Heuristic battery-protective strategy for energy management of an interactive renewables–buildings–vehicles energy sharing network with high energy flexibility

Energy flexibility investigation of advanced grid-responsive energy control strategies with the static battery and electric vehicles: A case study of a high-rise office building in Hong Kong

Multi-objective optimisation of an interactive buildings-vehicles energy sharing network with high energy flexibility using the Pareto archive NSGA-II algorithm

Frontier ocean thermal/power and solar PV systems for transformation towards net-zero communities

This study presents advanced control and energy management strategies for uncertain wind energy systems using a Takagi–Sugeno (T-S) fuzzy modeling framework. To address key challenges, such as system uncertainties, external disturbances, and input delays, the study integrates a fuzzy H∞ robust control approach with a neural network-based delay compensation mechanism. A fuzzy observer-based H∞ tracking controller is developed to enhance robustness and minimize the impact of disturbances. The stability conditions are rigorously derived using a quadratic Lyapunov function, H∞ performance criteria, and Young’s inequality and are expressed as Linear Matrix Inequalities (LMIs) for computational efficiency. In parallel, a neural network-based controller is employed to compensate for the input delays introduced by online learning processes. Furthermore, an energy management layer is incorporated to regulate the power flow and optimize energy utilization under varying operating conditions. The proposed framework effectively combines control and energy coordination to improve the systems’ performance. The simulation results confirm the effectiveness of the proposed strategies, demonstrating enhanced stability, robustness, delay tolerance, and energy efficiency in wind energy systems.

This thesis explores innovative data-driven approaches to enhance energy management in smart buildings, addressing challenges through advanced deep reinforcement learning (DRL) methods. The thesis follows a logical progression, starting from the smallest unit of a smart building, a multi-energy smart home, and then moving to the residential building level, focusing on a typical scenario of an apartment building often found in city areas. Finally, the thesis addresses a general case of building energy management, proposing an improved approach that integrates advanced forecasting algorithms to enhance the performance of building energy management systems (BEMSs). Chapter 2 models a multi-energy smart home equipped with various appliances, battery energy storage systems (BESS), thermal energy storage systems (TES), micro combined heat and power systems (mCHP), electrical heat pumps (EHP), rooftop photovoltaic (PV), and electric vehicles (EV). The home energy management (HEM) problem is formulated as a cost minimization task with strict constraints to manage energy generation, storage, and consumption efficiently. The chapter introduces a safe DRL approach, primal-dual deep deterministic policy gradient (PD-DDPG) algorithm. Unlike existing DRL methods, this approach learns to minimize costs from accumulated cost functions and automatically tunes the cost function coefficients. Additionally, a convolutional neural network-long short-term memory (CNN-LSTM)-based dynamic electricity price forecasting model addresses future price uncertainties. Simulations with Singapore wholesale electricity price data demonstrate the proposed method's effectiveness in minimizing energy costs and avoiding constraint violations. This chapter addresses the research gap of efficiently managing time-coupled operational constraints in smart homes by proposing a novel approach that ensures constraint satisfaction and convergence, surpassing the limitations of empirical penalty functions used in previous studies. Chapter 3 explores the concept of smart sustainable apartment buildings (SSABs), focusing on installing renewable energy systems in common areas, integrating smart meters for precise energy consumption tracking, and coordinating energy use across multiple residential units. The internal energy coordination scheme is formulated as a hierarchical partially observable Markov decision process (HPOMDP), emphasizing efficient RES utilization and introducing an equitable internal electricity settlement mechanism to ensure fair benefits for all residents. The self-attention prioritized deep deterministic policy gradient (SAP-DDPG) algorithm addresses complex challenges related to RES integration, privacy considerations, and energy consumption patterns within SSABs. Real-world data simulations show that SAP-DDPG significantly enhances energy efficiency, provides cost savings for residents, increases revenue for building operators, and boosts local RES generation utilization. This chapter addresses the unique challenges of energy management in SSABs, including hierarchical coordination, privacy preservation, and efficient internal electricity settlement schemes. The chapter also proposes the use of multi-agent DRL to effectively manage interactions and energy distribution among multiple agents within the SSAB framework. Chapter 4 introduces the multi-step interval forecast soft actor-critic (MSIF-SAC) algorithm. This approach incorporates multi-step interval forecasts of PV generation and electricity prices into the soft actor-critic (SAC) algorithm's state representation. By redesigning the actor and critic network architectures, MSIF-SAC effectively extracts vital information from complex input data, enabling BEMSs to make informed decisions. Real-world dataset evaluations show that MSIF-SAC outperforms existing DRL-based strategies, demonstrating superior learning efficiency, stability, and cost-reduction capabilities. This chapter addresses the research gap of integrating multi-step interval forecasts into DRL-based BEMSs to improve decision-making under uncertainty, especially with the challenges posed by uncertainties in PV generation and fluctuating electricity prices. In conclusion, this thesis presents advanced data-driven energy management strategies for smart buildings that aim to enhance efficiency, reduce costs, and maximize renewable energy utilization. By pushing the boundaries of deep reinforcement learning applications in smart building energy management, this thesis makes significant contributions to the development of more sustainable, cost-effective, and intelligent energy management systems.

Transformation towards a carbon-neutral residential community with hydrogen economy and advanced energy management strategies

Electric vehicles (EVs) are crucial for reducing greenhouse gas emissions and promoting sustainable transportation. However, optimizing energy management in EVs is challenging due to the variability in driving conditions and the impact of battery degradation. This paper proposes an advanced energy management and control strategy that accounts for these factors, aiming to enhance both vehicle performance and battery longevity. We integrate real-time data on driving conditions with detailed battery degradation models to develop a comprehensive control framework. Our methodology employs a combination of rule-based and optimization-based algorithms to dynamically adjust energy usage, ensuring optimal performance under diverse driving scenarios. Our strategy significantly improves energy efficiency and mitigates battery degradation compared to conventional approaches. Specifically, findings show an increase in overall driving range and a reduction in battery wear. Additionally, a sensitivity analysis underscores the robustness of our approach across different driving conditions and battery states. This research offers critical insights for the development of next-generation EV energy management systems, promoting longer-lasting and more efficient electric vehicles. Future work will focus on real-world testing and further refinement of the control algorithms to ensure practical applicability and enhanced performance in varied driving environments.

The future technological potential of hydrogen fuel cell systems for aviation and preliminary co-design of a hybrid regional aircraft powertrain through a mathematical tool

Advances of machine learning in multi-energy district communities‒ mechanisms, applications and perspectives

The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities

Co-optimization of speed planning and cost-optimal energy management for fuel cell trucks under vehicle-following scenarios

There are various approaches to energy and carbon management and the most appropriate approach is likely tochange over time. Selecting the appropriate approach is pivotal in determining how successful an organisation will be in achieving its energy and carbon management objectives. Over the last fifteen years, Gosford City Council has undergone numerous shifts in its approach to the management of energy and carbon across its operations. Council initially focused on reducing its carbon footprint, firstly by setting aspirational targets and followed by the setting of evidence based targets. In response to rising energy costs, Council shifted from a “carbon abatement” to an “energy management” focus in 2012. At this time, the sophistication of Council’s energy management program was vastly improved with the introduction of a corporate energy management information system and a revolving energy fund. In 2014, Council’s energy management program focused on “use less” and“pay less” levers. The first lever “use less” covered much the same ground as previous carbon abatement approaches, however, the second energy management lever, “pay less” unlocked significant additional value to Council. Pay less initiatives, such as energy procurement, load shifting, energy account management and bill validation resulted in energy cost savings of hundreds of thousands of dollars for Council. Council is now shifting from a tactical to a more strategic approach to energy management. An Energy Management Strategy is under development. The Energy Management Strategy will introduce an Energy Productivity Improvement Objective. This objective will focus on recognising the complete economic value of improved energy and carbon management. This should yield organisational productivity improvements and economic value in the local community. The strategy also introduces advanced energy metrics such as an energy cost index and asset class energy intensity metrics. The appropriate approach for Gosford City Council’s energy and carbon management has advanced in line with wider organisation objectives, values and maturity of its energy management systems.

One of the latest innovations in the industrial environment was the emergence of Industry 4.0. Along with numerous challenges, there are many chances for companies in it. Besides automation, energy efficiency and energy flexibility is becoming increasing important. Because of the planned phasing-out of nuclear power and high prices for energy, it is important for companies to reduce their energy consumption in order to reduce cost and stay compatible in market place. Therefore strategic energy management plays an important role in overcoming these obstacles. Implementation of energy management is often based on standards and norms with the DIN EN ISO 50001 being the most important. Energy monitoring is a key factor for the successful execution of energy management. For energy monitoring of Industry 4.0 production plants, a cloud based energy monitoring and management system was developed. This solution allows users to monitor their production in real time enabling them a possibility of condition monitoring, energy flexible planning of production based on historic data and energy management along with load management. The detailed concept of the cloud based totally integrated energy management system, beginning with data acquisition to data analysis and visualization is presented in this paper.

A regression learner-based approach for battery cycling ageing prediction―advances in energy management strategy and techno-economic analysis

Improving thermal efficiency and reducing carbon emissions are the permanent themes for internal combustion (IC) engines. In the past decades, various advanced strategies have been proposed to achieve higher efficiency and cleaner combustion with the increasingly stringent fuel economy and emission regulations. This article reviews the recent progress in the improvement of thermal efficiency of IC engines and provides a comprehensive summary of the latest research on thermal efficiency from aspects of thermodynamic cycles, gas exchange systems, advanced combustion strategies, and thermal and energy management. Meanwhile, the remaining challenges in different modules are also discussed. It shows that with the development of advanced technologies, it is highly positive to achieve 55% and even over 60% in effective thermal efficiency for IC engines. However, different technologies such as hybrid thermal cycles, variable intake systems, extreme condition combustion (manifesting low temperature, high pressure, and lean burning), and effective thermal and energy management are suggested to be closely integrated into the whole powertrains with highly developed electrification and intelligence.