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 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.

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.

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.

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.

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.

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.

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.

Blockchain-based Peer-to-Peer Energy Trading Method

For grid-connected neighbors within communities,blockchain-enabled peer-to-peer energy trading proves to be a coherent approach to trade energy from locally produced and distributed renewable energy resources.Effective matching among peers enables enhanced energy efficiency during energy transactions,thereby improving the power quality and preferentially increasing user welfare.The proposed algorithm builds upon work to develop a system of scoring an energy transaction.It employs a McAfee-priced double auction mechanism and assigns the scores based on the preference of factors like price,locality,and the type of energy generation,in addition to the quantity of energy being traded.These transactions are pre-evaluated by the said algorithm to determine the optimal transactional pathway.As a result,the transaction that is finally executed is the one holding the highest cumulative score.The proposed algorithm is simulated over a range of scenarios and tends to boost the user welfare percentile by an average of 75%.From an economic perspective,the algorithm may be implemented in small to large settlements while remaining stable.By reducing power loss,this energy trading algorithm empowers consumers to save around 25%on their energy costs and offers prosumers a 50%increase in revenue.

Optimal Operation Strategy of Electricity-Hydrogen Regional Energy System under Carbon-Electricity Market Trading

Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors.However,further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen(P2H)technology,focusing on participating in combined carbon-electricity market transactions.This study introduces an innovative Electro-Hydrogen Regional Energy System(EHRES)in this context.This system integrates renewable energy sources,a P2H system,cogeneration units,and energy storage devices.The core purpose of this integration is to optimize renewable energy utilization and minimize carbon emissions.This study aims to formulate an optimal operational strategy for EHRES,enabling its dynamic engagement in carbon-electricity market transactions.The initial phase entails establishing the technological framework of the electricity-hydrogen coupling system integrated with P2H.Subsequently,an analysis is conducted to examine the operational mode of EHRES as it participates in carbon-electricity market transactions.Additionally,the system scheduling model includes a stepped carbon trading price mechanism,considering the combined heat and power generation characteristics of the Hydrogen Fuel Cell(HFC).This facilitates the establishment of an optimal operational model for EHRES,aiming to minimize the overall operating cost.The simulation example illustrates that the coordinated operation of EHRES in carbon-electricity market transactions holds the potential to improve renewable energy utilization and reduce the overall system cost.This result carries significant implications for attaining advantages in both low-carbon and economic aspects.

Toward a sustainable future:Examining the interconnectedness among Foreign Direct Investment(FDI),urbanization,trade openness,economic growth,and energy usage in Australia

The energy demand in Australia is increasing with the industrialization and rapid economic growth.This study analyzed the relationships among the economic growth,Foreign Direct Investment(FDI),trade openness,urbanization,and energy usage in Australia based on the data from World Development Indicators(WDI)from 1972 to 2021.The results indicates that there is a cointegration among economic growth,FDI,trade openness,urbanization,and energy usage,which was traced through the autoregressivedistributed lag(ARDL).The Zivot-Andrews unit root test reveals that energy usage,economic growth,FDI,urbanization,and trade openness show significant structural breaks in 1993,1996,1982,2008,and 1994,respectively.The ARDL model shows that economic growth has a positive and significant effect on energy usage in the long-run(0.814)and short-run(0.809).Moreover,the results also show that FDI(0.028)and trade openness(0.043)have positive impacts on energy usage in the long-run.However,urbanization shows a negative and significant influence on energy usage in the long-run(–0.965).Then,the research demonstrates a unidirectional causation between energy usage and trade openness,with energy usage significantly causing trade openness.The current study endorses energy consumption policies and investment strategies for a paradigm shifting from a reliance on fossil fuels as the primary energy source to renewable energy sources.These findings have profound implications for sustainable energy usage.

Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.

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.

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.

A Consortium Blockchain-enabled Double Auction Mechanism for Peer-to-peer Energy Trading among Prosumers

This paper investigates a double auction-based peer-to-peer(P2P)energy trading market for a community of renewable prosumers with private information on reservation price and quantity of energy to be traded.A novel competition padding auction(CPA)mechanism for P2P energy trading is proposed to address the budget deficit problem while holding the advantages of the widely-used Vickrey-Clarke-Groves mechanism.To illustrate the theoretical properties of the CPA mechanism,the sufficient conditions are identified for a truth-telling equilibrium with a budget surplus to exist,while further proving its asymptotical economic efficiency.In addition,the CPA mechanism is implemented through consortium blockchain smart contracts to create safer,faster,and larger P2P energy trading markets.The proposed mechanism is embedded into blockchain consensus protocols for high consensus efficiency,and the budget surplus of the CPA mechanism motivates the prosumers to manage the blockchain.Case studies are carried out to show the effectiveness of the proposed method.

A coordinated operation method of wind-PV-hydrogenstorage multi-agent energy system

Wind-photovoltaic(PV)-hydrogen-storage multi-agent energy systems are expected to play an important role in promoting renewable power utilization and decarbonization.In this study,a coordinated operation method was proposed for a wind-PVhydrogen-storage multi-agent energy system.First,a coordinated operation model was formulated for each agent considering peer-to-peer power trading.Second,a coordinated operation interactive framework for a multi-agent energy system was proposed based on the theory of the alternating direction method of multipliers.Third,a distributed interactive algorithm was proposed to protect the privacy of each agent and solve coordinated operation strategies.Finally,the effectiveness of the proposed coordinated operation method was tested on multi-agent energy systems with different structures,and the operational revenues of the wind power,PV,hydrogen,and energy storage agents of the proposed coordinated operation model were improved by approximately 59.19%,233.28%,16.75%,and 145.56%,respectively,compared with the independent operation model.

Planning Model for Energy Routers Considering Peer-to-Peer Energy Transactions Among Multiple Distribution Networks

A high proportion of renewable energy affects the power quality of distribution networks,and surplus energy will be sold to the upstream grid at a low price.In this paper,considering peer-to-peer energy transactions,the energy router-based multiple distribution networks are analyzed to solve the above problems and realize collaborative consumption of renewable energy.Presently,the investing cost of an energy router is high,and research on the economic operation of energy routers in distribution networks is little.Therefore,this paper establishes a planning model for energy routers considering peer-to-peer energy transactions among distribution networks,and explores the benefits of peer-to-peer energy transactions through energy router based multiple distribution networks.A structure of an energy router suitable for peer-to-peer energy transactions is selected,and a power flow calculation model based on a multilayer structure is established.The energy router’s scheduling model is established,and unique functions of the energy router and revenue of each distribution network are considered.A power flow calculation model based on peer-to-peer interconnection of multiple distribution networks through energy routers is also established.Finally,simulation results verify the effectiveness of the proposed planning model.Results show that peer-topeer energy transaction among distribution networks through energy routers can effectively reduce the comprehensive cost of distribution networks,significantly improve the power quality of the distribution networks,and reduce the impact of power fluctuation on the upstream grid incurred by the distribution network.

Analysis on Scale, Level and Structure of China-Kazakhstan Intra-Industry Trade of Energy Products-Based on Silk. Road Economic Zone as Strategic Background

论文依次从规模、水平、结构三个角度,分析1999年-2014年中国与哈萨克斯坦能源产品产业内贸易发展状况D 结果表明：中哈能源产品贸易规模扩大,但以产业间贸易为主,产业内贸易集中于石油产品,其中垂直型产业内贸易占主要地位,能源产品产业内贸易有较大提升空间D 在此基础上对中哈能源产品产业内贸易影响因素进行探究,考察经济规模、收入差异、贸易公平程度、外商直接投资等四个因素对产业内贸易的影响,为发展中哈能源贸易提供实证依据及合理建议.

Peer-to-Peer Trading in Distribution Systems with the Utility's Operation

To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.

Effect analysis of carbon trading on Economy-Energy-Environment system and calculation of reasonable carbon price intervals

This paper constructs a 4-lier computable general equilibrium model which includes such modules as modeling carbon emission constraints and carbon trading(CT),and incorporates the cost of carbon emissions into constant elasticity of substitute production function.Under scenario settings under different carbon abatement targets,based on 2007 national social accounting matrix and related statistical data about energy consumption and carbon emission,effects on economic outputs,energy consumption,and carbon abatement are estimated and analyzed at both macro and sector level.By calculating selected novel indicators that compromise between macroeconomic opportunity cost and achievable carbon abatement,reasonable carbon price intervals are given for enhancing the robustness and liquidity of carbon market.Further,by decomposition and share-weighted methods,expected carbon abatement and energy price are measured and analyzed in details.Some results are meaningful for fundamental design of the future carbon market.Given constant energy utilization and carbon abatement technologies at the macro level,the higher the carbon price the more actual carbon abatement;the more gross domestic product loss,the less energy consumption.Accwding to the overall situation estimated for 2007 in China,the advice given is to introduce a carbon abatement target rate(R_c)of-10%,which is helpful to make carbon market stable against unexpected carbon price shocks between[6.9,35]/tC with less economic loss.According to Kaya decomposition,after introduction of carbon pricing,carbon abatement is mainly contributed by the effects of energy intensity(EI)and technical progress.Further,CT may help reduce energy consumption and induce transformation to a low-carbon energy structure.At the sector level,the introduction of CT could induce economic recession in all sectors,especially energy.However,the overall economic structure remains unchanged to some extent.CT will help reduce energy consumption in all sectors,especially energy.Overall utilization costs of the energy composite can be divided in two,market price and carbonrelated costs.Carbon-related costs mainly contribute to variation in the utilization cos of the energy composite;carbon pricing may help non-energy sectors achieve sufficient carbon abatement by pushing up energy utilization cost.However,despite achievable carbon abatement by the energy sector being relatively high,induced by carbon pricing,there is still significant potential for other incentive policies to stimulate further abatement,such as energy resources taxation and transportation fuel taxation,especially in the sectors of coal and transportation.Finally,some advice is proposed in regard to policy decisions and further research.