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This paper proposes a novel, smart energy management scheme for a microgrid, consisting of a diesel generator and power electronic converter interfaced renewable energy-based generators, such as photovoltaic (PV) and fuel cell, for frequency regulation without any storage. In the proposed strategy, output of the PV is controlled in coordination with other generators using neurofuzzy controller, either only for transient frequency regulation or for both transient and steady-state frequency regulation, depending on the load demand, thereby eliminating the huge storage requirements. The option of demand response control is also explored along with the generation control. For accurate and quick tracking of maximum power point and its associated reserve power from the PV generator, this paper also proposes a novel adaptive-predictor-corrector-based tracking mechanism.

Sluggish tracking for change in solar irradiations is the main demerit of the perturb and observe (P&O) algorithm because of its fixed perturb. To overcome this, an adaptive tracking algorithm based on Takagi-Sugeno fuzzy implications is proposed in this study. Input to the fuzzy controller is the error between conductance and incremental conductance which is otherwise zero at the maximum power point. The P&O algorithm and the proposed algorithm along with the recently published adaptive incremental conductance algorithm are examined for their performance efficacy on a photovoltaic (PV) generating system with fabricated as well as real irradiation data. As a case study, all the considered algorithms are validated under partial shading conditions also. The effectiveness of the proposed algorithm is verified for the tracking of the maximum power point of a PV system in steady as well as changing irradiations and the conclusions are supported through some experimental validations.

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