Power Generation Benefit Research Articles

Risk-benefit comparative analysis of different control methods for reservoirs under the water to electricity mode

Abstract For some small hydropower stations that are not under the control of power dispatching department, there are many ways to implement the reservoir operation scheme formulated under the premise of determining power by water. This paper respectively selects the output, flow and water level process as the control conditions, explores the risks and benefits of reservoir operation with different control variables, and selects the optimal operation scheme through comprehensive comparative analysis. Simulation results of a hydropower station in the Han River Basin show that when the output is taken as the control condition, the waste water is small, the power generation benefit is high, and the risk of the water level outside boundary is within the acceptable range. Considering the risk and benefit of reservoir operation and taking output as the control condition, it has significant advantages over water level and flow.

Performance prediction of ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon module based on experimental fitting method

The novel ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon modules (VL-BIPV) has a self-weight of only about 6 kg/m2, which helps to address weight-bearing challenges on low-capacity industrial building rooftops. However, the unique thermal dissipation features of the system pose challenges for the analysis of its photovoltaic performance and thermal processes. Therefore, a comparative experimental testing approach was employed, comparing the VL-BIPV system with a conventional rooftop photovoltaic system. Using the efficiency equations of the conventional photovoltaic system as a reference, the functional equations were fitted based on experimental results. Based on typical meteorological year data in Nanjing, annual power generation and PV cell temperature variations were evaluated, along with power generation and carbon reduction benefits over a 25-year lifetime. The results indicate that the VL-BIPV system achieves an annual power generation of 262.22 kWh/m2, a 6.52% increase compared to the conventional system. Additionally, the highest average and maximum cell temperatures in the VL-BIPV system during the year are 58.39 °C and 64.74 °C, respectively, both lower than the conventional system's peak at 68.34 °C. Over a 25-year lifespan, the VL-BIPV system reduces carbon emissions by 20.17 kgCO2/Wp, exceeding the conventional system's reduction by 1.25 kgCO2/Wp.

MOFs/MXene nano-hierarchical porous structures for efficient ion dynamics

Nature's various hierarchical structures exhibit exceptional performance in enhancing mass transport. However, how to bionic nano-hierarchical structures to enhance ion dynamics and achieve charge regulation is a significant challenge. Herein, a nano-hierarchical porous hybrid membrane inspired by nature was designed by integrating 1D metal-organic frameworks (MOFs) on the 2D MXene substrate utilizing MOF nanoparticles with tailorable positive and negative surface charges. The synergistic effects of the bioinspired nanohierarchical porous structure and surface charge interactions significantly enhance the performance and stability of the nanoconfined membrane, as corroborated by experimental characterizations and theoretical simulations. As a result, the optimized nanohierarchical porous membrane exhibits high a cation selectivity of 0.95, an outstanding power density of 35.04 W m−2, and excellent stability over one month. Synergizing enhanced mass transport and ion dynamics in nano-hierarchically structured materials with tailored charge improves osmotic power generation efficiency and benefits logic circuits with controlled charge transport.

Study on photovoltaic combined air source heat pump heating system under typical meteorological conditions

Abstract This paper proposes a photovoltaic (PV) combined air source heat pump heating system for rural areas in severe cold regions of northern China, where the current clean heating methods have high costs and low efficiency. The paper uses meteonorm8 and TRNSYS software to derive the typical meteorological parameters and simulate the system performance in a guard room in Shenyang city. The paper analyzes the heat pump power consumption, PV cell power generation, and economic and environmental benefits of the system under different outdoor temperatures in the heating season. The results show that the system can meet the heating demand and maintain a comfortable indoor temperature, with an annual cost of 1603 RMB, which is 61.33% lower than a single heat pump system. The system can also reduce the emissions of carbon dioxide, sulfur dioxide, and dust by 2619.43 kg, 10.78 kg, and 6.91 kg per year, respectively, demonstrating its economic feasibility and environmental contribution.

Research on Multi-Objective Optimization Scheduling Strategy for Cascaded Small Hydropower Stations Based on Improved HBA

Abstract Effective optimization scheduling strategy is the premise and key to improving the power generation and capacity benefits of cascade small hydropower stations (CSHS). However, the power generation of CSHS is significantly affected by complex hydraulic and electrical constraints. To effectively solve this problem, an improved honey badger algorithm (HBA) is proposed by updating the mutation strategy and introducing non-dominated sorting to achieve the multi-objective optimization scheduling solution of CSHS. The following improvements have been made to the standard HBA: Firstly, the Tent chaotic mapping is applied to the population initialization stage, its strong ergodicity and randomness ensure the randomness of the initialization stage and improve the global search ability of CSHS scheduling. Secondly, the powerful optimization ability and fast convergence speed of the Golden-Sine strategy make updating and mutation more efficient, greatly enhancing the local search ability of CSCH scheduling. And then combining the non-dominated sorting of the non-dominated sorting genetic algorithm-II (NSGA-II), an improved multi-objective Honey Badger Algorithm (IMOHBA) is further proposed to achieve multi-objective solutions for CSCH scheduling. Finally, abundant field experiments were tested for validation. The results expressed that compared to other algorithms, the effect of IMOHBA in CSHS scheduling can further increase power generation, while also improving the peak shaving ability of CSHS.

Rooftop PV Development Suitability and Carbon Benefits: An Anhui Province Case Study

As one of the most rapidly developing provinces in China in the past two decades, Anhui Province has seen an increasing demand for clean energy in recent years due to industrial transformation and the requirements of dual carbon targets. This paper opts to investigate roof-mounted distributed photovoltaics, which are more suitable for development in densely populated areas. Current research on distributed photovoltaics largely focuses on vague estimations of power generation potential, without adequately considering the specific development conditions of different regions. This paper starts from the actual situation affecting the development of roof-mounted distributed photovoltaics and selects a smaller number of factors that are more in line with reality for hierarchical analysis, constructing a relatively simple but practical evaluation system (“meteorological-geographical-socio-economic”). At the same time, this paper innovatively proposes different schemes for the full lifecycle power generation and emission reduction benefits of roof-mounted distributed photovoltaics and compares them, providing a foundation for subsequent in-depth research. Key findings include the following: The northern regions of Anhui Province exhibit higher suitability for rooftop distributed PV, with residential areas being the primary influencing factor, followed by solar radiation considerations; the annual power generation potential of rooftop distributed PV in Anhui Province constitutes around 80% of the total electricity consumption in 2021, but the potential is predominantly concentrated in rural areas, resulting in spatial disparities in power generation and consumption across the province; developing the rooftop distributed PV industry based on suitability can yield substantial power generation and emission reduction benefits, translating to an estimated reduction of approximately 1.28 × 108 tCO2 annually, representing around one-third of Anhui Province’s carbon emissions in 2021.

Thermo-economic analysis of a pumped thermal energy storage combining cooling, heating and power system coupled with photovoltaic thermal collector: Exploration of low-grade thermal energy storage

As renewable energy penetration grows, traditional power systems face significant challenges due to their intermittency and volatility. Pumped thermal energy storage (PTES) is a potential energy storage technology that has a low specific cost and geographical restriction. In this paper, a PTES system which is coupled with solar photovoltaic thermal (PVT) collectors is proposed to satisfy the demand for cooling, heating and electricity supply, and achieve energy cascade utilization. An ejector refrigeration cycle and the turbine extraction heating are accompanied with a PTES system. Thermodynamic analysis of the system is performed by evaluating the influence of key parameters on system performances for 1 MW power output. The economic analysis adopted levelized cost of energy storage (LCOS), which mainly includes investment costs, operation and maintenance costs and system depreciation. The results show that the maximum amplification of roundtrip efficiency with PVT is 12.04 % compared to the system without PVT. Moreover, the maximum average power generation benefit of PVT is 4.85 %. The increase in cold energy storage tank temperature can effectively improve the roundtrip efficiency of the system. The lower cold energy storage tank temperature and higher hot energy storage tank temperature have a negative impact on system thermal efficiency (ηthermal) but benefits for LCOS. Multi-objective optimization is carried out to obtain the optimal design performance that ηthermal and LCOS are 51.06 % and 0.533$/kWh respectively.

Hydrogen production to combat power surpluses in hybrid hydro–wind–photovoltaic power systems

The complementary operation of hydropower, photovoltaic, and wind power can promote the integration of renewable energy resources into the grid. However, the competition of power transmission channels among multiple energy raises surpluses significantly. This study equips a hydrogen production plant for the hydro–wind–PV hybrid system to utilize the power surplus. First, a multi-scale nested joint operation model that considers both long-term and short-term operation strategies is proposed to simulate the operation process of the hybrid system. Then, the potential surplus of the system is evaluated and used as the power source of the hydrogen production plant. Lastly, the optimal hydrogen production plant size is determined based on economic and technical analysis. A case study is performed with the Yalong River basin of China. The results show that (McElroy and Chen, 2017 (1)) The deployment of a 260 MW hydrogen production system has the potential to boost the annual power generation benefit of the hybrid system by 610 million CNY； (2) The fluctuation of hydropower station output is effectively reduced, subsequently mitigating operational risks. Especially during flood season, the power variation coefficient experiences a notable decrease of approximately 75%； Liu and Xu (2022) (3) The deployment of hydrogen production plants is more economically feasible than expanding power transmission channels, particularly when considering long-distance power transmission. Furthermore, given that hydrogen production plants frequently operate under fluctuating power conditions, the electrolyzers and compressors must exhibit prompt responsiveness to these power variations.

Design comparison and effects of a chamber structure on wave dissipation

ABSTRACT Among all the ocean energy sources that have been widely studied, chamber structures such as oscillating water column (OWC) technologies are effective and low-cost methods to develop hundreds of wave power generation devices as the disadvantage is its lack of efficiency. Even if the OWC system does not perform any power generation operations, it is a wave-absorbing structure. A large part of the wave energy may be blocked by the structure’s acting chamber or could be dissipated during the interaction of the waves in the air chamber, which affects the actual power generation benefit evaluation. Nevertheless, a better wave attenuation structure may present a relatively poor design of the OWC generator chamber. A series of experiments were conducted with regular and irregular wave conditions to study the effects of energy dissipation caused by a chamber structure under varied barrier and orifice designs.

Thermal stratification characteristics and cooling water shortage risks for pumped storage reservoir–green data centers under extreme climates

Utilizing the thermal stratification of reservoirs to obtain cold water for cooling green data centers (GDCs) is a new mode of energy conservation and emission reduction. However, global warming is expected to alter this phenomenon in deep reservoirs and thus may affect the digitalization process. While the frequency and intensity of extreme climate (EC) events are increasing under a changing climate, the impacts of these highly destructive events on thermal stratification and the related cascading effects on GDC cold-water supplies are still unclear. A two-lateral-dimensional thermohydrodynamic model was established to determine the characteristics of reservoir thermal stratification changes and its potential water environment impacts based on the heat extreme experienced by the Jinshuitan Reservoir, a large, pumped storage power station (PSPS) with a GDC in southeastern China. Fourteen different PSPS inlet/outlet schemes under 2 climate scenarios were designed, and two new indexes, namely, the risk index of cold-water shortage and the index of thermal stratification, were proposed to explore the impact of extreme climates on GDC water extraction. The results showed that compared to the present climate conditions, the reservoir experienced stronger thermal stratification with a 20.6 % average increase in thermal stability, a longer thermal stratification duration with a transitional trend from seasonal to permanent stratification, and a thinner cold-water layer with a 15-m average decrease. Changes in the thermal stratification of reservoirs under EC conditions will benefit some species, such as diatoms (except for early July and the period from mid-August to early October), green algae and cyanobacteria and the migration of warm-water fish, but adversely impact dissolved oxygen, diatoms and cold-water fish. Furthermore, the risk of cold-water shortages under EC conditions was consistently greater than that under average climate (AC) conditions (traditional assessment) when the elevation of pumped storage intake/outlet was less than 145 m. According to the traditional assessment method, the intake/outlet elevation of pumped storage reservoirs resulted in a 60.27 % increase in the risk of cold-water shortages during climate extremes. Furthermore, the demand for green cold-water withdrawal and optimal pumped storage power generation benefits were met when the PSPS intake/outlet elevation was 145 m. Beyond solving this specific case, this study elucidates the importance of linking the thermal stratification and data center of cold-water withdrawal to ECs. These findings provide new insights for increasing climate change resilience.

Multi-Objective Synergetic Operation for Cascade Reservoirs in the Upper Yellow River

The Yellow River, a critical water resource, faces challenges stemming from increasing water demand, which has led to detrimental effects on hydropower generation and ecological balance. This paper will address the complex task of balancing the interests of hydropower generation, water supply, and ecology within the context of cascade reservoirs, specifically Longyangxia and Liujiaxia reservoirs. Employing a systemic coupling coordination approach, we constructed a multi-objective synergetic model of the upper Yellow River in order to explore synergies and competitions among multiple objectives. The results reveal that there is a weak competitive relationship between hydropower generation and water supply, a strong synergy between hydropower generation and ecology, and a strong competitive relationship between water supply and ecology. The Pareto solution set analysis indicates a considerable percentage (59%, 20%, and 8% in wet, normal, and dry years, respectively) exhibiting excellent coordination. The probability of excellent coordination decreases with diminishing inflow. The optimization scheme with the highest coupling coordination demonstrates significant improvements in power generation, water supply, and ecological benefits in the upper Yellow River without compromising other objectives, fostering the sustainable operation of hydropower generation, water supply, and ecology in the upper Yellow River.

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

The health benefits of solar power generation: Evidence from Chile

Renewable energy can yield social benefits through local air quality improvements and their subsequent effects on human health. We estimate some of these benefits using data gathered during the rapid adoption of large-scale solar power generation in Chile over the last decade. Relying on exogenous variation from solar irradiation and incremental solar generation capacity over time, we find that solar energy displaces coal generation and curtails hospital admissions due to respiratory diseases. These effects are largely manifested in cities downwind of and near coal plants that are displaced by the introduction of new solar. The reduction in exposure to air pollution from these displaced coal plants seems to be driving this relationship. Our results help quantify the health benefits that can be achieved through greater renewable energy investments.

Study on the effect of PV tilt angle on power generation

The power generation of a photovoltaic (PV) system is significantly influenced by the tilt angle of the module. The system achieves the highest power efficiency when operated at the optimal tilt angle, which is typically considered in the design of system installation. In this study, the optimal tilt angle of photovoltaic (PV) modules is determined by using PVsyst software and analyzed through shadow simulation under specific boundary conditions. The study examines the power generation of photovoltaic (PV) panels at various inclination angles and illustrates the impact of inclination angle on PV panel power generation by using project-specific data. The study results indicate that the secondary optimization of the tilt angle by PVsyst, in conjunction with the geographic factors of a specific region in Hainan, reveals that the optimal installation tilt angle for maximum power generation benefit is 9°. This finding aligns with the best power generation and benefits from the actual installation, which is at 10° (adjacent to 9°). The conclusions drawn from the exploration emphasize the essential nature of EPC optimization for future projects, as it can serve as a valuable reference for the design of photovoltaic power generation systems.

신에너지 자동차배터리 산업 공급망 네트워크의 최적화

In this study, an optimization of supply chain network for New Energy Vehicle Power Batteries (NEVPB) from the perspective of resource circulation is proposed. The model consists of seven stages in terms of collection center (CC), repairing center (RC), distribution center (DC), recycling center (RY), part supplier (PS), material supplier (MS), and disposal center (DP). The model is formulated as a nonlinear integer programming model and a comparison of LINGO and genetic algorithm (GA접근법) are conducted respectively. In numerical experiment, it has found a way to repair and recycle NEVPB. Retrieving as many NEVPBs in use demonstrates sustainable power generation benefits by maximizing the use of recovery through resource recycling and searching for recovery classification of batteries as well as shortening lifespan that occur during the process of producing initial parts and modules from raw materials.

Experimental study of bifacial photovoltaic wall system incorporating thermochromic material

In this study, we innovatively propose a bifacial PV wall system combined with thermochromic materials (BPVW-TC). The system combines thermochromic materials and bifacial PV technology on a building facade to passively and adaptively improve the overall solar efficiency of the building facade by taking advantage of the passive temperature response properties of the thermochromic materials and the power generation benefits of bifacial PV. The designed thermochromic glass with a transition temperature of 35.8 °C maintains a high visible light transmittance of 88.6 % in the transparent state, while the transmittance drops sharply to 0.6 % in the opaque state; in addition, a bifacial PV module with a coverage of 70 % is designed, and an experimental setup is set up based on it to validate the power generation gain of the system under indoor steady state conditions. Subsequently, the BPVW-TC system model was tested under different weather conditions in Hefei, China, and the results showed that the system could achieve solar energy utilisation of up to 38.8 % and solar thermal efficiency of up to 83 % on a sunny summer day.

Optimal Operation Model of Ecological Flow of Hydropower Station Based on Genetic Algorithm and Neural Network

In view of the contradiction between the ecological flow regulation of the reservoir River and the power generation flow regulation of the reservoir with the largest power generation, this paper takes the maximum power generation and the maximum average ecological protection degree as the objective function, and uses the genetic algorithm back propagation neural networks (GA-BP) algorithm to establish the reservoir flow regulation model under the constraints of water balance, reservoir capacity, unit output and unit overflow, The optimal solution of the dispatching scheme is carried out, and the Shulehe hydropower station is taken as an example to verify the optimal scheme. The results show that the optimized dispatching model can ensure the demand of ecological flow, increase 32680 kW·h power generation and 23.85% power generation benefit, so as to provide scientific reference for the rational planning, dispatching and utilization of reservoir water resources.

Multi-objective cooperative optimization of reservoir scheduling and water resources allocation for inter-basin water transfer project based on multi-stage coupling model

The efficient coupling of a water allocation model with a reservoir operation model is key to maximizing the benefits of inter-basin water transfer (IBWT) projects. Most existing studies of IBWT operations and management do not account for matching water transfer and demand, and instead seek to optimize either the water allocation model or the reservoir scheduling model. In this paper, we propose a multi-stage joint optimization framework for reservoir scheduling and water allocation models that considers the key operation indicators of the project in the water source area and the multi-dimensional objectives in the receiving area. We derived a functional relationship that balances considerations regarding water, the economy, and ecology (WEE) in the receiving area and applied it to the water allocation model. The reservoir operation model was developed by considering the multiple benefits of water transfer and power generation in the water resource area. To reduce the complexity of the model and the tightness of its coupling, the model was divided into multiple stages of optimization and solved using the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) algorithm. The results showed the allocation schemes of water resources influenced the competition between the economic and ecological benefits. Using the WEE model, a more reasonable water transfer and allocation method can be selected according to different development needs. As such, the water shortage rate of each user in the receiving area is kept below 8%, and there is significant improvement in all indicators compared with the conventional scheme. In addition, this model enables the reservoir group to achieve a power generation capacity of more than 5.64 kWh with the goal of meeting the water transfer demand, maintaining the reservoir empty rate at about 10%, and reducing the risk of water shortages. Our study can serve as a reference for effectively addressing the challenges associated with IBWT projects, and it can inform socioeconomic development in receiving areas while optimizing water transmission and allocation.

A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

With growing attention focused on energy generation and environmental conservation, the operation of cascade reservoirs that considers power generation benefits and ecological flow requirements plays an increasingly important role in water resource and power systems. However, the traditional optimization methods may fail to solve complex cascade reservoirs operation models with strong spatial-temporal coupling physical constraints. To effectively address this problem, this study proposes an efficient multi-objective cooperation search algorithm (MOCSA) that utilizes the modified team communication, reflective learning operator and internal competition operators to enhance global exploration and local exploitation. MOCSA is tested on a group of multi-objective benchmark functions and real-world constrained engineering problems. The results demonstrate that MOCSA can provide better results for most benchmark test functions in comparison with the competitive algorithms. Besides, MOCSA exhibits excellent search ability to find feasible solutions of constrained engineering problems. The proposed method is then applied to resolve a real-world reservoir system under different operation scenarios. Simulations indicate that MOCSA provides diversified decision options for the operation of cascade reservoir system and find a more diverse set of non-dominated solutions within the feasible solution space. Overall, this research presents MOCSA as an effective multi-objective optimization tool for complex cascade reservoir operation problems.

A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models

One of the most important sources of energy is the sun. Taiwan is located at a 22–25° north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long–short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model.