• Energy Research
• 2021-2025close

How can data and digitalisation bring new insights to operational efficiency in shipping and help capitalise on the opportunity presented? Our new Insight Brief explores this question as part of a series that examines the undervalued opportunity presented by operational efficiency.

The global energy landscape is going through major shifts triggered by consumer preferences, regulations, and technological development. My dissertation develops optimization models to de-rive insights into strategic decisions in energy operations management. In the first essay, I examine how blockchain-enabled peer-to-peer energy trading shifts electricity consumers’ investment in renewable energy. Using the equilibrium model, I show that electricity consumers are always better off by participating in the virtual network, with their resulting cost savings averaging 9.7%. I also prove that blockchain is able to fully coordinate heterogeneous participants in the network to minimize the total cost in the system. The second essay addresses how a Transmission System Operator (TransCo) can optimally invest in a long-distance transmission line to allow renewable energy development by a Power Generation Company (GenCo) in a geographically remote region. Using a continuous-time, infinite-horizon, Stackelberg game between TransCo and GenCo, I show that transmission and generation act as complements with regard to the value functions for both companies. I derive the value-maximizing transmission fee charged by TransCo to GenCo for each unit of energy exported via transmission lines. I characterize a Pareto-improving cost-sharing contract through which both companies can improve the value of their investment. The third essay focuses on how to better manage a decentralized supply chain of an oil-field service company. To minimize the transportation and inventory holding costs of different members in a cross-docking supply chain, I formulate multi-period, mixed-integer programming models. I use structural properties of optimal solutions to show that different collaborations in the supply chain can generate significant cost savings for individual supply chain members, whereas the quantified cost savings exhibit significant variations depending on product weight and holding cost. I also develop a Stackelberg pricing game ...

As the demand for energy grows, there is a substantial push to adopt sustainable and inherently carbon-neutral renewable energy sources. However, the intermittency and non-dispatchability of renewables requires measures to improve grid flexibility. These measures include the frequent cycling of the conventional, fossil-based generating units and energy storage. While conventional plant cycling reduces its efficiency and plant lifetimes, grid-scale energy storage is capital-intensive with only a limited number of suitable candidate technologies. Due to these integration challenges associated with renewables, it is difficult to completely replace the dispatchable fossil generators. CO₂ capture, utilization and storage provides a means to reduce emissions from fossil power plants, but its large-scale deployment is currently limited by high cost and high energy requirement. In this Ph.D. work, the limitations of these decarbonization technologies are addressed through the development of computational frameworks and methodologies. To begin with, a flexible operation strategy is proposed for CO₂ capture in dynamic, uncertain pricing-driven electricity markets to reduce its high energy consumption. This framework is then extended to leverage the operational synergies between renewables and CO₂ capture to address their individual challenges together. A decentralized scheme is further proposed for the integration of energy storage with individual fossil power plants to reduce the costs associated with power plant cycling as well as grid-scale energy storage while accommodating renewable energy. A comprehensive software prototype, THESEUS, is presented to enable the optimal design and operation of the decentralized system, as well as the systematic downselection and comparison of energy storage technologies for different clean energy applications. Using THESEUS, one can determine the best-suited storage technology from a suite of nine technologies at different technology maturity levels, perform detailed techno-economic ...

The detailed derivation processes of Eq. (26)-(31) in the paper "An Energy-efficient Timetabling Approach with Consideration of Varying Train Mass and Real-world Speed profiles for Metro Systems".

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems.

Decision-making under uncertainty has been studied for a long time by the operations management research community. In the past, uncertainty models were often derived based on domain knowledge. However, the availability of vast amounts of data in the recent years has shifted interests towards data-driven approaches for uncertainty quantification. More specifically, statistical models are employed within this framework for characterizing the uncertain components of a stochastic optimization problem based on historical data.In this dissertation, we focus on applications of data-driven decision-making under uncertainty in the healthcare and energy management sectors. The first part of our work provides a mathematical framework for efficient call assignment under Direct Load Control (DLC) contracts (i.e. an incentive-based demand-response program that is widely used by utility firms for balancing the supply and demand of electricity during peak times). Specifically, we employ a model for forecasting energy consumption and develop a large-scale integer stochastic dynamic optimization problem. We then propose a novel hierarchical approximation scheme for efficient execution of the contracts. We evaluate the quality of our proposed approach using real-world data obtained from California Independent System Operator (CAISO), which is the umbrella organization of utility firms in California. A large utility firm in California has implemented our model and informed us that they have experienced a 4\% additional r duction in their cost.Following a similar predict-then-optimize methodological framework, the second part of this dissertation studies data-driven healthcare intervention planning. Specifically, we develop a continuous-time latent-space Markovian model for describing disease progression based on discrete-time irregularly-spaced observations. Our model is capable of incorporating the effect of interventions on progression of disease. We discuss the computational challenges of parameter estimation for this model and ...

What are the legal implications of operational efficiency, and how can the inefficient artefacts of shipping contracts be phased out while driving uptake of specific clauses that encourage the transparency, cooperation, and benefit sharing that allow for more efficient operation of ships? This paper explores this question as part of a series that examines the undervalued opportunity presented by operational efficiencies to reduce shipping emissions in the short term and pave the way for long-term decarbonisation solutions. The learnings presented here have emerged from a series of meetings and workshops gathering perspectives from experts across the maritime value chain—shipowners, operators, charterers, ports, and NGOs—as part of the Short Term Actions Taskforce. Other papers in the series provide an overview of the issue, and dive deeper into the identified solutions and enablers: the role of data, and the role of pilots.

The paper covers the initial development of a prototype visualisation tool designed to enable live projects to track emerging operational energy and emissions. Verified changes to rated power, run times and load factors are visualised relative to a default (worst case) trajectory derived from published building performance studies. The default trajectory follows the S-curve concept of over-promise and underdelivery (1). The tool aims to help practitioners identify key risk factors that could compromise building performance and mitigate these risks at different stages of procurement. The visualisation will link directly to the Operational Energy and Carbon (OpEC) workbook, the subject of a complementary Symposium paper by Field and Bunn (2). A prototype OpEC Visualisation will be presented at the Symposium.

The adverse effects of climate change, the steady depletion of fossil fuels, and the industrialization of developing countries have resulted in an increased supply and demand of renewable thermal energy. Renewable thermal energy sources like solar thermal energy produce fewer local emissions but have a temporally inconsistent power output. The consumer space heating and domestic hot water demands also vary as a function of time. This creates a mismatch between thermal energy supply and demand. Energy storage is one method of solving this problem. However, conventional methods, like hot water storage, are voluminous and can only store heat for short periods of time. Therefore, compact long-term energy storage technologies, like sorption-based energy storage systems, require research and development. The current work aims to identify and develop suitable materials for sorption-based energy storage systems and to determine the effects of operating conditions on the performance of thermal energy storage systems. A material screening study was performed, which identified MCM-41, SAPO-34, and silica gel, which are all silica-based materials, as suitable materials for sorption-based energy storage. The effects of key operating variables for a silica gel/water-vapour adsorption-based energy storage system were quantified and optimized. The optimized system energy storage density value was nearly double that of unoptimized systems. The effects of salt impregnation were investigated by impregnating different hosts with MgSO4 salt and varying the concentration of the salt in the host material. All composites were stable after three hydration/dehydration cycle. A silica gel/MgSO4 hybrid containing 33 wt% MgSO4 was found to have the highest energy storage density of all of the MgSO4-based composites. Finally, CaCl2, a promising hygroscopic for thermal energy storage was stabilized via impregnation into silica gel and encapsulation in methylcellulose. A novel synthesis technique involving the simultaneous impregnation of ...

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping.