Capacity Evaluation of AC/DC Hybrid Micro-grid-Distributed Generation Considering V2G

The increasing number of DC loads, such as electric vehicles (EVs), has resulted in micro-grid undergoing difficulty in satisfying the various demands of such loads. The study develops a multi-objective capacity optimization allocation model for hybrid micro-grid on the bases of users’ satisfaction and the orderly charging/discharging of EVs. The proposed model aims to reduce the cost of the capacity allocation of AC/DC hybrid micro-grid and improve users’ satisfaction. Particle swarm optimization algorithm is used to address the capacity optimization of hybrid micro-grid in EVs across scenarios. This study verified the rationality and effectiveness of the proposed capacity optimization model by comparing and analyzing the influence of capacity optimization on the orderly/disorderly charging/discharging of EVs and users’ satisfaction.

An Initialization-free Distributed Algorithm for Power Dispatch Problem with Multiple Resources of Future Distribution Network

With the gradual development of bidirectional interacted future distribution network, it is necessary to enter distributed clean power sources with various characteristics, including wind turbine and solar panel. Traditional centralized control has difficulties to fulfill the demands of future distribution networks for safe, stable, and efficient operation. Aiming at the constrained power allocation problem widely studied in smart grid, with the cooperative control algorithm of continuous multi-agent system, this paper proposed a distributed optimization allocation strategy, which is free by the initial state. The proposed distributed algorithm implements parameterization by adding auxiliary variables. In the iterative process, the algorithm only needs to know the state of the distributed power supply of the neighbor, and finally solve the global optimal solution of the system. The simulations prove that the proposed scheme can effectively improve the economic dispatch performance. Furthermore, comparing to the existing algorithm, the proposed algorithm achieves faster optimal solution.

A Resource Optimization Allocation Strategy for China’s Shipbuilding Industry Green Innovation System

In order to realize the resource optimization allocation in the green innovation system of China’s shipbuilding industry under the internet environment, to improve the level of green innovation and to reduce the resource consumption, a resource optimization allocation model and the corresponding allocation strategy are proposed. The model integrates and shares the innovation resource data through Internet of Things (IOT) technology, and optimizes the allocation decision by using the cooperative differential game and Particle Swarm Optimization (PSO) algorithm. At the same time, it ensures the robustness of green innovation system and realizes the optimization allocation of resources. A case study is given to illustrate the feasibility of the model. The results show that the green innovation subject can carry out strategic interaction by adjusting the allocation proportion of innovation resources through the proposed model, so as to optimize the overall green innovation benefits of the system.