Solar Microgrid Research Articles

Economic and strategic challenges in microgrid integration: Insights from operational dynamics and renewable energy potential

With the integration of a large number of microgrids in the power distribution network operation, economic and strategic challenges arise. To address these challenges, this research provides a comprehensive investigation into the operational, economic, and strategic dynamics of microgrids. Through careful data analysis, the study interprets the complications of microgrid responses to seasonal changes. It also investigates energy consumption patterns and production dynamics. The detailed analysis of microgrid configurations reveals the unique attributes and challenges of PV, wind, and hydropower microgrids. Moreover, the research explains the financial implications of microgrid integration, from setup costs to potential ROI. It is also examining the cooperative relationship between microgrids and conventional grids. Key findings highlight that solar microgrids contribute 3.2% to 5.3%, wind microgrids provide 5.9% to 7.4%, and hydropower microgrids contribute 24.4% of total power. Energy purchase peaks at 850,000 kWh in August and declines to 580,000 kWh in May. 170,000 kWh of energy is sold back to the grid in May. Moreover, the grid energy costs reduced from $0.178 kWh to $0.30 kWh. The total net present cost of the system is achieved at $37,880,023.33. Renewable energy production ranges from 480 kW to 2,300 kW, with a penetration level reaching 160%.

Optimizing K-means clustering center selection with density-based spatial cluster in radial basis function neural network for load forecasting of smart solar microgrid

Optimizing K-means clustering center selection with density-based spatial cluster in radial basis function neural network for load forecasting of smart solar microgrid

Wellbeing, infrastructures, and energy insecurity in informal settlements

IntroductionUnderstanding the intricate relationship between energy and wellbeing in informal urban settlements is essential for developing effective interventions that address the diverse needs of residents. This paper explores this nexus through a multi-dimensional lens, examining the complexities and dynamics involved in off-grid renewable energy interventions, focusing on solar microgrids in an informal settlement in Cape Town, South Africa.MethodsThe paper is based on empirical research analysed through a theoretical framework articulating the pathways through which energy insecurity influences various dimensions of wellbeing, encompassing economic, physical, social, and environmental aspects. By applying this framework to our empirical research, the paper reveals the intricate interplay between neighbourhood factors, housing conditions, social processes, and economic insecurities, shedding light on both the challenges and opportunities associated with off-grid energy interventions.ResultsThe understanding of wellbeing presented in the paper is based on what we term the energy-wellbeing-informality nexus. Understanding the nexus necessitates: (a) moving past universalist and technocratic understandings of wellbeing, and towards a relational and networked basis for wellbeing analysis; (b) moving beyond conventional narratives of off-grid electrification as mere technical fixes, emphasizing the importance of recognizing informal settlements as sites of innovation and experimentation; (c) understanding the multi-sectoral nature of energy-related wellbeing impacts, extending beyond energy provision to encompass broader dimensions such as education, health, and social cohesion.DiscussionThe paper not only advances theoretical understanding but also offers practical insights for policymakers and practitioners. It emphasizes the need for context-sensitive policymaking that acknowledges the complexities of informal settlements and fosters innovative approaches to energy service provision. By integrating energy interventions into broader development strategies and adopting a multi-sectoral perspective, stakeholders can work towards more equitable and resilient solutions that enhance the overall wellbeing of residents in informal urban contexts.

Modelling and optimizing microgrid systems with the utilization of real-time residential data: a case study for Palapye, Botswana

Microgrids are becoming a realistic choice for residential buildings due to the increasing need for affordable and sustainable energy solutions in developing nations. Through modeling and simulation, the main goal is to evaluate the viability and performance of a solar microgrid system. Residential load modeling is used, which is vital to developing an effective Energy Management System (EMS) for the microgrid. A residential household’s load metering data is examined using statistical methods, including time series and regression analysis. For the residential community load in this research, Proportional-Integral-Derivative (PID) controllers and Fuzzy Logic Controllers (FLC) are used to generate the necessary Direct Current (DC) microgrid voltage. The simulation research shows that FLC have benefits over PID controllers. The FLC technique performs better at reducing total harmonic distortion, which improves the microgrid system’s overall power quality. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was found to be the most appropriate and reliable model for the dataset after the performance of the models was evaluated using the metrics. The optimization results also showed that FLC optimization improves the microgrid system’s stability. The exponential Gaussian process regression (GPR) produced the highest R-squared measure of 0.49 and RSME measure of 7.9646, making it the best goodness fit for modeling the total daily energy usage and the peak daily usage.

Hourly load prediction based feature selection scheme and hybrid CNN‐LSTM method for building's smart solar microgrid

AbstractThe short‐term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the rooftop with the battery management system (BMS) to enhance energy efficiency management. However, the traditional methodologies only processed the weather parameters and power demand information for short‐term load prediction, ignoring the collected data from SSM and BMS by the advanced metering infrastructures (AMI), which probably improved prediction accuracy. In this research, many accumulated data of building and SSM are collected before methodology implementation. Considering the diversities of accumulated parameters from SSM and BMS, an adaptive convolution neural network long short‐term memory (CNN‐LSTM) is proposed for hourly electrical load prediction. The CNN could extract the critical large‐scale input feature, while the LSTM could achieve better accurate forecasts. The Pearson correlation matrix is calculated for the feature selection scheme from different data units. The hyperparameter tuning is utilized for obtaining the optimized hybrid CNN‐LSTM algorithm. The K‐fold cross‐validation is employed for deep learning algorithm verification, which includes LSTM, GRU, CNN, and Bi‐LSTM methodologies. The results prove that the hybrid CNN‐LSTM achieved outperformed improvements, which are 20.57%, 29.63%, 19.06% in MSE, MAE, MAPE, and 21.24%, 22.02%, 3.82% in validating MSE, MAE, MAPE, respectively. The hybrid CNN and LSTM combined with the feature selection scheme achieve superior predicting accuracies, proving the adaptability ability for integrating into the energy management system (EMS) of the building's SSM.

A Coordinated Optimal Operation of a Grid-Connected Wind-Solar Microgrid Incorporating Hybrid Energy Storage Management Systems

The hybrid-energy storage systems (ESSs) are promising eco-friendly power converter devices used in a wide range of applications. However, their insufficient lifespan is one of the key issues by hindering their large-scale commercial application. In order to extend the lifespan of the hybrid-ESSs, the cost functions proposed in this paper include the degradation of the hydrogen devices and the battery. Indeed, this paper aims to develop a sophisticated model predictive control strategy for a grid-connected wind and solar microgrid, which includes a hydrogen-ESS, a battery-ESS, and the interaction with external consumers, e.g., battery/fuel cell electric vehicles. The integrated system requires the management of its energy production in different forms, i.e., the electric and the hydrogen ones. The proposed strategy consists of the economical and operating costs of the hybrid-ESSs, the degradation issues, and the physical and dynamic constraints of the system. The mixed-logic dynamic framework is required to model the operating modes of the hybrid-ESSs and the switches between them. The effectiveness of the controller is analyzed by numerical simulations which are conducted using solar and wind generation profiles of solar panels and wind farms located in Abu Dhabi, United Arab Emirates. Such simulations, indeed, show that the proposed strategy appropriately manages the plant by fulfilling constraints and energy requests while reducing device costs and increasing battery life.

Analysis of Battery Testing Protocols in the Transition from the Lab to the Field: A Test Case Using Advanced Zn-MnO2 Batteries in Off-Grid Solar Microgrids

Understanding cycling performance of batteries under varying conditions is crucial for continued development of emerging chemistries. Currently much of this work is focused on cycling single cells in well-controlled lab experiments. There are few module cycling studies in the open literature. Studies of full-sized fielded systems are even rarer. As a result of this data gap, there is limited understanding of how single cell cycling can be used to predict performance of fielded systems prior to their deployment.Sandia National Laboratories (SNL) recently deployed an advanced Zn-MnO2 secondary battery energy storage system in an off-grid solar plus storage system. Using this deployment as a test case, we will detail how performance and degradation for batteries can vary between the lab and the field. We will also discuss ways that single cell and module lab cycling can be improved to better predict operation in fielded systems.In preparation for this deployment, SNL conducted single cell cycling experiments in the lab to simulate operation in the field and determine the expected life of the systems. The first test program utilized elements of the IEC 61427-1 standard for photovoltaic off-grid application to simulate the temperature and predicted state of charge (SOC) range the cells will be cycled during field operation. The simulated field cycling targeted SOC ranges of 10 to 40% and 80-100% over 150 cycles with temperature varied between -4 and 35oC to match the ambient conditions in the field during each season. The second type of cycling test was a temperature dependence study to determine how temperature impacted cell degradation. Cells were cycled at C/10 at their full SOC range and at a single temperature until they reached 40% capacity retention. Both cycling studies predicted that low temperature operation would increase the rate of degradation.The system was deployed in May of 2022, and since that time the SNL team has collected a significant amount of performance data to help analyze system level behaviors as compared to observations from cell level testing. These include battery interactions with the solar charge controller, limitations on power inverter settings, and variance between the average system level state of charge and the test protocol. Additionally, the team is analyzing conditions found in the field, with regards to ambient temperature, solar insolation and battery charging rates, and customer usage patterns for comparison with cell-level testing protocol.Observations of the deployed system’s performance indicate there is a need for more nuanced testing protocols for lab testing that can better approximate field conditions. These may include dynamic temperature ranges that vary throughout a charge/discharge cycle, and solar charging assumptions that account for reduced insolation due to cloud cover. This can help further inform the development of charge control algorithms, system hardware and software that can help improve the overall performance of solar microgrid energy storage systems.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2023-02502A

An Assessment of Solar Micro-Grid System in the Islands of Bangladesh for Sustainable Energy Access

The isolated islands in the northern Bay of Bengal face difficulties accessing electricity from the central grid and use fossil fuel-based generators, which causes health risks, environmental damage, and high expenses. So, this study aims to replace these fossil fuel-based power sources with Solar Photovoltaic (SPV) microgrids to provide continuous power to remote islands and contribute to reducing emissions. A specific location on the island of Manpura is selected for the SPV plant installation, considering land availability and vulnerability to erosion. Solar power density and other technical parameters such as Direct Normal Irradiation, Global Horizontal Irradiation, Diffuse Horizontal Irradiation, Global Tilted Irradiation, the optimum tilt angle of Photovoltaic (PV) modules, air temperature, and terrain elevation are analyzed using some prominent online analytical tools named “Global Solar Atlas (GSA)”, “Photovoltaic Geographical Information System (PVGIS)” etc. Based on these datasets, a 10kW ground-mounted PV system using monocrystalline silicon solar panels is designed for off-grid operation. The developed system can generate 14.808 MWh of energy per year. This design’s environmental and social impacts are critically analyzed considering the Sustainable Development Goals (SDGs). Compared with conventional energy sources, the results show that the 10kW microgrid SPV system can reduce CO2 emissions by 284 tons. Financial analysis shows the recovery period of the investment based on electricity production at around 8.8 years, making solar PV microgrids a viable option for remote areas like Manpura Island.

Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems

The aim of this paper is the design and implementation of an advanced model predictive control (MPC) strategy for the management of a wind–solar microgrid (MG) both in the islanded and grid-connected modes. The MG includes energy storage systems (ESSs) and interacts with external hydrogen and electricity consumers as an extra feature. The system participates in two different electricity markets, i.e., the daily and real-time markets, characterized by different time-scales. Thus, a high-layer control (HLC) and a low-layer control (LLC) are developed for the daily market and the real-time market, respectively. The sporadic characteristics of renewable energy sources and the variations in load demand are also briefly discussed by proposing a controller based on the stochastic MPC approach. Numerical simulations with real wind and solar generation profiles and spot prices show that the proposed controller optimally manages the ESSs, even when there is a deviation between the predicted scenario determined at the HLC and the real-time one managed by the LLC. Finally, the strategy is tested on a lab-scale MG set up at Khalifa University, Abu Dhabi, UAE.

Experimental investigation of a novel smart energy management system for performance enhancement of conventional solar photovoltaic microgrids

Solar photovoltaic microgrids are reliable and efficient systems without the need for energy storage. However, during power outages, the generated solar power cannot be used by consumers, which is one of the major limitations of conventional solar microgrids. This results in power disruption, developing hotspots in PV modules, and significant loss of generated power, thus affecting the efficiency of the system. These issues can be resolved by implementing a smart energy management system for such microgrids. In this study, a smart energy management system is proposed for conventional microgrids, which consists of two stages. First power production forecasting is done using an artificial neural network technique and then using a smart load demand management controller system which uses Grey Wolf optimiser to optimize the load consumption. To demonstrate the proposed system, an experimental microgrid setup is established to simulate and evaluate its performance under real outdoor conditions. The results show a promising system performance by reducing the conventional solar microgrids losses by 100% during clear sunny conditions and 42.6% under cloudy conditions. The study results are of relevance to further develop a smart energy management system for conventional microgrid Industry and to achieve the targets of sustainable development goals.

Optimal sizing of battery energy storage in solar microgrid considering peak load shaving

The power system faces a lot of challenges due to incorporation of a large number of renewable energy sources. Here, Battery Energy Storage System (BESS) can be used effectively to deal with the grid’s rising peak demands without the necessity of upgrading the grid capacity. An energy management strategy is considered based on peak load shaving combined with transformer power limit and BESS State of Charge (SOC). Cooperation Search Algorithm (CSA) is utilised to determine the number of batteries in BESS and Photovoltaic (PV) panels with the goal of maximising economic benefits. The optimisation model of solar microgrid energy storage is established and solved for three scenarios of the grid only (Base Case), BESS without PV (Case-A) and BESS with PV (Case-B). From the result, it is verified that in Case-B, the customer will pay 31% and 34% less compared to the Base Case and Case-A, respectively, towards energy costs in a day.

Demand side management in a microgrid to reduce the peak consumption cost

Microgrid stands for the participation of renewable sources such as solar and wind in the existing energy system in order to increase the availability and reliability of energy for consumers and to make the atmosphere carbon-free by reducing dependence on the main grid. However, the non-linearities inherent in the nature of the microgrid and network components make the programming process complex. Therefore, the efficient but linear model for microgrid resource planning algorithms is gaining interest today due to its simplicity and computational speed. On the contrary, for demand-side management, reducing the peak demand price by using different methods such as load trimming and valley filling by varying the use of flexible loads on the consumer side, which is an easier option for microgrid operators, rush hour price for consumer satisfaction. This paper gives an idea of the above purpose by designing an average model of a solar microgrid with the implementation of the maximum power point tracking (MPPT) algorithm method for stability of the system frequency and a battery storage system with a controller. You then plan the microgrid using the demand side management (DSM) method in order to reduce the cause of the price spikes.

Towards Energy Sustainability in University Campuses: A Case Study of Beirut Arab University

Lebanon has been suffering from severe challenges in its electric sector for decades owing to chronic supply shortages and faults in its aging power grid infrastructure. The deplorable situation of the Lebanese electric sector has been made worse by the economic meltdown that started in 2019, which eventually led to total power blackouts across the country. In this paper, we present a case study on the design and implementation of a solar microgrid system for Beirut Arab University, Lebanon. As a first step, simulation software for a microgrid and a distributed generation power system is used to compare different design scenarios. Considering the available installation area and the fact that the greatest demand occurs during the daytime, when both the educational and managerial facilities are running, it is found that a 500-kW photovoltaic system tied to the university’s already present diesel generators is the optimal solution in terms of return on investment. The second step details the actual implementation of the system in the Beirut campus and the evaluation of the system’s performance in terms of diesel cost savings and emissions reduction. We expect that the results of this case study will encourage other institutions and communities to adopt sustainable and renewable energy sources.

Review and Demonstration of the Potential of Bitcoin Mining as a Productive Use of Energy (PUE) to Aid Equitable Investment in Solar Micro- and Mini-Grids Worldwide

Despite the climate commitments made by countries in the Paris Climate Agreement adopted in 2015 and reinforced during COP 21 and with notably less success during COP 22, world carbon emissions increased in both 2021 and 2022. It is increasingly unlikely that the world will achieve the targeted 50% carbon reduction by 2030, the reduction approximately needed for reducing global temperature rise since the beginning of the Industrial Revolution to less than 1.5 deg. C. At the same time, there remain nearly 2 billion people in the world who have no or highly unreliable access to power. In developed countries, access to both clean energy and energy efficiency investment in residences within low to moderate income communities has also lagged. This paper provides a review of the “Productive Use of Energy (PUE)”, which is a means to add value to solar energy mini- and micro-grids to ensure investment worthiness and add more value to the communities being served. In this context, it posits an opportunity to leverage Bitcoin mining as a common PUE strategy applicable to new solar installations. Several actual pilot cases are described to demonstrate this potential throughout the world and at multiple scales. These include: (i) existing micro-grids with significant stranded energy to generate income that could be used to reduce the cost per kWh for the community; (ii) new solar micro-grids optimized to meet community load and mining operations; (iii) dedicated solar-powered Bitcoin mining mini-grids developed solely to create a funding stream for self-investment by communities for their benefit; and (iv) a low-income residential solar-powered Bitcoin miner to reduce the energy cost burden for residents. Several of these scenarios show significant potential to aid investment worthiness.

Energy efficient behavior modeling for demand side recommender system in solar microgrid applications using multi-agent reinforcement learning model

Electricity consumers are often faced with challenges relating to the choice of an optimal energy saving plan. Increasing integration of transient renewable energy sources promises tantalizing solutions but also poses emerging stability challenges for the electricity grid. Demand side management using battery energy storage systems (BESSs) is crucial towards extending the physical limits of existing electricity grid. However, problems related to consumer behavior towards adoption of energy/battery efficiency measures and consumer comfort feedback exist. In this study, we present BESS technologies that are embedded into the grid and further enhanced with the use of reinforcement learning control and recommendation system technologies for improving the grid reliability, attaining self-consumption and demand response goals. The novelty of the proposed work is highlighted by using a separate class of active controller for BESS technologies, thereby separating it from loads which determine user comfort. Similarly, an adaptive demand side recommender scheme was used to provide recommendations targeting various microgrid entities. The result of the study shows that operating BESS using the multi-agent reinforcement learning control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. The linear reduction in peak load was further enhanced by the BESS efficiency-related recommendations when compared to the baseline scenario.

An optimised solar-based microgrid integrated with desalination to enhance exergy sustainability

The size of a solar microgrid system should be designed based on aspects of sustainability, such as cost, exergy, etc. This study by the division algorithm finds the optimal size with maximum reliability vs. the cumulative exergy demand. Larak Island, Iran, is used as an example of how the concept could be applied to the real world. According to the introduced optimal size, 1 m3 of freshwater generated by the solar energy system leads to an average cumulative exergy demand of 17 MJ. While integrating a diesel generator into system means, exergy demand reaches 33 MJ/m3 of freshwater.

Photovoltaic power prediction for solar micro-grid optimal control

In a solar micro-grid, a hybrid renewable energy system generates electricity for a building’s onsite use. The battery storage and the main power grid connection are used to facilitate the matching between the demand and production. To control energy flows optimally, an accurate day-ahead prediction of the photovoltaic (PV) panels output is required. However, this is a challenging task due to the fluctuating nature of solar radiation availability. The accuracy of the prediction is influenced by the modelling method and input parameters. In this study, the measured power and weather data is gathered from an experimental installation of PV panels to predict PV output for a 24-hours horizon in 15 min intervals. The multiple linear regression (MLR) and artificial neural network (ANN) methods are considered in the prediction modelling and compared using performance indicators. The micro-inverter technology is used to gather the individual PV panel output in addition to the overall system output. The results show that the modelling methods have different accuracy performances and the ANN model built with the individual PV output data results in the highest accuracy. Utilizing the micro-inverter technology leads to an advantage of having more accurate PV prediction for the control purpose.

Quantification of Hydrogen Evolution on a Zinc Rotating Disk Electrode in Traditional Alkaline Electrolytes and Acetate-Based Water-in-Salt “Wise” Electrolytes

Rechargeable batteries as energy storage devices for the electric grid are advantageous to bridge the intermittency in the daily energy demand and production by renewable energy sources such as solar microgrids. Another important consideration to satiate this energy landscape is the need for inexpensive, safe, and non-toxic rechargeable battery technology and chemistry. Zinc aqueous (alkaline) batteries are one of the prime candidates that satisfies the aforementioned criterion and has thus warranted a lot of scientific and technological research for commercial applicability. There has been considerable work conducted by the researchers in this field to bolster the cycle life and active material utilization of zinc in alkaline systems. However, zinc is thermodynamically susceptible to hydrogen gas evolution in alkaline solutions as witnessed by its Pourbaix diagram. On a device level, this affects the columbic efficiency due to the deleterious side reaction, which in turn negatively impacts the energy density of the battery. Water-in-salt electrolytes (WiSE) are a recently developed class of electrolytes that directly address this shortcoming of dilute electrolytes due to the modified solvation structure of water which lowers the activity of free water molecules in solution.In this work, we show a type of non-toxic and cheap acetate-based WiSE that has lower hydrogen gassing rates when compared to traditional alkaline potassium hydroxide-based electrolytes. Specifically, we quantify hydrogen gas evolution at the zinc electrode-electrolyte interface using the rotating disk electrode technique at different overpotentials to show the remarked improvement in gassing observed. At overpotentials of 100, 300 and 500 mV, 27 m (molal) potassium acetate is observed to have gassing rates of up to 16 times less than that of 25 weight percent potassium hydroxide. Using the Koutecky-Levich equation, mass-transfer and kinetic parameters for the hydrogen evolution reaction were also determined. The results establish the applicability of this class of WiSE in reducing gassing on zinc and thus, improving the overall efficiency of zinc-based aqueous batteries.

Power Quality Disturbance Detection and Monitoring of Solar Integrated Micro-Grid

Due to the popularity of microgrids and power quality disturbances (PQD) induced by renewable energies, monitoring in microgrids has risen in popularity in recent years. For monitoring the PQD, many strategies based on artificial intelligence have been proposed. However, when the electrical parameters change, the need to retrain the Artificial neural network (ANN) becomes a significant issue. This paper presents a new approach to the power quality disturbance detection and monitoring of integrated solar microgrids. The power quality event detection is accomplished by analyzing the frequency signal with Wavelet transformation (WT). The classification of power quality disturbance is achieved based on the features. For the classification of PQDs, the retrieved features are fed into a Convolutional neural network (CNN) classifier.

Three-Phase Load Prediction-Based Hybrid Convolution Neural Network Combined Bidirectional Long Short-Term Memory in Solar Power Plant

The economic renewable energy generations have been rapidly developed because of the sharp reduction in the costs of solar panels. It is imperative to forecast the three-phase load power for more effective energy planning and optimization in a smart solar microgrid installed on a building in the Linyuan District, Taiwan. To alleviate this problem, this article proposes a convolution neural network bidirectional long short-term memory (CNN-Bi-LSTM) to accurately predict the short-term three-phase load power in building the energy management system in the smart solar microgrid with the collected data from advanced metering infrastructure (AMI), which have not been investigated before. The three-phase load-predicting methodology is developed using weather parameters and different collected data from AMI. The project evaluates the performance of the CNN-Bi-LSTM model by utilizing hyper-parameter optimization to attain the optimum parameters. The prediction models are trained based on hourly historical input features, selected based on the Pearson correlation coefficient. The performances’ optimal structure CNN-Bi-LSTM are validated and compared with the bidirectional LSTM (Bi-LSTM), LSTM, the Gated Recurrent Unit (GRU), and the recurrent neural network (RNN) models. The obtained optimized structure of CNN-Bi-LSTM demonstrates the effectiveness of the proposed models in the short-term prediction of three-phase load power in a smart solar microgrid for building with a maximum enhancement of 68.36% and 8.81% average MSE, and 30.26% and 36.36% average MAE during the testing and validating operations.