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.

[1]  K.M. Nor,et al.  Development of unbalanced three-phase distribution power flow analysis using sequence and phase components , 2008, 2008 12th International Middle-East Power System Conference.

[2]  Lieven Vandevelde,et al.  Three-phase inverter-connected DG-units and voltage unbalance , 2011 .

[3]  Francisco Jurado,et al.  Technical impact of photovoltaic-distributed generation on radial distribution systems: Stochastic simulations for a feeder in Spain , 2013 .

[4]  F. Jurado,et al.  Probabilistic load flow for photovoltaic distributed generation using the Cornish–Fisher expansion , 2012 .

[5]  Francois Vallee,et al.  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 , 2013 .

[6]  Francois Vallee,et al.  Probabilistic Simulation Framework of the Voltage Profile in Balanced and Unbalanced Low Voltage Networks , 2016 .

[7]  Math Bollen,et al.  Integration of Distributed Generation in the Power System , 2008 .

[8]  Roy Billinton,et al.  Reliability/cost implications of utilizing photovoltaics in small isolated power systems , 2003, Reliab. Eng. Syst. Saf..

[9]  K. M. Nor,et al.  Improved three-phase power-flow methods using sequence components , 2005, IEEE Transactions on Power Systems.

[10]  Wenyuan Li,et al.  Reliability assessment of photovoltaic power systems: Review of current status and future perspectives , 2013 .

[11]  Evangelos Tzimas,et al.  Demand shifting analysis at high penetration of distributed generation in low voltage grids , 2013 .

[12]  Roy Billinton,et al.  Generating capacity adequacy evaluation of small stand-alone power systems containing solar energy , 2006, Reliab. Eng. Syst. Saf..