Flexible operation of shared energy storage at households to facilitate PV penetration

This paper proposes a new methodology to enable high penetration of photovoltaic (PV) generation in low voltage (LV) distribution networks by using shared battery storage and variable tariffs. The battery installed at customer premises is shared between customers and local distribution network operators (DNOs) to achieve two goals-minimizing energy costs for customers and releasing distribution network constraints for DNOs. The two objectives are realised through a new concept - “charging envelope”, which dynamically allocates storage capacity between customers and the DNO. Charging envelope first reserves a portion of storage capacity for network operator's priority to mitigate network problems caused by either thermal or voltage limit violation in order to defer or even reduce network investment. Then, the remaining capacity is used by customers to respond to energy price variations to facilitate in-home PV penetration. Case study results show that the concept can provide an attractive solution to realise the dual conflicting objectives for network operators and customers. The proposed concept has been adopted by the Western Power Distribution (UK) in a smart grid demonstration project SoLa Bristol.</p

User Engagement with Mobile Technologies: A Multi-Dimensional Conceptualization of Technology Use

Our study conceptualizes user engagement – a form of technology use targeting the emerging ubiquitous mobile technology generation such as mobile health (mHealth) and social network applications. User engagement manifests in three dimensions, including behavioral, cognitive, and emotional engagement. We validated the measures (in both objective and subjective forms) for the three-dimension user engagement in two different mobile technology contexts, i.e., an e-nursing mobile application and a question-and-answer social network application. We further delineated the relationships among the three dimensions: 1) prior behavioral engagement contributed to both emotional and cognitive engagement, 2) emotional engagement lead to post behavioral engagement, and 3) emotional engagement, compared with prior behavioral engagement and cognitive engagement, exerted a stronger influence predicting post behavioral engagement. Our study enriches both technology use and engagement literature

Examining Drivers and Impacts of Informatization in Shanghai Manufacturing Firms

With careful theoretical development and empirical data examination, this paper investigates several key factors that influence the IT usage in Shanghai firms: technology resource, human resource and environment resource. On the basis of the resource-based view and the process model, the study imports government regulation policies, as well as e-government actions, as environmental resource to affect firms’ IT usage. By surveying 398 manufacturing firms in Shanghai and statistically analyzing the field data using structural equation modeling technique, the study contributes several insights to the IT usage in Chinese firms. First of all, this study sheds lights on the value creation process of firms’ informatization in Shanghai manufacturing industry and validates the route from IT investment to value realization. Second, the findings suggest that government promotion policies have significant impacts on manufacturing firms’ technology infrastructure and IT management decision. However, there is no evidence showing the government impact on firms’ IT usage level

W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-training techniques to weather forecasting and propose W-MAE, a Weather model with Masked AutoEncoder pre-training for multi-variable weather forecasting. W-MAE is pre-trained in a self-supervised manner to reconstruct spatial correlations within meteorological variables. On the temporal scale, we fine-tune the pre-trained W-MAE to predict the future states of meteorological variables, thereby modeling the temporal dependencies present in weather data. We pre-train W-MAE using the fifth-generation ECMWF Reanalysis (ERA5) data, with samples selected every six hours and using only two years of data. Under the same training data conditions, we compare W-MAE with FourCastNet, and W-MAE outperforms FourCastNet in precipitation forecasting. In the setting where the training data is far less than that of FourCastNet, our model still performs much better in precipitation prediction (0.80 vs. 0.98). Additionally, experiments show that our model has a stable and significant advantage in short-to-medium-range forecasting (i.e., forecasting time ranges from 6 hours to one week), and the longer the prediction time, the more evident the performance advantage of W-MAE, further proving its robustness

Adaptive energy management for hybrid power system considering fuel economy and battery longevity

The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs.</p

Reliability-based Probabilistic Network Pricing with Demand Uncertainty

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration.</p

4-(4-Fluoro­anilino)-N-(4-fluoro­phen­yl)-3-nitro­benzamide

In the title compound, C19H13F2N3O3, the anilinobenzamide unit is essentially planar, with a maximum deviation of 0.036 (3) Å. The nitro group and the benzene ring form dihedral angles of 9.6 (5)and 62.20 (8)°, respectively, with the anilinobenzamide unit. An intra­molecular N—H⋯O inter­action occurs. In the crystal, mol­ecules are linked by weak inter­molecular C—H⋯O, N—H⋯O and C—H⋯F hydrogen bonds, which stabilize the structure

LMP-based Pricing for Energy Storage in Local Market to Facilitate PV Penetration

Increasing Photovoltaic (PV) penetration and low-carbon demand can potentially lead to two different flow peaks, generation, and load, within distribution networks. This will not only constrain PV penetration but also pose serious threats to network reliability. This paper uses energy storage (ES) to reduce system congestion cost caused by the two peaks by sending cost-reflective economic signals to affect ES operation in responding to network conditions. First, a new charging and discharging (C/D) strategy based on binary search method is designed for ES, which responds to system congestion cost over time. Then, a novel pricing method, based on locational marginal pricing (LMP), is designed for ES. The pricing model is derived by evaluating ES impact on the network power flows and congestions from the loss and congestion components in LMP. The impact is then converted into an hourly economic signal to reflect ES operation. The proposed ES C/D strategy and pricing methods are validated on a real local grid supply point area. Results show that the proposed LMP-based pricing is efficient to capture the feature of ES and provide signals for affecting its operation. This work can further increase network flexibility and the capability of networks to accommodate increasing PV penetration.</p