Microgrid Research Articles

A two‐stage, four‐layer robust optimisation model for distributed cooperation in multi‐microgrids

AbstractAs the integration of microgrids (MG) and energy storage continues to grow, the need for efficient distributed cooperation between MGs and common energy storage (CES) becomes paramount. A robust optimisation model for the distributed cooperation of MG‐CES is presented, taking into account distributed generation under uncertainty. The proposed model follows a two‐stage, four‐layer ‘min‐min‐max‐min’ structure. In the first stage, the initial layer ‘min’ addresses the distributed cooperation problem between MG and CES, while the second stage employs ‘min‐max‐min’ to optimise the scheduling of MG. To enhance the solution process and expedite convergence, the authors introduce a column‐constrained generation algorithm with alternating iterations of U and D variables (CCG‐UD) specifically designed for the three‐layer structure in the second stage. This algorithm effectively decouples subproblems, contributing to accelerated solutions. To tackle the convergence challenges posed by the non‐convex MG‐CES model, the authors integrate the Bregman alternating direction method with multipliers (BADMM) with CCG‐UD in the final solution step. Real case tests are conducted using three zone‐level MGs to validate the efficacy of the proposed model and methodology. The results demonstrate the practical utility and efficiency of the developed approach in addressing distributed cooperation challenges in microgrid systems with energy storage.

An intelligent Island detection scheme to enhance grid resilience

The importance of strengthening grid resilience has grown with the increase in environmental destruction and modern power grid complexity, as a consequence of power outages inflicted by human intrusion and extreme weather events. Micro-grids (MGs) have proven to be a viable alternative in such circumstances. However, these occurrences are highly unpredictable, resulting in unintended islands of MGs with negative consequences. As a response, alerting its distributed generations about unintended island is indeed a crucial issue for enhancing grid resilience with MG. Therefore, it is essential to develop a technique for the efficient and accurate detection of unintended islands. There has been an increase in the use of micro-phasor measurement units (µ-PMUs) in MG. In the perspective of this, using an efficient µ-PMU, the research provides a method for finding unintended islands in a MG. The µ-PMU analyses the solar generator bus voltage and analyzes it with symmetrical components for island identification. This study introduces a µ-PMU based Fortescue-transform and random forest algorithm method for rapid detection of unintended islanding in distribution generation system. The approach monitors voltage phasor of zero and negative sequence, calculating angular sum over time to distinguish islanding event from other disturbance. Using Matlab/Simulink, the proposed method is evaluated on the IEEE-34 node distribution network. Multiple simulations provide validation for the method’s resilient performance. The methodology proposed has a detection time of 20 ms.

Challenges Hindering Electric Vehicle Adoption in India and Proposed Solutions

The inductive approach aims to investigate the factors influencing EV adoption in India, while the deductive approach concentrates on understanding how these factors unfold in developed nations and can be applied in developing countries. The iterative application of the inductive-deductive approach results in the creation of a taxonomy that classifies the factors as micro-, macro-, and meso-level antecedents. This taxonomy can serve as a framework for organizing systematic and cohesive initiatives to encourage EV adoption in developing countries. The article also underscores the importance of tailoring these factors to the distinctive infrastructural, economic, and market requirements of developing nations. A hurdle in embracing electric vehicles (EVs) lies in concerns about their restricted driving range. Recent suggestions consider the implementation of dynamic wireless charging, allowing power exchange between the vehicle and the grid while in motion. This paper emphasizes optimizing the routes of EVs requiring charging to maximize the use of mobile energy disseminators (MEDs), serving as mobile charging stations. The surge in electric vehicle (EV) popularity has led to a corresponding rise in associated challenges. Extended waiting periods at charging stations pose a significant obstacle to widespread EV adoption. Consequently, battery swapping stations (BSSs) present an efficient solution, addressing short waiting times and promoting healthy recharging cycles for battery systems. Furthermore, these swapping stations offer opportunities not only for EVs but also for power systems, providing regulation services to the grid, particularly for smaller networks like microgrid (MG) systems. This study explores the optimal location and size to maximize the revenue of a swap station within an MG system.

Assessment of Cyber-Physical Inverter-Based Microgrid Control Performance under Communication Delay and Cyber-Attacks

The integration of communication infrastructures into traditional power systems, transforming them into cyber-physical power systems (CPPS), accentuates the significance of communication in influencing system performance and sustainability. This paper presents a versatile, innovative cyber-physical co-simulation framework that integrates the physical power system and communication networks, uniting OPAL-RT, a network simulator (ns3), and Docker containers into a sophisticated platform, facilitating intensive studies into CPPS dynamics. The proposed experimental study provides an innovative way to assess the frequency control response of a cyber-physical inverter-based microgrid (MG), addressing the MG sustainability challenges. We consider diverse real-world scenarios, focusing on communication delays and distributed denial of service (DDoS) attacks within the communication channels. We propose a precise ns3-based communication model that bridges the MG’s primary and secondary control layers, an aspect often overlooked in previous studies; this is a noteworthy contribution to elucidating the adverse impacts of communication latency on MG frequency performance. The experimental results demonstrate the effectiveness of the centralized secondary controller in eliminating the frequency deviations. Furthermore, the findings offer insights into stable and unstable regions, revealing how the communication delay value affects the frequency stability under different operating conditions. In addition, the developed real-time DDoS attacks model within the proposed communication surface unveils crucial insights into the MG’s resilience to cyber threats. This work’s revelations offer a foundational awareness of MG vulnerabilities, paving the way for designing robust and resilient communication networks and control strategies within the cyber-physical inverter-based microgrids.

Modeling and Control of Micro Grid Powered by Maximum Power PV Array and Fixed Speed Wind Energy Conversion System

In this paper, the dynamic modeling of the studied micro grid (MG) is presented for steady state and transient operation conditions. Moreover, control strategies to obtain the maximum converted power and stabilize the voltage under different operating conditions are derived. The integrated system under study has four key subsystems to supply the required electric loads. The first and second subsystems include the layout of the studied MG and the renewable generation sources from Wind turbine and PV array. The third represents the boost converter between the PV and the DC collection bus as well as the interfaced inverter to the point of common coupling (PCC) with the MG. The fourth subsystem comprises mainly the required PI controllers to regulate the modulation index of the inverter and the duty cycle of the boost converter for PV maximum in addition of regulating the boost inverter DC link voltage. The proportional and integral parameters of these controllers are tuned off-line to enhance the grid performance. The integrated system and the associated subsystems are fully validated for efficient operation under normal or fault conditions using the Matlab/Simulink/Sim Power software.

Implementation of an Optimal Energy Management within Islanded Microgrid

This paper proposes an optimal Energy Management System (EMS) based on Mixed Integer Linear Programming (MILP) optimization technique for an islanded Microgrid (MG). A local energy market (LEM) platform is employed which attempts to obtain the best electricity market price and to maximize the utilization of existing distributed energy resources. Besides, a single side auction market structure is used for the LEM which takes the offer prices from all microsources to calculate market clearing price. Simulation and experimental results from the proposed method over a real MG are compared with those from a Modified Conventional EMS (MCEMS). The proposed algorithm is flexible, extendable and too fast to be utilized for different MG structures with various capacities. The results demonstrate the effectiveness of the proposed method to solve the optimal operation problem in an isolated MG with 25% reduction in the cost of operation.

Environmental/Economic Power Dispatch of MicroGrid Using Multiobjective Optimization

This paper proposes a generalized formulation to determine the optimal operating strategy and cost optimization scheme as well as the reduction of the emissions for a MicroGrid (MG). Multiobjective (MO) optimization is applied to the environmental/economic problem of the MG. The proposed problem is formulated as a nonlinear constrained MO optimization problem. Prior to the optimization, models for system components from real data are constructed. The problem formulation takes into consideration the operation and maintenance costs as well as the emissions NOx, SO2, and CO2 reduction. The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the costumer demand and safety of the system. We also add a daily income and outgo from sale or purchased power. The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions. The comparison with other techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.

Droop parameter-based economic dispatch for islanded microgrid considering demand response

The droop parameter setting of the distributed generator (DG) has an essential role in islanded microgrid (MG) systems. This paper presents droop-based economic load dispatch (ELD) in an islanded MG. In this approach, the Power–frequency (P–f) droop characteristic of DG is integrated into its incremental cost, which is the derivative of the cost function of DG. The optimisation problem is formulated by considering the standard deviations of the incremental costs of DGs. Under steady-state conditions, DGs operate at a single frequency and the P–f droop-based ELD ensures that the incremental costs of DGs are equal, minimising the overall cost of active power generation, following the equal incremental cost principle. Furthermore, an incentive-based demand response (DR) programme is integrated into the system to maximise the utility benefit function by incentivising customers for power curtailment. The results exhibit significant power curtailment during peak load hours, indicating the effectiveness of the DR programme. The proposed combined mathematical model is applied to the modified 33-bus islanded MG and solved using basic particle swarm optimisation. It calculates optimal droop parameters for a day-ahead scenario and emphasises the efficiency benefits of optimised droop settings and the integration of DR in islanded MGs.

A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, which is a crucial consideration for the emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication links. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyber-attacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control as well as modern probabilistic and data-driven approaches for modeling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

Determination of Controllers Constraints for Frequency Stability in Islanded Microgrid

With large scale integration of micro-generation into low voltage grids, stability becomes an important issue for a Microgrid (MG). The unique nature of the MG requires that it becomes stable in both grid-connected and islanded modes. In grid-connected mode, the main grid guarantees the stability of MG but in islanded mode, the stability is related to all elements of the system and their control units. In this paper, the changes in droop coefficient influencing the frequency stability of the MG are investigated in islanded MG. The simulation results determine the new constraint for controllers in MG

Droop Controller Limitation for Voltage Stability in Islanded Microgrid

The motivation to develop microgrids (MG), as a particular form of active networks is explained and presented as an effective solution for the control of grids. In MGs, with high level of penetration of Distributed Energy Resources, the stability becomes an important issue. Some control characteristics of elements such as droop coefficients in VSI highly affect voltage and frequency stability. In this paper, the changes in droop coefficient influencing the voltage stability of the microGrid are under investigation in islanded MG. The simulation results show the importance of the proposed limitation for the droop controller.

Decision-making for sustainable urban transportation: A statistical exploration of innovative mobility solutions and reduced emissions

This research aims to investigate sustainable urban transportation decision-making through innovative mobility solutions, aiming to curtail emissions. To this end, this research presents a smart stochastic architecture to optimize renewable energy-dependent systems, addressing challenges in networked microgrids. By integrating electric vehicle (EV) charging demands and harnessing the unpredictability of renewable energy resources into the urban networks, the research introduces a vehicle-to-grid layout and a novel machine learning-enabled probabilistic Technique. These advancements cover the way for stable, sustainable, and efficient urban transportation systems with reduced environmental impact. A smart stochastic architecture to optimize the operation and management of such systems that rely heavily on renewable energy is proposed in this study, taking into account the poor reliability and complicated energy management in networked microgrids (MGs). The stochastic and variable charging EVs and also the unpredictability of renewable energy resources (RERs) are incorporated in the suggested layout. A vehicle to grid layout, which has become suitable for the MG cost function, is being used for mitigating adverse impacts of vehicles on MGs. In order to support the use of RERs, this study proposes a new machine learning-enabled probabilistic method utilizing support vector machine and point estimation methods for keeping the system in a stable state taking into account their random behavior. Furthermore, this paper develops a novel optimization algorithm using modified particle swarm optimization to determine what is optimal. IEEE systems evaluate the suitability of the suggested smart layout.

Active Disturbance Rejection Control for Distributed Energy Resources in Microgrids

Motivated by the significant efforts developed by researchers and engineers to improve the economic and technical performance of microgrids (MGs), this paper proposes an Active Disturbance Rejection Control (ADRC) for Distributed Energy Resources (DER) in microgrids. This approach is a nonlinear control that is based on a real-time compensation of different estimated disturbances. The DER operates along with the electrical grid to provide the load requirements. This load has a nonlinear and uncertain character, which presents a source of unmodeled dynamics and harmonic perturbations of the MG. The main objective of this paper is to ensure the stability and the continuity of service of the distributed generation resources by controlling the DC-AC converter. The ADRC as a robust control technique is characterized by its ability to compensate for the estimated total disturbances caused by the load variation and the external unmodeled perturbations to guarantee the high tracking performance of sinusoidal reference signals in the DER system. The ADRC technique is characterized by its nonlinear function, which provides a high robustness to the controlled system. However, in order to simplify the control structure by keeping its high reliability, this paper proposes to replace the nonlinear function with a simple error (termed linear ADRC), compares the impact of this modification on the system performances, and evaluates its operation in the presence of linear and nonlinear load variations. Simulation results are presented to demonstrate the efficiency of the proposed control approach for a three-phase DER.

An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties.

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considerssix various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effectivethan itspeers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.

Optimization of frequency dynamic characteristics in microgrids: An improved MPC-VSG control

For the power imbalance caused by the load switching in microgrids (MGs), which in turn causes the frequency crossing limit problem. In this paper, we propose an improved model predictive control (MPC) based on the existing MPC-VSG control, combining adaptive inertia damping control and adaptive weight coefficient control for joint control, and adjusting the inertia damping coefficient of VSG in real-time by adaptive inertia damping control. In MPC control, the measured parameters at the current moment are used to predict the state quantities at the future moment, and then the target function is designed with the angular frequency deviation and the VSG power output as performance indicators, and the weight coefficients in the target function are updated in real-time by detecting the frequency deviation. After solving the objective function to obtain the required power compensation, the power reference value of the VSG is updated to optimize the dynamic response of the frequency. Experiment and comparison analysis with two existing methods show that the proposed method can further optimize the frequency response and smooth the output power of other power supply components in the microgrid when they are disturbed.

Business Model to the LASSE performance at Microgrid environment

The Microgrid (MG) scenario has a socioeconomic and technical character seeking to promote the sustainable, technological and economic advance. Through the MG, the consumer may set up its own business with a the sale of its own energy, thus, the Automation and Electrical Systems Simulation Laboratory (LASSE) is interested in participating of the MG market. Creating and/or innovating a business requires knowledge of the application environment. This reality has as consequence the determination of strategies for the company’s management in the vision of the market dynamics. This research aims to define the necessary requirements for the performance of LASSE in the MG environment and to develop the systemic LASSE diagram associating the structural elements of the business model with the characteristics of LASSE. The information was generated through the Business Model Generation (BMG) methodology, among which the Business Model Canvas tools stand out. The research interests LASSE for creating ideological guidelines for members of the LASSE Smart Energy Project. In this way, it is possible to clearly understand the process of introduction to the MG environment, as well as the necessary characteristics for the elaboration of Business Models (BM).

Optimal selection and analysis of microgrid energy system using Markov process

This study investigated the reliability of a microgrid (MG) energy system using the Markov process reliability method. Twenty possible MG configurations based on photovoltaic (PV), wind turbine generator (WTG), biomass generator (BMG), battery bank (BB), diesel generator (DG) and an electric vehicle (EVs) have been considered. This study also selects an optimal configuration based on reliability indices (failure rate, repair time, unavailability, expected energy not supplied (EENS) and loss of load probability (LOLP)), annually load demand, availability of the system and its components. The results show that the hybrid PV/WTG/BMG/BB/EVs/DG microgrid system has the lowest system failure rate (0.01012 /year), repair time (1.1858 hrs), unavailability (0.0120 hrs/year), EENS (4.5 kWh/year) and LOLP (1.36986E-06 /year). This MG configuration has an availability of 99%. The PV array (97.55%) and the changeover switch (99.5%) are the parts of the MG system that are the most reliable. The least stable elements of an MG system are battery (63.64%), diesel generator (80%) and biomass generator (83.89%). PV (62%) and WTG (28% of total) are the main contributors to electricity generation, with the battery, electric vehicles, and diesel generator acting as backup sources. Under different weather and load conditions, the hybrid PV/WTG/BMG/EVs/BB/DG microgrid was found to be a reliable and efficient system for remote areas. This research carries a significant impact for policymakers, government, energy planners, and researchers working on stand-alone microgrid systems as it provides valuable insights into optimizing system configuration for reliability and efficiency.

Power allocation scheme for grid interactive microgrid with hybrid energy storage system using model predictive control

Grid-interactive microgrids have become a promising alternative to traditional centralized power systems as a result of the rising demand for reliable and sustainable energy solutions. DC microgrids (MG) based upon renewable energy sources (RES) are on the rise due to high energy efficiency and compact size than the AC microgrids. Microgrid equipped with hybrid energy storage system is sensible for a reliable and stable power supply to the system. Implementation of a potent control system makes sure of the system stability. A model predictive control (MPC) based control strategy is proposed due to its easy implementation approach and inclusion nonlinear dynamics & constraints of the controlled system. An efficient power allocation scheme is developed in this paper for a grid-interactive photovoltaic microgrid featuring a hybrid energy storage system (HESS). The system-level power allocation scheme (PAS) considers the real-time data of load demands, generation, market energy cost, and energy storage state-of-charge to actively manage the power flow within the microgrid and also with the utility grid. The proposed control scheme uses a sophisticated model predictive current control for the DC/DC bi-directional converters control and model predictive combined power and voltage control is used for bidirectional interlink converter. The proposed control approach provides faster DC bus voltage recovery. The HESS consisting of battery and supercapacitor is used as energy buffers for responding to the fluctuation of power generation and demand for smoothing the PV power. The potency of the proposed controller is verified using MATLAB/Simulink (2021b) environment. Also, the real-time implementation of the proposed approach is carried out using the OPAL-RT OP4510 simulator.

A new approach for reliability assessment of the smart microgrids using k-shortest path algorithms

The reliability of cyber-physical microgrids (MGs) is crucial in the development of smart grids. The reliability of MGs can be affected by cyber network failures, which have a significant impact on the physical components. Since MGs have interdependent cyber and physical elements, a smart MG can be viewed as a system of n dependent two-mode components. This paper proposes an approach to finding the k most likely configurations of the system. The method involves three phases. Firstly, the multi-mode model is obtained for physical components, considering the operation and topology of the cyber network. Then, the problem is transformed to finding the k most likely state of a system consisting of n independent multi-mode components. In the second phase, a transformation using new transformed metrics is applied. This results in the problem being converted to finding the k shortest path, which can be solved using efficient algorithms. Finally, the states are evaluated using a DC load flow, and reliability indices such as loss of load probability (LOLP) and expected energy not supplied (EENS) are calculated. Moreover, we have incorporated the dynamic thermal rating (DTR) system into our proposed model, addressing the safe enhancement of system component ratings. The results indicate that the most probable states of the system are related to the failure of distribution generators. The most severe events occur due to failure in the cyber network, and cyber network malfunction has a higher effect on EENS compared to LOLP. Additionally, we observe a significant enhancement in reliability indices when considering the DTR system over the static thermal rating (STR) system. This approach is efficient in reliability calculation using fewer system states.

Reliable Power Supply of Islanded Locations through Microgrids

Due to the increase in the price of oil and the attempt to meet the limits of established CO2 emissions, the technology of microgrids (MGs) has been growing considerably for some years. Its implementation is recommended above all in weak electrical networks, such as the networks of isolated areas and islands, with the aim of solving the typical quality problems in the supply of such networks. In this paper, the analysis to estimate the implementation of an isolated MG is carried out, located on islands in the Hordaland region of southwestern Norway. Special importance is given to the fact that the MG in question contemplates high penetration of renewable energies and storage technologies.