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 cost-efficiency 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 generation 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 deployment of SM devices

Planning Tools for the Integration of Renewable Energy Sources Into Low- and Medium-Voltage Distribution Grids

This chapter presents two probabilistic planning tools developed for the long-term analysis of distribution networks. The first one focuses on the low-voltage (LV) level and the second one addresses the issues occurring in the medium-voltage (MV) grid. Both tools use Monte Carlo algorithms in order to simulate the distribution network, taking into account the stochastic nature of the loading parameters at its nodes. Section 1 introduces the probabilistic framework that focuses on the analysis of LV feeders with distributed photovoltaic (PV) generation using quarter-hourly smart metering data of load and generation at each node of a feeder. This probabilistic framework is evaluated by simulating a real LV feeder in Belgium considering its actual loading parameters and components. In order to demonstrate the interest of the presented framework for the distribution system operators (DSOs), the same feeder is then simulated considering future scenarios of higher PV integration as well as the application of mitigation solutions (reactive power control, P/V droop control thanks to a local management of PV inverters, etc.) to actual LV network operational issues arising from the integration of distributed PV generation. Section 2 introduces the second planning tool designed to help the DSO, making the best investment for alleviating the MV-network stressed conditions. Practically, this tool aims at finding the optimal positioning and sizing of the devices designed to improve the operation of the distribution grid. Then a centralized control of these facilities is implemented in order to assess the effectiveness of the proposed approach. The simulation is carried out under various load and generation profiles, while the evaluation criteria of the methodology are the probabilities of voltage violation, the presence of congestions and the total line losses

Probabilistic framework for evaluating droop control of photovoltaic inverters

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Development of a Probabilistic Tool Using Monte Carlo Simulation and Smart Meters Measurements for the Long Term Analysis of Low Voltage Distribution Grids with Photovoltaic Generation

peer reviewedConnections of distributed generation (DG) units based on the use of photovoltaic 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. Consequently, it is important for them to have an analysis tool that computes statistical voltage profiles and allows to assess maximal penetration rates of photovoltaic generation (PV) on low voltage (LV) distribution feeders. In previous studies, it has been shown that such a tool 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 using collected measurements from smart meters that are expected to be massively deployed in the future, it will be possible for the DSO to directly assess voltage profiles at all the nodes of the LV grid. Moreover, in the context of alleviating the impact of photovoltaic generation on the recorded voltage profiles, smart meters data will also be used in order to not only quantify the influence of reactive power flows on the collected results but also to estimate the auto-consumption potential over some critical nodes of the grid

Probabilistic Simulation Framework of the Voltage Profile in Balanced and Unbalanced Low Voltage Networks

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A Probabilistic Framework for Evaluating Voltage Unbalance Mitigation by Photovoltaic Inverters

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A probabilistic framework for evaluating voltage unbalance mitigation by photovoltaic inverters

In three-phase Low Voltage (LV) networks, distributed photovoltaic (PV) units can contribute to voltage unbalance mitigation in case they are connected with the use of three-phase inverters integrating unbalance mitigation control schemes. This paper presents a probabilistic framework that simulates the time-varying action of voltage magnitude and unbalance mitigation schemes, locally implemented by PV inverters in LV feeders. The scope includes evaluating the effect of such strategies in the context of a long term techno-economic planning of the LV network and characterizing LV network operation for increasing the observability of state estimation techniques applied in the Medium Voltage level. The presented framework evaluates the action of four distributed control schemes in an extensive range of possible network states assembled with the use of feeder-specific smart metering (SM) data. The simulation of a real LV feeder with distributed PV generation and long term SM measurements is presented. A control strategy that acts resistively towards the negative- and zero-sequence voltage components without modifying the total nodal injected power (three-phase damping control strategy) results to be more efficient compared with traditionally applied voltage control scheme

On the Correlation between Prosumers in Probabilistic Analysis of Low Voltage Distribution Systems

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