In residential microgrids, an energy storage system (ESS) can mitigate the intermittence and uncertainty of renewable energy generation, which plays an important role in balancing power generation and load consumption. Distributed energy storage (DES) is a common form of ESS. However, the high investment cost and fixed energy storage capacity limit their application in residential areas. This study proposes an improved service mechanism based on an alternative form of DES, cloud energy storage (CES). The energy transaction service is added in traditional CES service mechanism to enhance the power interaction between users. In addition, the pricing scheme of CES service fee is formulated, which is calculated based on the battery life loss caused by charging/discharging behaviour during the service period. This study considers that CES can improve energy storage utilisation and meet the energy storage requirements of users at a lower cost than DES. Finally, the CES service decisions are solved by the solver LINGO, including charging/discharging power decisions and energy trading decisions of users. Simulation results show that users' electricity costs are further reduced under the improved CES model. The rationality and economic feasibility of the improved CES model are demonstrated.

In order to improve the scientific and rationality of power quality (PQ) comprehensive evaluation, an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) strategy in consideration of the correlation between indices is proposed to evaluate PQ. The strategy overcomes the shortcomings of the traditional methods that ignore the correlation between PQ performance parameters. Firstly, the AHP-entropy weight (EW) method is obtained by combining the improved analytic hierarchy process (AHP) and the EW method, and the combined weights of the PQ indices are calculated. Secondly, the Mahalanobis distance is used to replace the Euclidean distance in the traditional TOPSIS method, and the PQ samples that need to be evaluated are sorted. The Mahalanobis distance nonlinearly correlates the components inside the evaluation matrix through its own covariance matrix, which solves the problem that the dimensions of each index are not uniform and eliminates the correlation interference between the indices. The example shows that the improved TOPSIS method effectively avoids the misjudgment caused by the correlation between the indices, and the evaluation results are more reasonable and scientific, with greater superiority and effectiveness.