Evaluation method for assessing the auxiliary service transaction mechanism in regional power markets

Accurate and seamless auxiliary services in the power market can guarantee smooth and continuous power system operation. China’s new round of power system reform has entered a critical period, and reform implementation requires comprehensive improvements in the maturity of the supporting auxiliary service market. This study reviews the development status and evolution path of the European unified power market and the US regional power market, provides experience for the development of China’s regional power market, then identifies the key influencing factors of auxiliary service trading mechanism design in regional power markets. To analyze the rationality of the auxiliary service trading evaluation index, this paper established an evaluation model for assessing regional power markets. Using combined weight optimization, the gray correlation TOPSIS method was applied to comprehensively evaluate auxiliary service trading in the regional power market. Finally, the application of the proposed evaluation method was briefly analyzed to examine four regional power markets in China and evaluate the effectiveness of current market construction in different regions and provide suggestions for future market construction.

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.

Power Purchase Model of Provincial Grids Based on Diverse Energy Coordination Hedging Optimization

Under the background of energy conservation, the grid companies should give priority to consumptive hydropower, wind power and other clean electricity to fulfill their social responsibility and promote the carbon emission reduction in power industry. But under the current power purchase mode, grid companies must first perform the contract. This is extremely uneconomical and not environmentally friendly. Based on hedging theory, this paper proposes a power purchase optimization model using the strategy of “compression and compensation”. If outer price is lower than the contract price, the grid can compress contract power appropriately, leaving more space for purchasing electricity;if outer price is not attractive enough, the grid should timely improve contract proportion, compensating the deviations of contract caused by "compression". Based on the strategy of "compression and compensation", it can effectively reduce the abandoned wind and water, enhance the economic and social benefits of provincial power grid.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

Intelligent Models of Long-Term Security Correction in Energy Conversion Dispatching

This paper is research on the dispatching models for monthly generation plan which is manage the congestion considering the security constrains of the power grid. Since the monthly generation plan is the result of vary monthly power exchange, including long-term power contract, power exchange among provinces and generation constitution exchanges, The application of monthly security constrained dispatching is with significant meaning for the security and stability of power grid. This paper brings forward the purpose and contents of security dispatching and introduces the working procedure and mathematic models. At last, the practical example of the Hunan Province power grid is introduced to explain the models.

The Transformer Equipment Selection’s Update Decision Technical and Economic Analysis Model

Based on the current situation of the domestic transformer equipment selection, the paper analyses the SH15, S11 and S9 transformer from the angles of annual running power consumption, payback period, energy efficiency of the transformer, furthermore, determine the best optimal capacity, load factor and update Year of the SH15-type transformers. Example analysis results show that, from the point of view of the technical and economic the SH15-type transformer has better economic and environmental benefits, and large capacity SH15 transformer better comprehensive benefits.

Correction Scheme for Scheduled Network Loss Rate Applied to UHV Transmission Trading

The size and allocation method of transmission network loss have major impact on particular users involved with the electricity market trading process, therefore, the accuracy of scheduled network loss rates will be vital to the economic concerns from the stakeholders. In view of the shortcoming of existing loss rate scheme in 1,000 kV demonstration project, three scheduled loss rates applied to the UHV AC （ultra high voltage alternating current） transaction settlement are revised. By taking relationship between loss rate and power into consideration, the new segmented correction scheme greatly reduces the deviation between scheduled loss rates and actual loss rates, and is more accurate, flexible and expansible than the old one.

Study on detecting main ingredients of silicone rubber based on terahertz spectrum

The authors investigated the ingredient detection technique of silicone rubber based on the Terahertz spectrum.For this purpose,18 diverse high-temperature vulcanised silicone rubber(HTVSR)formulations were customised,8 of which are used as calibration set while the rest 10 as prediction set.Based on the Beer-Lambert Law,the partial-least-square(PLS)regression model and the least-squares support-vector machines(LS-SVM)regression model were used to yield the relationships between the absorption spectrums and the content percentages of polydimethylsiloxane(PDMS),alumina tri-hydrate(ATH),and silica in HTVSR.The results showed that for the formulations tested,the prediction accuracy of all three main ingredients by the PLS regression model could be improved by changing the spectrum range from 0.2–4 to 0.5–2 THz.If the data were pre-processed by the Savitzky–Golay smoothing method or multiplicative scatter correction method,the prediction accuracy of PDMS could be further enhanced.However,this would lead to a slight decrease in the prediction accuracy of ATH.For the LS-SVM regression model,the radial basis function(RBF)kernel and the linear kernel were studied.It was found that the prediction accuracy of both kernels was better than that of the PLS regression model.With the LS-SVM regression model using the RBF kernel,the correlated coefficients of PDMS and ATH in the prediction set could be up to 0.9915 and 0.9742,respectively.

An Efficient Method for Estimating Capability Curve of Virtual Power Plant

To incorporate the operating constraints of a virtual power plant(VPP)in transmission-level operation and market clearing,the concept of the VPP capability curve(VPP-CC)is proposed which explicitly characterizes the allowable range of active and reactive power outputs of a VPP.A two-step projection-based calculation framework is proposed to approximate the VPP-CC by the convex hull of critical points on its perimeter.The output of the proposed algorithm is concise and can be easily incorporated in the existing system operation and market clearing.Case studies based on the IEEE 33 and 123 test feeders show the computational efficiency of the proposed method outperforms existing methods by 4~7 times.Additionally,many fewer inequalities are needed to depict the VPP-CC while achieving the comparative approximation accuracy compared to sampling-based methods,which will relieve the communication and computation burden.

Collusion Potential Assessment in Electricity Markets Considering Generation Flexibility

The collusion among various generating units has been a problematic issue affecting the fairness and transparency of electricity markets.Therefore,it is of great significance to assess the potential of such collusion in the electricity market.However,the previous assessment studies primarily focused on the bidding behaviors of collusive generating units,without considering the influences of generation flexibility,such as ramp rates.In this paper,a novel assessment method is proposed to evaluate the collusion potential in the electricity market considering generation flexibility.First,a bi-level optimization model is developed to simulate the collusive strategies of dif-ferent generating units,including the withholding of generation capacities and ramp rates,as well as the uplifting of minimum outputs and bidding prices.In the upper-level problem,collusive generating units optimize their offering strategies to optimize the generation profits without violating the regulatory laws.The lower-level problem is a day-ahead economic dispatch model which minimizes the dispatching costs.Based on the optimal collusive strategies determined by the bi-level model,a framework is then proposed to assess the collusion potential in electricity markets.Moreover,price-based and profit-based indices are proposed to quantitatively evaluate the collusion potential of different generating units.Finally,the proposed assessment method is validated on a modified IEEE 39-node system.The numerical results demonstrated that generation flexibility can be exploited collusively for making excessive profits,particularly during load peaks and valleys.Index Terms-Collusionpotential,economic cdispatch,generation flexibility,ramp rates,strategic withholding.

Bias Current Influence on the Characteristic of the Magnetic-controlled Switcher Type Fault Current Limiter

A novel magnetic-controlled switcher type fault current limiter （FCL） for high voltage electric network is presented. The current limiting principle of the FCL and the bias current influence on the characteristic of the FCL axe discussed. The experiments on the 220 V/50 A test model show that the FCL can limit the fault current swiftly and effectively. Under the normal state, the bias current adjustment can change the FCL voltage loss; under the fault state, the steady fault current can be easily adjusted to the preset level by bias current regulating. The experimental result is in accordance with the principle analysis and the FCL has the advantages of flexible control strategy and simple and reliable structure.