Optimization of Power Flow in Hybrid Microgrids Using AI-Based Algorithms

Guest Editorial: Situational awareness of integrated energy systems

Energy management in DC microgrid with an efficient voltage compensation mechanism

This article proposes new control architecture to improve the dynamic performance of DC microgrid by enhancing system inertia. The DC microgrids are gaining more importance due to the controlled and combined operation of multiple distributed energy resources (DERs). The DC microgrid is dominated by controlled power electronics converters. These converters are not having inbuilt inertia like rotating generators. Therefore, the DC microgrid has a fast rate of change of bus voltage under fluctuations of load, change in available power capacity of sources due to their randomness, etc. This affects the voltage profile and stability of the system under dynamic variation. The proposed closed loop control scheme estimates the amount of inertia required in the DC microgrid to minimize the voltage variation. The new power references are given to sources based on virtual generation to improve the inertia in the system. The proposed control scheme maintains the stability of DC microgrid. It also achieves effective power management based on available power in DERs with a minimum burden on the energy storage system (ESS). The bus voltages are regulated at reference value. The stability analysis of the system is carried out for DC microgrid under different operating conditions. The proposed control method is tested experimentally under diverse operating conditions.

This paper proposes a hierarchical look-ahead framework to conduct conservation voltage reduction (CVR) when distributed energy resources such as solar photovoltaic (PV) systems and energy storage systems (ESSs), and demand response programs are integrated into distribution systems. With the increasing deployment of PV systems in distribution systems, their frequently varying power output due to cloud movements could have a detrimental impact on the consumer’s voltage quality, consequently leading to degraded CVR performance. A two-level CVR framework for the coordination of an on-load tap changer (OLTC), capacitor banks (CBs), and the smart inverters of PV systems/ESSs is presented, in which these elements operate to reduce the voltage profile along the distribution feeder at different temporal scales. At the global level, the operations of the OLTC and the CBs are scheduled every hour to achieve the best CVR performance in an optimization problem using mixed-integer linear programming. When voltage violations occur rapidly, the smart inverters of PV systems and ESSs help to maintain a lower voltage profile every second based on the proposed piecewise droop control functions at the local level. A simulation study is carried out in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and ESSs, and our results demonstrate the advantages of the proposed approach in terms of voltage level and energy savings. Furthermore, the impact of demand reduction on the proposed approach is quantified, and we verify that a higher demand reduction yields more energy savings in the proposed framework.

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids

The ever-increasing development in the world causes load demand to increase exponentially each year. The current transmission and distribution systems cannot meet this load demand. Moreover, the peak demand occurs only for a short duration. However, it stresses the power system and results in system losses and damages, which can be avoided by load shedding at the cost of customer discomfort and economic losses to utilities. Energy Storage System (ESS) is a feasible option to counter this problem. In this paper, an optimal mixed integer linear programming (MILP) based computational energy arbitrage model of ESS is proposed for efficiently scheduling its charging and discharging power to avoid overloading of distribution feeder. The proposed optimization model ensures satisfaction of feeder thermal limit constraints. The case study of distribution feeder with ESS deployment is presented for proving the effectiveness of the proposed model. The results clearly demonstrate the ESS economical operation to reduce feeder peak load and maximize ESS profit.

This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows’ memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic–emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper.

Prospective hydrogen-based microgrid systems for optimal leverage via metaheuristic approaches

A device-level service-oriented middleware platform for self-manageable DC microgrid applications utilizing semantic-enabled distributed energy resources

Microgrids have become valuable assets because they improve the reliability of consumers while integrating renewables via distributed energy resources (DERs). Thus, making them cost-efficient is essential to secure their proliferation. This paper proposes a new method for the optimal design of microgrids. The proposed two-stage method optimizes the size and the location of the DERs, i.e., the renewable energy sources (RESs), distributed generation (DG) units, and battery energy storage systems (BESSs). Furthermore, the overall operation of the microgrid is optimized using a stochastic scenario-based approach, considering grid-connected and unintentional islanded modes. The proposed method also considers internal network reinforcements. Thus, the first stage is an energy-based approach, formulated as a mixed-integer linear programming (MILP) problem, and it is used to size the DERs, whereas the second stage uses an optimal AC power flow (AC-OPF) to formulate a mixed-integer nonlinear programming (MINLP) model that allocates the DERs and selects the best conductor for each circuit. The multi-objective nature of the problem is addressed via Pareto optimization to analyze the trade-off between operational and capital costs. The MINLP model is linearized through piece-wise approximations and solved using commercial solvers. Furthermore, the impact of battery degradation is analyzed through a simple adaptation of the Stage 1 model. Results were obtained with data from the real university campus microgrid CampusGrid, located at the State University of Campinas (UNICAMP), in São Paulo, Brazil.

Many consumers in production plants like industrial robots or tool machines perform repetitive movements, which lead to a cyclic load demand. However, these load profiles can usually only be roughly estimated at the planning stage. Hence, a subsequent online adaptation of the energy distribution is useful for cases, such as balancing between the charging and discharging amount of energy storage systems to improve those lifetime and usage. This paper presents a novel method of online adaptation for the load distribution of production processes within industrial direct current (DC) microgrids. The online load profile cycle recognition was used to adapt the energy distribution among the sources and loads in the DC microgrid. These sources can be inverters, rectifiers, energy storage systems or decentralized power supply units, such as photo voltaic systems. The approach consists of three major points, the load profile cycle recognition, the load profile analysis and the online adaptation of the energy distribution. This solution was tested in simulation and in experiment with a test rig, that contains an inverter and an energy storage system. The results show, that the load profile will be recognized latest from the third cycle and that the imbalance between charging and discharging amounts of the energy storage is less than 0.6% for each cycle after adaptation.

This comprehensive review critically analyses the complex correlation between DC microgrids and the incorporation of Distributed Generation (DG). It offers a full evaluation of fundamental principles, advanced control strategies, technology advancements, and practical implementations in real-world scenarios. This research examines the ability of Direct Current (DC) microgrids to effectively integrate and optimize various DG sources within the context of a paradigm shift towards decentralized energy solutions. The paper critically examines the obstacles and opportunities associated with renewable energy systems and different DG technologies. It provides essential insights for researchers, engineers, and politicians who are involved in the complex process of integrating distributed energy into existing systems. This review serves as a crucial resource that contributes to the understanding of DG in DC microgrids. It plays a significant role in developing conversations surrounding energy solutions that are both resilient and sustainable. This paper not only discusses the difficulties related to the integration of DG, but also emphasizes practical examples that demonstrate the flexibility and effectiveness of DC microgrid designs. This study serves as a crucial resource for stakeholders who aim to use the potential of DC microgrids, since it encompasses a comprehensive analysis of power electronics, energy storage, and sophisticated control systems. By combining critical evaluation with practical implementation, this review offers valuable insights and guidance in the field. The complete investigation plays a significant role in advancing the integration of DG, which is essential for creating a resilient and sustainable energy environment. It provides basic information that is needed for achieving effective integration. This review paper provides a comprehensive examination of the integration of DG within DC microgrids. From laying the groundwork with fundamental principles to scrutinizing challenges, technological advancements, and practical applications, this review aims to offer a holistic understanding of the dynamic landscape where DC microgrids intersect with distributed energy generation.

The new energy resources in distribution systems have led researchers to consider the operation of such elements during service restoration. Hence, the current state-of-the-art regarding the restoration problem considers the operation of distributed resources in both island and grid-connected modes. Few articles also address the system's restoration capacity changes throughout the time needed to repair the network after an outage, given the variations in the system's demand and availability of energy sources. However, the solutions proposed in the literature converge in proposing a single topology that is feasible throughout the entire repairing time. In this article, we propose addressing the mutable nature of the system's restoration capacity by splitting the repairing time into several time intervals and associating each of them with a specific configuration that minimizes the out-of-service load. Energy storage systems, intermittent and dispatchable generators, and voltage-control devices are also considered. The problem is formulated as a mixed-integer linear programming (MILP). The model and its scalability are tested using the 53-, 217-, and 404-node systems, considering different fault scenarios and reliability indices. The results show that the nonsupplied energy during the repairing time decreases when dynamic changes in the system topology are considered. Furthermore, the effects of storage systems operation and their integration with dynamic topology changes are highlighted.

The direct current (DC) systems have outstanding advantages compared to the alternating current (AC), especially when it comes to renewable energy resources (RER) and microgrids (MGs). Therefore, the DC microgrid (DCMG) has become popular and drawn the attention of researchers. Due to the integration of different power sources in the single DC bus, including solar photovoltaics (PV), wind farms, diesel generators, and energy storage systems, maintaining stable bus voltage requires a complicated control system to ensure a fast-decision-making procedure. This paper presents an energy management strategy for the DCMG using a fuzzy logic-based controller. The proposed DCMG uses a hybrid energy storage system (HESS) for energy balancing and a diesel generator as a backup. The proposed strategy uses generation and loads demand and battery state of charge (SoC) to control the power flow within the MG. The energy management system keeps the SoC in a suitable range to increase the battery life. By controlling the charging and discharging of the HESS, the proposed method has a fast response to the load variation and RER intermittency to prevent the fluctuation of the voltage level of the DC bus and keep it constant. The obtained results validate the efficacy of the proposed fuzzy logic controller.

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.