Traditional Distribution Network Research Articles

Validation of a Holistic System for Operational Analysis and Provision of Ancillary Services in Active Distribution Networks

The advent of distributed renewable energy sources (DRESs) has led to the progressive transformation of traditional distribution networks to active components of the power system. This transformation, however, may jeopardize the reliable grid operation due to the advent of new technical problems, such as network overloading, over-/under-voltage events, abnormal frequency deviation and dynamic instability. In this challenging scenery, the installation of a modern measuring infrastructure has created new sources of data and information that facilitate the provision of ancillary services (ASs) via measurement-based analysis. The ACTIVATE (ancillary services in active distribution networks based on monitoring and control techniques) project aims to design innovative AS solutions for power system operators. These solutions aim to tackle the technical issues emerged by the ever-increasing DRES penetration and their volatile nature. In this context, in ACTIVATE, a holistic system is proposed comprising centralized and decentralized control features to enhance the overall network performance. Additionally, a network monitoring system is designed to support a number of online and offline dynamic analysis applications by exploiting measurements obtained at the transmission, primary and secondary distribution network. This paper presents a validation of the overall system, which is performed by using simulation and power-hardware-in-the-loop results in combined transmission and distribution network models.

Optimal Planning of Hybrid Electricity–Hydrogen Energy Storage System Considering Demand Response

In recent years, the stability of the distribution network has declined due to the large proportion of the uses of distributed generation (DG) with the continuous development of renewable energy power generation technology. Meanwhile, the traditional distribution network operation mode cannot keep the balance of the source and load. The operation mode of the active distribution network (ADN) can effectively reduce the decline in operation stability caused by the high proportion of DG. Therefore, this work proposes a bi-layer model for the planning of the electricity–hydrogen hybrid energy storage system (ESS) considering demand response (DR) for ADN. The upper layer takes the minimum load fluctuation, maximum user purchase cost satisfaction, and user comfort as the goals. Based on the electricity price elasticity matrix model, the optimal electricity price formulation strategy is obtained for the lower ESS planning. In the lower layer, the optimal ESS planning scheme is obtained with the minimum life cycle cost (LCC) of ESS, the voltage fluctuation of ADN, and the load fluctuation as the objectives. Finally, the MOPSO algorithm is used to test the model, and the correctness of the proposed method is verified by the extended IEEE-33 node test system. The simulation results show that the fluctuation in the voltage and load is reduced by 62.13% and 37.06%, respectively.

Reliability Assessment of Cyber–Physical Distribution Systems Considering Cyber Disturbances

With the development of communication technology, traditional distribution networks have gradually developed into cyber–physical systems (CPSs), from which the cyber system provides more protection for the grid and brings new security threat–cyber disturbances. Current research cannot scientifically measure the impact of cyber disturbances on the system and lacks reliability indices for a comprehensive quantitative assessment of CPS reliability from the perspective of cyber–physical fusion. If the impact of information disturbances on system reliability is not assessed accurately, it will not be possible to provide a scientific and reasonable decision basis for system planning and operation. Therefore, a set of reliability assessment methods and indices for distribution network CPSs considering cyber disturbances is proposed. Firstly, a reliability modeling method combining fault tree and Petri net is proposed to model the reliability of a distribution network CPS, which can improve the efficiency and accuracy of the modeling. Secondly, the system state is divided into two categories: normal operation state and cyberattack state. Then, generalized reliability indices considering cyber disturbances for distribution network CPSs are defined. Finally, through the tests on the modified IEEE RBTS BUS2 distribution network CPS, an analysis of the effects of information component failures, cyberattacks, and access network structures on system reliability is conducted in this paper to verify the efficiency of the proposed method and the rationality of the newly defined reliability indices.

Stochastic Strategic Participation of Active Distribution Networks With High-Penetration DERs in Wholesale Electricity Markets

With the increasing penetration of distributed energy resources (DERs), traditional distribution networks as load-serving entities in wholesale electricity markets, now evolve towards active distribution networks (ADNs) which can proactively participate in wholesale markets by optimally controlling the DERs in their networks. A stochastic bilevel optimization model is proposed in this paper for the strategic participation of ADNs and DERs to provide energy and grid services in wholesale electricity markets. The bilevel optimization model can capture the interactions between the ADN and the wholesale energy and ancillary service markets, considering the uncertainties of DERs in the ADN. In the upper-level model, the ADN makes optimal decisions on energy and reserve bidding considering the availability, uncertainties, and flexibility of DERs. The joint energy and reserve market-clearing of the independent system operator (ISO) is modeled as the lower-level problem. Using strong duality theory and Karush-Kuhn Tucker (KKT) conditions, the proposed bilevel optimization problem is reformulated as mathematical programming with equilibrium constraints (MPEC) problem and further converted into a computationally-solvable mixed-integer second-order-cone programming (MISOCP) model. The simulation results demonstrate the effectiveness of the model and the interactions between an ADN and wholesale electricity markets.

Research on electric vehicle charging load based on the monte Carlo

With the increasing number of electric cars, they may become the most important load in the grid of the future. Due to the randomness of electric vehicles access to the grid, when large-scale electric vehicles are linked to the grid, they will fluctuate on the grid, resulting in the failure of the power supply mode and operation control means of the traditional distribution network to attain the optimal result. In view of this phenomenon, based on the Monte Carlo calculation and simulation, the charging load of electric vehicles is predicted, which can achieve comprehensive prediction under different kinds of electric cars, different battery capacities, different charging methods, etc, and provide theoretical and technical support for the effect analysis of electric vehicles access to power grid.

Analysis of the Influence of Distributed Generation on Distribution Network Voltage

This paper introduces the development and application background of distributed generation and analyzes its technical characteristics. According to the relationship between distributed generation and traditional distribution networks, the models of different types of distributed generation are equivalent to PQ, PI, PV, and PQ (V) nodes. Besides, this paper analyzes the impact of distributed generation on the voltage of the distribution network from the perspective of position and capacity. It points out the direction for further research.

A Hierarchical Coordinated Control Strategy for Power Quality Improvement in Energy Router Integrated Active Distribution Networks

The energy router (ER) is a current power electronic device which can integrate distributed energy, provide power for different types of loads, and simultaneously realize the free flow of energy. In traditional active distribution networks, power quality is affected due to the access of photovoltaics (PV) and various loads. Hence, this problem can be improved by accessing the ER. This paper shows the power quality improvement of the grid when the ER is used to integrate PV, energy storage, and AC/DC loads. At the same time, an energy coordination strategy for ER is proposed. The IEEE 13 node model is developed to analyze power quality fluctuations when distributed energy and AC/DC loads are directly connected to the grid. For the power quality analysis, five indicators were selected and the hierarchical analysis method was used to obtain the indicators of power quality. After the use of ER under the coordinated control of ER, the energy is distributed twice and the power quality of the grid improves. The feasibility of ER topology and the control strategy have been verified through an established active distribution networks model with ER. It is verified that when the ER is connected to active distribution networks, the power quality improves accordingly, and it can effectively deal with the characteristics of distributed energy fluctuations and improve the flexibility of the power grid.

Distribution network reconfiguration control and protection method based on multi-source data interaction

Distribution network reconfiguration technology is the key technology of distribution network fault treatment and power supply recovery. Given the problem that the traditional distribution network reconfiguration algorithm needs a lot of power flow calculation and low efficiency to find the optimal target, the simulated annealing algorithm is proposed to optimize the computing speed and searching ability of the distribution network reconfiguration algorithm. Firstly, the information interaction technology of distribution network protection and control equipment is studied, and the real-time data rapid peer-to-peer exchange and data and information interaction model is built, which improves the fault detection and timely removal ability of the distribution network. Secondly, the simulated annealing algorithm is used to reduce the number of power flow calculations, reduce the calculation time, improve the calculation speed of network reconstruction problems, and ensure that the optimal or better network topology can be obtained. Finally, the simulation analysis of the IEEE37 node system verifies the effectiveness of the proposed reconfiguration protection method in a complex environment with massive access to distributed energy.

Multilevel optimization of economic dispatching in active distribution network based on ADMM

With the continuous improvement of the penetration rate of renewable energy and the continuous integration of advanced network control technology and measurement equipment, the traditional distribution network is developing into an active distribution network (ADN) with the characteristics of flexible scheduling control, high user interaction, and high energy utilization. This article fully considers the economy of the overall operation of the distribution network, and proposes a hierarchical optimization economic dispatch method for active distribution networks based on the alternating direction multiplier method (ADMM). Firstly, a hierarchical optimization scheduling model of active distribution network is established with the goal of minimizing the overall operating cost of the distribution network. The alternating direction multiplier method algorithm is decomposed into upper and lower layers to solve. The upper layer is optimized with the goal of minimizing the overall operating cost of the distribution network, and the lower layer considers the distribution network. The distributed photovoltaic and energy storage units connected to the internal nodes of the network are optimized with the goal of minimizing the local energy storage operation cost and power purchase cost. The upper and lower layers, through the exchange of limited boundary information, iterate each other until the convergence conditions are met, and the optimal solution is obtained. Finally, a design example is tested to verify the effectiveness and feasibility of the proposed scheduling method.

Power distribution network expansion planning to improve resilience

AbstractHigh‐impact, low‐probability events that cause significant annual damages seriously threaten the health of distribution networks. The effects of these events have made the expansion planning for distribution systems something beyond the traditional reliability criteria, so there is an ever‐increasing need for modifications in current planning approaches and focusing on the resilience in the expansion planning of distribution networks. The new attitude dealing with resilience and distributed generation sources in distribution networks necessitates a fundamental reconsidering of traditional distribution network planning methods. Here, by modelling common natural disasters such as floods and storms, an appropriate index is introduced to evaluate the distribution network resilience in the presence of distributed generation (DG) sources, including conventional gas‐fired and photovoltaic sources. Then, by presenting an appropriate model for load and photovoltaic production, the problem of comprehensive distribution network planning, including substations, feeders, and DG sources, is mathematically formulated as a multi‐objective optimization problem to improve resilience and optimize costs. Furthermore, a non‐dominated sorting genetic algorithm is used to solve the problem of comprehensively planning a resilient distribution network. Implementation of the proposed model on the IEEE 54‐bus sample network shows that network resilience can be improved with minimum cost by optimal network planning.

Distribution Network Disaster Early Warning and Production Decision Support System Based on Multisource Data

Aiming at the problems of long warning time and low warning accuracy in the traditional distribution network disaster early warning and production decision support systems, a distribution network disaster early warning and production decision support system based on multisource data is designed. The forecast information is collected through the data collector, the wind load and lightning trip rate of the line are calculated, all of the information is integrated together for multisource data fusion processing, and the distribution network disaster early warning model is constructed in accordance with the system hardware, which is designed with a data collector, gateway, man‐machine interface, fault analysis module, disaster early warning module, and expert decision support module. According to the system hardware and software design, the design of a distribution network disaster early warning and production decision support system based on multisource data is realized. The simulation results show that the system has high accuracy and a short warning time.

Planning and Operation of an Intelligent Power Electronics Series Voltage Regulator for PV-Rich Distribution Feeders

The connection of photovoltaic systems on weak electrical distribution networks can lead to the occurrence of excessive overvoltages or undervoltages that lead toward unsatisfactory operating conditions for renewable energy producers. In such critical situations, where electrical parameters are outside standard regulatory levels, the untimely tripping of electrical protections can lead to losses of renewable energy production and thus to the reduction of producers’ economic revenues. In this context, this article describes aspects related to the planning and operation of an intelligent voltage regulator capable of solving voltage regulation problems in PV-rich low-voltage networks. The results achieved with the application of the power electronics device in electric distribution networks are shown with a real case study in a network with a high penetration of photovoltaic-type renewable energy installations. The results show the effectiveness of the application of the device considering both technical and economic aspects; these results are also compared with traditional distribution network planning strategies, highlighting the advantages for the distributor and the owners of photovoltaic systems.

Artificial Neural Network Based Algorithm for Fault Detection in a Ring DC Microgrid Under Diverse Fault Conditions

The DC microgrid has become a greater power system in modern power technology due to its wider acceptance as compared to the AC-based traditional power distribution network. Nevertheless, protection of the DC microgrid is a difficult and complicated task due to numerous types of fault scenarios such as pole-to-ground and pole-to-pole faults, variation in fault current magnitude during grid connected and islanded mode, as well as bidirectional behaviour of the converters. In addition to the abovementioned challenges, fault detection during varying fault resistance and intermittency is also a crucial and tricky task because the level of the fault current can vary due to the distinct value of the fault resistance. Therefore, in this manuscript, an ANN-based protection scheme is proposed to detect the fault under varying fault conditions. Furthermore, to investigate the appropriateness of the protection scheme, DT and kNN-based techniques have also been considered for analysis purpose. In the proposed protection scheme, the tasks of mode identification, fault detection/classification, as well as section identification, have been proposed. The results in Section 5 indicate that the protection scheme is capable and accurate for fault detection in any type of faulty condition.

Power Flow Analytical Method for Three-Phase Active Distribution Networks Based on Multi-Dimensional Holomorphic Embedding Method

With the increasing penetration of distributed generations (DGs) in three-phase distribution networks, the traditional radial passive distribution networks are gradually transformed into active distribution networks (ADNs) with DGs, accompanied by a dramatic increase in operational scenarios. To fast analyze the massive scenarios of ADNs, this brief proposes a multi-scenario-based power flow analytical method based on the multi-dimensional holomorphic embedding method. First, DGs are partitioned according to their spatial correlation, and then a power flow analytical method is cast for unbalanced three-phase ADNs. Finally, the method is applied to the ADN power flow analysis for multiple scenarios. Comparisons with other power flow calculation methods validate the high efficiency and computational tractability of the proposed method.

Deregulated Distribution System Planning - Incremental Capacity Auction Mechanism With Transactive DERs

High penetrations of distributed energy resources (DERs) are driving the transformation of traditional distribution networks into transactive energy distribution systems (TEDS). TEDS depart from conventional distribution system constructs to 1) enable a local distribution operator to maximize social welfare, 2) enable peer-to-peer energy transactions, 3) extract maximum participation and benefits from DERs, 4) usher in competition in the distribution sector to supply electricity via DERs, and 5) hold the potential for a lower asset cost solution, greater customer choice, and higher reliability. The conventional distribution system planning is inadequate for this purpose. Following the new TEDS paradigm, an incremental capacity auction (ICA) is proposed in this paper considering: 1) bids for power capacity from all energy sources (DERs and transmission), 2) bids for network asset upgrades from equipment vendors, and 3) bids from new loads. A holistic auction settlement procedure is developed for the proposed ICA mechanism that can concurrently determine the optimal power capacity to be procured and network asset upgrading, while maximizing social welfare. Further, locational marginal price for power capacity demand is estimated as a by-product of the ICA solution. Benefits are demonstrated via case studies and include increased participation of DERs to supply energy, maximizing the social welfare, and enabling assets deferral considering the available local resources.

Reactive power optimization of a distribution network with high-penetration of wind and solar renewable energy and electric vehicles

As high amounts of new energy and electric vehicle (EV) charging stations are connected to the distribution network, the voltage deviations are likely to occur, which will further affect the power quality. It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode. If the reactive power regulation potentials of new energy and EVs can be tapped, it will greatly reduce the reactive power optimization pressure on the network. Keeping this in mind, our reasearch first adds EVs to the traditional distribution network model with new forms of energy, and then a multi-objective optimization model, with achieving the lowest line loss, voltage deviation, and the highest static voltage stability margin as its objectives, is constructed. Meanwihile, the corresponding model parameters are set under different climate and equipment conditions. Ultimately, the optimization model under specific scenarios is obtained. Furthermore, considering the supply and demand relationship of the network, an improved technique for order preference by similarity to an ideal solution decision method is proposed, which aims to judge the adaptability of different algorithms to the optimized model, so as to select a most suitable algorithm for the problem. Finally, a comparison is made between the constructed model and a model without new energy. The results reveal that the constructed model can provide a high quality reactive power regulation strategy.

Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

Promoted by the rapid development of information technology, 10kV overhead line has been widely used in the majority of cities, and it is of great significance to monitor the distribution network effectively, in order to ensure the normal operation of the system. Most of traditional distribution network monitoring methods are based on manual work, which causes inconvenience to the distribution network fault location, repair, maintenance and real-time monitoring, and reduces the efficiency of the distribution network emergency repair and the reliability of power supply. Aiming at the automatic monitoring problem of 10kV overhead network, this paper adopts an intelligent wireless monitoring technology, where a monitoring node is employed to monitor the network transmission status through wireless links. We evaluate the system monitoring performance by using the metric of outage probability, depending on the wireless data rate over wireless channels. For the considered system, we derive analytical outage probability, in order to measure the system performance in the whole range of signal-to-noise ratio (SNR). The simulation results are finally presented to verify the analytical expressions on the system monitoring outage probability in this paper.

Swadisth Company Pvt Ltd: A Survival Plan during COVID-19

The case highlights a problem faced by Swadisth: a confectionary manufacturing firm. The company could barely sell its products through the traditional distribution network during the nationwide lockdown due to COVID-19. In just 30 days, there was a 90% decline in sales. The manufacturing plants closed, the workers were sitting idle and the costs associated with inventory, workforce and machinery were piling up. The people were not leaving their houses because of the government guidelines and relied on online platforms for purchases. Many competitors shifted their businesses online, and the rest were planning to launch soon. It was time for Swadisth to quickly respond to the market changes to survive the pandemic. Previously, Swadisth distributed its products through a network of manufacturing plants, distributors and retailers located in multiple places. Entry into e-commerce would require changing their supply chain structure and distribution strategies. For this purpose, Mr Keshav Rao (Chief Operating Officer) was tasked to evaluate alternative distribution strategies and present a report to Mr Chaudhary (Managing Director).

REVIEW ON SMART POWER DISTRIBUTION NETWORKS: A SUSTAINABLE DEVELOPMENTAL APPROACH

Smart power grid distribution network is very paramount in building a sustainable Electricity network for smart grid operations. As stipulated and stated in the agenda 2030 sustainable development goals project (agenda goals; 3, 7, 9, 11, and 13), it’s pertinent to create, research and develop an enabling environment within the power grid horoscope with these agendas in view. This helps the design of reliable and quality distribution system to replace the traditional distribution networks which produce carbons and other harmful chemical substances in to the environment. Recently, the distribution system has been burdened with numerous dynamic loads and energy demands due tothe current Covid-19 situation which has brought many to work from home. The Smart distribution network as reviewed in this paper is a critical component and final stage in providing optimum power solutions to customers. Discussed in this paper is the reviews of developmental approaches to smart power distributions, since it networks perform critical functions and bears social responsibility by adopting large and small hydro-dams, solar, wind, and other clean energy sources, for delivering power to its end users safely and effectively.

Optimal scheduling of a wind energy dominated distribution network via a deep reinforcement learning approach

With the development of clean energy systems, large-scale renewable energy is being connected to the traditional distribution network, which also brings new challenges to the reliable and economic scheduling of the power grid. To address these challenges, this paper proposes an intelligent scheduling strategy for a wind energy dominated distribution network, which aims to reduce the fluctuation caused by the wind energy. First, the energy scheduling model and objective function of the distribution network system are established and the constraints of various types of components are considered. Then, deep reinforcement learning is introduced to realize real-time decision in distribution network to solve the problem of fluctuation caused by the uncertain wind power output. The energy scheduling method is developed into a Markov decision process based on deep deterministic policy gradient (DDPG) algorithm. Finally, the simulation is verified on the IEEE14 node system. The results verify that the proposed approach can effectively reduce power fluctuations in the distribution network. The superiority of the adopted DDPG algorithm is demonstrated by comparing with the deep Q network algorithm.