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The high expansion rate of renewable generation in the last years was mainly driven by the green transition and the net zero emission targets for 2050. Concerns on the volatility of wind and solar energy, can be mitigated by combining these renewable energy sources with energy storage and operating them coordinately, creating hybrid power plants. Price of batteries has also dropped significantly reaching US$139/kWh in 2023 (battery pack price) and it is expected that by the end of this decade the price will fall below US$100/kWh. Thus, more developers and owners of large renewable plants are looking into their hybridization for both off-grid and grid-connected configurations. The advantages for such a hybrid renewable power plant include complementarity of energy production, better utilization of grid connection while reducing the connection costs, firm capacity of energy production, increase of revenue streams by providing ancillary services, etc. However, the control of a wide range of technologies i.e. wind turbines, solar PV and batteries, from different suppliers poses challenges during their integration into a hybrid power plant.This paper aims to provide an overview of the most common configurations of co-located hybrid power plants including their control architectures. The advantages and challenges for each configuration are discussed in detail from the perspective of OEMs and system integrators.The work presented is part of the ongoing activities in the IEA Wind TCP Task 50 on Hybrid Power Plants that aims to provide a systematic framework and guidelines in this area.

Understanding the causes of past atmospheric methane (CH4) variability is important for characterizing the relationship between CH4, global climate and terrestrial biogeochemical cycling. Ice core records of atmospheric CH4 contain rapid variations linked to abrupt climate changes of the last glacial period known as Dansgaard-Oeschger (DO) events and Heinrich events (HE)1,2. The drivers of these CH4 variations remain unknown but can be constrained with ice core measurements of the stable isotopic composition of atmospheric CH4, which is sensitive to the strength of different isotopically distinguishable emission categories (microbial, pyrogenic and geologic)3-5. Here we present multi-decadal-scale measurements of δ13C-CH4 and δD-CH4 from the WAIS Divide and Talos Dome ice cores and identify abrupt 1‰ enrichments in δ13C-CH4 synchronous with HE CH4 pulses and 0.5‰ δ13C-CH4 enrichments synchronous with DO CH4 increases. δD-CH4 varied little across the abrupt CH4 changes. Using box models to interpret these isotopic shifts6 and assuming a constant δ13C-CH4 of microbial emissions, we propose that abrupt shifts in tropical rainfall associated with HEs and DO events enhanced 13C-enriched pyrogenic CH4 emissions, and by extension global wildfire extent, by 90-150%. Carbon cycle box modelling experiments7 suggest that the resulting released terrestrial carbon could have caused from one-third to all of the abrupt CO2 increases associated with HEs. These findings suggest that fire regimes and the terrestrial carbon cycle varied contemporaneously and substantially with past abrupt climate changes of the last glacial period.

The training process of learning-based distribution system state estimation (DSSE) methods relies on accurate state variables, which typically contain unknown noise and outliers in practice. To this end, this paper proposes an adaptive noise-resistant graphical learning-based DSSE method considering the impact of inaccurate state variables. Specifically, two global-scanning graph jumping connection networks are first developed to capture the regression rules between measurements and state variables considering the structure constraints. To mitigate the negative impact caused by inaccurate labels, a collaborative learning framework is further developed, within which Gaussian mixture model-based discriminators are employed to adaptively select clean samples in each mini-batch. These allow the method to obtain robustness against noisy state labels in historical data, as well as anomalous measurements during online operations. Comparative tests show the superiority of the proposed method in tackling abnormal data in both the training and test phases.

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.

In a hybrid AC/DC microgrid (MG), power quality issues arise when an unbalanced load connects to the AC subgrid, which are not confined to the AC subsystem but extend to affect the DC subsystem as well. This paper investigates the potential power quality issues caused by AC imbalance, including DC voltage fluctuation and AC current harmonics. Multiple control objectives are developed, aiming to eliminate DC fluctuation, reduce AC distortion and imbalance, and achieve negative sequence current sharing among distributed generations in the AC subgrid. To realize these control objectives, a two-layer coordinated control strategy is proposed. The first layer involves local interlinking converter (IC) control to improve the power quality of the DC subgrid, while the second layer focuses on distributed unbalance compensation control to improve the power quality of the AC subgrid. Finally, several experiments are conducted to verify the effectiveness of the proposed control strategy.

Abstract This project explores the integration of battery energy storage systems (BESS) in residential settings to optimize energy management with a novel focus on standalone BESS configurations independent of solar photovoltaic (PV) systems. The objective is to analyze electricity price patterns, evaluate different BESS configurations, and develop strategies for efficient charging and discharging. This enables homeowners to store excess electricity during low-demand periods and discharge it during peak demand, reducing grid reliance and saving on electricity costs. The analysis reveals that a standalone BESS can reduce electricity costs by leveraging low-demand periods with electricity prices averaging 0.4 DKK/kWh, compared to peak prices exceeding 0.8 DKK/kWh. For June 2023, optimal charging (6:00–14:00, 18:00–22:00) and discharging (14:00–18:00, 23:00–6:00) periods were identified, leading to potential cost savings of up to 30 %. The findings highlight the potential of BESS to transform energy management and suggestions such as a net metering program are proposed to mitigate these challenges.

With the rapid development of electric vehicles, the accuracy requirement for lithium-ion battery state feedback is increasing. However, traditional algorithms cannot achieve the desired accuracy. For this purpose, this article focuses on the ternary lithium-ion battery as the research object to achieve high-precision feedback. The results show that the proposed second-order resistor capacitance-partnership for a new generation vehicle (RC-PNGV) equivalent circuit model for ternary lithium-ion batteries and gradually decaying memory recursive least squares method improve the online parameter identification accuracy of battery equivalent models, successfully reducing the overall precision error of State of charge (SOC) estimation from 7.99 % to only 0.35 %. The improved particle swarm optimization algorithm-adaptive dual extended Kalman filter method effectively improves the accuracy and stability of joint estimation of SOC and SOE for ternary lithium-ion batteries. The error in joint estimation is reduced from 3.16 % to 0.89 %, demonstrating that the improved algorithm has high precision, adaptability, and correction capability. This study uses the improved particle swarm optimization - adaptive dual extended Kalman filter algorithm to research the joint estimation of SOC and SOE for lithium-ion batteries, aiming to provide efficient state feedback for batteries to ensure their operational efficiency and safety.

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.