A Review on the Driving Forces, Challenges, and Applications of AC/DC Hybrid Smart Microgrids

The majority of Medium Voltage (MV) and Low Voltage (LV) power systems are based on and operate using Alternating Current (AC) infrastructures. Yet, modern energy market needs, which promote more decentralized concepts with a high Renewable Energy Sources (RES) penetration rate and storage integration, bring Direct Current (DC) to the forefront. In this sense, AC/DC hybrid smart microgrids constitute a newly-introduced research field with a variety of potential applications that combine the benefits of both AC and DC systems. The purpose of this chapter is to review the advantages and disadvantages of AC/DC hybrid grids and analyze potential applications that would benefit from such infrastructures. Also, the most significant efforts and requirements for the constitution of a solid regulatory framework for AC/DC hybrid grids are presented, to pave the way towards their wider adoption by the market

Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty

Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint

Dynamic Modeling and Control Issues on a Methanol Reforming Unit for Hydrogen Production and Use in a PEM Fuel Cell

Abstract: The presented research work focuses on the mathematical description and control analysis of an integrated power unit that uses hydrogen produced by methanol autothermal reforming. The unit consists of a reformer reactor where methanol, air and water are co-fed to produce a hydrogen rich stream through a series of reactions. The hydrogen main stream is fed to a preferential oxidation reactor (PROX) for the reduction of CO at levels below 50ppm with the use of air. In the end, the PROX outlet stream enters the anode of a PEM fuel cell where power production takes places to serve a load demand. The operation of the two reactors is described by a combination of partial differential equations (mass and energy balances) and non-linear equations (kinetic expressions of the reactions), while the power production in the fuel cell is based on the inlet hydrogen flow and on operational characteristics. A simple case sceanrio is employed when a step change on methanol flowrate is imposed. Main target is to identify and analyze the changes occuring in the main variables of concern (H 2 , CO and temperature levels) that affect the overall system operation. Based on the results, an insight on the challenging control scheme will be applied in order to identify possible ways of setting up a reliable and robust control structure according to the developed mathematical model

Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation

Dynamic Modeling and Control of a Coupled Reforming/Combustor System for the Production of H2 via Hydrocarbon-Based Fuels

The present work aims to provide insights into the dynamic operation of a coupled reformer/combustion unit that can utilize a variety of saturated hydrocarbons (HCs) with 1–4 C atoms towards H2 production (along with CO2). Within this concept, a preselected HC-based feedstock enters a steam reforming reactor for the production of H2 via a series of catalytic reactions, whereas a sequential postprocessing unit (water gas shift reactor) is then utilized to increase H2 purity and minimize CO. The core unit of the overall system is the combustor that is coupled with the reformer reactor and continuously provides heat (a) for sustaining the prevailing endothermic reforming reactions and (b) for the process feed streams. The dynamic model as it is initially developed, consists of ordinary differential equations that capture the main physicochemical phenomena taking place at each subsystem (energy and mass balances) and is compared against available thermodynamic data (temperature and concentration). Further on, a distributed control scheme based on PID (Proportional–Integral–Derivative) controllers (each one tuned via Ziegler–Nichols/Z-N methodology) is applied and a set of case studies is formulated. The aim of the control scheme is to maintain the selected process-controlled variables within their predefined set-points, despite the emergence of sudden disturbances. It was revealed that the accurately tuned controllers lead to (a) a quick start-up operation, (b) minimum overshoot (especially regarding the sensitive reactor temperature), (c) zero offset from the desired operating set-points, and (d) quick settling during disturbance emergence

Integrated Design and Control of Various Hydrogen Production Flowsheet Configurations via Membrane Based Methane Steam Reforming

This work focuses on the development and implementation of an integrated process design and control framework for a membrane-based hydrogen production system based on low temperature methane steam reforming. Several alternative flowsheet configurations consisted of either integrated membrane reactor modules or successive reactor and membrane separation modules are designed and assessed by considering economic and controller dynamic performance criteria simultaneously. The design problem is expressed as a non-linear dynamic optimization problem incorporating a nonlinear dynamic model for the process system and a linear model predictive controller aiming to maintain the process targets despite the effect of disturbances. The large dimensionality of the disturbance space is effectively addressed by focusing on disturbances along the direction that causes the maximum process variability revealed by the analysis of local sensitivity information for the process system. Design results from a multi-objective optimization study, where only the annualized equipment and operational costs are minimized, are used as reference case in order to evaluate the proposed design framework. Optimization results demonstrate the controller’s ability to track the imposed setpoint changes and alleviate the effects of multiple simultaneous disturbances. Also, significant economic improvements are observed by the implementation of the integrated design and control framework compared to the traditional design methodology, where process and controller design are performed sequentially

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

This work presents an optimum energy management framework, which is developed for integrated Polymer Electrolyte Membrane (PEM) fuel cell systems. The objective is to address in a centralized manner the control issues that arise during the operation of the fuel cell (FC) system and to monitor and evaluate the system’s performance at real time. More specifically the operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. To achieve these objectives a novel Model Predictive Control (MPC) strategy is developed and demonstrated. A semiempirical experimentally validated model is used which is able to capture the dynamic behaviour of the PEMFC. Furthermore, the MPC strategy was integrated in an industrial-grade automation system to demonstrate its applicability in realistic environment. The proposed framework relies on a novel nonlinear MPC (NMPC) formulation that uses a dynamic optimization method that recasts the multivariable control problem into a nonlinear programming problem using a warm-start initialization method and a search space reduction technique which is based on a piecewise affine approximation of the variable’s feasible space. The behaviour of the MPC framework is experimentally verified through the online deployment to a small-scale fully automated PEMFC unit. During the experimental scenarios the PEMFC system demonstrated excellent response in terms of computational effort and accuracy with respect to the control objectives

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

This work presents an optimum energy management framework, which is developed for integrated Polymer Electrolyte Membrane (PEM) fuel cell systems. The objective is to address in a centralized manner the control issues that arise during the operation of the fuel cell (FC) system and to monitor and evaluate the system’s performance at real time. More specifically the operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. To achieve these objectives a novel Model Predictive Control (MPC) strategy is developed and demonstrated. A semiempirical experimentally validated model is used which is able to capture the dynamic behaviour of the PEMFC. Furthermore, the MPC strategy was integrated in an industrial-grade automation system to demonstrate its applicability in realistic environment. The proposed framework relies on a novel nonlinear MPC (NMPC) formulation that uses a dynamic optimization method that recasts the multivariable control problem into a nonlinear programming problem using a warm-start initialization method and a search space reduction technique which is based on a piecewise affine approximation of the variable’s feasible space. The behaviour of the MPC framework is experimentally verified through the online deployment to a small-scale fully automated PEMFC unit. During the experimental scenarios the PEMFC system demonstrated excellent response in terms of computational effort and accuracy with respect to the control objectives

Toward Optimum Working Fluid Mixtures for Organic Rankine Cycles using Molecular Design and Sensitivity Analysis

This work presents a Computer-Aided Molecular Design (CAMD) method for the synthesis and selection of binary working fluid mixtures used in Organic Rankine Cycles (ORC). The method consists of two stages, initially seeking optimum mixture performance targets by designing molecules acting as the first component of the binaries. The identified targets are subsequently approached by designing the required matching molecules and selecting the optimum mixture concentration. A multiobjective formulation of the CAMD-optimization problem enables the identification of numerous mixture candidates, evaluated using an ORC process model in the course of molecular mixture design. A nonlinear sensitivity analysis method is employed to address model-related uncertainties in the mixture selection procedure. The proposed approach remains generic and independent of the considered mixture design application. Mixtures of high performance are identified simultaneously with their sensitivity characteristics regardless of the employed property prediction method