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In power system, the end consumer receives the electricity through the distribution system. Typically, the distribution system has three types. i.e. doubly fed, ring type and radial in nature. Worldwide, the radial type of distribution system is more dominant due to its technical advantages. However, it possesses high resistance to reactance ratio, which eventually consumes about 13% of power losses (I2 x R) out of total power generation (Ali, Mohd Nor, Ibrahim, & Fakhizan Romlie, 2017; Devabalaji, Ravi, & Kothari, 2015; Kumar, Nallagownden, & Elamvazuthi, 2017; Mahesh, Nallagownden, & Elamvazuthi, 2016). On the other hand, the load demand in the distribution system is also escalating linearly due to urbanization and population growth (Anand & Suganthi, 2017; Barisal, Panigrahi, & Mishra, 2017). The majority of these loads are inductive in nature. So, an increase in load increases the reactive power demand, which reduces more voltage profile of the system. Therefore, integration of DG and DSTATCOM has overall positive effects on the distribution system. The DG and DSTATCOM are the small sources of power generations connected near to the load center. They help in peak power shaving, base-load power reduction and reduces power losses and increases voltage profile. The DG has the ability to provide the active power whereas, the DSTATCOM provides the reactive power. The installation of DG and DSTATCOM optimally provides safe, reliable, economical, and sustainable power alternates in the distribution system. Whereas, non-optimal integration will over-rule the distribution system performance and badly impacts the system losses and voltage quality.
In literature, numerous methods such as analytical, numerical programming, and heuristic or iterative based optimization methods have been used for optimal placement and sizing of DG and DSTATCOM. The overall objective is to reduce the power losses and reinstate the system security constraints. The analytical method called the two-third method for power loss reduction was proposed by H. L. Willis (Willis, 2000). According to this method, the DG at a two-third distance with two-third size will give maximum benefit and reduce a portion of system losses. Author C. Wang et al. (Wang & Nehrir, 2004) derived two analytical expressions for power loss reduction. The one expression is used for radial distribution whereas the second expression was used for the meshed network. N. Acharya et.al (Acharya, Mahat, & Mithulananthan, 2006) and D. Q. Hung (Hung, Mithulananthan, & Bansal, 2010) proposed the analytical method for optimal allocation of DG in the distribution system. The optimal sitting and sizing of DG using analytical method were also proposed by T. Gozel et al. (Gözel & Hocaoglu, 2009). The linear programming approach based optimal DG placement has been proposed in (Keane & O'Malley, 2005, 2007) considering financial and technical constraints. Author Y. Atwa et al. proposed the non-linear programming method to solve the optimal DG placement and sizing problem. A probabilistic based generation-load model was designed for optimal integration of wind (Atwa & El-Saadany, 2011) and renewable DG sources in the distribution system (Atwa, El-Saadany, Salama, & Seethapathy, 2010). The optimal sitting and sizing of DG considering multi-constraints with single or multi-objective using GA have been used in (El-Ela, Allam, & Shatla, 2010; Kim, Lee, Rhee, Lee, & You, 2002). Optimal placement and sizing of DSTATCOM and DG using an immune algorithm for reduction of power loss and DSTATCOM installation cost have been carried out by S. A. Tahir et al. (Taher & Afsari, 2014). Minimization of active power loss, improvement in voltage profile, improvement in VSI index and operational cost with weighting technique has been proposed by K. Devabalaji et al. (Devabalaji & Ravi, 2015). The author used the bacterial forging optimization (BFOA) algorithm for optimal placement and sizing of DG and DSTATCOM.