by enzyme-linked immunosorbent assay. Antibodies to IFX were measured by drug-sensitive bridging assay. Results: During follow-up, patients were defined according to treatment outcome. At week 6, median IFX TL in patients requiring a switch to another treatment due to LOR (LOR switched group) (2.32 &mgr;g/mL [0.12–19.93 &mgr;g/mL]) was lower than in patients with long-term response (long-term responders) (8.66 &mgr;g/mL [0.12–12.09 &mgr;g/mL], P = 0.007) and in patients responding to optimization (LOR optimized group) (7.28 &mgr;g/mL [0.17–14.91 &mgr;g/mL], P = 0.021). At week 2, median IFX TL was lower in the LOR switched group (5.7 &mgr;g/mL [0.15–12.09 &mgr;g/mL]) compared with the long-term responders (11.92 &mgr;g/mL [0.14–19.93 &mgr;g/mL], P = 0.041) but no significant difference was reached with the LOR optimized group (11.91 &mgr;g/mL [0.23–12.09 &mgr;g/mL], P = 0.065). In the LOR switched group, median IFX TL at induction (weeks 2 and 6) was significantly lower when patients had been previously exposed to anti–tumor necrosis factor compared with naive patients (0.91 &mgr;g/mL [0.12–4.4 &mgr;g/mL] versus 6.6 &mgr;g/mL [0.15–19.93 &mgr;g/mL], P = 0.044). Conclusions: This study suggests that patients who do not respond to any optimization strategy have lower IFX TLs during induction at week 6. IFX TLs measured early on at induction might predict treatment failure to IFX during maintenance.

Estimating the Photovoltaic Hosting Capacity of a Low Voltage Feeder Using Smart Meters’ Measurements

Maximizing the share of renewable resources in the electric energy supply is a major challenge in the design of the future energy system. Regarding the low voltage (LV) level, the main focus is on the integration of distributed photovoltaic (PV) generation. Nowadays, the lack of monitoring and visibility, combined with the uncoordinated integration of distributed generation, often leads system operators to an impasse. As a matter of fact, the numerous dispersed PV units cause distinct power quality and costefficiency problems that restrain the further integration of PV units. The PV hosting capacity is a tool for addressing such power system performance and profitability issues so that the different stakeholders can discuss on a common ground. Photovoltaic hosting capacity of a feeder is the maximum amount of PV generation that can be connected to it without resulting in unacceptable power quality. This chapter demonstrates the usefulness of smart metering (SM) data in determining the maximum PV hosting capacity of an LV distribution feeder. Basically, the chapter introduces a probabilistic tool that estimates PV hosting capacity by using customer-specific energy flow data, recorded by SM devices. The probabilistic evaluation and the use of historical SM data yield a reliable estimation that considers the volatile character of distributed genera‐ tion and loads as well as technical constraints of the network (voltage magnitude, phase unbalance, congestion risk). As a case study, an existing LV feeder in Belgium is analysed. The feeder is located in an area with high PV penetration and large deploy‐ ment of SM devices.

Statistical load and generation modelling for long term studies of low voltage networks in presence of sparse smart metering data

The lack of monitoring data in Low Voltage (LV) networks has lately become a major concern since their operation is currently undergoing significant changes driven by the worldwide desire to support and facilitate the energy transition. It is therefore essential to improve network observability, which leads to the deployment of smart metering (SM) devices at the end-user level. However, their actual roll-out is confronted to technical, financial as well as social barriers, and is therefore still limited to some sparse areas. This paper aims at overcoming this data deficiency in the context of long-term studies of the system. The objective is thus to establish reliable individual stochastic models for every LV consumers (e.g. residential, commercial, etc.) and distributed generators, even those without metering devices. The first step of the work focuses on the segmentation of end-users into representative clusters. Afterwards, within each cluster, all available SM information is used to extrapolate statistical profiles of all components thanks to an innovative load modelling methodology. The accuracy of our implemented approach is then validated on a real LV feeder thanks to a Monte Carlo simulation. In particular, the improvement of this new modelling method compared to currently used approaches, such as Synthetic Load Profiles, is statistically highlighted.

A new formulation of state estimation in distribution systems including demand and generation states

Distribution system state estimation faces a major difficulty: the lack of real-time measurements. This imposes to add information, usually pseudo-measurements from historical data. This paper proposes a different, novel formulation of state estimation relying on the classification of loads into components (e.g. residential, commercial, etc.) and accounting for dispersed generation. The approach “by-passes” the use of pseudo-measurements by expressing the medium-voltage bus injections as functions of a small number of active power components at low-voltage level, treated as additional state variables. The injections at medium-voltage buses become equality constraints. A procedure to identify the above functions is detailed, which takes advantage of data collected by smart meters.

SARMA Time Series for Microscopic Electrical Load Modeling

In the current context of profound changes in the planning and operations of electrical systems, many Distribution System Operators (DSOs) are deploying Smart Meters at a large scale. The latter should participate in the effort of making the grid smarter through active management strategies such as storage or demand response. These considerations involve to model electrical quantities as locally as possible and on a sequential basis. This paper explores the possibility to model microscopic loads (individual loads) using Seasonal Auto-Regressive Moving Average (SARMA) time series based solely on Smart Meters data. A systematic definition of models for 18 customers has been applied using their consumption data. The main novelty is the qualitative analysis of complete SARMA models on different types of customers and an evaluation of their general performance in an LV network application. We find that residential loads are easily captured using a single SARMA model whereas other profiles of clients require segmentation due to strong additional seasonalities.

Optimal Allocation Process of Voltage Control Devices and Operational Management of the Voltage in Distribution Systems Using the Experimental Design Method

This paper presents a new approach to control the voltage in medium voltage (MV) networks by using the Experimental Design method. This method, also referred to as Design of Experiments (DOE), is a powerful tool to establish and study the effects of multiple inputs (factors) on a desired output (response). In this work, the output will be the sum of the absolute values of the deviations between the voltage at each node of the network and the rated voltage. The response should therefore be minimized to ensure the best possible voltage profile along the feeders of the grid. The different studied factors are: on-load tap changer (OLTC) of transformer, curtailment of distributed generation (DG) and reactive power compensation devices. In this study, the DOE is employed for two purposes. Firstly, the parameters which have the most significant impact on the output are selected. Secondly, the values of these parameters are optimised to reduce the voltage variation in the distribution network by using a response surface methodology (RSM) approach. This two-step process is then applied on a 28-bus radial MV network in order to study an overvoltage problem caused by an increased active power generation coming from DG units.

Medium-Term Multimarket Optimization for Virtual Power Plants: A Stochastic-Based Decision Environment

This paper presents a decision-making tool tailored for a portfolio manager aiming at maximizing its profit by participating in both energy and balancing services markets. The proposed formulation is modular and flexible so as to comply with any portfolio configuration and to follow evolutions of the market regulation policy. Detailed formulations of both medium-term (i.e., typically from one week up to one year-ahead) and short-term (i.e., day-ahead) perspectives are jointly considered and solved using surrogate-based optimization. The objective is to adequately account for the interdependencies and possible conflicting objectives between these time horizons. Then, in order to overcome the resulting computational burden associated with the different sources of uncertainty, an innovative method for generating time and space-dependent scenarios is developed. The approach is based on nonparametric copulas and, in contrast to traditional methods, allows including a large number of uncertain parameters into the formulation. Finally, the procedure is tested and illustrated for a portfolio manager with diversified assets. The case study is developed to emphasize the advantages of the proposed optimization tool in terms of accuracy and computational burden of the proposed models as well as subsequent generated profit.

Trough Levels at Induction: Impact on Long Term Response when Re-Initiating Infliximab

Infliximab (IFX) is indicated for the treatment of inflammatory bowel disease (IBD) (ulcerative colitis(UC) or Crohn disease(CD)). Nevertheless, a significant proportion of patients will experience a loss of response (LOR) to IFX over time which may require despite optimization a switch to another anti-TNF or to swap out to another biotherapy. We have recently reported that week 2 and 6 IFX through levels (TLs) can be predictive of treatment failure and long term response. Only one study has shown that week 14 TLs can be predictive of long term response on re-initiation of IFX therapy. Our objective is to evaluate early on at induction IFX TLs and antibodies to IFX (ATI) in patients previously exposed to anti-TNF. 269 IBD patients (194 CD-75 UC) have been treated with IFX on follow-up. 2331 samples were prospectively collected but measured retrospectively by ELISA in parallel with clinical data. 91 samples (TL measured <1μg/ml) were analyzed for IFX ATI using drug-sensitive bridging ELISA. At follow-up, patients were subdivided into three groups: long-term responders, patients who had LOR but responded to optimization or patients who had LOR but did not respond to optimization and were switched to another biotherapy. Each group was subdivided according to naïve or previous treatment with anti-TNF (IFX or Adalimumab) status. During induction (week 2 and 6 combined), in the LOR switched group, median IFX TL was significantly lower in previously exposed patients than in naïve patients (0.92μg/ ml[0.12-4.4μg/ml]VS6.6μg/ml[0.15-19.93μg/ml], p=0.044)(Figure 1A). Inversely, there was no statistical difference between median TL in the LOR optimized group between naïve and previously exposed patients(9.38μg/ml[0.17-14.91μg/ml]vs11.82μg/ml[0.17-14.91μg/ ml], p=0.52) as well as in naïve and previously exposed Long-term responders(9.57μg/ ml[1.44-11.97μg/ml] vs 11.91μg/ml[0.12-19.93μg/ml], p=0.92). Overall, among the previously exposed patients, the LOR switched group had a lower median IFX TL (0.92μg/ ml[0.12-4.40μg/ml]) compared to the Long-term responders(9.57μg/ml[0.44-11.97μg/ml], p=0.015) and LOR optimized group(11,82μg/ml[0.23-12.09μg/ml], p=0.005)(Figure 2). The percentage of ATI occurrence was statistically lower in the Long-term responders(5.7%) than in the LOR optimized(37.5%), p= 0.002 and LOR switched groups(40%), p=0.002. Interestingly, among the LOR switched group, the percentage of ATI occurrence was similar in patients whether naïve or previously exposed to anti-TNF (38,8%VS42,9%, p= 0.86)(Figure 1B). The same observation was found in the LOR optimized group(25%VS45% p=0.45). In LOR switched group, patients previously exposed to anti-TNF seem to have lower IFX TLs at induction (at week 2 and 6) than naïve patients. This may not be related to immunogenicity as the presence of ATI was similar in patients whether naïve or previously exposed to anti-TNF.

Photovoltaic integration in smart city power distribution: A probabilistic photovoltaic hosting capacity assessment based on smart metering data

Maximizing the share of renewable resources in the electric energy supply is a major challenge in the design of smart cities. Concerning the smart city power distribution, the main focus is on the Low Voltage (LV) level in which distributed Photovoltaic (PV) units are the mostly met renewable energy systems. This paper demonstrates the usefulness of smart metering (SM) data in determining the maximum photovoltaic (PV) hosting capacity of an LV distribution feeder. Basically, the paper introduces a probabilistic tool that estimates PV hosting capacity by using user-specific energy flow data, recorded by SM devices. The probabilistic evaluation and the use of historical SM data yield a reliable estimation that considers the volatile character of distributed generation and loads as well as technical constraints of the network (voltage magnitude, phase unbalance, congestion risk, line losses). As a case study, an existing LV feeder in Belgium is analysed. The feeder is located in an area with high PV penetration and large deployment of SM devices. The estimated PV hosting capacity is proved to be much higher than the one obtained with a deterministic worst case approach, considering voltage margin (magnitude and unbalance).

Impact of the geographical correlation between wind speed time series on reliability indices in power system studies

In order to help system (transmission and/or distribution) operators to account for the stochastic nature of wind during the grid planning phase, univariate AutoRegressive Moving Average (ARMA) time series models for the long term modeling of wind speed are generally considered. Practically, different geographical correlation levels are observed in the bibliography when sampling wind generation in the framework of long-term analysis tools (e.g. sequential Monte Carlo algorithms). The traditional approach is to assess extreme correlation scenarios (entire independence, entire correlation). Some recent works try to better capture the correlation patterns of real data by using matrix methods, like the Cholesky decomposition. In this work, two methods for reproducing the actual correlation are compared on a statistical basis on the one hand, and in the framework of a Monte-Carlo reliability analysis on the other hand. It is shown that a good correlation model is mandatory for obtaining correct reliability indices.