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Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning

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Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning Mohammad Jafari, Vahid Sarfi, Amir Ghasemkhani, Hanif Livani, Lei Yang, and Hao Xu Abstract—In this paper, a biologically-inspired adaptive intel- control signals to maintain the voltage and frequency stabil- ligent secondary controller is developed for microgrids to tackle ity. Secondary control acts over primary control by sending system dynamics uncertainties, faults, and/or disturbances. The compensation references to restore the voltage and frequency developed adaptive biologically-inspired controller adopts a novel computational model of emotional learning in mammalian limbic deviations to the nominal values. The highest level of control, system. The learning capability of the proposed biologically- the so-called tertiary level is required to specify the optimal inspired intelligent controller makes it a promising approach to set-points for operation of the generation resources by consid- deal with the power system non-linear and volatile dynamics ering the power system requirements [4]. Reviewing the related without increasing the controller complexity, and maintain the works in MGs control schemes shows that most of the state- voltage and frequency stabilities by using an efficient reference tracking mechanism. The performance of the proposed intelligent of-art methods require detailed information about the system secondary controller is validated in terms of the voltage and dynamics. In this sense, developing a model-free adaptive frequency absolute errors in the simulated microgrid. Simulation controller becomes of practical importance due to nonlinear results highlight the efficiency and robustness of the proposed and complicated nature of the DERs dynamics in MGs. intelligent controller under the fault conditions and different system uncertainties compared to other benchmark controllers. B. Related works Index Terms—Intelligent Secondary Controller, Microgrids, BELBIC, Emotional Learning Previous works have addressed the challenges in MG oper- ation by proposing several control methods. The autonomous I. INTRODUCTION operation of MGs during the transition to the islanded mode A. Motivation have been proposed in [5]. Nonlinear heterogeneous dynamics of DERs has been transformed into linear dynamics using an Microgrids (MGs) with their corresponding control systems input-output feedback linearizion approach in [1] to design a are independent distribution power systems which provide secondary controller for MGs. Recently, authors in [2] have guaranteed power quality for various loads [1]. MGs are proposed an adaptive PI based frequency controller for MGs operational in both islanded and grid connected modes. This by leveraging a combination of the fuzzy logic and the particle operational flexibility provides an opportunity for the scalable swarm optimization techniques to improve the conventional PI integration of local generators including distributed energy controller performance against dynamical changes. An intelli- resources (DERs) into power system. However, integrating gent pinning based cooperative secondary control of DERs DERs puts forth stability and operational issues for the MGs’ for an islanded MG has been developed in [6]. Moreover, operators. To this end, MG energy management system (EMS) decentralized and distributed secondary controllers have been needs to incorporate controlling schemes for MGs in order to investigated for MGs in islanded mode in [7] and [8], respec- maintain the stability of the system and address the operational tively. All of these controllers propose an efficient performance issues in both steady and faulty states. Additionally, these with respect to the changes in the system operating conditions. controlling schemes should be efficient and robust enough to However, they require the detailed information about the account for various system uncertainties in different opera- system dynamics to update the MG control parameters in real tional modes and states [2], [3]. time. To tackle this problem, a model-free secondary controller A hierarchical control scheme is applied to address the has been studied in [9]. In this sense, an adaptive neuro- challenges of MGs operation in both islanded and grid- fuzzy inference system (ANFIS) method has been proposed for connected modes [1]. A hierarchical control scheme comprises simultaneous voltage and frequency control in an islanded MG. three levels: primary, secondary and tertiary control levels. Although the suggested controller performs well with respect Primary control level is responsible for generating fast control to tracking the changes in the normal operating conditions, responses including inner voltage/current and power sharing the proposed control scheme is trained by a desired I/O *This work is partially supported by NSF award, # 1723814. data set offline which is not appropriate since it needs to M. Jafari is with the Department of Applied Mathematics, Jack Baskin be applied to an actual system. Hence, there is a pressing School of Engineering, University of California, Santa Cruz, CA 95064. need to develop control strategies with less dependency on the [email protected] V. Sarfi, H. Livani and H. Xu are with the Department of Electrical full knowledge of the system dynamics which can adjust MG and Biomedical Engineering, University of Nevada, Reno, NV 89557-0260. operating parameters online. [email protected],[hlivani, haoxu]@unr.edu In the past few years, diverse complex problems have been A. Ghasemkhani and L. Yang are with the Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557-0260. successfully solved by extensively employing the intelligent [email protected], [email protected] techniques [10]–[13]. Brain Emotional Learning Based Intel- 978-1-7281-1981-6/19/$31.00 ©2019 IEEE Authorized licensed use limited to: UNIVERSITY OF NEVADA RENO. Downloaded on September 22,2020 at 17:57:06 UTC from IEEE Xplore. Restrictions apply. Sensory Orbitofrontal ligent Controller (BELBIC) [14], is a biologically-inspired Cortex Cortex intelligent model-free controller which can be successfully Emotional implemented into complex problems by assigning appropriate Signal Max(SI) functions to the Sensory Inputs (SI) and Emotional Signal (ES) Thalamus Amygdala which are the two main inputs of the BELBIC model [15], [16]. Finally, BELBIC has shown promising performance even Sensory Model when the model dynamics are fully or partially unknown Inputs (SI) Output and/or there exist noise and system uncertainty [14]. Fig. 1. Computational model of emotional learning. C. Main Contributions II. BRAIN EMOTIONAL LEARNING-BASED INTELLIGENT CONTROLLER In this paper, we focus on two issues of the MGs secondary controller design, i.e., the effect of the system dynamics in BELBIC is a neurobiologically-inspired intelligent model- the design process while they are fully or partially unknown, free controller which takes advantage of the mathematical and the robustness of the controller with respect to the system model of the emotional learning in the mammalian limbic uncertainties in different operational conditions. Our main system introduced in [17]. This model (depicted in Fig. 1), contributions are summarized as follows: has Amygdala, and Orbitofrontal Cortex as its primary compo- • A model-free adaptive intelligent secondary controller nents. According to the mammalian limbic system, the role of is developed for voltage and frequency stabilization in Amygdala is immediate learning, while Orbitofrontal Cortex MGs with system uncertainties and disturbances. This plays an inhibitory role to avoid any inappropriate learning controller not only is able to track the changes in dif- happening in the Amygdala. Furthermore, BELBIC model has ferent operating conditions, but also updates the required Sensory Inputs (SI), and Emotional Signal (ES) as its two controlling commands in real time. external inputs. • The intelligent secondary controller is designed by lever- Amygdala outputs are calculated by the summation of all aging the concept of brain emotional learning mechanism. its corresponding nodes, where the output of each node is BELBIC is a model-free controller which performs well described as equation (1) and the equation (2) is employed in presence of system noise an uncertainties. Moreover, for updating its weights (i.e., Vi). it has a low computational complexity which makes Ai = Vi × SIi (1) ! it a promising method in a real-time application. The X proposed controller not only reduces the system complex- ∆Vi = Kv × SIi × max 0; ES − Ai (2) ity, but also provides a controller with multi-objective i where, Kv is the learning rate. properties (i.e., control effort optimization, uncertainty The maximum of all SIs is another input considered in the handling, and noise/disturbance rejection). model. This signal (i.e., Ath), which is directly sent from the • Lyapunov analysis is provided to show the convergence of Thalamus to the Amygdala, is defined as: the proposed intelligent controller. The learning capability Ath = Vth × max (SIi) (3) of the proposed approach is validated for stabilizing where Vth is the weight and the corresponding update law is the voltage and frequency of the MGs. In order to the same as Equation (2). demonstrate the effectiveness of the proposed approach, To calculate the Orbitofrontal Cortex outputs, all its corre- a comparison between the proposed approach and both sponding nodes are added
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