Haiyun Wang

Bio: Haiyun Wang is an academic researcher from Xinjiang University. The author has contributed to research in topics: Electric power system & Computer science. The author has an hindex of 7, co-authored 16 publications receiving 193 citations.

A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction

Abstract: In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distribution system. The optimization process is applied using a newly developed type of Cayote Optimization Algorithm (COA). The proposed technique includes two different approaches. In the first approach, the optimization for allocation and the sizing are performed one by one and in the second approach, the optimization has been done simultaneously. To analyze the proposed system, four different scenarios have been analyzed which include different conditions without/with PVs and also using single/two BESS. The results showed that using two BESS can reduce the total error of the distribution system. the results also showed that using PVs can also decrease the total losses. Finally, the proposed approach based on ICOA is compared with Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to show the proposed method's prominence efficiency.

A Chaos Disturbed Beetle Antennae Search Algorithm for a Multiobjective Distribution Network Reconfiguration Considering the Variation of Load and DG

Abstract: As the distributed generation (DG) in a power supply and the user load demand constantly change in an actual distribution network, multiobjective optimal network reconfiguration considering variations in load and DG has become a major concern, which is important and required to make system operations safe and economical. The aim is to minimize the sum of the active power loss, the sum of the load balancing index and the sum of the maximum node voltage deviation index simultaneously during the reconfiguration period. Here, this article proposes a new Chaos Disturbed Beetle Antennae Search (CDBAS) algorithm to reduce the computational time and solve the multiobjective optimal problem of network reconfiguration. To adopt the Chaos Disturbed Beetle Antennae Search algorithm for solving this multiobjective problem, grey target decision-making technology is used to rank the beetles. Additionally, to the enhance the system static voltage stability and voltage quality, a grey target decision-making model is established to achieve a layer relationship between each index and the switching operation index. The plausibility and effectiveness of the presented methodology is verified on the modified IEEE 33, 69 and 118-Bus Test Radial Distribution Network. Finally, compared with other research methods in the literature, the CDBAS algorithm outperforms other algorithms and produces a quality decision solution.

Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm

Abstract: Accurate state of health (SOH) is a crucial factor for the regular operation of the electric vehicle. Compared with the equivalent circuit methods, the data-driven methods do not rely on the battery model and do not need to measure the open-circuit voltage. This paper proposes an on-line method based on the fusion of incremental capacity (IC) and wavelet neural networks with genetic algorithm (GA-WNN) to estimate SOH under current discharge. Firstly, IC curves are acquired, and the important health feature variables are extracted from IC curves using Pearson correlation coefficient method. Second, The GA is used to optimize the initial connection weights, translation factor and scaling factor of WNN; then, the GA-WNN model is applied to estimate battery's SOH. Third, the established model is verified by battery data. Finally, the experiment results show that the SOH estimation error of this method is less than 3%.

Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks

Abstract: This paper presents a methodology for the optimal location, selection, and operation of battery energy storage systems (BESSs) and renewable distributed generators (DGs) in medium–low voltage distribution systems. A mixed-integer non-linear programming model is presented to formulate the problem, and a planning-operation decomposition methodology is proposed to solve it. The proposed methodology is separated into two problems (planning and operation problems). The planning problem is related to the location and selection of these devices, and the operation problem is responsible for finding the optimal BESS operating scheme. For solving the planning problem is used a simulated annealing algorithm with a defined neighborhood structure that uses a sensitivity analysis based on the Zbus matrix. A novel decomposition method, which guarantees near-global-optimal solutions with low computational effort, is proposed for solving the operation problem. The effectiveness and accuracy of the proposed decomposition method is validated and tested on an 11-node test system from the specialized literature, and the robustness of the proposed method is assessed and tested on a modified version of an IEEE 135-node test system. The proposed planning-operation decomposition methodology is tested on a real medium–low voltage distribution system of 230 nodes. To verify the efficiency of the proposed methodology, four cases are compared: (I) without BESS and DGs, (II) with DGs, (III) with BESS, and (IV) with BESS and DGs. The numerical results demonstrate the effectiveness and robustness of the proposed methodology.