Power Distribution Network Planning Research Articles

Evaluation of Distributed Power and Electric Vehicle Grid-connected Carrying Capacity Based on Genetic Algorithm Optimisation Neural Network

Abstract The integration of new types of loads, such as large electric vehicles (EVs) and distributed energy sources, and the conventional structure and operation of the power distribution network have been significantly transformed. This presents a significant challenge in maintaining the network’s capacity to handle the load. To address this challenge, a carrying capacity assessment method based on a machine learning model optimized by a bionic optimization algorithm is proposed. Firstly, the assessment model’s index system is established, and the distribution network data are collected and preprocessed. Secondly, considering the complexity of the distribution network carrying capacity assessment, a backpropagation (BP) neural network is constructed within the machine learning model. The genetic algorithm, inspired by biological optimization, is used to fine-tune the machine learning model’s parameters. Subsequently, the model’s accuracy and generalization capability are evaluated, and the optimized machine learning model is used to assess the carrying capacity of EVs and distributed power sources. The results of the carrying capacity assessment can provide an effective basis for subsequent power distribution network planning. This method combines the benefits of machine learning with bionic optimization algorithms to tackle the challenges associated with incorporating new load types into the power distribution network. The model’s ability to accurately assess the carrying capacity can support effective planning and management of the evolving distribution network.

Multi‐objective interval planning for 5G base station virtual power plants considering the consumption of photovoltaic and communication flexibility

AbstractLarge‐scale deployment of 5G base stations has brought severe challenges to the economic operation of the distribution network, furthermore, as a new type of adjustable load, its operational flexibility has provided a potential way to promote the consumption and utilization of photovoltaic. In this paper, a multi‐objective interval collaborative planning method for virtual power plants and distribution networks is proposed. First, on the basis of in‐depth analysis of the operating characteristics and communication load transmission characteristics of the base station, a 5G base station of virtual power plants participating in the cellular respiratory demand response model is constructed. In view of the inherent contradiction between system economy and environmental performance, a multi‐objective interval optimization model for collaborative planning of virtual power plants and distribution networks is established with the lowest system investment and operating costs and the lowest carbon emissions as the optimization goals. The established model is transformed into a deterministic optimization problem, which is solved by NSGA‐II algorithm. The modified IEEE‐33 node system is used in the case analysis to analyse the impact of different planning schemes and response characteristics on the system economy. The calculation results verify the effectiveness of the proposed method.

Design and application of comprehensive evaluation index system of smart grid based on coordinated planning of major network and power distribution network

Design and application of comprehensive evaluation index system of smart grid based on coordinated planning of major network and power distribution network

Research on Grid Planning of Dual Power Distribution Network Based on Parallel Ant Colony Optimization Algorithm

A distribution network plays an extremely important role in the safe and efficient operation of a power grid. As the core part of a power grid’s operation, a distribution network will have a significant impact on the safety and reliability of residential electricity consumption. it is necessary to actively plan and modify the distribution network’s structure in the power grid, improve the quality of the distribution network, and optimize the planning of the distribution network, so that the network can be fully utilized to meet the needs of electricity consumption. In this paper, a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm. For the distribution network structure planning of dual power sources, the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies, and the dual power distribution network structure model is established based on the principle of the lowest cost. The artificial ants in the algorithm were compared with real ants in nature, and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem (TSP). Then, the limitations of the ant colony algorithm were analyzed, and an improvement strategy was proposed by using python for digital simulation. The results demonstrated the reliability of model-building and algorithm improvement.

Optimal joint operation of coupled transportation and power distribution urban networks

The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).

Optimal Planning of Power Distribution Network by a Novel Modified Jaya Algorithm in Multiobjective Perspective

This article presents a novel improved Elitism oppositional Jaya algorithm for the optimal power distribution network reconfiguration and optimal sizing, siting of multiple distributed generations (DGs) with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> , PQV buses. Here, PQV bus is a voltage-specified PQ type bus controlled by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> bus. In this proposed planning model, the basic Jaya algorithm is modified by incorporating variable inertia weight, vicinity searching capability, elitism property, and blended with an opposition learning-based strategy. This establishes an effective approach between global search and local search in quest of an optimal solution. A multiobjective framework is developed to optimize technical and economic objectives simultaneously. Optimal weights are allocated to each objective using an improved analytical hierarchy process method. Novel indices are proposed to identify the optimal locations of DGs also considering critical loads. During reconfiguration, the line data sequence is rearranged by a smart graph theory mechanism. Furthermore, different schemes have been studied by the suggested method on IEEE 33 and 69 bus distribution test systems. The findings of the study by using the proposed algorithm in comparison with particle swarm optimization (PSO) and enhanced leader PSO show its effectiveness and validity.

Firefly analytical hierarchy algorithm for optimal allocation and sizing of DG in distribution network

&lt;span lang="EN-MY"&gt;Distributed generation (DG) can be beneficially allocated in distribution power systems to improve the power system's efficiency. However, specious DG's allocation and sizing may cause more power loss and voltage profile issues for distribution feeders. Therefore, optimization algorithms are vital for future intelligent power distribution network planning. Hence, this study proposes a multi-objective firefly analytical hierarchy algorithm (FAHA) for determining the optimal allocation and sizing of DG. The multi-objective function formulation is improved further by integrating analytical hierarchy process (AHP) with FA to obtain the weight of the coefficient factor (CF). The performance of the proposed approach is verified on the 118-bus radial distribution network with different bus voltage at DG location (VDG) as regulated PV-bus during load flow calculations. The calculated CF and impact of the unregulated voltage at the PV-bus on the objectives function have been analysed. The findings show that the proposed techniques could allocate the DG at the most voltage deviation while minimizing the power loss and improving the radial distribution’s voltage stability index (VSI). The experimental results indicate that the approach is able to improve the overall voltage profile, especially at PQ-buses, minimize the power loss while improving the network's stability index simultaneously.&lt;/span&gt;

Probabilistic spatial load forecasting for assessing the impact of electric load growth in power distribution networks

Recently, high rates of urbanization reveal the need for more granular approaches of long-term load forecasting. The development of modern spatial load forecasting (SLF) methods can contribute to more effective planning of power distribution networks, while serving as decision making tools in the hands of regional planners and distribution system operators (DSOs). Nonetheless, the ever-present error of spatial allocation of future electric loads arises the need for probabilistic assessment of the long-term point forecasts. This paper proposes a methodology of probabilistic SLF that can be employed to assess the impact of load growth in power distribution networks. Firstly, an hierarchical trend method forecasts the peak of the electric loads at each subarea of the study area. Subsequently, the spatial root-mean-squared error across the service territory is calculated to be used for the construction of prediction intervals. Finally, a probabilistic power flow study based on Monte Carlo simulation seeks potential technical issues on the distribution network. The proposed methodology is demonstrated on a real-world distribution network. The results prove that the proposed probabilistic SLF method can lead DSOs to more reliable decisions in comparison to business-as-usual approaches.

Reliability Based Power Distribution Network Planning Using Fuzzy Logic

This paper presents a fuzzy system for reliability-based power distribution network planning. The proposed Mamdani type fuzzy inference system with subsequent application of the Bellman-Zadeh decision-making method is used to evaluate the reliability of the power line feeders as criteria for power system planning. Unplanned outages of system components, the Energy Not Supplied (ENS) and age of the power lines are used as input variables of the system and are fuzzified using triangular fuzzy functions. The proposed model was tested on a model of a realistic distribution network in order to prove its relevance and applicability. Results demonstrated that this model could make a contribution in this field as it can be used in practical planning situations for project priority ranking.

Service Restoring Reconfiguration for Distribution Networks Considering Uncertainty in Available Information

The recent growth in the penetration of photovoltaic generation systems (PVs) has brought new difficulties in the operating and planning of electric power distribution networks. This is because operators of the distribution networks normally cannot monitor or control the output of the PVs, which introduces additional uncertainty into the available information that operations must rely on. This paper focuses on the service restoration of the distribution networks, and the authors propose a problem framework and its solution method that finds the optimal restoration configuration under extensive PV installation. The service restoration problems have been formulated as combinatorial optimization problems. They do, however, require accurate information on load sections, which is impractical in distribution networks with extensively installed PVs. A combined framework of robust optimization and two-stage stochastic programming adopted in the proposed problem formulation enables us to deal with the PV-originated uncertainty using readily available information only. In addition, this problem framework can be treated by a traditional solution method with slight extensions. The validity of the authors’ proposal is verified through numerical simulations on a real-scale distribution network model and a discussion of their results.

Planning of active power distribution network based on source-network-load collaborative optimization

With the fast development of renewable energy generation, the randomness and volatility seriously threaten the stability of the power system. In order to improve the consumption rate of renewable energy and the economic benefit, the source-network-load collaborative optimization is put forward and widely used in the power system. And the active power distribution network which consists of the energy storage devices and flexible load is the best carrier to realize it. Based on the active power distribution network planning, this paper firstly designs an economic index covering construction cost and operation cost as the optimization objective. Then with the physical constraints consisting of the physical features of generation units, energy storage devices and flexible load, the source-network-load collaborative optimization model of active power distribution network is established. Finally, a case of city distribution network is presented to verify the effect of source-network-load collaborative optimization and the efficiency of the optimization model.

Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.

Application of Power Load Forecasting in Urban Distribution Network Planning Based on 3D Real Scene Platform

The 3D real-view platform technology builds a power load forecasting platform in urban distribution network planning. The completion of this system can provide integrated support for power load forecasting in power distribution line planning, and further provide a three-dimensional foundation platform for power load forecasting analysis and calculation software in power distribution planning. Urban distribution network planning is an important task for power supply companies. The quality of its construction directly affects the development of urban economy and the improvement of people’s quality of life. This paper combines the years of work experience to introduce the importance of power distribution network planning, which optimizes the structure of the power distribution network, which can ensure the safe operation of the power grid, reduce power loss, improve power supply reliability, and accurately predict power loads. To meet the needs of urban power load development.

Research on Urban Distribution Network Planning and Optimization Measures Based on Real 3D Scenes

With the continuous advancement of smart grid construction, the structure of the power grid is becoming increasingly large and complex, and it is increasingly difficult to adapt to the requirements of grid development using traditional 3D drawings for grid design and operation and maintenance. The emerging 3D real-world modeling technology can visually and comprehensively display terrain, landforms, equipment appearance dimensions, wiring methods and other information. To promote the application of real-world 3D scene technology, electric power companies have repeatedly carried out 3D design of power grid projects and construction of data centers Optimized distribution network planning. Realize the linkage of real-world 3D scene plan maps, building information models, etc. through geographic connections, provide real feedback on site conditions, reduce the number of site surveys, save manpower and resources, and effectively implement urban power distribution network planning.

Planning of the Power Distribution Network in the Industrial Area under the Liberalization of Incremental Power Distribution Business

The so-called incremental power distribution network is a newly added power distribution network serving the public. At present, the enhanced power distribution network is often used in industrial parks with voltage levels of 220KV, 110KV and below. The basic conditions for the construction of the incremental power distribution network are in line with the provincial planning of power distribution network, which avoids conflicts such as repeated construction and a cross power supply between the incremental power distribution network and the provincial power distribution network. This paper studies the ways to plan the power distribution network in the industrial area under the liberalization of incremental power distribution business to provide reference for relevant practitioners.

Planning of power distribution networks in densely populated cities. Through distributed generation and capacitive compensators

This paper analyses different options that can be used to solve the problem of the planning of power distribution networks by including capacitive compensation and distributed generation. The methodology for planning aims to determine the size of the units, the bus where the units have to be located, and the year in which investments should be made, in order to minimize the total energy losses on the network during the planning period. The work analyses four different cases: planning using neither capacitive compensation (SC) nor distributed generation (DG), planning using only SC, planning using only DG, and planning using reactive compensation and distributed generation simultaneously. Results show that simultaneous use of SC and DG reduce the total energy losses and improve the voltage profiles on the network, so good results for the planning are obtained.

Expansion Planning of Active Distribution Networks With Multiple Distributed Energy Resources and EV Sharing System

The ever-increasing energy demand and high penetration rate of distributed renewable generation brings new challenges to the planning of power distribution networks. This paper proposes an expansion planning model for distribution networks by considering multiple types of energy resources in distribution side, including shared electric vehicle (SEV) charging stations, solar-based distributed generation sources, and battery energy storage systems. The operational characteristics of SEV are considered and modeled. The proposed planning model aims to minimize the weighted sum of network investment cost, energy losses, and queue waiting time of SEVs. A stochastic scenario generation method is introduced to address the stochastic feature of SEVs’ driving behaviors. Numerical studies are tested on the systems with 54-node distribution network and 25-node traffic network.

Philosophy and Practice of Classification and Hierarchization of Power Distribution Network Planning and Design Standard System

The integration of distributed power sources and other new-type equipment to power distribution network places higher requirements on the planning of power distribution network. Due to lack of scientific and reasonable classification and hierarchization methods, overlap or even contradiction among China’s existing power distribution network planning and design standards is becoming increasingly severe. Based on the principles of standard classification and the existing standard system, a classification and hierarchization method suitable for the power distribution network planning and design standard system is proposed in this paper. The power distribution net-work planning and design standards are classified into five classes. And then, after analysing the content attributes of standards at each level and the relation of inclusion among standards at different levels, two hierarchization methods including Content Evaluation Method and Comparative Analysis Method are proposed to determine the level of a standard. Finally, this paper illustrated the effectiveness of the two methods by examples. These research results provided theoretical guidance and reference for the effective management and efficient application of standards and the establishment of a modern power distribution network planning and design standards system.

Optimization of Distribution Network Construction Based on GIS System

With the rapid development of China’s economy and science and technology, people’s daily life and scientific research work demand more and more electricity. The traditional distribution network structure and load can hardly meet the actual demand. Therefore, it is very necessary to construct a more stable distribution network system in accordance with the actual demand for electricity. This paper mainly introduces the application of GIS system in power distribution network planning and design, and realizes the optimization and upgrading of power distribution network in coordination with the geographic information of the construction site, so as to meet the increasing demand for electricity.

Planning of a Resilient Underground Distribution Network Using Georeferenced Data

This study describes a practical methodology for a resilient planning and routing of power distribution networks considering real scenarios based on georeferenced data. Customers’ demand and their location are the basis for distribution transformer allocation considering the minimal construction costs and reduction of utility’s budget. MST (Minimum Spanning Tree) techniques are implemented to determine the optimal location of distribution transformers and Medium voltage network routing. Additionally, the allocation of tie points is determined to minimise the total load shedding when unusual and extreme events are faced by the distribution grid, improving reliability and resilience reducing downtime during those events. The proposed methodology provides a coverage of 100%, supplying electricity to the totality of customers within statutory limits during normal and unusual conditions.