Energy Trading In Smart Grid Research Articles

Decentralized Energy Trading in Smart Grids: A Blockchain-Based Framework for Efficiency and Transparency

Decentralized Energy Trading in Smart Grids: A Blockchain-Based Framework for Efficiency and Transparency

A novel socio-economic-environmental model to maximize prosumer satisfaction in smart residential complexes

As the adoption of distributed and renewable energy resources helps consumers evolve into prosumers who generate and trade their own energy, optimizing distributed renewable energy resources, electric vehicles, and energy storage in smart residential complexes is crucial to enhance satisfaction and capitalize on their potential for environmental and economic benefits. Nonetheless, limited progress has been made to quantify the impact of prosumer and energy manager preferences and the integrated socio-economic-environmental dynamics on energy scheduling and stakeholder satisfaction within smart communities. This study aims to overcome this challenge by proposing a socio-economic-environmental optimization model based on a preference-oriented management approach, which quantifies the impact of energy scheduling on stakeholder satisfaction in smart communities. The model inputs include infrastructure configurations, energy consumption patterns, and weather data. The simulation method includes the application of deep learning and System Advisor Model software in predicting weather conditions and photovoltaic and wind turbine systems energy generation. The simulation results are then imported as parameters in GAMS software to optimize the stakeholder satisfaction using CPLEX solver. Quantitative results show that with the simultaneous use of electric vehicles and energy exchange between energy communities, the highest level of satisfaction of the stakeholders in the smart grid including energy communities has been achieved at hour 24 and at the rate of 0.99 per-unit. The model reveals notable enhancements in stakeholder satisfaction, particularly through the integration of electric vehicles and energy trading in smart grids. It underscores the critical impact of aligning renewable energy strategies with prosumer socio-economic-environmental preferences, offering valuable insights for energy managers, policymakers, and community planners in fostering efficient, community-oriented smart grid systems.

Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids

The proliferation of electric vehicles (EVs) and the advancement of Vehicle-to-Everything (V2X) energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This paper proposes a novel Federated Reinforcement Learning (FRL) system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of Proof of State of Charge (PoSOC) as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system.

Decentralized Energy Trading In Smart Grids: A Blockchain-Based Framework for Efficiency and Transparency

Decentralized Energy Trading In Smart Grids: A Blockchain-Based Framework for Efficiency and Transparency

Decentralized peer-to-peer model of energy trading in smart grid considering price differentiation

An increase in electricity demand has sparked a new trend to restructure energy markets as consumer-centric marketplaces. One of the possible techniques for establishing decentralized energy market models is peer-to-peer (P2P) energy trading. In P2P energy trading, market players are encouraged to trade energy among themselves through direct negotiations. This paper presents a decentralized P2P model of energy trading considering price differentiation in the smart grid. Price differentiation offers different pricing for different market players based on their demands and preferences. Here, market participants exchange energy at decided price to optimize their welfare without exceeding constraints. Simulation studies are used to analyze the effectiveness of the proposed approach. To validate the efficiency of the proposed method, P2P energy trading with price differentiation results are compared with the results of P2P energy trading without price differentiation. It is clearly observed that the social welfare of the market players with price differentiation is increased by 49.64%

Blockchain and cooperative game theory for peer-to-peer energy trading in smart grids

Peer-to-peer energy trading allows smart grid-connected parties to trade renewable energy among each other. It is widely considered as a scheme to mitigate the supply–demand imbalances during peak-hour. However, the main challenge of such a scheme is the lack of secure, efficient, and transparent systems that foster economic incentives for users. This paper proposes a novel system that combines cooperative game theory and blockchain technology to stimulate users to maximize their profit and trade energy securely. In our model, users can store renewable energy credits as assets in the blockchain and trade them with others. We also develop a modified version of Proof of Energy Generation (PoEG) by including distribution line loss as a consensus mechanism among blockchain energy trading members. Then, we propose a coalition formation algorithm based on PoEG protocol and optimization technique to determine the winning coalition as block miners. The system is validated through comprehensive theoretical analysis and simulation experiments utilizing a standard IEEE 14-bus system and a real-life dataset (Ausgrid). Also, the developed model is implemented on the Avalanche blockchain platform. The test results show that the proposed scheme improves prosumers’ financial benefits by 6.5% in terms of energy savings compared to the baseline model. The experiments demonstrate the effectiveness, capabilities, and robustness of the proposed system.

Incentive-compatible and budget balanced AGV mechanism for peer-to-peer energy trading in smart grids

Peer-to-peer (P2P) energy trading refers to a type of decentralized transaction, where the energy from distributed energy resources is directly traded between peers. A key challenge in peer-to-peer energy trading is designing a safe, efficient, and transparent trading model and operating mechanism. In this study, we consider a P2P trading environment based on blockchain technology, where prosumers can submit bids or offers without knowing the reports of others. We propose an Arrow-d’Aspremont-Gerard-Varet (AGV)-based mechanism to encourage prosumers to submit their real reserve price and determine the P2P transaction price. We demonstrate that the AGV mechanism can achieve Bayesian incentive compatibility and budget balance. Kernel density estimation (KDE) is used to derive the prior distribution from the historical bid/offer information of the agents. Case studies are carried out to analyze and evaluate the proposed mechanism. Simulation results verify the effectiveness of the proposed mechanism in guiding agents to report the true reserve price while maximizing social welfare. Moreover, we discuss the advantages of budget balance for decentralized trading by comparing the Vickrey-Clarke-Groves (VCG) and AGV mechanisms.

Peer-to-peer energy trading in smart grid: Frameworks, implementation methodologies, and demonstration projects

• A comprehensive review of existing research and pilot projects on P2P energy trading. • Detailed analysis of the implementation methodologies with mathematical formulations. • Discussion on the developed P2P testbed and live demonstration projects. • Directions for future research work in developing P2P energy trading frameworks. Energy sector is undergoing a massive transformation that includes key aspects such as integrating renewables, improving operational efficiency, leveraging smart grid infrastructure, and handling the dynamics of transactive energy; all of which necessitates ecosystem players to refine their role, devise efficient regulatory/policy frameworks, and experiment with new business models. Given these circumstances, Peer-to-Peer (P2P) energy trading appears to be one of the viable solutions in which an end-user can sell/buy power to/from other users instead of fully relying on the utility for the same. However, the implementation of P2P energy trading in the distribution networks introduce new challenges, such as network constraint violations, increased communication requirements, compromised end-user privacy, the threat to the financial viability of the utility companies, etc. Various pilot projects and research are being carried out at different levels worldwide in this regard. This paper provides a detailed analysis of the existing research and implementation activities on P2P energy trading, and suggests potential future research avenues. Readers gain an understanding of P2P energy trading frameworks, implementation methodologies, and demonstration projects through this article.

Rating-protocol optimization for blockchain-enabled hybrid energy trading in smart grids

Rating-protocol optimization for blockchain-enabled hybrid energy trading in smart grids

Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm (MOGOA) with Deep Extreme Learning Machine (DELM)-based short-term load predictive technique i.e., MOGOA-DELM model for P2P Energy Trading (ET) in SGs. The proposed MOGOA-DELM model involves four distinct stages of operations namely, data cleaning, Feature Selection (FS), prediction, and parameter optimization. In addition, MOGOA-based FS technique is utilized in the selection of optimum subset of features. Besides, DELM-based predictive model is also applied in forecasting the load requirements. The proposed MOGOA model is also applied in FS and the selection of optimal DELM parameters to improve the predictive outcome. To inspect the effectual outcome of the proposed MOGOA-DELM model, a series of simulations was performed using UK Smart Meter dataset. In the experimentation procedure, the proposed model achieved the highest accuracy of 85.80% and the results established the superiority of the proposed model in predicting the testing data.

A blockchain-based efficient data storage and sharing scheme for renewable energy trading in smart grid

The popularity of renewable energy is increasing due to its cost effectiveness. However, not everyone can generate and fulfill their energy demand, so energy trading is necessary. Current solutions are centralised and charged at a high fee for energy trading as they have a monopoly in the market. Energy trading requires the storage, verification, and sharing of data related to the trade while keeping records tamper-proof. Traditional database solutions are centralised and susceptible to data tempering. In our proposed scheme, we aim to solve those problems with the help of blockchain technology by storing data on blockchain and verifying transactions with the PoA consensus algorithm for faster processing. We tested our scheme against the Ethereum network and found that our scheme has a significant improvement in cost and processing. In the future, with the help of machine learning, pricing for each transaction can be optimised.

An Ethereum-based solution for energy trading in smart grids

The need for a flexible, dynamic, and decentralized energy market has rapidly grown in recent years. As a matter of fact, Industry 4.0 and Smart Grids are pursuing a path of automation of operations to insure all the steps among consumers and producers are getting closer. This leads towards solutions that exploit the paradigm of public blockchain, which represents the best platform to design flat and liquid markets for which providing trust and accountability to mutual interactions becomes crucial. On the other hand, one of the risks arising in this situation is that personal information is exposed to the network, with intolerable threats to privacy. In this paper, we propose a solution for energy trading, based on the blockchain Ethereum and Smart Contracts.The solution aims to be a concrete proposal to satisfy the needs of energy trading in smart grids, including the important feature that no information about the identity of the peers of the network is disclosed in advance.

P2P Energy Trading Application In Smart Grids Using Blockchain

Abstract: This paper helps one to understand the concept of smart grids, blockchain, and their interaction with one another in a real-world application of the energy sector, specifically in the smart grids. Initially, we will focus on the working of smart grids and compare them with traditional models. Formalize a list of existing gaps in the present technology and solve them with the introduction of blockchain technology as the principle solution. In the current energy distribution system, when a supplier produces electricity from a renewable energy source (RES), the distribution process is highly inefficient and mismanaged. The meter readings are first logged onto a spreadsheet and then sent to the registry provider to issue a certificate. Then multiple other intermediaries are involved in the whole process, such as one to broker a deal between producers and consumers. Then another external intermediary is tasked with verifying the certificates. This causes an increase in the operational costs, errors, and multiple third parties/mediators involved, which causes a lack of trust in the system. The current system is also highly centralized, and our proposed system; this is P2P energy trading in smart grids using blockchain aims to eliminate the above drawbacks. Keywords: blockchain, certificates, IoT, smart grid.

Distributed Energy Trading in Smart Grid Over Directed Communication Network

This paper presents a distributed solution for energy trading in smart grid with voltage and congestion management. In the proposed approach, the energy trading is formulated with a distributed consensus algorithm to optimize both generation- and demand-side cost functions based on incremental cost. The proposed approach is designed for directed communication networks.

Blockchain and Cooperative Game Theory for Peer-to-Peer Energy Trading in Smart Grids

Blockchain and Cooperative Game Theory for Peer-to-Peer Energy Trading in Smart Grids

Peer-to-Peer Energy Trading in Smart Grid Through Blockchain: A Double Auction-Based Game Theoretic Approach

In a smart grid, each residential unit with renewable energy sources can trade energy with others for profit. Buyers with insufficient energy meet their demand by buying the required energy from other houses with surplus energy. However, they will not be willing to engage in the trade if it is not beneficial. With the aim of improving participants' profits and reducing the impacts on the grid, we study a peer-to-peer (P2P) energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, the auctioneer controls the game, and the seller does not participate in the game but finally achieves the maximum social welfare. The proposed method not only benefits the participants but also hides their information, such as their bids and asks, for privacy. We further study individual rationality and incentive compatibility properties in the proposed method's auction process at the game's unique Stackelberg equilibrium. For practical applicability, we implement our proposed energy trading system using blockchain technology to show the feasibility of real-time P2P trading. Finally, simulation results under different scenarios demonstrate the effectiveness of the proposed method.

Peer-to-Peer Energy Trading in Smart Grid Considering Power Losses and Network Fees

Peer-to-peer (P2P) energy trading is one of the promising approaches for implementing decentralized electricity market paradigms. In the P2P trading, each actor negotiates directly with a set of trading partners. Since the physical network or grid is used for energy transfer, power losses are inevitable, and grid-related costs always occur during the P2P trading. A proper market clearing mechanism is required for the P2P energy trading between different producers and consumers. This paper proposes a decentralized market clearing mechanism for the P2P energy trading considering the privacy of the agents, power losses as well as the utilization fees for using the third party owned network. Grid-related costs in the P2P energy trading are considered by calculating the network utilization fees using an electrical distance approach. The simulation results are presented to verify the effectiveness of the proposed decentralized approach for market clearing in P2P energy trading.

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

SPB: A Secure Private Blockchain-Based Solution for Distributed Energy Trading

Blockchain is increasingly being used to provide a distributed, secure, trusted, and private framework for energy trading in smart grids. However, existing solutions suffer from a lack of privacy, processing and packet overheads, and reliance on trusted third party (TTP) to secure the trade. To address these challenges, we propose a secure private blockchain (SPB) framework. SPB enables energy producers and consumers to directly negotiate the energy price. To reduce the associated overheads, we propose a routing method which routes packets based on the destination public key (PK). SPB eliminates the reliance on TTP to ensure both energy producer and consumer commit to their obligations by introducing atomic meta-transactions. The latter consists of two transactions: first the consumer generates a CTP transaction, committing to pay the energy price to the producer. On receipt of the energy, the smart meter of the consumer generates an energy receipt confirmation (ERC) which triggers a smart contract to transfer the committed price in CTP to the energy producer. To verify that the ERC is generated by a genuine smart meter, SPB supports authentication of anonymous smart meters to prevent malicious nodes from linking ERC transactions and thus enhance the user privacy. Qualitative security analysis shows the resilience of SPB against a range of attacks. Implementation results demonstrate that SPB reduces monetary cost and delay compared to existing solutions.

An Auction Mechanism for Profit Maximization of Peer-to-Peer Energy Trading in Smart Grids

In this paper, we propose an optimization technique applying an auction-based mechanism for peer-to-peer energy trading in a smart grid. In our proposed system model we have assumed proper infrastructure and communication to allow connected users to trade energy in a peer-to-peer fashion. The mechanism requires complete knowledge of the different constraints tied to the grid infrastructure such as the status of the paths connecting the suppliers and consumers. This view on the underlying infrastructure is imperative not only to prevent power losses and over utilization of the power lines but also to factor the cost of energy transfer in the trading process. We investigate a sealed bid auction model where each player can participate in the bidding process to maximize its benefit according to current and future energy availability, price variations, the distance between traders and energy transfer costs. The evaluation of the proposed model is examined through theoretical analysis as well as experimental results. This work further demonstrates a profit maximization algorithm (PMA) for energy suppliers which generates high profit return.