Open AccessOptimal coordination of PV smart inverter and traditional volt‐VAR control devices for energy cost savings and voltage regulation
Author(s) -
Pamshetti Vijay Babu,
Singh Shiv Pujan
Publication year - 2019
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12042
Subject(s) - voltage reduction , photovoltaic system , voltage , volt , voltage regulation , inverter , control theory (sociology) , engineering , ac power , computer science , automotive engineering , reliability engineering , electrical engineering , control (management) , artificial intelligence
Summary Traditional volt‐VAR control (VVC) devices such as on‐load tap changers (OLTC), voltage regulators (VRs), and shunt capacitor banks (SCBs) may not be capable to handle the sudden voltage violations because of slow response and large delay time. The voltage fluctuations may result from various disturbances such as intermittence in power output from distributed energy sources (DERs) such as photovoltaic (PV) and wind generation, change in network configuration, and load demand (especially in the case of flexible loads). Hence, there is a need of fast‐acting voltage regulation device such as smart inverter along with traditional VVC devices to encounter these issues. However, without proper coordination, these devices may cause a detrimental impact on distribution operations and network assets. In order to resolve this issue, a hierarchical coordinated volt‐VAR optimization (VVO) methodology has been introduced in this paper. In the proposed methodology, centralized as well as local control algorithm has been considered. The VVO objective of present study is to minimize the total operating cost considering conservation voltage reduction (CVR) and voltage deviation at all nodes simultaneously. Besides, the impact of battery energy storage (BES) on total operating cost, voltage deviation, and CVR has been examined. The ϵ‐constraint method and fuzzy decision‐making method have been employed for the solution of the abovementioned multi‐objective optimization problem. The performance of the proposed scheme has also been verified considering the forecast errors in PV generation and load demand. The proposed VVO methodology has been validated on a well‐known 33‐bus distribution systems. The test results demonstrate the significance of the proposed scheme on minimization of energy consumption, losses, OLTC switching operations, and voltage deviation.