A MILP model for optimal renewable wind DG allocation in smart distribution systems considering voltage stability and line loss

被引:13
作者
Alanazi, Mohana S. [1 ]
机构
[1] Jouf Univ, Coll Engn, Elect Engn Dept, Sakaka 42421, Saudi Arabia
关键词
Renewable distributed gen-eration; Voltage stability; Mixed integer linear pro-gramming; Energy losses; Optimal power flow; Active network management wind generation; PARTICLE SWARM OPTIMIZATION; LEARNING BASED OPTIMIZATION; ECONOMIC EMISSION DISPATCH; NETWORK RECONFIGURATION; TECHNICAL BENEFITS; OPTIMAL PLACEMENT; POWER-FLOW; GENERATION; LOCATION; LOAD;
D O I
10.1016/j.aej.2021.11.017
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper introduces a new model for optimal wind distributed generation (WDG) allo-cation based on mixed integer linear programming (MILP) in todays' smart grids considering on-load tap changing (OLTC) and power factor control (PFC) strategies. The objectives of the pro-posed optimization model are improving voltage stability and energy loss minimization subject to power flow constraints, line thermal limits, maximum number and size of WDG units, penetra-tion limit, discrete OLTC tap position constraints, and PFC constraints. An efficient voltage phasor-information-based probabilistic voltage stability index (VSI) is extracted to measure the improvement of voltage stability with WDGs. Besides energy losses, reduction index is used to mea-sure the energy loss reduction with the integration of renewable WDGs in the distribution system. An efficient linearized power flow (LPF) model as well as piecewise linearized model of the quad-ratic terms associated to the PFC constraints are represented to convert the original nonconvex problem into the form of a well-known MILP problem, which guarantees optimal solution and computationally is efficient to be solved using powerful commercial solvers. The effectiveness and validity of proposed model is investigated in compared to the state-of-the-art population -based methods and classical models.
引用
收藏
页码:5887 / 5901
页数:15
相关论文
共 52 条
[41]   Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index [J].
Niknam, T. ;
Narimani, M. R. ;
Aghaei, J. ;
Azizipanah-Abarghooee, R. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2012, 6 (06) :515-527
[42]   Evaluating distributed time-varying generation through a multiobjective index [J].
Ochoa, Luis F. ;
Padilha-Feltrin, Antonio ;
Harrison, Gareth P. .
IEEE TRANSACTIONS ON POWER DELIVERY, 2008, 23 (02) :1132-1138
[43]  
OConnell R. P, 2005, PRINCIPLES EFFICIENT
[44]   Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm [J].
Prabha, D. Rama ;
Jayabarathi, T. .
AIN SHAMS ENGINEERING JOURNAL, 2016, 7 (02) :683-694
[45]   Cost–benefit analysis for optimal DG placement in distribution systems by using elephant herding optimization algorithm [J].
Prasad, C. Hari ;
Subbaramaiah, K. ;
Sujatha, P. .
Renewables: Wind, Water, and Solar, 2019, 6 (01)
[46]   Assessment of energy distribution losses for increasing penetration of distributed generation [J].
Quezada, VHM ;
Abbad, JR ;
San Román, TG .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) :533-540
[47]   Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems [J].
Sultana, Sneha ;
Roy, Provas Kumar .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 63 :534-545
[48]   Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods [J].
Vatani, Mohammadreza ;
Alkaran, Davood Solati ;
Sanjari, Mohammad Javad ;
Gharehpetian, Gevork B. .
IET GENERATION TRANSMISSION & DISTRIBUTION, 2016, 10 (01) :66-72
[49]   Analytical approaches for optimal placement of distributed generation sources in power systems [J].
Wang, CS ;
Nehrir, MH .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2004, 19 (04) :2068-2076
[50]  
Wang H., 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), P1