Abstract

The rapid growth of photovoltaic (PV) power generation has led to an increasing need for effective battery energy storage systems to address the intermittency and variability of PV output. This comprehensive review focuses on the optimization models used for battery sizing in photovoltaic power stations. It presents an in-depth analysis of various approaches, including mathematical programming, heuristic algorithms, and hybrid methods. The review examines the objective functions, constraints, and key parameters considered in these models. Additionally, it discusses the performance evaluation metrics and the impact of different factors such as load profiles, PV generation patterns, and cost structures on battery sizing decisions. The findings of this review provide valuable insights for researchers and practitioners aiming to optimize battery sizing in PV power stations to enhance system reliability and economic viability.

Abstract

The intermittency and instability of solar power generation present significant challenges for its effective integration into the grid. This project introduces an advanced Particle Swarm Optimization (PSO) based model for optimizing the configuration of photovoltaic (PV) and battery systems in power stations. The model tackles the intermittency and unpredictability of solar energy by incorporating an uncertainty set within the PSO algorithm, ensuring robust optimization under varying conditions. It also integrates an attention mechanism to enhance the accuracy of PV power output predictions. The study further develops an economic assessment model to evaluate the financial performance of different configurations, considering equipment, maintenance costs, and market prices. The project’s findings demonstrate the model’s effectiveness in improving both the operational efficiency and economic viability of PV-battery systems.

Abstract

The heat pump is a highly efficient and environmentally friendly technology for converting electricity into heat with efficiencies larger than one. Recently, air-source heat pumps (ASHPs) have seen widespread deployment across various climate zones for building heating and cooling purposes. Conversely, ground-source heat pumps (GSHPs) offer even greater efficiencies but have seen 15% of total heat pump installations. This article intends to conduct a comprehensive thermodynamic comparison analysis of adopting an Air-source heat pump (ASHP) and a ground-source heat pump (GSHP) for building heating and cooling in Beijing using TRNSYS simulation. The TRNSYS model leverages city-level building information to simulate the load profile of the building. Additionally, it incorporated the impact of occupants’ energy behavior including the window-opening schedule, heating and cooling temperature, effective area, and number of heat pumps to form 3 scenarios. As a result, the GSHP outperform the ASHP with around 30% higher efficiency and 45% reduction of total power consumption.

Abstract

Smart homes use devices that automate tasks like security, lighting, and temperature control. These homes let people control appliances remotely through the Internet of Things (IoT), adjusting to their schedules for better energy use. But as energy use rises, it causes more pollution, and climate problems, and puts more strain on energy sources. Therefore, it’s important to track energy use closely as the world moves into the use of renewable energy to avoid power outages, save money, and protect the environment especially because of the intermittent nature of renewable energy. This paper proposed an Ensemble-Tree Model Based on Bayesian Optimization or Solar Energy Generation Prediction in Smart Homes. First, three tree-like machine learning models training hyperparameters were optimized using the Bayesian optimization technique. Secondly, their output was concatenated based on Mean Aggregation Methods in mathematics. Lastly, the prediction was done based on k-fold cross-validation. The ‘smart-home-dataset with weather-information dataset is used while using the R-squared (R2), Mean Squared Error (MSE), and Mean Absolute Error (MAE) to observe how accurate the predictions are. Results show that the proposed model outperforms other machine learning models with R2 value of 0.988 as compared in this paper.

Abstract

Transportation electrification is an important strategy of addressing the rapid growth of carbon emissions from the transportation sector. However, rapid and widespread adoption of electric vehicles (EVs) may result in adverse effects such as increased grid emissions, power equipment degradation, and reduced grid efficiency. This study employs HOMER software to develop a hybrid EV charging station model with deferrable charging, offering a potential solution to these issues. To determine the most effective energy configuration, a multi-scenario simulation using real-world charging load data is performed. Findings indicate that hybrid charging stations equipped with smart charging technology can significantly alleviate these negative impacts by reducing peak loads, cutting carbon emissions, and enhancing cost efficiency. In these simulations, the optimal solution for public charging involves integrating wind power with grid electricity. This approach yields an annual production of 1,567.5 MWh, achieves an levelized cost of energy (LCOE) of $0.051/kWh, and results in a negative net emission through the sale of excess electricity to the grid. Furthermore, comparative simulations illustrate three key points. First, the system can sell back the excess electricity to achieve a negative net emission of -266 t carbon dioxide per year. Secondly, in the public charging scenario, managed charging has a significant effect on reducing peak loads (92% reduction in the maximum peak load), lowering grid emissions by selling back excess clean electricity (-74.5t per year), and optimizing the energy cost ($0.030/kWh reduction in LCOE). Lastly, in the domestic charging scenario, on-demand charging may outperform the deferrable charging. This paper highlights the limitations of REs and smart charging, the need for region-specific strategies and energy configurations, potentially contributing valuable insights to the fields of charging strategies and energy systems.

Abstract

Addressing global challenges such as climate change, energy crises, housing shortages, and regional inequality, this paper explores sustainable building design workflow in Yunnan’s rural area. This study developed a data-driven approach to achieve climate-adaptive, affordable rammed earth house design. It assesses environmental impacts based on cradle-to-grave life cycle assessment and assesses indoor comfort and energy efficiency through dynamic building performance simulations. The study culminates in guidelines for rammed earth house design based on extensive data analysis. Several design plans for the rammed earth house project were developed and evaluated, together with a conventional brick house. The outcome of our study offers a sustainable housing design workflow that could inspire similar initiatives in regions with comparable social and climatic conditions.

Abstract

Facade photovoltaic (FaPV) can significantly enhance the capabilities of building-integrated photovoltaic (BIPV) systems as prosumers, while also mitigating the additional electricity demands of electric vehicles (EVs). This study presents a comprehensive analytical framework for optimizing the operation of BIPV-EV systems and evaluating their environmental and economic benefits with a case study in Xiong’an New Area. The Economy-Energy-Environment(3E) parameters are measured for multiple scenarios, and sensitivity analyses are applied to validate the impacts of the changes in electricity prices and BIPV costs on the BIPV-EV system. The simulation results demonstrate that BIPV-EV systems effectively reduce carbon emissions and electricity costs. The installation of FaPV can increase power generation by 67.60% compared to the BIPV system using RPV stand-alone. Regarding urban decarbonization, the adoption of BIPV system can lead to a reduction in CO2 emissions by 41.91% and 34.99%. In each scenario, the lowest levelized cost of energy (LCOE) scenarios in the short-term and long-term are RPV+FaPV (SE)-EV and RPV+FaPV (SW)-EV, where BIPV generates 48.10% and 31.90% of the total electricity generation, respectively. In terms of EV power consumption, The BIPV-EV system can decrease the grid sell ratio by 15.38% compared to the single BIPV scenario. This framework analyzes the potential of integrated systems with EVs under various BIPV scenarios. In cities with nascent BIPV and EV markets, the framework can be used as a strategic tool for urban planners to plan BIPV-EV systems that improve sustainability and energy efficiency.

Abstract

For hydrogen polymer electrolyte membrane fuel cell (PEMFC), the structure design of the bipolar plate (BP) and gas diffusion layer (GDL) greatly affects the water and gas transport and the performance of the PEMFC. Starting with the length and cross-section size of the flow channel (FCH), the thickness of the GDL, the average porosity and the degree of porosity gradient in the flow direction, this paper studies the effect of the collaborative matching optimization of the BP and GDL structure on the performance of the PEMFC. Firstly, five variables including average porosity, GDL thickness, porosity gradient degree, FCH cross-section and length are selected as matching factors (five-level numbers for each variable), and two performance parameters including maximum power density and pressure drop are used as performance evaluation indexes. Secondly, the orthogonal table is established by orthogonal experimental design. The range analysis and variance analysis are carried out on the Orthogonal test results after the data is obtained through simulation. Finally, the Entropy weighting method is used to evaluate the two performance evaluation indexes of 26 groups of data. The results show that Case 18 has the highest comprehensive evaluation index, with an evaluation index of 0.06055 and the base group is 0.04655. Compared to the base case, the maximum power density of case 18 is increased by 19.8%.

Abstract

Renewable energy continues to rise, with supercritical water gasification (SCWG) emerging as a potential biofuel production technique. Conventional approaches for estimating CH4 and H2 generation in complex systems frequently fail due to the complicated and dynamic nature of these processes. Machine learning (ML) has emerged as a disruptive technology in various industries, including energy, where it is used to optimize operations and improve prediction accuracy. Conventional techniques lack the versatility and scalability of ML models, resulting in less accurate and efficient prediction capabilities. This gap emphasizes the importance of incorporating machine learning into the energy domain, notably for optimization and prediction in SCWG processes. Furthermore, for any machine learning model, determining the appropriate hyperparameter setting has a direct and significant influence on its performance. In this study, we investigate the influence of three distinct types of hyperparameter optimization techniques on CH4 and H2 production prediction based on supercritical water gasification. Grid Search Optimization, Random Search Optimization, and Bayesian Optimization were analyzed utilizing six machine learning models: Ridge Lasso, Elastic Net, Decision Tree, Random Forest, and XGBoost, as well as two ensemble models: linear-based and tree-based models. The dataset from supercritical water gasification of Yimin lignite was used based on three evaluation metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results were evaluated based on 5-fold cross-validation and results show that the Random Forest stood out with an R2 of 0.995, RMSE of 0.109, and MAE of 0.085 for the CH4 production prediction while recording an R2 of 1.00, RMSE of 0.112 and MAE of 0.088 for H2 production prediction. The analysis carried out in this study shows that the choice of optimization technique does not significantly impact the performance of the deployed models, which indicates that the hyperparameter space is relatively well-behaved for this CH4 and H2 Production Prediction based on Supercritical Water Gasification, and thus even simpler optimization strategies like Random Search can perform nearly as well as more sophisticated ones like Bayesian Optimization. The result implies that if computation time or resources are a factor, Random Search would be a more efficient approach without a significant trade-off in model performance

Abstract

Countries worldwide are adopting carbon neutrality targets to address global warming challenges. High-density cities, viewed as a promising path toward a sustainable future, exhibit a unique emission pattern dominated by the building sector. Therefore, this study devised a lightweight and adaptable modeling framework to project long-term carbon emissions from building operations, outlining a decarbonization roadmap for the building sector in high-density cities, with Hong Kong as a case. Using multi-source data, the features of each district are considered, such as land planning, technology, and socioeconomic factors. The model was built and validated by data over ten years, with a mean percentage error of 5%. Results reveal that radical policies are necessary for carbon neutrality, necessitating simultaneous energy demand and supply decarbonization. Limited space for renewables in such cities makes demand-side technologies vital. Electrifying home appliances display significant carbon reduction potential. Among building types, public facilities, multi-functional commercial buildings, and private housing offer substantial reduction potential. The proposed carbon mitigation roadmap underscores the complex nature of achieving carbon neutrality in high-density cities, calling for comprehensive strategies from various sectors.