Distribution Network Planning Research Articles

Cluster partition-based two-layer expansion planning of grid–resource–storage for distribution networks

In order to realize the optimal planning of grid–resource–storage for distribution networks (DNs) with high penetrated distributed photovoltaics (PVs), a cluster partition-based two-layer expansion planning for DNs is proposed. First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size. Second, a cluster partition-based two-layer expansion planning model is proposed. In the upper layer, a line planning model for clusters is established to carry out the planning of cluster connection lines. In the lower layer, a robust source-storage planning model is established with the uncertainty of PVs and loads, and then, the optimal location and capacity of PVs and energy storages (ESs) can be obtained. In addition, the uncertainty regulation parameter is utilized to control the range of uncertainty sets, which can reduce the conservatism of the optimization. Finally, the proposed method is carried out in a real DN in China, which can effectively improve the economy of DN planning.

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

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.

Automated feeder routing for underground electricity distribution networks based on aerial images

The process towards a carbon-neutral society requires a substantial amount of investment into electricity distribution networks for integrating more sustainable technologies such as photovoltaic systems, electric vehicles, heat pumps, and others. In order to execute the large number of network expansion measures associated with this effort, strategic and operational planning of distribution networks benefit from higher degrees of automation. A key component in these planning processes is the routing of feeder segments, i.e., the task of finding feasible and cost-efficient paths for underground power lines between two geographic locations, such as residential buildings and transformer stations. In this paper, we present FEEDAIR, a novel and innovative approach to automate feeder routing based exclusively on aerial images. FEEDAIR integrates deep neural networks, which encode geoinformation from aerial images, and A* search, which identifies effective feeder routes based on this encoding. We train and evaluate FEEDAIR using a large data set of more than 7000 real-world electricity distribution network feeder segments. Thereby, we compare FEEDAIR to state-of-the-art approaches from related work and find that the feeder segments generated by FEEDAIR are on par or superior with regard to several characteristics and metrics. FEEDAIR achieves this performance by relying exclusively on aerial images, whereas competing approaches require data on geocoded land cover and estimated construction cost, which is not as widely available and is frequently based on potentially subjective expert opinion.

Cyber-Physical Integrated Planning of Distribution Networks Considering Spatial-Temporal Flexible Resources

Cyber-Physical Integrated Planning of Distribution Networks Considering Spatial-Temporal Flexible Resources

Grid Planning Method of Medium Voltage Distribution Network Including Distributed Power Sources based on Timing Characteristics

Aiming at the problems of unbalanced load distribution of feeders and low utilization rate of system resources in existing medium voltage(MV) distribution network planning, it is difficult to meet the requirements of future grid construction, Propose a grid planning method of MV distribution network including distributed generator(DG) based on timing characteristics. Firstly, based on the existing grid planning achievements and temporal characteristics analysis, the grid structure and planning strategy of the MV distribution network are formed. Secondly, based on the network planning strategy, establish a dual level planning model for both internal and external distribution networks. The inner model uses comprehensive performance indicators as fitness functions to solve the optimal division of the distribution network grid, while the outer model calculates the distributed power capacity configuration of each grid with the goal of maximizing the consumption of DG. Then, based on the established dual layer planning model for the distribution network, genetic algorithms and self programming algorithms are used to solve the model by interconnecting the internal and external layers. Finally, the practicality and effectiveness of the proposed model and method were verified through practical examples combined with evaluation indicators.

Relay protection sensitivity integrated optimal placement and capacity of inverter interfaced distributed generators in distribution networks

The relay protection sensitivity is one of the determined factors in the power system, however, it is often overlooked in current distribution network (DN) planning. The relay protection sensitivity can be decreased to below the minimum values, failing to meet the requirements for electrical installations. To address this challenge, a new optimization model integrated with the relay protection sensitivity to maximize the inverter interfaced distributed generator (IIDG) penetration level while minimizing IIDG investment was proposed in this paper. The IIDG effect on the relay protection sensitivity was analysed and the relay protection sensitivity re-evaluation method was developed. The relay protection sensitivity evaluation was integrated into the proposed model and the particle swarm optimization (PSO) algorithm was developed to solve the nonlinear issue. The proposed optimization method was tested on different cases, and results confirmed the effectiveness of the method. Furthermore, the relay sensitivity profiles obtained through the proposed method and the optimization without considering the relay sensitivity limits were compared. The proposed method improves the average and minimum sensitivity factors by 28.77 % and 51.76 %, respectively, when the DTO protection functions as the backup for the protected line in the thirty-three-node system. When DTO acts as the backup for the adjacent line, the average and minimum values increase by 29.91 % and 50.95 %, respectively. Comparative analysis confirms the efficacy of the proposed method. The new method extends the power system panning approaches and can be integrated into the DN planning tools to support the low-carbon initiatives.

Optimization and analysis of distributed generation units in distributed system for minimizing losses

The proposed study presents a novel methodology aimed at mitigating losses in distributed generation (DG) systems within distributed networks. This methodology involves the integration and implementation of DG units that utilize non-conventional, sustainable resources, potentially enhancing traditional DG systems. When DG units are located near the point of consumption, they create favorable conditions for voltage support, reduction in energy losses, and lower emissions. The strategic placement of DG units, in terms of both size and location within an existing generation network, is critical for the construction, execution, and operational planning of real-time distribution networks. This optimal positioning is key to maximizing voltage stability and minimizing power loss. The study proposes an innovative strategy to decrease real power losses and improve voltage profiles, which includes optimizing substation capacity by introducing DG units.

Assessing the Nationwide Benefits of Vehicle–Grid Integration during Distribution Network Planning and Power System Dispatching

The diffusion of electric vehicles is fundamental for transport sector decarbonization. However, a major concern about electric vehicles is their compatibility with power grids. Adopting a whole-power-system approach, this work presents a comprehensive analysis of the impacts and benefits of electric vehicles’ diffusion on a national power system, i.e., Italy. Demand and flexibility profiles are estimated with a detailed review of the literature on the topic, allowing us to put forward reliable charging profiles and the resulting flexibility, compatible with the Italian regulatory framework. Distribution network planning and power system dispatching are handled with dedicated models, while the uncertainty associated with EV charging behavior is managed with a Monte Carlo approach. The novelty of this study is considering a nationwide context, considering both transmission and distribution systems, and proposing a set of policies suitable for enabling flexibility provision. The results show that the power and energy demand created by the spread of EVs will have localized impacts on power and voltage limits of the distribution network, while the consequences for transmission grids and dispatching will be negligible. In 2030 scenarios, smart charging reduces grid elements’ violations (−23%, −100%), dispatching costs (−43%), and RES curtailment (−50%).

Optimization for cost-effective design of water distribution networks: a comprehensive learning approach

AbstractThe Comprehensive Learning Gravitational Search Algorithm (CLGSA) has demonstrated its effectiveness in solving continuous optimization problems. In this research, we extended the CLGSA to tackle NP-hard combinatorial problems and introduced the Discrete Comprehensive Learning Gravitational Search Algorithm (D-CLGSA). The D-CLGSA framework incorporated a refined position and velocity update scheme tailored for discrete problems. To evaluate the algorithm's efficiency, we conducted two sets of experiments. Firstly, we assessed its performance on a diverse range of 24 benchmarks encompassing unimodal, multimodal, composite, and special discrete functions. Secondly, we applied the D-CLGSA to a practical optimization problem involving water distribution network planning and management. The D-CLGSA model was coupled with the hydraulic simulation solver EPANET to identify the optimal design for the water distribution network, aiming for cost-effectiveness. We evaluated the model's performance on six distribution networks, namely Two-loop network, Hanoi network, New-York City network, GoYang network, BakRyun network, and Balerma network. The results of our study were promising, surpassing previous studies in the field. Consequently, the D-CLGSA model holds great potential as an optimizer for economically and reliably planning and managing water networks.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Multi-Stage Planning of Distribution Network With High Penetration Renewable Energy Considering Reliability Index

Multi-Stage Planning of Distribution Network With High Penetration Renewable Energy Considering Reliability Index

Optimal distributed generators allocation with various load models under load growth using a meta-heuristic technique

Distribution network planning and operation are facing several problems, including asset congestion, voltage fluctuations, and system instability. The adequate planning and modeling of distributed generators and capacitor banks must quantify these problems. This article presents the optimal allocation of distributed generators in parallel with capacitor banks in distribution networks with single and multi-objectives using the Gazelle Optimization Algorithm (GOA) and Mountain Gazelle Optimization Algorithm (MGOA). The single objective framework includes technical objectives like minimization of active power losses. The multi-objective framework includes technical and non-technical objectives like simultaneously minimization of active power losses, voltage stability, and voltage deviation, and minimization of polluting greenhouse gases and total electricity purchase cost. Furthermore, these planning problems are investigated by three case studies on different nonlinear voltage-dependent models at two different loading conditions from future planning perspectives. The effectiveness and feasibility of the MGOA are evaluated on the IEEE standard 33 bus system. As a result, the MGOA demonstrates a remarkable reduction in technical and non-technical objectives in all types of distributed generator placement. Moreover, a comparative analysis of other existing research works validated the efficiency and feasibility of established algorithms at each use case with different load models by improving all the objective functions of network planning. In single-objective and multi-objective frameworks, the active power losses reduce to 94.42% and 93.57% in the voltage-independent model, respectively. Meanwhile, the non-technical objectives are also significantly improved for each load model, further validating the efficiency of the proposed algorithms.

The spatiotemporal distribution prediction method for distributed photovoltaic installed capacity based on power supply measurement data

Abstract With the anticipated expansion of distributed power grid integration in the foreseeable future, the consideration of distributed power’s impact on power balance becomes paramount in distribution network planning. In this research, we presented a novel approach for predicting the spatial and temporal distribution of distribution network planning areas, with a specific focus on estimating the installed capacity of distributed photovoltaic (PV) systems. Our method leveraged the saturated capacity of distributed PV, requiring minimal data inputs. By establishing a quantitative model that elucidated the relationship between installed distributed PV capacity and land area, we generated PV installed capacity evolution curves for various types of land. Subsequently, we derived the development coefficient of distributed PV installed capacity. By combining this coefficient with the current status of installed distributed PV capacity in the target area’s land parcels, we forecasted the spatial and temporal distribution of future distributed PV capacity within the region. The proposed prediction model held significant implications for the planning of new distribution networks. Additionally, this study predicted the installed distributed PV capacity for distinct land use types, including residential, commercial, and industrial land, using a regional power supply unit as a representative example. We employed the installed PV capacity unit to forecast the electricity loss rate and energy saving rate within the planning area. By validating the model and method through exemplary test results, we demonstrated the model’s feasibility and accuracy. Furthermore, our model effectively predicted the impact of distributed PV integration on overall load forecasting, thereby offering the power grid more precise load forecasting capabilities.

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

Statistical Machine Learning for Power Flow Analysis Considering the Influence of Weather Factors on Photovoltaic Power Generation.

It is generally accepted that the impact of weather variation is gradually increasing in modern distribution networks with the integration of high-proportion photovoltaic (PV) power generation and weather-sensitive loads. This article analyzes power flow using a novel stochastic weather generator (SWG) based on statistical machine learning (SML). The proposed SML model, which incorporates generative adversarial networks (GANs), probability theory, and information theory, enables the generation and evaluation of simulated hourly weather data throughout the year. The GAN model captures various weather variation characteristics, including weather uncertainties, diurnal variations, and seasonal patterns. Compared to shallow learning models, the proposed deep learning model exhibits significant advantages in stochastic weather simulation. The simulated data generated by the proposed model closely resemble real data in terms of time-series regularity, integrity, and stochasticity. The SWG is applied to model PV power generation and weather-sensitive loads. Then, we actively conduct a power flow analysis (PFA) on a real distribution network in Guangdong, China, using simulated data for an entire year. The results provide evidence that the GAN-based SWG surpasses the shallow machine learning approach in terms of accuracy. The proposed model ensures accurate analysis of weather-related power flow and provides valuable insights for the analysis, planning, and design of distribution networks.

A data-driven chance-constrained BESS planning in distribution networks by a decoupling solution method

In this study, a novel approach for the battery energy storage system (BESS) planning considering the integration of photovoltaic (PV) is proposed. A data-driven chance-constrained BESS programming model including the dynamic network reconfiguration (DNR) is developed. Then, a decoupling solution approach for sitting selection and sizing optimization is proposed to obtain optimal solution. According to the approach, comprehensive cost sensitivity factor (CCSF) is formulated for the sitting selection, and the particle swarm optimization (PSO) combined with the Minty algorithm and the switch operation time reduction algorithm is developed to obtain more accurate sizing. In addition, based on the scenarios generated by the spectral clustering approach, a continuous scenario increment approach is proposed to handle chance-constrained constraints. The numerical simulations on a practical 31-bus distribution network show the superiority of the proposed algorithm over existing methods.

An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers

AbstractAmong the distribution network planning problems, allocation of transformers is one of the most important and challenging ones. On the other hand, owing to the widespread growth of distributed generations (DGs), their inclusion in the planning problems is vital. This paper proposes an efficient optimization framework for simultaneous allocation of photovoltaic (PV) systems and service transformers in the distribution network. For this aim, to improve the network performance, location and size of the transformers and PV units are optimally determined. Due to the difficulty of the planning problem, four variants of crow search algorithm (CSA) including original CSA, differential CSA (CSAd), directed differential CSA (CSAdd) and chaotic directed differential CSA (CSAcdd) are introduced and applied to the planning problem. To evaluate the impact of PV cost on the results, the planning problem is solved considering different PV system costs. Simulation results show that at the price of $1.25/W, installation of PV systems is cost‐effective, so that over the case study, 400 kW PV system was installed. By decreasing the PV price to $1.2/W, the maximum capacity considered for a PV system is installed. Moreover, on average, CSAd produces more accurate and robust results than the other studied algorithms.

Multistage reliability-constrained stochastic planning of diamond distribution network: An approximate dynamic programming approach

In this paper, a multistage reliability-constrained stochastic planning model is established for the diamond distribution network, a new type of distribution system grid structure, with the goal of optimizing the total cost of investment and operation. The uncertainty of load growth is simulated by generating multiple load growth scenarios, and the power supply loss caused by the reliability of components in the system is also considered. The multistage stochastic dynamic decision process is modeled by The Markov decision process. In order to solve the “curse of dimensionality” problem in multi-stage stochastic planning model, the approximate dynamic programming algorithm is used to solve it. The case study is based on the typical structure of the diamond distribution network and realistic data of an actual district in Shanghai, China. The planning results verify the effectiveness of the model and the solution algorithm, and demonstrate the reliability and economy of the diamond distribution network are superior to the traditional double-ring network.

Feeder Capacity Planning and Reliability Evaluation Model of Distribution Network Based on Support Vector Machine

Capacity planning and reliability evaluation models of the traditional distribution network feeder mainly use multi-option balance constraints for multi-objective solutions, which are prone to capacity constraints, resulting in lower reliability indicators for planning and evaluation. Therefore, a brand-new distribution network feeder capacity planning and reliability evaluation model based on a support vector machine is designed. By using support vector machines to design the reliability evaluation process of distribution network feeder capacity planning, we establish an optimization model for reliability evaluation of distribution network feeder capacity planning. The experimental results show that the designed feeder capacity planning and reliability evaluation model based on a support vector machine has good evaluation results, high-reliability indicators for planning and evaluation, reliability, and certain application values.