Power Generation Scenarios Research Articles

A multicriteria modeling approach for evaluating power generation scenarios under uncertainty: The case of green hydrogen in Greece

Clean energy technological innovations are widely acknowledged as a prerequisite to achieving ambitious long-term energy and climate targets. However, the optimal speed of their adoption has been parsimoniously studied in the literature. This study seeks to identify the optimal intensity of moving to a green hydrogen electricity sector in Greece, using the OSeMOSYS energy modeling framework. Green hydrogen policies are evaluated, first, on the basis of their robustness against uncertainty and, afterwards, against conflicting performance criteria and for different decision-making profiles towards risk, by applying the VIKOR and TOPSIS multi-criteria decision aid methods. Although our analysis focuses exclusively on the power sector and compares different rates of hydrogen penetration compared to a business-as-usual case without considering other game-changing innovations (such as other types of storage or carbon capture and storage), we find that a national transition to a green hydrogen economy can support Greece in potentially cutting at least 16 MtCO2 while stimulating investments of EUR 10–13 bn. over 2030–2050.

Forecasting sustainable power generation profiles to achieve net zero emissions using multi-objective techno-ecological framework: A study in the context of India

Climate change is becoming the most pronounced issue worldwide, with its origins dating back to the time of industrialization. It has now become imperative to take remedial steps to mitigate its harmful effects throughout the globe. In today's era, climate change is expected to worsen due to air emissions from power generation systems as they primarily depend on coal and fossil fuels. Moreover, as the global population increases, the demand for electricity will rise, further exacerbating the problem. Given this situation, populous nations like India and China are seen as key players in achieving net-zero emissions worldwide by transitioning to sustainable power generation. This study presents a multi-objective optimization method to meet the future electricity demand while ensuring net-zero emissions by mitigating pollution through a techno-ecological synergy (TES). In turn, this study incorporates the ecosystem as a process system, unlike the conventional decision-making methods that ignore the ecosystem's ability to mitigate pollution. As a case study, Delhi, the capital of India, which is known as one of the world's most polluted cities, is selected to showcase the proposed methodology. The study proposes various scenarios for sustainable power generation for Delhi to achieve net-zero emissions. The findings reveal that solar energy could be a viable alternative to conventional fossil fuels to meet Delhi's energy demand by 2050. This is achievable by implementing an energy transition plan that involves the aggressive phasing out of the coal power plant, which aligns with the urgent requirement to reduce emissions.

Applying the solar solid particles as heat carrier to enhance the solar-driven biomass gasification with dynamic operation power generation performance analysis

Solar-driven biomass gasification is a promising technology to improve the utilization efficiency of solar energy and biomass, which also enhances the thermochemical storage and the subsequent stable utilization. Whereas, the intermittence of solar energy deteriorates the stability and continuity of the thermochemical reaction process, which limits the efficient conversion of the reaction. In this regard, this work proposes a new solar-driven biomass gasification technology based on solid particle heat carrier. Different from the direct solar-driven gasification, solid particles are performed as medium to receive the concentrated solar radiation energy and transfer energy to biomass feedstocks. In addition, the combined power generation scenario is adopted to evaluate the thermodynamics and economics of the technology. The integration of solid particle heat storage, the system dispatch and stable operation will be further enhanced. Results show that under the optimal condition, the system maximum thermal and exergy efficiencies are increased by 12.94% and 12.51%, respectively. Considering the system off-design operation, with the evaluated typical periods of four seasons, the averaged power generation efficiency is increased by 5.16%, with the highest efficiency improvement of 9.41% occurring in the typical winter days. With the change of receiver price and biomass price, the maximum decline of levelized cost of electricity reaches to 17.6%, which presents the favorable economic benefits. As a new solar thermochemical technology, this technology exhibits competitive characteristics, significantly improving the utilization efficiency of solar and biomass energy.

Medium and Long Term Scenario Generation of Multi-Temporal Renewable Energy Based on GAN

With China’s “double carbon” target, the future power system will show A significant percentage of the energy generated comes from renewable sources, specifically wind power, which has obvious randomness, volatility and intermittency, and will have a greater impact on the safety of the power system. How to effectively characterize the randomness and volatility of its power output is a major challenge for power system dispatch. The method we suggest in this paper utilizes generative adversarial networks to produce medium- and long-term scenarios for wind power generation. This approach allows for the creation of multiple sets of wind power generation data that share the same characteristics as the historical data., Our approach takes into account the spatial correlation between wind power generation at wind farms situated in various geographic locations, fully describing the randomness and spatio-temporal correlation of wind power, and providing an important reference for power system planning and scheduling.

Ce‐filled Ni1.5Co2.5Sb12 Skutterudite Thin Films with Record‐High Figure of Merit And Device Performance

AbstractRealizing high thermoelectric performance in CoSb3 skutterudite‐based thin films and their devices is historically challenging, especially due to the lack of high‐performing thin‐film‐based device working at medium‐to‐high temperatures. Here, a record‐high ZT of 1.1 is achieved at 683 K in an n‐type Ce0.3Ni1.5Co2.5Sb12 thin film, fabricated from a self‐designed target via advanced pulsed laser deposition. Both experimental and computational results confirm that the Ce‐filling and metal‐featured nanoinclusions such as CeSb contribute to high electrical conductivity, while the Ni‐doping and significantly strengthen the energy filtering effect that occurs at the dense interfaces between the Ce0.3Ni1.5Co2.5Sb12 matrix and the nanoinclusions which leads to a large Seebeck coefficient, giving rise to such a high ZT. In addition, a new‐type CoSb3 thin‐film‐based device is successfully fabricated, which exhibits a high output power density of 8.25 mW cm−2 at a temperature difference of 140 K and a cold‐side temperature of 573 K, indicating the potential for application to medium‐to‐high‐temperature power generation scenarios.

Smart City Facility Location Recommendation Algorithm Using Multimedia Data and Improved Generative Adversarial Networks

Internet of Things, low energy consumption, and intelligent and functionally integrated urban infrastructure construction are crucial elements in the development of smart cities. Distributed generation (DG) and electric vehicle charging infrastructure play a vital role in the planning and construction of smart cities. However, the uncertainty associated with the power output of distributed generation significantly impacts the planning of distribution networks. To address this issue, this paper proposes a site-selection recommendation algorithm that leverages urban multimedia data and improved generative adversarial networks. The proposed algorithm begins by modeling the uncertainty of wind power and photovoltaic (PV) generation using an enhanced conditional generative adversarial network model. To generate multimedia datasets with time-series characteristics for wind power and PV generation scenarios, monthly multimedia data labels are incorporated into the model. These multimedia datasets, representing a wide range of scenarios, are then clustered using the K-means clustering method. Furthermore, a distributed generation planning model is established, aiming to minimize the annual integrated cost. The planning problem is efficiently solved using CPLEX, a mathematical programming solver. In the simulation experiments, the proposed scheme is compared with alternative schemes. The results demonstrate that the proposed scheme achieves a significant total cost saving of 21.95% compared to the comparison scheme. Moreover, the experimental comparison reveals that the proposed scheme exhibits higher stability. Additionally, in terms of algorithm efficiency, the proposed algorithm outperforms the other three algorithms tested in terms of the number of iterations and speed. The experimental results highlight the effectiveness of the proposed planning model in improving the economy and stability of the distribution network. Furthermore, it enhances the computational efficiency of the planning problem associated with distributed power supply and electric vehicle charging stations. The findings of this research hold substantial research significance for the site selection planning of distributed power supply.

Long-Term Scenarios of Indonesia Power Sector to Achieve Nationally Determined Contribution (NDC) 2060

This study aims to assess the feasibility of achieving Indonesia’s net-zero emissions target by 2060 through a model of future power generation using renewable energy sources using the Low Emissions Analysis Platform (LEAP) software. There are five projected power generation scenarios in this research: the reference (REF) scenario, the conservative (CON) scenario, the moderate (MOD) scenario, the progressive (PRO) scenario, and the advanced (ADV) scenario. The availability of renewable energy technology differentiates each scenario. The ADV scenario, which utilizes nuclear power and energy storage, achieves the 100% renewable energy target by 2060 at the lowest total cost. However, the costs of CON and MOD are not significantly higher. Indonesia should decommission existing fossil fuel power plants and construct more renewable energy power plants to achieve the net-zero emissions target. Based on the simulation, biomass energy is the least favorable type of energy. Solar becomes an option only when other renewable energies are at their maximum potential capacity. Furthermore, nuclear energy and energy storage is essential for Indonesia to achieve the renewable target.

A benchmarking framework for performance evaluation of statistical wind power forecasting models

Lack of benchmarks for wind power forecasting models undermine their potential and consequently their implementation for industry applications. Despite the extensive existing literature in the field, a unified framework has not been created yet. Therefore, we propose a benchmark set-up where statistical wind power forecasting models can be tested under standardized criteria with respect to data, time resolution, and prediction horizon, while evaluating them under varied representative operational conditions of wind farms. The utility of this framework is shown with an example case applied to mode decomposition models, which have shown a higher performance compared to other statistical models in recent times. Data collected from two Irish wind farms are used to calculate the accuracy of statistical wind power forecasting models. Their robustness is also examined by providing an assessment of such models under several scenarios of power generation. In the example case of this benchmark, results indicate that the use of variational mode decomposition as decomposition algorithm together with advanced recurrent networks provide the best performance among the evaluated forecasting models.

Analysis of demand, generation, and emission for long-term sustainable power system planning using LEAP: The case of Bangladesh

The availability of quality power is a foremost need for a nation's sustainable development. The government of Bangladesh has the vision to be a high-income country by 2041. To meet the power challenges in the near future associated with the vision, there should be a well-planned master plan for the power system. Bangladesh has a power system master plan (PSMP) up to 2041. However, it is unclear whether the PSMP is the most adaptable plan considering different power generation scenarios by considering the demand, generation, and emissions. Hence, the long-range energy alternative planning (LEAP) tool is employed for scenario analyses of Bangladesh's electricity sector from 2022 to 2041. On the demand side, the final electricity demand has been projected as 335.25, 314.76, 376.59, and 398.10 TWh in 2041 for business-as-usual (BAU), low growth (LG), medium growth (MG), and high growth (HG) scenarios, respectively. Considering technical and environmental parameters, eight generation scenarios are also analyzed on the supply side. The analysis projected 58,230 MW capacity for BAU and LG under P1 to P8 generation scenarios and 68,830 MW capacity for MG and HG under Q1–Q8 generation scenarios in 2041. In terms of emission in 2041, 167.4 and 165 MMt CO2 equivalent are found for the P8 scenario in the case of BAU and LG. In Q8, for MG and HG, the emissions are found to be 206.5 and 209.4 MMt CO2 equivalent, respectively. The generation scenarios of P8 for BAU and LG and Q8 for MG and HG are found to be suitable ones with respect to energy reliability and reduced emission. A similar analysis could also be performed to identify suitable power generation plans for other developing countries.

Adversarial Representation Learning for Intelligent Condition Monitoring of Complex Machinery

Condition monitoring (CM) of machinery is important for ensuring the reliability of industrial processes. To adapt to the rareness of data from faulted machinery, semi-supervised CM can be implemented by training on only healthy samples. However, the performance of CM can be impaired by the variability of operating data acquired from complex machinery. Additionally, the accuracy of results is limited by the impractical assumption that samples under different health conditions are naturally separable. To address these problems, an adversarial representation learning method is developed herein. The method is trained by reconstructing operating data in both signal and latent spaces, and adversarial evolution is adopted to avoid the convergence at local optima. In this case, data representations of health conditions can be obtained to suppress the volatility of measurements, and redundant information can be reduced by latent codes. Moreover, a strategy of representation embedding is developed to impose constraints on unhealthy data, guaranteeing separable samples under distinct health conditions in the monitoring stage. Furthermore, feature fusion is conducted to avoid missing detailed information on health conditions. The satisfactory performance of the proposed method is demonstrated by experiments in test benches and actual scenarios of wind power generation.

Wind Power Scenario Synthesis With Smoothing Effect Through Spectral Decomposition and Its Application to Flexible Resource Adequacy

We synthesize future scenarios of total wind power generation (WPG) of future wind farms by considering the smoothing effect, in which mid-term variability fades out, but periodicity and short-term variability remain as more geographically distributed wind farms are aggregated. First, we decompose the WPG into a periodic component (PC) and non-periodic component (NC). We design the future PC by modifying the standard deviation and mean of the current PC. Then, we assume that the NC can be decomposed into a variable component (VC), which is a source of mid-term variability, and an uncertain component (UC), which is a source of short-term variability. We separate the VC and UC in the frequency domain. We design the VC by modifying the slope of power spectral density (PSD) to represent the smoothing effect and the UC by synthesizing the PSD distribution to represent randomness. Based on our scenarios, we develop a new flexibility index and a novel recursive algorithm to calculate the required amount of future flexible resources and the value of the smoothing effect to maintain the flexible resource adequacy. Finally, we verify the smoothing effect and the amount of flexible resources by simulating the IEEE test network based on actual WPG in Texas and Australia.

Wind Energy and the Energy Transition: Challenges and Opportunities for Mexico

We present a review of wind energy development in Mexico, factors hampering this development, and proposals for solutions to address this hampering. This review is relevant in the context of climate change mitigation strategies and the achievement of the United Nations’ sustainable development goals. Wind energy can be harvested at competitive costs to solve society’s energy poverty and climate change problems. Firstly, we present the current wind energy installed capacity and wind power generation status globally and in Mexico and discuss why Mexico is lagging behind, particularly since 2020. Despite this lag, several state governors are still considering wind energy developments. The current economic context is then considered, with community wind energy as a solution forward for wind energy development, using a successful case study from the UK that has addressed energy poverty and provided an additional income source for an island community. Any community energy project using wind as its main energy resource relies on accurate wind energy assessment in its feasibility analysis. Thus, an evaluation of different wind energy atlases for Mexico was performed, which showed that models considering microscale processes could lead to a relative difference of more than 50% when compared to those that do not consider them. This led to the conclusion that microscale effects must be considered in wind energy characterization models. Furthermore, it is acknowledged that wind faces other challenges, such as the effect of future climate change scenarios, grid planning, and vulnerability and risk associated with tropical storms, which can be substantial in Mexico. Solutions are proposed in the form of possible wind power generation scenarios, planning and implementation of centralized and distributed transmission lines, and possible wind siting and technological choices to reduce the vulnerability and risk to tropical storms. Finally, we close with some future perspectives for researchers and decision-makers. The main conclusions are that sustainable growth can only be compatible with a transition to renewable sources of energy, energy community projects can address energy poverty and achieve sustainable development goals, wind energy feasibility studies need to include microscale effects, return of investment can be improved by siting the wind farms in regions of low vulnerability and risk to extreme events, and high-voltage transmission lines are crucial for sustainable development, even with the important role that distributed systems play. Finally, turbine growth and materials recycling, among other factors, must be considered when assessing the environmental impacts of wind farm decommissioning.

Nuclear Fusion impact on the requirements of power infrastructure assets in a decarbonized electricity system

Electrification of final energy use and electricity generation by low-carbon technologies are key points of the path toward carbon-neutrality. Carbon free electricity can be generated by both nuclear and renewable energy sources. Nevertheless, although all of them can be economically viable in terms of ‘Levelized Cost Of Energy‘, their exploitation involve variable renewables, so as to require power system upgrades with adequate energy storage systems, dispatchable generation capacity and transmission/distribution grid enhancements (power infrastructure assets) that may lead to relevant additional system costs. To this respect, each power system is almost unique, due to its peculiarities as far as renewable potentials is concerned. In order to find the least cost feasible and reliable generation mix, detailed hourly simulations are necessary. In this paper, long term power generation scenarios will be simulated with the COMESE code, a dispatch model able to perform detailed regional power systems analysis. Carbon-free Italian power system long term scenarios are simulated, with high share of photovoltaic and to less extent wind electricity together with a possible contribution of nuclear fusion power plants. The operation of the transmission grid is simulated, through a transport model, in order to assess the necessary grid enhancement and to estimate the related costs. In this context the impact of fusion will be assessed in terms of mitigation of the overall system cost of electricity.

PROCSIM: An Open-Source Simulator to Generate Energy Community Power Demand and Generation Scenarios

Reducing our carbon footprint is one of the biggest challenges facing humanity in the current millennium. In the last few years, researchers have focused their attention on balancing the demand and supply, thereby allowing better management of renewable energy resources. In this regard, many energy management strategies have been developed. Nevertheless, testing, evaluating, and comparing such approaches in multiple scenarios, and above all, assessing their generalization, is currently a hard, or even impossible, task. Furthermore, analyzing the impact of such strategies in Energy Communitys (ECs) is an underexplored task. This is due to the lack of existing EC datasets and simulators that allow users to evaluate and compare their approaches. Although there are some tools to generate demand and production profiles, they are all developed with a single purpose. To address these challenges, PROCSIM is presented: an open-source simulator designed especially to create energy community datasets for multiple purposes—in particular, to test and evaluate different algorithms and models. It includes integration with a consumption-profiles generator, tools to simulate Solar Photovoltaic (PV) and wind production, a module that generates an EC dataset, and finally, a set of metrics to evaluate the generated community. To conclude, a case study comprised of two experiments is presented. The first experiment shows how an EC dataset can be created using PROCSIM. In the second experiment, an exemplification of how this dataset can be used to evaluate an optimization algorithm is provided, namely, to optimize the control of a battery. Ultimately, it is shown that the simulator can generate energy community power demand and generation scenarios. The scenarios can be fully customized by the user, considering different sizes (power capacity) and numbers of assets, and diverse generation/consumption characteristics. The datasets generated by PROCSIM can be useful for different purposes, such as optimal scheduling of EC generation resources and consumption flexibility, and for designing battery energy storage systems.

Design of Bi2Te3-based thermoelectric generator in a widely applicable system

A Bi2Te3-based thermoelectric generator (TEG) system cooled by heat radiation fin (HRF) applies to most scenarios of low-temperature power generation and waste heat recovery, while the high performance of the TEG system requires the precise design of the structure. Here we developed a theoretical model to simulate the thermal-electric coupling of the TEG system and analyzed the Bi2Te3-based TEG system with the aid of numerical and experimental methods. Results show that the increase in height of TEG not only improves the conversion efficiency but leads to a peak value of output power, which is much different from the traditional view that the output power solely decreases with the increase of height of TEG. Compared to the thickness of the fin, the height of the fin plays an essential role in optimizing the performance of TEG. By adding an electrical fan to improve the heat exchange coefficient, the net output power is doubled and the net conversion efficiency is improved by more than 80%. Besides, the structure of the TEG system is designed for different material parameters via the theoretical method. We anticipate that our results will guide the design and optimization of the TEG system.

Long‐Term Optimal Operation of the Cascade Hydro‐Wind‐Photovoltaic Hybrid System considering Transmission Section Constraints

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro‐wind‐photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long‐term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario‐generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro‐wind‐PV complementary operation; finally, the long‐term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption.

Techno-economic assessment of green hydrogen production using different configurations of wind turbines and PV panels

In this work, a hybrid system is comprised of wind turbines (WT) and photovoltaic (PV) panels to generate green Hydrogen via water electrolysis. Consideration is given to the influence of five electrical power generation scenarios on system performance and Hydrogen production cost. This study adopts the solar radiation, wind speed, and ambient temperature for Mersa-Matruh in Egypt. The system performance is studied using MATLAB-Simulink over one year. The winter months have high wind speed and low sun radiation compared to other months, whereas additional months have high solar radiation and lower wind speed than the winter months. The findings show that the amount of Hydrogen produced for all scenarios varies from 12,340 m3 to 13,748 m3 per year. The system efficiency and LCOH are 7.974% and 3.67 USD/kg, 9.56%, and 3.97 USD/kg, 10.7% and 4.12 USD/kg, 12.08%, and 4.3 USD/kg, and 16.23% and 4.69 USD/kg for scenarios 1 to 5, respectively. Finally, the introduced system can reduce CO2 emissions by 345 tons over the lifetime and gain about 13,806 USD.

Optimal Location and Operation of PV Sources in DC Grids to Reduce Annual Operating Costs While Considering Variable Power Demand and Generation

Due to the need to include renewable energy resources in electrical grids as well as the development and high implementation of PV generation and DC grids worldwide, it is necessary to propose effective optimization methodologies that guarantee that PV generators are located and sized on the DC electrical network. This will reduce the operation costs and cover the investment and maintenance cost related to the new technologies (PV distributed generators), thus satisfying all technical and operative constraints of the distribution grid. It is important to propose solution methodologies that require short processing times, with the aim of exploring a large number of scenarios while planning energy projects that are to be presented in public and private contracts, as well as offering solutions to technical problems of electrical distribution companies within short periods of time. Based on these needs, this paper proposes the implementation of a Discrete–Continuous Parallel version of the Particle Swarm Optimization algorithm (DCPPSO) to solve the problem regarding the integration of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grids, with the purpose of reducing the annual costs related to energy purchasing as well as the investment and maintenance cost associated with PV sources in a scenario of variable power demand and generation. In order to evaluate the effectiveness, repeatability, and robustness of the proposed methodology, four comparison methods were employed, i.e., a commercial software and three discrete–continuous methodologies, as well as two test systems of 33 and 69 buses. In analyzing the results obtained in terms of solution quality, it was possible to identify that the DCPPSO proposed obtained the best performance in relation to the comparison methods used, with excellent results in relation to the processing times and standard deviation. The main contribution of the proposed methodology is the implementation of a discrete–continuous codification with a parallel processing tool for the evaluation of the fitness function. The results obtained and the reports in the literature for alternating current networks demonstrate that the DCPPSO is the optimization methodology with the best performance in solving the problem of the optimal integration of PV sources in economic terms and for any kind of electrical system and size.

Forecasting methods for wind power scenarios of multiple wind farms based on spatio-temporal dependency structure

In this paper, we first proposed a new wind power generation scheme for multiple wind farms that considers both spatial and temporal dependence of wind power time series based on copula theory in a computationally efficient way. Different vine copulas were studied and compared. Then a novel two-stage spatio-temporal sampling method is used to generate wind power scenario samples. Finally, we propose a conditional quantile sampling method that converts the samples into specific wind power generation scenarios. A case study was conducted on Wind Integration National Dataset (WIND) of National Renewable Energy Laboratory (NREL) to benchmark the performance of the new method with existing methods. The results show that the proposed method can generate scenarios with more accuracy and efficiency, which is conducive to subsequent operations.

Predicting the Impact of Compressor Flexibility Improvements on Heavy-Duty Gas Turbines for Minimum and Base Load Conditions

The increasing importance of renewable energy capacity in the power generation scenario, together with the fluctuating consumer energy demand, forces conventional fossil fuel power generation systems to promptly respond to relevant and rapid load variations and to operate under off-design conditions during a major fraction of their lives. In order to improve existing power plants’ flexibility in facing energy surplus or deficit, retrofittable solutions for gas turbine compressors are proposed. In this paper, two different operation strategies, variable inlet guide vanes (IGVs) and blow-off extraction (BO), are considered for enabling partial load and minimum environmental load operation, and thus to identify implementation opportunities in existing thermal power plants. A typical 15-stage F-class gas turbine compressor is chosen as a test case and some energy demand scenarios are selected to validate the adopted solutions. The results of an extensive 3D, steady, CFD analysis are compared with the measurements coming from an experimental campaign carried out in the framework of the European Turbo-Reflex project. It will be shown how the combined strategies can reduce gas turbine mass flow rate and power plant output, without significantly penalizing efficiency, and how such off-design performance figures can be reliably predicted by employing state-of-the-art CFD models.