Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems

Symbiotic Organism Search Algorithm for Optimal Size and Siting of Distributed Generators in Distribution Systems

Tri Phuoc Nguyen, Vo Ngoc Dieu, Pandian Vasant
Copyright: © 2017 |Pages: 28
DOI: 10.4018/IJEOE.2017070101
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This paper presents a new approach for solving optimal placement of distributed generation (OPDG) problem in distribution systems for minimizing active power loss. In this research, the loss sensitivity factor is used to identify the optimal locations for installation of DGs and symbiotic organisms search (SOS) is used to find the optimal size of DGs. The proposed SOS approach is defined as symbiotic relationships observed between two organisms in the ecosystem, which does not need control parameters like other meta-heuristic algorithms. The OPDG problem is considered with two different scenarios including Scenario I for DGs installed at candidate buses to supply only active power to the system and Scenario II for same as Scenario I except that DGs are controlled to supply both active and reactive powers at a 0.85 p.f. The effectiveness of the proposed SOS method has been verified on the IEEE 33-bus and 69-bus radial distribution systems. The result comparison from the test systems has indicated that the proposed SOS is effective to obtain the optimal solution for the OPDG problem.
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Distributed generation (DG) (also called decentralized generation, dispersed generation, and embedded generation) is small generating plants connected directly to the distribution systems or on the customer site of the meter. DGs include synchronous generators, induction generators, reciprocating engines, micro turbines, combustion gas turbines, fuel cells, solar photovoltaic, wind turbines and other small power sources (Georgilakis & Hatziargyriou, 2012).

Distribution systems have more losses and poor voltage regulation. Almost 13% of the generated power is wasted as IJEOE.2017070101.m01 losses. Loss reduction in distribution systems by applying the optimization methods is the current potential area of research (Reddy, Reddy, & Manohar, 2016).

Recently, the application of DG has been considered to address the issue of loss reduction in distribution systems. DGs are commonly used to provide the active and reactive power compensation in distribution systems. The proper installation of DG will contribute to improve system performance in terms of voltage profile, reduce flows and system losses, and improve power quality and reliability of supply. Besides, the reasonable use of DG also helps reduce pressure on upgrading the grid in investments, fuel costs, operating costs and reserve requirements. Depending on the structure of power grid and the technology of DG, the effect of DG is different. However, the DGs if placed without reasonable calculation results into deterioration of system performance (Gupta, Pandit, & Kothari, 2014). Thus, it is important to find the optimal placement and size of DG in distribution systems to achieve the above mentioned objectives.

Based on the above benefits, a lot of DG operation problems have been posed. The typical optimal placement of DG (OPDG) problem deals with the determination of the optimum locations and sizes of DGs to be installed into existingdistribution systems, subject to electrical network operating constraints, DG operation constraints, and investment constraints. The OPDG is a nonlinear, highly constrained, multi-objective, mixed-integer, multimodal optimisation problem (Jordehi, 2016). Solving constrained problems is difficult for optimisation algorithms. Different constraint handling strategies used in heuristic optimization algorithms are reviewed by (Jordehi, 2015).

With the advantages of OPDG problem brought, over the last decade many researchers have contributed a lot in terms of effort and time to figure out algorithms solve this problem. Many algorithms have been used from the classic to artificial intelligence and evolution such as analytical method (Wang & Nehrir, 2004; Acharya, Mahat, & Mithulananthan, 2006; Hamedi & Gandomkar, 2012; Gozel & Hocaoglu, 2009), lagrange multiplier (LM) (Gautam & Mithulananthan, 2007), interior point method (IPM) (Khoa, Binh, & Tran, 2006), teaching-learning based optimization (TLBO) (Garcíal & Mena, 2013), tabu search (TS) (Nara et al., 2001), genetic algorithm (GA) (Borges & Falcao, 2006; Pisică, Bulac, & Eremia, 2009; Kalantari & Kazemi, 2011), differential evolution (DE) (Arya, Choube, & Arya, 2011), ant colony optimization (ACO) (Falaghi & Haghifam, 2007), particle swarm optimization (PSO) (El-Zonkoly, 2011; Reddy, Dey, & Paul, 2012), bacterial foraging optimization algorithm (BFOA) (Mohamed Imran & Kowsalya, 2014), flower pollination algorithm (FPA) (Reddy, Reddy, & Manohar, 2016), grey wolf optimizer (GWO) (Sultana et al., 2016), cuckoo search (CS) (Moravej & Akhlaghi, 2013), gravitational search algorithm (GSA) (Mistry, Bhavsar, & Roy, 2012), bat algorithm (BA) (Behera, Dash, & Panigrahi, 2015), and harmony search algorithm (HSA) (Kollu, Rayapudi, & Sadhu, 2012), etc. Besides, many researchers have hybridized two optimization algorithms to obtain a better solution. A new hybrid method of GA and TS is introduced by (Gandomkar, Vakilian, & Ehsan, 2005), while (Celli et al., 2005) implemented a approach based on a GA and an IJEOE.2017070101.m02-constrained method. (Gonzalez, López, & Jurado, 2012) employed discrete PSO and OPF to overcome the optimal DG placement and sizing in distribution systems. To find an near-optimal solution for nonlinear function with constraints, the classical algorithms previously used iterative method, it took a long time to seek the solution and sometimes the solution was not near-optimal with large systems. Nowadays, the optimization calculations inspired by the movement and development of the organism has proven to be a potential alternative method to solve difficult OPDG problem.

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