Distribution Network Capacity Research Articles

Research on assessment method of maximum distributed generation hosting capacity in distribution system with high shares of renewables and power electronics.

With the rapid development of distributed generation (DG) within the framework of modern power systems, accurately assessing the maximum DG hosting capacity in distribution networks is crucial for ensuring the safe and stable operation of the power grid. This paper first introduces an assessment model of maximum DG hosting capacity in distribution network based on optimal power flow (OPF). Then, a two-step method that combines the linearization method and the recursive method is proposed, which consists of two parts: firstly, using linearization method to quickly calculate the preliminary assessment value of maximum DG hosting capacity, and then using a recursive method to accurately correct the preliminary assessment value. Additionally, the proposed improved comprehensive sensitivity index and safety constraint verification method can enhance the computational efficiency and accuracy of the recursive algorithm. Finally, the proposed methods were simulated and validated on the IEEE 33-bus system.

Enhancing PV Hosting Capacity of Electricity Distribution Networks Using Deep Reinforcement Learning-Based Coordinated Voltage Control

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study.

EV charging load forecasting and optimal scheduling based on travel characteristics

Accurate prediction of EV charging load distribution is not only the basis of evaluating the capacity of distribution network to receive EV, but also the necessary premise to promote the research of charging station planning and vehicle-network interaction. Based on this, firstly, according to the travel characteristics of electric private car and taxi users, this paper constructs the models of speed-flow and EV power consumption per mileage, thus, the real-time changes of vehicle speed and power consumption per mileage are simulated. Secondly, consider factors such as charging price, time, and power consumption along the way, as well as multiple sources of information such as traffic networks, vehicles, public quick charging stations, and distribution networks, the OD matrix analysis method is introduced to simulate EV moving characteristics for EV trip assignment matrix, and the EV charging load forecasting system with real-time interaction of multi-source information is established. Finally, the second-order cone optimization method is used to optimize the space-time distribution of regional charging load. The model and method proposed in this paper are more consistent with the actual situation and can improve the accuracy of prediction.

Power system benefits of simultaneous domestic transport and heating demand flexibility in Great Britain’s energy transition

The increasing share of variable renewables in power generation leads to a shortage of affordable and carbon neutral options for grid balancing. This research assesses the potential of demand flexibility in Great Britain to fill this gap using a novel linear optimisation model PyPSA-FES, designed to simulate optimistic and pessimistic transition pathways in National Grid ESO Future Energy Scenarios. PyPSA-FES models the future power system in Great Britain at high spatiotemporal resolution and integrates demand flexibility from both smart charging electric vehicles and thermal storage-coupled heat pumps. The model then optimises the trade-off between reinforcing the grid to align charging and heating profiles with renewable generation versus expanding dispatchable generation capacity. The results show that from 2030, under optimistic transition assumptions, domestic demand flexibility can enable an additional 20–30 TWh of renewable generation annually and reduce dispatchable generation and distribution network capacity by approximately 20 GW each, resulting in a total cost reduction of around £5bn yearly. However, our experiments suggest that half of the total system cost reduction is already achieved by only 25% of electric vehicles alone. Further, the findings indicate that once smart electric vehicle charging reaches this 25% penetration rate in households, minimal benefits are observed for implementing smart 12-hour thermal storages for heating flexibility at the national level. Additionally, smart heating benefits decrease by 90% across all metrics when only pre-heating (without thermal storages) is considered. Spatially, demand flexibility is often considered to alleviate the need for north–south transmission grid expansion. While neither confirmed nor opposed here, the results show a more nuanced dynamic where generation capacities are moved closer to demand centres, enhancing connectivity within UK sub-regions through around 1000 GWkm of additional transmission capacity.

Distributed PV carrying capacity prediction and assessment for differentiated scenarios based on CNN-GRU deep learning

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson’s correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.

A dynamic reference voltage adjustment strategy for Open-UPQC to increase hosting capacity of electrical distribution networks

Future electrical grids, particularly the distribution networks, may face more severe voltage rises/drops, and in general, more power quality problems in the presence of new loads such as electric vehicle chargers and renewable energy generation units like photovoltaic systems. This necessitates investing in additional high-cost infrastructure to increase the capability of the feeder in hosting higher levels of loads and generation units while the existing capacity is not utilized effectively. In the stated condition, effective voltage stabilization strategies in electrical distribution networks can contribute to hosting capacity improvement and the better utilization of the existing infrastructure. Accordingly, in this paper, the application of Open-UPQC in voltage profile improvement and hosting capacity enhancement is evaluated in low-voltage distribution networks. Furthermore, a dynamic reference voltage adjustment strategy is applied to the device to improve its capabilities in power quality improvement and hosting capacity enhancement. Simulation studies have been implemented to evaluate the capability of Open-UPQC either with static reference voltage or the dynamically-adjusted one in low-voltage networks with real measured data while different cases are assessed regarding the topology and the length of the feeder. The simulation results approved the capability of Open-UPQC especially with the dynamic reference voltage in hosting capacity enhancement while providing the highest level of voltage profile improvement among all the assessed custom power devices in the studied low-voltage networks.

Optimal configuration method of photovoltaic energy storage in distribution network based on source-load coordination

Abstract To enhance the configurability of photovoltaic energy storage within distribution network systems and foster synchronized development of power sources and loads, a source-load coordinated approach for optimal photovoltaic energy storage configuration in distribution networks is introduced. An alternative multi-objective framework for optimal allocation of photovoltaic energy storage capacity in distribution networks is formulated, which is the optimal goal of maximum economic benefit of photovoltaic energy storage, the optimal goal of minimum network loss and the optimal goal of source-network load coordination. The constraints of the system’s power flow, energy storage charging and discharging capabilities, and an optimized allocation strategy for energy storage are established, and the objective function is solved with full consideration of source-network load coordination factors. The energy storage optimization is updated in the iterative process. Based on the update results, the process for optimal allocation of photovoltaic energy storage in the distribution network has been devised to attain the most efficient allocation. Experimental results indicate a minimal discrepancy between the actual and specified energy storage output, along with a reduced average output power resulting from the optimized photovoltaic energy storage configuration, which shows excellent performance in energy storage optimization configuration.

Investigation methods for enhancing the energy consumption capacity of novel PV access distribution networks

Abstract This paper presents a method for reconstructing distribution networks using soft open point (SOP) and demand response (DR) energy storage, which can effectively solve the problems of voltage overruns, line overcurrent, and bidirectional flow currents caused by the photovoltaic (PV) getting into the distribution network. First, the model for the rebuilding of the distribution network is created by using the energy storage method of DR and SOP. Then, MISOCP, which stands for mixed-integer second-order cone programming, is the model that is used to change the distribution network reconfiguration, and the transformed model is solved by the MOSEK algorithm package in MATLAB. Finally, the strategy suggested in this research may successfully enhance the distribution grid’s consumption capacity after PV access, as shown by the verification in the IEEE33 node distribution grid system.

Robust Assessment of Hosting Capacity of Renewable Generation in Distribution Systems Considering the Active Network Management

Abstract As the penetration rate of renewable distributed generation (RDG) increases, its random fluctuation characteristics have brought many problems to the safe operation of the active distribution network (ADN), such as voltage over-limit, reverse flow, and deterioration of power quality. Therefore, it has effectively affected the new energy hosting capacity of the ADN. Therefore, the effective assessment of the new energy hosting capacity of the ADN is a key part of the distribution network planning. In order to cope with the random fluctuation characteristics of new energy, this paper presents a two-stage robust assessment method of RDG hosting capacity of distribution networks by considering active network management (ANM) techniques such as network reconfiguration, reactive power compensation and on-load voltage regulation. Finally, the improved IEEE-33 distribution system is used to verify the results. The proposed method has been shown to be effective for evaluating the ADN’s RDG hosting capacity.

Hierarchical control on EV charging stations with ancillary service functions for PV hosting capacity maximization in unbalanced distribution networks

PV hosting capacity of a practical three-phase unbalanced distribution network is expected to increase, thus increasing safe PV penetration and speeding up the decarbonization of power systems. However, power unbalances and bus voltage violations are two major obstacles limiting further increases in the PV hosting capacity. With the popularization of electric vehicles (EVs), EV charging stations (EVCSs) as controllable loads can effectively provide grid ancillary services, which helps enhance the PV hosting capacity. In this regard, this paper proposes a hierarchical control method for EVCSs in an unbalanced network, consisting of central day-ahead scheduling and local real-time dynamic control. It is novel that the local control capability of EVCSs is reserved in the central scheduling. A three-phase day-ahead EV charging scheduling optimization model is developed to reduce the power unbalance and the bus voltage violation, and in turn, improve the PV hosting capacity. Moreover, active power transfer and voltage droop control are developed as two ancillary service functions for local real-time control responding to dynamic network operating conditions. Numerical simulations verify the effectiveness of the proposed method in enhancing PV hosting capacity and providing grid ancillary services.

Shaped operating envelopes: Distribution network capacity allocation for market services

The transition from centralised, fossil fuel-powered generating units to distributed energy resources (DER) represents a significant step forward, offering numerous benefits. However, this shift also presents operational challenges for distribution network service providers (DNSPs) and the electricity markets. In this paper, we introduce our innovative solution for allocating network capacity in the form of operating envelopes, shaped to enhance customer participation in energy and reserve markets. Our approach also provides DNSPs with network support flexibility of DER which can be leveraged to either increase network throughput for market services or postpone network augmentation. This study outlines our initial findings from Project Converge11https://arena.gov.au/projects/project-converge-act-distributed-energy-resources-demonstration-pilot/.—a comprehensive real-world trial involving 1000 active customers (the largest participant cohort in Australia) located in the Australian Capital Territory. Within, we discuss the challenges we have encountered, the opportunities that have arisen, potential avenues for future expansion, and the invaluable insights gained during the pre-trial phase of the project.

Introducing storage operators for coordinating residential batteries in distribution networks under time-of-use tariffs and adaptive power limits

The transition towards Time of Use (ToU) tariffs has become a promising solution for addressing power system challenges resulting from increased installations of renewable energy systems. ToU tariffs encourage residential Battery Energy Storage System (BESS) adoption to reduce customers' bills through maximizing energy storage during low-price intervals (e.g., middle of the day). However, simultaneous BESS charging affects diversity of load, which may lead to the violation of distribution networks constraints. Traditional network management with conservative fixed and static power limits leads to inefficient network capacity use since they do not consider changes in network operating conditions and status of BESS facilities. Specially, these approaches do not allow higher import limits when proportion of BESS facilities are in idling state. To better allocate the capacity of distribution networks to active BESS facilities (charging/discharging), this work introduces an independent storage operator to coordinate BESS control actions by employing time-varying and adaptive power limits. For this purpose, a Mixed Integer Linear Programming (MILP) algorithm is proposed for storage operator to manage BESS facilities while respecting network constraints and customers' desired bills. At each time step, the algorithm decides power limits for active BESS facilities based on predefined linear functions. These functions are generated offline by using Optimal Power Flow (OPF) to establish relationships between power limits and number of active BESS. The application of the algorithm using a real Jordanian distribution network demonstrates its effectiveness to allow a larger number of customers achieving their desired bills compared to using fixed power limits.

Evaluation of maximum power supply carrying capacity of medium-voltage distribution network considering feeder segment transfer

Evaluation of maximum power supply carrying capacity of medium-voltage distribution network considering feeder segment transfer

Photonic WDM switches architecture for multi-band optical networks

Addressing the capacity, low cost, and low power challenges of 6G distribution networks, this paper proposes and demonstrates a multi-band optical metro-access network architecture employing semiconductor optical amplifier (SOA)-based wavelength division multiplexing (WDM) switches as low-cost and low-power multi-band optical add-drop multiplexers (MB-OADMs) to extend the capacity beyond C-band limits. We implemented and evaluated the performance of an SOA-based MB-OADM prototype in the C- and O-bands including the network reconfigurability, the node scalability, and the capability to support high-capacity transmission. Experimental results show that the MB-OADM-based network maintains high optical signal-to-noise ratio (OSNR) values up to 35.38 dB in the C-band and 33.56 dB in the O-band over 100 km across five nodes with a 20 km linkspan in between (a total of 100 km) without additional optical amplifiers at 25 Gbps. This work also assesses the MB-OADM-based network scalability in terms of nodes and data rate. Results indicated that the architecture supports cascading through nine C-band nodes over 45 km with a 3.6 dB power penalty at 25 Gbps for a bit error rate (BER) of 10−6 and through four O-band nodes with a 3 dB penalty at 25 Gbps for a BER of 10−6, maintaining nearly uniform power levels across channels at a BER under the FEC threshold. It successfully demonstrates PAM-4 at 50 Gbps and 100 Gbps data rate transmission operation crossing four nodes over a 4 km distance, with 2 dB and 2.4 dB power penalty at a BER of 10−3 in the C-band and 1.85 dB and 2.2 dB power penalty at a BER of 10−3 in the O-band, respectively.

The planning method of new energy distribution network in plateau area based on local accommodation

Abstract The planning method of new energy distribution network in plateau area based on local accommodation is studied to improve the local accommodation capacity of new energy distribution network in plateau area. The framework of new energy distribution network planning in the plateau area is constructed, and the distribution network equipment suitable for the plateau environment is selected based on the harsh environment in the plateau area; in the framework of new energy distribution network planning in plateau area, a bi-level planning model considering multi-flexible resource coordinated scheduling and energy storage is established. The upper level planning model takes the maximum accommodation of new energy as the objective, and the lower level planning model takes the optimal daily operation benefit of energy storage under the given configuration as the optimization objective; the probabilistic power flow method and C-PSO are used to solve the bi-level model. The experiment shows that this method ensures the stability of the new energy distribution network in the plateau area, reduces the amount of abandoned wind energy and the operating cost, and improves the local accommodation capacity of the core higher than the distribution network.

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.

Renewable energy hosting capacity assessment in distribution networks based on multi‐strategy improved whale optimization algorithm

AbstractThe increasing penetration of distributed renewable energy poses significant challenges to the safety and stability of distribution system, rendering the renewable energy hosting capacity of distribution networks a major concern. To effectively evaluate the renewable energy hosting capacity in distribution networks, this paper introduces a comprehensive hosting capacity assessment model considering various safety and stability constraints. Given the numerous non‐linear constraints inherent in the hosting capacity assessment model for distribution networks, a multi‐strategy improved Whale Optimization Algorithm is proposed. Improvements are made to the population initialization and population position update strategies, and a mutation strategy is introduced to prevent premature convergence, thereby enhancing the algorithm's performance. Finally, the effectiveness of the proposed model and the method is validated through the utilization of three distribution systems. Moreover, the key factors that restrict the improvement of hosting capacity are revealed from the perspective of distribution network topology.

Construction of Measurement Model for Ultimate Carrying Capacity of Medium Voltage Distribution Network Based on Genetic Neural Network

Construction of Measurement Model for Ultimate Carrying Capacity of Medium Voltage Distribution Network Based on Genetic Neural Network

Distributed Voltage Reactive Power Control of Distribution Network Considering the Coordination Between DPV and SVC

With the increasing permeability rate of DPV in the distribution network, the safe and economic operation of the distribution network is faced with many challenges, among which the problem of voltage exceeding the limit must be solved urgently. A method for distributed voltage reactive power control, which involves coordinating DPV and SVC, is suggested to enhance the voltage regulation capacity in high-permeability distribution networks. Firstly, considering the limited reactive power regulation capacity of DPV, a coordinated control strategy is proposed to enable SVC when the reactive power capacity of DPV is insufficient. Secondly, a voltage-reactive power control model for distribution networks is established, with a focus on minimizing node voltage deviations and network losses. An adaptive penalty factor adjustment strategy, based on the alternating direction multiplier method, is introduced to achieve a comprehensive solution for this model. Finally, the enhanced IEEE 33-node system is utilized for verification and analysis. The outcomes demonstrate that the suggested coordination strategy involving DPV and SVC, alongside the distributed optimization control method, offers economic advantages and effectively achieves the safe and cost-effective operation of the distribution network.

Hosting Capacity of an Australian Low-Voltage Distribution Network for Electric Vehicle Adoption

Hosting Capacity of an Australian Low-Voltage Distribution Network for Electric Vehicle Adoption