Vasiliki Klonari

Probabilistic modeling of short term fluctuations of photovoltaic power injection for the evaluation of overvoltage risk in low voltage grids

Dispersed photovoltaic generation integrated in the low voltage distribution grids leads their operation towards an active approach. In that context, new methodologies are needed in order to identify and to accurately quantify the impact of this massive renewable penetration on the grid operation. For this purpose, the authors of this study have developed in a previous analysis a probabilistic load flow tool directly based on the use of smart meters data. The goal of this tool was to quantify probabilities of overvoltage in radial low voltage grids with increased photovoltaic penetrations. However, the drawback of this approach was the too long (15 minutes) sampling time step of smart meters that did not allow the consideration of fast fluctuations of photovoltaic generation. In the present paper, an original adaptation of the existing tool is therefore presented, which enables the consideration of short temporary photovoltaic power fluctuations. Thanks to this new development, it is shown that a more accurate estimation of overvoltage situations, due to photovoltaic generation, is permitted.

Implementation of an e-bike sharing system: The effect on low voltage network using pv and smart charging stations

Nowadays distribution systems are becoming more and more complicated mainly due to the new methods of producing and storing electricity (PV, fuel cells, battery storage systems) as well as due to the new tensions of consuming electric energy (smart appliances, e-vehicles, e-bikes, etc.). Uncertainty in load, generation, and cost requires modeling power systems with a probabilistic approach. In such a way, the probabilistic nature of demand side management (DSM) problem can also be addressed. This work presents the design of an e-bike sharing system, in terms of system components and user mobility patterns. The integration of the designed system in the Low Voltage (LV) grid is simulated with a probabilistic analysis framework that uses real smart metering (SM) data. The stochastic character of the loading parameters at the network nodes is studied taking into account the charging energy needs of the proposed e-bikes sharing system. PV generation produced on the parking roof of the e-bikes smart charging stations (SCS) along with the energy stored in a local battery is also studied.

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.

Effect of electric vehicles' optimal charging-discharging schedule on a building's electricity cost demand considering low voltage network constraints

Nowadays, one of the dominant reasons of excessive energy consumption is the high energy demand in corporate and/or public buildings. At the same time, electric vehicles (EVs) are becoming more and more popular worldwide being a considerable alternative power source when parked. In this work we initially propose an energy management framework which optimizes the control of the charging-discharging schedule of a fleet of EVs arriving at a university building for two typical load-days in February and May aiming at the minimization of the energy demand and, thus, the electricity cost of the building. To this end, a mixed integer linear programing (MILP) model containing binary and continuous variables was developed. Uncertainties in load, generation, and cost require modeling power systems with a probabilistic approach. In such a way, the probabilistic nature of demand side management (DSM) problem is also possible to be addressed. The integration of the EVs in the Low Voltage (LV) grid is simulated with a probabilistic analysis framework that uses real smart metering (SM) data. The stochastic character of the loading parameters at the network nodes is studied taking into account the charging energy needs of the corresponding EVs fleet.

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.

Study of the combined impact of auto-consumption behaviour and correlation level between prosumers on overvoltage probabilities in low voltage distribution grids

Connections of distributed generation (DG) units based on the use of photovoltaic (PV) cells are highly increasing in low voltage distribution grids. In that way, one of the major problems met by the Distribution System Operators (DSO) comes from overvoltage in the neighbourhood of dispersed units. In previous studies, it has been shown that probabilities of overvoltage in such grids could be obtained by using a Probabilistic Load Flow based on analytical techniques or Monte Carlo methods. In this paper, given its simplicity of implementation, a pseudo-chronological Monte Carlo simulation is used and the statistical behaviour of prosumers (consumers with PV units) is directly based on smart meters measurements. Thanks to this tool and, in the context of alleviating the impact of photovoltaic generation on the recorded voltage profiles, smart meters data are employed in order to not only estimate the auto-consumption potential of prosumers connected to the grid but also to quantify the impact of the correlation level between those prosumers.

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).

Long term assessment of a voltage unbalance mitigation scheme implemented by photovoltaic inverters

This paper applies a long term network observability analysis for investigating the potential contribution of photovoltaic (PV) inverters to the mitigation of voltage unbalance in low voltage (LV) feeders. For this purpose, a probabilistic offline state estimation algorithm is used, which simulates the time-varying action of local voltage (magnitude and unbalance) control schemes. The paper focuses on a control scheme that acts resistively towards the negative- and zero-sequence voltage components without modifying the total nodal injected power (three phase damping control scheme) For this long term evaluation, feeder- and user-specific smart metering (SM) data are used. The volatile character of PV generation and loads is modelled on a 15-min time scale. As a case study, a real LV feeder with distributed PV generation and long-term user-specific SM measurements is simulated. The three phase damping control results to be more advantageous compared with currently applied voltage control schemes.

Application of voltage sensitivity analysis in a probabilistic context for characterizing low voltage network operation

The massively dispersed nature of power distribution networks and their current unobservability will drive distribution utilities to hierarchize the integration of automation in their systems. Given the complete lack of real-time information in Low Voltage (LV) networks, state estimation techniques specifically tailored for these systems will not come for the foreseeable future. Considering delayed Smart Meter (SM) recordings as the only current source of information in LV networks, this paper presents a probabilistic method that uses sensitivity analysis and quarter-hourly SM measurements for characterizing and setting boundary values for LV network operation indices. Such information can be useful in the preprocessing phase of state estimation techniques focusing on the Medium Voltage (MV) or on the LV (in a later phase) level. The proposed method is applied for analyzing a real LV feeder and its outputs are compared to the ones of a deterministic direct sensitivity analysis method, whose accuracy has been previously demonstrated, as well as to the ones of a probabilistic Monte Carlo (MC) simulation.

Probabilistic assessment of a distribution tariff scheme for incentivizing demand side management in the small energy usage sector

Lately distribution utilities worldwide have been incentivized to reduce operating expenses that have severely increased with the integration of distributed generation. Enabling demand flexibility in the low voltage network seems a promising means at this direction. Given the current (low) electricity prices and the stochasticity of distributed generation and loads, this paper explores whether a capacity-based distribution tariff that rewards low power withdrawal during peak hours could incentivize small end-users to participate in demand side management. The assessment deploys a pseudo-sequential Monte Carlo simulation that uses quarter-hourly energy measurements and accounts for network constraint management. The presented case study highlights that a coordinated technical implementation is required, both during peak and non-peak periods, for not stressing LV network operation with the integration of flexibility. The potentially generated revenue for end-users, thanks to their response to the considered distribution tariff, results sufficiently motivating for engaging them in flexibility actions.