Generation Resources Research Articles

Implementation of Sustainable PBL towards Affordable and Clean Energy: Industry, Innovation

Abstract— Sustainable development is way of life need to adopt to preserve resources for future generation. Adopting this idea will help to keep the ecosystem in balance. Sustainable development is defined as development that fully recognizes, supports human needs while explicitly endorsing ecological limitations within the framework of economic activity. The Sustainable Development Goals (SDGs) are examined in this study as a critical international framework for addressing environmental, socioeconomic issues. It breaks down intricacies of SDG implementation by carefully examining advancements, obstacles, strategic paths. This research work focuses on different Sustainable Project based learning methods of Industry, Innovation, Infrastructure; Sustainable Cities. To obtain clean energy, industry innovation education plays key role in energy transition by raising awareness, training, sector players to promote renewable energies. In order to give high-quality education on renewable energy, it is imperative that educators stay up to date on current industry trends, emerging technology, and successful pedagogical approaches. This can be achieved through ongoing professional development programmers. For energy transition a continuous monitoring and SDG indicators, baseline assessment, promotion of energy policies, examining investment trends, regulatory frameworks, publicprivate partnerships supporting industrial development, innovation ecosystems are required. For this a new project-based learning (PBL) must be involved to obtain specified goals. PBL teachers should support acquisition of interdisciplinary learning goals via a variety of learning activities that include direct instruction organized under an inquiry, typically with significant student voice. Keywords—Sustainable Development Goals (SDG), Sustainability, Renewable Energy, PBL, SDG indicators.

Achieving zero liquid discharge through water recycle in Muara Karang Combine Cycle Power Plant

Abstract The study evaluates a water recycling program implemented at the Muara Karang Power Plant (MKPP), which plays a crucial role in reducing wastewater and enhancing the operational efficiency of the power plant. Water is an essential resource in power generation, but its quality has been declining over time, impacting plant performance. Regular checks on the boiler water at MKPP reveal that the plant collects around 1-1.4 m3/second, which was traditionally discarded after inspection. However, considering the quality and quantity of this water, the study explores its potential for recycling. Since 2023, approximately 2,741.46 m3 of water has been reused, improving efficiency by 160 m3 per month while reducing wastewater discharge. The program demonstrates that simple yet strategic water management interventions can significantly improve operational efficiency and environmental sustainability in power plants. By recycling boiler water within the system, the plant conserves valuable water resources and lowers pollution levels. This study introduces a pioneering approach to water recycling in power plant by utilizing boiler water that was previously discarded. This innovative approach not only reduces the environmental impact but also sets a new benchmark for sustainable water management in the energy sector, providing a model for other power plants to follow.

A novel economic dispatch of energy-renewable multi-area power systems with group-based differential evolution

Spinning reserve capacity can handle the effects of load and renewable energy output forecasting errors. To ensure the safe and stable operation of the power systems and enhance economic benefits, it is meaningful to develop reasonable reserve capacity model for multi-area power systems. In this paper, a calculation method for reserve capacity, based on multi-area joint reserve capacity, is proposed to handle the uncertainty of renewable energy output. Multi-area interconnected economic dispatch with renewable energy, based on multi-area joint reserve capacity, can comprehensively consider the characteristics of power generation resources in each areas, and then reasonably allocate reserve capacity in each areas. Furthermore, to solve the multi-area interconnected economic dispatch with renewable energy, a sorted grouping-based adaptive multi-strategy differential evolution algorithm is designed, which divides the population into four groups according to the current convergence and balance, then adopts several new mutation strategies, and uses adaptive schemes for each group of populations separately. Notably, the sorted grouping-based adaptive multi-strategy differential evolution algorithm has advantages over several differential evolution variants and several well-known algorithms. Finally, the proposed method is compared with individual reserve capacity, which proves that the superiority of the proposed method in reserve capacity programming.

Heuristics-Based Energy Demand Forecasting with Scarce Data in the Department of Huila, Colombia

Within the framework of the energy transition, electrical distribution grid operators require effective tools to predict the demand of individual users. These tools are necessary for an adequate planning of future generation resources and infrastructure modernization. However, understanding future electricity needs poses a significant challenge, especially in emerging economies, where historical data are manually collected on a monthly or bi-monthly basis and exhibit a significant amount of missing information. In response to the above, this work proposes a novel heuristics-based method for medium-term energy demand forecasting with scarce data. Qualitative and quantitative information was abstracted into a mathematical model representing the trend and noise components of historical energy consumption observations. In addition, external factors were considered as an additional layer for the mathematical model, in order to account for events that could not be foreseen by merely using the dataset. A train-test data split was proposed to iteratively search for the best parameters to predict electricity demand with respect to different categorical divisions of users (social stratum, rural or urban location, and municipality). For testing and validation, real historical data were used, as provided by the regional distribution system operator (DSO) of the department of Huila, Colomba. The results suggest a trade-off between accuracy and computational intensity, as well as the fact that a non-categorical approach leads to the algorithm with the best performance (average absolute error of 1.61%) at a low computational cost.

ANALYSIS OF THE USE OF GROUNDWATER FOR DRINKING WATER SUPPLY OF THE POPULATION OF THE MYKOLAIV CITY

The analysis of groundwater use as a source of drinking water supply is an important task in the context of ensuring sustainable development and conservation of natural resources. Groundwater, a vital source of fresh water for the population, industry, and agriculture, can be subject to overuse, leading to a decrease in its level, pollution, and other negative consequences. This article provides a comprehensive analysis of the situation with groundwater use in the Mykolaiv region, highlighting the main causes of this phenomenon, its consequences, and possible solutions. Analyzing the reasons for the overuse of groundwater, it was found that the key factors are insufficient supervision by government agencies, underestimation of the role of groundwater in the water supply and sewerage system, and imperfect water management. These problems require immediate attention and resolution. Another important aspect is the lack of public awareness of the methods of rational water use, which also contributes to excessive groundwater consumption. The second aspect of the analysis concerns the consequences of excessive groundwater use. These include lowering the groundwater table, depletion of water sources, contamination of groundwater with chemicals and microbiological pollutants, reduced quality of drinking water, and environmental damage. These effects can have long-term impacts on ecosystems and human health. In addition, a decline in groundwater levels can lead to degradation of natural landscapes, loss of biodiversity, and changes in the region's hydrological regime. Possible solutions to the problem may include improving legislation and regulatory mechanisms, implementing effective systems for monitoring and controlling groundwater use, and creating and applying innovative technologies for water treatment and conservation. It is also important to raise the level of environmental awareness among the population through educational programs and information campaigns aimed at promoting the rational use of water resources. Thus, an integrated approach to groundwater management, including legal regulation, technological innovations, educational activities, and international cooperation, is a key to ensuring reliable water supply to the population of the Mykolaiv region and preserving natural resources for future generations.

GIS-based approach including social considerations for identifying locations for solar and wind power plants

This paper presents a methodology for evaluating and identifying potential areas for the installa- tion of solar and wind power plants, including consideration of social aspects. The methodology is based on Geographic Information Systems (GIS) and maps derived from open data provided by various institutions. Multi-criteria analysis is used, which considers environmental and physical conditions, as well as social indicators and the specific cultural and economic characteristics of the area under analysis. The objective of this is to explore the intersection of potential power plant locations with social maps that represent different problematics that can be critical to the successful implementation of a generation project in a given area. The results indicate that solar energy is the most viable resource for electric generation in the Colombian territory. This can be equivalent to a total of 8.24T W · km2. In contrast, when considering the environmental impact, it becomes evident that viable zones begin to shift away from bodies of water and areas that are natural reserves. The results obtained are not only relevant for energy planning in Colombia, but may also be applicable in other contexts, contributing to the development of sustainable energy strategies at a global level. Furthermore, the methodology can serve as a model for other developing countries that seek to transition towards a more sustainable energy future.

Prospects of Nuclear Energy for Clean and Sustainable Energy Development in Rwanda

Rwanda, just like many other developing countries, still faces challenges in the energy sector. The country is still not able to supply reliable and affordable electricity to meet the current and future energy needs, as a result of rapid economic growth, increasing population and urbanization. Rwanda currently has limited generation resources, particularly during the dry season, when several hydropower stations are experiencing water shortages. Sometimes, leased diesel generation is needed to meet peak demand, which comes at a hefty cost. Efforts are being made to determine the true number of existing resources in the country for energy generation. Diversifying sources of energy inevitably becomes a strategic option. Rwanda has decided to embark on an ambitious programme for nuclear development as one of the alternatives to tackle energy challenges. This paper briefly presents plans featuring in the nuclear science and technology programme with the ultimate goal to leverage nuclear applications for sustainable social-economic development. A review of the country’s energy sector status is presented with focus on prospects of the nuclear programme as the solution to the country’s high energy demand and applications in different socio-economic sectors. In efforts to join the rest of world in the “net zero” greenhouse gas emission by 2050, Rwanda embraces the perspective under which nuclear energy is foreseen as a source of energy that most supports the efforts.

Constructing high-performance and versatile liquid–solid triboelectric nanogenerator with inflatable columnar units

Abstract The use of water resources for energy generation has become increasingly prevalent, encompassing the conversion of kinetic energy from streams, tides, and waves into renewable electrical power. Water energy sources offer numerous benefits, including widespread availability, stability, and the absence of carbon dioxide and other greenhouse gas emissions, making them a clean and environmentally friendly form of energy. In this work, we develop a droplet-based liquid–solid triboelectric nanogenerator (LS-TENG) using sophisticatedly designed inflatable columnar structures with inner and outer dual-electrodes. This device can be utilized to harvest both the internal droplet-rolling mechanical energy and the external droplet-falling mechanical energy, capable of being assembled into various structures for versatile applications. The design incorporates a combined structure of both internal and external TENG to optimize output performance via multiple energy harvesting strategies. The internal structure features a dual-electrode columnar-shaped LS-TENG, designed to harvest fluid kinetic energy from water droplets. By leveraging the back-and-forth motion of a small amount of water within the air column, mechanical energy can be readily collected, achieving a maximum mass power density of 9.02 W·Kg−1 and an energy conversion efficiency of 10.358%. The external component is a droplet-based LS-TENG, which utilizes a double-layer capacitor switch effect elucidated with an equivalent circuit model. Remarkably, without the need for pre-charging, a single droplet can generate over 140 V of high voltage, achieving a maximum power density of 7.35 W·m−2 and an energy conversion efficiency of 22.058%. The combined LS-TENG with a sophisticated inflatable columnar structure can simultaneously collect multiple types of energy with high efficacy, exhibiting great significance in potential applications such as TENG aeration rollers, inflatable lifejacket, wind energy harvesting, TENG tents, and green houses.

Capacity investment portfolio optimization considering EV charging flexibility: A heuristic algorithm-informed system dynamics approach

Orderly charging of electric vehicles (EVs) functions as a flexible tool for peak shaving to help integrate renewable energy sources effectively. Coordinating investments in charging resources and generation resources can enhance social benefits while ensuring the charging needs of EVs are met. This study aims to optimize the long-term investment strategies for these resources using a heuristic algorithm-informed system dynamic (SD) approach. Firstly, a SD model is developed to simulate the long-term interactions among generation resources, EVs, and power demand, capturing their dynamic relationships. Then, the fixed feedback functions of investment parameters in the SD model are replaced with an optimization model, enabling parameter optimization for environmental and economic objectives. Given that the process must quantify the aforementioned long-term interrelationships with potential high-order, nonlinear feedback loops, the solution is implemented using Particle Swarm Optimization (PSO). Data from a specific province in China was used for the case study. The results show that the optimized investment portfolio reduced carbon costs and total investment by 2.4% and 22%, respectively, while total social costs decreased by 17% compared to the pre-set investment strategy. Lastly, we also conducted sensitivity analysis and algorithm comparison.

Utilizing Fuzzy DANP to Rank Factors for Optimal Siting of Renewable Energy Farms

Energy consumption, much higher than the global average in developing countries, witnessed a growing and alarming trend in past years. An accepted reality for human communities is that the world’s energy demand is hastily increasing, and cheap fossil energy resources will gradually, yet certainly, be exhausted in future decades. To conserve these worthy fossil resources for future generations, prevent environmental damages due to burning these fuels, and respond to the increasing demand for energy, humans should use clean and renewable energies and discover proper locations to generate renewable energies. Today, locating studies prevail to determine the sites of renewable energy farms and reach industrial development. These studies consider various criteria to select the best location for establishing renewable energy farms. The present research attempted to identify and rank factors influencing the location of renewable energy farms using the fuzzy DANP approach.

Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty

Microgrids (MGs) can enhance the consumers’ reliability. Nevertheless, besides significant outcomes, some challenges arise. Regarding the intermittent nature of Renewable Energy Resources (RESs), MGs are not operated radially. Accordingly, the reliable protection of MGs considering uncertainty in RESs is crucial for planners and operators. This paper uses data analysis to extract knowledge from locally available measurements using RMS values of symmetrical components. The learning-based characteristic of the suggested technique with a low computational burden exempts the need for an available communication infrastructure in the MG. The Support Vector Machine (SVM) technique is applied to train the Intelligent Electronic Devices to have a reliable MG protection scheme. The proposed method, which performs fault detection and classification together, just requires local information and functions effectively to discriminate faulty from normal conditions considering different uncertainty of resistance faults. Digital simulations on an MV test network were conducted to construct an appropriate database to consider some aspects of uncertainty in the network. The various faults considering their uncertainty, the different modes of operation, the uncertainty of RESs generation, and the load levels are combined to produce myriad scenarios. The simulation results confirm the effectiveness of the proposed adaptive protection approach in accurately distinguishing different system modes and consistently protecting the MG, achieving an accuracy rate of 99.75%. Furthermore, it offers the MG an optimal protection scheme that is not limited by selectivity constraints across diverse conditions.

Impacts of Climate Change: Examining Precipitation, Temperature Anomalies, and Effects on Water Resources in Turkey

Climate change is a significant global issue that affects our everyday lives and water resources. One of the most critical impacts of climate change is on our water resources, which are vital for sustaining life and supporting various ecosystems. Understanding the complex relationship between climate change and water resources is crucial for developing effective mitigation and adaptation strategies. This study aims to investigate the impact of climate change on water resources and to shed light on the importance of addressing this issue. Water is a limited resource, and its availability and quality directly affect human well-being, agriculture, energy production, and ecosystem health. However, climate change alters the patterns and dynamics of precipitation, temperature, and evaporation, leading to significant changes in water availability and distribution. Increasing global temperatures cause glaciers and ice caps to melt, progressively depleting freshwater resources. Moreover, changing precipitation patterns are leading to more frequent and severe droughts in some regions, while others experience increased rainfall and flooding events. The effects of these changes are profound. As water scarcity becomes more widespread, competition for limited resources intensifies, potentially leading to conflicts and social unrest. Given the importance of water resources and the undeniable impact of climate change on their availability and quality, it is imperative that we deepen our understanding of this complex relationship. This study aims to assess the socio-economic and environmental impacts of climate change on water resources. By doing so, it seeks to provide policymakers, scientists, and communities with information that will guide decision-making processes and direct effective climate change adaptation and mitigation strategies. In conclusion, the impact of climate change on water resources cannot be overstated. This study aims to contribute to our understanding of this critical issue by highlighting the urgency of addressing climate change and its consequences on water resources and sustainability. This study examines the effects of changing precipitation patterns and increasing temperatures due to climate change on underground and surface water resources in Turkey. By conducting this research, we hope to pave the way for informed decision-making and encourage collective responsibility to protect our water resources for future generations.

Multifractal Analysis of Standardized Precipitation Evapotranspiration Index in Serbia in the Context of Climate Change

A better understanding of climate change impact on dry/wet conditions is crucial for agricultural planning and the use of renewable energy, in terms of sustainable development and preservation of natural resources for future generations. The objective of this study was to investigate the impact of climate change on temporal fluctuations of dry/wet conditions in Serbia on multiple temporal scales through multifractal analysis of the standardized precipitation evapotranspiration index (SPEI). We used the well-known method of multifractal detrended fluctuation analysis (MFDFA), which is suitable for the analysis of scaling properties of nonstationary temporal series. The complexity of the underlying stochastic process was evaluated through the parameters of the multifractal spectrum: position of maximum α0 (persistence), spectrum width W (degree of multifractality) and skew parameter r dominance of large/small fluctuations). MFDFA was applied on SPEI time series for the accumulation time scale of 1, 3, 6 and 12 months that were calculated using the high-resolution meteorological gridded dataset E-OBS for the period from 1961 to 2020. The impact of climate change was investigated by comparing two standard climatic periods (1961–1990 and 1991–2020). We found that all the SPEI series show multifractal properties with the dominant contribution of small fluctuations. The short and medium dry/wet conditions described by SPEI-1, SPEI-3, and SPEI-6 are persistent (0.5<α0<1); stronger persistence is found at higher accumulation time scales, while the SPEI-12 time series is antipersistent (0<α0−1<0.5). The degree of multifractality increases from SPEI-1 to SPEI-6 and decreases for SPEI-12. In the second period, the SPEI-1, SPEI-3, and SPEI-6 series become more persistent with weaker multifractality, indicating that short and medium dry/wet conditions (which are related to soil moisture and crop stress) become easier to predict, while SPEI-12 changed toward a more random regime and stronger multifractality in the eastern and central parts of the country, indicating that long-term dry/wet conditions (related to streamflow, reservoir levels, and groundwater levels) become more difficult for modeling and prediction. These results indicate that the complexity of dry/wet conditions, in this case described by the multifractal properties of the SPEI temporal series, is affected by climate change.

Integrating canonical correlation analysis with machine learning for power quality disturbance classification

Abstract Recently, the rapid growth of Renewable Energy Resources (RER) in power generation has resulted in the frequent occurrence of Power Quality Disturbances (PQDs) within the power system. The timely and accurate detection of these PQDs is critical for maintaining good power quality while integrating RER into hybrid power systems to make them more robust and stable. In this paper, a multi-view dimensionality reduction approach based on Canonical Correlation Analysis (CCA) is proposed to differentiate different types of PQDs. Here, a dataset of 29 types of PQDs which include nine single types and twenty multiple types of PQDs have been generated using their mathematical model in MATLAB for experimentation. CCA being multi-view dimensionality reduction technique maximizes the correlation between two different views of the data. Here two cases of datasets have been considered for further exploration, Case 1: PQDs without noise and with 20 dB noise, Case 2: PQDs with 20 dB and 30 dB noise. Furthermore, to test the efficacy of CCA in both cases, the extracted features have been tested using four different classifiers i.e. K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF). The performance of each of the classifiers has been tested on five different performance metrics such as precision, recall, F1 score, hamming loss and accuracy and the results shows that the proposed technique of multi-view dimensionality reduction is capable of classifying the PQDs with two different views at a time.

Positive Impacts and Challenges of School-Based Management in New Generation Schools and Resource Schools in Cambodia

Positive Impacts and Challenges of School-Based Management in New Generation Schools and Resource Schools in Cambodia

Analysis of Indicators of the Electrical Power Production Enterprises’ Pollution and Harm Risk Degree in Terms of the Paris Agreement

Abstract In the current decade, electricity consumption will increase by more than 16%. The main resources for electricity generation are oil – 2.53%; gas – 22.9%; coal – 35.98%; nuclear industry – 9.84%; hydraulic power regeneration – 15.01%; renewable sources – 12.85%; other – 0.89%. This causes global warming of the planet due to greenhouse gases increased emissions into the atmosphere. A resource capable to cover the thermal energy field’s balance is the nuclear energy. To compare the resources’ polluting capacity, it is necessary to introduce a general indicator that takes into account their properties. This study proposes an integrated approach based on the formation of groups of indicators reflecting greenhouse gas emissions; power consumption; economic activity, air quality, etc. For comparison needs, all indicators are normalized in dimensionless form and compiled with reference to their specific weight. Calculations of the pollution index for fossil resources and nuclear energy, carried out using this algorithm proved that the nuclear power, with a careful consideration of all possible polluting radionuclides (11 components), exceeds this indicator for gas by 22%, but is lower than ciphers for coal and oil by 36 and 26%, respectively. For two latters only 3 specific components being taken into account. Therefore, it seems advisable to use the considered complex indicator of environmental pollution to assess the resource safety level for electricity generation.

РАЗВИТИЕ НА УСТОЙЧИВИЯ ТУРИЗЪМ В БЪЛГАРИЯ

The present report explores how Bulgaria integrated sustainable tourism strategies in the global sustainable tourism practices. The strategies aim to use tourism as a tool for socio-economic benefits while also preserving its environmental and cultural resources for future generations. The document covers the economic and social aspects of sustainability, emphasizing the importance of progress in harmony with environmental conservation. It discusses policies that promote responsible tourism practices, with a focus on preserving natural ecosystems and cultural heritage. The report also highlights the significance of waste management and investment in renewable energy sources in advancing the tourism sector sustainably. The main objective is to demonstrate Bulgaria's commitment to sustainable tourism, which aims to cater to the evolving demands of global travelers while ensuring the long-term viability of its tourism industry. This approach aligns economic interests with the preservation of natural and cultural treasures, positioning Bulgaria as a favored destination for sustainable tourism on the international stage.

Exploring the Photocatalytic Cleavage Pathway of the β-5 Linkage Lignin Model Compound on Carbon Nitride.

As a globally abundant source of biomass, lignocellulosic biomass has been the centre of attention as a potential resource for green energy generation and value-added chemical production. A key component of lignocellulosic biomass, lignin, which is comprised of aromatic monomers, is a potential feedstock for value added chemical production. The cleavage processes of the linkages between monomers to obtain high value products, however, requires significant investigation as it is a complex, non-facile process. This study focuses on the photocatalytic valorization of a β-5 lignin model compound, a key linkage in the lignin structure. It was found that greater yields of aromatic products were obtained from the photocatalytic conversion of β-5 lignin model compound using carbon nitride (CN) when compared to Evonik P25 titanium dioxide (TiO2). Products of the β-5 model compound photocatalytic conversion were determined and C-C bond cleavage was observed. It was also determined that the solvent participated in the reactions with the introduction of a cyano group to one of the products. Radical quenching experiments revealed that superoxide radicals participated in the CN photocatalytic conversion. These results reveal for the first time the products and possible mechanism of the photocatalytic transformation of β-5 model compounds using CN photocatalysis.

Harnessing Artificial Intelligence in Stock Market Investments: A Neomarxist Approach to Alternative Socioeconomic Funding Agendas for National State Revenue Accumulation

In the contemporary economic, sociological and political landscape, the successful integration of Artificial Intelligence (AI) computer programs into the predicted behaviour of stock market value in the financial markets represents a profound burgeoning frontier in economics, with profound implications for both wealth generation and national budget resource allocation. This paper explores the potential sociopolitical economic strategy of using AI-driven stock market prediction predictions to cumulatively accumulate capital funded for by taxpayer sourced representative income. As the baseline return accumulates in the form of investment potential then as a means of revenue accumulation for the state then the burden of the national budget upon its citizens decreases then ceases. This is the theorisation of the establishment of a Marxist economic social superstructure for the funding of social institutions through the powerful potential of artificial intelligence. By leveraging a portion of the national budget on advanced AI technologies, this strategy could dramatically facilitate the financial sustenance of political budgets aimed at social equity and economic justice. Through a critical review of sociological and economic theories, this theoretical research delineates exactly how such an approach could potentially reconfigure the mode of production and contribute to the reformation of a sustainable socialist economy.

Adaptive Two-Stage Stochastic Programming with an Analysis on Capacity Expansion Planning Problem

Problem definition: Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expansion planning problem with different generation resources including renewable energy. We demonstrate the value of adopting adaptive two-stage approach against the existing policies under limited flexibility and highlight the efficiency of the proposed heuristics along with practical implications on the studied problem. Funding: This work was supported by the National Science Foundation [Grant 1633196] and the Office of Naval Research [Grant N00014-18-1-2075]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0157 .