Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

Distributed photovoltaics with peer-to-peer electricity trading

Distributed electricity generation technologies,like solar photovoltaic(PV),have achieved rapid development in recent years,but are constrained by some problems such as low marketization and lag in public services.The peer-to-peer(P2P)electricity trading,which allows direct electricity transactions between local consumers and prosumers,has the potential to efficiently distribute the profits coming from self-consumption of PV electricity among all participants.This study proposes a three-layer P2P electricity trading system for communities with high penetration of household distributed PV.The trading system includes a physical layer based on the power grid,an information layer based on a virtual agent network,and a market layer based on a continuous double auction(CDA)mechanism combined with market clearing.By using simulation of trading experiments,a community with 60 houses and 50%PV occupancy is studied as a case.Results show that in one day,62.5%of the surplus PV electricity of all prosumers actually can be consumed within the community.Through P2P electricity trading of this part of electricity,total incomes of prosumers can increase by 11.5%,and total expense of all users decrease by 7.5%,resulting in that the net expenses of the whole community decrease by 13.8%.It is concluded that P2P electricity trading can unite the whole PV community as a bigger prosumer,and make every residential house in the community share the profits from local PV consumption,which could promote the subsidy-free development and grid parity of household distributed PV in the future.

Influences of uncertainties to the generation feasible region for medium- and long-term electricity transaction

For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium-and Iong-term electricity tradi ng in terms of their in flue nces on the GFR.In this paper,we prese nt a graphic distortio n pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to dem on strate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transacti ons.

A Reinforcement Learning approach for the continuous electricity market ofGermany: Trading from the perspective of a wind park operator

With the rising extension of renewable energies, the intraday electricity markets have recorded a growingpopularity amongst traders as well as electric utilities to cope with the induced volatility of the energysupply. Through their short trading horizon and continuous nature, the intraday markets offer the abilityto adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice. Producersof renewable energies utilize the intraday market to lower their forecast risk, by modifying their providedcapacities based on current forecasts. However, the market dynamics are complex due to the fact that thepower grids have to remain stable and electricity is only partly storable. Consequently, robust and intelligenttrading strategies are required that are capable to operate in the intraday market. In this work, we proposea novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms as a possiblesolution. For this purpose, we model the intraday trade as a Markov Decision Process (MDP) and employ theProximal Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework is introducedthat enables the trading of the continuous intraday price in a resolution of one minute steps. We test ourframework in a case study from the perspective of a wind park operator. We include next to general tradeinformation both price and wind forecasts. On a test scenario of German intraday trading results from 2018,we are able to outperform multiple baselines with at least 45.24% improvement, showing the advantage of theDRL algorithm. However, we also discuss limitations and enhancements of the DRL agent, in order to increasethe performance in future works.

CMC International Advertising & Exhibition Co.Ltd.-Bridging the Sino-foreign Mechanical and Electrical Trade

CMC International Advertising & Exhibition Co. Ltd.(CMC-IAE) is a specialized subsidiary of China National Machinery Import & Export Corporation (CMC) with corporate status. In the past 47 years, the company has played a positive role in the conveyance of information and development of China’s mechanical and electrical trade with other countries in the forms of specialized advertising and exhibitions, contributing greatly to the bridging of Sino-foreign mechanical and electrical trade and expansion of international economic and trade exchanges. CMC-IAE deals mainly in and acts as agent for the advertising business of both Chinese and foreign enterprises, and trading companies. It sponsors large-scale exhibitions and promotional activities both at home and abroad; undertaking to design and produce various advertisements,pamphlets and printing matters; engaging in

China's Processing Trade Development for Mechanical and Electrical Products

Since the launch of the economic reform and opening to the outside world, China has seen rapid growth in its export of mechanical and electrical products, with its export

Gaining Currency

South africa was africa’s economic giant,creating the miracle of accounting for one third of the continent’s GDP though its population was only 6 percent of the entire African population.However,subsequently,exceptionally high unemployment,socio-economic inequalities,an ailing electricity generation capability,and the recent xenophobic attacks have challenged its position, in 2013, Nigeria became Africa＇s new biggest economy, with the ＂Rainbow Nation＂ on a downward spiraL.