Optimal planning of uncertain renewable energy sources in unbalanced distribution systems by a multi‐objective hybrid PSO–SCO algorithm

Abstract

High penetration of Renewable Energy Sources into unbalanced distribution systems faces many challenges due to the uncertainty nature of both Renewable Energy Sources and loads as well as the unbalance degree of the distribution systems. This paper proposes the planning of Renewable Energy Sources in unbalanced distribution systems. Different types of Renewable Energy Sources, photo-voltaic and Wind are considered. A multi-objective optimisation problem is formulated using non-dominated sort and crowing distance for multi objective hybrid algorithm that combines the merits of Particle Swarm Optimisation and Sine Cosine Optimisation algorithms. Three objectives, improving voltage profile, power losses minimisation, and minimisation of voltage unbalance, are considered. The proposed method determines the optimal Renewable Energy Sources' specifications such as site/size/type of distributed generators, number of photo-voltaic modules or wind turbines in each distributed generator and their power factor and connection phases. Also, it determines the optimal setting of substation tap changer and system voltage regulators. Moreover, both Renewable Energy Sources and loads uncertainty are considered and modelled using Monte Carlo simulations. An improved matrix-based backward-forward load flow method is developed by using coefficient matrices. The proposed procedure is conducted on IEEE 13-node, 37-node, and 123-node unbalanced distribution systems. The proposed algorithm results compared with individual optimisers show significant performance improvements. The optimal plans resulted by using the proposed hybrid algorithm achieve a reduction in power losses that reached 89% in some cases. Also, the system unbalance index is reduced with a ratio reached 34%. Large improvement in the system voltages profile all over the year is achieved as the system voltage profile index is reduced by a ratio reached 70%.

On Integrating and Operating Distributed Energy Resources in Distribution Networks: A Review of Current Solution Methods, Challenges, and Opportunities

“…In addition, this research does not allow quantifying the economic and environmental impact of this type of strategies, implying the need for future works that integrate different objective functions into a multi-objective optimization model within the framework of a three-phase analysis. • The work presented by Ali et al [119] proposes a methodology to improve the operating conditions of EDS through the correct integration of DG units. To this effect, the authors use a hybrid optimization algorithm that combines the qualities of the PSO algorithm and the sine-cosine algorithm (SCO).…”

Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems

“…Equation (19) shows that white sharks position themselves in accordance with the best site, very close to the prey. The great white sharks reach their location and thus the best position of the great white sharks is somewhere within the investigation zone, very close to the optimum prey.…”

“…Most of these works used some optimization techniques to solve the optimal location and size issues for DGs. Genetic Algorithm [17], Particle Swarm Optimization [18,19], Modified Bacterial Foraging Optimization [20], Bat Approach [21], Invasive Weed Optimization [22], Water Cycle Algorithm [23], Ant Colony Algorithm [24,25], Modified Teaching-Learningbased Optimization Algorithm [26], Hybrid Big Bang-Big Crunch Approach [27], Gray Wolf Optimization [28], Cuckoo Search Algorithm [29][30][31], Heuristic Methods [32], Chaotic Symbiotic Organisms Search Algorithm [33], and Marine Predators Optimizer [34] were introduced to deal with the DG placement process. Using three typical radial systems, IEEE 33 [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], 69 [52][53][54][55][56][57][58][59][60][61], and 85 …”

Maximization Approach of Hosting Capacity Based on Uncertain Renewable Energy Resources Using Network Reconfiguration and Soft Open Points

“…In [6], e uncertainties of both WT and load demand were taken into consideration while presenting a hybrid HC enhancement technique using an on-load tap changing (OLTC) transformer and voltage regulators. In [7][8][9], the authors presented various stochastic scenarios for representing and reducing the uncertainty of WT, PV, and load demand.…”