Electric Grid Research Articles

Multi-Criteria Decision Making for Selecting the Location of a Solar Photovoltaic Park: A Case Study in UAE

The high availability of solar energy in the Gulf Cooperation Council (GCC) makes it the most attractive source of energy in this region, especially due to the global shift toward eco-friendly systems. A significant increase in the implementation of solar PV projects has been noticed in the United Arab Emirates (UAE). For this reason, this study conducted a multi-criteria decision-making process to compare four locations for building a solar PV park in the UAE, namely, Abu Dhabi, Dubai, Sharjah, and Umm Al Quwain. Various criteria were taken into account, including the solar radiation, wind speed, distance from the electricity grid, distance from seaports, and land cost. A hybrid AHP-TOPSIS model was employed to evaluate the criteria weights and alternatives’ scores, which were also compared with the no priority-TOPSIS to check the effects of the criteria weights on the overall ranking. According to the findings, Dubai and Abu Dhabi were ranked first and second, with final scores of 0.7108 and 0.4427, respectively, when using the AHP-TOPSIS model. Furthermore, Umm Al Quwain scored slightly less than that of second place, with a value of 0.4252. The top two ranked alternatives were consistent between the two compared models, as Dubai also performed the best among all locations, which confirmed the reliability of the proposed approach and verified the obtained results and rankings.

Development of a Cost-Driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

Improving the Expansion of Electricity Services Considering Affordability Issues: A Case Study in Brazilian Low-Income Households

The assurance of universal access to electricity refers to not only physical access to electricity, heating, and/or cooling services but also making them affordable to families. This is the case in Brazil, where physical electricity access reaches more than 99% of the urban population, but a high proportion of households are disconnected due to lack of payment. Affordability is a key factor in planning the expansion and maintenance of the electricity grid. In this paper, therefore, we propose the development of a strategy, called the Payment Capability Indicator (PCI), that provides new information about the relationship between energy cost and family income. The classic indicator, the Ten Percent Rule (TPR), was the base, and empirical research was conducted in two low-income neighborhoods in Paraná state, southern Brazil. The results consider variables that add new information to guide local public policies, combining interventions to improve affordability, energy efficiency, alternatives to energy services and consumer behavioral changes.

Study on technical, economic, environmental efficiency of self-consumption rooftop solar power using lithium-ion battery for households in Vietnam

Rooftop solar power (RSP) systems are important to ensure electrical system safety and reduce investment pressure on the national power grid. The Vietnamese Government stopped the FIT price mechanism for solar power and determined that the development solution of RSP in the near future is seft-consumption. In addition, the world is currently developing many RSP projects using lithium batteries to serve self-consumption needs and use the most of excess solar power during the day. However, Vietnam does not have in-depth technical and economic analysis for grid-tied solar power projects using lithium batteries for households, so these projects receive little investment attention, especially in areas with low solar radiation. In this paper, the technical and economic efficiency of a self-consumption RSP system using lithium batteries is specifically analyzed according to different sensitivity ranges to clarify investment possibilities in 3 locations with different climate characteristics in Vietnam. The study results show that the project in Vung Tau city (Southern region) has the highest amount of solar electricity output and the net present value (NPV) is 2669$ while the projects in Hai Phong city (Northern region) and Binh Dinh (Central region) only reach $129 and $1661, respectively. The payback period of the project at the locations in Hai Phong, Binh Dinh, Vung Tau is 12.4 years, 10.7 years, and 9.9 years, respectively. Finally, the actual operating results of a typical RSP system using lithium battery present that the storage system can increase self-consumption and reduce the use of grid electricity for household loads.

Decarbonizing of power plants by ammonia co-firing: design, techno-economic, and life-cycle analyses

ABSTRACT This research investigates the decarbonization of India’s electricity grid using ammonia in power plants. It focuses on ammonia produced in Western Australia and transported to India, co-fired with high rank coal, and compared with power plants utilizing carbon capture and sequestration (CCS). The study assesses the overall costs and the life cycle greenhouse gas (LC GHG) emissions for both new plants and retrofits. For 20% gray, blue, and green ammonia, the levelized cost of electricity is 86, 89, 125 $/MWh, with corresponding LC GHG emissions of 1,234, 1,079, and 1,062 kg CO2e/MWh. Co-firing with green ammonia, though more expensive than blue ammonia, yields lower CO2 emissions. Conversely, reducing the same amount of direct CO2 emission via CCS costs $84/MWh and a LC GHG emission of 1,227 kg CO2e/MWh. While CCS is cheaper, it results in higher LC GHG. There is a trade-off between cost and emissions across the strategies. Under scenarios with low capacity factors or reduced ammonia production costs, coal-ammonia co-firing could become more economical and greener than the CCS. This study provides quantitative insights for policymakers and project developers. However, it is crucial for decision-makers to consider several factors: (1) the potential impact of social resistance to CCS; (2) the time required for large-scale commercialization of CCS technology, which is expected to be significantly longer than the implementation time for a coal-ammonia co-firing decarbonization strategy; (3) the potential of either CCS or ammonia-coal co-firing strategy to enhance India’s electricity mix, thus contributing to energy security.

Manufacture and testing of biomass-derivable thermosets for wind blade recycling.

Wind energy is helping to decarbonize the electrical grid, but wind blades are not recyclable, and current end-of-life management strategies are not sustainable. To address the material recyclability challenges in sustainable energy infrastructure, we introduce scalable biomass-derivable polyester covalent adaptable networks and corresponding fiber-reinforced composites for recyclable wind blade fabrication. Through experimental and computational studies, including vacuum-assisted resin-transfer molding of a 9-meter wind blade prototype, we demonstrate drop-in technological readiness of this material with existing manufacture techniques, superior properties relative to incumbent materials, and practical end-of-life chemical recyclability. Most notable is the counterintuitive creep suppression, outperforming industry state-of-the-art thermosets despite the dynamic cross-link topology. Overall, this report details the many facets of wind blade manufacture, encompassing chemistry, engineering, safety, mechanical analyses, weathering, and chemical recyclability, enabling a realistic path toward biomass-derivable, recyclable wind blades.

Modelling and operation of electrical grids considering techno-economic multi objective functions

Analysing the functionality of electrical systems allows for an evaluation of the characteristics of different power sources over varying periods and enables the coordination of multiple units. This is crucial for assessing the effects of uncertainty on the system considering load and energy generation for the effectiveness of power system monitoring and smart scheduling and cost-effectiveness of electrical grid operation. This research introduces an approach for simulating the operation of an electrical grids considering different time scales based economic and technical indices. First, a series of standard source load scenarios is established. Subsequently, multiple scenarios are produced to show the stochastic nature of renewable energy and demand and utilizing the probability features of uncertain data. The system described in this paper shifts the power balance issue from a yearly scale to weekly and daily electricity balances based on different time scales. The performance of the stochastic simulation solution is significantly enhanced by effectively coordinating in different time periods. The total coal consumption, economic index, abandoned renewable energy, and the rate of abandoned renewable energy are all closely monitored to maintain a high level of accuracy in reflecting the system's performance.

Resilient bus-bar protection scheme for DC microgrids connected to AC electric grids

ABSTRACT In recent years, there has been a growing interest in incorporating microgrids into existing electrical power networks to reduce reliance on conventional grids. This is further due to the numerous benefits of microgrids, most notably the ability to operate in either autonomous or grid-connected mode, making microgrids a highly versatile structure to incorporate intermittent production and energy storage. However, despite the many benefits, it is difficult to present resilience protection for DC bus bar to ensure the efficient operation of a DC microgrid, as there is a non-zero crossing phenomenon of DC fault current, while zero crossing phenomenon of an AC fault. Additionally, the DC bus bar has both an AC side with rectified DC and a DC line current, creating protection concerns for microgrids. To address these issues, this article proposes a novel fast fault detection scheme for DC microgrids with multiple renewable energy sources, energy storage, and loads. The scheme incorporates current and voltage sensing through current and voltage transformers, along with advanced digital relays, to detect and isolate faults on the DC microgrid. By analyzing rectified AC and DC line data, the scheme employs a differential current-based protection strategy to safeguard the DC bus bar. Validated through comprehensive fault simulations in PSS/SINCAL, the method proves effective in maintaining DC bus voltage stability and enhancing microgrid flexibility.

A new hybrid fixed and adaptive gains control algorithm to reduce power losses on LLCL filter-based renewable energy conversion systems with systematic parametrization using Grey Wolf optimizer

Currently, there is a transition in the energy matrix around the world, where traditional sources of energy generation are continually being replaced by energy generation systems based on renewable sources to mitigate the climate crisis. In this bias, this work presents the mathematical modeling of an LLCL filter, used to connect power generation systems based on renewable energy sources to the electrical grid, and presents a novel hybrid fixed-and adaptive gains control strategy for current injection into the grid using this system. The developed hybrid controller is composed of a proportional–integral controller and a direct robust adaptive controller. The first term of the controller guarantees the reference current, while the second term of the controller is used for disturbance rejection. Furthermore, a systematic procedure for the controller’s parametrization based on Grey Wolf Optimizer is also provided. The control of the current injected into the grid is carried out considering the LLCL filter without passive damping resistors in the filter structure to avoid power losses due to the passive filter elements. Additionally, the LLCL filter model considers minimal parasitic resistances to evaluate the controller’s performance and optimize it for the application of interest, aiming to maximize the system performance by ensuring a short transient regime due to the fast closed-loop system response. Simulation results indicate high performance of this optimized control strategy with small tracking error even considering grid impedance variations.

Lost in transaction?: An institutional analysis of households’ transaction costs from demand-side grid management

Better utilization of the electricity grid is urgently called for, and demand-side management (DSM) measures are implemented to influence consumers' electricity consumption to off-peak periods. Little is known about how DSM measures generate private transaction costs for households, being crucial for the effectiveness and fairness of such measures. This article contributes to the energy transition literature by exploring sources of private transaction costs for households from a time-of-use tariff scheme for the grid in Norway. Data from nine group interviews reveal that uncertainty about the transaction (grid capacity), distrust of the transactor (grid provider) and the complexity of the institutional context (uncertainty about the electricity sector) increase transaction costs for households and reduce effectiveness of the tariff. Providing households with information on the reasons and methods for adjusting electricity consumption is essential, including the economic impacts on households of the new tariff. Additionally, households should receive enhanced guidance of technology use and behavior changes. For these two steps to work, trust in energy actors must be strengthened. Lastly, one should pay attention to the potentially undermining effect of such DSM measures on legitimacy of energy policies in general, given the unequal levels of transaction costs depending on households’ capital.

Optimizing solar power generation forecasting in smart grids: a hybrid convolutional neural network -autoencoder long short-term memory approach

Incorporating zero-carbon emission sources of energy into the electric grid is essential to meet the growing energy needs in public and industrial sectors. Smart grids, with their cutting-edge sensing and communication technologies, provide an effective approach to integrating renewable energy resources and managing power systems efficiently. Improving solar energy efficiency remains a challenge within smart grid infrastructures. Nonetheless, recent progress in artificial intelligence (AI) techniques presents promising opportunities to improve energy production control and management. In this study, initially, we employed two different Machine learning (ML) models: Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM), to forecast solar power plant parameters. The analysis revealed that the LSTM model performed better than RNN in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Following a review of the LSTM model’s graphical results, it was further enhanced by combining Autoencoder with LSTM, creating the Autoencoder LSTM (AELSTM) model. Next, a new hybrid model was introduced: Convolutional Neural Network-Autoencoder Long Short-Term Memory (HCAELSTM), designed to boost prediction accuracy. These models were trained on a one-year real-time solar power plant dataset for training and performance assessment. Ultimately, the hybrid HCAELSTM model surpassed the AELSTM model in terms of MAPE, MAE, and MSE. It excelled in MAPE scores for Daily Power Production, Peak Grid Power Production, and Solar Radiance, achieving low scores of 1.175, 2.116, and 1.592 respectively, demonstrating superior accuracy. The study underscores the importance of AI and ML, in particular, the hybrid model HCAELSTM, in enhancing the smart grid’s ability to integrate renewable energy sources. The hybrid model excels at accurately forecasting key measurements, improving solar power generation efficiency within the smart grid system which also plays a key role in the broader shift toward the fourth energy revolution.

Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

Optimized the locations and sizes of FACTS devices on electrical network involving wind power using a new hybrid stochastic algorithm

This article focuses on utilizing a new hybrid optimization algorithm called the Fitness-Distance Balance-based Archimedes Optimization Algorithm (FDB-AOA) to solve the Optimal Power Flow (OPF) problems within a recently adopted electrical transmission grid, specifically the modified IEEE-30 bus system. This system integrates thermal and wind -based generating units, including various types of Flexible AC Transmission System (FACTS) devices. Several tests are performed where the stochastic wind energy is modeled using probability density functions. The optimization goal takes into account the cost of thermal generation, the direct cost of scheduled wind power, and the penalty cost for underestimating wind power. The locations and sizing of FACTS devices are optimized with aim of reducing several fitness functions. The optimization results achieved by the proposed method in solving single objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results show the superiority of the proposed method in the majority of case studies, as it achieved a better optimum solution with a total generation cost ( Cgen ) value of 806.9817 $/h, and a real power loss ( Ploss ) value 1.7619 MW, also yields a competitive gross cost ( Cgross ) value of 1104.6652 $/h compared to those obtained by the other algorithms. In contrast, the statistical analysis proven the superiority of this algorithm where the standard deviations (SD) required in solving the single objective problem ( Cgen ) is 0.0996, which are better compared to other techniques. the simulation results demonstrate that the FDB-AOA optimizer is robust than other approaches, like the Success history based-adaptive differential evolution (SHADE) algorithm, MSA (Moth Swarm Algorithm), and ABC (artificial bee colony) integrated with the SF (superiority of feasible solutions) approach, in solving OPF problems involving the integration of both thermal and wind power plants along with FACTS devices.

Physics and data‐driven approach for online joint state and parameter estimation of electricity and steam networks

Physics and data‐driven approach for online joint state and parameter estimation of electricity and steam networks

Revolution in Renewables: Integration of Green Hydrogen for a Sustainable Future

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies.

Assessing the emissions reduction potential and economic feasibility of small-scale (

Small-scale combined heat and power (CHP) for residential applications has been demonstrated in different regions throughout the world. CHP can enable resilience in energy supply and serve as a transitional technology as the grid progresses to higher fractions of low carbon electricity production. However, there are very limited feasibility assessments in the context of US climate zones, policies, and economic factors. To address this gap, we assess how the economic feasibility and emissions impact of a small-scale, natural gas CHP system compares considering the local electrical grid in seven climate zones and six subgrid regions across the United States. This is accomplished using an in-house Python program alongside EnergyPlus that is used to size the CHP and/or thermal storage systems according to representative electrical and thermal demand profiles for multifamily residential buildings, optimizes CHP and thermal storage energy dispatch throughout the year to meet demands, assesses the system’s economic feasibility, and calculates the carbon emission reduction or increase relative to the local electrical grid. For the baseline cases considered, carbon emissions can be reduced by up to 12% for regions with high required thermal load and/or electricity derived from high carbon emitting sources. In many scenarios, carbon emissions increased. Furthermore, the economic feasibility of the systems, even considering recent incentives, remains a significant barrier. The resulting Python program is open-source and designed for use by both researchers and building owners to conduct their own small-scale CHP feasibility assessments.

Investigation of static and dynamic mechanical loads on light-weight PV modules for offshore floating applications

Photovoltaic (PV) systems are significant for the transition towards renewable energy. However, their expansion is constrained by the scarcity of suitable land in proximity to electric grids. To address this issue, floating PV is a promising solution. Ensuring the robustness of floating PV installations remains a major concern, particularly regarding the mechanical load due to strong winds in open water. This study aims to assess the impact of wind on floating PV modules, by considering real wind speed values occurring at the North Sea. To achieve this objective, the influence of PV module configuration, such as inclination, is examined. Moreover, different thicknesses of PV glass are considered to find an optimum between mechanical stability and material consumption. Lastly, a resonance vibration test is performed and compared to simulations to estimate the effects of vibration on PV module stress. The findings indicate that a low inclination installation is preferable, and a glass-glass PV module with a 2.5 mm glass thickness can withstand static and dynamic mechanical loads, although long-term durability requires further investigation. Additionally, it is crucial for the standard Dynamic Mechanical Load (DML) test to include higher pressure values and an extended vibration test at the resonance frequency with the highest measured acceleration, identified after a frequency sweep.

The role of electric grid research in addressing climate change

The role of electric grid research in addressing climate change

PENGARUH DISIPLIN KERJA DAN KOMPETENSI TERHADAP KINERJA PEGAWAI DENGAN MOTIVASI SEBAGAI VARIABEL MODERATING PADA PT. PLN UNIT PELAKSANA PELAYANAN PELANGGAN (UP3) SITUBONDO

The PT. PLN Situbondo Customer Service Implementation Unit (UP3) is oriented towards fulfilling environmentally friendly energy policies, distributing electricity for the public interest, general tasks of providing services to customers, carrying out maintenance and repairs to the electricity network, ensuring stable electricity supply for customers, providing efficient customer service , including complaint handling, new installations, electricity replacement, and temporary outages. The aim of this research is to analyze the influence of Work Discipline, Competency and Motivation on employee performance at the Customer Service Implementation Unit (UP3) PT PLN Situbondo. The population in this study was employees of PT. PLN Situbondo Customer Service Implementation Unit (UP3). This research was conducted using a quantitative approach and the sampling technique was determined by quota sampling. The data analysis and hypothesis testing in this research used the Structural Equation Model– Partial Least Square (PLS-SEM). The results of hypothesis testing using the Smart PLS 3.0 application show that Work Discipline has a significant effect on Performance, Competence has a significant effect on Performance. The results of hypothesis testing which is moderated by the Motivation variable show that the Work Discipline variable has a negative effect on Performance but is not significant, the Competency variable which is moderated by Motivation shows a positive effect on Performance but is not significant.

Determining onshore or offshore hydrogen storage for large offshore wind parks: The North Sea Wind Power Hub case

The large-scale integration of renewable energy sources leads to daily and seasonal mismatches between supply and demand and the curtailment of wind power. Hydrogen produced from surplus wind power offers an attractive solution to these challenges. In this paper, we consider a large offshore wind park and analyze the need for hydrogen storage at the onshore and offshore sides of a large transportation pipeline that connects the wind park to the mainland. The results show that the pipeline with line pack storage, though important for day-to-day fluctuations, will not offer sufficient storage capacity to bridge seasonal differences. Furthermore, the results show that if the pipeline is sufficiently sized, additional storage is only needed on one side of the pipeline, which would limit the needed investments. Results show that the policy which determines what part of the wind power is fed into the electricity grid and what part is converted into hydrogen has a significant influence on these seasonal storage needs. Therefore, investment decisions for hydrogen systems should be made by considering both the onshore and offshore storage requirements in combination with electricity transport to the mainland.