Tuning of Microgrid Controllers using Cuckoo Search Algorithm

Author Name(s): G. Mallesham , B. Priyadarshini, G. Balaji , Krishna Degavath
Author Email: malleshamg@yahoo.com


Microgrid at islanding mode is operated with renewable energy sources like Solar, Wind and non-renewable energy sources like Diesel generator and Battery which supply load to the system efficiently. With the change in load there is frequency deviation and controllers are required. There is a requirement to tune controllers to have optimal utilization of electrical energy and to maintain frequency at desired level. Cuckoo Search Algorithm (CSA) has been implemented to tune the controllers of microgrid. CSA gives optimal solutions in MATLAB using Integral Time Square Error principle (ITSE). The proposed results using CSA in comparison to the trial and error method is improving the steady state response of the considered microgrid, maintaining the system frequency constant. We have proposed a method for tuning the controller to have the frequency of the system at desired level.


Microgrid, Tuning, Diesel generator, Battery, Cuckoo Search Algorithm.


Electrical power has been the greatest demand of all times, irrespective of time. This leads to increase in demand, thus emphasizing to continuously meet the increases demand. Main grids meet the loads catering to optimal economic dispatch. A microgrid is a small-scale power grid that can operate independently or in conjunction with the area’s main electrical grid [1], [2], [3]. Any small scale localized station with its own power resources, generation and loads and definable boundaries qualifies as a microgrid. These grids being smaller in size are subjected to larger deviations. To overcome this, controllable sources are used to supply power, to balance out the increase in load demand or the reduction in power generation. However due to the delay in the output characteristics of controllable sources, the frequency oscillations are still present in the microgrid. Hence there is a necessity of designing proper controller parameters to controllable sources for optimal utilization of energy and to maintain minimum frequency deviations. In addition, microgrid if integrated with the mega grid will allow bulk consumers to save on electricity costs by using their generators during high peak demand periods when power from the mega- grid becomes expensive thereby enhancing the efficiency, reliability and security of large and centralized plants [4]. The various types of small scale generation systems used in a microgrid can be categorized into two groups namely primary sources consisting of solar and wind energy systems and secondary sources (controllable sources) such as diesel generator, fuel cell, aqua electrolyser, battery and flywheel. Keeping view of the above, CSA has been implemented for optimization of all parameters of controllers of microgrid with variations in load demand and power from renewable sources [5]. In this paper, diesel generator and battery are considered, where parameters of Proportional (P), Proportional Integral (PI) and Proportional Integral Derivative (PID) are tuned. This paper elucidates the following. Section II explains the microgrid modeling, its components and their transfer functions. Section III illustrates the Cuckoo Search Algorithm. Simulation analysis and results for various conditions are discussed in Section IV. The conclusions are presented in Section V.


In this paper the optimal values of the controller gains in the microgrid were calculated using the conventional methods and CSA. In this work renewable and non renewable energy sources are used to meet the load. The principle behind the new stratagem adopted is to somehow or other steady the system at the output end and independent of variations in the system due to load fluctuations on all counts. Thus the Cuckoo Search Algorithm has been implemented in the tuning of controller parameters MATLAB atmosphere. By trial and error method, the results achieved were comprehensively compared with Cuckoo Search Algorithm. The resultant conclusion proved that the latter is more efficient and better choice among other optimization techniques.


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