• Energy Research

The advantages of hybrid energy ship with photovoltaic in energy conservation and emission reduction are becoming more and more prominent with increasing tension of global fossil energy. However, how to deal with photovoltaic uncertainty in real time and make photovoltaic efficiently connected to ship microgrid has become a key technical problem. Therefore, we propose a real-time energy management strategy for hybrid energy ship based on approximate model predictive control. Firstly, aiming at minimizing the operating cost and deviation from the reference state of charge, an energy management framework based on model predictive control is established. Secondly, the machine learning algorithm is trained to approximate the optimal control action of model predictive control offline, and the performance of different machine learning algorithms is analyzed quantitatively. Finally, taking a ferry equipped with photovoltaic as an example, the appropriate machine learning algorithm and sample number are selected. The results show that the proposed strategy can not only ensure the optimization performance, but also effectively reduce the amount of calculation and realize the real-time operation of energy management in hybrid energy ship.

A reasonable plan for charging stations is critical to the widespread use of electric vehicles. In this paper, we propose an optimal planning method for electric vehicle charging stations. First of all, we put forward a forecasting method for the distribution of electric vehicle fast charging demand in urban areas. Next, a new mathematical model that considers the mutual benefit of electric vehicle users and the power grid is set up, aiming to minimize the social cost of charging stations. Then, the model is solved by the Voronoi diagram combined with improved particle swarm optimization. In the end, the proposed method is applied to an urban area, simulation results demonstrate that the proposed method can yield optimal location and capacity of each charging station. A contrasting case is carried out to verify that improved particle swarm optimization is more effective in finding the global optimal solution than particle swarm optimization.

With the popularization of electric vehicles (EVs), the charging behavior of users will inevitably affect the power grid in the process of vehicle-to-grid (V2G). It is particularly important to consider the behavior characteristics and charging habits of EV users to guide their charging behavior. This paper presents a hybrid demand response (HDR) strategy based on price-sensitive demand response (PSDR), as well as incentive-based demand response (IBDR) strategy. PSDR strategy based on time-of-use (TOU) price is to guide EV users charging behavior by implementing dynamic TOU price. IBDR strategy is to put forward a charging point accumulation mechanism based on incentive subsidies and set charging reward and punishment points for different types of users according to the type of users. Then converting the TOU price into the form of points and setting a limited value for incentive points in combination with peak and valley periods to obtain the HDR strategy. Considering the uncertainty of users’ response, the model of users’ participation response is established. The multi-objective optimization model of reward and punishment coefficient and unit integral value is established by comprehensively considering various benefits. Finally, simulation results show that this strategy can effectively improve the adhesion of EV users and reduce the impact of their charging on the power grid.

Typhoons can have disastrous effects on power systems. They may lead to a large number of power outages for distribution network users. Therefore, this paper establishes a model to predict the power outage quantity of distribution network users under a typhoon disaster. Firstly, twenty-six explanatory variables (called global variables) covering meteorological factors, geographical factors, and power grid factors are considered as the input variables. On this basis, the correlation between each explanatory variable and response variable is analyzed. Secondly, we established a global variable model to predict the power outage quantity of distribution network users based on Random Forest (RF) algorithm. Then the importance of each explanatory variable is mined to extract the most important variables. To reduce the complexity of the model and ease the burden of data collection, eight variables are eventually selected as important variables. Afterward, we predict the power outage quantity of distribution network users again using the eight important variables. Thirdly, we compare the prediction accuracy of a model called the No-model that has been used before, Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), RF-global variable model, and RF-important variable model. Simulation results show that the RF-important variable model proposed in this paper has a better effect. Since fewer variables can save prediction time and make the model simplified, it is recommended to use the RF-important variable model.