An Energy Trading Method Based on Alliance Blockchain and Multi-Signature

Blockchain,known for its secure encrypted ledger,has garnered attention in financial and data transfer realms,including the field of energy trading.However,the decentralized nature and identity anonymity of user nodes raise uncertainties in energy transactions.The broadcast consensus authentication slows transaction speeds,and frequent single-point transactions in multi-node settings pose key exposure risks without protective measures during user signing.To address these,an alliance blockchain scheme is proposed,reducing the resource-intensive identity verification among nodes.It integrates multi-signature functionality to fortify user resources and transac-tion security.A novel multi-signature process within this framework involves neutral nodes established through central nodes.These neutral nodes participate in multi-signature’s signing and verification,ensuring user identity and transaction content privacy.Reducing interactions among user nodes enhances transaction efficiency by minimizing communication overhead during verification and consensus stages.Rigorous assessments on reliability and operational speed highlight superior security performance,resilient against conventional attack vectors.Simulation shows that compared to traditional solutions,this scheme has advantages in terms of running speed.In conclusion,the alliance blockchain framework introduces a novel approach to tackle blockchain’s limitations in energy transactions.The integrated multi-signature process,involving neutral nodes,significantly enhances security and privacy.The scheme’s efficiency,validated through analytical assessments and simulations,indicates robustness against security threats and improved transactional speeds.This research underscores the potential for improved security and efficiency in blockchain-enabled energy trading systems.

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.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.

V2V Energy Trading Considering User Satisfaction under Low-Carbon Objectives via Bayesian Game

In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) power exchange processes, this paper explores a multi-party energy trading model considering user responsiveness under low carbon goals. The model takes into account the stochastic charging and discharging characteristics of EVs, user satisfaction, and energy exchange costs, and formulates utility functions for participating entities. This transforms the competition in multi-party energy trading into a Bayesian game problem, which is subsequently resolved. Furthermore, this paper primarily employs sensitivity analysis to evaluate the impact of multi-party energy trading on user responsiveness and green energy utilization, with the aim of promoting incentives in the electricity trading market and aligning with low-carbon requirements. Finally, through case simulations, the effectiveness of this model for the considered scenarios is demonstrated.

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 optimalDELM 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.

State-of-the-Art Analysis and Perspectives for Peer-to-Peer Energy Trading

As a promising solution to address the“energy trilemma”confronting human society,peer-to-peer(P2P)energy trading has emerged and rapidly developed in recent years.When carrying out P2P energy trading,customers with distributed energy resources(DERs)are able to directly trade and share energy with each other.This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers,research projects,and industrial practice.Key aspects in P2P energy trading are identified and discussed,including market design,trading platforms,physical infrastructure and information and communication technology(ICT)infrastructure,social science perspectives,and policy.For each key aspect,existing research and practice are critically reviewed and insights for future development are presented.Comprehensive concluding remarks are provided at the end,summarizing the major findings and perspectives of this paper.P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world.

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the grid.The classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the grids.The TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them.At the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)models.Though some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,etc.into account.In this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in TEM.The proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence rate.Moreover,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading systems.In order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome.The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

Design of a Peer-to-Peer Energy Trading Platform Using Multilayered Semi-Permissioned Blockchain

A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of blockchain makes it a befitting candidate to actualize an efficient P2P energy trading market. However, for a sustainable and dynamic blockchain-based P2P energy trading platform, few critical aspects such as security, privacy and scalability need to be addressed with high priority. This paper proposes a blockchain-based solution for energy trading among the consumers which ensures the systems’ security, protects users’ privacy, and improves the overall scalability. More specifically, we develop a multilayered semi-permissioned blockchain-based platform to facilitate energy transactions. The practical byzantine fault tolerant algorithm is employed as the underlying consensus for verification and validation of transactions which ensures the system’s tolerance against internal error and malicious attacks. Additionally, we introduce the idea of quality of transaction (QoT)—a reward system for the participants of the network that eventually helps determine the participant’s eligibility for future transactions. The resiliency of the framework against the transaction malleability attack is demonstrated with two uses cases. Finally, a qualitative analysis is presented to indicate the system’s usefulness in improving the overall security, privacy, and scalability of the network.

Energy Blockchain in Smart Communities: Towards Affordable Clean Energy Supply for the Built Environment

The rapid growth of distributed renewable energy penetration is promoting the evolution of the energy system toward decentralization and decentralized and digitized smart grids.This study was based on energy blockchain,and developed a dual-biding mechanism based on the real-time energy surplus and demand in the local smart grid,which is expected to enable reliable,affordable,and clean energy supply in smart communities.In the proposed system,economic benefits could be achieved by replacing fossil-fuel-based electricity with the high penetration of affordable solar PV electricity.The reduction of energy surplus realized by distributed energy production and P2P energy trading,within the smart grid results in less transmission loss and lower requirements for costly upgrading of existing grids.By adopting energy blockchain and smart contract technologies,energy secure trading with a low risk of privacy leakage could be accommodated.The prototype is examined through a case study,and the feasibility and efficiency of the proposed mechanism are further validated by scenario analysis.

URJA:A sustainable energy distribution and trade model for smart grids

Energy has always been one of the fundamental elements in the growth of human society.It is crucial that we improve our energy management due to the ever-growing imbalance in demand-supply.The new wave in the energy sector is characterized by the three D's of decarbonization,decentralization,and digitalization.For a sustainable life,it is necessary that energy is tapped from alternative sources that are renewable to leave a minimal carbon footprint.To integrate different sources with the main power grid,we require two-way communication among them.This makes it indispensable that consumers are empowered to protect their privacy and make them the sole owners of their data.A good access control scheme would ensure security of data,and an efficient trading platform would ensure judicious use of the resources.Here,we propose a framework URJA with an access control scheme and an energy trading platform based on blockchain for a smart grid.The locally available renewable sources of energy are connected to the grid such that demand-supply is managed effectively without loss of efficiency and privacy.A customized consensus scheme based on trust ensures a quick operation in real-time.

China-Arab Energy Cooperation: Construct new Energy Silk Road

It is an important part of the Belt and Road initiative that Chinaand Arab countries to strengthen energy cooperation and jointlybuild the energy Silk Road. China-Arab has made remarkable progressin energy cooperation, especially in the fields of oil and gascooperation and new energy cooperation. China and Arab countriesare also strengthening strategic docking, promoting energytransition and cooperation in the field of new energy, and workingto form a clean energy production and modern energy consumptionsystem. China and Arab countries should deepenenergy cooperation by taking their complementary advantages,improve the docking mechanism of energy cooperation, promotecooperation in the whole energy industry chain, and avoid geopoliticalrisks through co-development. In the future, the energytransition will also bring great opportunities for deepening China-Arab energy cooperation. At the same time, political and economicrisks will still be an important challenge for China-Arabenergy cooperation. However, the sound political foundationbetween China and Arab countries is the long-term advantage ofChina-Arab energy cooperation.

Worldwide carbon neutrality transition?Energy efficiency,renewable,carbon trading and advanced energy policies

Climate change and energy shortage crisis promptly necessitate achievement of sustainable development goals.However,there is no straightforward pathways for low-carbon transformation on building sectors,and energy/carbon trading and reverse promotion on decarbonization strategies are not clear.In this study,a literature enumeration method with dialectical analysis was adopted for state-of-the-art literature review and comparison.Low-carbon transformation pathways in buildings were holistically reviewed,with a series of integrated techniques,such as energy saving,clean energy supply,flexible demand response for high self-consumption,and even smart electric vehicle(EV)integration.Afterwards,energy/carbon flows and trading in building-related systems were provided,such as peer-to-peer energy trading,building and thermal/power grids,building and energyintegrated EVs,and carbon trading in buildings.Last but not the least,worldwide decarbonization roadmaps across regions and countries are analysed,to identify the most critical aspects and immediate actions on decarbonization.Results indicate that tradeoff strategies are required to compromise the confliction between insufficient feed-in tariff(FiT)incentives(low renewable penetration in the market)and great economic pressures(high investment in renewable systems).Low-carbon building pathway is further enhanced with first priority given to passive/active energy-saving strategies,onsite clean energy supply and then flexible demand response.Energy/carbon trading will significantly affect renewable energy utilization,and acceptance from end-users to actively install renewable systems or participate in EV interactions.Worldwide decarbonization pathways mainly focus on industries,transportation,buildings,renewable sources,carbon sink and carbon capture,utilization and storage(CCUS).This study can contribute to technical roadmaps and strategies on carbon neutrality transition in both academia and industry,together with advanced policies in grid feed-in tariff,energy/carbon trading and business models worldwide.

Bi-level Energy Trading Model Incorporating Large-scale Biogas Plant and Demand Response Aggregator

Increasing intermittent renewable energy sources(RESs)intensifies the imbalance between demand and generation,entailing the diversification of the deployment of electrical energy storage systems(ESSs).A large-scale biogas plant(LBP)installed with heating devices and biogas energy storage(BES)usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas,which is addressed in this paper.By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic,this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator(DRA)simultaneously.In this model,social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level.Compared with cases only with DRA,the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic(PV)generation up to 6.3%,18.1%,and 18.9%at 30%,40%,and 50%PV penetration levels,respectively,with a better economic performance.This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints(MPEC)using Karush-Kuhn-Tucker(KKT)conditions and solved by the Cplex solver.The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.

P2P energy trading via public power networks:Practical challenges,emerging solutions,and the way forward

Peer-to-peer(P2P)energy trading is an emerging energy supply paradigm where customers with distributed energy resources(DERs)are allowed to directly trade and share electricity with each other.P2P energy trading can facilitate local power and energy balance,thus being a potential way to manage the rapidly increasing number of DERs in net zero transition.It is of great importance to explore P2P energy trading via public power networks,to which most DERs are connected.Despite the extensive research on P2P energy trading,there has been little large-scale commercial deployment in practice across the world.In this paper,the practical challenges of conducting P2P energy trading via public power networks are identified and presented,based on the analysis of a practical Local Virtual Private Networks(LVPNs)case in North Wales,UK.The ongoing efforts and emerging solutions to tackling the challenges are then summarized and critically reviewed.Finally,the way forward for facilitating P2P energy trading via public power networks is proposed.

Accelerating the renewable energy sector through Industry 4.0:Optimization opportunities in the digital revolution

The fourth industrial revolution introduced the concept of Industry 4.0,which refers to the digitalization of the modern world and the inclusion of digital industry in major industrial sectors.Within this context,we investigated how Industry 4.0 technologies could transform conventional factories into smart factories and contribute to the development of a circular economy with significant economic,environmental,and social advantages.The primary goal of this study was to assess the potential of Industry 4.0 technologies in optimizing energy production from renewable resources.Specifically,we examined how various Industry 4.0 technologies,including Blockchain,the Internet of Things(loT),and Cloud Computing,contributed to energy generation,transmission,and trade.In addition,we investigated solutions to issues such as intermittent power supply,low efficiency,grid bottleneck,wastage of excess power,and unpredictable energy markets.Furthermore,we discussed two case studies based on circular economies,sustainable development goals,and Industry 4.o-based data analysis platforms.The study concludes by discussing the challenges faced by industries in adapting to the digital revolution and the potential uses of modern-day digital technologies to advance the energy sector and society as a whole.

Security Constrained Decentralized Peer-to-Peer Transactive Energy Trading in Distribution Systems

Peer-to-peer(P2P)transactive energy trading offers a promising solution for facilitating the efficient and secure operation of a distribution system consisting of multiple prosumers.One critical but challenging task is how to avoid system network constraints to be violated for the distribution system integrated with extensive P2P transactive energy trades.This paper proposes a security constrained decentralized P2P transactive energy trading framework,which allows direct energy trades among neighboring prosumers in the distribution system with enhanced system efficiency and security in which no conventional intermediary is required.The P2P transactive energy trading problem is formulated based on the Nash Bargaining theory and decomposed into two subproblems,i.e.,an OPF problem(P1)and a payment bargaining problem(P2).A distributed optimization method based on the alternating direction method of multiplier(ADMM)is adopted as a privacy-preserving solution to the formulated security constrained P2P transactive energy trading model with ensured accuracy.Extensive case studies based on a modified 33-bus distribution system are presented to validate the effectiveness of the proposed security constrained decentralized P2P transactive energy trading framework in terms of efficiency improvement,loss reduction,and voltage security enhancement.

Blockchain-based Peer-to-Peer Transactive Energy System for Community Microgrid with Demand Response Management

Interest in transactive energy frameworks(TEFs)is proliferating due to the modern smart grid paradigm.This paper proposes a TEF,which applies auction-theory,incorporates a system of agents,and facilitates a transactive energy market(TEM)through an auctioneer.Further,it also enables peer-to-peer(P2P)energy trading among the residential buildings in community microgrid for possible monetary benefits.In this framework,there are three agents,namely,auctioneer,participants,and utility.The auctioneer is a managing agent modeled using auction theory to determine day-ahead internal market-clearing price and quantity.The participants are autonomous and rational decision-makers;they aim to minimize their electricity bills through the demand response(DR)management.Two types of architectures,one with the third-party agent demonstrated using the MATLAB environment and the other with the virtual agent(without third-party)implemented using the blockchain environment are presented.The simulation results reflect significant monetary benefits to each market participant,improved community selfsufficiency,self-consumption,and reduced reliance on the utility grid.

A Two-stage Peer-to-peer Energy Trading Model for Distribution Systems with Participation of Utility

With the growing penetration of distributed energy resources(DER)in distribution systems,the traditional utility dominated tariff-based business model may no longer meet the need for further development.As a result,the transformation from the traditional tariff-based business model to the emerging peer-to-peer energy trading model has been acknowledged by researchers and policy makers.In this paper,a two-stage peer-to-peer energy trading model is proposed while considering the role of the utility.Specifically,energy transactions between buyers and sellers are optimized in the first stage;the cleared transactions are submitted to the utility for approval in the second stage,which solves a transaction approval model to verify the transactions from the perspective of secure system operations.Indeed,certain transactions mav be disapproved to ensure that all network constraints,such as voltage and line flow limitations,are satisfied.In addition,a comprehensive trading tariff is designed to recover the hidden costs of the utility,such as those associated with network usage,system losses,and ancillary service provision.A modified 33-bus distribution system is adopted to verify the proposed model.

Multi-commodity Optimization of Peer-to-peer Energy Trading Resources in Smart Grid

Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing(P2P-ETS).However,as more distributed energy resources integrate into the distribution network,the impact of the communication link becomes significant.We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS.On one hand,the proposed algorithm minimizes the cost of energy generation and communication delay.On the other hand,it also maximizes the global utility of prosumers with fair resource allocation.We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss.The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms.It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others.