Abstract
This paper proposes two novel strategies for determining the bilateral trading preferences of households participating in a fully Peer-to-Peer (P2P) local energy market. The first strategy matches between surplus power supply and demand of participants, while the second is based on the distance between them in the network. The impact of bilateral trading preferences on the price and amount of energy traded is assessed for the two strategies. A decentralized fully P2P energy trading market is developed to generate the results in a day-ahead setting. After that, a permissioned blockchain-smart contract platform is used for the implementation of the decentralized P2P trading market on a digital platform. Actual data from a residential neighborhood in the Netherlands, with different varieties of distributed energy resources, is used for the simulations. Results show that in the two strategies, the energy procurement cost and grid interaction of all participants in P2P trading are reduced compared to a baseline scenario. The total amount of P2P energy traded is found to be higher when the trading preferences are based on distance, which could also be considered as a proxy for energy efficiency in the network by encouraging P2P trading among nearby households. However, the P2P trading prices in this strategy are found to be lower. Further, a comparison is made between two scenarios: with and without electric heating in households. Although the electrification of heating reduces the total amount of P2P energy trading, its impact on the trading prices is found to be limited.

Abstract
The interconnection of AC and DC microgrids results in a hybrid AC/DC microgrid (HMG). In light of HMGs, the future smart grid implementation will be facilitated. One important aspect in HMGs is the interconnection of AC and DC microgrids and control of bidirectional interlink power converters (BILPCs), which has taken a lot of research attention in the last decade. The BILPCs are the most prevalent method for interconnection of HMGs. Thus, the current study first reviews different interconnection methods and control challenges of AC and DC microgrids in HMGs and then overviews various control strategies of BILPCs presented in literature, all carried out in a comprehensive manner.

Abstract
During the recent years, the power system has entered a new technological era. The trends associated with increased commitment to wind farms (WFs) and energy storage systems (ESSs) as well demand side flexibility require disruptive changes in the existing power system structures and procedures. Being at the heart of a paradigm shift from passive users of the grid to active prosumers, storage owners and demand responsive actors, this paper expresses a flexible coordinated power system expansion planning (CPSEP) while considering local WFs, ESSs and incentive-based demand response programs (DRPs). This model minimizes the summation of the expansion planning, operation and reliability costs while taking the network model based on AC optimal power flow constraints, and the reliability and flexibility considerations into account. The proposed framework is firstly formulated by mixed integer non-linear programming (MINLP), then to have a well-handed optimization model it is converted to mixed integer linear programming (MILP). Additionally, the uncertainties of load, energy price, maximum WF generation and availability/unavailability of the network equipment are included in the proposed model where the first three parameters are modeled based on the bounded uncertainty-based robust optimization (BURO), and the scenario-based stochastic programming (SBSP) is used to model the last uncertain parameter. Finally, the proposed method is examined on several test networks to assess the performance of the proposed framework for flexi-reliable transmission network operation and planning.

Abstract
Wind power forecast evaluation matters greatly as wind power has an ever-increasing proportion in the power system. Generally speaking the forecasting result can be divided into lead-lag scenarios and common scenarios which depends on whether the wind process is predicted on time. During the lead-lag scenarios the errors usually change from large positive numbers to negative ones (or the opposite), especially in the both ends of the period. Compared with the common scenarios in the same value of root mean square error (RMSE), large changes in errors from positive to negative in a short time can cost nearly two times of spinning reserve but get the same assessment score. For power system the two scenarios should be evaluated differently, however, few metrics in the evaluation can indicate the lead-lag scenarios in that they dispose the errors ignoring the signs or time continuity of the errors, or analysis the errors in a macro-scale sight like 24 hours horizon scale. This paper proposes a new metric based on RMSE which detects the changes of signs of errors in a process of moving average. Except for normal advantages like objectivity, adaptability, unity, symmetry and stability, the new metric has the ability to reflect both the lead-lag scenarios and common scenarios. The new metric can be used in the evaluation of wind and solar power, load, price, demand response forecasting and the process of neural network parameter training.

Abstract
The direct power purchase by large consumers (DPLC) is an important part of the reform of the electricity market, and the development of renewable energy has led to a trend of decentralization on the supply side. Blockchain, as an emerging distributed database technology, has a good application prospect in the context of the current energy Internet construction. The article first introduces the principle of blockchain technology in detail and analyzes its application value in electricity trading. Starting from the traditional direct purchase transaction model, a framework for direct purchase of electricity for large consumers is proposed. Combining the characteristics of direct power purchase transactions, the distributed consensus mechanism is researched and improved, the smart contract is designed in combination with the transaction process, and the communication protocol and interaction relationship at each level are analyzed from the overall system architecture. Finally, the challenges faced by the system in practical application are analyzed, which provides ideas for follow-up research.

Abstract
In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and non-parametric approach termed as the lower upper bound estimation (LUBE) model. On the other hand, due to the high complexity raises in designing manually the deep learning architectures, as well as the enhancing the performance of the prediction intervals, we develop a novel deep neuroevolution algorithm based on the two-stage modification of the Gaining-Sharing Knowledge (GSK) optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. We also utilize coverage width criterion (CWC) to establish an excellent correlation appropriately between both the the PI coverage probability (PICP) and PI normalized average width (PINAW). We also indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada. The performance of the proposed model is examined on the practical tidal current data collected from the Bay of Fundy, NS, Canada.