Impact of D-STATCOM in distribution systems with load growth on stability margin enhancement and energy savings using PSO and GAMS

Abstract

Summary Optimal reactive power deployment as per the statutory provisions of Indian Electricity Grid Codes is essential for better operation of the system. In this paper, a multiobjective optimization problem is solved for sizing and siting of the reactive compensation device. The proposed scheme comprises combination of particle swarm optimization and General Algebraic Modeling System to obtain location and rating of Distribution Static Synchronous Compensator (D‐STATCOM). The main highlights of the proposed work are (i) optimal sizing and siting of D‐STATCOM under load growth and load variations scenario; (ii) voltage stability margin enhancement with D‐STATCOM in a stressed distribution system with load variations and growth; (iii) impact of single and multiple D‐STATCOM units on the losses, voltage profile, and voltage stability margin improvement; and (iv) cost of energy loss savings and annual energy savings. The proposed method is tested on 33‐bus, 69‐bus, and 30‐bus real‐time distribution system of the East Delta Network of the Unified Egyptian network. Benefits to distribution network operator are assessed in reduction in cost of energy loss, annual cost savings, and enhanced voltage stability margin. The results are compared with other methods reported in literature to verify the competency of the proposed multiobjective optimization problem using particle swarm optimization and General Algebraic Modeling System. Annual cost savings of $27 776.97 and $33 297.17 are obtained with proposed technique for 33‐bus and 69‐bus, respectively, which is higher than existing methods. After installation of D‐STATCOM, voltage stability margin enhanced from 0.6956 to 0.75845 for 33‐bus RDS, from 0.68339 to 0.74555 for 69‐bus RDS, and from 0.80223 to 0.83001 for 30‐bus RDS.

A Techno-Economic Centric Integrated Decision-Making Planning Approach for Optimal Assets Placement in Meshed Distribution Network Across the Load Growth

“…The evaluation comparison of achieved results in C1_OLG and C2_OLG are shown in Table 36, the multi-aspect results outperform the reported results in [57]. The evaluation results of C3_OLG and C4_OLG are compared in Table 37 with hybrid particle swarm optimization (PSO) and GAMS in [36], and sensitivity-based approach in [57]. The results from the proposed work outperforms the reported works from the perspective of better performance and optimal sizing assets, as shown in bold text shown throughout this section.…”

“…The D-STATCOM placement on different load levels [36] and multiple asset sets (DG and D-STATCOM) on different buses [29][30][31][32][33][34] and the same buses [37] have been evaluated from various objectives. It is also important to mention that the works reported in [14,[32][33][34][35][36][37] have mostly aimed at RDN, centered on a single branch (two buses) model and cannot encompass the core dynamic of LDN and MDN that is usually fed by more than on sending end.From the viewpoint of MCDM, hybrid methodologies have been put forward to achieve multiple objectives or evaluation under various criteria. In the research works [38][39][40][41], the prominent MCDM methods employed are weighted sum method (WSM), weighted product method (WPM), technique for order preference by similarity to ideal solution (TOPSIS), preference ranking organization method for enrichment of evaluations (PROMETHEE), and RDN is reconfigured in terms of asset optimization to achieve objects such as active power losses, reliability, and average energy not served (AENS).…”

“…The D-STATCOM placement on different load levels [36] and multiple asset sets (DG and D-STATCOM) on different buses [29][30][31][32][33][34] and the same buses [37] have been evaluated from various objectives. It is also important to mention that the works reported in [14,[32][33][34][35][36][37] have mostly aimed at RDN, centered on a single branch (two buses) model and cannot encompass the core dynamic of LDN and MDN that is usually fed by more than on sending end.…”

Optimal coordination of static VAR compensators, fixed capacitors, and distributed energy resources in Egyptian distribution networks

“…The maximum power flow in each line ( ij ) must not exceed the maximum thermal capacity of the line for each loading level 35 as expressed in Equation (20). where S max is the maximum allowable power flow of each line.…”

Application of Bacterial Foraging Algorithm in The Allocation of DSTATCOM in 50-bus Canteen Feeder

“…The drawbacks encountered by analytical approaches in DSTATCOM allocation have been addressed to a large extent by several meta-heuristic techniques such as crow search algorithm [13], hybridized PSO and general algebraic modeling system [14], differential evolution algorithm [15,16], imperialistic competitive algorithm [17], cuckoo search algorithm [18], hybridized fuzz-ACO [19], particle swarm optimization algorithm [20], immune algorithm [21], harmony search algorithm [5], bacterial foraging algorithm [4], quadratic adaptive bacterial foraging algorithm [6], firefly algorithm [22], grasshopper optimization algorithm [2] and weighted artificial fish swarm algorithm [23].…”