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The optimal selection, sizing, and location of small-scale technologies within a grid-connected distributed energy system (DES) can contribute to reducing carbon emissions, consumer costs, and network imbalances. This is the first study to present an optimisation framework for obtaining discrete technology sizing and selection for grid-connected DES design, while simultaneously considering multiphase optimal power flow (MOPF) constraints to accurately represent unbalanced low-voltage distribution networks. An algorithm is developed to solve the resulting Mixed-Integer Nonlinear Programming (MINLP) formulation. It employs a decomposition based on Mixed-Integer Linear Programming (MILP) and Nonlinear Programming (NLP), and utilises integer cuts and complementarity reformulations to obtain discrete designs that are also feasible with respect to the network constraints. A heuristic modification to the original algorithm is also proposed to improve computational speed. Improved formulations for selecting feasible combinations of air source heat pumps (ASHPs) and hot water storage tanks are also presented. The algorithms outperform the existing state-of-the-art commercial MINLP solver, which fails to find any solutions in two instances. While feasible solutions were obtained for all cases, convergence was not achieved for all, especially for those involving the larger network. Where converged, the algorithm with the heuristic modification has achieved results up to 70% faster than the original algorithm. Results for case studies suggest that including ASHPs can support up to 16% higher renewable generation capacity compared to gas boilers, albeit with higher ASHP investment costs. The optimisation framework and results can be used to inform stakeholders such as policy-makers and network operators, to increase renewable energy capacity and aid the decarbonisation of domestic heating systems. 47 pages, 10 figures, 14 Tables

Feature-based machine learning models for capacity and internal resistance (IR) curve prediction have been researched extensively in literature due to their high accuracy and generalization power. Most such models work within the high frequency of data availability regime, e.g., voltage response recorded every 1–4 s. Outside premium fee cloud monitoring solutions, data may be recorded once every 3, 5 or 10 min. In this low-data regime, there are little to no models available. This literature gap is addressed here via a novel methodology, underpinned by strong mathematical guarantees, called ‘path signature’. This work presents a feature-based predictive model for capacity fade and IR rise curves from only constant-current (CC) discharge voltage corresponding to the first 100 cycles. Included is a comprehensive feature analysis for the model via a relevance, redundancy, and complementarity feature trade-off mechanism. The ability to predict from subsampled ‘CC voltage at discharge’ data is investigated using different time steps ranging from 4 s to 4 min. It was discovered that voltage measurements taken at the end of every 4 min are enough to generate features for curve prediction with End of Life (EOL) and its corresponding IR values predicted with a mean absolute percentage error (MAPE) of approximately 13.2% and 2.1%, respectively. Our model under higher frequency (4 s) produces an improved accuracy with EOL predicted with an MAPE of 10%. Full implementation code publicly available.

The virtual energy storage system (VESS) is one of the emerging novel concepts among current energy storage systems (ESSs) due to the high effectiveness and reliability. In fact, VESS could store surplus energy and inject the energy during the shortages, at high power with larger capacities, compared to the conventional ESSs in smart grids. This study investigates the optimal operation of a multi-carrier VESS, including batteries, thermal energy storage (TES) systems, power to hydrogen (P2H) and hydrogen to power (H2P) technologies in hydrogen storage systems (HSS), and electric vehicles (EVs) in dynamic ESS. Further, demand response program (DRP) for electrical and thermal loads has been considered as a tool of VESS due to the similar behavior of physical ESS. In the market, three participants have considered such as electrical, thermal and hydrogen markets. In addition, the price uncertainties were calculated by means of scenarios as in stochastic programming, while the optimization process and the operational constraints were considered to calculate the operational costs in different ESSs. However, congestion in the power systems is often occurred due to the extreme load increments. Hence, this study proposes a bi-level formulation system, where independent system operators (ISO) manage the congestion in the upper level, while VESS operators deal with the financial goals in the lower level. Moreover, four case studies have considered to observe the effectiveness of each storage system and the simulation was modeled in the IEEE 33-bus system with CPLEX in GAMS.

The potential widespread adoption of Electric Vehicles (EVs) has received considerable attention across the globe. However, as a promising technology for both EVs and smart grid, Vehicle-to-Grid (V2G) tended to receive much less attention. This paper developed an agent-based joint EV and V2G model to simultaneously simulate how EVs and V2G might diffuse across space and over time, with empirical findings from a questionnaire survey in Beijing. In particular, random forest models were developed with the survey data to generate each agent’s preferences and attitudes towards EVs and V2G. The joint model also considered three typical levels of social influence, i.e., global influence, neighbor effect, and friendship effect, in the diffusion of EVs and V2G. Finally, the joint model was tested through several “what-if” scenarios, considering different V2G prices, EV/V2G advertisement intensities, and vehicle purchase restrictions. The survey results suggested that 67.7% of the respondents were familiar with EVs, but only 3.3% of them were familiar with V2G. However, over 70% of them would/might try V2G given that they had an EV. The model results suggested that the number of CV applicants was 6.19 times that of BEV applicants in 2030 in the baseline scenario, and only 27.8% of BEV users adopted V2G. Furthermore, V2G selling price, EV/V2G advertisement, and dedicated PHEV purchase permits were not very influential to the diffusion of V2G. The outcomes would be helpful for EV- and V2G-related stakeholders in policy making and technology investment.

This study was planned to offer the roadmap for lifecycle clean shipping by addressing the fundamental question of ‘what are the promising energy solutions for the shipping sector?’. This goal was attempted to be achieved by a lifecycle comparative analysis of the viability of three zero-carbon fuels, ammonia, hydrogen, and inland electricity, based on the operational practicality as well as Well-to-Wake environmental impacts. Credible business scenarios were designed with a high-level screening of 27 short-route ferries currently engaged in 26 West-Scotland coastal routes. Then a series of comparative analyses between the diesel and the proposed alternative fuel sources was conducted. While carbon-free fuels are in the early stages of development in the UK, there are various views on how these fuels can be produced, distributed, and used onboard for the clean shipping economy. To determine the optimal energy solutions, all credible scenarios for the upstream pathways for these fuels were developed, based on the current and future prospected UK energy infrastructure and grids. Those scenarios were examined for West-Scotland shipping and extended to the UK targets. Their technical aspects for maritime application were also investigated in consideration of safety, regulation, infrastructural availability, supply chain constraints, barriers, and the downstream emission pathways to their uptake onboard. Ship conceptual designs were briefly conducted to evaluate the systems, technologies, and equipment required for onboard installation to utilise zero-carbon fuels. As a result of the study, when hydrogen was used as a fuel in fuel cells and electricity was supplied as a backup from batteries that store inland power and the solar PV system, GHG emissions were 25.7% of the conventional fossil fuel-using scenario. In addition, it was confirmed that GWP was 22.2% compared to MGO when ammonia was used as fuel without reforming into hydrogen and backup power was supplied from batteries and the solar PV system. It is ...

Abstract The reliability of proton exchange membrane fuel cell (PEMFC) tightly depends on the suitable operating conditions during dynamic operations. This study proposes an optimization framework to determine the optimal control strategy for PEMFC cold starts underpinned by a novel artificial intelligence method, to improve cold-start capacity and shorten the start-up time. The effects of constant and dynamic currents on PEMFC cold starts under various initial temperatures are studied. The numerical results from a developed PEMFC dynamic model show that the constant current slope strategy (CCSS) is more efficient than the constant current strategy (CCS) in respect of the cold-start time. In the CCSS study, a too-large current slope can lead to a voltage undershoot and then cause a failed cold start, but a too-small current slope can result in a long start-up process in the investigated range of the operating conditions. A data-driven model is developed for dynamic prediction and real-time optimization during the cold start by a semi-recurrent sliding window (SW) method coupled with artificial neural networks (NN) with the simulation data. Based on this NN-SW model, the specific safety–critical operating condition curve under the CCSS has been identified. A real-time adaptive control strategy (RACS) is further proposed to optimize the operating current during the PEMFC cold starts with various initial temperatures. Compared to the optimal CCSS, RACS proves to be more robust and efficient for PEMFC cold-start startups. Based on RACS, the start-up time for an initial temperature of −20 °C can be cut down by 26.7%. Furthermore, the ice predictions by the NN-SW model are also tested and the results are satisfying with an average R2 = 0.9773.