ABSTRACT
In recent times, the issue of the gap between electricity supply and demand has been addressed using distributed generation (DG) technology. When DG technology is properly placed within a transmission and distribution network, there is always improvement in power loss reduction, voltage profile and system reliability. Proper placement can be achieved by evaluating the optimal values for voltage deviation, real power loss and bus voltage. Thus, this study presents a multi-objective DG optimisation model that minimizes of voltage deviation and real power loss, while maximising voltage stability factor. The model was formulated as a multi-objective problem and solved using an improved Strength Pareto Evolutionary Algorithm (SPEA-2) technique of optimisation to DG problem. The Nigeria-31 bus, 330 KV, transmission network was considered as test case. The results obtained were validated with the standard IEEE 30-bus system. In addition, this study presented scenarios where 1, 2 and 3 DGs were placed into these test cases. Based on the SPEA-2 implementation, the optimisation run time for the Nigerian network and the IEEE network were 2229.55 and 2039.42 secs, respectively. The optimal bus location of the three DGs (whose capacities are: 8.7793 MW/6.1272 MVAr, 8.1806 MW/4.7778 MVAr and 7.9567 MW/4.6281 MVAr respectively) for the IEEE 30 bus were 5,7 and 26 buses, respectively, while for the Nigerian-31 bus were 14, 15 and 17 (whose capacities are: 22.9693 MW/15.2956 MVAr, 27.3711 MW/21.7274 MVAr and 30.9910 MW/15.4069 MVAr respectively) respectively. For the placement of these DGs, the power loss reduction in the IEEE-30 bus is 16.17 MW, 15.28 MW, 14.07 MW respectively and 64.21 MVAr, 61.19 MVAr, 56.13 MVAr respectively. While for the Nigeria-31 bus system; the reduction in power loss is: 34.66 MW, 33.99 MW, 33.82 MW and 411.85 MVAr, 403.45 MVAr, 400.54 MVAr respectively. The results obtained showed that the total power losses reduced as DGs are sited at the optimal locations for the two test cases when compared with cases when DGs were not considered.
Oluseyi, P & Iro, I (2021). Development Of Optimal Placement Of Distributed Generators In Electrical Network Using Improved Strength Pareto Evolutionary Algorithm. Afribary. Retrieved from https://track.afribary.com/works/development-of-optimal-placement-of-distributed-generators-in-electrical-network-using-improved-strength-pareto-evolutionary-algorithm
Oluseyi, P. and I. Iro "Development Of Optimal Placement Of Distributed Generators In Electrical Network Using Improved Strength Pareto Evolutionary Algorithm" Afribary. Afribary, 05 May. 2021, https://track.afribary.com/works/development-of-optimal-placement-of-distributed-generators-in-electrical-network-using-improved-strength-pareto-evolutionary-algorithm. Accessed 27 Nov. 2024.
Oluseyi, P., I. Iro . "Development Of Optimal Placement Of Distributed Generators In Electrical Network Using Improved Strength Pareto Evolutionary Algorithm". Afribary, Afribary, 05 May. 2021. Web. 27 Nov. 2024. < https://track.afribary.com/works/development-of-optimal-placement-of-distributed-generators-in-electrical-network-using-improved-strength-pareto-evolutionary-algorithm >.
Oluseyi, P. and Iro, I. . "Development Of Optimal Placement Of Distributed Generators In Electrical Network Using Improved Strength Pareto Evolutionary Algorithm" Afribary (2021). Accessed November 27, 2024. https://track.afribary.com/works/development-of-optimal-placement-of-distributed-generators-in-electrical-network-using-improved-strength-pareto-evolutionary-algorithm