This paper proposes a novel controller for the secondary load frequency control of a shipboard microgrid. The suggested controller is based on the linear matrix inequality technique and the Lyapunov stability theory. In the controller design procedure, the effects of the sensor-to-controller and controller-to-actuator delay communication links are considered, and a robust controller against the delay is proposed by utilizing a Lyapunov–Krasovskii functional. In addition, due to the practical space limitation of a ship, low-space, high-efficient renewable energy sources (RESs), and energy storage systems are considered. Furthermore, a diesel generator and a proton exchange membrane fuel cell are employed to effectively mitigate the frequency oscillations arise by the loads and RES power variations. Finally, to show the merits of the proposed method, several hardware-in-the-loop real-time simulations are performed. Comparison results illustrate the effectiveness of the proposed approach to the state-of-the-art methods.

Due to the increasing impacts of ships pollutants on the environment and the preventive laws that are tightening every day, the utilization of all-electric ships is a recent emerging technology. Being a promising technology, the usage of fuel cells as the main energy resource of marine vessels is an interesting choice. In this paper, an all-electric hybrid energy system with zero-emission based on fuel cell, battery and cold-ironing is proposed and analyzed. To this end, actual data of a ferry boat, including load profiles and paths is considered to assess the feasibility of the proposed energy system. The configuration of the boat and energy resources as well as the problem constraints are modeled and analyzed. Finally, the boat's energy management in hourly form for a one-day period is implemented. The Improved Sine Cosine Algorithm (ISCA) is used for the power dispatch optimization, and all models are implemented in MATLAB software. Based on the analysis results, the proposed hybrid system and energy management method have high performance as an applicable method for the marine vessels. In addition, to be a zero-emission ship, the proposed system has an acceptable energy cost.

The utilization of green energy resources for supplying energy to ships in the marine industry has received increasing attention during the last years, where different green resource combinations and control strategies have been used. This paper considers a ferry ship supplied by fuel cells (FCs) as well as batteries as the main sources of ship’s power. Based on the designers’ and owners’ preferences, different scenarios can be considered for managing the operation of the FCs and batteries in all-electric marine power systems. In this paper, while considering different constraints of the system, six operating scenarios for the set of FCs and batteries are proposed. Impacts of each proposed scenario on the optimal daily scheduling of FCs and batteries and operation costs of the ship are calculated using a Mixed Integer Non-Linear Programming (MINLP) model. Model predictive control (MPC) is also applied to consider the deviations from forecasted hourly demand. Moreover, since the efficiency of FCs varies for different output powers, the impacts of applying a linear model for FCs’ efficiency are compared with the proposed non-linear model and its related deviations from the optimal operation of the ship are investigated. The proposed model is solved by GAMS software using actual system data and the simulation results are discussed. Finally, detailed real-time hardware-in-the-loop (HiL) simulation outcomes and comparative analysis are presented to confirm the adaptation capability of the proposed strategy.

Nowadays, sea traveling is increasing due to its practicality and low-cost. Ferry boats play a significant role in the marine tourism industry to transfer passengers and tourists. Nevertheless, traditional ferry ships consume massive amounts of fossil fuels to generate the required energy for their motors and demanded loads. Also, by consuming fossil fuels, ferries spatter the atmosphere with CO2 emissions and detrimental particles. In order to address these issues, Ferry-building industries try to utilize renewable energy sources (RESs) and energy storage systems (ESSs), instead of fossil fuels, to provide the required power in the ferry boats. In general, full electric ferry (FEF) boats are a new concept to reduce the cost of fossil fuels and air emissions. Hence, FEF can be regarded as a kind of DC stand-alone microgrid with constant power loads (CPLs). This paper proposes a new structure of a fully electric ferry ship based on RESs and ESSs. In order to solve the negative impedance induced instabilities in DC power electronic based RESs, a new Intelligent Single Input interval type-2 fuzzy logic controller based on sliding mode control is proposed for the DC/DC converters feeding CLPs. The main feature of the suggested technique is that it is mode-free and regulates the plant without requiring the knowledge of converter dynamics. Finally, we conduct a dSPACE-based real-time experiment to examine the effectiveness of the proposed energy management system for full electric ferry vessels.

It is undeniable that marine vessel systems play an important role to transfer huge loads and weapons with low cost. However, ship power systems produce a lot of greenhouse gases, which in turn lead to serious environmental pollution. Hence, the utilizing of wind turbines (WTs), solar generation, sea wave energy (SWE), and energy storage systems (ESSs) in marine vessel power systems have been attracting a lot of attention in recent years. In this paper, it is assumed that a marine vessel power system with photovoltaic (PV), WT, SWE, and ESS can be regarded as a mobile-islanded MG. Then, a novel topology for hybrid shipboard microgrids (MGs) is presented. Next, in order to make a balance between consumption and power generation in shipboard MGs, an optimal modified model-free nonlinear sliding mode controller is introduced for the secondary load frequency control. Since the quality of the control actions of the proposed model-free approach depends on its parameters, a hybrid version of the sine-cosine algorithm (SCA) and wavelet-mutation (WM), called SCAWM, is employed to find the best value of these coefficients. Comparisons are conducted with other existing methodologies, such as model predictive control, interval type-2 fuzzy logic controller, and conventional PI (PI) to establish the supremacy of the newly suggested control strategy. Finally, a real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT is accomplished to affirm the applicability of the suggested controller, from a systemic perspective, for the load frequency control problem in the shipboard MG.

This paper proposes a new adaptive controller for air-feed on proton exchange membrane fuel cell (PEMFC) plants. The oxygen excess ratio is first regulated by a single input interval type-2 fuzzy PI (SIT2-FPI) controller during the current variation of the PEMFC system. Then, a deep deterministic policy gradient (DDPG) algorithm is adopted to adaptively adjust the baseline PI coefficients of the SIT2 controller by taking the benefits of the online learning and model-free features of reinforcement learning. In the DDPG structure, an actor-network determines the policy signals, while a critic-network evaluates the quality of the policy provided by the actor. The suggested DDPG algorithm naturally takes into account the baseline PI coefficients into the design objective and offers the SIT2-FPI structure with online coefficient adjusting ability through learning. Based on reward feedback of the oxygen excess ratio error, the weights of the actor and critic networks are updated by the gradient descent technique. Detailed real-time model-in-the loop (MIL) simulation outcomes and comparative analysis are presented to confirm the adaptation capability of the DDPG-based online SIT2-FPI coefficient tuning strategy.