Distribution Network Service Providers Research Articles

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

Economic Viability Assessment of Neighbourhood versus Residential Batteries: Insights from an Australian Case Study

Amidst the evolving paradigms of the contemporary energy landscape, marked by the imperative of sustainability and efficiency, the integration of energy storage has emerged as a transformative strategy that seeks to recalibrate the dynamics of electricity distribution and consumption. However, there remains a pressing need to determine the most economically viable approach for deploying energy storage solutions in residential low-voltage (LV) feeders, especially in rural areas. In this context, this paper presents the results of an economic evaluation of energy storage solutions for a residential LV feeder in a rural town in Australia. Specifically, the study compares the financial viability of a front-of-the-meter (FTM) battery installed on the feeder with that of a fleet of behind-the-meter (BTM) batteries. The FTM battery, with a size of 100 kW/200 kWh, is assumed to be operated by the retailer but owned by the community, with any profits assigned to the community. In this scenario, we studied a battery operating under standard network tariffs and three different trial tariffs that distribution network service providers currently offer in Australia. On the other hand, the fleet of BTM batteries (3 kW, 3.3 kWh) are individually owned by households with solar installations, and their cumulative capacity matches that of the FTM battery. The comparison is based on key economic parameters, including network charges, retail margins, frequency control ancillary service (FCAS) revenues, wholesale energy costs, technology costs associated with community batteries, and net profit or loss for the community, as well as considerations of utility grid arbitrage and solar photovoltaic (PV) self-consumption. The study also assumes different grant levels to assess the impact of subsidies on the economic feasibility for both battery configurations. The findings indicate that, while both require some form of subsidy for profitability, the BTM batteries outperform the FTM battery in terms of economic viability and so would require lower grant support. The FTM battery case finds a need for grants ranging from 75% to 95% to break even, while the BTM fleet requires approximately 50% in grants to achieve a similar outcome. In conclusion, this study highlights the importance of grant support in making energy storage solutions economically feasible. In particular, it highlights how the less mature segment of FTM batteries will need higher support initially if it is to compete with BTM. The outcomes of this study inform decision-making processes for implementing energy storage solutions in similar communities, fostering sustainable and cost-effective energy systems.

Management of prosumers using dynamic export limits and shared Community Energy Storage

Integration of Distributed Energy Resources (DERs) can introduce challenges such as Over-Voltage (OV) and line congestion in distribution networks. Recently, the concept of dynamic export limits as well as Community Energy Storage (CES) have gained attention as potential solutions for these challenges. Within these concepts, this paper presents a novel framework where the Distribution Network Service Provider (DNSP) provides prosumers with dynamic export limits for networks with a CES. Moreover, this framework enables prosumers (through leasing) and the DNSP to collaboratively manage the CES. In each time step, the DNSP calculates the charging/discharging power for the unleased portion of the CES and collects data on the expected export and charging/discharging power from prosumers leasing the CES. Subsequently, while considering network voltage and current limits and a fairness objective, the DNSP computes and communicates dynamic export limits to prosumers, allowing them to manage their assets accordingly. The performance of the proposed framework is evaluated via various case studies conducted on a real-world LV test feeder with 49 prosumers. The results emphasize the critical role of dynamic export limits in resolving OV and congestion issues in DER-rich networks. Furthermore, the CES proves to be effective in reducing active power curtailment and increasing total generation, with a notable increase of 4.6%.

Machine learning based energy demand prediction

Energy demand prediction can benefit electricity consumers, distribution network service providers, and system operators. It heavily depends on auxiliary factors, such as weather parameters (e.g., ambient temperature), which makes this problem more complex. Moreover, for many industrial applications, instead of aiming for the highest prediction accuracy, a more easily understandable and interpretable model that can lead to higher accuracy against the baseline model is the priority. Therefore, this problem still requires more investigation, especially when there is a specified prediction baseline to be compared with. This paper proposes a machine learning (ML) based prediction framework that investigates how temperature combined with energy consumption and simple and interpretable ML methods can be used to provide more precise demand forecasts and thus baselines closer to actual load profiles. The proposed framework is tested on two different real-world energy demand datasets. The analysis shows that using a simple ML model, such as a polynomial regression model, results in a more accurate prediction than the current baselines used in the energy market. The proposed ML models are not black-box type models, and thus are easier to explain and interpret. The ML-based forecasted demand is used as a baseline for demand response (DR) and is compared with the existing baselines used in the demand response market of Australia’s national grid.

A Hierarchical Multi-Stage Coordination of Inverters for Voltage Control in Active Distribution Networks with a µPMU-PDC

This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface.

Visualisation of spheres of influence of distributed generation through critical line flow analysis

Power System around the world are transitioning into the phase of accommodating large scale and small scale Distributed Generation (DG). To harvest the benefits of DG, determining the most influential generator and associated network-wide impacts are crucial. This paper proposes a novel visualisation method to determine the sphere of influence of DG in the network using line current flows. The proposed visualisation technique trades off tedious calculations and provides information-rich overview of a network and enables distribution network service providers to make judicious decisions in order to determine sphere of influence of DG. The visualisation concept is tested on a 33 bus distribution network and implemented using DigSILENT Power Factory, Python and Matlab.

Hosting Capacity Assessment Strategies and Reinforcement Learning Methods for Coordinated Voltage Control in Electricity Distribution Networks: A Review

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed.

Anonymization of distribution feeder data using statistical distribution and parameter estimation approach

Distribution networks are undergoing a transition due to the rapid increase in distributed energy resources (DERs). Concerns over revealing critical infrastructure information and breaching consumer privacy have significantly hindered their development. Many applications that require data sharing are still impractical due to the lack of sufficient datasets, privacy concerns, and data access problems. To address these issues, a statistical distribution approach for parameter estimation is proposed. The statistical patterns of real distribution feeders are examined by accessing the confidential database of a local distribution network service provider (DNSP). An algorithm based on the Maximum Likelihood Estimate (MLE) is applied to estimate the statistical distribution parameters that represent the actual data. Then, these statistical distribution parameters are used to generate anonymized datasets that are realistic. A Kolmogorov–Smirnov (K-S) test is conducted to confirm the effectiveness of anonymized datasets, and results are compared with the actual feeder datasets. Validation is carried out with existing methods and comparisons are shown on the different portions of the datasets (25 percent, 50 percent, 75 percent, and 100 percent). The comparison results indicate superior performance over traditional methods, with a performance improvement ranging from 1 to 13 percent. The practical application of the method is demonstrated on the IEEE 123-node test feeder. The method achieves consistent results on voltage profiles, with a maximum difference of 0.420 percent between actual and anonymized datasets.

Integrated Volt/Var Control Method for Voltage Regulation and Voltage Unbalance Reduction in Active Distribution Networks

The emergence of distributed generation such as solar systems has introduced new challenges in distribution networks that are becoming more apparent with increasing penetration levels. The time mismatch between peak load and peak generation can make voltage levels in distribution networks swing towards extreme limits during a day. Distribution network service providers are struggling to cater for new distributed generation installations while ensuring that the quality of steady state supply voltage meets stipulated requirements. The segregation between medium and low voltage networks in control strategies may result in unnecessary or worse, opposing control actions leading to voltage issues and control on one side of the distribution network influencing the other. By developing an efficient volt/var control method, the steady state voltage level and voltage unbalance in a comprehensive distribution network can be controlled simultaneously. This paper analyses voltage issues and volt/var control in medium and low voltage networks as a whole, unabridged problem. This paper proposes a pragmatic and effective volt/var control method that addresses voltage regulation and voltage unbalance simultaneously using existing infrastructure. The proposed control method is implemented on three types of representative Australian distribution networks and results obtained demonstrate that the proposed volt/var control can simultaneously manage voltage level and voltage unbalance whilst reducing the number of tap change operations and maximizing solar penetration.

Prospects of photovoltaic rooftops, walls and windows at a city to building scale

Many cities across the world are committing to deep decarbonisation efforts. While solar photovoltaics (PV) will play a critical role in this pursuit, the role of rooftop and facade-integrated PVs within the urban landscape is yet to be fully understood. This work presents an analysis into the solar energy harvesting potential of PVs integrated as building rooftops, walls, and windows at various spatial resolutions that range from city to building scale within the City of Melbourne, Australia, as a contemporary case study. It further investigates the relationship between calculated electricity production from such PVs with the urban morphology, seasonal variation and the measured electricity consumption by the local distribution network service providers. The results indicate that PV rooftops are responsible for the largest share of the city’s solar energy potential. However, for individual blocks with high densities of high-rise and glazed buildings, it is shown that the PV potential from windows becomes more prominent. The technical workflow presented here will enable different cities to facilitate decision-making on the PV implementation in urban environments.

A comprehensive day-ahead scheduling strategy for electric vehicles operation

Distribution networks are envisaged to host significant number of electric vehicles and potentially many charging stations in the future to provide charging as well as vehicle-2-grid services to the electric vehicle owners. The main goal of this study is to develop a comprehensive day-ahead scheduling framework to achieve an economically rewarding operation for the ecosystem of electric vehicles, charging stations and retailers using a comprehensive optimal charging/discharging strategy that accounts for the network constraints. To do so, an equilibrium problem is solved using a three-layer iterative optimisation problem for all stakeholders in the ecosystem. EV routing problem is solved based on a cost-benefit analysis rather than choosing the shortest route. The proposed method can be implemented as a cloud scheduling system that is operated by a non-profit entity, e.g., distribution system operators or distribution network service providers, whose role is to collect required information from all agents, perform the day-ahead scheduling, and ultimately communicate the results to relevant stakeholders. To evaluate the effectiveness of the proposed framework, a simulation study, including three retailers, one aggregator, nine charging stations and 600 electric vehicles, is designed based on real data from San Francisco, the USA. The simulation results show that the total cost of electric vehicles decreased by 17.6%, and the total revenue of charging stations and retailers increased by 21.1% and 22.6%, respectively, in comparison with a base case strategy.

Comparison of economic model predictive control and rule‐based control for residential energy storage systems

This study quantifies the benefits of implementing model predictive control on residential solar PV and energy storage systems considering a time-of-use demand tariff, feed-in tariff and varying PV system sizes and battery life-cycle costs. The control system analysed makes use of economic model predictive control (EMPC) whereby the objective function is directly tied to the economics of the system. Using residential load and PV data from an Australian distribution network service provider, the EMPC controller is compared to a rule-based controller, highlighting the benefits of EMPC in regards to annual economic performance and battery energy throughput. The EMPC algorithm is then tested using 10 residential customers at the low voltage feeder level showing the capacity for the EMPC controller to shift peak demand and flatten the aggregated load profile of 30 residential customers.

A Probabilistic Reverse Power Flows Scenario Analysis Framework

Distributed Energy Resources (DER), mainly residential solar PV, are embedded deep within the power distribution network and their adoption is fast increasing globally. As more customers participate, these power generation units cause Reverse Power Flow (RPF) at the edge of the grid, directed upstream into the network, thus violating one of the traditional design principles for power networks. The effects of a single residential solar PV system is negligible, but as the adoption by end-consumers increases to high percentages, the aggregated effect is no longer negligible and must be considered in the design and configuration of power networks. This article proposes a framework that helps to predict the RPF intensity probability for any given scenario of DER penetration within the distribution network. The considered scenario parameters are the number and location of each residential DERs, their capacity and the daily net-load profiles. Classical simulation-based approach for this is not scalable as it relies on solving the load-flow equations for each individual scenario. The framework leverages machine learning techniques to make fast and precise RPF prediction within the network for each scenario. The framework enables the Distribution Network Service Providers (DNSPs) to assess DERs penetration scenarios at a granular level, derive and localise the RPF risks and assess the respective impacts on the installed assets for network planning purpose. The framework is illustrated with scenario analysis conducted on an IEEE 123 bus system and OpenDSS and shown that it can lead to multiple orders of magnitude savings in computational time while retaining an accuracy of 94% or above compared to classical brute force simulations.

Cue utilisation predicts control room operators’ performance in a sustained visual search task

This research was designed to determine whether qualified practitioners’ cue utilisation is predictive of their performance during a sustained visual search task in an operational context. Australian Distribution Network Service Provider (DNSP) operators were recruited for two experiments, and were classified with either higher or lower cue utilisation based on an assessment of cue utilisation within the context of power distribution. Operators’ performance was assessed using a domain-related sustained visual search task. In both experiments, power distribution operators with higher cue utilisation demonstrated shorter mean response latencies during the sustained visual search task, compared to operators with lower cue utilisation. Further, no differences in accuracy based on cue utilisation were observed during the sustained visual search task. The results are consistent with the proposition that power operators with higher cue utilisation have a greater capacity to sustain visual search during domain-related tasks, compared to operators with lower cue utilisation. Practitioner summary: Power distribution system operators’ cue utilisation was used to predict performance during a domain-related sustained visual search task. Power distribution operators with higher cue utilisation demonstrated shorter mean response latencies during the sustained visual search task, but no differences in accuracy, compared to operators with lower cue utilisation. Abbreviations: DNSP: distribution network service provider; EXPERTise 2.0: EXPERT intensive skills evaluation; FAT: feature association task; FDT: feature discrimination task; FIT: feature identification task; fNIRS: functional near infrared spectroscopy; FPT: feature prioritisation task; FRT: feature recognition task; SCADA: supervisory control and data acquisition

Can applying renewable energy for Australian sugarcane irrigation reduce energy cost and environmental impacts? A case study approach

Abstract In Australian sugarcane production, 90% of irrigation pumps are connected to the national electricity grid. In regional Queensland, where irrigated sugarcane is grown, both the retailer and distribution network service providers are government owned and highly regulated. This study investigates options for on-farm embedded generation from a range of commercially available components, to reduce energy costs of furrow, centre pivot, and Big Gun® irrigation. This study confirms that demand-side management crucially affects the economic feasibility of embedded generation. Connection rules, such as feed-in tariffs and export limits affecting renewable embedded generation can also influence emissions abatement costs and investment returns. When export limits are allowed on larger sites (solar PV systems >40 kW), abatement costs fall from $109/t CO2e to $18/t CO2e and the present value of the investment improves substantially. The analysis reveals economically feasible opportunities exist for small-scale solar PV system installations (under 40 kW), reducing NPC of pumping from 12 to 25% and emission reductions ranging from 1245 t CO2e to 1314 t CO2e per installation over 25 years. Where a site is not eligible for a feed-in tariff, high renewable energy utilisation rates are required to make the site feasible. Batteries did not feature as an optimal component, even when battery storage and replacement values were discounted by 60%, indicating that seasonal load profiles under-use a battery investment. Therefore, batteries are inefficient and can be avoided in an irrigation microgrid.

Control Room Operators' Cue Utilization Predicts Cognitive Resource Consumption During Regular Operational Tasks.

This study was designed to examine whether qualified practitioners’ cue utilization is predictive of their sustained attention performance during regular operational tasks. Simulated laboratory studies have demonstrated that cue utilization differentiates cognitive load during process control tasks. However, it was previously unclear whether similar results would be demonstrated with qualified practitioners during familiar operational tasks. Australian distribution network service provider (DNSP) operators were classified with either higher or lower cue utilization based on an assessment of cue utilization within the context of electrical power distribution. During two, 20-min periods of operators’ regular workdays, physiological measures of workload were assessed through changes in cerebral oxygenation in the prefrontal cortex compared to baseline, and through eye behavior metrics (fixation rates, saccade amplitude, and fixation dispersion). The results indicated that there were no statistically significant differences in eye behavior metrics, based on levels of cue utilization. However, as hypothesized, during both sessions, operators with higher cue utilization demonstrated smaller increases in cerebral oxygenation in the prefrontal cortex from baseline, compared to operators with lower cue utilization. The results are consistent with the proposition that operators with higher cue utilization experience lower cognitive load during periods of regular activity during their workday, compared to operators with lower cue utilization. Assessments of cue utilization could help identify operators who are better able to sustain attention during regular operational tasks, as well as those who may benefit from cue-based training interventions.

Identification of PV system shading using a LiDAR-based solar resource assessment model: An evaluation and cross-validation

Photovoltaic (PV) systems are subject to several different systematic de-rating factors, such as soiling, degradation, inverter mismatch and shading. With increasing penetration of PV in the local grid, Distribution Network Service Providers (DNSPs) are inclined to assess such losses, in order to accurately estimate the total regional power output of distributed PV. The most influential de-rating factor is shading, which can cause ramps on the generated power output, similar to clouds. In this study we evaluate and compare two fundamentally different methods for module orientation parametrisation and shading analysis of PV systems that have been developed in previous work. In the first method, LiDAR (Light Detection and Ranging) data are used to derive the PV module orientation and shading, referred to herein as the LiDAR model. The second method, referred to as the QCPV-Tuning model, is based on reported PV power generation, which is firstly quality controlled and parameterised in order to derive module orientation and a loss factor, LF, representing systematic de-rating factors. Secondly, variations in de-ratings throughout the day, mainly due to shading, are explored in a process referred to as Tuning. For both methods, binary time series are derived expressing the presence of shading, which are used to evaluate how the methods corroborate. We evaluate four cases; (case 1) evaluates the original versions of the LiDAR and QCPV-Tuning models, while in cases 2–4 improvements to the models are introduced. A new filter for extracting representative LiDAR data points for the shading analysis was introduced for the LiDAR model (case 2). For the QCPV-Tuning model significant inaccuracies in the parametrisation of the module orientation were identified due to strong shading in either morning or evening and were thus corrected to observed parameters (case 3). For (case 4) improvements on both models were introduced. The Pearson correlation coefficients of shading events for the methods were 0.28, 0.36, 0.42 and 0.50 for cases 1–4, respectively. A mismatch in the timing of shading events motivated the comparison of the mean hourly shading, with correlation coefficients of 0.34, 0.43, 0.49 and 0.57 for cases 1–4, respectively. The results of this study show that both methods can confidently be used for solar resource assessment, given the suggested improvements.

Large Scale Proactive Power-Quality Monitoring: An Example From Australia

In Australia and many other countries, distribution network service providers (DNSPs) have an obligation to their customers to provide electrical power that is reliable and of high quality. Failure to do so may have significant implications ranging from financial penalties theoretically through to the loss of a license to distribute electricity. In order to ensure the reliability and quality of supply are met, DNSPs engage in monitoring and reporting practice. This paper provides an overview of a large long-running power-quality monitoring project that has involved most of Australia's DNSPs at one time or another. This paper describes the challenges associated with conducting the project as well as some of the important outcomes and lessons learned. A number of novel reporting techniques that have been developed as part of the monitoring project are also presented. A discussion about large-volume data management, and issues related to reporting requirements in future distribution networks is included.

Probabilistic load flow for distribution systems with uncertain PV generation

Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.

Value of net-FiT PV policies for different electricity industry participants considering demand-side response

Net-metering feed-in-tariffs (net-FiT) policies for residential photovoltaics (PV) have financial implications not only for PV customers but also for all other electricity industry participants. They may also incentivise households to adjust their daily load patterns to either minimise or maximise PV export depending on the FiT design and wider retail electricity arrangements. In this paper, we study the financial implications of both residential PV systems and such demand-side response (DSR) on the financial returns of PV for households, their retailers and their distribution network service providers (DNSPs). We use half-hourly PV generation and household consumption data for 61 houses in the Australian city of Sydney and consider two net-FiT designs offering tariffs either significantly higher or lower than retail electricity rates. We use a simple model of DSR that allows households to increase PV exports or self-consumption by moving load between daylight hours and the evening. We find that such DSR modestly improve household revenue but has potentially greater implications for retailers and DNSPs. DSR to increase exports reduces the adverse impacts of PV on retailer and DNSP revenues, while increased self-consumption worsens them. Conversely, increased exports might drive DNSP expenditures in constrained network areas while increased self-consumption might help reduce them. The study highlights the importance of designing PV policies with regard to their implications for retailers and DNSPs as well as PV households. Furthermore, the broader policy settings of retail electricity markets will become increasingly important as PV deployment grows, opportunities for DSR expand and current inadequacies in retail electricity markets become more marked. Copyright © 2014 John Wiley & Sons, Ltd.