Smart Distribution Transformer Research Articles

Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

The microservice-based smart grid service (SGS) organization and the heterogeneous multi-cores-based computing resource supply are the development direction of edge computing in smart grid with high penetration of renewable energy sources and high market-oriented. However, their application also challenges the service schedule for edge computing apparatus (ECA), the physical carrier of edge computing. In the traditional scheduling strategy of SGS, an SGS usually corresponds to an independent application or component, and the heterogeneous multi-core computing environment is also not considered, making it difficult to cope with the above challenges. In this paper, we propose an SGS scheduling strategy for the ECA. Specifically, we first present an SGS scheduling framework of ECA and give the essential element of meeting SGS scheduling. Then, considering the deadline and importance attributes of the SGS, a microservice scheduling prioritizing module is proposed. On this basis, the inset-based method is used to allocate the microservice task to the heterogeneous multi-cores to utilize computing resources and reduce the service response time efficiently. Furthermore, we design the scheduling unit dividing module to balance the delay requirement between the service with early arrival time and the service with high importance in high concurrency scenarios. An emergency mechanism (EM) is also presented for the timely completion of urgent SGSs. Finally, the effectiveness of the proposed service scheduling strategy is verified in a typical SGS scenario in the smart distribution transformer area.

Distributed PV Management Based on Edge-cloud Computing Model of Smart Distribution Transformer Terminal Unit

With the growing installation of distributed PV at a low voltage level and the resulting concerns about good power quality, power facilities see a greater need for energy management programs, especially for flexible distributed PV management. In the dawn of a new generation of ICT technologies, an edge-cloud computing model widely applied to smart distribution transformer terminal units (SD-TTU) is proposed to achieve lifecycle management of power facilities. Firstly, existing distribution management systems’ current practices and limitations are presented. According to the development trend, the reconstructed computing model involves three layers, and data interaction among the cloud layer, edge layer, and device layer is described. To support the huge scale of sensed data integration and transmission, the architecture of the edge node is established. Then the technical details for the distributed PV management are presented, including equipment configuration, main monitoring information, and specific procedures.

An LSTM-SAE-Based Behind-the-Meter Load Forecasting Method

Nowadays, modern technologies in power systems have been attracting more attention, and households can supply a portion of or all of their electricity based on on-site generation at their location. This can be challenging for utilities in terms of monitoring and recording the data because the households’ facilities can generate or consume the energy without passing it through a meter, increasing the complexity of a distribution network. The speed of transferring data to utilities is another important concern. There is a necessity to send the smart meter (SM) data of each house to a distribution management system (DMS) for more analysis in the shortest possible time. This paper presents a novel deep learning framework collaborating with sequence-to-sequence (seq2seq), long short-term memory (LSTM), and stacked autoencoders (SAEs) to forecast residential load profiles considering the photovoltaic (PV), battery energy storage system (BESS), and electric vehicle (EV) loads with more capability based on pre-defined patterns. Experimental results show that the proposed method achieves outstanding performance in the forecasting process of residential load profiles in comparison with other algorithms. Also, a smart distribution transformer can help utilities to receive the data instantly via wireless communication, which can reduce the transfer duration to every minute and make the prediction and monitoring more manageable considering the different combinations of distributed energy resources (DERs) in residential locations.

State estimation of distribution network based on hybrid measurement combined with multi-source asynchronous data

At present, the redundancy of real-time measurement in distribution network is low and the mixed measurement is incompatible, Hybrid measurement combined with multi-source asynchronous data such as SCADASCADA/ smart distribution transformer information acquisition system (SDTIAS), anew state estimation based on SCADASCADA and SDTIAS is proposed in this paper. Firstly, SDTIAS and SCADA measurements are matched and synchronized; Then, the quadratic multi-source asynchronous measurement model is constructed, the quadratic equality constraint is established, the unified modeling of multi-source asynchronous measurement is realized; Finally, the proposed model and algorithm are simulated and verified in the standard test system, The results show the error seven times lower than traditional methods, and the calculation efficiency (100 Statistics) is increased by 35%, indicating better accuracy and higher efficiency.

A configuration method of computing resources for microservice-based edge computing apparatus in smart distribution transformer area

With the development of the Internet of Things technology in the smart grids, terminal devices such as advanced sensors and smart meters present a trend of massive access in distribution network, which poses great challenges to the information perception, analysis and processing ability of distribution automation system. The microservice-based edge computing apparatus is a novel technology in the smart distribution transformer area to improve the adaptability of the distribution automation system. Research on modeling methods and computing resource capacity planning methods for the microservice-based edge computing apparatus has become an urgent and significant research topic in the modern electrical power system. Therefore, this paper proposes a configuration method of computing resources for the microservice-based edge computing apparatus in the smart distribution transformer area. The suitability of the microservice technology and edge computing apparatus is analyzed, and a model of microservice-based edge computing apparatus is proposed. Inspired by power demand and supply in the power system, a computing resource demand and supply model is proposed to represent the computing resource demand of power Internet of Things services and the computing resource supply of the microservice-based edge computing apparatus. Furthermore, a sequential logic-based evolution algorithm is designed to determine the configured capacities of computing resources for the microservice-based edge computing apparatus considering multi-aggregated service flows. Finally, simulation results validate the effectiveness of the proposed method.

Application of Transformer Heat Circuit Model in Power Substation Station Area

The hot spot temperature of transformers operating in the station area is calculated in real-time using the transformer heat path model as it is impossible to measure the internal winding hot spot temperature directly and modifying the transformer will increase the cost. The smart distribution transformer terminal is an important device for collecting and processing data on the side of the distribution transformer, with a container built from Linux inside to run an application (App) with specific functions developed in the Linux environment. Based on that, the heat circuit model of the transformer is developed into an App installed into the container of the smart distribution transformer terminal to realize the function of the real-time computing and warning of transformer hot spot temperature. It is expected to provide some reference for the transformer operating normally and stably.

Development of Smart Distribution Transformer Terminal Based on Software Defined Technology

In order to cope with the development trend of distribution network, the demand for terminal to have the ability of IoT has become increasingly prominent. This paper firstly expounds the development trend of low-voltage distribution network, and emphasizes the technical transformation of the core equipment of low-voltage distribution network-smart terminal; secondly, it puts forward the software and hardware architecture of smart terminal suitable for the IoT, and emphasizes the cooperative capability of the software and hardware platform; and then it introduces the key technologies of the smart terminal, integrates model standardization and virtualization technologies, realizes the plug and play of information flow, and supports application through engineering; finally, it analyzes the work plan of the next stage, and designs to improve the functions of the smart terminal.

DYNAMIC VOLTAGE CONTROL OF A SMART DISTRIBUTION TRANSFORMER

With the increased penetration of distributed generation, maintaining voltages across distribution networks within statutory limits becomes challenging. To maintain the voltage within statutory limits with the variability of the distributed generation, a dynamic voltage control (DVC) mechanism should be implemented. Such voltage control also facilitates load control especially at peak load periods as the demand depends on the load busbar voltages to some extent. An electronically controlled series compensator based on smart distribution transformer was investigated to achieve the DVC in the low voltage distribution system. The controller design and the operation of the three-phase series compensator and its control are discussed. The proposed topology and control strategies are evaluated on a typical distribution network. From simulations, it was found that the smart distribution transformer can be controlled with less than two cycle response to obtain performance similar to a conventional transformer. Further, the DVC enhances the loadability of the smart distribution transformer.

An Approach for Distribution Transformer Management With a Multiagent System

The objective of this paper is to propose an approach for smart distribution transformer (TR) management to help implement emergency demand response (DR) at a TR. In this paper, a DR algorithm embedded in a multiagent system is presented. The proposed algorithm ensures that an instantaneous power demand at a TR is kept below a certain demand limit during a DR event while the impacts of demand restrike are minimized. At the same time, homeowners' goals of securing critical loads, mitigating comfort level violation, and minimizing appliance's compensation time during a DR event are taken into account. To validate and evaluate the effectiveness of the proposed DR algorithm, a case study of a TR serving three homes is demonstrated.

Spatial-Temporal Solar Power Forecasting for Smart Grids

The solar power penetration in distribution grids is growing fast during the last years, particularly at the low-voltage (LV) level, which introduces new challenges when operating distribution grids. Across the world, distribution system operators (DSO) are developing the smart grid concept, and one key tool for this new paradigm is solar power forecasting. This paper presents a new spatial-temporal forecasting method based on the vector autoregression framework, which combines observations of solar generation collected by smart meters and distribution transformer controllers. The scope is 6-h-ahead forecasts at the residential solar photovoltaic and medium-voltage (MV)/LV substation levels. This framework has been tested in the smart grid pilot of Evora, Portugal, and using data from 44 microgeneration units and 10 MV/LV substations. A benchmark comparison was made with the autoregressive forecasting model (AR-univariate model) leading to an improvement on average between 8% and 10%.

Energoelektroniczne moduły funkcjonalne dla realizacji inteligentnych transformatorów dystrybucyjnych SN/nn dla sieci Smart Grid

With an observed increase in the involvement of active consumers in activities aimed at improving energy efficiency and increasing interest in producing energy from renewable sources, there is a need for the development of new technologies enabling the distribution network operators to offer new services and functionalities. Smart MV/LV distribution transformers are character ized by a compact three-stage design, including an input stage in the form of a controlled power electronic AC-DC converter on the MV side, intermediate stage in the form of a DC-DC converter with isolation implemented at high frequency and an output stage in the form of controlled power electronic DC-AC converter on the LV side. Topologies and functionalities of basic subsystems of smart distribution transformer are discussed in the paper using the Power Electronics Building Blocks concept. The recent results of investigations carried out at Gdansk University of Technology are also presented.