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The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable‐based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro‐based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi‐objective optimisation method proposed in this paper for optimal integration of renewable‐based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33‐bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24‐hr time‐varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

This study investigates the optimal power flow (OPF) problem for distribution networks with the integration of distributed generation (DG). By considering the objectives of minimal line loss, minimal voltage deviation and maximum DG active power output, the proposed OPF formulation is a multi-object optimisation problem. Through normalisation of each objective function, the multi-objective optimisation is transformed to single-objective optimisation. To solve such a non-convex problem, the trust-region sequential quadratic programming (TRSQP) method is proposed, which iteratively approximates the OPF by a quadratic programming with the trust-region guidance. The TRSQP utilises the sensitivity analysis to approximate all the constraints with linear ones, which will reduce the optimisation scale. Active set method is utilised in TRSQP to solve quadratic programming sub-problem. Numerical tests on IEEE 33-, PG&E 69- and actual 292-, 588-, 1180-bus systems show the applicability of the proposed method, and comparisons with the primal–dual interior point method and sequential linear programming method are provided. The initialisation and convergence condition of the proposed method are also discussed. The computational result indicates that the proposed algorithm for DG control optimisation in distribution system is feasible and effective.

Voltage instability in the electrical power distribution system is becoming a major problem. The sharp increase in power consumption throughout electrical distribution networks is the primary cause of this instability. This study offers a systematic solution by introducing allocation and sizing of distributed generation (DG) in order to enhance voltage stability, lower power losses and raise the voltage profile. Genetic algorithm (GA) and particle swarm optimization (PSO) are two optimization methods that were developed and tested on the IEEE 33-bus and the actual Bahir Dar distribution system in Ethiopia to assess their applicability and effectiveness. Comparative analyses were conducted against existing techniques to assess the performance of the developed GA and PSO-based approaches. The results demonstrate that the integration of DG using the proposed optimization methods led to substantial improvements in the loading factor of the distribution systems. Specifically, the 35-bus case study achieved an 11.553% and 17.529% increase in loading factor using GA and PSO, respectively. Similarly, the 53-bus system gained loading factor improvements of 5.538 and 6.153% with GA and PSO. Notably, the PSO algorithm outperformed GA in terms of voltage stability index (VSI), voltage profile enhancement, and loss minimization through DG integration.HighlightsDistributed generation placement improves stability and reduces power losses in distribution systemsParticle swarm optimization outperforms genetic algorithm in enhancing voltage stability and efficiencyCase study on Bahir Dar network validates effectiveness of solar DG for reliable power distribution