Natural Power Generation Research Articles

Research on Provincial Distribution of Total Carbon Emissions in the Power Generation Industry under Carbon Neutrality Constraints

The power generation industry in China is at the forefront of implementing carbon trading market policies, playing a crucial role in carbon emission reduction. Carbon allocation in each province is pivotal for future economic development and achieving a gradual and orderly carbon peak. By analyzing total carbon emissions from 2012 to 2021 and examining emission responsibilities from a producer perspective, this study extends the STIRPAT model to evaluate factors influencing carbon emissions in the national and provincial power generation sectors. Key findings highlight the urgent need for industrial structure changes in provinces like Xinjiang, Tibet, Guangdong, Shandong, and Jiangsu, which bear significant emission reduction responsibilities. The spatial and temporal distribution of carbon emissions is shifting from the southeast coast to central and western regions. Factors such as energy consumption, electric power intensity, urbanization rate, power structure, economic development level, and industrial composition significantly impact carbon emissions. To achieve carbon peak goals, a multifaceted approach is essential for reducing emissions in China's power generation sector, emphasizing the need for strategic interventions across various perspectives.

Impacts of electric vehicle policy on the New Zealand energy system: A retro-analysis

Policy in New Zealand supports a transition to Electric Vehicles (EVs), which are projected to comprise 40% of the light vehicle fleet by 2040. The impact of EVs cannot be decoupled from the power generation. Therefore, in this study, New Zealand’s energy system (transport and national power generation) is modelled in LEAP and the impacts of a 40% transition to EVs assessed with a multiple-scenario 2019-based retro-analysis. Two charging scenarios are simulated, modelling “Optimistic” and “Concentrated” charging. Two methods of generation expansion are modelled: “Mixed Renewables” and “Non-Renewables”. Results show existing generation can support a 16% EV transition with Concentrated charging, and a 43% transition with Optimistic charging, without decreasing the reserve margin. It is expected a 40% EV transition with a Non-Renewable power system expansion will reduce energy sector GHG emissions by 2% and costs by 7%, and a Mixed Renewables expansion will reduce emissions by 17%–18% and costs by 4%–5%. These results suggest an EV transition is an effective method for New Zealand to meet GHG emission targets, particularly with renewable generation expansion. Important areas of future research are identified, including: alternative EV charging strategies, peak travel events, embodied vehicle emissions, and the costs of power-system upgrades.

Optimizing the sustainable energy transition: A case study on Trinidad and Tobago

Trinidad and Tobago is a small island developing state (SIDS) with one of the largest emitters of CO2 per capita globally - linked to a reliance on oil and gas. With the country’s commitment to sustainable development goals and climate change agreements, rapid redesign of the national power sector is critical to promoting a sustainable energy transition. Our study examines the minimisation of electricity costs (LCOE) and greenhouse gas emissions (GHGLC), using a mixed-integer linear programming model (MILP) across 5 scenarios describing the national power generation system. Our results show an LCOE of $USD 61.72 per MWh and GHGLC of 756 kgCO2eq per MWh, attributed to current take-or-pay Power Purchasing Agreements (PPA) aligned to Scenario 1 A. Coupled improvements in power generation technology and resource efficiency (Scenario 2), results in LCOE and GHGLC reductions of up to 40% ($USD 36.94 per MWh) and 24% (572 kgCO2eq per MWh), respectively. Furthermore, the incorporation of low carbon H2, MeOH and NH3 into the process system was deemed infeasible at current prices, despite potential avoided emissions when compared to local business-as-usual operations. Lastly, looking ahead to 2030 shows a general increase in LCOE of $USD 8.5 – 14 per MWh and GHGLC of 8 – 27 kgCO2eq, attributed to higher grid demand and natural gas prices. Thus, our results support the need for new policies linked to active decarbonisation within the national energy sector and removal of current legislation that discourages the application of low carbon technologies linked to sustainable development. Ultimately, through a multi-objective optimisation framework, our results highlight the value of systems-based planning and implementation in the sustainable energy transition across the Caribbean region, in accordance with sustainable development goals (SDGs).

The carbon footprint response to projected base stations of China's 5G mobile network

While the rapid expansion of China's 5G mobile network helps to speed up the nation's economic and social development, it tends to release more CO2 due to the 5G's significant energy demand, hampering sustainable development of the 5G network. Previous assessments of CO2 emissions from China's 5G development were based on a projected 5G network ranging from six to fifteen million base stations with the absent of a convincing business model in 5G's application. Under the scenario of business-estimated six million base stations in 2030, the share of electricity consumed by China's 5G networks in 2030 could reach 8.4 % of the national total power generation, causing 0.44 GtCO2/yr CO2 emissions. We collected 5G base station numbers in 2020 and 2021 in 31 provinces and province-level municipalities (PLM), the period with the rapid growth of the 5G base stations in China. We linked these provincial base stations with provincial Gross Domestic Product (GDP), population (POP), and big data development level (BDDL) and established a statistical model to predict 5G base stations by 2030. The model predicted 2–5 million 5G base stations by 2030, considerably lower than the business-projected base station number. Under the model predicted 5G base stations, China's 5G network could yield 0.15–0.29 GtCO2/yr emissions subject to the nation's BDDL from 40 to 80 % by 2030. Both 5G base stations and CO2 emissions are significantly lower than the previous estimates. We decomposed the CO2 footprint of China's 5G networks and assessed the contribution of the number of 5G base stations and mobile data traffic to 5G-induced CO2 emissions. We find that increasing the application of clean energy and promoting energy efficiency can reduce CO2 emissions in the 5G network. To more accurately estimate 5G's climate effect, we propose that it urgently needs to improve vivid 5G business models.

Present and Future Losses of Storage in Large Reservoirs Due to Sedimentation: A Country-Wise Global Assessment

Reservoir sedimentation is often seen as a site-specific process and is usually assessed at an individual reservoir level. At the same time, it takes place everywhere in the world. However, estimates of storage losses globally are largely lacking. In this study, earlier proposed estimates of sedimentation rates are applied, for the first time, to 47,403 large dams in 150 countries to estimate cumulative reservoir storage losses at country, regional, and global scales. These losses are estimated for the time horizons of 2022, 2030, and 2050. It is shown that 6316 billion m3 of initial global storage in these dams will decline to 4665 billion m3 causing a 26% storage loss by 2050. By now, major regions of the world have already lost 13–19% of their initially available water storage. Asia-Pacific and African regions will likely experience relatively smaller storage losses in the next 25+ years compared to the Americas or Europe. On a country level, Seychelles, Japan, Ireland, Panama, and the United Kingdom will experience the highest water storage losses by 2050, ranging between 35% and 50%. In contrast, Bhutan, Cambodia, Ethiopia, Guinea, and Niger will be the five least affected countries losing less than 15% of storage by 2050. The decrease in the available storage by 2050 in all countries and regions will challenge many aspects of national economies, including irrigation, power generation, and water supply. The newly built dams will not be able to offset storage losses to sedimentation. The paper is an alert to this creeping global water challenge with potentially significant development implications.

A novel matrixed nonlinear grey Bernoulli model for interval prediction of power generation

Development trends of power generation sequences have nonlinearity and uncertainty. The nonlinearity can be reflected by the nonlinear grey Bernoulli model. The uncertainty can be reflected by the interval sequence. A matrixed nonlinear grey Bernoulli model based on interval sequences is proposed in this paper. In order to make the model directly applicable to interval sequence, the traditional grey Bernoulli model is linearized by power function transformation, then the parameters are matrixized, and finally the recursive prediction formula is obtained by Cramer's law. Particle swarm optimization algorithm is used to estimate the nonlinear parameter. By adjusting the parameter, the new model can be used to predict exponential, saturated and parabolic interval sequences. The new model is used to predict the intervals of China's thermal power, hydro power, nuclear power, wind power, national power generation and power generation in east China. The prediction results show that the new model has higher prediction accuracy than the competing models. Finally, the power generation of four modes and national power generation from 2022 to 2025 are predicted and analyzed.

Optimal Strategy for Low-Carbon Development of Power Industry in Northeast China Considering the ‘Dual Carbon’ Goal

With China’s carbon peak and target proposed and the power system as an important source of carbon emissions, its carbon reduction issues are of great concern. However, the mismatch between power demand, resources, and generation facilities in different regions of China creates enormous carbon reduction pressures, especially in the region covered by China’s Northeast Power Grid (NE Grid). Therefore, in this study, a two-stage power optimization model considering Carbon Capture, Utilization, and Storage (CCUS) was established to optimize the national power generation and transmission in China from 2020 to 2060 first and then to optimize the regional road map of energy structure adjustment for China’s NE grid and its CO2 storage strategy. The first stage optimization results show that the national power interaction in China can make full use of the energy resource advantages of the NE grid, especially Jilin and Mengdong, delivering clean electricity to the North China grid as it reaches its “dual carbon” goal. Additionally, the second-stage optimization results show that the NE grid can make good use of CCUS for its thermal power transition. Annual CO2 storage will peak at 204 Mt in 2050 as thermal power withdraws in the NE grid region, which provides a reference for other regions to realize carbon neutrality of their power system by using CCUS.

Assessment of Hybrid Renewable Energy System in Beledweyne city Somalia, Technical and Economical Analysis

The rapid depletion of conventional fossil fuels with each passing day and increasing environmental concerns have given a great impetus to the studies on clean and renewable alternative energy sources. This study aims to investigate the feasibility of an on-grid, and off-grid hydro-wind-solar-battery hybrid system for electricity generation in Beledweyne, Somalia. The model is simulated with the HOMER program based on the energy-efficient system, has been successfully applied to find the best optimization result for the required amount of load. The economic and technical performance of a stand-alone combination of 100 kW hydropower, 200 kW solar photovoltaic, 400 kWh battery, and 80 kW wind power was investigated. Grid-connected megaprojects, such as large dams and energy pools, are essential but slow and expensive to increase national and regional power generation and transmission. This study is expected to shed light on investments to be made in Africa's off-grid and mini-grid solutions that are cheaper and faster to install and emphasizes that investments to be made in this direction can be a solution to Somalia's electricity crisis.

Effects of Climate Change on Hydropower Generation in China Based on a WEAP Model

Changes in climate patterns not only affect precipitation and precipitation patterns, but also cause the spatiotemporal redistribution of precipitation and runoff, affecting hydrogeneration in turn. Based on the coupling relationship between the Coupled Model Intercomparison Project 5 (CMIP5) climate change model and surface runoff in China, a database of China’s major hydropower stations was constructed in this study and the Water Evaluation and Planning model was applied to analyze the impacts of climate change on hydropower generation in China by region and basin under the Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios. During the forecast period, national power generation compared with base year first decreased in the 2030s and then increased in the 2070s, while a risk of excessive hydropower generation was concentrated in the southwestern provinces, Yangtze River Basin, and giant hydropower stations. During the 2030s, hydropower generation may face a risk of electricity generation decrease which will limit its contribution to the Nationally Determined Contribution target.

Bagasse-Based Sugar Cogeneration Potential in India: A Source of Renewable Energy

India has been suffering from an acute shortage of electricity for several decades. The main reason is that there is a large gap between the demand and supply of electricity. Therefore, the government of India has recently promoted cogeneration in the sugar industry. The national power generation potential from bagasse cogeneration, according to the Ministry of New and Renewable Energy (MNRE), a study by the Government of India, is about 3,500 MW, while Maharashtra's potential is 1,250 MW. Indian Sugar Industries has the potential to produce an excess capacity of up to 5,000 MW in all sugar mills. Maharashtra again became India's leading sugar-producing state five years later. It overtook Uttar Pradesh in sugar production. The total sugar production in Maharashtra is 138 thousand metric tonnes for 2021–2022. In India, the sugar industry is the second-largest agro-industry and contributes significantly to the socioeconomic development of the country. Sugarcane is the main raw material for sugar production in India. Sugar cogeneration is the concept of producing two types of energy from one fuel. One form of energy must always be heated, and the other may be electricity or mechanical energy. In the cogeneration industry, very high-efficiency levels in the range of 75% to 90% can be achieved.

Long-term transition of China's power sector under carbon neutrality target and water withdrawal constraint

Deep carbon mitigation and water resources conservation are two interacted environmental challenges that China's power sector is facing. We investigate long-term transition pathways (2020–2050) of China's power sector under carbon neutrality target and water withdrawal constraint using an integrated capacity expansion and dispatch model: SWITCH-China. We find that achieving carbon neutrality before 2060 under moderate cost decline of renewables by 10–20% depends heavily on large scale deployment of coal-fired power generation with carbon capture and storage (CCS) since 2035 in China's water-deficient northwestern regions, which may incur significant water penalties in arid catchments. Introducing water withdrawal constraints at the secondary river basin level can reduce the reliance on coal-CCS power generation to achieve carbon neutrality, promote the application of air-cooling technology, and reallocate newly built coal power capacities from northwestern regions to northeastern and southern regions. If levelized cost of renewables can decline rapidly by about 70%, demand for coal power generation with CCS will be significantly reduced by more than 80% and solar photovoltaic (PV) and wind could account for about 70% of the national total power generation by 2050. The transition pathway under low-cost renewables also creates water conservation co-benefits of around 10 billion m3 annually compared to the reference scenario.

Design and Simulation of an Off-Grid Photovoltaic System with Duty Cycle Prediction Using Neural Network Controller

Global concerns over the inappropriate utilization of abundant renewable energy sources, the damages due to instability of fuel prices, and fossil fuels' effect on the environment have led to an increased interest in green energy (natural power generation) from renewable sources. In renewable energy, photovoltaic is relatively the dominant technique and exhibits non-linearities, leading to inefficiencies. Maximum Power Point is required to be tracked rapidly and improve the power output levels. The target is to use a Neural network controller by training historical data of ambient irradiance and temperature levels as inputs and voltage levels as output for the photovoltaic module to predict duty cycles across the DC-DC converter. The DC-DC converter is the electrical power conditioner at the Botswana International University of Science and Technology, Palapye Off-Grid photovoltaic system. Perturb and Observe algorithm on PSIM environment is only implemented to acquire the historical data for the training and Matlab for the modeling of the network. Relatively long period ambient irradiance and temperature data of Palapye were acquired from the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) WeatherNet in Botswana. Matlab environment was used for the simulation of the backpropagation algorithm for training. The Neural network's feedforward to optimize the non-linear nature of the PV module input and output relationship with relatively fewer processes is required. The results show promising, and the Mean Errors appear to be typically about 0.1 V, and the best performance is 193.5812 at Epoch 13, while the regression delivered a relatively low measured error. The maximum power delivered by the duty cycles from the model with 90 % prediction accuracy. The article demonstrates Neural Network controller is more efficient than the conventional Perturb and Observe Maximum Power Point algorithm.

Status of electric vehicles in South Africa and their carbon mitigation potential

Road transport remains a key source of greenhouse gas (GHG) emissions in South Africa. The objective of this work is to investigate the status of electric vehicles (EVs) in South Africa and their potential to mitigate carbon emissions. Charging of EVs from the current electricity grid is more carbon intensive than new internal combustion engine (ICE) vehicles in the country. This study advances knowledge about GHG abatements by new motorcars in South Africa and demonstrates potential mitigation growth and the contribution thereof between EVs and technology improvement in ICE vehicles and their overlap. The study shows that mitigation impact of EVs in South Africa will start to increase from 2040 because of decarbonized power system and growth of electrified vehicles. Electric motorcars are projected to reduce baseline GHG emissions of motorcars by 19% in 2050 while ICE motorcars will contribute 63%. These results show that electric motorcars and overall technology improvements in motorcars will have significant influence in the reduction of road transport GHG emissions. Accumulated abated emissions of electric motorcars will overtake those of their ICE counterparts in 2050. Power grid is expected not to be fully decarbonized by 2050 and recharging batteries of EVs by non-renewable energy sources will produce net emissions. The main constraints to adoption of EVs in the country are the cost of EV purchase and limited charging infrastructure around the country. Furthermore, power supply constraints in South Africa are not conducive to rapid adoption of EVs as charging would burden the energy system. This shows that increasing national power generation with renewable sources would be invaluable. Provision of policy direction by the government with set EV sales, charging infrastructure targets and incentives would provide impetus for adoption of EV technologies in the country.

Agricultural Residue Management for Sustainable Power Generation: The Poland Case Study

The European Union has set targets for renewable energy utilization. Poland is a member of the EU, and its authorities support an increase in renewable energy use. The background of this study is based on the role of renewable energy sources in improving energy security and mitigation of climate change. Agricultural waste is of a significant role in bioenergy. However, there is a lack of integrated methodology for the measurement of its potential. The possibility of developing an integrated evaluation methodology for renewable energy potential and its spatial distribution was assumed as the hypothesis. The novelty of this study is the integration of two renewable energy sources: crop residues and animal husbandry waste (for biogas). To determine agricultural waste energy potential, we took into account straw requirements for stock-raising and soil conservation. The total energy potential of agricultural waste was estimated at 279.94 PJ. It can cover up to 15% of national power generation. The spatial distribution of the agricultural residue energy potential was examined. This information can be used to predict appropriate locations for biomass-based power generation facilities. The potential reduction in carbon dioxide emissions ranges from 25.7 to 33.5 Mt per year.

The prediction of CO2 emissions in domestic power generation sector between 2020 and 2030 for Korea

In the last ten years, reducing CO2 emissions has been a very important focus across all industries. To efficiently achieve carbon reduction in the power generation sector, various policies, regulations, and legislation have been proposed. In addition, new energy sources and technologies have been developed and widely adopted. In this study, current and future CO2 emissions from the domestic power generation sector were calculated and predicted based on two national power generation plans. The overall power plant efficiencies, operation rate of power plants, power capacities, and CO2 emissions for 2030 were predicted based on the 7th and 8th basic plan for long-term electricity supply and demand in Korea. In addition, the CO2 emissions policies of several major countries announced in accordance with the Paris Climate Agreement were identified and compared with Korea's climate change policy. Finally, the improvement of power generation efficiencies and co-combustion of biomass with coal is recommended to help the reduction of the BAU-based CO2 emissions by 19.4%.

Study of the emissions and spatial distributions of various power-generation technologies in China

The power sector is the main contributor to climate change and atmospheric pollution, and reducing the emissions from the power sector is essential for achieving sustainable development. However, few studies have accounted for the emissions from various power-generation technologies. In this paper, a high-resolution inventory of power plants with more than 6 MW accounting for 93% of the national power generation in China is established for the first time. Based on this inventory, the CO2, NOx and SO2 emissions of China's hard coal power, natural gas power, hydropower, wind power, photovoltaic power, biomass power and nuclear power are calculated by using the life cycle assessment method, and their spatial distributions in 2014 are further analyzed. The results show that China's power plants emitted 3.44 E+09 t CO2, 8.56 E+06 t NOx, and 1.19 E+07 t SO2, and the uncertainties were (−18.66%, +19.14%), (−67.10%, +66.41%) and (−88.69%, +87.61%) in 2014, respectively. Renewable-energy power plants accounted for approximately 15% of the national power generation but only contributed less than 1% of the total emissions. Meanwhile, in China's eastern cities, renewable-energy power accounts for the lowest proportion of the total installed capacity. In addition, both the emission amount and intensity were significantly higher in eastern cities than central and western cities. This study will help improve research on the emissions of all power-generation technologies in China and help achieve a comprehensive and systematic emissions reduction strategy.

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

Buildings’ energy efficiency measures effect on CO2 emissions in combined heating, cooling and electricity production

Valid tools for building designers and building simulation professionals to evaluate transparently CO2 saving potential of energy efficiency measures are needed to achieve CO2 reduction targets. Earlier studies have pointed out existing tools' weak interconnection between buildings and energy systems and the lack of transparency. This study developed and validated a multimode district energy system model including district heating, cooling, CHP and heat pumps, and connection to a national power generation, to evaluate buildings' energy efficiency measures effect on CO2 emissions. A calculation example demonstrates how important it is to take into account an interconnection between buildings and a wider energy system. Implementation of the robust model as a calculation tool is fully reported and validated against the measured data so that it fulfilled the calibration criteria. Calculation tool overestimated regional CO2 emissions only by 3% with small variation and correlation to real data was very good. Calculation example shows that buildings’ energy efficiency measures effect on CO2 emissions was highly dependent on CO2 intensity of marginal national electricity generation and electricity import when regional CHP production exists, and existing building stock was considered. Thus, interconnection between buildings, regional energy production and national electricity generation must be taken into account if regional CHP production and district heating demand changes exist. Annual average CO2 factors failed to provide reliable results when the CO2 saving potential of energy efficiency measures was evaluated and therefore should not be used.

Meteorological Drivers of European Power System Stress

A rapid decarbonisation of power systems is underway in order to limit greenhouse gas emissions and meet carbon-reduction targets. Renewable energy is a key ingredient to meet these targets; however, it is important that national power systems still maintain energy security with increasing levels of renewable penetration. The operating potential of renewable generation at times of peak demand (a critical time for power system stress) is not well understood. This study therefore uses a multidecadal dataset of national demand, wind power, and solar power generation to identify the meteorological conditions when peak demand occurs and the contribution of renewables during these events. Wintertime European peak power demand events are associated with high atmospheric pressure over Russia and Scandinavia and are accompanied by lower than average air temperatures and average wind speeds across Europe. When considering power demand extremes net of renewable power production, the associated meteorological conditions are shown to change. There is considerable spatial variability in the dates of national peak demand events and the amount of renewable generation present. Growth in renewable generation has the potential to reduce peak demands. However, these impacts are also not uniform with much larger reductions in peak demand seen in Spain than in central Europe. The reanalysis-derived energy models have allowed recent peak demand events to be put into a long-term context.

풍력자원지도를 이용한 한반도 풍력자원의 연간 변동성 분석

An UM-WRF numerical weather prediction system was established accommodating Korea Meteorological Agency’s regional/local data assimilation and prediction system, then the wind resource map of the Korean Peninsular for 2010~2018 was produced to analyze inter-annual variability of wind resource. The reliability of the map was validated by comparing with the national wind power generation data of Korea Power Exchange, and the agreement showed high correspondence of R2=0.83. The inter-annual variability of wind speed over inland and offshore were 11.4% and 4.3% of coefficient of variation, respectively. The wind resource in mountain region where most of wind farms located showed higher variability so that more accurate analysis is needed to predict an impact on the electricity grid by continuously increasing the spatial resolution of the wind resource map and extending its temporal coverage.