Search

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the orderly control and flexible adjustment capabilities of large-scale distributed power sources connected to the distribution network, it is currently an effective method to reasonably divide the distributed power sources into clusters and use the cluster as the basic unit for regulation. To this end, a distributed power cluster partitioning method that considers the adjustable capacity and response speed of flexible resources is proposed. Firstly, the modularity index and the active power balance index are proposed to characterize the structural and power balance capability of clusters. Secondly, based on the analysis of the cluster's flexible response capability, and in view of the problem that it is difficult to quantify the impact of the flexible resource response capacity and response speed on the adjustment capability, it is proposed to take the cumulative area of deviation between the response curve of cluster output and the net load curve as the indicator of the flexible response capability of clusters, and the influence of the allocation of energy storage capacity on the delineation of clusters is also taken into account. Then, a cluster division objective function that comprehensively considers the modularity index, active power balance index, cluster flexible response capability index and energy storage unit allocation index is proposed, and this function is solved using an improved adaptive genetic algorithm. Finally, the proposed method is verified in the improved IEEE 33-node system and IEEE 69-node system. The results show that the cluster partitioning method based on the proposed comprehensive performance index can improve the cluster flexible response capability by more than 20% on the basis of ensuring the structural strength of the cluster, which verifies the effectiveness and superiority of the cluster partitioning method proposed in this paper.

Fossil energy consumption and environmental protection issues have pushed electric vehicles (EVs) to become one of the alternatives to traditional fossil-fuel vehicles. EV refers to a vehicle that uses electric energy as power and is driven by an electric motor. The electric energy of EVs is stored in batteries. When the EV is not traveling, the battery can provide power for other loads. Therefore, with the increase in the number of EVs and the load of the power grid, the EV-to-grid (V2G) mode, which uses EVs to supply power to the power grid, has gradually entered the field of vision of researchers. The physical connection mode, charge and discharge technology, and energy management strategy are the main topics of the current review papers; however, there is a lack of systematic research on V2G modeling, framework, and business models. This paper describes the concepts of the spatio-temporal distribution model and the adjustable capacity of EVs. In addition, common constraints and methods in optimization are introduced. Moreover, this paper introduces the interactive relationship among power grids, load aggregators, and EV users. Furthermore, the business model of V2G is introduced and analyzed from various perspectives. Finally, the future development of V2G is pointed out. This paper's goal is to provide an overview of the present V2G application scenarios and to identify any challenges that must be overcome. [ABSTRACT FROM AUTHOR]

Fossil energy consumption and environmental protection issues have pushed electric vehicles (EVs) to become one of the alternatives to traditional fossil-fuel vehicles. EV refers to a vehicle that uses electric energy as power and is driven by an electric motor. The electric energy of EVs is stored in batteries. When the EV is not traveling, the battery can provide power for other loads. Therefore, with the increase in the number of EVs and the load of the power grid, the EV-to-grid (V2G) mode, which uses EVs to supply power to the power grid, has gradually entered the field of vision of researchers. The physical connection mode, charge and discharge technology, and energy management strategy are the main topics of the current review papers; however, there is a lack of systematic research on V2G modeling, framework, and business models. This paper describes the concepts of the spatio-temporal distribution model and the adjustable capacity of EVs. In addition, common constraints and methods in optimization are introduced. Moreover, this paper introduces the interactive relationship among power grids, load aggregators, and EV users. Furthermore, the business model of V2G is introduced and analyzed from various perspectives. Finally, the future development of V2G is pointed out. This paper’s goal is to provide an overview of the present V2G application scenarios and to identify any challenges that must be overcome.

Offshore wind power has developed rapidly in recent years, but its scale still lags far behind onshore wind power. Offshore wind power still has great development potential. One of the key factors restricting the development of offshore wind power is the unsatisfactory control effect of offshore wind farms, and the reason is that the adjustable capacity of the wind farm cannot be obtained accurately and quickly. Aims to meet the high precision requirements for adjustable capacity evaluation of offshore wind farms, this paper establishes a step-by-step power mapping framework based on the division of power transmission processes in offshore wind farms, considering the loss of each transmission process in detail. By establishing a step-by-step mapping from the wind turbine power to the injected power at the grid connection point of the offshore wind farm, the adjustable capacity of the offshore wind farm can be estimated based on the maximum theoretical power of the wind turbines. The performance of proposed method has been demonstrated in a real offshore wind farm. [ABSTRACT FROM AUTHOR]

Offshore wind power has developed rapidly in recent years, but its scale still lags far behind onshore wind power. Offshore wind power still has great development potential. One of the key factors restricting the development of offshore wind power is the unsatisfactory control effect of offshore wind farms, and the reason is that the adjustable capacity of the wind farm cannot be obtained accurately and quickly. Aims to meet the high precision requirements for adjustable capacity evaluation of offshore wind farms, this paper establishes a step-by-step power mapping framework based on the division of power transmission processes in offshore wind farms, considering the loss of each transmission process in detail. By establishing a step-by-step mapping from the wind turbine power to the injected power at the grid connection point of the offshore wind farm, the adjustable capacity of the offshore wind farm can be estimated based on the maximum theoretical power of the wind turbines. The performance of proposed method has been demonstrated in a real offshore wind farm.

This article addresses the production control problem of an adjustable capacity unreliable manufacturing cell responding to a single product type demand. The manufacturing cell is composed of an unreliable machine, called the 'central machine'. Due to availability fluctuations, the central machine may fall short of meeting the long-term demand rate. In order to quickly adjust the production capacity and thus meet the demand, a reserve machine is called upon in support if the finished product inventory level drops below a specific threshold. Such a machine involves higher production costs compared with the central one. This article aims to determine the optimal production control policy for the involved machines in order to minimise production, inventory and backlog costs over an infinite horizon. This article proposes a continuous dynamic programming formulation of the problem and adopted a numerical scheme to solve the optimality conditions equations. The optimal production policy is shown to be described by a state dependent hedging point policy (SDHPP). To determine the optimal control policy parameters, an experimental approach based on design of experiments, simulation modelling, and response surface methodology is proposed. Several sensitivity analyses have been carried out and have shown the robust behaviour of the developed policy facing expected variations of the system parameters. The results also show that the proposed SDHPP policy outperforms classical stand-by and parallel machines based control policies. The usefulness of the proposed approach is outlined for more complex situations where the system must deal with non-exponential failure and repair time distributions. [ABSTRACT FROM AUTHOR]