Smart Electrical Distribution Research Articles

Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning

The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals to obtain a home energy management system (HEMS), which allows reducing the use of electrical energy. This article incorporates artificial intelligence techniques to demand response, allowing control, switching, turning on and off of appliances, modifying and reducing consumption, and achieving improvements in the quality of life in the home. In addition, an architecture based on a smart socket and an artificial intelligence model that recognizes the consumption of electrical appliances in high resolution (sampling every 10 s) is proposed. The system uses the Wi-Fi communication protocol, ensuring that the smart sockets wirelessly provide the data obtained to the public cloud. The use of Deep Learning allows us to obtain a central control model of the home, which, when interconnected to the smart electrical distribution networks of companies, could generate a positive impact on the environmental effects and CO2 reduction.

Moment-Based Distributionally Robust Peer-to-Peer Transactive Energy Trading Framework Between Networked Microgrids, Smart Parking Lots and Electricity Distribution Network

Moment-Based Distributionally Robust Peer-to-Peer Transactive Energy Trading Framework Between Networked Microgrids, Smart Parking Lots and Electricity Distribution Network

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

Game-Theoretic Decision Making for Intelligent Power Consumption Analysis

With smart electricity distribution and dependable electric appliances, the revolutionary impact of the Internet of Things (IoT) technology has considerably improved the service-oriented features of the power grid industry. In the current study, a methodology for IoT-based electricity distribution for intelligent homes is described to detect power consumption efficiently. Although effective power resource allocation remains a primary issue for every power grid house, poor energy distribution has significantly influenced everyday living. The current study focuses on the effective distribution of electricity resources by power grid houses over a spatial-temporal basis. Specifically, the Spatial-Temporal Consumption Index is calculated for each home in a geographical region based on electricity usage, which enables the effective allocation of power resources. Additionally, an automated game-theoretic decision-making model is proposed to assist power grid house managers in optimizing the spatial-temporal distribution of electricity resources. For validation purposes, a simulated environment is used to monitor 4 smart houses for 60 days. A comparative analysis with state-of-the-art data assessment methodologies shows that the presented approach is significantly better in terms of statistical parameters of temporal delay (113.24s), Classification Efficacy (Precision (93.23%), Sensitivity (92.34%), and Specificity (92.34%), Decision-making Efficiency, Reliability (88.45%) and Stability (72%).

Concepts, Configurations, and Challenges of Solid-State Transformer: A Review

Abstract: Rapid and suddenly increasing use of small distributed renewable energy resources (DRER) and distributed energy storage (DES), reduced compact dynamic electrical power consumer-like electrical vehicle loads, the requirement of bidirectional power flow communication network to exchange critical information, other ancillary services, etc. has to be envisaged and fulfilled by a future smart distribution grid system. A solid-state transformer (SST) has more potential solutions to provide stable and efficient distribution system operation to overcome the above problem.From various designs of converter control schemes, the solid-state transformer concept has been evaluated and summarised. The benefits and downsides of converters in the AC/DC, DC/DC, and DC/AC stages are studied for the best configuration. The High Frequency Transformer(HFT) is a main part of SST.The design and optimization of a HFT is approached on the objective of minimising the weight and size of the SST.Researchers also face several challanges in the prototype design and implementation of SST before operating effectively in the distribution system which is presented . Some of expected solutions and future recommendations for the establishment of the solid state transformer for future smart electrical distribution systems are discussed.

Operational concerns and solutions in smart electricity distribution systems

Interfacing high penetration of Renewable Energy Sources (RES) with energy storage and microgrid control systems is an essential feature of future distribution grids for optimal utilization and management of Distributed Energy Resources (DERs). This feature allows distributed system operators to follow the right path to transform their traditional grids into smart grids. With the continued expansion of DERs, the current distribution grid is moving toward a carbon-free electricity system and greener energy-transportation applications. However, there are some practical concerns as well as technical and operational challenges in an interconnected power network. Therefore, this paper reviews the most prominent issues associated with smart grid, such as large-scale distributed generation integration, energy storage, energy trading, operational planning, service reliability, and cyber resiliency, in light of recent technological advancements. Through reviewing several emerging technologies, this study shows the necessary methodologies for establishing smart energy distribution. Finally, this study highlights future research prospects for a fully functional smart grid system.

A hybrid robust-stochastic approach for unit commitment scheduling in integrated thermal electrical systems considering high penetration of solar power

With increasing environmental concerns, limited oil reserves and governmental incentives, installation of photovoltaic (PV) systems have gained substantial momentum in smart electric distribution systems (EDS), which is considered to be a great success. That said, widespread integration of these PV units, comes with uncertainty issues that puts the flexibility of the system at stake. In this regard, local district heating systems (DHS) are considered to be an effective tool in enhancing the flexibility of the power system on account of their high thermal inertia. In this study, we propose hybrid robust (RO)- stochastic programming (SP) optimization framework for commitment scheduling of distributed generation units and local DHS considering high penetration of PV units. Furthermore, the impact of flexible thermal and electrical loads on total operational cost and overall flexibility of the system is evaluated. The SP is utilized to deal obscurity of solar radiation, and RO is utilized to handle price uncertainties. The uncertainty management via the electrical storage system is also scrutinized. The results are evaluated by different cases studies implemented on an integrated IEEE 33-bus EDS and an 8-node DHS. Eventually, the mixed integer linear problem is solved with a standard CPLEX solver.

Design and development of RFID based Multi-User Smart Energy Distribution System at Sunyani Technical University, Ghana

In Ghana families living in large and compound houses desire to have separate energy meters from other occupants. This is because individuals cannot monitor and control the energy consumption of other tenants and results in misunderstanding in energy bill sharing, overcrowding of energy meters on the wall of a building and likelihood of fire outbreak. It is therefore expedient to have a very accurate, efficient and peaceful means of delivering electrical energy to multiple users within a household with just a single energy meter and this has necessitated the introduction of Smart Multi-user Energy Distribution Meter (SEDS). In this paper microcontroller based smart electrical distribution switch that has the capability of allocating independent energy units to several users in a household is proposed. Experiment was done by assigning Energy units of 4kWh, 8kWh and 6kWh to three logging points on the SEDS device representing three different users. A load of 100W was connected representing the consumption of individual user. The results indicate that the rate of consumption is dependent on the total load connected. The SEDS device has different switches allocated to each user which monitor the consumption rate of the user and automatically isolate a user when power gets exhausted which serve the same purpose as several users having different energy meters. However, individual consumption can be monitored and controlled by the user.

Real-time day ahead energy management for smart home using machine learning algorithm

Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error.

Digital twin for smart electricity distribution networks

This paper presents the results of composing a digital model of critical states of the power distribution network with intelligent control. A specific example of a power network with thirty transformer substations connected by main and reserve cables is discussed. The analysis of critical events occurring in the power system was carried out, and a set of decision rules was formed to counter these events. A practical need was noted for regular updating and expansion of the list of critical events and corresponding parrying rules. It is shown that it is expedient to use faceted active neural networks with a structure similar to that of the controlled object as a digital twin model of intelligent control of the system under consideration. The use of such neural networks based on the rule “one event - one ensemble of neurons” provides the digital duplicate model with the ability to unlimited cumulative expansion, similar to the evolutionary property of natural biological systems.

Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems

Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability.

Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources

Abstract High penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in electrical distribution grids has changed the energy balance of distribution system operators (DSO). For this purpose, the energy scheduling problem of a Residential smart electrical distribution grid (RSEDG) with RESs and demand side management (DSM) is modeled as a tri-objective model consisting of: (1) minimization the operation cost, and emission pollutions in generation side; (2) minimization the loss of load expectation (LOLE) in demand side, and (3) minimization the deviation between the demand curve and output power of RESs. The third objective function is used as a DSM strategy to model the joint scheduling of RESs and the deferrable loads (DLs) where the DLs can be shifted regarding the output power of RESs to improve the load factor (LF). The uncertain behavior of the RESs are modeled using the stochastic optimization approach. The proposed model is solved using the epsilon-constraint method. Since the proposed approach has three objective functions; different Pareto solutions are obtained and the best solution is determined by the decision-making method. To investigate the effectiveness of the proposed method, it is applied on the 83-bus distribution grid and its results are compared for four cases studies.

Technology, business model, and market design adaptation toward smart electricity distribution: Insights for policy making

The transformation of the electricity sector towards a sustainable energy supply and use has a disruptive potential for infrastructure and utilities. The spread of digital technologies, renewable energy, and prosumers requires a swift and well-guided adaptation of the electricity distribution industry to smart grid technologies and related business models. This paper, based on the large technical systems (LTS) conceptual framework, investigates the complex evolution and company and market design adaptation needs. Challenges and opportunities are analyzed through nine multi-stakeholder workshops, held in two EU member states (Germany and Portugal) in 2016–2017, engaging distribution system operators, researchers, academics, and integrated utility companies. The results indicate considerable uncertainty for distribution system operators regarding the value of large-scale smart meter rollouts. Also, a corporate culture with resistance to change is observed, challenging the integration of novel technologies and processes. Traditional regulation is seen as a barrier to smart grid investments, and is associated with job losses and knowledge destruction. Policy-makers can benefit from these insights on the dynamics of DSOs, which can contribute to public policy design and market reform which traditionally has often been mainly concerned about operational efficiency in a steady-state, stable economy.

Assessment of electricity distribution business model and market design alternatives: Evidence for policy design

The transition toward smart electricity distribution grids is transforming the European electricity sector. This has contributed to increased attention by policy makers regarding the future role of electricity distribution system operators in a more flexible, digital, and renewables-based electricity system. A better understanding on how to support the adaptation of the electricity distribution industry can contribute to the introduction of an effective policy framework. Our research provides evidence for policy design by presenting the results of a Policy Delphi study focused on business model and market design adaptation alternatives. We highlight the importance of supporting innovation and a more proactive approach to adaptation from both distribution system operators and regulators. Our findings support the importance of electricity distribution for neutral market facilitation, contributing to market development and enabling new market players. The results obtained support policy makers working on electricity sector adaptation and can contribute to the ongoing market redesign efforts under the Energy Union.

Lifetime-Based Power Routing in Parallel Converters for Smart Transformer Application

The use of a smart transformer-based electrical distribution could be an effective approach to reorganizing the electric grid and solving the problems and challenges of distributed generation systems and active loads. While lower efficiency and higher cost compared with the conventional transformer are well known and investigated limiting factors, the required higher maintenance, related to the use of electronics systems in the electric grid, is seldom targeted. In fact, high maintenance cost would make the use of smart transformers inapplicable even if new services could justify higher initial cost and new semiconductor development could increase the overall efficiency. This work proposes a modular repairable system based on condition monitoring, which aims at equalizing the lifetime consumption of the single modules to make possible prognostic maintenance.

Technology, Business Model, and Market Design Adaptation Toward Smart Electricity Distribution: Insights for Policy Making

The ongoing European Union sustainable energy transition has a disruptive potential regarding the role of infrastructure and utilities in the electricity sector. The increased spread of digital technologies, renewable energy sources, and prosumers calls for a swift and well-guided adaptation of the electricity distribution industry towards a smart grids context. We analyze the challenges and opportunities associated with this adaptation through nine multi-stakeholder workshops, held in Germany and Portugal in 2016 – 2017, engaging distribution system operators (DSOs), researchers, academics, and integrated utility companies to obtain up-to-date insights. Our results indicate uncertainty regarding the value of large-scale rollout of smart meters for DSOs. Also, a corporate culture with resistance to change is observed, challenging the integration of novel technologies and processes. Traditional regulation is seen as a barrier to smart grid investments, is associated with job losses, and knowledge destruction. Policy-makers can benefit from these insights by taking them into account in policy design and market restructuring.

Smart electricity distribution networks, business models, and application for developing countries

The electricity distribution industry in the developing world is dominated by public sector utilities. These utilities are technically and economically inefficient and face large financial losses that prevent further development of the networks. The reforms are introduced to unbundle the vertically integrated utilities and introduce private investment and management, but these efforts often fail due to poorly designed market and incentive models. Focusing on the electricity distribution in developing countries, we propose a business model based on organizing the utilities into distinct activities and businesses. In many developing countries political economy and weak investment conditions do not favour full privatization of utilities. However, in some activities ‘competition for the market’ and ‘management contracts’ can provide a partial role for the private actors. The model is based on the concepts of enterprise innovation, emphasizing external collaboration and partnerships and can be operationalized via specialization, where some specific tasks are outsourced. The proposed model can also be applied to developed countries as the energy sectors of both developing and developed countries are undergoing transformation due to new technologies such as communications, distributed generation (DGs), and active demand response.

An open source tool for reliability evaluation of distribution systems with renewable generators

Future smart electrical distribution systems could adopt switch remote control and advanced automation systems to operate in autonomous mode (islanding) some portions of the network in order to improve system reliability. In this perspective, a free code able to assess distribution reliability by using analytical or Monte Carlo simulation methods accounting for intentional islanding operation is presented. An effective network representation and data structure, which have led up to the implementation of two automatic and general procedures, are proposed. The procedures share common underlying information in order to speed up calculation. A further merit of the tool is that it enables the user to quickly and simply create the input files. The reliability indices of three distribution networks have been computed by means of the developed software for different scenarios, that is when island mode of operation is not permitted by regulation, and when island mode is permitted (intentional islanding) with the local load either partially or totally supplied by renewable distributed generators. The free code, the input files and the results related to the considered networks are available online at: http://www.dieei.unict.it/users/sconti/psrs.htm.

On Study of Construction of New Generation Intelligent Communication Network for Distribution System

many new technological means are integrated in the new network of smart electricity distribution including electric power technology, control technology and information technology, and in this area, people carry out profound research to the construction of communication network and power distribution network. This paper analyzes specific structures of the communication network for distribution system, discusses the development trend of it, explores its technical system in depth, and finally proposes some concrete constructive strategies, hoping to be a valuable reference for related research persons.

Enabling European electrical transmission and distribution smart grids by standards

Smart electrical transmission and distribution grids are seen as key facilitators of the industry innovation that are required to meet the challenges of de-carbonization, operation performance excellence, and consumer centricity. Electricity grids represent a complex system of systems with intense interactions between them and require the collection of large amounts of data from a variety of sources to improve the energy ecosystem operation. In this paper, we describe an IBM framework that combines industry standards and advanced technologies—including analytics, mobile, and IoT (Internet of Things)—to meet the smart grid industry challenge. The Smart Grid Architecture Model (SGAM) and accompanying standards are used as a reference to ensure the openness, loose coupling, and flexibility of the framework. A consolidated and standardized data model bridging traditional utility silos is essential for analytics in this framework and represents a canonical model for integration. While offering advanced service-oriented architecture and application-programming interface integration concepts, the framework allows for retention of traditional utilities' legacy applications, preserving investments made. The framework permits for mix-and-match of the flexible building blocks linked by industry standards, promoting an incremental deployment of services as per utility needs.