Smart grid for a sustainable future

Advances in micro-electro-mechanical systems (MEMS) and information communication technology (ICT) have facilitated the development of integrated electrical power systems for the future. A recent major issue is the need for a healthy and sustainable power transmission and distribution system that is smart, reliable and climate-friendly. Therefore, at the start of the 21st Century, Government, utilities and research communities are working jointly to develop an intelligent grid system, which is now known as a smart grid. Smart grid will provide highly consistent and reliable services, efficient energy management practices, smart metering integration, automation and precision decision support systems and self healing facilities. Smart grid will also bring benefits of seamless integration of renewable energy sources to the power networks. This paper focuses on the benefits and probable deployment issues of smart grid technology for a sustainable future both nationally and internationally. This paper also investigates the ongoing major research programs in Europe, America and Australia for smart grid and the associated enabling technologies. Finally, this study explores the prospects and characteristics of renewable energy sources with possible deployment integration issues to develop a clean energy smart grid technology for an intelligent power system

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

Achieving High Renewable Energy Integration in Smart Grids with Machine Learning

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. A large number of works have been devoted to the optimization integration of the RES and ESS system to the traditional grid, along with combining the ESS scheduling control with the traditional Optimal Power Flow (OPF) control. Cybersecurity problem focusing on the RES integrated grid has also gradually aroused researchers’ interest. In recent years, machine learning techniques have emerged in different research field including optimizing renewable energy integration in smart grids. Reinforcement learning (RL), which trains agent to interact with the environment by making sequential decisions to maximize the expected future reward, is used as an optimization tool. This dissertation explores the application of RL algorithms and models to achieve high renewable energy integration in smart grids. The research questions focus on the effectiveness, benefits of renewable energy integration to individual consumers and electricity utilities, applying machine learning techniques in optimizing the behaviors of the ESS and the generators and other components in the grid. The objectives of this research are to investigate the current algorithms of renewable energy integration in smart grids, explore RL algorithms, develop novel RL-based models and algorithms for optimization control and cybersecurity, evaluate their performance through simulations on real-world data set, and provide practical recommendations for implementation. The research approach includes a comprehensive literature review to understand the challenges and opportunities associated with renewable energy integration. Various optimization algorithms, such as linear programming (LP), dynamic programming (DP) and various RL algorithms, such as Deep Q-Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), are applied to solve problems during renewable energy integration in smart grids. Simulation studies on real-world data, including different types of loads, solar and wind energy profiles, are used to evaluate the performance and effectiveness of the proposed machine learning techniques. The results provide insights into the capabilities and limitations of machine learning in solving the optimization problems in the power system. Compared with traditional optimization tools, the RL approach has the advantage of real-time implementation, with the cost being the training time and unguaranteed model performance. Recommendations and guidelines for practical implementation of RL algorithms on power systems are provided in the appendix

Achieving High Renewable Energy Integration in Smart Grids with Machine Learning

The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. A large number of works have been devoted to the optimization integration of the RES and ESS system to the traditional grid, along with combining the ESS scheduling control with the traditional Optimal Power Flow (OPF) control. Cybersecurity problem focusing on the RES integrated grid has also gradually aroused researchers’ interest. In recent years, machine learning techniques have emerged in different research field including optimizing renewable energy integration in smart grids. Reinforcement learning (RL), which trains agent to interact with the environment by making sequential decisions to maximize the expected future reward, is used as an optimization tool. This dissertation explores the application of RL algorithms and models to achieve high renewable energy integration in smart grids. The research questions focus on the effectiveness, benefits of renewable energy integration to individual consumers and electricity utilities, applying machine learning techniques in optimizing the behaviors of the ESS and the generators and other components in the grid. The objectives of this research are to investigate the current algorithms of renewable energy integration in smart grids, explore RL algorithms, develop novel RL-based models and algorithms for optimization control and cybersecurity, evaluate their performance through simulations on real-world data set, and provide practical recommendations for implementation. The research approach includes a comprehensive literature review to understand the challenges and opportunities associated with renewable energy integration. Various optimization algorithms, such as linear programming (LP), dynamic programming (DP) and various RL algorithms, such as Deep Q-Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), are applied to solve problems during renewable energy integration in smart grids. Simulation studies on real-world data, including different types of loads, solar and wind energy profiles, are used to evaluate the performance and effectiveness of the proposed machine learning techniques. The results provide insights into the capabilities and limitations of machine learning in solving the optimization problems in the power system. Compared with traditional optimization tools, the RL approach has the advantage of real-time implementation, with the cost being the training time and unguaranteed model performance. Recommendations and guidelines for practical implementation of RL algorithms on power systems are provided in the appendix

Smart Grid Voltage Sag Detection using Instantaneous Features Extraction

International audienceSmart grids have initiated a radical reappraisal of distribution networks function where the integration of renewable energy sources, load demand control, and effective use of the network are indexed as the most important keys for smart grid expansion and deployment regardless each country policies. One of the most efficient ways of effective use of these grids would be to continuously monitor their conditions. This allows for early detection of power quality degeneration facilitating therefore a proactive response, prevent a fault ride-through the renewable power sources, minimizing downtime, and maximizing productivity. In this smart grid context, this paper proposes the evaluation and comparison of advanced signal processing tools, namely the Hilbert transform and the ensemble empirical mode decomposition method for the detection of voltage sags as they are the most commonly encountered power quality disturbances

Electric vehicles in Smart Grids: Performance considerations

Distributed power system is the basic architecture of current power systems and demands close cooperation among the generation, transmission and distribution systems. Excessive greenhouse gas emissions over the last decade have driven a move to a more sustainable energy system. This has involved integrating renewable energy sources like wind and solar power into the distributed generation system. Renewable sources offer more opportunities for end users to participate in the power delivery system and to make this distribution system even more efficient, the novel Smart Grid concept has emerged. A Smart Grid: offers a two-way communication between the source and the load; integrates renewable sources into the generation system; and provides reliability and sustainability in the entire power system from generation through to ultimate power consumption. Unreliability in continuous production poses challenges for deploying renewable sources in a real-time power delivery system. Different storage options could address this unreliability issue, but they consume electrical energy and create signifcant costs and carbon emissions. An alternative is using electric vehicles and plug-in electric vehicles, with two-way power transfer capability (Grid-to-Vehicle and Vehicle-to-Grid), as temporary distributed energy storage devices. A perfect fit can be charging the vehicle batteries from the renewable sources and discharging the batteries when the grid needs them the most. This will substantially reduce carbon emissions from both the energy and the transportation sector while enhancing the reliability of using renewables. However, participation of these vehicles into the grid discharge program is understandably limited by the concerns of vehicle owners over the battery lifetime and revenue outcomes. A major challenge is to find ways to make vehicle integration more effective and economic for both the vehicle owners and the utility grid. This research addresses problems such as how to increase the average lifetime of vehicles while discharging to the grid; how to make this two-way power transfer economically viable; how to increase the vehicle participation rate; and how to make the whole system more reliable and sustainable. Different methods and techniques are investigated to successfully integrate the electric vehicles into the power system. This research also investigates the economic benefits of using the vehicle batteries in their second life as energy storage units thus reducing storage energy costs for the grid operators, and creating revenue for the vehicle owners

Artificial Intelligent Based Energy and Demand Side Management for Microgrids and Smart Homes Considering Customer Privacy

The rapid development of various power electronics applications facilitates the integration of many smart grid applications in recent years. However, integration of intermittent renewable energy sources, highly stochastic electric vehicles (EVs) activities on the grid and time-varying smart loads have increased the level of grid vulnerability to unusual and high complexity and quality-related problems. Among these problems is to accurately estimate the real contribution and consumption of household loads, in the era of smart appliances and interoperability operation, and its relative impact to the grid’s operation. Specifically, household loads represent a significant percentage of electrical energy consumption and, therefore, could offer great prosperity to the rise of the demand-side management (DSM) programs, which subsequently improve the stability of the grid’s operation. As a result, our main focus in this dissertation is to develop DSM strategies based on Artificial Intelligence (AI) techniques to properly model and estimate the amount of support smart homes could offer to the smart grids and microgrid’s operation. Throughout the way to achieve our goals, we develop an energy management framework for smart homes that operate in efficient and reliable microgrids with multiple energy sources and energy storage applications to meet the demands at a stable voltage and frequency limits. Furthermore, we develop a precise short-term load forecasting (STLF), which is a critical tool needed to manage a DSM program for residential loads that have very high uncertainty and volatility in load consumption. We also develop an energy exchange portal with communication sources, demands, and connectivity information between each consumer and the local power utility at the distribution level. Finally, creative AI methodologies have been developed throughout the way to facilitate the integration, control, and management of the DSM programs taking into account the consumers’ own privacy and security. The security of the DSM is provided by preserving the indoor privacy of the smart homes by sharing limited and encoded data among household appliances controllers

Potential and Impacts of Smart Transformer in Green Harbours

Harbour grids are undergoing rapid transformation due to the increased interest in green harbour initiatives such as ship cold ironing, renewable energy integration, battery-powered marine vessels, etc. In this scenario, better controllability over the power flow is important to maintain the voltage and current quality within the grid-code specified limits and ensure a stable and efficient power supply. This paper aims to explore the potential of the smart transformer (ST) in providing various support features to green harbours. The features include the integration and control capability of ST in accommodating renewable energy sources, electric vehicle charging stations and storage. In addition, the impact of the ST in the green harbour is analyzed with the focus of addressing the key issues and challenges such as voltage variations, peak loads and poor power factor

Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consumption, utilizing various techniques like artificial neural networks, support vector machines, and decision trees. Furthermore, data preprocessing methods, such as data splitting, normalization, decomposition, and discretization, are employed to enhance prediction accuracy. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency, more effective responses to demand, and better integration of renewable energy sources. Nevertheless, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed. The research investigates various ML applications within the realms of solar energy, wind energy, and electric distribution and storage, illustrating their potential to optimize energy systems. To sum up, this research demonstrates the evolving landscape of the electricity sector as it shifts from centralized to decentralized solutions through the application of ML innovations and distributed decision-making, ultimately shaping a more efficient and sustainable energy future

Communication requirements for risk-limiting dispatch in smart grid

The existing power grid infrastructures in many countries are primarily based on technologies that have been developed as centralized systems in which power is generated at major power plants and distributed to consumers through transmission and distribution lines. In the recent decade, with the increasing penetration of renewable energy sources such as solar and wind power, and smart electrical appliances, the centralized model may no longer hold, and the supply and demand for electricity become more dynamic. Moreover, the latest developed information and communication technologies (ICT) and power electronic technologies could enhance the efficiency and performance of power system operations. Recently, concerns with global warming have prompted many countries to announce research programs on smart grid, which is the transformation of the traditional electric power grid into an energy-efficient and environmentally friendly grid by the integration of ICT, power electronic, storage and control technologies. With the smart grid, there is an opportunity for a new operating paradigm that recognizes the changing structures of the power grid with renewable generation, and the high-resolution data, high speed communications, and high performance computation available with the advanced information infrastructure. A new operating paradigm, namely, risk-limiting dispatch, is proposed for the smart grid in this paper. In addition, we have identified the requirements of a communication infrastructure to support this new operating paradigm. ©2010 IEEE.published_or_final_versionThe IEEE International Conference on Communications Workshops (ICC 2010), Capetown, South Africa, 23-27 May 2010. In Proceedings of ICC, 2010, p. 1-