AC‐coupled hybrid power system optimisation for an Australian remote community

Abstract

Summary This paper investigates the operation of various optimised AC‐coupled hybrid power systems for an isolated community in South Australia. In addition to diesel generators, various combinations of distributed generators and storage technologies are considered to form different hybrid power systems. Optimal design of the hybrid power systems is implemented using particle swarm optimisation (PSO) algorithm. The performance of the PSO in optimizing the above hybrid systems is discussed in detail. Whilst meeting the power system operational constraints, the minimum levelised cost of electricity (LCOE) of all systems, with various combinations of system components, are calculated. Simulation results obtained for each optimized system are carefully described and analysed. The annual operations of various components in each system are then compared and discussed in detail. A sensitivity analysis by varying the cost of wind turbines and the photovoltaic is also conducted for all hybrid systems. It has been found that the inclusion of energy storage systems (battery and flywheel) can reduce the LCOE and CO2 emission significantly compared to the inclusion of renewable generations (solar and wind) alone. The diesel‐wind‐solar‐battery system is the eco‐friendliest with an annual CO2 emission of 451 t compared to the reference diesel system (1064 t). In addition, the hybrid system consisting of diesel, wind, solar, battery and flywheel is found to be the most economic with a LCOE of 58.94 ¢/kWh compared to the diesel system with a LCOE of 67.17 ¢/kWh.

Optimal planning of solar PV and battery storage with energy management systems for Time‐of‐Use and flat electricity tariffs

“…The number of runs determines the repetition of the optimization process, and the final optimal results are obtained from the best global best solution of all runs. It is notable that the other hyper-parameters of the PSO algorithm are inertia weight, cognition weight, and social weight that are selected as 0.5, 2, and 2 [23].…”

“…It is notable that the optimization algorithm is not a contribution of this study. The superiorities of PSO over other optimization methodologies are easy handling of system nonlinearities, simplicity of the concept, easy implementation, high convergence rate, less dependency on initial points, and computational efficiency [23, 24]. Figure 2 shows the optimization process of the particle swarm optimization algorithm.…”

“…Although the optimization model can be optimized using suitable optimization tools in MATLAB, particle swarm optimization (PSO) has been proven as a reliable tool for such a study and provide reliable solutions. This method has been efficiently applied for power system planning studies [10,18,19], and its results have been approved for optimal sizing compared to other methodologies [22,23]. It is notable that the optimization algorithm is not a contribution of this study.…”

Optimal Sizing of Rooftop PV and Battery Storage for Grid-Connected Houses Considering Flat and Time-of-Use Electricity Rates

“…Energies 2021, 14,3520 The formulated optimization model can be solved with different solvers in In this study, particle swarm optimization (PSO) was used due to its salient fea as computational efficiency, ease of implementation, capacity of finding glob suitable convergence rate, and lesser dependency on initial points [18]. The PSO has been broadly used for optimal sizing of power systems [28][29][30][31].…”

Review on the state‐of‐the‐art multi‐objective optimisation of hybrid standalone/grid‐connected energy systems

“…Levelised cost of energy : LCOE is the ratio of net annual payment to the net annual electricity consumption [49, 50]. It is calculated based on the components and electricity NPCs, the capital recovery factor (CRF), and the annual energy demand of system : where d denotes the discount rate.…”