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"Energy trading"
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Day‐ahead continuous double auction‐based peer‐to‐peer energy trading platform incorporating trading losses and network utilisation fee
Integration of distributed energy resources, such as photovoltaic solar (PV), introduces new opportunities to establish local energy market frameworks to improve renewable energy utilisation in residential sectors. Such peer‐to‐peer (P2P) energy trading refers to a local market structure where customers (and prosumers) interact to share excess PV generation to enhance the individual and community social welfare. In this work, a day‐ahead continuous double auction (CDA)‐based P2P market structure considering network losses and network utilisation fees was designed. Day‐ahead PV energy is modelled using fractional integral polynomials and the output is forecasted using an autoregressive integrated moving average model for each market interval. Based on the customer load and excess PV energy, the CDA market is cleared using a bid/ask matching mechanism. The performance of the P2P market was evaluated by computing different welfare metrics while analysing the effect of network constraints. The results show that the designed CDA‐based P2P market structure increases the social welfare of all participants by an average of 17.75% compared to the baseline for the presented cases. Moreover, the impact of the forecasting error between the day‐ahead and real‐time market was also quantified. This article presents a modified continuous double auction‐based peer‐to‐peer trading market structure incorporating the network constraints. The proposed market framework has been evaluated for different practical test scenarios to evaluate the performance of the market under the varying real‐time parameters. The manuscript contributes to develop a practical energy trading platform where different residential customers can interact autonomously to enhance their welfare.
Journal Article
Framework of locality electricity trading system for profitable peer‐to‐peer power transaction in locality electricity market
This paper proposes an architecture of locality electricity market (LEM) for peer‐to‐peer (P2P) energy trading among a group of residential prosumers (consumers and producers) with renewable energy resources, smart meters, information and communication technologies, and home energy management systems in a smart residential locality. Prosumers may sell(buy) their excess generation(demand) in LEM at a profitable prices compared to the utility prices in P2P fashion. In order to manage the trading in LEM, a common portal named as locality electricity trading system (LETS) is introduced. The purpose of LETS is to prepare a trading agreement between the participants by fixing a price for every deal based on the quoted price and day‐ahead power trading schedule given by the participants. An enhanced intelligent residential energy management system (EIREMS) is proposed at the prosumers' premises to enable their participation in the day‐ahead energy trading process and in real‐time scheduling of schedulable loads and battery for reducing the electricity bill with due consideration to the operational constraints and LETS agreement. The performances of proposed LETS and EIREMS are validated through a few case studies on a locality with ten prosumers. The proposed methodology endorses marginal economic benefit for all the participants.
Journal Article
Recent Trends and Issues of Energy Management Systems Using Machine Learning
Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.
Journal Article
A Review of Peer-to-Peer Energy Trading Markets: Enabling Models and Technologies
This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies.
Journal Article
Emergence of blockchain-technology application in peer-to-peer electrical-energy trading: a review
Abstract
Renewable-energy resources require overwhelming adoption by the common masses for safeguarding the environment from pollution. In this context, the prosumer is an important emerging concept. A prosumer in simple terms is the one who consumes as well as produces electricity and sells it either to the grid or to a neighbour. In the present scenario, peer-to-peer (P2P) energy trading is gaining momentum as a new vista of research that is viewed as a possible way for prosumers to sell energy to neighbours. Enabling P2P energy trading is the only method of making renewable-energy sources popular among the common masses. For making P2P energy trading successful, blockchain technology is sparking considerable interest among researchers. Combined with smart contracts, a blockchain provides secure tamper-proof records of transactions that are recorded in distributed ledgers that are immutable. This paper explores, using a thorough review of recently published research work, how the existing power sector is reshaping in the direction of P2P energy trading with the application of blockchain technology. Various challenges that are being faced by researchers in the implementation of blockchain technology in the energy sector are discussed. Further, this paper presents different start-ups that have emerged in the energy-sector domain that are using blockchain technology. To give insight into the application of blockchain technology in the energy sector, a case of the application of blockchain technology in P2P trading in electrical-vehicle charging is discussed. At the end, some possible areas of research in the application of blockchain technology in the energy sector are discussed.
Peer-to-peer (P2P) energy trading can create new markets and help consumers save money, but not without new tools. Blockchain can allow safe, secure P2P energy trading. This paper provides a thorough review and case studies.
Graphical Abstract
Journal Article
A Smart Microgrid Platform Integrating AI and Deep Reinforcement Learning for Sustainable Energy Management
Smart microgrids (SMGs) have emerged as a key solution to enhance energy management and sustainability within decentralized energy systems. This paper presents SmartGrid AI, a platform integrating deep reinforcement learning (DRL) and neural networks to optimize energy consumption, predict demand, and facilitate peer-to-peer (P2P) energy trading. The platform dynamically adapts to real-time energy demand and supply fluctuations, achieving a 23% reduction in energy costs, a 40% decrease in grid dependency, and an 85% renewable energy utilization rate. Furthermore, AI-driven P2P trading mechanisms demonstrate that 18% of electricity consumption is handled through efficient decentralized exchanges. The integration of vehicle-to-home (V2H) technology allows electric vehicle (EV) batteries to store surplus renewable energy and supply 15% of household energy demand during peak hours. Real-time data from Saudi Arabia validated the system’s performance, highlighting its scalability and adaptability to diverse energy market conditions. The quantitative results suggest that SmartGrid AI is a revolutionary method of sustainable and cost-effective energy management in SMGs.
Journal Article
Peer-to-Peer Energy Trading Case Study Using an AI-Powered Community Energy Management System
The Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.
Journal Article
Efficient Simulator for P2P Energy Trading: Customizable Bid Preferences for Trading Agents
Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator’s ability to analyze trading outcomes while accounting for participant risk aversion preferences.
Journal Article
Blockchain and Secure Element, a Hybrid Approach for Secure Energy Smart Meter Gateways
This paper presents a new hybrid approach that is suitable for application to energy smart meter gateways, based on combining both blockchain and Secure Element (SE) technologies serving the roles of a secure distributed data storage system and an essential component for building a “root of trust” in IoT platforms simultaneously. Blockchain technology alone may not completely secure a transaction because it only guarantees data immutability, while in most cases, the data has to be also secured at the point of generation. The proposed combinational approach aims to build a robust root of trust by introducing the SE, which will provide IoT devices with trusted computed resources. The feasibility of the proposed method is validated by testing three different implementation scenarios, using different Secure Element systems (SES) combined with blockchain and LPWAN communication technologies to encrypt, transmit, and save data. This hybrid approach aids in overcoming the obstructions of using any one technology alone, and its use is demonstrated with a case study for an Energy Smart Metering gateway that enables the implementation of a local Peer to Peer energy trading scheme that is end-to-end secure and decentralized.
Journal Article