Incorporating variable lifetime and self-discharge into optimal sizing and technology selection of energy storage systems

Technology selection and sizing are key aspects of the design procedure for energy storage systems (ESSs) for power system applications. Here, the authors extended existing methodologies for optimal sizing and technology selection by introducing self-discharge effects, and variable ESS lifetime as a function of energy throughput, which results in a non-convex optimisation problem. Simulation results confirmed that making operational lifetime a variable has a significant impact on the results of the optimal sizing and technology selection problem. More specifically, considering the variable ESS lifetime as a function of energy throughput showed that ESSs of various technologies tend to operate such that their operational lifetimes would far exceed their calendar lifetimes. This has confirmed the importance of considering operational lifetime as a variable rather than a fixed value, as without doing this could result to underutilised and/or oversized systems. Taking into account, the self-discharge effect showed that the electrochemical technologies considered here, with the exception of supercapacitors, have low levels of self-discharge, which are largely obscured by the significant impact of the roundtrip efficiency characteristic

Digitalization for Port Decarbonization: Decarbonization of key energy processes at the Port of Tyne

This article presents findings of the Clean Tyne Project. This project was part of the Clean Maritime Demonstration, funded by the UK’s Department for Transport and delivered in partnership with Innovate UK. Announced in March 2020, and part of the Prime Minister’s Ten Point Plan to position the UK at the forefront of green shipbuilding and maritime technology, the Clean Maritime Demonstration Competition was a £20m investment from government alongside a further £10m from industry to reduce emissions from the maritime sector. The contribution of Newcastle University in the project was to provide quantifiable evidence around the benefits of digitalization, by means of a real-time supervisory and data acquisition platform, in the reduction of carbon emissions, as well as operating and infrastructural costs, at the Port of Tyne.The main aim of this article is to report and discuss the key outputs originating from the modelling performed by Newcastle University around specific operational scenarios at the port. These are intended to highlight the value of intelligent coordination of key energy processes and reduced uncertainty of associated data, both enabled by digitalization. For this purpose, we have designed and modelled current and future operational scenarios, in which Emission Reduction Technologies (ERTs)1 and infrastructure are introduced, alongside increased capability for coordination of energy assets and data availability. In our analysis we consider a centralized decision-making process where energy costs and carbon emissions are minimized subject to available infrastructure and data.Our results can be divided into three categories: impact of emission reduction technologies, impact of coordination, and impact of uncertainty on investment deferral. Under certain credible modelling and data assumptions, and considering energy operational costs and emissions, our findings are that: ERTs can yield significant emissions reductions of up to 93% in year 2040 compared to a present scenario, even if imported power is not 100% zero-carbon; energy costs related with key operations can be reduced up to 45% in year 2050 compared to a scenario where assets are not coordinated; and finally, confidence in data can yield significant reductions in infrastructural investment costs for key energy assets such as energy storage; we have noted that reduction of uncertainty through data availability (due to digitalization) led to a £3.35 Mreduction of CapEx for a particular case considering energy storage installed at the Port of Tyne. We now continue by showing how we modelled port operational scenarios. We then perform a quantitative analysis of cost and carbon emission savings that can be achieved by intelligent coordination of key processes, as well as savings in the form of deferral of network reinforcement and investment in new assets and technologies due to reduced uncertainty around historical data. We subsequently present our results, key findings, and conclude this article, including some suggestions for future work

Sustainable Services to Enhance Flexibility in the Upcoming Smart Grids

Global efforts are already focusing on future targets for even more increases in renewable energy sources contribution, greater efficiency improvements and further greenhouse gas emission reductions. With the fast-paced changing technologies in the context of sustainable development, new approaches and concepts are needed to cope with the variability and uncertainty affecting generation, transmission and load demand. The main challenge remains in developing technologies that can efficiently make use of the available renewable resources. Alternatives in the form of microgrids or virtual power plants along with electricity storage are potential candidates for enhancing flexibility. However, intelligence must be added at all levels in the grid and among all the equipment comprising each subsystem, in order to achieve two-way communications and bidirectional flow of power. Then, the concept of smart grid can be realized and, relying upon software systems, it can remotely and automatically dispatch and optimize generation or storage resources in a single, secure and Web-connected way. Deploying smart configurations and metering promises new possibilities for self-managed energy consumption, improved energy efficiency among final consumers and transition to more consumer-centric energy systems via demand response and demand-side management mechanisms