Due to inherent advantages of DC system over AC system such as compatibility with renewable energy sources, storage devices and modern loads, Direct Current Microgrid (DCMG) has been one of the key research areas from last few years. The power and energy management in the DCMG system has been a challenge for the researchers. MG structure and control strategies are the integrated part of the power and energy management system. This paper covers all the aspects of the control of DCMG, whether it is DC bus voltage, power or energy related. Different MG Structures with their comparative analysis has been given in this paper. Various control schemes: Basic control schemes like centralized, decentralized and distributed control and multilevel control scheme such as hierarchal control has been discussed. The Power management in grid-connected, Islanded mode and transition mode has been presented. Different energy management strategies have been presented as energy management plays very important role in optimizing the size and rating of energy storage system and their maximum utilization. The energy management of a battery and super capacitor based HESS in all configurations has also been discussed and finally, future trends in further research are presented.

A review on overall control of DC microgrids

As the emerging technology offers more economic and efficient mechanisms for hydrogen production, fuel cell electric vehicles (FCEVs) are expected to be deployed more extensively in the near future. Proliferation of hydrogen fueling stations throughout the transportation network and justifying their economic viability are key factors to the success of the FCEVs. In today’s deregulated market environment, many governments are encouraging private investors to invest in key infrastructures including the hydrogen fueling stations. To that end, this paper proposes a new model for optimal scheduling of privately owned hydrogen storage stations to both serve the transport sector and the electricity market operator. The model mainly aims to: 1) exploit the lower electricity market prices to reduce the power purchase cost and 2) contribute to the capacity–based demand response program to further enhance the economic feasibility of the investment. The profitability constraints and dynamic hydrogen pricing mechanisms are incorporated into the optimization process to guarantee the economic feasibility of the investment. Through such constraints, hydrogen sale prices would dynamically change to maintain system profitability at the lowest possible hydrogen price. Numerical studies reveal that the stacked profit from the two aforementioned sources of revenue under the proposed model would lead to a stronger rate of return.

Hydrogen Storage Optimal Scheduling for Fuel Supply and Capacity-Based Demand Response Program Under Dynamic Hydrogen Pricing

Rapid industrialization and its automation on the globe demands increased generation of electrical energy with more reliability and quality. Renewable energy (RE) sources are considered as a green form of energy and extensively used as an alternative source of energy for conventional energy sources to meet the increased demand for electrical power. However, these sources, when integrated to the utility grid, pose challenges in maintaining the power quality (PQ) and stability of the power system network. This is due to the unpredictable and variable nature of generation by these sources. The distributed flexible AC transmission system (DFACTS) devices such as distributed static compensator (DSTATCOM) and dynamic voltage restorer (DVR) play an active role in mitigating PQ issues associated with RE penetration. The performance of DFACTS devices is mostly dependent on the type of control algorithms employed for switching of these devices. This paper presents a comprehensive review of various conventional and adaptive algorithms used to control DFACTS devices for improvement of power quality in utility grids with RE penetration. This review intends to provide a summary of the design, experimental hardware, performance and feasibility aspects of these algorithms reported in the literature. More than 170 research publications are critically reviewed, classified, and listed for quick reference for the advantage of engineers and academician working in this area.

/pdf/comprehensive-review-of-distributed-facts-control-algorithms-3abt9977a4.pdf

Comprehensive Review of Distributed FACTS Control Algorithms for Power Quality Enhancement in Utility Grid With Renewable Energy Penetration

In this article, a normalized gradient adaptive regularization factor neural filter based control is presented for a three-phase grid interfaced solar photovoltaic (PV)-battery energy storage microgrid system. An incremental conductance (INC) technique is utilized for the peak power extraction of a solar PV array. A battery is connected through a bidirectional converter at dc-link of voltage-source converter (VSC), and it charges and discharges as per load variation and enhances the reliability of the system. This dc–dc converter also regulates the dc-link voltage for maximum power point tracking (MPPT). This neural filter based current controller improves the dynamic behavior of the proposed system and feeds active power to the utility grid by utilizing the feed-forward term of solar PV power under variable atmospheric scenarios. A power electronics switch is used for VSC mode shifting operation between current control in the grid-connected mode and voltage control in an islanded mode to ensure continuous and adequate power to the nonlinear load. The discrete proportional and resonant (PR) controller is used for the voltage control in an islanded mode to reduce the steady-state error between sensed and reference load voltages. The voltage controller also regulates the frequency. Simulations of the microgrid system are carried out by utilizing MATLAB/Simulink software to show the effectiveness of control technique. The performance of the system is found satisfactory for various operating conditions such as load variation, load unbalancing, and solar insolation change, and validated through test results on a developed laboratory prototype.

Three-Phase Grid-Interactive Solar PV-Battery Microgrid Control Based on Normalized Gradient Adaptive Regularization Factor Neural Filter

This paper presents a grid-forming control (GFC) scheme for two-stage photovoltaic (PV) systems that maintains power reserves by operating below the maximum power point (MPP). The PV plant in GFC mode behaves like a voltage source that supports the grid during disturbances in full or limited grid-forming mode as per the reserve availability. This is a model-free method that avoids the estimation of MPP power in real-time commonly done in the literature, which makes it simpler and more reliable. The proposed control also features an enhanced current limitation scheme that guarantees containment of the current overshoots during faults, which is not trivial in voltage-sourced GFC inverters. A thorough investigation is done, exploring various generation mixtures of synchronous machines (SM), GFC and grid-following (GFL) inverters, and all common disturbances, e.g., load change, faults and irradiance transients. The results show very favorable dynamic performance by the GFC inverters, far superior to GFL inverters and directly comparable to SMs. It is found that replacing SMs with GFC inverters may improve the frequency profile and terminal voltage during disturbances, despite losing out in the mechanical inertia and the strict inverter overcurrent limits.

/pdf/grid-forming-control-for-solar-pv-systems-with-power-3wz8vs64a8.pdf

Grid-Forming Control for Solar PV Systems With Power Reserves

This paper presents an adaptive power management and control for a self-reliant dc microgrid powered by photovoltaic (PV) array and fuel cell stack that is escorted with the supercapacitor- and electrolyzer-based hybrid energy storage device. The prime operational impediments associated with this dc microgrid are the unpredictable variations in the PV generation and load demand, fuel starvation phenomenon of the fuel cell, the time constant associated with the electrolyzer operation, and state-of-charge limitation of the supercapacitor. In this paper, the above-mentioned issues are addressed by employing an adaptive dynamic power management strategy (ADPMS) that supervises the overall power flow in the system. The ADPMS enables the co-existence of high power density and high energy density storage devices to deliver the power flow required by the loads. The expeditious support extended by the supercapacitor helps in swift regulation of the dc-link voltage during unforeseen transient load/source power variations. The electrolyzer–fuel cell combination employed in this system can supplement a battery bank and can equip high energy density and self-reliance to the system. The set-reset flip-flop based fixed frequency current controller is simple and effective in accurately tracking the reference currents established by the ADPMS. The proposed ADPMS along with the control is validated for transient unforeseen load profiles and renewable generation changes via simulation studies and its performance is also validated through an experimental analysis on a laboratory-scale dc microgrid testbed.

A Self-Reliant DC Microgrid: Sizing, Control, Adaptive Dynamic Power Management, and Experimental Analysis

To maximise functional efficacy and reliability of distributed generations (DGs), this study leads to modelling, control, stability analysis, and hardware validation of a new adaptive current controller in the application of an on-grid voltage source converter (VSC) system. For effective mitigation of power system hindrances without affecting the power quality (PQ), self-tuning of weights associated with the proposed variable leaky adaptive step-size-least mean square (VLAS-LMS) control algorithm based on artificial neural network (ANN) is updated in natural frame and felicitous shaping of VSC outputs are witnessed. The selection of a constant step-size associated with proposed controller usually yields updating of same weights by all the sampling periods and hardly takes care of rate of convergence factor, which decides the stability of the controller. It can no longer avoid wavering of weights during grid disturbances, resulting in high-filtering gains. Again, a constant leaky factor may lead to over- or under-parameterisation of regularisation component. These disputes can be overcome in the proposed algorithm by the introduction of an adaptive step size along with a variable leaky factor. Furthermore, PQ is maintained as trade-off by the inclusion of detuned LC filter. Experimental outcome ensures the validation and effectiveness of the proposed controller.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2017.0142

ANN-based adaptive current controller for on-grid DG system to meet frequency deviation and transient load challenges with hardware implementation

Islanding detection is an important operational aspect of microgrids. Early detection of islanding is essential to avoid unsynchronised reclosing. The possibility for minimal or near zero power mismatch between generation and load of a microgrid is high in a modern reconfigurable distribution network. Hence, at the instant of islanding, the voltage at the Point of Interconnection (PoI) will not change significantly and may lead to failure of over/under voltage and frequency relays in detecting islanding. In this work, second harmonic voltage at PoI of a voltage source inverter-based distributed generation (DG) is extracted through a sliding window discrete Fourier transform and is used for generating a second harmonic current reference, through virtual admittance. The second harmonic impedance at DG terminal is estimated. This second harmonic impedance remains insignificant in the case of grid-connected mode but drifts beyond a threshold in islanding mode. The proposed method is validated on two test cases, and the comparative results from simulation studies indicate that the islanding detection is achieved in less time with minimal effect on the power quality, fundamental voltage, and frequency at the PoI.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-gtd.2018.6838

Second harmonic impedance drift-based islanding detection method

The technology of solar photovoltaic (PV) systems has seen remarkable
boom the past decade and remains central in the decarbonization and
post-COVID19 recovery plan of many countries worldwide. However, global
experience shows that PV installations start to level off in networks with
certain solar integration levels. Among the various reasons, such as financing,
regulations, markets etc., this report focuses and takes a deep look on
technical challenges that act as barriers against further solar integration and
develops a roadmap with technology innovations and transformations that will
allow a high-solar future. The UK and India are the focal points of this
investigation, examining closely how the challenges and future directions relate
or differ between the two countries. This report arises out of the UK-India
Joint Virtual Clean Energy Centre (JVCEC), which is a research consortium that
involves ten UK universities and thirteen Indian institutes within three
virtual centres namely, the Joint UK-India Clean Energy Centre (JUICE), the
India-UK Centre for Education and Research in Clean Energy (IUCERCE) and the UK
India Clean Energy Research Institute (UKICERI).

Solar integration in the UK and India: technical barriers and future directions

A growing number of electric industry leaders agree that it is only a matter of time before renewable energy resources dominate their grid systems. In the context of increasing interest for smart grids, grid-connected renewable energy systems play a major role. However, the increasing renewable energy penetration poses typical challenges for the existing power system like stability, protection, and power quality issues. This indicates the necessity of a proper channel for the efficient and stable inclusion of such sporadic generation. DC microgrid acts as an effective platform for such purposes due to its renowned characteristics like increased efficiency, reduced complexity in control, negligible power quality issues, etc. In this perspective, this chapter presents the control and energy management in a grid-connected DC microgrid comprising of solar PV array, battery, and supercapacitor banks in order to maximize the synchronized grid integration of the renewable energy along with the objective of stabilizing the DC microgrid. Modeling and analysis of the system are carried out in MATLAB/SIMULINK environment and vital operating scenarios associated with the system are tested to evaluate its performance. The results presented in this chapter convey the effectiveness of the control and energy management employed to the system in stably integrating the renewable energy against the operating constraints associated with the system components.

Bonu Ramesh Naidu

Papers

A Self-Reliant DC Microgrid: Sizing, Control, Adaptive Dynamic Power Management, and Experimental Analysis

ANN-based adaptive current controller for on-grid DG system to meet frequency deviation and transient load challenges with hardware implementation

Second harmonic impedance drift-based islanding detection method

Solar integration in the UK and India: technical barriers and future directions

Dynamic energy management and control of a grid-interactive DC microgrid system