Renewable Energy Microgrids Research Articles

Efficient voltage control strategy: observability design for multistage DC–DC buck converter

This paper emphasizes the significance of implementing an effective control system to enhance the performance of a three-stage DC–DC buck converter (TDDC). Herein, we present the development of an observer controller (OC) for TDDC, where average modeling of the DC–DC converter was employed alongside an observer algorithm for the OC. The primary focus is on the adoption of an observation topology aimed at enhancing system responsiveness under various conditions, including variable input voltages, reference voltage changes, load fluctuations, integration with photovoltaics, and battery charging. This approach ensures improved stability and performance, addressing the dynamic challenges inherent in such systems. A comparative analysis was conducted on two distinct control topologies for the proposed TDDC system, namely proportional–integral (PI) and advanced OC. Simulink software was used to model and simulate the proposed system. The results demonstrate lower rising and settling times of the TDDC for the OC and PI implementations, respectively. Additionally, the system with the OC eliminates 20% of the overshoot, while the system with OC minimizes the output voltage oscillations from 13% to 2% compared to the PI system. The OC was integrated into the TDDC to enhance system stability and improve the control loops, particularly the third loop. These improvements make the TDDC with OC suitable for application to renewable energy systems, microgrids, telecommunication networks, and electric vehicles, where precise control and stability are essential.

Short‐term energy forecasting using deep neural networks: Prospects and challenges

AbstractThis study presents an in‐depth overview of deep neural networks (DNN) and their hybrid applications for short‐term energy forecasting (STEF). It examines DNN‐based STEF from three perspectives: basics, challenges, and prospects. The study compares recent literature using metrics like mean absolute error (MAE), mean average percentage error (MAPE), and root mean square error (RMSE). Findings indicate that combining automated data‐driven models with enhanced DNNs effectively addresses forecasting challenges. It also highlights the role of DNNs in integrating energy prosumers, renewable energy systems, microgrids, big data, and smart grids to improve STEF.

Capacity planning of storage batteries for remote island microgrids with physical energy storage with CO2 phase changes

Energy storage devices based on the CO2 thermal cycle (CTC) lose competitiveness in cold regions because of the decreased energy volume density and charge–discharge efficiency at low ambient temperatures. The CO2 hybrid thermal cycle (CHS) combines the CTC with the CO2 hydrate thermal cycle (CHT) to maintain a high energy volume density and charge–discharge efficiency regardless of the ambient temperature. The CHS-based energy storage system has been shown to have an energy volume density of 80–140 kWh/m3 and charge–discharge efficiency of 60 %–106 %. However, the economic feasibility of this system has not been considered. In this study, a numerical analysis was performed on the practical application and economic feasibility of CHS-based energy storage for the 100 % renewable energy microgrid of a pair of remote islands. In a case analysis of CHS installation in a microgrid for a remote island with 100 % renewable energy, the construction cost of CHS was 2/3 of that of pumped storage; the equipment cost reduction of CHS strongly depends on the type and amount of renewable energy to be interconnected, and therefore, the overall optimization of the power system is necessary.

Optimal rule-based energy management and sizing of a grid-connected renewable energy microgrid with hybrid storage using Levy Flight Algorithm

The study addresses the integration of hybrid hydrogen (H2) and battery (BT) energy storage systems into a renewable energy microgrid comprising solar photovoltaic (PV) and wind turbine (WT) systems. The research problem focuses on improving the effectiveness and computational efficiency of energy management systems (EMS) while ensuring high system reliability. Despite the existing optimization methods for hybrid microgrids, challenges remain in optimizing energy storage and capacity planning in grid-connected microgrids. To solve this, we propose the use of the Levy Flight Algorithm (LFA) to optimize the capacities of PV, WT, H2 tanks, electrolyzers (EL), fuel cells (FC), and BT, which presents a complex nonlinear optimization challenge. The novelty of this study lies in integrating the LFA with a rule-based EMS, enhancing system reliability and efficiency. The proposed approach significantly reduces the annualized system cost (ASC) and the levelized cost of energy (LCOE). The result demonstrate that the LFA outperforms methods like the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) and Genetic Algorithm (GA), yielding cost savings of $3,309, $5,297, $4,484, and $5,129 respectively. The LFA achieves the lowest LCOE at $0.275/kWh, compared to $0.278/kWh with SSA, $0.289/kWh with GA, $0.280/kWh with PSO and $0.283/kWh with GWO. This research contributes to the broader scientific community by providing a more efficient approach to optimizing renewable energy microgrids with hybrid storage systems, thus promoting eco-friendly and cost-effective energy solutions. The proposed system design offers a pathway to future energy systems with high renewable integration, especially as technology advances and costs continue to decrease.

Energy management of hybrid AC/DC microgrid considering incentive‐based demand response program

AbstractIncreasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming.

A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties

The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.

The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid

This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants’ typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64).

Real-Time Optimization of Ancillary Service Allocation in Renewable Energy Microgrids Using Virtual Load

The stability of global economies relies heavily on power systems (PS) that have sufficient operating reserves. When these reserves are insufficient, power systems become vulnerable to issues such as load shedding or complete blackouts. Maintaining grid stability becomes even more challenging with a high penetration of renewable energy sources (RES). However, RES, connected through power electronic devices, offer significant potential as ancillary service (AS) sources. Renewable energy-based microgrids (MG), which aggregate various RES resources and have substantial load control potential, further enhance the capability of AS provision from RES. The presence of diverse AS resources raises the question of how to dispatch ancillary service signals optimally to all resources. Most of the previous research work related to AS allocation relied on single-bus MG models. This paper proposes a detailed MG model for the optimal dispatching of ASs among the resources using Virtual Load, along with an optimization procedure to achieve the best results. The model incorporates voltage profiles and power losses for AS dispatching, and a comparative analysis is conducted to quantify the significance of grid modeling. The model and proposed procedure are tested using the CIGRE microgrid benchmark model. The results indicate that detailed modeling of MG can impact the results by 11%, compared to single-bus modeling, which qualifies detailed MG modeling for all future research work and shows the impact that modeling can have on technical and economic indicators of MG operation.

Analysis and Design of Frequency Regulation Controllers with Considering Communication Delays and Environmental Effects of Renewable Energy Microgrids

Analysis and Design of Frequency Regulation Controllers with Considering Communication Delays and Environmental Effects of Renewable Energy Microgrids

Policy and regulatory framework supporting renewable energy microgrids and energy storage systems

Policy and regulatory framework supporting renewable energy microgrids and energy storage systems

Hybrid energy-based electric vehicles charging station integrated with INVELOX wind turbine: A case study of Kermanshah

Global warming from fossil fuels is a growing concern for governments. A potential solution is using renewable energy microgrids, which reduce dependence on the power grid. On the other hand, Electric vehicles are a solution to combat environmental pollution as they emit no harmful pollutants compared to conventional vehicles. However, charging these vehicles can lead to congestion, reliability, and resiliency issues for power systems. To address this issue, renewable energy sources are being utilized to ease the burden of electric vehicles on power grids. Therefore, this paper assumes a proposed microgrid's planning and management in Kermanshah city and uses unique renewable energy sources such as the INVELOX wind turbine and common photovoltaic panels by utilizing Homer software. Results show that for the first scenario, excluding PV panels, the NPC is $432,173. Adding PV panels reduces the optimized NPC by 53 % to $282,523. Sensitivity analysis highlights the impact of inflation, fuel prices, and PV longevity. In the second scenario with the INVELOX wind turbine, the NPC without PV is $373,199, with $26,070 in annual repairs. Adding PV panels decreases the NPC to $253,405 and maintenance to $14,288, showing significant cost reductions. Novel technologies like INVELOX and PV panels enhance microgrid cost efficiency and environmental impact.

Digital twin technology for renewable energy microgrids

Digital twin technology for renewable energy microgrids

Leveraging media for demand control in an optimal network of renewable microgrids with hydrogen facilities in South Korea

Abstract In pursuit of a sustainable 2030 strategy in the Republic of Korea, this study addresses the oversight in recent optimal renewable energy microgrid designs, which, despite encompassing all feasible renewable sources, neglected the pivotal role of hydrogen as an energy carrier. This research explores the feasibility of reprogramming media platforms to dynamically shape energy consumption during peak intervals. It further proposes the retrofitting of microgrids with industrial hydrogen production and storage facilities, aligning with controlled electricity demand. A comprehensive social survey investigates the impact of media content on energy-conscious behaviour and cooperation, specifically targeting energy savings during peak hours. Utilizing a probabilistic model, the study quantifies responses from the surveyed sample and decomposes the energy demand time series to reveal three new consumption patterns: demand reduction by lowering residential electricity consumption at peak intervals without shifts, intense demand shifting by redistributing electricity consumption from peaks to valleys without human intervention, and moderate demand shifting achieved through cooperation with consumers. With these novel energy demand patterns in hand, the study optimally designs renewable microgrids in 17 sites in South Korea, comparing two strategies: Plan A, involving electrolysis-based hydrogen production and storage tanks, and Plan B, which excludes hydrogen facilities. Comparative results demonstrate that media content contributes to a 10.28% and 16.11% reduction in peak electricity consumption, with and without human intervention, respectively. In Plan B, a demand cut saves 937.3 MWh/yr, resulting in a 12.88% reduction in the levelized costs of electricity (LCOE) and a 4.67% reduction in net present costs (NPC) of optimal renewable microgrids in Korea. Conversely, in Plan A, intense demand reduction exhibits superior performance, leading to $981K less NPC, 1,046 MWh/yr less excess electricity, and a 3.76% smaller LCOE. The study recommends the implementation of smart gadgets to control residential electricity consumption, producing industrial hydrogen at Korean sites based on consumer attention and agreement with specific media content. However, it underscores the importance of studying the socio-psychological effects of this plan in future research.

Capacity Optimization of an Isolated Renewable Energy Microgrid Using an Improved Gray Wolf Algorithm

Capacity Optimization of an Isolated Renewable Energy Microgrid Using an Improved Gray Wolf Algorithm

Research on coordinated control technology of hybrid energy storage under grid-connected/off-grid dual mode

Abstract Aiming at the multi-time scale intermittency of renewable energy in microgrids and frequent power fluctuations will lead to a series of power quality issues. So, it is necessary to propose appropriate strategies for controlling the energy storage systems of microgrids to ensure energy balance. In microgrids, two types of battery energy storage are used to form a hybrid energy storage system, namely, zinc-bromine flow battery for energy storage and lithium titanate battery for energy storage. By analyzing the functionality of various energy storage systems in the grid-connected/off-grid dual style, the operation objectives of the hybrid energy storage system in different modes are proposed. In the meantime, the control strategy is formulated by combining the smoothing fluctuation characteristics of power-type energy storage and the peak load shifting features of energy-type energy storage. Finally, the efficiency of the hybrid energy system control strategy is checked by the simulation software in the connected/off-the-grid mode.

Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

Optimization and performance enhancement of renewable energy microgrid energy system using pheasant bird optimization algorithm

The main aim of this research work is to design and select an optimal renewable energy resource based microgrid (MG) system for rural area electrification of India. MG system consists of diesel generator (DG), battery bank (BB), wind turbine (WT), biomass generator (BMG) and solar photovoltaic (PV) modules with seven different configurations. The optimal system is selected based on technological, economic, environmental, social and reliability parameters. For this objective, a novel algorithm called pheasant bird optimization algorithm (PBOA) is presented and compared with particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), cuckoo search algorithm (CSA) and HOMER Pro. Results show that PV/WT/BMG/BB/DG is an optimal configuration with sizes of 1148 units of PV, 42 units of WT, 20 units of BMG, 36 units of BB and 1 unit of DG. PBOA has determined the optimal resource size for the MG system. In terms of social and reliability parameters for the PV/WT/BMG/BB/DG system, PBOA generated values of 93.58 %, for RF, 0.08754 for EGJC and 0.01 % for LPSP. For the optimal configuration, PBOA estimated NPC, COE, CE values are ₹66185779, ₹9.3 and 650901 kg/yrs respectively. PBOA reaches the minimal NPC of ₹66185779 in just 180 iterations, whereas PSO, ACO, GA, and CSA required 360, 200, 360 and 250 iterations respectively. PBOA also achieved minimal COE at a rate of 1.52, 1.11, 1.11 and 1.94 times faster than PSO, GA, CSA and ACO respectively. The contribution of PV and WT sources to the total generated electricity is approximately 50 % and 35 % respectively. PBOA exhibits faster convergence time and iteration compared to the PSO, ACO, GA and CSA algorithm. The comparison results indicate that the optimal PV/WT/BMG/BB/DG configuration select for rural areas. This research provides valuable information for policy makers and investors on the implementation of MG system in rural regions.

Innovative hierarchical control of multiple microgrids: Cheetah meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques. This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights. HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities. The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600 V-bus According to IEEE519–2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 s. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Review Study on Recent Advancements in Islanding Detection and Diagnosis in Microgrids Using Signal Processing and Machine Learning Techniques

The integration of renewable energy sources and microgrids has become a key focus in the pursuit of sustainable and resilient power systems. Microgrids, being decentralized and often operating in isolation from the main grid, face unique challenges, including the need for accurate islanding detection and diagnosis to ensure safe and efficient operations. This review article comprehensively investigates and evaluates the application of signal processing and machine learning techniques in the context of islanding detection and diagnosis within microgrids. The significance of islanding detection and diagnosis is highlighted in this review study which emphasizes grid stability, safety risk mitigation, and energy efficiency enhancement during islanding. Further, the study explores the technical aspects, covering signal processing techniques and machine learning approaches used for islanding detection, including harmonic analysis, wavelet transforms, and neural networks. This research explores the most recent developments in the domain, encompassing a variety of strategies that harness sophisticated algorithms and data analysis to improve the dependability and effectiveness of microgrid operations. Subsequently, this review sheds light on the state-of-the-art methodologies, challenges, and promising avenues in islanding detection and diagnosis, ultimately contributing to the advancement of microgrid technology and the broader vision of sustainable and resilient energy systems. As the transition toward renewable energy sources accelerates, microgrids represent a promising solution for enhancing grid resilience and integrating distributed generation. However, ensuring the safety and efficiency of microgrid operations during islanding events is a critical concern. This study explores the intersection of signal processing and machine learning, offering a comprehensive examination of islanding detection and diagnosis techniques in microgrids.

Energy-efficient scheduling for a hybrid flow shop problem while considering multi-renewable energy

At present, the concept of green and sustainable development has been recognised by everyone. For industrial applications, the concept is both a matter of energy consumption and a matter of production methods. Based on the hybrid flow shop problem, a multi-energy supply system is constructed, and external renewable energy microgrid is introduced to further improve the low-carbon level. From the perspectives of energy supply and internal load, the output of wind power and photovoltaic, energy storage cost and energy consumption cost in the flow shop on a certain day are analysed under the demand–response electricity price. After considering the natural endowments and demand differences of different microgrids, construct a multi-energy complementarity model, determine the coordination transaction strategy among microgrids and obtain the solution of the subproblem and the equilibrium solution of the whole problem. Finally, an example is given to evaluate the effectiveness of the proposed strategy under time-of-use prices. The results show that the proposed strategy can effectively improve the utilisation rate of renewable energy in the flow shop while reducing the overall operating cost.