Distribution Network Load Research Articles

A multi-source data collection and information fusion method for distribution network based on IOT protocol

With the construction of Global Energy Internet, Intelligent distribution network has more and more characteristics of multi scene, multi service and changeable measurement and control objects. In order to realize data collection and information exchange among the intelligent terminal and energy, power grid, load, energy storage and other control resources in distribution network, we should research an architecture which can adapt to various data sources Based on dynamic self describing protocol.

Power Sharing in Island Microgrids

The growth of local renewable energy sources and heavy loads in power distribution networks, such as the increasing electric vehicles charging stations, causes several issues with a direct impact on the stability of the electrical grid. An attempt to overcome such issues is the microgrid concept, which has the grid structured into local sub-grids that manage their power and energy balancing. A microgrid may operate connected or disconnected from the main grid, being dynamically necessary to guarantee a power balancing between local loads and sources. Furthermore, as several power units are connected to the same microgrid, equity is also required in terms of power sharing. Current work explores a scenario of an island operation of a microgrid with multiple sources, including battery storage systems and sharing power with multiple loads, including electric vehicle chargers, a scenario appropriated to a city grid. A local control solution for a stable operation of the microgrid in terms of both power balancing and power sharing is presented and validated through numerical and experimental results.

Energy-Efficient Frame Aggregation Scheme in IoT Over Fiber-Wireless Networks

With the tremendous growth of traffic demand caused by the conventional service and emerging Internet-of-Things (IoT) applications, it is vitally challenging to support seamlessly access for billions of IoT devices along with existing Internet service. Fiber-Wireless (FiWi) access networks is an ideal solution for the next-generation access networks to support both IoT and conventional service. One of the main challenges faced by the design of an FiWi access network is the high energy consumption due to low utilization of optical network units (ONUs) and high control overhead of data transmission. In this article, An adaptive frame aggregation scheme with load transfer is proposed to reduce the energy consumption in FiWi. By evaluating the quality of wireless channel, the proposed scheme adaptively adjusts the length of the aggregated frame to reduce the energy consumption caused by the frequent preemption of wireless channel and the numerous retransmission of data frames resulted by poor channel quality. In the optical backhaul, according to the delay requirements of services with different priorities, the proposed scheme calculates the optimal frame length for each queue and performs load transfer among ONUs to dynamically distribute network load and maximize the sleep rate of ONUs for energy saving. Simulation results and theoretical analysis both unanimously show that the proposed scheme achieves significant amounts of energy saving, while preserving delay performance of various services.

A Multi-port DC Solid-state Transformer for MVDC Integration Interface of Multiple Distributed Energy Sources and DC Loads in Distribution Network

In traditional DC distribution networks with both a low-voltage DC (LVDC) and a medium-voltage DC (MVDC) bus, DC units are connected to the LVDC bus through LVDC converters. In this approach no galvanic isolation is provided and two power conversion stages are needed between the DC units and the MVDC grid, hence leading to a high number of converters and higher costs. To address these shortcomings, this paper proposes a novel multi-port DC solid-state transformer (MDCSST) to interface DC units directly to the MVDC bus. Multiple modules are connected in series on the medium voltage side to the MVDC bus, whereas DC units are independently connected on the low-voltage side of the MDCSST. Compared to a traditional DC distribution network, the proposed scheme connects DC units to the MVDC bus without an extra converter or an LVDC bus, therefore saving cost and reducing the number of converters. In addition, DC units are galvanically isolated by high-frequency transformers. The main challenge of the MDCSST is to resolve the voltage-imbalance problem on the MVDC side which is caused by the DC units power differences. An LC-branch is used to balance the voltages among the modules and to transfer their differential powers.

IGDT-based Robust Power Dispatch for Active Distribution Network Considering Demand Response

Considering increasing uncertain renewable energy sources (RES) and flexible loads in active distribution network (ADN), this study proposes a novel optimal model for robust hourly energy scheduling of ADN. Firstly, a deterministic optimal dispatching model is formulated, which aims at minimizing the total operation cost of distribution network; Secondly, the information gap decision theory (IGDT) is employed to handle uncertainties of RES generation. One of the features of the proposed model is to take into account the impact of demand response of flexible loads and energy storage system (ESS) as the effective tools to reduce unintended costs due to uncertainty of RESs. Also, the uncertainty of RESs is handled in a way that maximum tolerable uncertainty is achieved for a given worsening of total operation cost. The model is formulated as a mixed integer nonlinear optimization problem and solved in the genetic algorithm. Numerical simulation on the IEEE 33-bus system has been performed. Comparisons with two types of probabilistic techniques demonstrate the effectiveness and benefits of the proposal.

On the k Nearest-Neighbor Path Distance from the Typical Intersection in the Manhattan Poisson Line Cox Process

In this paper we calculate the exact cumulative distribution function (CDF) of the path distance (L1 norm) between a randomly selected intersection and the k-th nearest node of the Cox point process driven by the Manhattan Poisson line process. The CDF is expressed as a sum over the integer partition function <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$p\!\left(k\right)$</tex></formula> , which allows us to numerically evaluate the CDF in a simple manner. The distance distributions can be used to study the k-coverage of broadcast signals in intelligent transportation systems (ITS) transmitted from a \ac{RSU} that is located at an intersection. They can also be insightful for network dimensioning in urban vehicle-to-everything (V2X) systems, because they can yield the exact distribution of network load within a cell, provided that the \ac{RSU} is located at an intersection. Finally, they can find useful applications in other branches of science like spatial databases, emergency response planning, and districting. We corroborate the applicability of the distance distribution model using the map of an urban area.

A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network

The Wireless Sensor Network (WSN) has seen rapid growth in the development of real-time applications due to its ease of management and cost-effective attributes. However, the balance between optimization of network lifetime and load distribution between sensor nodes is a critical matter for the development of energy-efficient routing solutions. Recently, many solutions have been proposed for constraint-based networks using the cloud paradigm. However, they achieve network scalability with the additional cost of routing overheads and network latency. Moreover, the sensors’ data is transmitted towards application users over the uncertain medium, which leads to compromised data security and its integrity. Therefore, this work proposes a light-weight secure and energy-efficient fog-based routing (SEFR) protocol to minimize data latency and increase energy management. It exploits the Quality of Service (QoS) factors and facilitates time-sensitive applications with network edges. Moreover, the proposed protocol protects real-time data based on two levels of cryptographic security primitives. In the first level, a lightweight data confidentiality scheme is proposed between the cluster heads and fog nodes, and in the second level, a high-performance asymmetric encryption scheme is proposed among fog and cloud layers. The analysis of simulation-based experiments has proven the significant outcomes of the proposed protocol compared to existing solutions in terms of routing, security, and network management.

A Scheme for Position and Capacity Determination of Distributed Generation Considering Load Distribution and System Voltage Stability

The purpose of this study was to investigate a simplified distributed generation (DG) Position and Capacity Determination model (DG-PCD model) based on the coupling relationship between load distribution and voltage stability in the distribution network. First, based on the relationship between voltage stability and system equivalent impedance and the relationship between system equivalent impedance and load distribution, the relationship between voltage stability and load distribution is deduced, and the concept of influencing impedance mode is proposed and used in DG site selection. Then, build a DG-PCD model considering voltage stability, active power loss and line thermal stability margin, and use genetic algorithm (GA) to solve the model. Finally, an improved IEEE33-node system calculation example is analyzed. The results show that compared with the existing methods, the proposed method can get better results faster. This Proposed method not only simplifies the DG-PCD model, but also quantifies the relationship between voltage stability and load distribution. This provides a new reference index for the voltage control of the power grid.

Resilience‐oriented service restoration modelling interdependent critical loads in distribution systems with integrated distributed generators

The exponential increase in the frequency and intensity of high impact low probability weather-related events have pivoted the paradigm of research pertaining to power systems towards resilience. Power system is considered as a critical infrastructure, directly linked to the nation's economy, security and health. Therefore, recent researchers have proposed several techniques to enhance the resilience of power systems. In those techniques, critical loads have been considered independent in nature and different metrics have been proposed to evaluate the resilience of the network. To enhance the resiliency, this paper incorporated distributed generators in the distribution network and critical loads are modelled interdependently. Furthermore, a novel resilience metric is proposed in this paper to evaluate the resilience of a distribution system. The proposed model is formulated as a mixed integer second-order cone programming problem and the efficacy of the proposed model is evaluated on IEEE 33- and 69-bus systems. The competence of the proposed resilience metric is evaluated after comparison with existing resilience metrics.

Multi-Stage Distribution Network Space Load Forecasting Method Considering Demand Side Resources

Under the background of the further development of electric power, this paper forecasts the spatial load of distribution network, and proposes a multi-stage spatial load forecasting method considering the demand side resources. Firstly, the load of distribution network is pretreated to improve the prediction function of the processing system, and the working efficiency of the whole system is enhanced to solve the maximum load value. Then, the different conditions of demand side resources are considered step by step to realize the fine analysis, confirm the saturation density value of load, understand the specific information of spatial load, master the predicted data status, and finally carry out the comprehensive prediction method research of spatial load to realize the prediction research of spatial load of distribution network. The experimental results show that the multi-stage spatial load forecasting method considering demand side resources has high accuracy and reliability, and its forecasting effect can improve the system forecasting performance to a certain extent, reduce unnecessary operation time, reduce energy and resource consumption, and promote the development of load forecasting research.

Optimal network reconfiguration and DG allocation using adaptive modified whale optimization algorithm considering probabilistic load flow

The optimal DG allocation (ODGA) and network reconfiguration (NR) are significant tools to improve voltage stability and minimize power losses in a distribution network. In a real power distribution network loads are random variables, thus ODGA and NR processes are incorporated using a probabilistic load flow (PLF). The reactive power capability of DGs has a significant impact on the network parameters which can be studied using PLF. In the present work, an ODGA and NR processes have been incorporated to improve the voltage stability and loss profile of the distribution system considering probabilistic loads and DGs which are operated at varying pfs. The optimization problem has been solved using an adaptive modified whale optimization algorithm (A-MWOA). A novel technique based on depth first search algorithm has been employed to check radiality constraints. The effectiveness of proposed technique has been tested on two standard distribution networks.

Feeder Fault Warning of Distribution Network Based on XGBoost

In this paper, the historical observation data of the power grid are used to build a predictive model of power outages for distribution network by using machine learning methods. By judging whether the distribution transformer network is about to fail, the maintenance and troubleshooting of the distribution network can be achieved in advance, thereby fundamentally reducing the occurrence of power fault of the distribution transformer. The data covers several dimensions such as distribution network loads, equipment ledgers, historical faults, weather and so on. The experiments show that the proposed method based on XGBoost is valid and efficiency for feeder fault early warning.

Chance-Constrained Water Pumping to Manage Water and Power Demand Uncertainty in Distribution Networks

Water pumping in drinking water distribution networks (WDNs) can be treated as a flexible load in the power distribution network (PDN). In this article, we formulate an optimization problem to minimize the electricity costs associated with pumping subject to WDN and PDN constraints. In practice, both water and power demands are uncertain and pumps should be scheduled to ensure that pump operation does not violate either networks’ constraints for nearly all possible uncertainty realizations. To address this problem, we formulate a chance-constrained (CC) optimization problem that simultaneously determines pumping schedules along with the parameters of real-time control policies that can be used to respond to water and power demand forecast errors. We use approximations and relaxations along with the scenario approach for CC programming to reformulate the optimization problem into a convex deterministic problem. We demonstrate the performance of the approach through case studies and also explore the impact of the relaxations, an approach to improve computational tractability, and tradeoffs associated with the way in which we define the cost of real-time control actions. We find that optimal scheduling and real-time control of water pumping can effectively manage water and power demand uncertainty, meaning water demand is satisfied and both the WDN and PDN operate within their limits; however, the approach is conservative leading to high reliability at high cost.

Data‐driven robust extended computer‐aided harmonic power flow analysis

The surge in the uptake of harmonic producing loads in 21st-century smart distribution networks has necessitated that robust data-driven approaches for harmonic assessment be developed. Moreover, there exists abundant harmonic data which can be leveraged on for the construction of this harmonic assessment tool. This is the objective of this study. Large volumes of time-stamped data acquired from a practical distribution network in Edmonton, Canada, have been used to construct a time-dependent cross-coupled harmonic model that has been combined with a formulated iterative time-dependent robust extended computer-aided harmonic power flows. The constructed harmonic power flow formulation also considered the constraints relating to the power conservation principle at the fundamental frequency. Practical network conditions including untransposed lines, load unbalance and skin effects of conductors have been thoroughly modelled. The proposed method has been applied to practical radial and weakly meshed medium voltage distribution networks and it demonstrated robustness to initialisation of the iterative procedure. Moreover, findings from this detailed data-driven technique reveal that harmonic impacts in the distribution networks depend on the quantum of harmonic levels, type of network and location of the harmonic sources.

An Optimal Placement Model for Electric Springs in Distribution Networks

The electric spring (ES) is a type of demand-response technology that improves the voltage quality of critical loads (CLs) in a distribution network. This paper, proposes an optimal placement model to determine the optimal placement locations and power ratings of ESs in a radial distribution network. The total voltage deviations with respect to the buses are proposed as the placement criteria. The non-critical loads (NCLs) with ESs are extended to power-electronics-based loads, and they are modeled as constant-power-type loads. The operation characteristics of the individual ESs are investigated and linearized. On the basis of the linearized Distflow equations, an optimal placement model is proposed for ESs in a radial distribution network. The proposed model is solved as a mixed integer linear program problem. As per the case-study results, the installation locations and power ratings of the ESs are mainly associated with the network topology, weight coefficients of the voltage deviations on different buses, and bus loads in the radial distribution network.

Extraction of Dynamic Frequency Response Characteristics and Modelling of Modern Air Conditioners

Modern inverter-based air-conditioners (IACs) are generally equipped with power-electronic components to achieve higher efficiency and more advanced controllability. Thus, their operating behaviours can be very different from the conventional air-conditioners with direct motor connections, and their new characteristics have not been adequately studied in the current literature. Thus, hardware experiments are conducted to extract the novel dynamic frequency response features of modern inverter-based air-conditioners under a range of different frequency disturbances. Then, a new IAC model with physical meanings is developed by accommodating the discovered new features such as time delays, load frequency relief and the minimum operating power limit. Further, this letter also demonstrates that the developed air-conditioner model can accurately represent the dynamics of the IACs under various frequency events. The extracted dynamic frequency response behaviours and the developed air-conditioner model will provide a solid foundation for the modern appliance modelling in a large scale and will assist to improve the accuracy of the inertia response representations of distribution network's loads.

An auxiliary unit with selective harmonic suppression and inherent reactive power compensation for civil distribution networks

In view of the fact that there are usually two dominant harmonic currents and large amounts of inductive loads in residential distribution networks and low voltage distribution networks (called civil distribution networks in the paper), an auxiliary unit with selective harmonic suppression and inherent reactive power compensation for civil distribution networks is proposed in this paper. The proposed auxiliary unit consists of a passive part and an active inverter part. The passive part is composed of a compensation capacitor and two series resonance circuits tuned at the frequencies of the two dominant current harmonics. The passive part is used to compensate reactive power with a constant capacity and reduces capacity requirements of the active inverter part as well as voltage ratings of active inverter part. Owing to the low-impedance of the series resonance circuits, the vast majority of compensation currents generated by the active inverter part are injected to grid. These characteristics have been verified by experiment results. It is indicated that the proposed auxiliary unit is a preferred topology for improving power quality in civil distribution networks.

Enhancement of two-step state estimation performance in unbalanced distribution networks

The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method.

The Control Strategy of Three-Phase Unbalance Load in Low Voltage Distribution Networks Based on SVG

For the three-phase imbalance management, this paper points out that the capacity of static var generator (SVG) can be configured based on smart meter measurement data, and the SVGs coordinated control strategy is used to compensate the three-phase imbalance load of the distribution network. Communication bus and coordinated control strategy are introduced in the proposed system. so that the SVG connected downstream of the low-voltage line can use the remaining capacity after compensating its downstream unbalanced current to sequentially compensate for the insufficient capacity of the upstream SVG. The low voltage problem of each node is further reduced by the reverse power flows from the downstream SVG to the upstream SVG. Simulations for actual effect evaluation of the proposed compensation system were done in Matlab/Simulink. Validity of the proposed system is confirmed by the simulation.

Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm

Existing research on cascading failures of wireless sensor networks (WSNs) fails to take into account the role of the sink node on network load distribution, and rarely involves how to improve network robustness, so it has obvious limitations. To this end, this paper presents a sink-oriented cascading model for WSNs. On this basis, a memetic algorithm MA-TOSCA is proposed to help WSNs resist cascading failures via topology optimization, in which the local search operator is designed based on a new network balancing metric “sink-oriented betweenness entropy”. Moreover, we apply network statistics to identify the correlation between typical network properties and network robustness. Extensive simulations have shown that the proposed model can properly characterize the cascading process of WSNs and MA-TOSCA can find more robust topology with less time compared to existing algorithms. In addition, we discover that a network with the “onion-grid topology structure” is highly robust; the network communication efficiency, the modularity and the clustering coefficient is positively related to network robustness and the average shortest path length is negatively related to network robustness.