This paper proposes a coordinated multilayer control strategy for energy management (EM) of grid-connected AC microgrids. The strategy predicts the customer's load demand and photovoltaics (PV) power generation for day-ahead energy management. It utilizes the PV power generations and the bidirectional energy transactions from electric vehicles and battery storage to provide a combined response for load-support. The system also predicts any uncertainties in customers demand and power generations, and implements day-ahead precautionary measures to tackle that uncertainty. Two different prediction strategies are used, autoregressive moving average and artificial neural networks, and their performance for a dayahead EM are investigated. The reference power from the tertiary layer EM are sent to the local controllers for power regulation at the inverter level. Additionally, the varying power output reference obtained from day-ahead EM is classified into slow, medium and fast variations. The performance of the local controller employed in the interfacing grid connected three-phase inverter is tested during the above-mentioned power reference variations. Total harmonic distortion (THD) incorporating moving window technique is calculated for the AC output current during each class of power variations over a day.
Inverters in Microgrids (MGs) face significant challenges during their parallel operations; such as accurate power sharing, deviations in system voltage magnitude and frequency, imbalance between generation and load demand. To solve these techno-economic challenges, hierarchical control structures are implemented in MGs. The structure consists of three layers as primary, secondary and tertiary controls. The control approach can be either communication-based or communication-less at the various layers. The use of communication at primary and secondary layers faces problems like communication latency, data drop-up, and expense issues. On the other hand, improved decentralized control techniques being communication-less can avoid the disadvantages of using communication. This paper presents an insight into the limitations with the communication-based approach by briefing about the centralized and distributed control techniques at the secondary control layer. Subsequently, the communication-less control techniques and algorithms to achieve accurate power sharing along with restoration of MG voltage and frequency are described. A comparison among different decentralized droopbased power sharing methods in the primary control layer are done based on review and simulations. In addition to that, improved communication-less secondary restoration techniques are explained. Finally, future research directions in these areas are listed, aiming to improve the reviewed techniques.
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