Smart Grid Management System Research Articles

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Certificateless Provable Data Possession Scheme Suitable for Smart Grid Management Systems

A large amount of data are generated at each section of the smart grid. Safely and efficiently storing and managing this data is a crucial challenge faced by the smart grid. Cloud-based smart grid management system (CB-SGMS) is a great choice for addressing this problem. Various departments in the smart grid upload the collected data to the cloud for storage. Due to the lack of physical control over the data stored in the cloud, it is a significant issue to judge that the data are intact. Provable data possession (PDP) scheme can solve this problem. Each department sends data to the cloud and entrusts it to a third-party verifier (TPV) to conduct integrity checks on data. In 2018, He et al. put forward a certificateless provable data possession (CL-PDP) scheme for CB-SGMS and professed that their scheme is secure. Unfortunately, we pointed out that there are security flaws in the scheme by showing two concrete attacks. We then came up with an improved scheme to make up for the security flaws. We presented the security requirements and gave the security proofs based on the discrete logarithm problem. In the end, we execute a comparison on performance for the improved scheme and the other five schemes. The results show that the improved scheme is more secure and efficient than other ones, which makes it suitable for CB-SGMS.

Stability analysis of smart grid management system on campus building

Medan Area University is one of the private universities in North Sumatra that is also involved in the development of renewable energy technology. This technological development has initiated the University's efforts to develop an electrical system by developing a smart grid system in order to reduce the University's operational costs. However, in its realization, it has its own challenges, such as power flow stability and transient stability. Both problems can be analyzed using ETAP 12.6 software. In one of the cases analyzed, the smart grid system can reduce the use of the active power of the transformer by 11 kW and a current of 19.7 Amp. Meanwhile, in the Indonesian State Electricity Company (PLN) off condition, the smart grid system has not been able to supply the load because the availability of renewable energy sources is not sufficient for the load requirement. As well as the results of the voltage and frequency analysis, the highest voltage is 376.4 Volts, the lowest is 375.9 Volts, which is still in the stable category of 380 Volt voltage deviation because it is not +5% and -10%, and the overall frequency of the cases discussed is consistent stable at nominal 50 Hz.

Edge computing Helps the Development of Smart Grid

With the rapid development of smart grids, the number of terminals connected to them has sharply increased. Using these terminals, the smart grid management system can monitor real-time information such as power consumption and power quality in the power grid to ensure safe and stable operation of the power grid. However, the access of a large number of terminals has also increased the amount of data that needs to be processed in the power grid. The traditional centralized cloud computing model can no longer meet the needs of rapid data processing, and a new model is urgently needed to solve the problem. As an emerging distributed computing model, edge computing is an ideal solution to realize large-scale distributed management of smart terminals in the future smart grid by providing computing and storage capabilities at the network edge near device terminals and user sides, reducing data transmission delay, improving data reliability, protecting user privacy, and reducing the computing and storage pressure of cloud data centers.

Smart Grid Management System Based on Machine Learning Algorithms for Efficient Energy Distribution

This abstract describes the smart grid management system is an emerging technology that utilizes machine learning algorithms for efficient energy distribution. The paper presents an overview of the architecture, benefits, and challenges of smart grid management systems. The paper also discusses various machine learning algorithms used in smart grid management systems such as neural networks, decision trees, and Support Vector Machines (SVM). The advantages of using machine learning algorithms in smart grid management systems include increased energy efficiency, reduced energy wastage, improved reliability, and reduced costs. The challenges in implementing machine learning algorithms in smart grid management systems include data security, privacy, and scalability. The paper concludes by discussing future research directions in smart grid management systems based on machine learning algorithms.

A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment

A smart grid (SG), considered as a future electricity grid, utilizes bidirectional electricity and information flow to establish automated and widely distributed power generation. The SG provides a delivery network that has distributed energy sources, real-time asset monitoring, increased power quality, increased stability and reliability, and two-way information sharing. Furthermore, SG provides many advantages, such as demand response, distribution automation, optimized use of electricity, economical energy, real-time grid status monitoring, voltage regulation or VAR control, and electricity storage. In this survey, we explore the literature on smart Grid enabling technologies until 2022. We dig out four major systems: (1) the smart grid’s prominent features and challenges; (2) the smart grid standard system and legislations; (3) smart grid energy subsystem; and (4) the smart grid management system and protection system for new researchers for their future projects. The research challenges and future recommendations are also presented in the conclusion section to explore the new paradigm.

A green energy circular system with carbon capturing and waste minimization in a smart grid power management

Sustainable electricity production is an energy-efficient strategy aimed at both economic progress and environmental conservation. The power generation sector is the major contributor to global emissions and waste. The scope of circular economy in the power production sector with investments in waste minimization and emission reduction was not discussed in previous studies. In this context, a circular sustainable smart electric supply chain system is introduced in this paper with an aim of maximizing the profit with the minimum amount of emissions and waste generated from the power generation units. The power plant in this system consists of four power generation units that generate electricity from coal, waste, wind, and solar plants. A smart grid management system is used in this system for an efficient power supply according to the demands of the customer, and it enhances the system to distribute the power from renewable energies. In this system, the carbon dioxide from the thermal plants is captured and converted to natural gas to reduce emissions. Municipal and industrial waste is transformed into composite fuel to generate electricity. The profit for each unit and the customer’s demand for electricity depends on the circularity index. Linear demand versus linear and logistical cases of unit profit was considered. This study theoretically and numerically maximizes the profit of the system with optimal power consumption, circularity index of electricity, and green and waste minimization investment as per the carbon cap policy. To find the optimal strategies for maximum profit, an algorithm was developed. Sensitivity analysis is done to determine the fluctuations in the profit, emission cost, waste cost, circularity index, and power consumption due to the variations in parameters. The wise allocation of power generation from each unit and reduce energy loss by installing efficient storage devices will result in a higher profit. A system with the high efficacy of green techniques to reduce emissions, a smart grid to minimize waste, higher taxes to regulate emissions, and a rise in the amount of production of electricity from renewable sources will result in a greener power system that gains more profit than conventional systems. The key findings state that electric supply chain systems can obtain a good profit in a more sustainable manner than conventional systems.

IOT integrated smart grid management system for effective energy management

Customers' steadily increasing usage of power equipment causes a mismatch among requirements and supply, which presents a growing issue for power generation. Energy management is an essential tool in minimizing large supply-side shortages and boosting power effectiveness. The present practice in energy systems emphasizes lowering the total price of electricity without restricting use by deciding to decrease electricity usage duringpeaktimings. The aforementioned problem calls for the creation and advancement of an adaptable & moveable technology that serves a broad range of clients and maintains the overall energy balance. The goal of the Intelligent Electricity Management Solution seems to be to compensate for an energy loss of electricity in a territory with managed part-load reduction that caters to consumption habits. The execution of experiments conducted is demonstrated under the assumptions of a power storage occurrence, the maximum demand restriction using various scenarios, and adjusting the preference allocated to each equipment. In Intelligent Power Management System (IPMS), there are price-optimization techniques depending on the duration of use and flexibility using detector information elements. A home location network with efficient ZigBee connectivity has been constructed, and an IoT framework has been created for predictive analytics and archiving.

ПРОБЛЕМИ УПРАВЛІННЯ В СИСТЕМАХ SMART GRID УНІВЕРСИТЕТСЬКОГО ХАБА ЕНЕРГОЕФЕКТИВНОСТІ

The article attempts to tackle the issues of enhancing the performance of university energy efficiency management systems. An emphasis is put that in modern realia, alternative and renewable energy sources are becoming increasingly important in the electric power sector, thus contributing to environmental protection and enabling active electricity consumers to have their own sources of energy generation. However, it is observed that the relationships between energy generation sources and electricity consumers are complicated by new demands for setting balancing modes due to certain volatility of energy generation by alternative sources as well as the need to connect additional energy storage facilities. To identify opportunities of using Smart Grid technologies to manage the University energy consumption, a power balance equation was used to determine an active power balance between generated power, generation sources and power consumed by electricity consumers. In addition, the indicators of the total active power loss in the electrical network associated with the technological consumption of energy for its transmission was included into this equation. The study presents the results of an in-depth critical analysis on Smart Grid methodology and provides argument for the relevance of using artificial intelligence techniques in Smart Grid management systems of the University energy efficiency hub, along with suggesting a notion of electricity generating consumer in the concept of intelligent networks with two-way flow of energy and information as subsystems of a different nature. It is argued that the developed conceptual model of the electricity generating consumer for multilevel smart grid management systems and their infrastructure within the University energy efficiency hub allows establishing relationships between its structural elements and objects of different character. The findings reveal that the specifics of the developed method in setting priorities and regulatory standards for optimal management by a generating consumer within the University energy efficiency hub is the possibility of its automatic adaptation to changes in the external environment subject to interactions between electricity generating consumers.

Solar Energy Production Forecast Using Standard Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit

Solar radiation is among the renewable resources on which modern society relies to partially replace the existing fossil fuel-based energy resources. Awareness of how the energy is produced must complement awareness of how it is consumed. In the economic context, the gains derive from predictability across the entire supply chain. This paper represents a compressive study on how standard recurrent neural networks, long short-term memory, and gated recurrent units can be used to forecast power production of photovoltaic (PV) systems. This approach can be used for other use cases in solar or even wind power prediction since it provides solid fundamentals for working with weather data and recurrent artificial neural networks, being the core of any smart grid management system. Few studies have explored how these models should be implemented, and even fewer have compared the outcomes of different model types. The data used consist of weather and power production data with a one-hour resolution. The data were further pre-processed to unveil the maximum information. The most effective model parameters were selected to make the forecast. Solar energy plays a key role among other renewable energy sources in the European Union’s climate action and the European Green Deal. Under these initiatives, important regulations are implemented and financial resources made available for those who possess the capabilities required to solve the open points. The much-needed predictability that gives the flexibility and robustness needed for deploying and adopting more renewable technologies can be ensured by utilizing a neural-based predictive approach.

DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany

Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.

Real-Time Demand Response Using NB-IoT

The Internet of Things (IoT) already connects billions of devices and keeps growing exponentially. These devices are designed to be integrated with industrial machines, home appliances, and infrastructures. One such use case is to build a smart grid management system that relies on demand-response techniques to control the appliances automatically so that the power can be distributed optimally. The mission-critical smart grid communications require secure, reliable, two-way communicable, and latency bounded connections between the management system and the electrical appliances. To realize these, cellular technology is arguably the most feasible solution. 3GPP has already released the narrowband-IoT (NB-IoT) standards as the low-power dense-area coverage IoT cellular solution. In this article, we present an NB-IoT system to monitor and control the connected electrical appliances in a smart grid network. The platform is also capable of configuring the network dynamically. We assess the latency performance for our solution using the commercially available Orange network in Belgium. It is observed that NB-IoT enabled devices can be controlled and monitored with a maximum latency less than 8 s in the deep-indoor environment and within 2 s for the outdoor environment.

Optimal Smart Grid Management System in Campus Building

The utilization of well-managed electrical energy sources will result in high energy efficiency and reliability. Smart grid uses electricity management with 2-way communication that allows loads and sources to corporate each other. Campus is a place that requires priority in the availability of energy and it requires smart grid management. This research will contain smart grid management systems on campus that use multisource to fulfil dynamic loads conditions so as to produce optimal smart grid management. The method that use to analysis the system is conventional method. The optimal smart grid achieved by analysis the sources and loads energy needed and then create a management system that have substantial impact on campus electrical system. The results of this research that smart grid system ensures electrical conditions for the needs of these dynamic loads can be fulfilled which is without a smart grid there is lack of energy for 3 days, whereas with a smart grid there is no lack of energy in the campus building. Keywords : Smart Grid, Campus, Management

A Study on Development and Evolution of the Intelligent Power Grid Information Communication Architecture

According to the development plan of smart grid and the future demand of smart grid, the construction principles and development objectives of smart grid are discussed and analyzed. Based on the existing grid resources, and on the basis of ensuring full utilization of resources, a smart grid management system with significant advantages is realized, which fully guarantees the management convenience, reusability and portability of the system. On the basis of researching and analyzing the rules of the smart grid system architecture, an intelligent grid management system named “System of Systems” (SOS) is designed and developed, which is based on the open standard hierarchical service and replaces the old business silos with an integrated system. The smart grid system combines the scientific evolution route with service-oriented as the core feature to achieve a highly integrated management system to ensure the standardization of system data interaction protocols and the adequacy of data interfaces. In order to achieve good adaptability and compatibility of the smart grid system, the system can be optimized and adjusted flexibly and conveniently according to future changes in demand to ensure the reliability and stability of the system.

Agent‐based decentralised load flow computation for smart management of distribution system

Centralised operation of distribution system suffer from inherent difficulties like increased complexities with increased system size, consequent frequent upgradation, delayed decision making and so on. Paradigm shift towards decentralised operations overcomes the aforesaid problems and assist in automated grid operations. Load flow is an indispensable tool, where the system operator does centralised calculation of state variables. This study proposes a novel approach towards decentralised load flow computation for weakly meshed distribution system. Agent technology is employed to design the framework which is based on agent relaying concept. The agents are located on system buses, and have limited access to information pertaining to system topology and nodal injections. By coordinating with neighbouring agents, they aim towards the fulfilment of their own goal, i.e. bus voltage calculation. To achieve this, several behaviours such as forward sweep, backward sweep, break point calculations and scenario generation are incorporated in the proposed system design. The solution thus obtained is decentralised in nature. The agent designing is done under JAVA environment on JADE platform. Two case studies considering 15 bus and 69 bus radial distribution systems are simulated with the proposed concept to show the validity and applicability of the proposed framework for smart grid management systems.

Optimization of Experimental Model Parameter Identification for Energy Storage Systems

The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.

Impact of charging efficiency variations on the effectiveness of variable-rate-based charging strategies for electric vehicles

The huge energy demand coming from the increasing diffusion of plug-in electric vehicles (PEVs) poses a significant challenge to electricity utilities and vehicle manufacturers in developing smart charging systems interacting in real time with distribution grids. These systems will have to implement smart charging strategies for PEV batteries on the basis of negotiation phases between the user and the electric utility regarding information about battery chemistries, tariffs, required energy and time available for completing the charging. Strategies which adapt the charging current to grid load conditions are very attractive. Indeed, they allow full exploitation of the grid capacity, with a consequent greater final state of charge and higher utility financial profits with respect to approaches based on a fixed charging rate. The paper demonstrates that the charging current should be chosen also taking into account the effect that different charging rates may have on the charging efficiency. To this aim, the performances of two smart variable-rate-based charging strategies, taken as examples, are compared by considering possible realistic relationships between the charging efficiency and the charging rate. The analysis gives useful guidelines for the development of smart charging strategies for PEVs as well as for next-generation battery charging and smart grid management systems.