Photovoltaic power generation prediction is very helpful for real-time balanced operation in the power market, which in turn will benefit both energy suppliers and customers. Aiming at the prediction of photovoltaic power generation, the paper proposes a prediction method based on Markov chain and combination model. First, this paper preprocesses the data. Then, taking into account the variability and intermittency of photovoltaic power generation, discrete wavelet transform is used to de-noise the data. Next, the paper uses random forest for feature selection to extract feature vectors that are highly related to photovoltaic power generation. Finally, different prediction models are combined, and the weights of different prediction models are dynamically determined through Markov chains. The linear combination of the prediction values of the different prediction models under the dynamic weights is used as the final prediction value. The method in this paper is compared with other methods on real photovoltaic data sets. The experimental results show that the proposed method has a better prediction effect.

Unbalanced cost is an important issue in the design of power spot market mechanism. China’s power market reform has just begun and is still in a so-called “dual-track” system, in which the traditional administrative mode and the market mode both exist. This actual situation has made it difficult to deal with unbalanced costs in power spot market in China. To this end, this paper will focus on the problem of handling unbalanced costs under the dual-track system in China. Firstly, the problem of the dual-track system in China’s power market are introduced and the differences of different types of power entities are analyzed. Compared with the mature power market, the unbalanced cost of power spot market in China is characterized by non-market unbalanced cost due to the existence of scheduled power electricity and the composition of blocking costs is also different from foreign power market. This paper analyzes the generation of unbalanced costs due to non-market factors in detail and puts forward some suggestions for its allocation mechanism based on actual conditions.

In order to avoid the problem of uncallable spinning reserve caused by operation section block due to the new energy forecasting error and improve the system operation safety, a spinning reserve partitioning method considering new energy forecasting error is proposed. The core idea of this method is to determine the spinning reserve partition by evaluating the difference of locational marginal prices under the basic scenario and the extreme scenario. The power system congestion would be analyzed to evaluate the system operation cost increase caused by the new energy forecasting error. In order to simplify the discussion, this paper would focus on the case which only contains one single operation section block existed in a single time period. Other complex scenarios could be generalized accordingly. Finally, a case study based on IEEE-30 buses system is used to verify the effectiveness of the proposed method.

Nowadays, intermittent distributed generators (IDGs), represented by distributed wind turbine generators and photovoltaic generators, are developing rapidly. A multi‐scenario mathematical model with the objective of minimizing the annual carbon emission is proposed for the optimal allocation of IDGs in the active distribution network (ADN). The model takes into account three active measures, i.e. regulating the on‐load tap changer of the transformer, curtailing the active power of IDGs, and regulating the power factor of IDGs. K‐means clustering method is introduced to reduce the number of scenarios and obtain the probability of each scenario. A hybrid solving strategy combining the adaptive genetic algorithm and primal‐dual interior point method is developed to solve the model. Case studies are carried out on the IEEE 33‐bus ADN. The optimal allocation schemes under different cases are compared, and the potential contribution of carbon emission reduction from active measures is studied. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.