• Different digitalization techniques in the energy sector is briefly outlined • Impacts of digitalization in smart grids is critically analyzed. • Integration of the renewable energy sources are given priority during the selection of digitalization methods. • Maximum energy efficiency attainment using demand response is ensured in the processes. Decarbonization, decentralization, and digitalization are essential for advanced energy systems (AES), which encompass smart grids, renewable energy integration, and demand response initiatives. Digitalization is a significant trend that transforms societal, economic, and environmental processes globally. This shift moves us from traditional power grids to decentralized, intelligent networks that enhance efficiency, reliability, and sustainability. By integrating data and connectivity, these technologies optimize energy production, distribution, and consumption. This article presents a comprehensive literature review of four closely related emerging technologies: Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Digital Twin (DT) in AES. Our findings from the previous works indicate that AI significantly improves Demand Response strategies by enhancing the prediction, optimization, and management of energy consumption. Techniques like linear regression effectively predict power demand and aggregated loads, while more complex methods such as Support Vector Regression (SVR) and reinforcement learning (RL) optimize appliance scheduling and load forecasting. The integration of IoT technologies into Energy Management Systems (EMS) further enhances efficiency and sustainability through real-time monitoring and automated control. Additionally, DT technology aids in simulating energy scenarios and optimizing consumption in both residential and commercial smart grids. Our findings also emphasize blockchain’s role in creating decentralized energy trading platforms, facilitating peer-to-peer transactions, and enhancing trust through smart contracts. The insights gained from this review highlight the essential role of these emerging technologies in supporting decentralized, intelligent energy networks, offering valuable strategies for stakeholders to navigate the complexities of the evolving digital energy landscape.
Oct 1, 2024·Proceedings of the Twenty-fifth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers' strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers' private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers' private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers' truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers' equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by [EQUATION] when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism.
Christian Winzer, Héctor Ramírez-Molina, Lion Hirth, Ingmar Schlecht
Decarbonization involves a large-scale expansion of low-carbon generators such as wind and solar and the electrification of heating and transport. Both space heating and battery-electric cars have significant embedded flexibility potential. Granular price signals that convey abundance or scarcity of electricity are a precondition for customers or aggregators acting on their behalf to exploit this flexibility. However, unmitigated real-time prices expose customers to electricity price risks. To tackle the dual need of providing flexibility incentives while protecting customers from cost shocks, real-time tariffs with a hedging component can be a solution. In such contracts customers pre-agree an amount of energy and a consumption profile, while hourly deviations are charged at spot prices. In this paper we analyze design options by using a dataset of anonymized smart meter data and show that profile tariffs can bring electricity bill volatility to similarly low levels as fixed tariffs while providing full flexibility incentives from spot prices. • Profile contracts reduce bill volatility to similar levels as fixed price contracts. • Profile contracts restore flexibility incentives suppressed by fixed price contracts. • Profile contracts may reduce bill of flexible customers compared to fixed prices. • Demand for profile contracts expected to increase as load flexibility increases.
Park microgrids, valued for their efficiency and flexibility, require privacy-conscious energy management to ensure a trusted scheduling and trading environment. This paper, focusing on park microgrids with shared energy storage, designs an energy management strategy that comprehensively considers shared energy storage, scheduling transparency, and privacy security. First, a blockchain-based energy management platform is established, forming an energy dispatch consensus committee to execute decentralized scheduling management and decision-making. Next, an optimized energy scheduling smart contract for park microgrids is designed, considering Time-of-Use (ToU) pricing and storage arbitrage to formulate the day-ahead electricity purchase and sales plans as well as the shared energy storage operation plans. Then, a privacy protection strategy based on the Shamir secret sharing scheme is proposed, effectively preventing data leakage during blockchain interactions. Finally, through case analysis, the superiority of the proposed method in microgrid optimized scheduling, data tamper-resistance, and privacy protection is demonstrated.
G.B. Bhavana, R. S. Anand, J. Ramprabhakar, Veerpratap Meena · 6 authors
Countries all over the world are shifting from conventional and fossil fuel-based energy systems to more sustainable energy systems (renewable energy-based systems). To effectively integrate renewable sources of energy, multi-directional power flow and control are required, and to facilitate this multi-directional power flow, peer-to-peer (P2P) trading is employed. For a safe, secure, and reliable P2P trading system, a secure communication gateway and a cryptographically secure data storage mechanism are required. This paper explores the uses of blockchain (BC) in renewable energy (RE) integration into the grid. We shed light on four primary areas: P2P energy trading, the green hydrogen supply chain, demand response (DR) programmes, and the tracking of RE certificates (RECs). In addition, we investigate how BC can address the existing challenges in these domains and overcome these hurdles to realise a decentralised energy ecosystem. The main purpose of this paper is to provide an understanding of how BC technology can act as a catalyst for a multi-directional energy flow, ultimately revolutionising the way energy is generated, managed, and consumed.
• The article highlights the significance of smart grids in managing energy demand, addressing environmental issues, and ensuring energy security, emphasizing the need for blockchain technology and AI. • The study discusses data collection from smart city power consumption in a smart grid, utilizing techniques like Z-Score normalization and Spatial Temporal Correlation, and suggests blockchain technology for secure data transmission and storage. • The paper proposes a LSTM-RNN-ISSA for improved load forecasting accuracy and discusses the use of Blockchain-Based Smart Energy trading for effective communication in the smart grid. • Task-Oriented Communication facilitates real-time demand response, balancing electrical load and supply, outperforming existing approaches in smart city energy management. A smart grid (SG) is the financial benefit of a complicated and smart power system that can keep up with rising demand. It has to do with saving energy and being environmentally friendly. Growing populations and new technologies have caused a big rise in energy use, causing big problems for the environment and energy security. It is essential and significant to use blockchain technology and artificial intelligence (AI) to solve problems with power control. Data can be collected using a smart city in a power-consumed smart grid data and pre-process using a Z-Score normalization technique. It can extract features using a Spatial-Temporal Correlation (STC) to assess smart grid power usage within the context of a smart city using large-scale, high-dimensional data. Ensuring data integrity, privacy, and trust among grid applicants, transmit the data securely and reliably to a centralized or distributed cloud platform utilizing blockchain technology—a secure transmission and storage using Distributed Authentication and Authorization (DAA) protocol. To achieve precise load forecasting, a short-term recurrent neural network with an improved sparrow search algorithm (LSTM-RNN-ISSA) is incorporated. The smart grid may then record the projected results. Communication can be done on a smart grid with the users; the Blockchain-Based Smart Energy Trading with Adaptive Volt-VAR Optimization (BSET-AVVO) algorithm can be used for effective communication—a quick balancing electrical load and supply via a task-oriented communication mechanism in real-time demand response. Finally, our proposed method performs successfully better than the existing approaches.
Presently Rural Energy Communities (REC) are faced with challenges such as the inefficient distribution of energy from Renewable Energy Sources (RES), unfair pricing, and the inclusion of prosumers into the electricity market. Therefore, this article proposed an approach that employed enabling technologies such as Distributed Ledger Technologies (DLT), self-enforcing smart contracts-enabled Internet of Things (IoT), and Artificial Intelligence (AI) for sustainable energy sharing and tracking in REC. Additionally, a model is proposed based on key factors that influence the adoption of enabling technologies in REC. For the methodology qualitative data is collected from secondary sources and descriptive analysis is employed to present the key findings. Key findings from this study contributes to develop a decarbonized, decentralized, and digitized energy management approach to support the sustainability of REC. The deployment of AI can facilitate prediction short-term energy planning for RES production and consumption based on real-time data from IoT devices. More importantly, findings from this study presents use case scenarios of energy sharing and tracking, and green electric vehicle charging in REC suggesting that DLT based smart contracts, IoT, and AI offers an effective approach to accelerate the sharing and tracking of RES in REC. Besides, DLT and smart contracts enables real-time electricity consumption monitoring, energy trading management, and pricing.
Mithul Raaj A T, B. Saravana Balaji, Sai Arun Pravin R R, Rani Chinnappa Naidu · 10 authors
In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.
R. Mahesh, Kartik Anilkumar, S Shwetha, Dr.K.Pavan Kumar · 6 authors
Efficiency, security, and transparency have been improved by the smart grid energy management system's combination of IoT and blockchain technologies. Real-time data is collected by IoT devices, and safe transactions are recorded in a decentralized ledger using blockchain. In this chapter, the important elements have been discussed with distributed ledgers, smart meters, and sensors. Demand response, integration of renewable energy sources, and grid resilience have been enhanced by successful implementations. Issues related to interoperability, privacy, and scalability are tackled. The use of AI and ML in energy management, demand forecasting, and anomaly detection is also described in this chapter.
M. L. Sworna Kokila, R Sunitha, P. Kalyanakumar, J. Relin Francis Raj · 6 authors
This research investigates Blockchain-enabled energy trading within microgrids to enhance sustainable computing infrastructure. A novel algorithm, GridCoin, is proposed and evaluated against existing methodologies including Optimal Power Flow (OPF), Microgrid Energy Management System (MEMS), and Hierarchical Control Algorithms (HCA). The study employs simulation metrics such as Transaction Throughput, Energy Trading Efficiency, and Transaction Latency to compare the performance of these algorithms. GridCoin demonstrates superior capabilities in optimizing energy trading operations, achieving higher transaction throughput, improved efficiency in energy utilization, and reduced transaction latency. These results highlight the potential of Blockchain technology to revolutionize energy markets by providing secure, transparent, and decentralized transaction mechanisms. Future research directions include enhancing optimization techniques, integrating renewable energy sources, addressing scalability and security challenges, conducting real-world deployments, and analyzing economic and policy implications. This study contributes to advancing sustainable computing infrastructure through innovative Blockchain-based solutions.
Microgrids are regarded as vital components in contemporary realm of energy system improvement, resilience, and sustainability. In this paper a novel decentralized peer-to-peer energy trading system leveraging blockchain technology is proposed. The proposed model not only demonstrates the implementation of blockchain technology in microgrids but also transforms the energy sector by emphasizing decentralization, security and efficiency. This research aims to enhance the energy trading system by including smart contracts written on the Ethereum network. Energy Token and Demand Response contracts are integrated to enable dynamic interactions inside microgrids, which leads to a transparent, secure, and efficient energy trading system. Moreover, automation of energy transactions and elimination of intermediaries ensure cost effectiveness and utilization of excess energy potentially reduce dependency on the main grid. A microgrid system is designed in Simulink for distributed energy trading. Energy credits are represented by standard ERC-20 digital token and then demand response contract dynamically adjusts rewards and energy prices based on energy production and consumption. the ERC-20 contract manages the token transaction and then demand response contract enforces governance rules. The purpose of a demand response contract is to enable the automatic and efficient control of energy usage in response to changing demand and supply conditions. The use of the Web3 library further facilitates a direct and smooth connection between the blockchain network and microgrids. The results demonstrate the successful implementation of both smart contracts, making trading possible at noon when the combined generation from solar array and energy management system exceed the energy demand. • A P2P decentralized energy trading model leveraging blockchain technology to enhance microgrid efficiency and sustainability. • ERC-20 tokens are used for trading purpose. • Demand Response contract is written for balanced microgrid operation. • Employs Web3 library for direct and smooth interaction between microgrid and blockchain.
The convergence of financial technology and sustainability has given rise to green fintech, an innovative field leveraging cutting-edge technologies to address environmental challenges through financial solutions. This review explores the evolution of green fintech, focusing on the transformative roles of Artificial Intelligence (AI), Internet of Things (IoT), and smart contracts in developing sustainable financial services. Through a comprehensive analysis of recent literature and case studies, we examine how AI enhances ESG assessments, enables data-driven sustainable investment strategies, and facilitates green lending practices. We investigate IoT applications in environmental monitoring, supply chain transparency, and smart grid integration, highlighting their contributions to sustainable finance. The implementation of smart contracts for sustainability is explored, discussing their potential in green bonds, carbon credit trading, and renewable energy markets. The paper addresses key challenges facing green fintech, including data quality issues, privacy concerns, and regulatory uncertainties, proposing future directions for research and development. Our findings suggest that the integration of AI, IoT, and smart contracts in green fintech has significant potential to accelerate the transition to a sustainable global economy by embedding environmental considerations into financial decision-making at all levels. This article contributes to the growing body of literature on sustainable finance, providing insights for practitioners, policymakers, and researchers. It underscores the need for a multidisciplinary approach to overcome technological, regulatory, and socio-economic barriers, paving the way for a more sustainable and technologically advanced financial ecosystem.
Pavan Ramchandra Padghan, Arul Daniel Samuel, Raja Pitchaimuthu
Cooperative power-sharing (CPS) is one of the cutting-edge power management strategies the power system planners contemplate transforming from its traditional structure to a more decentralized framework as the globe prepares for a future with low carbon emissions. Cooperative microgrids are considered one of the future power system configurations where individual microgrids (MGs) can support each other through CPS. This paper introduces a decentralized energy market (DEM) to operate CPS hierarchically between MGs, groups of MGs, and the utility grid. The security of power-sharing transactions and the privacy of MGs pose challenges in terms of confidentiality and data integrity. To deal with these issues, a DEM framework based on distributed ledger technology (DLT) is proposed to ensure power transactions between cooperative MGs with a guarantee. A scheme is built on the Ethereum testnet to estimate the performance and validation of the proposed smart contracts. A case study with actual data for a group of four MGs is presented to validate the effectiveness of the proposed framework. The proposed blockchain approach results in money transactions resulting in MG1 receiving $485; while MG2 gets $525, MG3 and MG4 generates a revenue of $435 and $362.5 respectively. The numerical results demonstrate that MGs can actively share power with peers and achieve economic benefits.
This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.
The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework potential to support auxiliary grid services.
Around the world policymakers and regulators are struggling with the question of how to design retail electricity tariffs in the face of increasing penetration of local generation (e.g., solar PV), smart appliances, local storage, and electric vehicles. There is a widespread recognition that retail tariffs should vary dynamically across time and space, reflecting the changing conditions (congestion and losses) on the underlying networks. But, at the same time, there is recognition that such tariffs potentially expose retail customers to substantial risk. Risk averse retail customers desire protection against price spikes and volatile wholesale spot prices. This paper seeks to derive the optimal retail contract in the special case in which the uncertainty in the market is contractible (in the sense defined here). We show that the optimal retail contract exposes the prosumer to the wholesale spot price at the margin, but also perfectly insulates the customer from risk, achieving the first-best outcome. We show how the hedge component of this retail contract can be constructed from standard-form hedge contracts. We draw out several lessons for policymakers.
Animesh Giri, Abhishek V Sagarnal, R Chinmaya, Annapurna Dammur
The main goal of this research is to look at how smart grid can be implemented and automatic billing can be done by leveraging blockchain technology. It transforms the conventional way of managing energy using smart contracts. Self-executing contracts on blockchain can change the traditional approaches towards billing within the smart grid infrastructure. Our focus is to reduce manual work and minimal intervention by third parties. Smart contract approach ensures that billing is more efficient., timely and accurate. Blockchain technology is also mentioned in this analysis., which has made it possible to record transactions in an immutable manner using a distributed ledger. This transparency enhances trust-building among stakeholders as well as responsible action within the system. Lastly., the cryptographic mechanisms underlying blockchain provide transaction integrity and confidentiality., thus improving its security and reducing the risks of breaching or unauthorized access of data. The main objective of this research work is to show how blockchain-based systems for billing in smart grids can offer a more advanced foundation for energy management. They are very efficient., secure and open unlike any other option within that industry. Therefore., through blockchain technology and smart contracts can be used by major stakeholders in the energy industry to improve their billings and streamline operations that they do. Henceforth., this sets grounds for a sustainable and elastic future of energy.
Meeting the targets of Sustainable Development Goal (SDG) 7, which focuses on ensuring access to affordable, reliable, sustainable, and modern energy for all, poses significant challenges. Overcoming these hurdles requires innovative solutions that can bridge the gap between current capabilities and future needs. Swarm electrification emerges as a promising concept that could accelerate progress towards achieving SDG 7 goals by leveraging the collective power of decentralized energy resources. This paper presents a literature review on swarm electrification and related insights from case studies. The study delves into the concept of swarm electrification, placing it within the context of the prevailing trends in the power system sector: decentralization, decarbonization, and digitalization. It examines the role of digital technologies in enhancing swarm electrification and categorizes application areas according to the phases of swarm electrification. Particular attention is given to the technologies underpinning Deep Digitalization, such as distributed ledger technology, notably blockchain, and artificial intelligence, with a focus on machine learning. These technologies play pivotal roles in advancing swarm electrification. The review demonstrates how deep digitalization can facilitate the improvement of swarm electrification and ultimately support the integration of bottom-up initiatives with top-down grid expansion efforts over time.