Strategic Planning for Energy and the Environment

Feasibility Analysis of a Novel High-Production Saltworks Powered by Renewable Energy: An Indonesian Case Study

Makhfud Efendy, Fiki Milatul Wahyu, Nizar Amir, Misri Gozan — Wed, 30 Oct 2024 00:00:00 +0100

This research introduces an innovative solution combining wind-aided intensified evaporation (WAIV) technology with tunnel systems integrating renewable energy sources in salt production. The objective is to facilitate year-round salt production, regardless of unfavorable weather conditions. The study encompassed five Indonesian regions: Sampang, Indramayu, Pati, Bima, and Kupang. It employs a comprehensive analysis covering technical, economic, and environmental aspects. The results demonstrate that the proposed concepts can significantly enhance salt production. Feasibility indicators are positive across all regions, affirming the viability of implementing this concept successfully. Extensive sensitivity analyses were conducted, adjusting critical variables such as salt pricing to assess project feasibility.

Allocation of National Energy Saving Target to Provinces for Vietnam Using Cluster Analysis Method

Chau Dinh Van, Kien Duong Trung, Minh Nguyen Dat — Wed, 30 Oct 2024 00:00:00 +0100

Vietnam government has adopted a series solutions and policies to improve energy efficiency to meet the national energy efficiency target of period 2019–2030. The Vietnam’s provinces will act as main actor for the national achievement of energy efficiency. Thus, understanding the province’s potentiality of energy efficiency is useful for the harmonious and sustainable development between the economy and energy systems as well as for finding the best way of allocating energy saving target to each province. Based on the difference of GDP development and energy consumption levels, the target of energy efficiency for each province through clustering is set. The provinces of Vietnam clustered into 7 groups by using clustering method. The results show that 33 provinces included in the cluster 1, 2, 3, 4 and 6 should burden a heavy contribution of energy saving. Among them, the provinces in the cluster 2 and 3 need to focus on improvement of energy saving in the industry sector. The cluster 7 included the under-developed provinces can learn development’s experiences of the provinces in the cluster 1, 2, 3 and 4 to find the best way of their future development. The objective of this study is to clarify and provide a comprehensive target for energy saving allocation at provincial level of Vietnam.

Influence of Digital Economy on Ecological Carbon Environment in North China Under Spatiotemporal Synergistic Evolution

Dongfang Dai — Wed, 30 Oct 2024 00:00:00 +0100

North China is one of the significant geographic subregions in China, and it is quite important to analyze its digital economy and ecological carbon environment. Firstly, the digital economy level and ecological carbon environment in North China between 2010 and 2019 were measured. Then, the coupling coordination degree model (CCDM) was utilized to examine the impact of the digital economy on the ecological carbon environment. The digital economy level in North China consistently increased between 2010 and 2019. The average value rose from 0.163 in 2010 to 0.645 in 2019. Moreover, carbon emission intensity showed a year-on-year decrease, with an average reduction of 23.47% during the same period. The degree of coupling coordination displayed an upward trend, and Beijing showed the highest coordination among all provinces and cities. These results highlight the impact of the digital economy level on the ecological carbon environment. Some suggestions were proposed for the future progress of the digital economy and carbon emission in North China.

Real and Simulated Performance Assessment and Tilt Optimization of 5 MW Solar PV Plant Operating Under Tropical Climate of an Indian Island

Pushp Rai Mishra, C. H. Kiran Kumar — Wed, 30 Oct 2024 00:00:00 +0100

A comprehensive analysis conducted to evaluate thereal and simulated performance of a 5 MWp solar PV plant in installed in an Island of India. The average daily solar radiation ranged from 3.68 kWh/m² in July to 6.88 kWh/m2 in March, with an annual average of 4.92 kWh/m². Temperature data showed consistent trends throughout the year, with monthly averages ranging from 26.0^∘C to 32.1^∘C. Regarding electricity production, real data of the PV plant ranged from 13.51 MWh in October to 27.22 MWh in March, with simulated values showing similar trends. The performance ratio (PR) varied seasonally, with real PR ranging from 54.7% to 92.9% and simulation software predicting values above 73.8%. Additionally, capacity utilization factor (CUF) values fluctuated throughout the year, with real values ranging from 11.3% to 22.7% and simulations showing similar trends. The optimum tilt for the solar PV system was investigated estimating energy production at different panel tilt angle ranging from 0 to 26^∘C. The maximum energy production was found at 15 degrees due to maximum solar radiation. The study helps in identifying suitable locations for solar PV projects in tropical climates, with its simulation approach applicable to similar climates worldwide.

Forecasting of Solar Irradiation Based on the Deep Learning Model Using Complete Ensemble Empirical Mode Decomposition Technique

Anuj Gupta — Wed, 30 Oct 2024 00:00:00 +0100

Since solar energy plants have been developing so quickly in recent years, accurate solar power forecasting plays a significant role in contemporary intelligent grid systems and setup a bilateral contract negotiation between suppliers and customers. This paper introduces a novel three-step method for predicting two-channel solar irradiance that combines cutting-edge techniques like Bidirectional Long Short-Term Memory, Wasserstein Generative adversarial Networks and Complete Ensemble Empirical Mode Decomposition with adaptive noise. This methodology that is being suggested involves a framework of decomposition integration which allows for the effective isolation of the solar output signal into sub-sequences that are simple and can be differentiated based on their unique frequency disparities. This is achieved after the subsequences have been separated, and then they are subjected to individual prediction models. The high subsequences are predicted using the WGAN model, while the low subsequences are forecasted using the Bi-LSTM model. This approach ensures the specific characteristics of each subsequence are captured in the prediction process. The experimental result shows that the proposed model surpasses the performance of the suboptimal model in terms of evaluation metrics: RMSE, MAPE and R $^{2}$ . Compared to the suboptimal model, the RMSE, MAPE of all seasons

Unleashing the Power of Civil Society: Examining the Impact of the Saudi Green Building Forum in Advancing Eco-friendly Construction in Saudi Arabia

Faisal S. Alfadl, Medani P. Bhandari — Wed, 30 Oct 2024 00:00:00 +0100

This paper delves into the role of civil society in propelling green building practices, one key to a sustainable future. It focuses on the Saudi Green Building Forum (SGBF) “in Consultative Status with the United Nations Economic and Social Council, since 2017” as a case study that illuminates how civil society organizations contribute to environmental stewardship, social equity, and economic prosperity in the construction sector. The concept of green building is traced from its origins through its global development, explicitly emphasizing notable examples from Gulf Cooperation Council (GCC) countries and Saudi Arabia. The paper reassures the audience by outlining the unique and robust alignment of SGBF’s initiatives and its support for the Sustainable Development Goals (SDGs), underscoring its commitment to global sustainability.

The study presents a descriptive analysis based on desktop research, illustrating the transformative impact of civil society in promoting sustainable construction practices through the SGBF. Key aspects covered include the historical evolution of the green building movement, successful implementations of green building standards, and the activation of civil society institutions and organizations in driving sustainability. The paper showcases SGBF’s strategies and projects as exemplary models of civil society’s contribution to greening the built environment and fostering a sustainable future in Saudi Arabia and beyond. The objective is to inspire by highlighting civil society’s far-reaching impact on the construction sector’s environmental, social, and economic dimensions of sustainable development.

Climate Change Implications on Middle Eastern Geography and Stability – A Case Studies of GCC- with a Focus on the Kingdom of Saudi Arabia

Mohamad Mokdad, Haila Al-Mekaimi, Medani P. Bhandari — Wed, 30 Oct 2024 00:00:00 +0100

The environment constitutes the natural setting where humans reside, secure food sources, and utilize natural resources to fulfill their material and life necessities. Unlike other creatures, humans have not adapted to nature and the surrounding environment; instead, they have manipulated it to their advantage, disrupting the ecological balance. The primary catalyst for this disruption was the Industrial Revolution, which significantly increased pollution rates, harmful emissions, and the extraction of raw materials, thereby intensifying the exploitation of natural resources. The gravest concern is that the damage inflicted upon nature may reach a point of no return, with catastrophic and enduring consequences, such as global warming, ice melting, rising sea levels, and escalation in greenhouse gases, along with severe repercussions of these events, including an increase in mortality rates due to natural disasters. Effective responses necessitate actions at various levels, including cultural and educational initiatives and international measures, particularly in legislation. Such laws must enforce stringent controls on emissions from industrial and automotive sources. Individuals are called upon to conserve electricity and water, and adopting renewable energy sources, such as solar, hydro, wind, and tidal energy, that do not rely on fuel combustion is encouraged. Planting trees, which absorb carbon dioxide as a primary contributor to global warming, is among the most critical measures we can undertake. The Gulf Cooperation Council (GCC) is essential in combating climate change in this arena, and the Kingdom of Saudi Arabia is leading in this. This paper elaborates on the impact of climate change in Middle Eastern countries using a brief case study of the Saudi Green Initiatives. The paper also presents the policy recommendations to the concerned stakeholders of the GCC and the government of Saidi Arabia.

Harnessing Solar and Hydropower in Pakistan: A Strategic Approach to Climate Change Mitigation

Medani P. Bhandari, M. Zubair Rathore, Sadaf Zahoor — Wed, 30 Oct 2024 00:00:00 +0100

This study aims to conduct a thorough examination of Pakistan’s energy sector, with a specific focus on renewable energy (RE) and nonrenewable energy (non-RE) sources. It intends to delve into the potential of solar and hydropower resources within Pakistan and their capacity to mitigate the effects of climate change, thus centering the discussion on RE, particularly solar and hydropower. Adopting secondary data sources, this study seeks to provide a comprehensive analysis of the current state of Pakistan’s energy sector and the challenges that it faces.

One of the key findings highlighted in the analysis is the serious problem within Pakistan’s energy sector: existing institutions struggle to cope with the increasing demand for energy owing to insufficient growth in power generation capacity. This inadequacy has led to an increase in CO $_{2}$

emissions, primarily driven by the country’s growing reliance on fossil fuels to meet its energy needs. Despite this challenge, Pakistan possesses substantial potential for Resources, particularly solar energy and hydropower.

Following an evaluation of renewable energy resources, this study proposes recommendations to support the adoption and utilization of renewable energy as a critical component of sustainable growth. These suggestions are geared toward accelerating the transition to renewable energy sources, thus positioning Pakistan for a more sustainable and resilient energy future.

This research paper underscores the urgency of addressing the shortcomings in Pakistan’s energy sector and emphasizes the pivotal role of renewable energy, especially solar and hydropower, in mitigating climate change and fostering sustainable development. By leveraging its abundant renewable energy resources and implementing strategic measures, Pakistan can chart a path toward a more environmentally friendly and economically viable energy landscape.

Is Green Energy the Pathway to Sustainability? – An Explanation From the Perspective of Degrowth Theory

Yiming Yuan, Li Li, Rui Zhang — Wed, 30 Oct 2024 00:00:00 +0100

The discourse surrounding energy transition is intensifying due to insufficient global energy resources. Nonetheless, given that the capitalist economy prioritizes expansion and growth, it remains uncertain whether green energy solutions can genuinely pave the way towards sustainability and address the challenges of climate change, energy shortages, and food security concerns. Degrowth calls for a radical reorganization of politics and economics to reduce resource, energy consumption. This paper discusses the crisis of capitalism from multiple dimensions. It uses the Analytic Hierarchy Process (AHP) as an approach to explain degrowth and green energy. It also explores green energy in terms of three aspects of sustainable development: the profit-making industry, the high-efficiency paradox, and the re-challenge to the environment. Finally, it discusses the possible pathways for society to solve the global environmental crisis in the context of degrowth. These movements encourage critical thinking about the transition to green energy and offer a feasible pathway to sustainability without relying on growth ideology. This highlights the viability of the degrowth framework as a means to address a range of problems.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Shaohui Zou, Jing Zhang — Wed, 30 Oct 2024 00:00:00 +0100

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Comprehensive Evaluation Research on the Sustainable Development Level of Rural Energy Based on CRITIC-MABAC in Henan Province

Li Peng, Li Huixuan, Zu Wenjing, Wang Shiqian, Zhang Hongkai — Wed, 30 Oct 2024 00:00:00 +0100

Energy shortages are a key issue at present, and climate problems caused by the extraction and use of traditional fossil energy sources are also creating new challenges for rural development, with sustainable rural energy development being a priority at present. This paper takes the sustainable development of rural energy as the research object, and evaluates the development of rural energy in each prefecture-level city in Henan Province. On the one hand, rural development is viewed from the perspective of energy, on the other hand, the evaluation results provide a reference for each prefecture-level city, and most of the energy data can be obtained directly and with a high degree of accuracy, based on the CRITIC – MABAC methodology to determine the weights from an objective point of view and to rank the evaluation results, which is capable of obtaining the most accurate evaluation results. Finally, we used energy-related data from Henan Province in 2022 to conduct empirical analyses, and verified that Shangqiu City has a high level of sustainable rural energy development, which verifies the feasibility of the methodology proposed in this paper, and gives relevant recommendations.

A New Grey Prediction Model and Its Application in Renewable Energy Consumption

Bi Ge, Zhenyan Shang — Wed, 30 Oct 2024 00:00:00 +0100

Renewable energy is an energy resource that can be used continuously. At present, international oil prices continue to rise, the problem of global climate change is becoming increasingly prominent, and renewable energy and clean energy have ushered in a new round of development opportunities. Based on the new gray prediction model, this paper forecasts the consumption of renewable energy and further analyzes the sustainable development of renewable energy. In this paper, the combinatorial optimization method of cumulative order, background value coefficient, and initial conditions, parameter optimization combination, parameter combinatorial optimization process of the gray prediction model, and parameter optimization mechanism based on the PSO algorithm are proposed, and the reduction error analysis is carried out. The consumption of wind power and photovoltaic renewable energy is forecasted, and three different forecasting methods as exponential smoothing method, time series analysis method, and new gray forecasting method are compared, and the wind speed, irradiation intensity, and load are forecasted by these three different forecasting methods. Compared with the time series analysis method and the exponential smoothing method, the RMSE of the new grey prediction method is reduced by 127.12% and 160.59%, and the error rate is reduced by 3.16% and 4%, respectively. Based on the consumption forecast of renewable energy, this paper analyzes its sustainability from three directions economy, resource supply, and environment, and finally gives energy policy recommendations.

Modeling and Energy Flow Calculation of Integrated Energy System Based on Partial Differential Equation Model

Xiaofeng Xu — Wed, 30 Oct 2024 00:00:00 +0100

This paper discusses the modeling and energy flow calculation method of integrated energy system based on partial differential equation model. By constructing a model that integrates power, heat, and natural gas networks, we analyze in detail the process of energy transmission, conversion, and storage in the system. In the process of modeling, the influence of compressor in constant compression ratio, constant outlet pressure and constant natural gas flow is specially considered, and the accuracy of the model is verified by specific data. In terms of energy flow calculation methods, we compare the performance of the unified solution method and the decomposition solution method. Data analysis shows that the non-gradient descent iterative method, gradient descent iterative method and decomposition solution method show consistency in calculation accuracy, that is, the calculation results of the three methods are the same. However, in terms of computational efficiency, the gradient descent iterative method shows significant advantages. Specifically, under identical computing conditions, our analysis reveals that the gradient descent iterative method exhibits a convergence rate approximately 30% faster than the decomposition solution method, resulting in a notable reduction of around 25% in computational time. This pivotal observation serves as a solid foundation for selecting a more computationally efficient approach in practical applications. To further enhance the computational efficiency, we have delved into deriving the Jacobian matrix of the model and subsequently proposed an advanced gradient descent iterative calculation technique. Through the actual test, this method not only improves the calculation speed, but also ensures the stability and accuracy of the calculation. The research in this paper not only provides a strong theoretical support for the optimal operation of the integrated energy system, but also provides a valuable reference for future research in related fields. Through specific data analysis, we prove the effectiveness and practicability of the proposed method, laying a solid foundation for the sustainable development of integrated energy systems.

Hydro-thermal Power Reserve Allocation Under Outage Uncertainty

Michal Roubalik, Vaclav Dostal — Wed, 30 Oct 2024 00:00:00 +0100

In recent decades, the energy sector, namely energy markets have proved to be changing considerably. Most of these changes result in increasing volatility related to global political and environmental factors. This volatility affects all entities operating in the power sector. This paper focuses on the power plant operator’s perspective. First, the global context of the power sector is discussed and the power plant operator’s role is reviewed. Further, the problem of power reserve is discussed revealing the motivations of the presented work. The central contribution of this work lies in the formulation and development of a stochastic model of power reserve coupled with a mixed-integer programming scheduling model. In the results section, we show that the developed model allocates power reserve in an optimal way respecting the technical and economic parameters of the power plants as well as handling the stochastic nature of the power reserve allocation problem by minimizing the mean value of the sum of penalty for not delivering contracted power and lost opportunity.