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This paper proposes an economic and resilient operation architecture for a coupled hydrogen-electricity energy system operating at port. The architecture is a multi-objective optimization problem, which includes the energy system optimal economy as the goal orientation and the optimal resilience as the goal orientation. The optimal resilience orientation looks for the best resilience performance of the port through reasonable energy management including (1) reducing the amount of electricity purchased by the port power grid from the external power grid (2) improving the energy level of electric energy storage (3) improving the energy level of hydrogen energy storage. Taking the actual coupled hydrogen-electricity energy system of Ningbo-Zhoushan Port as an example, four typical scenarios were selected according to renewable generation and load characteristics, and a comparative analysis was carried out under the oriented operation. The results show that although the resilience orientation increases the operating cost compared with the economic orientation, the four scenarios reduce the load shedding by 44.84 %, 30.26 %, 48.49 % and 34.37 % respectively when the external power grid is disconnected. The impact of changes in resilience-oriented weight coefficients and hydrogen price on system resilience performance was investigated to provide more references for decision makers.

This paper proposes a multi-time scale scheduling strategy for a practical port coupled hydrogen-electricity energy system (CHEES) to optimize the integration of renewable energy and manage the stochasticity of port power demand. An optimization framework based on day-ahead, intra-day and real-time scheduling is designed. The framework allows coordinating adjustable resources with different rates to reduce the impact of forecast errors and system disturbances, thus improving the flexibility and reliability of the system. The effectiveness of the proposed strategy is verified by a case study of the actual CHEES in the Ningbo Zhoushan Port, and the impact of equipment anomalies on the port power system operation is studied through simulation of different scenarios. The results show that compared with a scheduling scheme without energy management strategy, CHEES with multi-time scale scheduling can save 25.42 % of costs and reduce 14.78 % of CO2 emissions. A sensitivity analysis is performed to highlight the impact of hydrogen price and soft open points (SOP) rated power on the system economy. This study not only provides a new perspective for the optimal scheduling of port energy systems, but also provides a practical framework for managing port energy systems to achieve green transformation and sustainable development.

Hydrogen-electricity integrated multi-energy systems are promising approaches to reduce carbon emissions in ports. However, the stochastic nature of renewable energy and the imbalance between the renewable generation and load demand in ports necessitate the design of an appropriate coupled hydrogen-electricity energy storage systems (CHEESS). This paper proposes a multi-objective optimization model for CHEESS configuration in random imbalanced port integrated multi-energy systems (PIMES), aiming to minimize its life-cycle cost and carbon emissions through co-optimization of sizing and energy management. A hierarchical two-stage framework is proposed to solve the multi-objective model. The proposed optimization framework is applied to a real PIMES at the Ningbo-Zhoushan Port. The results show that the proposed method can save 10.54 % of the monetary cost and 19.67 % of carbon emissions over the entire life-cycle of the system. The study demonstrates that the proposed framework has the potential to generate significant economic and environmental benefits and provides a feasible solution for port authorities seeking to implement CHEESS, aiming to promote sustainability in port operations.

In multiple appliance load monitoring, variations in learning difficulty and numerical scale across target appliances can create imbalances in network parameter optimization, resulting in degraded performance for certain appliances. To this end, a tailored dynamic multi-target loss function is designed to adaptively assign rational weights for target appliances at each epoch, mitigating model bias toward specific appliances. Specifically, a global percentage error metric is employed to evaluate each appliance's performance on a unified scale, allowing dynamic weight adjustment to balance parameter optimization across appliances. This enables the proposed method to build mapping relationships and learn correlations across multiple target appliances, even in the presence of substantial differences in their usage patterns. Furthermore, a transformer-structure monitor is designed to integrate multimodal signals, combining raw data series with multi-step differential signals. This improves the model's learning capability to capture pattern changes in target appliances while enhancing robustness against anomalies.

Future agriculture is poised to shift towards smarter, more sustainable production modes. This innovation are performed as the integration of greenhouse with photovoltaic energy storage systems (PESS). Agricultural park operators (APOs) may efficiently leverage solar energy to enhance both crop growth and overall energy management. Thus, APOs transform into prosumers via the deployment and management of PESS. Beyond benefits known to all, this transition presents a trade-off for APOs: 1) Using energy storage to save more solar energy, thereby extending growth time per day for crops utilize stored power. 2) Lease the energy storage to utilities for additional revenue or offset part of the electricity bill. In response to this future practical and meaningful challenge, this paper develops a bi-level optimization model of strategic decision-making and designs energy management for operators. The upper level highlighted maximizing profits of efficient and daily management for agricultural park. The upper level comprises two parts: (i) Maximizing profits in the ancillary services market and (ii) Minimizing the cost of electricity procurement. The bi-level model is reformulated as a mathematical program with equilibrium constraints (MPEC) problem via the Karush-Kuhn-Tucker (KKT) method. Simulations indicate that deploying photovoltaic and battery systems may reduce costs of electricity procurement and crop growth cycles, increase net profit up to 33 %. Additionally, crop prices and ancillary service prices significantly influence strategy options.