The installed capacity of distributed generation (DG) based on renewable energy sources has increased continuously in power systems, and its market-oriented transaction is imperative. However, traditional transaction management based on centralized organizations has many disadvantages, such as high operation cost, low transparency, and potential risk of transaction data modification. Therefore, a decentralized electricity transaction mode for microgrids is proposed in this study based on blockchain and continuous double auction (CDA) mechanism. A buyer and seller initially complete the transaction matching in the CDA market. In view of the frequent price fluctuation in the CDA market, an adaptive aggressiveness strategy is used to adjust the quotation timely according to market changes. DG and consumer exchange digital certificate of power and expenditure on the blockchain system and the interests of consumers are then guaranteed by multi-signature when DG cannot generate power due to failure or other reasons. The digital certification of electricity assets is replaced by the sequence number with specific tags in the transaction script, and the size of digital certification can be adjusted according to transaction energy quantity. Finally, the feasibility of market mechanism through specific microgrid case and settlement process is also provided.

Soft open points (SOPs) can transfer active power between feeders and compensate reactive power. These features help increase the integration capacity of distributed generation (DG), but the installation location and capacity of SOPs will affect DG planning. In addition, the distribution networks are usually unbalanced due to asymmetric line parameters, unbalanced loads, and DG. Converter-based DG units and SOPs have individual phase active and reactive power regulating ability and provide unbalance compensation. The objective of this paper is to develop a coordinated planning model of converter-based DG units and SOPs in an unbalanced distribution network (UDN) to incorporate their individual phase power control abilities. The individual phase power control characteristics of DG converters and SOPs are first analyzed. A bi-level optimization model of converter-based DG units and SOP planning is then established, in which the upper-level problem minimizes the total cost of the UDN and the lower-level problem minimizes the power loss and voltage unbalance. The bi-level model is transformed into a single-level mixed integer second-order cone programming problem that can be efficiently solved by widely used commercial solvers. Finally, the proposed model is verified on IEEE 33-node and Taiwan Power Company systems.

To improve energy efficiency and reduce pollution emissions of ports with electricity and hydrogen substitution, this paper proposes a collaborative scheduling method of port integrated energy and container logistics considering electric and hydrogen-powered transport. By analyzing the coupling relationship between energy production and consumption in a port that deploys electric and hydrogen-powered transport, the scheduling optimization models of the port integrated energy system (PIES) and the port container logistic system (PCLS) are collaboratively established and rendered as a mixed-integer bi-level programming model. Moreover, to simplify the complexity of PCLS scheduling which involves the allocation and operation of each type of container logistics equipment, an assembly line of port container logistics (ALPCL) model based on flexible job-shop scheduling (FJSS) is further constructed. Then, a distributed algorithm based on the Benders decomposition (BD) embedded with an improved non-dominated sorting genetic (INSGA-II) is designed to realize optimal dispatch with limited data exchange and great solution efficiency. The results of case studies demonstrate the effectiveness of the proposed method for economic and environmental benefits, including a reduction in scheduling costs and pollution emissions. This method can also achieve co-improvement of energy and logistics efficiency

This paper proposes an electric load demand model of the 5th generation (5G) base station (BS) in a distribution system based on data flow analysis. First, the electric load model of a 5G BS is developed according to its components and their characteristics. Second, critical factors of the power consumption of 5G BS, including area, data flow with uncertainty number of activated terminals and data usage of the terminal, are discussed. A statistical model of the number of activated terminals and data usage of the terminal are established using a probability distribution, and Monte Carlo sampling is used to handle the uncertainties. Then, the model is extended for multiple terminal connections. A case study is conducted to analyze the impact of the critical factors on the load of 5G BS and the influence of 5G BSs load on the other loads in three typical areas. Numerical results demonstrate that the proposed model is effective and can be employed as an accurate representation of the 5G BS load profile for the analysis of load characteristics. Case studies also show that the 5G BS loads have diverse impact on different typical areas in a distribution system.