In the net-zero emission journey, the energy industry is moving toward hybrid systems, which are able to process multiple feeds (solar irradiation, wind speed, biomass, geothermal, gas, wastes, etc.) and produce multiple products (e.g., power and chemicals). This indeed complicates the energy production system, especially in the face of intermittent and difficult-to-predict renewable resources. A consequence of high renewables penetration is the need for various energy storage technologies. Then comes the complexity of integrating hybrid storage and hybrid generation systems. 

In recent years (2010s), there has been some hyperemotionality about the role of photovoltaic (PV) and battery technologies to solve the entire energy network challenges. The main motto of our Lab is that our future energy network will be a colorful mix of diverse energy generation and storage technologies, integrated and interconnected with other networks. Rather than competition, storage technologies will complement each other. That's where we have coined the term "polystorage" to link with the "polygeneration" term. Our Lab works on polygeneration and polystorage system development and associated challenges in various industries, including power, chemical, oil and gas production, iron and steelmaking, food, and water (desalination). 

Increasing attention has been paid to the structure of future energy networks, considering wide ranges of renewable energies in response to market trends and climate change policies. However, relatively little work exists, which addresses the integration of future networks (with a wide range of intermittent renewable generations) with energy storage, especially at the distribution level. 

Most conventional energy system planning tools cannot accommodate energy storage features. One immediate reason is that most such models are coarse, with a time granularity of a year. Even tools with finer time frequency (such as hourly profiles) are mostly dispatch models, which cannot accommodate all features of energy storage. In fact, dispatch models noticeably underestimate the value of energy storage. These constraints leave us with finger-count models suitable to future energy system planning. Our team works on the development of storage-integrated unit-commitment framework for energy network planning using deterministic and probabilistic optimisation algorithms.

Often decision-making on utility (electricity, gas, water, etc.) service equity ends up with limited instruments, the most critical being tariff design. Our past works have shown the superiority of moving away from traditional flat tariffs to smart and market-connected tariffs. Despite some evolutionary fears, such tariffs may seemingly penalise and incentivise users fairer than traditional ones. Nevertheless, the tariff design in the emerging decentralised markets is becoming growingly complex. We have been using behavioural optimisation algorithms in sandboxing the feasibility of various tariff mechanisms for prosumer-based markets.

Knowledge is power, and knowledge of the future is superpower. Forecast plays a critical role in both operation and planning of supply chain systems. With network decentralisation, the forecast becomes growingly challenging as on a macro scale, a lot of variables filter out each other and aggregated data often becomes smooth. However, on a micro or distributed scale, noise reduces the accuracy of prediction. We use forecasting techniques for both future demand/price projection and anomaly detection.

Project schedule and redeployment issues are two major problems to consider in FPSO topside design. One of the advantages of FPSOs is that they can be redeployed to another location when the current field is exhausted. However, there are limitations to redeployment as hydrocarbon reservoirs vary with geographical locations and field characteristics, meaning, no two crudes are exactly alike.

These requirements have triggered an alternative approach to managing a fast track and multi-application FPSO project, the so-called “Generic” or “Standardised” approach. With a standardised design, it becomes beneficial to treat a group of FPSO projects as a portfolio rather than individual projects.

This project was focused on topside processes, a decision support algorithm was presented to help the designer in the selection of process packages and the eventual detailed design of selected equipment. The conceptual process design methodology detailed out the factors for the processing consideration, creating a selection path to be tailored for generic FPSO topside design. Upon understanding the methodology, the detailed engineering consideration was presented for the benefit of the designer, to go further in-depth for the step-by-step modularised topside equipment design.