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Abstract: Despite their non-renewability, high cost, and detrimental environmental impact, fossil fuels such as oil, gas, and coal are widely employed for energy-related purposes on a global scale. In contrast, despite being depletable, environmentally sustainable, and secure, renewable energy resources such as solar, wind, and hydro are still in their infancy on the energy market. With the potential for both small-scale and large-scale utilization, solar energy is the cleanest renewable energy resource available. For the sustainable utilization and extraction of solar energy, precise forecasting of solar irradiance is vital. As direct normal irradiance has a significant impact on solar power generation, it can be used to predict solar energy. However, the accurate prediction of direct normal irradiance is complicated by a number of factors, which makes it a difficult task. This investigation seeks to develop a novel hybrid model that combines the artificial bee colony optimizer and adaptive boosting algorithm in order to accurately predict direct normal irradiance. For the proposed model, input features were selected from ten features collected in Qinghai between July 1, 2022, and June 30, 2023. Comparing the alternative model in this investigation to the proposed model, which achieved the highest coefficient of determination value of 0.9790 during the testing phase, demonstrated its capability and efficacy. Thus, estimating direct normal irradiance for solar energy production can be accomplished with confidence using the proposed model. PubDate: 2025-04-18
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Abstract: The increasing deployment of inverter-based devices (IBRs) has significantly impacted the power system inertia level and frequency recovery under frequency disturbances. Real-time inertia estimation is crucial for power system operators to ensure the secure operation of the power grid. This paper presents a real-time inertia estimation method for power systems using a probing signal and phasor measurement unit (PMU) data. The probing signal is designed while considering the requirements of the power grid secure operation. Based on the probing signal response, a measurement-driven model can be identified to represent either the inertia dynamics of an individual generator or a regional electric power grid using the active power deviation and frequency deviation information from PMUs. The measurement-driven model can be further used to calculate the corresponding inertia of generators and the regional inertia. The performance of the inertia estimator is demonstrated through several test cases using the 23-bus 3-area system and the 240-bus WECC system. PubDate: 2025-04-14
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Abstract: The thermoelectric effect generates an electric voltage when there's a temperature difference between two junctions, making it promising for energy conversion applications. However, its low conversion efficiency has been a significant challenge for its technological development and practical use. This study aims to enhance the performance and efficiency of thermoelectric modules by maximizing the temperature difference across the module using a direct evaporative cooler as a heat sink. We conducted both experimental and numerical analyses to examine the effects of key parameters of the evaporative cooler on the thermoelectric module's performance. Specifically, we examined the influence of air velocity, water flow rate, and the staging of the cooling pad on the system's efficiency and power output. Our results identified the optimal conditions for these parameters: an air velocity of 2.5 m/s, a water flow rate of 3.2 g/s, and a three-stage cooling pad configuration. Under these optimized conditions, the thermoelectric module achieved a significant increase in performance, with a maximum power output of 37.35 W and a conversion efficiency of 6.1%. This was attained at a temperature difference of approximately 150 °C between the hot and cold surfaces of the module. PubDate: 2025-04-11
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Abstract: Robust decision-making analysis (RDM) can be applied to many planning problems that are characterized by deep uncertainties, which are risks that cannot be quantified in a consensual way. In this paper we extend the classic RDM methodology by integrating it with power system optimization models and modeling dynamic adaptation to changing conditions. We then develop an adaptive strategy for power system expansion in Bangladesh over the next decade and consider how uncertain climatic, technical, and policy factors might affect power generation capacity decisions. 200 future states are simulated using Latin Hypercube method to reflect various uncertainties and are then integrated into an optimization model of the Bangladesh power system. Instead of defining alternative strategies ahead of time as in the classic RDM approach, we use the optimization results to identify strategies as mixes of capacity of different technologies and fuel types in 2030. We show how RDM can be used to inform planners and policy makers about which strategies appear most robust and their vulnerability to future conditions. This understanding is then used to develop an ‘adaptive strategy’ that combines the most robust strategy and features tailored to the future possible vulnerabilities, achieving an acceptable performance in over 85% of the future states. PubDate: 2025-04-10
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Abstract: This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources based on high resolution sychro-waveform measurement technology. Leveraging recent progress in generative Artificial Intelligence (AI), machine learning, and networking technology, we develop a physics-based AI foundation model to enhance grid resilience and reduce economic losses from outages. The proposed framework adopts the AI Foundation Model paradigm, where a Generative and Pre-Trained (GPT) foundation model extracts physical features from power system measurements, enabling adaptation to a wide range of grid operation tasks. Replacing the large language models used in popular AI foundation models, this approach is based on the Wiener–Kallianpur–Rosenblatt innovation model for time series, trained to capture the physical laws of power flows and sinusoidal characteristics of grid measurements. The pre-trained foundation model causally extracts sufficient statistics from grid measurement time series for various downstream applications, including anomaly detection, over-current protection, probabilistic forecasting, and data compression for streaming synchro-waveform data. Numerical simulations using field-collected data demonstrate significantly improved fault detection accuracy and detection speed. The future grid will be rich in inverter-based resources, making it highly dynamic, stochastic, and low inertia. PubDate: 2025-03-26
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Abstract: The inherent nonlinearity, intermittency, and chaotic nature of wind speed make accurate forecasting challenging. Traditional approaches like standalone time series models and frequency domain analysis struggle to capture these complex characteristics effectively. In light of this, the present study utilizes three self-adaptive signal processing methods, namely empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD) and combines with ARIMA or window-sliding ARIMA (WSARIMA) to develop six hybrid models, namely EMD–ARIMA, EEMD–ARIMA, VMD–ARIMA, EMD–WSARIMA, EEMD–WSARIMA, and VMD–WSARIMA. To illustrate the efficacy of the proposed hybrid models in daily wind speed prediction, four study sites from India with different climates are considered. Based on the analysis of 7 years (08-2015–03-2023) of wind speed data, it is found that: (i) the extracted components of VMD overcome the limitations of EMD and EEMD methods; (ii) the combination of VMD and WSARIMA outperforms any other comparative model, such as ARIMA, WSARIMA, EMD–ARIMA, EEMD–ARIMA, VMD–ARIMA, EMD–WSARIMA, or EEMD–WSARIMA; the VMD–WSARIMA model reduces RMSE by 70–80% compared to the conventional ARIMA model; (iii) finally, as a part of post-processing, the residual analysis of the best fit VMD–WSARIMA model shows desirable characteristics. Therefore, the present study strongly recommends to consider adaptive decomposition based hybrid models in wind speed forecasting at shorter time horizon. PubDate: 2025-03-19
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Abstract: Global warming is causing an increasing number of severe weather events, and the current electrical infrastructures are unable to handle these rare but highly impactful disasters. To address highly unpredictable events with extensive state spaces during an extreme occurrence, standard model-based methods create optimization models to represent the effects on the power system. Nevertheless, the advanced models exhibit a significant level of computational complexity and lack the capacity for learning. This paper introduces a novel methodology for enhancing the resiliency of distribution systems by employing ensembled deep reinforcement learning. The proposed methodology reconfigures the system during outages to supply critical loads while maximizing the utilization of distributed energy resources (DER). This would enable the system to efficiently supply the maximum critical load by creating microgrids inside the system. Combine Resilient Microgrid Ensembled Deep Reinforcement Learning (RMG-EDRL) approaches with DERs to reduce penalties and DER running costs. The results show that our EDRL framework outperforms previous methods and may improve distribution system resiliency in unpredictable and dynamic operating situations. Ultimately, the suggested model was effectively tested on both a 33-bus and 123-bus distribution networks. The effectiveness of the proposed strategy was confirmed by the numerical findings. PubDate: 2025-03-19
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Abstract: Conventional power generation techniques such as fossil fuel based power are being discouraged owing to their exhaustive nature and cause of environmental pollution. Excessive and uncontrolled usage of fossil fuel, urbanization, growing trend of living in cities with compromised eco friendliness have brought in severe threat to human life. Progressive increase of atmospheric temperature, erosion, air pollution, river pollution, and more have significantly affected human health. Rise in human health issues, decrease in human life span, along with diverse psychological disorders have impacted our life significantly. Understanding the emergency of the situation United Nations has set sustainable energy goals which are being implemented by all the nations across the globe. Translation of all the energy sector into renewable energy sectors and mitigation of carbon footprints are the most important steps of current time and recent future. This short review, summarizes global scenario on renewables energy implantation-adoption, and initiatives for achieving net zero. Adoption of renewable energy solutions at different parts of the world, associated challenges, support from the respective government, statistical information of renewable power generation across the globe, and associated pros and cons are described in this article. Special emphasis is given to Indian energy sectors, net zero situation, challenges and opportunities. Renewable energy implementation across different states of India with associated scopes and challenges are discussed for understanding demographic variation inside the same country. Timelines for complete transition into renewable energies for different countries, different states of India are separately given and described. In this short review, we canvas renewable energy production shares of different countries across the globe, sustainable development goals, net zero target and renewable energy sectors in India, and future prospect in renewable energy sectors. This review will assist wider audience that covers scientists, technologists, environmentalists, entrepreneurs, policy makers, investors, and other stake holders. PubDate: 2025-03-19
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Abstract: Electricity prices in real-world markets can vary widely; prices in the New York Independent System Operator’s (NYISO) market, for example, can vary by two orders of magnitude: from a mean of $$\$ $$30/MWh up to $$\$ $$4000/MWh. However, simulated price data from electricity system production cost models (PCMs) result in much narrower distributions. This research has developed a new framework to better calibrate simulated price data from PCMs to real-world price data. This framework is based on Bayesian inference and utilizes two different types of Bayesian models to tackle two problems: Bayesian Ridge Regression to capture extreme price spikes and two Dual-head Bayesian Neural Networks (DBNN) to model prices within the normal range. The calibration framework is validated on real-world data from two regional wholesale electricity markets (California Independent System Operator (CAISO) and NYISO). It is shown that the calibrated PCM data much more closely follows the real-world data distributions, achieving closer skewness and kurtosis values and achieving overall improvement in similarity by as much as 73.58%. PubDate: 2025-02-28
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Abstract: Understanding how regulatory interventions shape electricity markets is essential. We posit that regulation is a behavioural adaptive process, influenced by internal and external forces. We develop a system dynamics model to explore the interdependencies between regulators' reactivity, their willingness to initiate major regulatory overhauls, and market reactivity. This creates an improved understanding of the behavioural aspects of regulation, thereby putting regulators in a better position to avoid misfits often observed in the regulation of infrastructure.The model highlights the dilemma regulators face when deciding when and how to intervene. While frequent regulatory changes are detrimental, creating a constant level of uncertainty, opting for fewer, larger, changes increases the volatility of the uncertainty perceived by the market participants. The former seems preferable, with a constant level of moderate uncertainty becoming a new normal: market participants can learn to work in this environment, without having to worry about large unpredictable changes.This stylised model thus provides a better understanding of the problems facing regulators, both in the electricity sector and in other regulated industries characterised by significant lags between a changing reality and the regulatory consequences. PubDate: 2025-02-21
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Abstract: This article proposes an efficient energy management system for optimally scheduling distributed energy resources based on photovoltaic static synchronous compensators (PV-STATCOMs) in medium-voltage distribution networks. The complete nonlinear programming (NLP) model is reformulated as a semi-definite programming (SDP) relaxation in the complex domain. The key feature of our approximation lies in utilizing a set of Hermitian semi-definite matrices $$\mathbb {X}_{n,n,h}$$, which allows transforming the NLP model from $$\mathbb {C}^{n}$$ into a linear programming one in the space of complex square matrices defined in $$\mathbb {C}^{n\times n}$$. The IEEE 33-bus grid was selected as a test feeder in order to evaluate the performance of the proposed relaxation through numerical experiments. Two objective functions are considered: (i) the minimization of the energy purchasing costs at the substation terminals and (ii) the minimization of the expected daily energy losses. The results demonstrate the effectiveness and robustness of our proposed SDP relaxation approach. PubDate: 2025-02-18
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Abstract: Solar Organic Rankine Cycles (ORC) based power production plants utilize solar irradiation for thermal power generation. Given the significant compatibility between the operating temperatures of solar irradiation-based technologies and the temperature needs of the cycle, they can be a promising renewable technology. Moreover, their higher performance compared to steam Rankine cycles in small-size applications makes them interesting within the smart grid context and microgrid communities. In this study, we inspect the impact that this technology can have on the peer-to-peer trading application in renewable-based community microgrids. Here the consumer becomes a prosumer (functioning both as energy producer and consumer), and engages actively in virtual trading with other prosumers at the distribution system level. Specifically, we concentrate on a microgrid where the solar ORC is combined with a storage system, to fulfill the final consumer’s demand. In fact, the combination of these plants with storage systems is fundamental to increasing their predictability and competitiveness with conventional plants, but it is quite challenging from a management perspective. Thus, a methodology based on operations research techniques has been developed to use this system at its optimal point. Moreover, we investigate how different technological parameters of the solar ORC may affect the final solution. Finally, the value of the solar ORC in the transactive energy trading context is studied under different configurations and scenarios. The results highlight an overall gain in the implementation of a predictable and manageable system as the one presented in this paper for a P2P transactive energy trading context, on average 16% in terms of operational costs. PubDate: 2025-02-14
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Abstract: Japan’s decarbonization transition towards carbon neutrality by 2050 will be more dependent on the long-term development of renewables. However, the renewable power generation technologies themselves are highly material- and energy-intensive. We estimated such materials and energy demands in response to power capacity changes. Our main results show that: (1) achieving a 100% reduction of GHG emissions requires enormous and urgent investment during 2020–2030; (2) the largest gap of material demands would show in 2020–2030, especially for cement-related products, petrochemical products, cables, wood products, and steel products, but with different degrees of dispersion; (3) the largest gap of industrial energy demands would show later in 2030–2040 as a result of early investment (inter-period iterations). Increasing material efficiency and benefiting more and earlier from the increasingly low-carbon energy supply would be the key to Japan’s industrial decarbonization. PubDate: 2025-02-12
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Abstract: This paper describes the Grid Optimization (GO) Competition Challenge 3, focusing on the problem motivation, formulation, solvers submitted by competition entrants, and analysis of the solutions produced. Funded by DOE/ARPA-E and led by a collaboration of national labs and academia members, the GO Competition addresses challenging problems in power systems planning and operations to drive research in advanced solution methods essential for a rapidly evolving electric power sector. Challenge 3 targets a multi-period unit commitment problem, incorporating AC power modeling and topology switching to reflect the dynamic grid management techniques required for future power systems. The competition results offer significant benefits to both researchers and industry practitioners. For researchers, it fosters innovation, encouraging the development of new algorithms to address the complexities of modern power systems. For industry practitioners, the competition drives the creation of more efficient and reliable computational tools, directly improving grid management practices. This collaboration bridges the gap between theory and practical implementation, advancing the field in meaningful ways. This paper documents the problem formulation, solver approaches, and the effectiveness of the solutions developed. PubDate: 2025-01-31
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Abstract: Given the strong decentralization of renewables and the raising interest in energy networks (e.g. heat, gas, hydrogen, etc.), spatial detail has become an important feature that energy system models have to take into account. However, a trade-off is needed between systems representation quality and models computational burden, also considering the temporal and technological resolution. In this context, clustering is a useful tool that can help reducing the complexity of energy system models maintaining at the same time a high quality of systems representation. The objective of this paper is to delve into the decision-making process behind the selection of parameters for clustering algorithms applied to energy system model data. This involves analysing the requirements of the algorithms and linking and adapting the clustering parameters to the specific physical quantities of these models. Furthermore, it aims to assist modelers in reading and interpreting the results obtained. As a case study, the Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) algorithm is applied to six Italian Municipalities with the aim of aggregating the possible input data of an Energy System Optimization Model (ESOM). A parametric analysis is then performed to assess the behaviour of clustering under different conditions and to assess technical-economical characteristics of the resulting aggregations. Results show that relationships between input data and DBSCAN behaviour (such as the relationship between the average distance between elements and the value of the parameter $$\epsilon$$ that returns the maximum number of clusters) might subsist for spatial domains where the elements to aggregate are homogeneous. Since the optimal choice of the clustering parameters depends on the modelling requirements, the paper finally proposes four different cases a modeller could focus on, specifying the most suitable parameters for each case. PubDate: 2025-01-19
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Abstract: Energy storage systems emerge as key elements in optimizing sustainable resource use, ensuring a steady flow of energy even when primary sources, such as sun or wind, are intermittent. In this scenario, the techno-economic integration of storage technologies becomes a key building block in the transition to a more resilient and efficient energy system. In this paper, we considered the possibility of using the electricity storage as a business, buying electric energy from the Italian electric day-ahead market when prices are low, and selling it back when prices are high. The theoretical modeling and the resulting simulations show that the use of storage in the day-ahead electricity market can actually be a profitable business, by quantifying the possible revenue on each of the last five years, showing that it is highly variable over time. In particular, the recent gas prices crisis changed the framework, showing very interesting revenue opportunities. About forecasting methods, we show that simple forecasting methods (e.g., daily averages) perform adequately, while optimization-based approaches outperform heuristic methods. PubDate: 2025-01-15
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Abstract: The rising global demand for power, allied with the compelling necessity to shift to sustainable energy sources, has heightened attention on renewable energy technologies, notably solar energy. Photovoltaic (PV) systems encounter efficiency challenges from the inherent nonlinearity associated with fluctuating atmospheric conditions. Solar charge controllers (SCC) are vital components in PV systems designed to improve the operational efficiency of solar panels by controlling voltage and current fluctuations. A comprehensive analysis of 100 publications extracted from the Scopus database was performed to assess the evolution and influence of SCC modules in PV applications. The analysis included growth trends, pros and cons, top keywords and topics, document types, authorship evaluations, constraints confronting solar PV systems, and the identified solution. The findings indicate that SCC modules, particularly those employing maximum power point (MPP) tracking techniques, significantly enhance system efficiency. The study emphasizes the potential of artificial intelligence (AI)-driven computer optimization techniques to improve energy efficiency, decrease pollutants, and alleviate greenhouse gas emissions. This research underscores the importance of SCC modules and AI-driven optimization techniques in enhancing energy efficiency and sustainability in renewable energy technologies, offering valuable insights for future advancements in the energy sector. PubDate: 2025-01-11
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Abstract: Technological advancements must keep pace with earth’s rising demand for energy, while minimizing the carbon footprint on earth. One such option is using space based solar (SBS) energy harvesting and radiofrequency (RF) microwave power beaming. In 1968, Dr. Peter Glaser published “Power from the Sun: Its Future”, qualitatively illustrating that SBS from GEO can be, at some time in the future, a solution to solar intermittency on earth. However, the high cost of this option and the drastically reduced cost of terrestrial solar energy combined in leaving this concept as aspirational as a trip to other planets. However, this technology is currently being explored under a renewed prism, to address terrestrial photovoltaic (PV) intermittent but only in high latitude remote areas and to transmit power to spacecraft in various orbits. A catalyst of this renewed interest is the promise of reusable launching vehicles (RLV) in drastically reducing the cost of bringing SBS components on orbit. This paper offers an overview of the current status on SBS research and space industry capabilities and highlights what additional research is needed to increase SBS success. This includes a discussion of reliance of SBS initiatives on RLV to place SBS spacecraft in a designated orbit, as well as the technological, economic, and operational challenges associated with power beaming to earth and other spacecraft. A complete orbital analysis covering three possible orbits, namely LEO, MEO and GEO and assessments of launching costs, satellite coverage, and transmission beaming show that MEO is likely the best orbit for hosting extensive SBS. PubDate: 2025-01-08
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Abstract: Nowadays, China is facing the threat of exhaustion of fossil fuels and negative impacts on the environment resulting from large-scale utilization of these traditional fuels since the majority of electricity in the country is supplied by coal-fired power plants. For the sake of overcoming the disadvantages of fossil fuels and utilizing renewable energy for sustainable development, the paper intends to explore the renewable energy potential of Changsha and propose a suitable renewable energy system for meeting the load demand of a small hotel located in the city. First, based on the natural resources and load demand, fundamental power system configuration and operation strategy for the project are put forward. Then, models of components for the system such as solar PV array, wind turbine, and battery bank are established. Some required data like climate condition, load demand, components parameters, and financial factors are taken into account and input into the HOMER software for simulation and optimization. The optimization results showed that compared to systems that use a single renewable energy source, a hybrid solar and wind energy system has the lowest cost of energy (COE) at US $0.310, which comprises 537 kW PV panels, 10 wind turbines (1,000 kW), 395 batteries (2,729.45 kWh) and a 150 kW converter. A sensitivity analysis of the impact of natural resources, costs of components, and financial factors on the COE of the optimal energy system is conducted, which indicates that wind speed and discount rate are significant contributors to the economic performance of our hybrid renewable energy system. The main contribution of the paper is to provide a concise and reasonable method for researchers to quickly develop hybrid renewable energy systems that can reduce carbon dioxide emission and pollutant discharge while supplying power for energy consumers, which is eco-friendly and helpful for achieving sustainable development. PubDate: 2025-01-08
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Abstract: In recent years, there has been a growing integration of geographic information systems (GIS) tools with multicriteria decision analysis (MCDA) methods to develop automated techniques for transmission line (TL) routing. The GIS-MCDA approach aims to integrate conflicting interests and incorporate, into the decision-making process, criteria encompassing social, environmental, technological, and economic aspects, rather than focusing solely on minimizing financial costs. This paper presents a thorough analysis of how sustainable development aspects are incorporated in the GIS-MCDA approach. We performed a comparative analysis of the criteria attractiveness scores of studies that employ GIS-MCDA for TL routing. The results indicate that certain criteria, such as linear infrastructure corridors and fields and pastures, are considered highly attractive. The criterion related to environmental preservation area have low attractiveness, however, the criterion related to areas with forest remnants and native vegetation showed considerable variation in attractiveness. In general, technical criteria present greater consensus regarding attractiveness, while some sensitive socio-environmental criteria present less consensus. This research highlights the importance of considering multiple criteria and perspectives within the GIS-MCDA approach to effectively evaluate the attractiveness of different areas for TL routing. It emphasizes the need to move beyond solely financial considerations and incorporate socio-environmental aspects in the decision-making process. The results presented in this paper can serve as a handy resource for researchers. PubDate: 2024-12-18
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