Stochastic Planning and Operational Constraint Assessment of System-Customer Power Supply Risks in Electricity Distribution Networks

Abstract

Electricity-distribution network operators face several operational constraints in the provision of safe and reliable power given that investments for network area reinforcement must be commensurate with improvements in network reliability. This paper provides an integrated approach for assessing the impact of different operational constraints on distribution-network reliability by incorporating component lifetime models, time-varying component failure rates, as well as the monetary cost of customer interruptions in an all-inclusive probabilistic methodology that applies a time-sequential Monte Carlo simulation. A test distribution network based on the Roy Billinton test system was modelled to investigate the system performance when overloading limits are exceeded as well as when preventive maintenance is performed. Standard reliability indices measuring the frequency and duration of interruptions and the energy not supplied were complemented with a novel monetary reliability index. The comprehensive assessment includes not only average indices but also their probability distributions to adequately describe the risk of customer interruptions. Results demonstrate the effectiveness of this holistic approach, as the impacts of operational decisions are assessed from both reliability and monetary perspectives. This informs network planning decisions through optimum investments and consideration of customer outage costs.

PV Hosting Capacity in LV Networks by Combining Customer Voltage Sensitivity and Reliability Analysis

“…This accurately captures the higher likelihood of component failure right after installation and towards the end of its service. More details on the enhanced MCS used in this research are provided in [21,22]. Then, different network scenarios are used to check how voltages at different network buses are affected by both the level of PV penetration and the location of PV generation along the feeder.…”

“…The feeder is supplied by a 0.5 MVA 11/0.415 kV secondary substation transformer and consists of underground cables (UC) as shown in Figure 1. This paper uses a time-varying residential demand profile that is derived from the after-diversity maximum demand (ADMD) for each load point in the network, as in [21]. Each customer is modelled to have an ADMD of 2.27 kW [34].…”

“…Accordingly, the System Average RMS (Voltage) Frequency Index (SARFIx) and System Average RMS (Voltage) Duration Index (SARDIx) are calculated by counting the number and duration of interruptions respectively at all load points and dividing by the total number of customers in the network as in ( 1) and (2). The system-wide indices, SARFIx and SARDIx are derived from [21] and calculate the average number and duration of interruptions per customer served on the entire feeder. An interruption is defined as a timestep when the bus voltage to which a customer is connected falls below the set voltage threshold x.…”

“…Accordingly, one of the objectives of this paper is to combine the impact of the frequency and duration of momentary and sustained interruptions, such as the methodology proposed in [20] where a single reliability metric gathers different classes of outages, at the same time, their frequency and duration, and thus the effective time of each interruption experienced by a customer can be established. These reliability results can be compared to a probabilistic methodology based on a Monte-Carlo Simulation of component failure rates and repair times as done in [21,22].…”

“…(c) A critical review of the existing reliability and power quality indices and using them as a basis for the creation and definition of novel system performance indicators. (d) Development of novel reliability indices to quantify the changes in network reliability based on the voltage-based reliability assessment methodology in [33] that and compare the results to a probabilistic reliability assessment that uses the time-sequential Monte-Carlo Simulation (MCS) as in [21].…”