In this paper, an adaptive multiobjective fractional-order fuzzy proportional-integral-derivative controller is proposed for the load frequency control (LFC) of islanded microgrids (MGs), while benefiting from the assets of electric vehicles (EVs) in this respect. Although the use of battery energy storage systems (BESS) can solve the unbalance effects between the load and supply of an isolated MG, their high cost and tendency toward degradation are restrictive factors, which call for the use of alternative power balancing options. In recent years, the concept of utilizing the BESSs of EVs, also known as vehicle-to-grid (V2G) concept, for frequency support of MGs has attracted a lot of attention. In order to allow the V2G controller operate optimally under a wide range of operation conditions caused by the intermittent behavior of renewable energy resources, a new multiobjective fractional-order control strategy for the EVs in V2G scenarios is proposed in this paper. Moreover, since the performance of the controller depends on its parameters, optimization of these parameters can play a significant role in promoting the output performance of the LFC control; hence, a modified black hole optimization algorithm is utilized for the adaptive tuning of the noninteger fuzzy PID controller coefficients. The performance of the proposed LFC is evaluated by using real world wind and solar radiation data. Finally, the extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller tracks frequency with lower deviation and fluctuation and is more robust in comparison with the prior-art controllers used in all the case studies.

Due to the fast development of renewable energy systems and the severe limitations enforced by the Marine Pollution Protocol, the utilizing of wind turbines, solar generation, sea wave energy and energy storage systems in marine vessel power systems has been attracting a lot of attention. Hence, a marine vessel power system with Photovoltaic (PV), WT, SWE and ESS can be considered as a specific mobile islanded microgrid. Consequently, the main target of this paper is to design a new optimal Fractional Order Fuzzy PD+I Load Frequency Controller (LFC) for islanded microgrids in a ship power system. Since the performance of the controller depends on its parameters, the optimization of these coefficients can play a significant role in improving the output performance of the LFC control. Accordingly, a modified black hole optimization algorithm (MBHA) is utilized for the adaptive tuning of the coefficients of non-integer fuzzy PD+I controller. The performance of the shipboard microgrid is evaluated by utilizing real world wind power fluctuation and solar radiation data. Finally, the extensive studies and hardware-in-the-loop (HIL) simulations are applied to prove that the proposed controller can track the reference frequency with lower deviation as well as it is more robust in comparison with the prior-art controllers utilized in the case studies.

This study utilizes a new optimal model-free controller based on a sliding mode technique to make a balance between load demands and power generations in the time-delay stand-alone microgrid system with electric vehicles. Forasmuch as the performance of the proposed controller is related to its parameter values, a new modified optimization algorithm, entitled the Black Hole optimization search algorithm, is applied to tune the coefficients of the proposed model-free technique. Finally, in order to validate the feasibility of the suggested approach under load disturbances, detailed simulation, and hardware-in-the-loop experimental results are shown.

The non-linearities and un-modeled dynamics inevitably degrade the quality and reliability of power conversion, and as a result, pose big challenges on higher-performance voltage stabilization of dc/dc buck converters. The stability of such power electronic equipment is further threatened when feeding the non-ideal constant power loads (CPLs) because of the induced negative impedance specifications. In response to these challenges, the advanced regulatory and technological mechanisms associated with the converters require to be developed to efficiently implement these interface systems in the Microgrid configuration. This paper addresses an intelligent proportional-integral (iPI) based on sliding mode observer (SMO) to mitigate the destructive impedance instabilities of non-ideal CPLs with time-varying nature in the ultra-local model (ULM) sense. In particular, in the current work, an auxiliary deep deterministic policy gradient (DDPG) controller is adaptively developed to decrease the SMO estimation error and further ameliorate the dynamic characteristics of dc/dc buck converters. The design of the DDPG is realized in two parts: i) an actor-network which generates the policy commands, while ii) a critic-network evaluates the quality of the policy command generated by the actor.

This paper proposes design of optimal and robust coordinated controller of hybrid energy storage system in a naval DC microgrid (MG) application. It is able to mitigate the negative effects of the pulsed power loads and meet the practical limitations of both converters input control and state variables signals based on IEEE standards. To do this, first, the dynamic model of the DC MG, which can represent in either all-electric aircraft or shipboard power systems is developed. Second, a novel model predictive controller (MPC) for energy storage converters is proposed such that all of the mentioned hard constraints are guaranteed. Third, a linear matrix inequality (LMI) approach is used to solve the MPC conditions. Finally, to evaluate the applicability and effectiveness of the proposed approach, some experimental tests are extracted. Obtained results verify better performance of the proposed approach over other state of the art control techniques.

The high-penetration of distributed generation technologies in the form of MicroGrids (MGs), in recent years, has increased the risk of frequency instability since their energy is supplied by renewable energy resources (RESs) with uncertain nature. Under such circumstances, providing an MG model with an efficient load frequency control (LFC) has a fundamental role in restoring the stability of the unstructured power system. In this study, a hybrid power system with the application of the Tidal Power Unit (TPU) and Vehicle-to-Grid (V2G) is effectively planed as an isolated MG. A new fractional gradient descent (FGD) based on a single-input interval type-2 fuzzy logic controller (SIT2-FLC) is suggested as the main LFC controller, where the footprint of uncertainty (FOU) coefficient of the SIT2-FLC is specifically adjusted to enhance the LFC performance. Additionally, a deep deterministic policy gradient (DDPG) with the actor-critic framework is considered to generate the supplementary control action, which is useful for the frequency stabilization by adapting to the randomness of load disturbances and RESs. Lastly, a model-in-the-loop (MiL) simulation is conducted to appraise the feasibility and applicability of the suggested design method from a systemic perspective.