Distribution Network Capacity Research Articles

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

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

The booming development of distributed power sources in power systems has drawn attention to the carrying capacity and stability of the power grid, becoming a key challenge for the power industry. This study aims to develop a comprehensive deep learning model by combining deep recurrent double Q network (DR-DQN) and deep convolutional neural network (DCNN), and use meta-learning to optimize the model as a whole to simultaneously optimize the power grid. Distributed power supply carrying capacity and predicting the voltage fluctuations of the grid. The comprehensive model is designed to consider distributed power capacity optimization and voltage fluctuation prediction holistically. Through the DR-DQN model, the maximum distributed power capacity is determined under different grid conditions and the distributed power configuration of the grid is optimized. At the same time, the DCNN model is used to analyze the power grid time series data and predict the voltage fluctuation of the power grid. The results are presented in graph form, showing trends in maximum capacity and voltage fluctuations under different grid conditions. Experimental results show that the overall model achieves satisfactory results in distributed power capacity optimization and voltage fluctuation prediction. Performance evaluation and comparison highlight the comprehensive model’s excellent performance in terms of prediction accuracy and computational efficiency, providing new possibilities for efficient management and reliable operation of power systems. The successful development of the model provides practical and reliable solutions for the future development of power systems.

Two-stage distributionally robust assessment method for distributed PV hosting capacity of flexible distribution networks

The large-scale grid-connection of distributed PV has brought great challenges to distribution network. The traditional stochastic optimization and robust optimization have conservative problems in dealing with the uncertainty of distributed PV. Therefore, based on the historical scenario data of PV output, this paper proposes a two-stage distributionally robust assessment method for distributed PV hosting capacity of distribution networks considering demand response. Firstly, a weighted K-means algorithm is used to cluster the historical data of distributed PV output to obtain typical scenario data and establish the demand response model of electricity price incentive. In order to maximize the grid-connected capacity of distributed PV, multiple operational constraints and adjustment strategies in flexible distribution network were considered. Then, a two-stage distributionally robust assessment model for distributed PV hosting capacity of flexible distribution network is established, and the column-and-constraint generation algorithm is used for iterative solution. The effectiveness of the algorithm is verified on a 70-bus system, and the influence of distributed PV output and different regulation strategies on the hosting capacity of flexible distribution network is analyzed.

Assessment of renewable energy hosting capacity in distribution networks considering demand-side management and network reconfiguration

Abstract A renewable energy carrying capacity assessment method for distribution grids considering demand-side management (DSM) and network reconfiguration (NR) was proposed. First, a DSM and NR model applicable to the renewable energy carrying capacity (NECC) assessment of the distribution network (DN) was established; second, an NECC evaluation model for DN considering DSM and NR was established with the aim of maximizing the access capacity of distributed new energies; second, the second-order conical relaxation method was applied to convex solve the model; third, the simulation example adopts an improved IEEE 33-node DN to evaluate the NECC of system with various scenarios. Finally, the simulation experiment based on improved IEEE 33-node DN was conducted to assess the distributed NECC of the system in some scenarios.

Allocation and smart inverter setting of ground-mounted photovoltaic power plants for the maximization of hosting capacity in distribution networks

As the integration of solar photovoltaic (PV) power plants into distribution networks grows, quantifying the amount of PV power that distribution networks can host without harmfully impacting power quality becomes critical. This work aims to determine the best number, location, and size of PV systems to be installed on a distribution feeder, as well as the best control set-points of the PV inverters, to maximize the PV hosting capacity (HC). Therefore, a simulation-optimization framework is proposed for siting and sizing ground-mounted PV power plants equipped with smart inverters (SIs). Single (decentralized) and multiple (distributed) allocations are analyzed by considering the connection of one, two, and three PV systems. Genetic algorithm (GA) and particle swarm optimization (PSO) metaheuristics are employed to solve the optimization problem. The simulation-optimization framework is tested on a real-world feeder model from an Ecuadorian utility. Installing two PV systems with their SIs operating with the Volt-VAr control function yields maximum PV HC, which is increased by 32.1 % compared to a single PV power plant operating at a unity power factor. Moreover, a comparative analysis of the two metaheuristic algorithms reveals that the PSO method provides better results than GA.

A Comprehensive Review of Renewables and Electric Vehicles Hosting Capacity in Active Distribution Networks

A Comprehensive Review of Renewables and Electric Vehicles Hosting Capacity in Active Distribution Networks

Multi-objective Configuration Method for Mobile Energy Storage Aimed at Enhancing the Resilience of Regional Power Grid

Mobile energy storage has the advantages of high mobility, environmental friendliness, and wide application scenarios. It is widely used in important load protection, outdoor emergency power supply, and distribution network capacity expansion scenarios. This paper proposes a multi-objective configuration method for flexibly supporting the optimal operation of regional distribution networks. It aims to address the configuration problem of multi-mobile energy storage supporting regional power grid operation. A multi-objective optimization operation model for renewable energy distribution networks has been established. A multi-objective optimization method combining the normalized normal constraint method and improved composite method is proposed. It can achieve optimization decision-making of mobile energy storage positions, charging and discharging states, and power at different periods. Finally, simulation analysis is conducted on the IEEE 33 node distribution system to verify the effectiveness of the control method and algorithm.

Allocation and sizing of dispatchable distributed generators considering value addition in resiliency and sustainability of power delivery infrastructure

ABSTRACT The increasing power demand growth and extreme weather conditions push utilities to search for a more sustainable and resilient power distribution infrastructure. Dispatchable distributed generators (DGs) can contribute to achieving the goals. This paper reveals a general framework for allocating dispatchable DGs with their suitable location, capacity, and type in the test distribution networks to bring sustainability and resilient planning synergy. A multiobjective optimization approach has been presented to minimize payback year (PBY) for DGs, enhance the network’s operational efficiency, and promote system resiliency by satisfying various operating constraints. The proposed planning model is structured to give the best possible solution in compliance with changes in seasonal peak demands. The efficacy of the proposed scheme has been tested on a 28-bus Indian distribution system and an IEEE 33-bus test system, and results concerning the number of customers served, and priority customers restored are presented.

Hosting capacity of distribution networks for controlled and uncontrolled residential EV charging with static and dynamic thermal ratings of network components

AbstractThe ongoing electrification of road transportation sector, which is expected to continue to strongly increase over the next years, will result in the connection of a significant number of electric vehicle (EV) chargers in LV and MV distribution networks, particularly in residential applications with on‐board (“slow”) EV chargers. In order to evaluate loading limits of existing distribution networks for the maximum number of EV chargers that can be safely connected (commonly denoted as a network EV “hosting capacity”, HC), this paper introduces a general approach to determine one commonly used network design parameter (after‐diversity maximum demand, ADMD) and one new parameter (maximum daily energy demand, MDED), which are both obtained from the load profiles of maximum per‐hour demands for uncontrolled residential EV charging. The presented approach uses actual EV charging data from the UK as the inputs in Monte Carlo simulations to generate daily EV charging profiles for arbitrary numbers of EVs, enabling to identify related ADMD, MDED and per‐hour maximum demand values, as well as their seasonal variations. The assessed ADMD, MDED and hourly maximum EV charging demands for uncontrolled EV charging are then combined with available UK residential daily load profiles before the EVs are connected (“pre‐EV demands”), where their combined coincidental and noncoincidental maximum demands are evaluated against the static thermal rating (STR) and dynamic thermal rating (DTR) loading limits of network components (transformers and overhead lines), taking into account relevant weather/ambient conditions. This is denoted as a network HC for uncontrolled EV charging. Finally, evaluating the resulting per‐hour maximum demand values against the STR and DTR loading limits and MDED values allows to select one particular scheduling method for controlled EV charging, which gives the absolute maximum number of EVs that can be safely connected in the considered network, that is, maximum network HC for fully controlled EV charging. The presented approach is illustrated on the example of the IEEE 33‐bus test network (modelled using typical UK network components), for the pre‐EV residential demands taken from the recordings at a UK MV substation, and for ambient data taken from a UK Met Office weather station. Obtained results allow to evaluate the range of network EV HC values for uncontrolled and controlled EV charging, that is, lower and upper HC limits, which can be correlated with the commonly used allocations of the firm and non‐firm network HC, respectively.

A novel estimation method for maximum PV hosting capacity in radial distribution networks using bus voltage and electrical distance

A novel estimation method for maximum PV hosting capacity in radial distribution networks using bus voltage and electrical distance

Risk-Aware Operating Regions for PV-Rich Distribution Networks Considering Irradiance Variability

This paper proposes a framework to identify, visualize, and quantify risk of potential over/under voltage due to annual energy consumption and PV generation growth. The stochastic modeling considers the following: (i) Active and reactive power profiles for distribution transformers, dependent on annual energy consumption and activity in the serviced areas. (ii) Variable solar irradiance profiles that allow a broader range of PV generation scenarios for sunny, overcast, and cloudy days. The proposed framework uses multivariate- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> copulas to model temporal correlations between random variables to generate synthetic scenarios. A probabilistic power flow is computed using the generated scenarios to define critical static operating regions. Results show that classical approaches may underestimate the maximum PV capacity of distribution networks when local irradiance conditions are not considered. Moreover, it is found that including annual energy consumption growth is critical to establishing realistic PV installation capacity limits. Finally, a sensitivity analysis shows that taking a 5% of overvoltage risk could increase up to 15% of the PV installed capacity limits.

Analysis Method for Photovoltaic Absorption Capacity of Distribution Network Based on Risk Quantification and Network Reconstruction

The random characteristics of photovoltaic (PV) power will cause changes in node voltage and line power, which is an important factor affecting the photovoltaic absorption capacity of the distribution network (DN). So as to accurately evaluate the impact of photovoltaic randomness on the absorption capacity of DN, an analysis method of photovoltaic absorption capacity of DN is proposed based on risk quantification. Firstly, a photovoltaic absorption capacity evaluation model is proposed based on risk quantification, which considers the randomness of load and PV power and can improve the accuracy of photovoltaic absorption capacity assessment. Secondly, the photovoltaic absorption capacity DN is calculated with the network topology and photovoltaic access capacity of the DN as the optimization variables. The probabilistic power flow is performed by linear power flow, which effectively improves the calculation speed. Finally, the proposed PV absorption capacity analysis method is simulated and analyzed. The simulation results show that the proposed method can effectively take into account the impact of load and photovoltaic randomness and improve the photovoltaic absorption capacity of DN.

A thorough comparison of optimization-based and stochastic methods for determining hosting capacity of low voltage distribution network

A thorough comparison of optimization-based and stochastic methods for determining hosting capacity of low voltage distribution network

Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving the voltage profile by up to 2.02%, relieving the network capacity, and increasing the load restoration capability and reliability. Statistical analysis is also carried out to highlight the performance of the proposed methodology.

Guest editorial: RES hosting capacity in distribution networks

Guest editorial: RES hosting capacity in distribution networks