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Using a new improved harmony search-based hybrid firefly algorithm (IHBFA), a comprehensive controller gain parameter estimation of all distributed resources-based microgrid is proposed. To ensure a fast convergence and to endeavour less randomisation to conventional firefly algorithm (FA), diversity of population is increased by an improved harmony search (HS) algorithm. To decrease local optima searching delay, a linear incremental pitch adjustment rate and exponential decaying bandwidth is considered for proposed HS-based hybrid FA. Photovoltaic (PV), an auxiliary battery energy storage system (BESS) with the second-order phase-locked loop control, is considered as a primary DG (DG1) for the proposed microgrid. Padѐ approximation delay-based governor control is used for the diesel generator unit, considered as a secondary DG (DG2). The overall gain optimisation improves the dynamic stability limits by minimising low-frequency network behaviour. The effectiveness of proposed IHBFA in terms of power oscillation damping and improved stability limits is clearly demonstrated for microgrid applications.

Summary In this paper, optimized controller design for multiple distributed generators (DGs) based microgrid network is discussed, where IEEE 1547 standards are followed for primary photovoltaic DG. According to standards, the primary photovoltaic/auxiliary battery energy storage system is integrated through voltage source converter's feedback controller, with operational modes: active reactive (P − Q) power control for grid synchronized operation and voltage frequency (V‐f) droop control for islanded microgrid. To overcome the inadequacy of conventional controller tuning in a multiple DGs‐based weak microgrid architecture, a new mutation based improved firefly algorithm is proposed in this paper. The worst stability operation is considered for voltage source converter's P‐Q and V‐f coordination during insufficiency in battery energy storage system management. The effectiveness of the proposed technique is validated on MATLAB Editor/ Simulink platform. Further, a hardware‐in‐loop test bench validation is achieved by TMS320 C6713 based DSP Starter Kit and embedded MATLAB coder.

An effective reduction in power prediction error profile and an improved battery management system design for photovoltaic (PV) based microgrid application are presented in this study, where battery life and power loss are considered to be effectiveness measures. For local energy management the prediction error has a direct influence on distributed generator (DG) control reference calculation and thus in system stability. The silent effect of prediction error in battery energy storage life deterioration is highlighted in terms of battery temperature and power losses. The PV power prediction challenge (null versus positive volatility nature) is addressed with effective error reduction by kernel-based feature mapping function. To obtain fast prediction (operational references to DG primary control) in an online manner, a new fast reduced Morlet kernel-based online sequential extreme learning machine is proposed in this study. The battery (lithium-ion) temperature effect is addressed by introducing a new secondary controller, which comprises battery temperature reference model (model reference) along with rule-based temperature tolerance switching of stacks. The effectiveness of the proposed design is presented by rigorous case studies (MATLAB and TMS320 C6713), where extreme performance is achieved by simultaneous prediction error and local uncertainty.

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