Optimal Power Management in DC Microgrids With Applications to Dual-Source Trolleybus Systems

A DC microgrid with DC bus signaling (DBS) for power management offers a promising and cost-effective solution for reliable electricity access in rural and remote communities, particularly in developing nations. In this article, a three-layer modularized model of a microgrid is presented, with emphasis on the “Basic DC Microgrid Model” (Layer 1), which ensures effective power management in the microgrid at all times. Through the framework of the Layer 1 model, various energy management scenarios (EMSes) can be implemented, by varying the voltage regulation priorities of the entities. This allows us to achieve different system objectives such as reduced battery requirement or less dependence on the AC Grid. Two of the EMSes are discussed, implemented, and examined through both digital simulations and experimental tests on a laboratory prototype. The results show that the DBS scheme achieves real-time power management in a DC microgrid and that different EMSes can be easily implemented to realize varied system-level objectives.

Instability problems caused by constant power loads as a result of the negative impedance effect are dealt with in this article. A comprehensive nonlinear model of a single-bus DC microgrid is developed, and a model predictive controller is designed to enhance voltage stability and optimize power management based on the developed model. Conventional control layers are replaced by a simplified optimal controller in the proposed approach in the presence of various load types, including constant power loads. Furthermore, the model and control strategy are extended to the multi-bus DC microgrid. Extensive stability analysis, including root locus methods and time-domain simulation, has been conducted in MATLAB/SIMULINK. The results confirm the effectiveness of the designed controller in maintaining system stability and improving performance under different operating conditions.

A hierarchical power management scheme is proposed in this paper for a typical DC Microgrid. Different from other Microgrids, the DC Microgrid can interface to the distribution system by Solid-State transformer (SST). The hierarchical power management strategy includes three control levels: 1) primary control for DC Microgrid to implement distributed operation 2) secondary control for the DC Microgrid bus voltage recovery to achieve seamless mode switch 3) tertiary control to manage the battery charge and discharge. The DC Microgrid can operate in islanding mode, including the individual control for distributed renewable energy source (DRER) and distributed energy storage device (DESD). In addition, the DC microgrid can operate in SST-enabled mode to interface to the distribution system. The DC Micorgrid can seamlessly switch between islanding mode and SST-enable mode. The consideration of state of charge (SOC) for battery is also involved into the tertiary control. To this end, a lab test-bed is constructed to verify the system performance. Lastly, several typical case studies are carried out and the experimental results verify the proposed power management strategy.

This paper proposes multiagent supervisory control for precise power management in isolated dc microgrids. Two power management aspects are considered: 1) equal power sharing, which is realized via a proposed distributed equal power sharing algorithm; and 2) optimal power dispatch, which is achieved through a proposed distributed equal incremental cost (DEIC) algorithm. Both algorithms offer the additional advantage of the ability to restore the average system voltage to its nominal value. The proposed algorithms are based on the application of the average consensus theory along with voltage sensitivity analysis. Each distributed generation (DG) unit exchanges information with its neighbors, thus locally updating its no-load voltage setting to achieve the supervisory control objectives. The incorporation of DG droop-based control renders the proposed algorithms fully distributed with a reduced number of agents. The stability of the proposed algorithms is addressed, as well as the convergence of the proposed DEIC algorithm. Real-time OPAL-RT simulations demonstrate the effectiveness of the proposed algorithms in a hardware-in-the-loop application.

A novel distributed power management scheme is proposed in this paper for a DC microgrid system, which is enabled by Solid-State transformer (SST). The proposed system includes distributed renewable energy resource (DRER) and distributed energy storage device (DESD). The proposed distributed control algorithm, which only relies on the local information and guarantees full utilization of each module in the system based on their characteristics, is applied to both SST and DC microgrid. To this end, a simulation platform is developed in MATLAB/Simulink, in which Photovoltaic (PV), fuel cell and battery are selected as the typical DRERs and DESD, respectively. Lastly, several typical case studies are carried out and the simulation results verify the proposed distributed power management.

In this paper, a nonlinear decentralized double‐integral sliding mode controller (DI‐SMC) is designed along with an energy management system (EMS) for the DC microgrid (DCMG). This DCMG includes having a hybrid energy storage system (HESS) that incorporates a battery energy storage system (BESS) and supercapacitor energy storage system (SCESS) while the load demand is met through the power generated from solar photovoltaic (SPV) units. First, dynamical models of each subsystem of DCMGs such as the SPV system, BESS, and SCESS are developed to capture highly nonlinear behaviors of DCMGs under various operating conditions. The proposed nonlinear DI‐SMC is then designed for each power unit in DCMGs to ensure the desired voltage level at the common DC‐bus and appropriate power dispatch of different components to fulfill the load requirement of the DCMG. On the other hand, an energy management system (EMS) is designed to determine the set point for the controller with an aim of ensuring the power balance within DCMGs under various operating conditions where the overall stability is assessed using the Lyapunov theory. Simulation studies along with the processor‐in‐loop validation, including a comparative study with a proportional‐integral (PI) controller, verify the applicability and effectiveness of the EMS‐based DI‐SMC under different operating conditions of the DCMG.

This study is focused on two areas: the design of a Battery Energy Storage System (BESS) for a grid-connected DC Microgrid and the power management of that microgrid. The power management is performed by a Microgrid Central Controller (MGCC). A Microgrid operator provides daily information to the MGCC about the photovoltaic generation profile, the load demand profile, and the real-time prices of the electricity in order to plan the power interchange between the BESS and the main grid, establishing the desired state of charge (SOC) of the batteries at any time. The main goals of the power management strategy under study are to minimize the cost of the electricity that is imported from the grid and to maximize battery life by means of an adequate charging procedure, which sets the charging rate as a function of the MG state. Experimental and simulation results in many realistic scenarios demonstrate that the proposed methodology achieves a proper power management of the DC microgrid.

An adaptive droop control with constant voltage regulation is proposed for the power and voltage management of a DC microgrid (DCMG) with multiple power sources, such as a utility grid (UG), a distributed generator (DG), an energy storage system (ESS), and an electric vehicle (EV). In the proposed scheme, the droop characteristics for the UG, ESS, and EV are adaptively changed according to electricity price conditions and state-of-charge (SOC) levels in order to optimize the DCMG operation flexibility as well as electricity cost. The proposed control method not only ensures the power-sharing of DCMG reliably without the use of a communication link, but also regulates the DC bus voltage stably at the nominal value. To achieve this, the proposed scheme consists of primary control and secondary control. The primary control is used to achieve power-sharing in the decentralized DCMG, while the secondary control is used to overcome the disadvantage of conventional droop control, i.e., to remove DC bus voltage deviations. Decentralized power management is also presented to enhance the DCMG system reliability in the presence of uncertainties such as DG generation power, the ESS and EV SOC levels, the grid and EV availabilities, the load demand, and electricity price conditions. The effectiveness of the proposed scheme is demonstrated in a comprehensive simulation and experiment under various conditions. The test results clearly confirm the control flexibility and overall performance of the proposed control scheme for decentralized DCMG system.

The paper presents a method of the power management in a DC microgrid supported by supercapacitor as an energy storage. Power management method is presented in an example system consisting of a variable speed power generation system with a permanent magnet generator PMG, power electronics converter between PMG and DC link, and additional converter between DC link and energy storage. The system is loaded on the DC side by resistor, which represents the inverter used for the load supply. The power management method allow to obtain required functionalities like DC link voltage stabilization and overvoltage protection, supercapacitor overcharging protection and the protection against too deep discharge of the energy storage. Simulation results and laboratory tests are presented in the paper.

Microgrids are receiving tremendous attention due to increasing need to integrate Distributed Generations (DG) and to provide reliable power to critical loads. Proper power management in microgrid is required to regulate the system voltage and frequency, maintain power quality, and manage DGs. A microgrid system has been studied in this paper, which includes two natural gas generators, an energy storage device, solar PV, and a wind turbine. The DGs are modeled in details using PSCAD software and the practical constraints of the components are considered. The Virtual Droop Control (VDC) method for microgrid power management is proposed and described in details. The proposed technique has been applied to adjust the active and reactive power of each component in order to manage the system. Unit commitment algorithm has also been implemented to improve the system efficiency.

Optimal power management of dependent microgrid considering distribution market and unused power capacity

Energy management in DC microgrid with an efficient voltage compensation mechanism

Hybrid renewable energy resource (RER) microgrids offer a sustainable solution to integrating multiple energy sources like solar and wind into modern power systems. However, managing the intermittent nature of these resources presents significant challenges in maintaining stable and efficient power distribution. This study explores innovative control approaches for optimizing power management in hybrid RER microgrids. By utilizing advanced algorithms, including real-time optimization and machine learning techniques, the proposed framework ensures efficient energy distribution, reduces dependency on the grid, and enhances system stability. The research focuses on integrating these control strategies to balance energy supply and demand while maximizing the utilization of renewable energy sources. Simulation results demonstrate the effectiveness of the proposed methods in improving system efficiency and reliability under varying environmental conditions, contributing to the broader adoption of hybrid RER microgrids.

In this chapter, the author presents the operation and power management of the hydrogen storage-based smart DC microgrid (DCMG). In this microgrid, several renewable distributed generations (DGs) such as wind turbine, solar photovoltaic system, solid oxide fuel cell (SOFC), and battery energy storage system are interconnected together and to the various DC and AC loads to form a ring-type low voltage distribution network. An additional storage as Hydrogen storage system has been connected to the dc microgrid for balancing the power at all times in the DCMG, under islanded mode operation, for all practical cases. An architecture of the hydrogen storage-based DC microgrid is suggested mainly for the remote rural area. For the regeneration of the electricity from the stored hydrogen, a SOFC DG system is also used in the proposed DCMG. A control technique is also developed for the operation of the hydrogen storage-based DCMG. The proposed DCMG system provides a reliable and high-quality power supply and will supply the power to all loads (both DC and AC) simultaneously.

To fulfill fast-growing energy needs, all energy sources should be utilized. Renewable energy is infinite and clean. However, its main disadvantage is that renewable energy sources are intermittent. A Hybrid Renewable Energy System (HRES) is built by integrating several distinct energy sources to deal with this problem. In regards to energy economy, economics, dependability, and flexibility, these hybrid systems can surpass the limits of individual energy producing technologies. The power capacity of HRESs increased from 700 GW to 3100 GW globally over the period 2000–2021. This study aimed to offer and analyze a comprehensive literature review of recently published works by several researchers in the area of HRESs. The HRES contains different Hybrid Energy Systems (HESs), which are categorized into three parts, namely, PV_Other, Wind_Other and PV_Wind_Other. These systems, based on different optimization techniques/software with techno-economic objective functions and constraints, are reviewed in this paper. The optimal sizing, control, and power management strategies of the HRES are elaborately discussed to harness its potential. It has been determined that Metaheuristic (MH) methods and HOMER software are mostly employed in the fields of HRES sizing, control, power management, and optimization. The review provides a critical analysis of the shortcomings of the existing HRES systems, while choosing optimization parameters, and control and power management schemes. Moreover, the study encapsulates the various challenges/barriers in adopting HRESs. Finally, this review highlights possible future opportunities for PV, Wind, and other HESs in the area of control, power management, optimization, and optimal sizing.