Trends and Technological Challenges in Green Energy

Power electronic converters have been used for different applications, including photovoltaics, wind energy conversion systems, automotive, boat propulsion systems, railway transport, and other applications. This is the reason for the great importance of power converters. Furthermore, bidirectional power converters can make the interaction between multiple energy sources and improve the performance of the mentioned systems in the case of several sources like in hybrid vehicles or photovoltaic systems. Two DC networks can be connected to more basic DC/DC power converters, and in this case a more robust solution can be achieved. This is the reason for the extensive analysis of DC/DC power converters worldwide. In this chapter, an improved variant of the bidirectional power converter has been presented and compared to up-to-date commercial solutions.

With the roadmap to carbon-neutral cities, offset and trading opportunities will leave the remnants of the unprecedented growth based on fossil fuels with urban air pollution and city impact of climate change. Promoting net zero and positivity in electricity generation can substitute the use of fossil fuels with clean energy but introduces considerable uncertainty on the engagement of different actors and in practice aspects of the energy transition process. In this work, a clear goal to carbon-free cities is proposed with a novel framework based on four pillars. Natural solutions such as greenery and solar energy technologies such as colored eco-designed photovoltaics are optimized at the variable climatic conditions of European cities as a first step to specific targets, performance indicators, and milestones for the urban transformation adventure of all European citizens.

The chapter highlights the significance of solar rebates and incentives in promoting the adoption of solar photovoltaic (PV) energy generation. Countries have adopted different approaches to renewable energy incentives depending on the local circumstances and desired renewable energy uptake rates. In South Africa, the implementation of a solar rebate program by the South African Revenue Service (SARS) and a net billing tariff by the eThekwini Municipality have marginally increased the practicality of investing in solar energy within the residential sector. The incentives strengthened the IRR of a 5 kVA solar PV system coupled with a 5 kWh battery energy storage system (BESS) from 12.80% to 15.50% and reduced the simple payback period (SPBP) from 6.18 years to 5.57 years. However, the current residential tariff structure lacks fixed charges for network cost recovery, leading to revenue losses for the municipality as customers with solar generation consume less energy from the grid. Depending on the uptake rate of solar PV in the residential sector, the losses vary accordingly. With an uptake rate of one hundred thousand, 5 kVA solar PV systems coupled with 5 kWh BESS, the potential loss to the municipality is R 649,000,000. To address the losses and for the city to remain revenue neutral, the Network Access Charge (NAC) within the net billing tariff needs to be increased from R 22/kVA per month to R 108/kVA. However, the increase in the NAC weakens the investment criteria of the solar project. With the increased NAC, the IRR dropped from 15.50% to 7.00%, and the SPBP increased from 5.57 years to 7.93 years. Raising the upfront rebate from the initial R 15,000 to R 60,000 eliminates the municipality's losses and provides an incentive for customers to install solar PV and BESS systems. With the current rebate mechanism and tariff structures, the eThekwini municipality is subject to financial losses as solar PV uptake rates increase. Increasing the national subsidy fourfold would effectively support eThekwini Municipality and, concurrently, incentivise solar projects. As the drive to reach net zero intensifies, solar rebates will continue to be essential in promoting renewable energy adoption.

In order to feed the ever-increasing global population, current food production must increase significantly in the near future. Increasing food production by expanding farmland or by farming intensification would lead to increasing land-use demand, which comes at the cost of biodiversity, increasing pressure on vulnerable ecosystems, and requiring large volumes of water and mineral fertilizer. Smart farming has been identified as an emerging solution to help address the need for increasing food production without an increase in land-use demand. In smart farming, crops are grown in vertical arrangements using artificial lighting and are fed hydroponically using nutrient solutions. With greatly improved water and nutrient use efficiency, it has been demonstrated that increases in crop yield and quality can be achieved by manipulating the properties, such as photoperiod, wavelength, and intensity of the artificial light through the use of light-emitting diodes (LEDs). LEDs require DC power supply, whereas the electric grid generates AC power, thereby requiring an LED driver to convert AC to DC power. Energy-efficient LED drivers can significantly reduce the operational cost of smart farming. This chapter presents a review of power electronic circuits and their control methods for driving LEDs from the grid. First, a short summary of the conventional circuit topologies is presented. Then, different control methods are critically reviewed. Finally, some potential research directions are provided to enhance the efficiency of LED drivers through control system improvement. The review presented in this chapter will assist researchers in making informed decisions when choosing or designing LED circuit topologies and control methods to improve the overall energy efficiency in smart farming systems.

The use of interferometric radar to track subsidence and wind turbine blade deformation has been examined in a number of studies. This technology has two main advantages over other nondestructive surveying methods: the ability to quickly conduct network-level surveys, and the supply of time-series evidence of displacements via multitemporal data gathering. In order to address the drawbacks of convolutional neural networks (CNNs), such as network resemblance to the linear neuron model, operational neural networks (ONNs) have recently been developed. In this study, we present the results of employing ONNs in interferometry monitoring to assess a wind turbine’s structural health.

In this chapter, we aim to generate rich data useful for understanding the essence of natural phenomena. Rich data generation is required to predict/forecast situations involving renewable power generators interacting with natural events. Renewable energy is an essential factor in guaranteeing the sustainability of society. The Tsugaru Strait, in the northern region of Japan, is an area that has attracted attention for the utilization of tidal/ocean energy. We propose a tidal/ocean power generator utilizing a flaring flanged diffuser (FFD) to harness the power effectively. To obtain rich data around FFD, generative deep learning is focused. Variable auto encoder (VAE) is used to reconstruct, encode and decode, the fluid field data. The original data of the generation is obtained from the measurement data in the experiment. The architecture of the generative deep neural network is constructed by exploiting the decoder in the learned VAE. The reconstruction of the fluid field is performed, and the performance is evaluated.

Large-span valve hall structure is frequently used in convertor stations due to the large-span and high-headroom features. For coastal areas, these structures will face extreme wind conditions, and the safety evaluation under extremely large wind velocities could be an important issue for large-span valve hall structure. A finite element model was proposed in this chapter to predict the displacement and stress response of the large-span valve hall structure under different wind loads. Elastoplastic material model is employed here. The wind load is utilized through wind pressure on the structure elements, and the wind field is composed through linear superposition of harmonic functions with random amplitude and frequency. Meanwhile, maximum wind load of the large-span valve hall structure is obtained. The results show that the structure is safe under the maximum wind load (37 m/s) that may occur in daily situations. Even at the wind speed of 40 m/s, the maximum axial stress in the structure is still less than the yield limit of the material. However, when the wind speed exceeds 45 m/s, plastic deformation will occur in some parts of the large-span valve hall structure, increasing the risk of structural failure.

Renewable energy penetration varies largely for different countries. A combination of factors, such as weather, availability of resources, technological level, politics, economic strength, and many others, play a role in deciding the level of renewables penetration. Malaysia, a country with a tropical climate, holds promising potential for a variety of renewable energy types, namely solar, biomass, hydroelectric, and wind. While renewables are encouraged for all countries to adopt, in alignment with Sustainable Development Goal 7, expanding carelessly would yield unintended consequences, especially for a developing nation such as Malaysia. As the deployment and expansion of renewable energies is a complex problem, this study utilizes a systems approach, namely system dynamics, to model and simulate the behaviors of identified key indicators for the prediction of penetration of renewable energies in Malaysia. Results show that renewable energy growth in the country cannot keep up with its economic growth and nonrenewable counterpart but could be useful in slowing down conventional energy expansions as long as there are available renewable resources.

Sizing and operational optimization are essential for a reliable and cost-effective hybrid renewable energy system (HRES). This study develops an optimization framework to improve the techno-economic performances of HRES, consisting of PV/WT generation and hydrogen/battery storage units. The operational problem is addressed by a long-duration operational (LDO) management strategy, with its operational variables adjustable by optimization algorithms to control discharge capacities under different periods. A residential building located in a temperate area is selected as a case study and is also virtually tested in a tropical climate zone to demonstrate the robustness of the optimization approach. The simulation results confirm the efficiency of the optimization approach as the selected solutions achieve 17.69% and 29.85% improvements in system operational efficiency with equivalent baseline investments. The LDO strategy shows its superiority in tropical locations with lower seasonal mismatches. Moreover, the combination of long-term hydrogen storage with short-term battery storage is necessary for seasonal storage or off-grid operation, that is, almost 100% renewable energy penetration. However, this is achieved with more than twofold costs due to the diminishing marginal benefit. Therefore, a nearly off-grid operation (90% SSR) can be a trade-off solution and save the most investment in energy storage units. The proposed optimization approach and its findings can support the application of HRES in zero-energy/carbon communities and microgrids.

Regenerative cooling is one of the most promising methods for the active thermal protection of hypersonic vehicles. The transient performance of regenerative cooling system using hydrocarbon fuels for combustion chamber before/after ignition is simulated. The cooling performance of the cooling system under different thermal boundaries is compared and discussed by referring to the distribution of temperature, velocity, and conversion of hydrocarbon fuels. Results show that the temperature of the fuel first decreases with the heat flux and then rises due to the endothermic reaction during the pyrolysis process. The relaxation time of the cooling system is longer than the ignition delay time. The heat transfer characteristics under uniform and distributed thermal boundary are different, which significantly affects the wall temperature distribution.

The development of a carbon labeling system is important for the realization of China’s “double carbon” goal, but the development of carbon labeling is limited by the interlocking interests of all parties involved in carbon labeling at this stage. In this chapter, we construct a “government–enterprise–consumer” evolutionary game model based on prospect theory, analyze the interaction mechanism of the main stakeholders and the key factors affecting the strategy evolution of the game subjects, and use MATLAB to conduct simulations, aiming to provide suggestions for the development of China’s carbon labeling system. The results show that the government is less sensitive to the carbon tax rate, while enterprises are a certain degree sensitive to the carbon tax rate; enterprises are more sensitive to government subsidies than consumers; and consumers’ low-carbon awareness has a certain impact on consumer strategy choice.