A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric vehicles.However,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust issues.In this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center.Based on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare.We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching.The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

The Research about the Trans-provincial Centralized Bidding Trading Market of East China Power Grid --I:Empirical Analysis

The clearing price and bidding price in electricity market are two key indicators to measure whether it is reasonable or not. Based on the grey incidence analysis, this paper studies the correlation coefficient between the clearing price and bidding price with the generation cost, the supervision and rules of the market, the supply and demand situation, the behavior of market members over the same period, which is based on the actual data of the trans-provincial centralized trading market of East China Power Grid. The results show that the factors affecting the clearing price and bidding price from largest to smallest are generation cost, supervision and rules of the market, the supply and demand situation, the behavior of market members. The conclusion is that the trans-provincial trading platform of East China Power Grid is a reasonable regional market which can discover the market cost, and regulate the market supply and demand balance, and promote healthy competition.

Research on key technologies of deduction of multinational power trading in the context of Global Energy Interconnection

When transnationalized electricity trade is conducted in the context of Global Energy Interconnection(GEI),the transaction settlement usually has a long cycle and high cost and is influenced by the volatility of the exchange rate.It is thus necessary to overcome the problems associated with the transaction settlement,change in the trading model data,and trading strategy in the transnational transaction deduction.To overcome the problem of trade settlement,this paper proposes the use of a digital currency(energy currency)for the cross-border electricity trading settlement based on the special drawing rights of the International Monetary Fund,which is controlled by the Global Energy Interconnection Development and Cooperation Organization(GEIDCO),to enable the proposed currency to become a stable digital currency.The traders can use the energy coins as a unit of currency for quotes,combined with the data pertaining to the changes in the energy information obtained from the GEI framework and data regarding the optimally extrapolated reference trading indicators.To realize the implementation of the multi-trader concurrent transaction deduction using a microservice architecture,this paper proposes a method of computing the microservice and synchronous interaction among the traders,based on the database table data,because the large amount of computation is required to be accomplished asynchronously with a single process.The key technology behind these cross-national electricity trading simulations can not only enable the GEI transnational traders to performed daily real-time trading,but it also demonstrates the advantages of the rapid settlement of the energy currency and the realization of a stable payment in the global energy interconnection cross-border electricity trading.

The Security Correction of Large Consumers Long-Term Direct Power Trading Based on DC Power Transfer Distribution Factor

The experiments of large consumers direct power trading is conducting in china nationwide, and it’s important to the reform of electricity market. To compensated efficiencies in security correction of large consumers direct power trading, a novel security correction method based on DC power transfer distribution factor was proposed. Using the presented method to comply security correction, all the transactions that satisfy the specific requirements of maximizing social welfare are able to enter security correction process, and when the power of transmission line is out of limit, this method avoid the transaction which causes this problem is abandoned directly by introducing supplement transactions. The simulation has shown that the proposed security correction method of large consumers direct power trading based on DC power transfer distribution factor is effective.

Blockchain-based Distributed Power Market Trading Mechanism

Distributed power market trading has the characteristics of large number of participants,scattered locations,small single trading scale,and point-to-point trading.The traditional centralized power trading model has the problems of large load,low efficiency,high cost,reliance on third parties and unreliable data.With the characteristics of decentralization and nontampering,blockchain can establish a point-to-point trusted trading environment and provide effective solutions to the above problems.Therefore,this paper proposed a distributed power market trading framework based on blockchain.In this framework,the distributed power supply characteristics and trading needs of each participant are analyzed,a complete distributed trading process based on blockchain is designed.In addition,we have studied the key technologies of distributed power market trading.With the goal of power service reputation and maximum revenue of distributed power providers,we have established a matching degree model,a distributed power market trading optimization model,and designed a smart contract-based power market trading optimization strategy and power trading settlement strategy.Finally,we designed experiments to verify the performance of the proposed framework.

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.

A Sustainable Energy Efficiency Solution in Power Plant by Implementation of Perform Achieve and Trade (PAT) Mechanism

An enhanced energy efficiency scheme, “Perform, Achieve and Trade” (PAT) is explored in relation to the existing carbon market in India, particularly the Clean Development Mechanism, Renewable Energy Certification and possible Nationally Appropriate Mitigation Actions. The PAT scheme incentivises energy-intensive large industries and facilities for Enhance Energy Efficiency, through technology upgrade and improvement in process. The PAT scheme currently identified 478 designated consumers from eight energy intensive industrial sectors namely, thermal power plants, iron and steel, cement, textiles, chlor-alkali, aluminum, fertilisers and pulp & paper. The threshold limit in thermal power plant sector to become a PAT designated consumer is 30,000 tonne of oil equivalent annual energy consumption. In the first PAT cycle, run through 2012 to 2015, total 144 designated consumers from various states have been identified with individual target. Thermal power plant sector has been categorized on the basis of their fuel input into three subsectors i.e. gas, oil and coal based plants. This paper reviews the state of the art in PAT mechanism design and operational features for implementation on thermal power plant sector. The possibility of implementing an Emission Trading Scheme (ETS) in India is explored from political and institutional perspectives.

The Research about the Trans-provincial Centralized Bidding Trading Market of East China Power Grid --II:Model Analysis

In this paper a novel cost function based on the relationship between operation cost of unit and generation load rate is employed in an agent-based model of Trans-provincial Centralized Bidding Trading Market of East China Grid. Simulation results are compared to real data to prove that the model is correct. Further analysis on simulation results point out the way to achieve an all-win game for power market members: generation companies improve their average load rates of the units by selling their electricity in the market, which makes units' cost drop and settlement price stay lower than benchmark price. Consequently electricity-demand provinces saved expenses, and units increase their profits. In conclusion, the trans-provincial electricity market of East China Power Grid is a successive case which improves the efficiency of the electricity industry by market-oriented measures.

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.

Algorithmic Optimization of BDDs and Performance Evaluation for Multi-level Logic Circuits with Area and Power Trade-offs

Binary Decision Diagrams (BDDs) can be graphically manipulated to reduce the number of nodes and hence the area. In this context, ordering of BDDs play a major role. Most of the algorithms for input variable ordering of OBDD focus primarily on area minimization. However, suitable input variable ordering helps in minimizing the power consumption also. In this particular work, we have proposed two algorithms namely, a genetic algorithm based technique and a branch and bound algorithm to find an optimal input variable order. Of course, the node reordering is taken care of by the standard BDD package buddy-2.4. Moreover, we have evaluated the performances of the proposed algorithms by running an exhaustive search program. Experi-mental results show a substantial saving in area and power. We have also compared our techniques with other state-of-art techniques of variable ordering for OBDDs and found to give superior results.

Thoughts on the Institutional Innovation from the Large Trading Nation to the Powerful Trading Nation

Since the reform and opening up,China’s economy has experienced rapid development and progress,and has gradually emerged as a large global economy and trading nation,and the import and export trade has made brilliant achievements.At the same time,however,China’s import and export trade activities still face enormous challenges and face the problem of turning the large trading nation into the powerful trading nation.Among them,institutional factors are the main obstacles restricting China’s trade development.Therefore,it is necessary to strengthen institutional innovation and reform from a large trading nation to a powerful trading nation.

Optimal Scheduling Strategy of Source-Load-Storage Based onWind Power Absorption Benefit

In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of China is limited,resulting in insufficient local wind power consumption capacity.Therefore,this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid’s wind power consumption capacity.The objective of the uppermodel is tominimize the peak-valley difference of the systemload,which ismainly to optimize the system load by using the demand response resources,and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid.The lower scheduling model is aimed at maximizing the system operation benefit,and the scheduling model is selected based on the rolling schedulingmethod.The load-side schedulingmodel needs to reallocate the absorbed wind power according to the response speed,absorption benefit,and curtailment penalty cost of the two DR dispatching resources.Finally,the measured data of a power grid are simulated by MATLAB,and the results show that:the proposed strategy can improve the power grid’s wind power consumption capacity and get a large wind power consumption benefit.

The Realization of a Strong Trade Power and China’s Direct Investment in SCO Countries

China’s path to becoming a strong trade power can be divided into three levels:the micro level of promoting factor cultivation,the meso level of achieving industrial dominance,and the macro level of participating in the establishment of the world system.As a feature of globalization,factor flow is the foundation and key to achieve the above three-level goals.In the first stage of reform and opening-up,China complied with the globalization characteristics of factor flow and gathered a large number of capital factors.It is now the second stage of reform and opening-up;that is,the stage of export-oriented investment.International investment may help in the path to become a strong trade power,or it may become an obstacle.The maximization of benefits and evasion of disadvantages are influenced by the grasp of investment risks.Therefore,special attention should be paid to identifying potential risks and controlling risks.

Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.

A Two-layer Framework for Mitigating the Con-gestion of Urban Power Grids Based on Flexible Topology with Dynamic Thermal Rating

The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing.This situation is expected to worsen with the proliferation of renewable energy and electric vehicles.In this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible topologies.In the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation transformers.This is known as the upper-layer PSD.The lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest.This optimum topology is tested by standard power flow to ascertain its feasibility.The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating.The proposed framework is tested on a 56-node test system.Results show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

FGD Capacity Prediction of Thermal Power Plants in China

Through analyzing the proportion of SO2 emission from thermal power plants in the nationwide SO2 emis- sion in USA, Japan etc. developed countries, and the developmental course of thermal power installed capacity and the FGD capacity in USA, the FGD capacity of thermal power plants in China is forecasted from two angles. One is to predict FGD capacity in accordance with the policy in force in China. The other is to predict FGD capacity based upon the emission right trading policy. As compared, it is held that FGD equipment should be mainly installed on the large size units burning high sulfur coal according to the emission right trading policy. Such a method of work not only can economize large amount of investments and operation costs, but also can realize the same environmental effect.

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%.

Trumponomics and China-US Economic and Trade Relations

Since he took office, the new US president, Donald Trump, has unveiled his broad economic policy--now called Trumponomics. It emphasizes neoliberalism at home, less government regulations, more growth and weakening the welfare state. Intemationally, Trumponomics embraces protectionism and nativism with a focus on US economic interests. Trumponomics caters to the lower-middle classes, a reflection of the country＇s current economic and diplomatic challenges. Trumponomics will bring uncertainty to China-US economic and trade relations. China should carefully study the policies of the Trump administration and prepare contingency plans.

适应新型电力系统的电力互替品市场——(一)理念与构想

新型电力系统重塑了能源生产消费结构,电力市场机制也需随之改变。首先,分析指出提升系统调节能力是新型电力系统下市场机制设计的首要目标;围绕该目标给出了市场机制应具备的特点,包括利于激发调节动力、释放调节能力、接纳各种新型调节资源等。其次,对世界各国现货市场普遍采用的分时交易机制进行了分析,指出其与新型电力系统存在诸多不适应之处,包括市场主体调节贡献无法被量化表征,市场主体连续运行特性与分时交易机制相矛盾、调节能力难以完全释放,以及难以满足海量柔性资源直接参与电力市场的需求等。为此,文中舍弃分时交易的方式,转而提出整体交易机制与电力互替品概念,介绍了形成与交易电力互替品的主要思路,并以电力互替品为核心构想了新型市场机制。该机制可通过调节责任划分量化各主体调节贡献,激发市场主体的调节动力;同时,也能促进市场主体主动优化自身运行方式,释放调节能力;具有易于扩展的优势,可轻松接纳新型电力系统下层出不穷的各类新型主体。