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Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this article: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as ‘active’ actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

Vehicle-to-grid (V2G) technologies can actively integrate electric vehicles (EVs) into power systems, thus playing a crucial role in stabilizing the grid and facilitating seamless integration of intermittent renewable energy. This article for the first time quantifies and integrates incentive factors into V2G management for realizing efficient, economical, and comfortable EV smart charging. A multi-element V2G incentive analysis model is established by analyzing grid power balancing requirements, considering vehicle charging costs, ancillary service benefits, battery aging, and range anxieties. The established model serves as a useful tool for quantitatively evaluating the incentive effect on V2G from both the grid operator and prosumer sides. Incentivized EV charging management is realized by a hybrid discrete-continuous policy network, where both vehicle charging state transition and power variation are fully controlled for reducing battery aging and maximizing V2G benefits. Charging management of EVs is realized by better balancing the cost-benefit tradeoff between different incentive factors. The effectiveness of the developed method is verified on a practical U.K. power grid. This research contributes to economical and computationally efficient solutions for managing smart EV charging, benefiting EV owners, network operators, and renewables.

The electrification of the aviation industry is a major challenge to realizing net-zero in the global energy sector. Fuel cell (FC) hybrid electric aircraft (FCHEV) demonstrate remarkable competitiveness in terms of cruise range and total economy. However, the process of simply hybridizing different power supplies together does not lead to an improvement in the aircraft economy, since a carefully designed power system topology and energy management scheme are also necessary to realize the full benefit of FCHEV. This paper provides a new approach towards the configuration of the optimal power system and proposes a novel energy management scheme for FCHEA. Firstly, four different topologies of aircraft power systems are designed to facilitate flexible power flow control and energy management. Then, an equivalent model of aircraft hydrogen consumption is formulated by analyzing the FC efficiency, FC aging, and BESS aging. Using the newly established model, the performance of aircraft can be quantitatively evaluated in detail to guide FCHEA design. The optimal aircraft energy management is realized by establishing a mathematical optimization model with the reduction of hydrogen consumption and aging costs as objectives. An experimental aircraft, NASA X-57 Maxwell, is used to provide a detailed performance evaluation of different power system topologies and validate the effectiveness of the energy management scheme. The new approach represents a guide for future power system design and energy management of electric aircraft.

There is a critical need to protect batteries from degradation to achieve the benefit of electric vehicles (EVs) in the energy and transportation sectors. However, existing battery degradation models have either failed to reflect the actual battery cycle aging mechanism, or are too complex for practical vehicle energy management. This paper presents a new approach for the efficient integration of battery cycles aging mechanisms into vehicle energy management by a prioritized experience-driven imitative learning (PExp-IL) framework. A prioritized degradation experience pool is constructed by comprehensively analyzing battery cycle aging mechanisms in vehicle standard driving schedules. Battery cycle aging features are mathematically presented by learning the expert experience that characterizes its degradation under different working conditions imitatively. The battery protective target is achieved by integrating the established gradient aging model into a vehicle energy management optimization problem. With the anti-aging policy gradient, battery aging mitigation can be easily incorporated into vehicle energy management scenarios that highly depend on algorithm computation efficiency. Two detailed demonstrative cases are further constructed to realize the anti-aging battery management in hybrid vehicles and grid integration of EVs. This approach provides a new practical solution for improving vehicle total economy by mitigating battery aging costs, which can contribute to net zero in the energy-transportation nexus.

The high renewables penetration results in increased imbalance volumes and balancing actions due to system stability requirements. The balancing market (BM) primarily turns down renewable generation and turns up traditional carbon-intensive generation in response to real-time energy imbalance. Existing dual-stage market mechanisms conflict with the carbon reduction trajectory by implementing balancing actions regardless of their carbon footprint. This paper proposes a novel carbon-oriented BM model to coordinate the environmental targets in the day-ahead (DA) and real-time BMs. The emissions of dispatchable generators and flexible loads are distinguished by their operation modes and flexibility types, respectively. Carbon signals are incorporated into their bid/offer prices through the proposed carbon emission flow (CEF) model. By integrating these carbon incentives, the dual-stage market model is formulated to minimize economic and environmental costs. Simulation results demonstrate that, overall, although the proposed BM mechanism results in an increased cost of balancing services (159.10 m£), there is a concurrent larger drop in carbon costs (294.14 m£), resulting in a reduction in total cost. It enables system operators to incentivize decarbonized energy resources in DA scheduling and real-time balancing actions.