Evolutionary Algorithms for Energy Scheduling under uncertainty considering Multiple Aggregators

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

The growth in renewable and distributed generation is driving the gradual evolution of the methods used to plan and operate transmission and distribution networks. Smart Grid technologies in the area of Active Network Management (ANM) are expected to facilitate the increased connection of renewable and distributed generators to electricity networks in the near-term. Other Smart Grid technologies are expected to emerge over time to enable the transition to a low carbon economy. ANM schemes are emerging in the areas of power flow management and voltage management and are the subject of much international research and development. This paper provides an overview of the Orkney Smart Grid, which involves the application of a novel ANM scheme to enable increased renewable generator connections to the existing Orkney network. A review of ANM scheme performance to date and lessons learned from the development and deployment of a multiple generator, multiple network constraint ANM scheme is presented. In addition, the authors discuss the desirable characteristics and challenges for ANM schemes to achieve near-term deployment.

Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. Due to the nature of the problem, which includes mixed-integer variables and non-linear constraints, Evolutionary Algorithms (EA) are considered a good tool to find optimal and near-optimal solutions to large-scale problems. In this paper, we analyze the application of Differential Evolution (DE) to solve the large-scale ERM problem in SGs through extensive experimentation on a case study using a 33-Bus power network with high penetration of Distributed Energy Resources (DER) and Electric Vehicles (EVs), as well as advanced features such as energy stock exchanges and Demand Response (DR) programs. We analyze the impact of DE parameter setting on four state-of-the-art DE strategies. Moreover, DE strategies are compared with other well-known EAs and a deterministic approach based on MINLP. Results suggest that, even when DE strategies are very sensitive to the setting of their parameters, they can find better solutions than other EAs, and near-optimal solutions in acceptable times compared with an MINLP approach.

Approaching to the Smartest technological use the whole world is being aware of using the smartest technology of generating and distributing power throughout the country in a simple but effective system. Smart Grid technology is providing with this unique feature. A lot of features make it possible to differentiate the Smart Grid from the existing traditional grid in various manners. This paper basically focused on the Smart Grid (SG) technology. A technology that has let us dreamt about solution of maximum power problems including generation to distribution ends. In this time of technological advancement where all the governments are facing challenges of meeting the power demand of the consumers, smart grid can bring harmony in demand supply ratio. In this paper a brief detail about Smart Grid Technology, its application has been discussed.

There is a growing interest in 'smart grid' technologies in both industry and academic circles. Few attempts have been made to develop a written specification consummated with standards agreed upon by members of both coteries, due to lack of government support. Utilities in the state of California are obligated, by state legislature, to create a more: efficient, reliable, and intelligent electric power system. This initiative along with Florida Power & Lighting's 'Smart Grid' pilot program has created a sense of exigency within the industry regarding smart grid technologies and standardizations. Their accomplishments are beginning to shape the policies and standards with marginal input from academic societies, ushering in a very lopsided, and business acclimatized set of standards. We will present and analyze, a SCE 'Smart Grid' use case, in which the utilities back office applications interact with the customer's meter, and provide technical recommendations for system security improvements.

Smart grid technologies are expected to increase system efficiency and reliability using advancement in automation, communication, computation, and optimization. Several control algorithms have been developed or enhanced for operation and control of smart distribution grid. In recent years, volt/var control (VVC) algorithms have been proposed for energy saving in distribution systems due to advancement provided by smart grid automation and control. VVC helps to lower distribution feeder voltage to decrease energy consumption. However, quantification of this impact is hard due to several external factors such as weather and end users' behavior. On the other hand, existing evaluation methods of VVC algorithms are either not suitable for long-term study or intrusive. This paper addresses these issues by proposing a new technique to estimate “distribution system demand without smart grid implementation” utilizing “data measured from system with smart grid implementation.” Interdependence of voltage and load is considered based on estimated load characteristic. Our key contribution is developing a method for quantifying the real-time energy saving of smart grid automation and control focused on VVC and implementing in an electric utility in eastern Washington, USA. The algorithm is developed using both commercial and open-source software.

In the smart grid (SG) era, the energy resource management (ERM) in power systems is facing an increase in complexity, mainly due to the high penetration of distributed resources, such as renewable energy and electric vehicles (EVs). Therefore, advanced control techniques and sophisticated planning tools are required to take advantage of the benefits that SG technologies can provide. In this paper, we introduce a new approach called multi-dimensional signaling evolutionary algorithm (MDS-EA) to solve the large-scale ERM problem in SGs. The proposed method uses the general framework from evolutionary algorithms (EAs), combined with a previously proposed rule-based mechanism called multi-dimensional signaling (MDS). In this way, the proposed MDS-EA evolves a population of solutions by modifying variables of interest identified during the evaluation process. Results show that the proposed method can reduce the complexity of metaheuristics implementation while achieving competitive solutions compared with EAs and deterministic approaches in acceptable times.

This paper explores the transformative role of smart grid technology in simplifying contemporary energy management complexities. By integrating advanced communication and automation, smart grids enhance efficiency, reliability, and sustainability in power distribution, paving the way for a more resilient and responsive energy infrastructure Through the integration of cutting-edge communication and automation, smart grids not only bolster efficiency but also elevate the reliability and sustainability of power distribution. This research sheds light on the potential of smart grids to create a more resilient and responsive energy infrastructure, addressing the challenges of our rapidly evolving energy landscape. The exploration extends beyond the surface benefits, delving into the nuanced ways in which smart grids empower energy systems to adapt and evolve. This adaptive prowess not only simplifies the complexities of energy management but also fosters a more resilient and responsive energy infrastructure. The intricate interplay of technological advancements within smart grids forms the bedrock for a paradigm shift in how we conceptualize, distribute, and consume energy resources. As the world grapples with the pressing need for sustainable energy solutions, this research sheds light on the multifaceted contributions of smart grid technology. By paving the way for a harmonized and dynamic energy ecosystem, smart grids stand poised to redefine the future of energy management, offering a promising path towards a more sustainable and resilient global energy landscape. Keywords-Advanced Metering Infrastructure (AMI), Distributed Energy Resources (DERs)

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

This study systematically reviews the cybersecurity challenges in smart grid technologies and their implications for sustainable energy infrastructure. The primary objective is to analyze the vulnerabilities inherent in smart grids, evaluate existing cybersecurity measures, and propose strategic recommendations for enhancing security. Employing a systematic literature review and content analysis, this research scrutinizes peer-reviewed articles, technical reports, and standards documents published between 2014 and 2024. The methodology focuses on identifying cybersecurity threats, vulnerabilities, and mitigation strategies within smart grids, alongside exploring the role of standards, regulatory frameworks, and stakeholder responsibilities in enhancing grid security. Key findings reveal that while smart grids offer enhanced efficiency and reliability, they also introduce significant cybersecurity vulnerabilities due to the integration of ICT. Advanced cybersecurity measures, including encryption and real-time intrusion detection, are critical in safeguarding these infrastructures. The study underscores the importance of collaborative efforts among stakeholders and the development of comprehensive cybersecurity standards tailored to smart grid technologies. Finally, the future of smart grid cybersecurity presents both challenges and opportunities, with the potential for leveraging emerging technologies like blockchain and AI to enhance security measures. The study proposes several policy recommendations, including the enforcement of cybersecurity standards and the promotion of threat intelligence sharing. Conclusively, the research highlights the need for innovative security solutions and interdisciplinary research to bridge technical and policy-making domains, ensuring the secure and sustainable development of smart grid technologies. Keywords: Smart Grid Cybersecurity, Cybersecurity Vulnerabilities, Cybersecurity Measures, Sustainable Energy Infrastructure.

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

This paper represents the necessity, challenges and development opportunities of smart grid technology in perspective of Bangladesh. In Bangladesh, power has been one of the major concerns from a long time. Smart grid technology is the modern concept of the grid system that has self-detection and management process. In the current power system, there has been a lot of mismanagements, lot of hazardous situations. So the necessity of smart grid technology establishment is very high now. In establishing the smart grid technology, there are lot of challenges to handle, lot of problems to solve. This paper gives an overall idea about the challenges that can be faced in establishing the smart grid technology. It also offers some suggestions about the establishment. In power system, security is a big issue. This paper focuses on all the possible security issues with the solution. Different development opportunities are also mentioned in the paper. The smart grid is not only associated with the production of electricity but also it has smart system of storage, transmission, distribution and use. From a smart home to a smart industry, a smart grid technology incorporates all the necessary things. For country like Bangladesh, smart grid technology can make the country to flourish for the further developments.

Achieving a low carbon economy has been the focus of Hong Kong in recent years. Strategies are put forth to support sustainable energy, because Hong Kong has inadequate local and renewable energy reserves. Smart grid technologies have benefited many countries in the world, and countries such as the United States, the United Kingdom, China, and South Korea are exploring this new green energy strategy. Hong Kong’s power companies have considered smart grid technologies, conducting pilot projects to test their local feasibility. In this research, stakeholders’ perspectives of the current status of smart grids are overviewed in Hong Kong from the angle of policy aspect. Stakeholder theory is utilized as the framework for understanding and analyzing the perceptions of stakeholder groups. An integrated and modified stakeholder power-interest matrix was developed for analyzing level of power and interest of stakeholder groups on smart grid from policy dimension.
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\nThrough the course of this research project, qualitative interviews were conducted for 11 interviewees with subjectsfrom1six stakeholder groups, including: academics, advisory bodies, business, government, non-governmental organizations, and power companies. Follow-up quantitative questionnaires were distributed to the same target group for stakeholder analysis and to map the level of power and interest of stakeholder groups regarding smart grid policy development in Hong Kong. The data collected from interviews were compared with and contrasted against each stakeholder groups and other countries, with a view to identifying common motivations, barriers, and policy suggestions. The findings reveal that the six stakeholder groups hold medium to high power and interest in smart grid policy development in Hong Kong.
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\nCommon motivations identified by local stakeholder groups include: the right to choose their own energy mix for power generation, potential behavioural change via smart meter and tariff restructuring, possible energy savings and emissions reductions for the environment, and corporate social responsibility. The most significant motivation for smart grid development shared with other countries is the goal of achieving low carbon economy and energy efficiency.
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\nShared barriers perceived by the six stakeholder groups are: unreliable renewable power supplies, significant capital investments required, impracticalities of supplying electricity back to the grid, transfers of additional power generation costs to consumers, and limited smart grid knowledge by the public. The most significant barrier shared with international countries is funding mechanism, yet it may be difficult for Hong Kong to secure funding from the government because of problems of trust. 
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\nThree stages of recommendations are suggested. Short-term recommendations centre on the policy development direction of smart grid in Hong Kong, and include implementing dynamic pricing, undertaking a large-scale pilot program and establishing an awareness campaign (funded by the government)for educating the general public. Medium-term recommendation is to establish a joint green fund with the contributions of companies with large carbon footprints. Long-term recommendations include market liberalization and increases in market competition.

India is truculent to meet the electric power demands of a fast expanding economy. Restructuring of the power industry has only increased several challenges for the power system engineers. The proposed vision of introducing viable Smart Grid (SG) at various levels in the Indian power systems has recommended that an advanced automation mechanism needs to be adapted. Smart Grids are introduced to make the grid operation smarter and intelligent. Smart grid operations, upon appropriate deployment can open up new avenues and opportunities with significant financial implications. This paper presents various Smart grid initiatives and implications in the context of power market evolution in India. Various examples of existing structures of automation in India are employed to underscore some of the views presented in this paper. It also reviews the progress made in Smart grid technology research and development since its inception. Attempts are made to highlight the current and future issues involved for the development of Smart Grid technology for future demands in Indian perspective.

As a result of the old-style electric grid's inability to handle the extensive incorporation of renewable energy resources and distributed renewable energy resources into the electric grid. In the twenty-first century, smart grid technology has entered the picture. Because smart grids operate on two-way communication, communication and information technology are critical to the smart grid's effective operation. The advanced version of the smart grid structure is presented in this article. Furthermore, this article discusses existing and advanced communication and information technology. Finally, the manuscript discusses future research advancements in communication and the most crucial information technologies, such as the Internet of Things (IoT), cloud computing, fog and edge computing, and blockchain in the smart grid. So, the smart grid can manage the massive integration of renewable energy resources while providing customers with efficient, sustainable, and reliable power.KeywordsIoT-Internet of ThingICT-Information and Communication TechnologyCC: Cloud Computing