Power Generation Structure Research Articles

Comparative Study on Environmental Impact of Electric Vehicle Batteries from a Regional and Energy Perspective

Against the backdrop of the global goal of “carbon neutrality”, the advancement of electric vehicles (EVs) holds substantial importance for diminishing the reliance on fossil fuels, mitigating vehicular emissions, and fostering the transition of the automotive sector towards a sustainable, low-carbon paradigm. The wide application of electric vehicles not only reduces the dependence on non-renewable resources such as oil, but also concurrently effectuates a substantial reduction in carbon emissions within the transportation sector. In the realm of electric vehicles, ternary lithium batteries (NCM) and lithium iron phosphate batteries (LFP) are two widely used batteries. This study examines the resource utilization and environmental repercussions associated with the production of 1 kW ternary lithium batteries and lithium iron phosphate batteries, employing a life cycle assessment (LCA) framework. The importance of clean energy in reducing environmental pollution and global warming potential is revealed by introducing five different power generation types and the regional power generation structure in China into the power battery production process. The findings of the investigation indicate that lithium iron phosphate batteries exhibit pronounced superiority in terms of environmental sustainability, while ternary lithium batteries are more advantageous in terms of performance. The mitigation of environmental pollution associated with battery production can be significantly achieved by the holistic integration of clean energy sources and the systematic optimization of manufacturing processes. Specific interventions encompass enhancing the energy efficiency of the production process, incorporating renewable energy sources for power generation, and minimizing the utilization of hazardous materials. By implementing these strategies, the battery sector can advance towards a more environmentally benign and sustainable trajectory.

Betavoltaic rechargeable Zn-ion battery based on hybrid cathode by combining betavoltaic structure with intercalation host

To address the issue of low power output of betavoltaic cells in practical applications, a 63Ni-powered betavoltaic rechargeable Zn-ion battery (BRZiB) is presented in this paper for simultaneously harvesting, converting, and storing beta-radioactive energy within one device. Experimentally, a patterned ZnO microrod array nanostructure was prepared using hydrothermal and lithography methods, in which a 63Ni beta source was deposited by chemical plating to form a 63Ni@ZnO structure for betavoltaic power generation. A carbon-coated VO2 nanocomposite (C@VO2), used as the intercalation host for Zn ions, was coated on the betavoltaic structure for fabricating the hybrid cathode of BRZiB. Without using any external power source, the 63Ni-powered BRZiB demonstrated a voltage rise of 0.3 V in 82 h, which was calculated to have an energy conversion efficiency of 5.1% and an energy density of 128 mWh/g over a half-life of 63Ni (∼100 years). The experimental results verify the effectiveness of multi-energy synergistic conversion in the electrochemical betavoltaic system.

Provincial inequalities in life cycle carbon dioxide emissions and air pollutants from electric vehicles in China

It is still unclear whether emissions reductions from electric vehicles can be achieved across different regions from a lifecycle perspective. Here we use the life cycle assessment model and Quasi Input-Output model to evaluate the carbon dioxide emissions and air pollutants of internal combustion engine vehicles, plug-in hybrid electric vehicles, and battery electric vehicles in different provinces of China, with the provincial electricity consumption data and the sales data of electric vehicles. We find that battery electric vehicles have achieved 11.8% and 1.1% reduction in carbon dioxide and nitrogen oxide emissions, respectively, compared to internal combustion engine vehicles. In contrast, the emissions of sulfur dioxide and particulate matter 2.5 increased by 10% and 20%, respectively. Due to the coal-based power generation structure and the cold weather, the emission intensity of battery electric vehicles in most northern provinces is higher than that in southern provinces. From 2020 to 2030, improving technological progress and optimizing electricity mix will greatly assist in achieving emissions reduction. The results can help policy-makers better understand the emission characteristics and reasonably plan future emission reduction strategies in transportation.

Addressing offshore wind farms compatibilities and conflicts with marine conservation through the application of modelled benchmarking scenarios

Offshore wind power generation structures are scheduled for development in the Canary Islands, potentially resulting in spatial overlap with various maritime activities, particularly fishing. It is crucial to point out that some areas that are considered suitable for installing these structures are in protected zones that are part of the Natura 2000 network, and do not have any prior environmental impact assessments. The research delved into the efficacy of utilizing Ecopath with Ecosim software to examine the consequences of implementing this technology in the study area, employing an ecosystem-based approach. To address this question, simulations were executed by assessing three distinct scenarios. The results suggest that there would be changes in the distribution of keystone species such as top predators, alongside a conspicuous decline in the abundance and catches of target species of the fishery. The Ecospace model holds the potential to forecast the impacts of offshore wind installations; however, crucial factors must be carefully considered, such as the lack of information. Notwithstanding the constraints, research like this demonstrates the efficacy of spatial ecosystem modelling in exploring this issue.

Low-carbon Development Path of Ordos Based on LEAP Model

To determine the low-carbon development path of Ordos, three scenarios （baseline scenario, low carbon scenario, and enhanced low carbon scenario） were constructed based on the LEAP model to forecast the energy demand and carbon emission in Ordos from 2020 to 2050 and to analyze the contribution of various policy initiatives to reduce carbon emission. The results showed that under the enhanced low carbon scenario, the energy demand in Ordos peaked at 52 million tons of standard coal equivalent in 2025 and decreased to 40 million tons of standard coal equivalent in 2050, and carbon emissions peaked at 163 million tons in 2025 and decreased to 16 million tons in 2050, which was 88% lower than that in 2020. Regarding emission reduction contribution, comparing the baseline scenario and the enhanced low-carbon scenario, the increase in renewable energy power generation installation, the reduction in energy consumption of terminal energy use, and the increase in terminal electrification rate contributed to the emission reductions of 43%, 25%, and 24%, respectively. The Ordos should vigorously develop renewable energy and make full use of the rich endowment of wind and light resources； at the same time, it should promote economic transformation and gradually increase the proportion of high-value-added and low-energy-consuming industries in the industrial structure. For the power sector, the power generation structure should be adjusted. Traditional thermal power generation should be replaced by zero-carbon and low-carbon power generation technologies. For the industrial and transportation sectors, the terminal electrification rate should be increased, and the energy intensity should be reduced.

Influence of Wind Power Generation on the Steel Structure Design of Green Buildings

Wind power generation in renewable energy is the current research focus, but the relationship between wind power generation and green buildings is the basis for ensuring the stable operation of wind power generation, so it has become the current research focus. I propose a composite method based on the heat energy limit theory to study the relationship between wind power generation and the steel structure of green buildings, find the weak points of the steel structure in time, and ensure the continuous operation of wind power generation. The simulation results show that there is a negative correlation between wind power generation and green steel structure, and there is a close relationship between the design characteristics of the steel structure, the material and the installation method of wind power generation. The composite method proposed in this paper can monitor the operation effect of power generation at noon in time, and the accuracy of heat energy monitoring of steel structure can reach 80~90%, and the whole monitoring time is less than 15 seconds, which shows that the composite method proposed by us can provide support for the design and analysis of wind power generation and green steel structure buildings, and has theoretical feasibility. Therefore, strengthening the relationship between wind power generation and green steel structure design is the technical condition for the operation and implementation of steel structure, and it is also the guarantee of wind power generation.

Design and experimental study of a stepped magnetorheological damper with power generation

Traditional vehicle suspension magnetorheological dampers have problems with low output damping force and require additional energy input to operate, to improve the performance of the vehicle suspension magnetorheological damper, in this paper, we propose and investigate a stepped magnetorheological damper structure with power generation, and conducts structural design and magnetic circuit analysis. The effects of different currents, damping gaps, coil slot positions, and coil turns on the damping performance of the stepped magnetorheological damper with power generation are numerically studied. The magnetic circuit sensitivity analysis of the power generation structure and the magnetorheological damper structure is also performed. Experiments have verified the effects of different input excitations on damping and energy-feeding performance, and the results of numerical analysis have been verified. The results show that when the excitation coil is wound for 257 turns, the magnetic circuit requirements are met. And the influence of different amplitudes, frequencies, and currents on the output damping force was studied through experiments. The results showed that the damping force would increase with the increase of single parameter values. When the amplitude was 7 mm, the frequency was 1 Hz, and the current was 2 A, the output damping force could reach 4500 N, meeting the requirements for use.

Transition towards a hybrid energy system: Combined effects of renewable portfolio standards and carbon emission trading

A hybrid energy system (HES) utilizing complementary energy sources allows for high-quality development of renewables. It is a viable pathway in achieving the low-carbon transition of the power sector. Both Renewable Portfolio Standards (RPS) and Carbon Emission Trading (CET) regulate the low-carbon behavior of conventional power enterprises. This highlights the importance of investigating how the policy mix can drive the transition towards hybrid power generation. This paper develops an evolutionary game between local governments and power enterprises to explore the influence of policy-related parameters on the transition to a hybrid energy system. We find that implementing the policy mix is imperative to the transition when the technology cost of a hybrid energy system is not competitive. Power enterprises can control investment costs through the structure of multi-energy power generation, but are also limited by the lack of renewable absorption capacity. We also find an additive effect between RPS and CET, albeit with differences. Raising the price of tradeable green power certificates (TGC) can effectively facilitate the transition under either a single RPS policy or the policy mix. Reducing initial carbon permits makes sense only under the policy mix. Moreover, the coordinated implementation of multiple policies can contribute to the transition more efficiently, but it deserves attention to avoiding policy redundancy.

Exploring hydrogen metallurgy to CO2 emissions reduction in China’s iron and steel production: An analysis based on the life cycle CO2 emissions – LEAP model

Previous research has primarily assessed the environmental impact of hydrogen metallurgy from technical or project perspectives, lacking a life cycle perspective. The impact of continued cleanliness of the hydrogen production mix and power generation structure on carbon dioxide (CO2) emissions from the hydrogen metallurgy production process has not been investigated on a medium to long-term scale, which does not provide an adequate and comprehensive view of the contribution of hydrogen metallurgy to China’s emissions reduction, resulting in short-sighted technology choices. To address this lack, we construct a bottom-up energy system model that combines life cycle CO2 emissions with long-range energy alternatives planning (LEAP), including iron and steel (IS) production, power generation, and hydrogen production. Setting development paths of two typical hydrogen metallurgy technologies, including business-as-usual (BAU) and hydrogen metallurgy (HM) scenarios, we carefully evaluate the CO2 emissions impact of hydrogen metallurgy from a whole industry perspective and in each IS production stage. The results suggest that the hydrogen metallurgy transition will help the iron and steel industry (ISI) to achieve peak and deep carbon reduction. Under the HM scenario, CO2 emissions from the ISI would decrease to 816.28 million tons (Mt) in 2050, representing an additional 281.13 Mt reduction over the BAU scenario. Hydrogen metallurgy can accelerate CO2 reduction in raw material acquisition and material processing phases but may increase CO2 emissions in manufacturing and recycling phases. In 2050, hydrogen metallurgy will contribute an additional 12.49 Mt and 300.95 Mt of CO2 reduction to the raw material acquisition and material processing phases, respectively. However, CO2 emissions in the manufacturing and recycling phases would be 32.15 Mt and 0.22 Mt more than in the BAU scenario due to the increased electricity consumption and scrap demand of electric arc furnace (EAF) steelmaking, respectively. Over time, the optimization of hydrogen production and power supply mix will gradually reduce CO2 emissions in each IS production process. In 2050, the CO2 intensity of the hydrogen-rich shaft furnace direct reduced iron (H2-DRI)-EAF process will drop to 753.41 kgCO2/t, which is lower than all other processes except the 100 % scrap-based EAF process. Advancing the development of the H2-DRI-EAF process can effectively achieve CO2 emissions reduction while alleviating dependence on scrap resources. The results are expected to inform government policy on the development of hydrogen metallurgy in China and other countries.

Transparent integrated pyroelectric-photovoltaic structure for photo-thermo hybrid power generation

Thermal losses in photoelectric devices limit their energy conversion efficiency, and cyclic input of energy coupled with pyroelectricity can overcome this limit. Here, incorporating a pyroelectric absorber into a photovoltaic heterostructure device enables efficient electricity generation by leveraging spontaneous polarization based on pulsed light-induced thermal changes. The proposed pyroelectric-photovoltaic device outperforms traditional photovoltaic devices by 2.5 times due to the long-range electric field that occurs under pulse illumination. Optimization of parameters such as pulse frequency, scan speed, and illumination wavelength enhances power harvesting, as demonstrated by a power conversion efficiency of 11.9% and an incident-photon-to-current conversion efficiency of 200% under optimized conditions. This breakthrough enables reconfigurable electrostatic devices and presents an opportunity to accelerate technology that surpasses conventional limits in energy generation.

Characteristics and mechanism analysis of the clean evolution of China's power generation structure

The power industry in China is the foremost emitter of carbon, necessitating a clean transition in its power generation segment for successful decarbonization. Identifying and quantitative analysis the critical factors that shape the transition and their operational mechanisms will help achieve a secure low-carbon transformation of the energy system. Nonetheless, the body of extensive research in this particular field remains notably limited. To bridge this gap, this paper initiates by developing a comprehensive mechanism framework (REEP) for the transition towards cleaner power generation including Resource endowment (abbreviated as R), Electricity market (abbreviated as E), Economic development (abbreviated as E) and Policy implementation (abbreviated as P) four fields. This framework is based on monthly power generation data derived from four categories of power generation sources at the provincial level in China, spanning the years 2014–2019. It dynamically and quantitative elucidates the multifaceted impacts of various influencing factors on the evolution of the power generation structure. Based on the REEP framework, we observe the power generation structure evolution trends. The study's findings underscore the significance of resource endowment as a paramount internal factor influencing the clean evolution of the power generation structure, while economic development emerges as a pivotal external driving force propelling its transformation. Despite wind and solar power generation accounting for a relatively small proportion at the end of the 14th Five Year Plan period, it is imperative for China to continue promoting the extensive development of renewable energy. While we should coordinate national power transmission and distribution on the foundation of incentivizing technological innovation in emission reduction and the development of energy storage technology. This approach is essential to comprehensively advance the low-carbon and stable development of the energy system, ultimately facilitating the realization of the dual-carbon goal.

A closed‐loop representative day selection framework for generation and transmission expansion planning with demand response

AbstractIn power systems with a high proportion of renewable energy resources (RES), the inherent stochasticity and volatility of RES necessitate careful consideration in power system planning. Scenario analysis is commonly employed to address the stochastic nature in power system planning. Existing studies generally adopt an open‐loop structure, where representative days are selected first and planning decisions are subsequently made. However, this method may not accurately represent the operating status of a system owing to changes in the power generation structure during the planning process. To address this limitation, this paper introduces a closed‐loop framework for representative day selection within the context of generation and transmission expansion planning (G&TEP), incorporating demand response (DR). The framework comprises three layers: representative day selection, planning decisions, and long‐term operational simulation. Initially, an approach for selecting representative days is proposed by combining the clustering and optimization‐based methods. Subsequently, a G&TEP model that incorporates DR is presented in the second layer. Lastly, the framework encompasses a three‐layer closed‐loop structure, enabling dynamic adjustments and enhancements to the representative day selection process to ensure optimality. Case studies on the reliability and operational test system of a power grid with large‐scale renewable integration (XJTU‐ROTS) demonstrate the effectiveness of our proposed framework.

LEAP model-based analysis to low-carbon transformation path in the power sector: a case study of Guangdong–Hong Kong–Macao Greater Bay Area

As a major carbon emitter, the power sector plays a crucial role in realizing the goal of carbon peaking and carbon neutrality. This study constructed a low-carbon power system based on the LEAP model (LEAP-GBA) with 2020 as a statistic base aiming of exploring the low-carbon transformation pathway of the power sector in the Guangdong–Hong Kong, and Macao Greater Bay Area (GBA). Five scenarios are set up to simulate the demand, power generation structure, carbon emissions, and power generation costs in the power sector under different scenarios. The results indicate that total electricity demand will peak after 2050, with 80% of it coming from industry, buildings and residential use. To achieve net-zero emissions from the power sector in the GBA, a future power generation mix dominated by nuclear and renewable energy generation and supplemented by fossil energy generation equipped with CCUS technologies. BECCS technology and nuclear power are the key to realize zero carbon emissions from the power sector in the GBA, so it should be the first to promote BECCS technology testing and commercial application, improve the deployment of nuclear power sites, and push forward the construction of nuclear power and technology improvement in the next 40 years.

Driving factors analysis and scenario prediction of CO2 emissions in power industries of key provinces along the Yellow River based on LMDI and BP neural network

IntroductionPower industry is one of the largest sources of CO2 emissions in China. The Yellow River Basin plays a supportive role in guaranteeing the effective supply of electricity nationwide, with numerous power generation bases. Understanding the drivers and peak of CO2 emissions of power industry in the Yellow River Basin is vital for China to fulfill its commitment to reach carbon emissions peak by 2030.MethodsThe Logarithmic Mean Divisia Index (LMDI) model was employed to explore the drivers to the change of CO2 emissions in power industries of three study areas, including Inner Mongolia Autonomous Regions, Shanxi Province, and Shandong Province in the Yellow River Basin. And Back Propagation (BP) neural network was combined with scenario analysis to empirically predict the trend of the amount of CO2 emitted by power industry (CEPI) from provincial perspective.ResultsCEPI in Inner Mongolia under the scenarios of a low degree of CO2 emissions promotion with a medium degree of CO2 emissions inhibition (LM) and a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) scenario can reach a peak as early as 2030, with the peak value of 628.32 and 638.12 million tonnes, respectively. Moreover, in Shanxi, only CEPI under a low degree of CO2 emissions promotion scenarios (LL, LM, LH) can achieve the peak in 2025 ahead of schedule, with amounts of 319.32, 308.07, and 292.45 million tonnes. Regarding Shandong, CEPI under scenarios of a low degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (LH) and a medium degree of CO2 emissions promotion with a high degree of CO2 emissions inhibition (MH) could achieve the earliest peak time in 2025, with a peak of 434.6 and 439.36 million tonnes, respectively.DiscussionThe earliest peak time of CEPI in Shandong Province and Shanxi Province is 2025, but the peak of CEPI in Shanxi is smaller than that of Shandong. The peak time of CEPI in Inner Mongolia is relatively late, in 2030, and the peak is larger than that of the other two provinces. The per capita GDP is the most positive driving factor that contributes to the CEPI. Shandong has a strong economy, and its per capita GDP is much higher than Shanxi’s. Therefore, even under the same peak time, the CEPI in Shandong is much higher than that of Shanxi. Inner Mongolia is extensive and sparsely populated, which makes its per capita GDP rank among the top in China. In addition, Inner Mongolia’s coal-based power generation structure and high power generation also contribute to its late CO2 peak time and large CO2 peak.

Environmental Benefits of Pollution and Carbon Reduction by Bus Fleet Electrification in Zhengzhou

Electrification of bus fleets is an effective approach to reducing transportation-related pollution and carbon emissions. Evaluating the impact of electrification on existing bus fleets can provide valuable insights for promoting full electrification of public transportation in large cities. Utilizing the fuel life cycle method, we analyzed the CO2 and pollutant emissions of Zhengzhou's bus fleet before and after electrification and evaluated emissions under different electrification scenarios. Our results indicated that after electrification, the fuel life cycle CO2 and PM2.5 emissions increased by 32.6% and 42.6%, respectively, whereas CO, NOx, and VOC emissions decreased by 28%, 34%, and 25%, respectively. Optimizing the power generation structure is a critical factor in reducing CO2 and PM2.5 emissions during the electrification process. The best scenario for comprehensive electrification and power generation structure optimization could result in a 38.7% reduction in CO2, as well as reductions of 80.1% in CO, 84.4% in NOx, 92.2% in VOC, and 30.2% in PM2.5. Prioritizing electrification on long-distance routes is recommended during the replacement process. Additionally, replacing plug-in hybrid natural gas vehicles with pure electric vehicles has both advantages and disadvantages in terms of emission reduction. Achieving pollution reduction and carbon synergies requires advancing fleet replacement and power structure adjustments simultaneously.

Power Generation Mix Optimization under Auction Mechanism for Carbon Emission Rights

As the international community attaches importance to environmental and climate issues, carbon dioxide emissions in various countries have been subject to constraints and limits. The carbon trading market, as a market tool to reduce greenhouse gas emissions, has gone through a development process from a pilot carbon market to a national carbon market in China. At present, the industries included in the national carbon market are mainly the electric power industry, and the carbon emissions of the electric power industry account for about 40% of the national carbon emissions. According to the construction history of foreign carbon markets, China’s future carbon quota allocation will gradually transition from free allocation to auction allocation, and the auction mechanism will bring a heavy economic burden to the electric power industry, especially the thermal power generation industry. Therefore, this study takes Guangdong Province as an example to optimize the power generation mix with the objective of minimizing the total economic cost after the innovative introduction of the carbon quota auction mechanism, constructs an optimization model of the power generation mix based on the auction ratio by comprehensively applying the system dynamics model and the multi-objective linear programming model, systematically researches the power generation structure under different auction ratios with the time scale of months, and quantitatively evaluates the economic inputs needed to reduce the greenhouse gas emissions. The results of the study show that after comprehensively comparing the total economic cost, renewable energy development, and carbon emissions, it is the most scientific and reasonable to set the auction ratio of carbon allowances at 20%, which achieves the best level of economic and environmental benefits.

Multi-source data-driven technology research on carbon emission dynamics prediction in electric power industry

Abstract This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

An integrated economic, energy, and environmental analysis to optimize evaluation of carbon reduction strategies at the regional level: A case study in Zhejiang, China

China plays a crucial role in responding to global climate change. Provinces are the main sources of energy consumption and greenhouse gas emissions in China's economic and social development. However, it is still unclear how to achieve dual-carbon goals by formulating and implementing local policies to adapt to climate change. In this study, we take Zhejiang Province in China as the research object, based on the LEAP (Low Emissions Analysis Platform) model to construct four social scenarios under different policies, comprehensively considering regional economic characteristics, population, and energy consumption patterns. The results show that to achieve Zhejiang Province's goal of carbon peaking by 2030 while maintaining steady economic growth, additional measures are required to reduce energy consumption intensity or improve the power generation structure. Otherwise, energy demand will increase to 228.06 million tonnes of coal equivalent and carbon emissions will be 487.76 million tonnes in 2050. Moreover, developing clean energy and promoting CCUS technology can continuously reduce carbon emissions to 293.59 and 210.76 million tonnes respectively. The economic viability of CCUS power generation is contingent upon the development of carbon taxes in the future. Once the growth rate reaches 7.2%, power cost will be 167.77 billion RMB and CCUS will become economically advantageous in 2050.

Identification of clean energy development routes under carbon emission constraints: A path towards structural adjustment of the power system

Clarifying the clean energy (CE) development path is significant for realizing energy system transformation, coping with climate risks, and ensuring energy and ecological security. This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints.

Cost-Effective Hybrid Wind-Photovoltaic Generation System for Isolated Critical Loads: A Case Study

This study provides a technical and economic analysis of integrating a microgrid structure for power generation with wind energy and photovoltaic (PV) generation. The study mainly focuses on providing optimal generation from renewable energy sources for supplying isolated critical loads with optimized cost. Using HOMER software, a financial study of the proposed wind-PV-based microgrid is presented. A model is built with artificial neural network (ANN) to further tune the simulated economic optimization results obtained for further economic optimization and validation analysis. A reduction of about 22% in cost of energy is obtained from the ANN based optimization of system cost. This indicates that the proposed hybrid generation system is a profitable one serving localized loads in isolation with 100% renewable energy utilization. The proposed system can modelled and established in an onshore area in eastern India with optimized utilization of renewable sources for generation with minimal cost as observed.