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China's electricity market reform is constantly advancing, and market rules change frequently. On the basis of sorting out the technical advantages of micro service and HMI configuration of electricity trading, and using the decoupling characteristics of front-end and back-end of micro service, a rapid response technology architecture of electricity trading platform with "front-end HMI configuration + back-end microservice" is proposed, and the corresponding key technologies are studied. The results of this paper have been applied in the construction of a new generation of ETP and achieved good results.

At present, electricity transaction in China is mainly conducted by medium- and long-term market contracts, while the spot electricity market has not yet been implemented. An appropriate energy imbalance processing mechanism for medium- and long-term power transactions is highly demanded for maintaining the stability of market operation, since it is supposed to guide market participants to reduce system imbalances and actively provide balancing services. Given this background, a bi-level optimization model for developing optimal bidding strategies of power producer in the monthly sequential contract and balancing markets is proposed based on the currently implemented monthly pre-listing balancing mechanism in China. In the upper level of the model, the monthly utility which makes a tradeoff between revenues and risks produced by the bidding strategy of the producer in the monthly sequential energy dual-market is maximized, considering the uncertainties of the monthly energy imbalances in the power system and the bidding strategies of rivals. In the lower level, the dispatch cost is minimized by simulating the three-stage process of sequential system operation, which includes contract market clearing by power exchange (PX), energy decomposition and modification by the system operator (SO) and balancing market clearing by PX. The simulation results of an eastern provincial electricity market in China show that the presented model can assist power producers in adjusting their monthly bidding strategies with relevant factors, such as the minimum output level of each producer, risk preference, and forecast information including system energy imbalance status and bidding strategies of rivals. In addition, the proposed method is also helpful for market designers in setting market parameters, such as government-authorized contract energy proportion and settlement thresholds in the balancing market.

With fast development of the retail market of China in recent years, the risk of the market needs to be quantified. This paper provides a detailed framework to assess and classify the risk of the market. The neural network is used to predict the risk. Time-dependent weight is used to describe cumulative effect. With the S-shaped curve, the risk factors are classified by safety, competition and elimination. The entropy method is used to set weights of the risk factors. The K-means clustering is used to classify the companies or periods. The risk factors, the operational risk, and the risk indicators form a hierarchical framework. The state aggregation based on serial tracking and incidence matrix finds transition rate matrix for early warning and coordination. The numerical results of a market with 9 companies are provided to validate the effectiveness of the proposed model.

The retail electricity market is becoming prosperous in China in the recent years. However, due to many uncertain factors, the retail providers may go bankrupt and have to withdraw from the retail market, thus the customers will suffer from unexpected supply interruption and economic loss, which will disturb the healthy operation of the electricity market. The purpose of the last resort service is to take the responsibility of the bankrupted providers, and maintain the power supply to the customers. This service is often provided by retail electric providers in foreign countries while undertaken by the power grid companies in China. In this paper, the experiences of the last resort service in several foreign countries are investigated. The qualification, pricing, customer allocation, and operating process about the last resort service are investigated. The benefit and risk of the last resort service are analyzed to determine the sharing of the responsibilities, interests, and punishments among the participants of the retail market. Based on the investigation, some practical suggestions to the last resort service are given to the provincial retail market in China.

The increasing capacity of the grid-connected solar system has negative influence on the power grid. This paper focuses on the quality evaluation of the solar power. The quality of the solar power is quantified by the penalty function, which considers the total output, the output fluctuation and the forecast error. Firstly, the K-means clustering technique to cluster the sample data of the indices is applied. Then, the significance test using the F-distribution is applied to validate the effectiveness of the proposed model. Finally, the penalty function is proposed. The logarithmic function, the power function, and the exponential function are applied to compare the influences of each index on the penalty function. The Analytic Hierarchy Process (AHP) and the entropy method are used to determine the weight coefficients. The feasibility and application value of the proposed model are validated by the numerical results.

An electric vehicle aggregator (EVA) can participate strategically in an electricity market, on behalf of a large number of electric vehicle (EV) owners. Strategic bidding optimization and charging management for each EVA can benefit both the power system and EVA. In this work, a hierarchical optimization model for developing optimal bidding strategy and charging management in a real-time (RT) electricity market is proposed for each EVA to minimize its costs. The developed model is formulated as a two-level optimization problem in the framework of the well-established model predictive control (MPC). The first level optimization is to determine the optimal hourly RT bidding strategy of the EVA by a two-stage stochastic predictive optimization, with the charging scheduling of EVs formulated as a linear recourse problem. The second level optimization is to manage the RT charging of EVs in each time interval according to the cleared bidding quantity in the RT market, which can be formulated as a predictive linear programming problem. Both the first level and second level problems are optimized using the commercial solver Gurobi interfaced with Python language. Case studies demonstrate that the EVA can strategically participate in the RT electricity market and attain more benefits by utilizing the proposed strategy.

The reliability of power electronic converters is a major concern in industrial applications because of the use of high-power semiconductor devices, which have high-power density and higher failure rates. Potential defects inside an insulated gate bipolar transistor (IGBT) module can be seen as precursors of being about to wear out, and are important for health monitoring of the IGBT module which is used to enhance reliability. Aiming to do reliability assessment about three-level inverters, this paper proposes an appropriate method based on fault-tolerant control circuit, uniquely fine-tuned for inverter (or general converter) evaluation. The proposed prognostic approach uses terminal voltages of IGBT as a precursors to monitor the bond wire health, which can give accurate predictions and allow operators to observe unhealthy IGBT modules inside a power converter, and then take appropriate measures timely to avoid breakdown.

With the development of modern electricity equipment, voltage dips may bring more and more economic losses, which need a proper method of evaluation. In this paper, a time varying quality loss function, utilizing the Tobit model, is used to predict economic losses due to voltage dips. This quality loss function is more flexible and closer to practical situation.