Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains

We consider using a battery storage system simultaneously for peak shaving and frequency regulation through a joint optimization framework which captures battery degradation, operational constraints and uncertainties in customer load and regulation signals. Under this framework, using real data we show the electricity bill of users can be reduced by up to 15\%. Furthermore, we demonstrate that the saving from joint optimization is often larger than the sum of the optimal savings when the battery is used for the two individual applications. A simple threshold real-time algorithm is proposed and achieves this super-linear gain. Compared to prior works that focused on using battery storage systems for single applications, our results suggest that batteries can achieve much larger economic benefits than previously thought if they jointly provide multiple services.Comment: To Appear in IEEE Transaction on Power System

Optimal Regulation Response of Batteries Under Cycle Aging Mechanisms

When providing frequency regulation in a pay-for-performance market, batteries need to carefully balance the trade-off between following regulation signals and their degradation costs in real-time. Existing battery control strategies either do not consider mismatch penalties in pay-for-performance markets, or cannot accurately account for battery cycle aging mechanism during operation. This paper derives an online control policy that minimizes a battery owner's operating cost for providing frequency regulation in a pay-for-performance market. The proposed policy considers an accurate electrochemical battery cycle aging model, and is applicable to most types of battery cells. It has a threshold structure, and achieves near-optimal performance with respect to an offline controller that has complete future information. We explicitly characterize this gap and show it is independent of the duration of operation. Simulation results with both synthetic and real regulation traces are conducted to illustrate the theoretical results

Analysis of the influence of side wall opening on the arch structure of metro station using the PBA method

In order to meet the traffic and commercial needs, it is sometimes necessary to open the side wall of the metro station, while the current research on the mechanical properties and safety of the arch caused by the opening of the side wall of the station by pile-beam-arch (PBA) method is rarely involved. In this paper, based on the Tianhe East Station project of Guangzhou Metro Line 11 located in soft-hard uneven stratum using PBA method, the settlement law and mechanical characteristics of the arch under different side wall opening conditions is analyzed, and the influence of opening construction and opening span on the safety of arch is also further studied. The results show that the settlement caused by the opening of the side wall is mainly concentrated in the upper part of the opening area, and gradually expands around the opening area with the increase of opening span, and the maximum settlement occurs in the middle part of the arch. Opening leads to the differential settlement at both ends of the arch. With the increase in opening span, the settlement growth trend of the right side of the arch is greater than that of the left side. The opening of the side wall leads to the increase of the safety factor of the arch body and the decrease of the safety factor of the right arch foot, while the change of the safety factor of the left arch foot is not obvious, and the safety factor meets the specification requirements

Demand‐side price‐responsive flexibility and baseline estimation through end‐to‐end learning

Abstract Time‐varying electricity tariffs provide consumers the flexibility to adjust their consumption patterns in response to price variations to reduce the cost of electricity while at the same time contributing to grid operation. As more homes and buildings utilize time‐varying tariffs, utilities and regulators must seek ways to model demand‐side flexibilities to predict future demands and design new incentives. This paper proposes a novel end‐to‐end deep learning framework that simultaneously identifies demand baselines and the price‐response model from the net demand measurements and price signals. A gradient‐descent approach is then proposed that backpropagates the net demand forecast errors to update the weights of the price‐response model and the weights of the baseline demand forecast jointly. The effectiveness of the approach is demonstrated through computation experiments with synthetic demand response traces and a large‐scale real‐world DR dataset. The results show that the approach accurately identifies the DR model, even without prior knowledge about the baseline demand

Black Phosphorus–Tungsten Oxide Sandwich-like Nanostructures for Highly Selective NO₂ Detection

Modern chemical production processes often emit complex mixtures of gases, including hazardous pollutants such as NO2. Although widely used, gas sensors based on metal oxide semiconductors such as WO3 respond to a wide range of interfering gases other than NO2. Consequently, developing WO3 gas sensors with high NO2 selectivity is challenging. In this study, a simple one-step hydrothermal method was used to prepare WO3 nanorods modified with black phosphorus (BP) flakes as sensitive materials for NO2 sensing, and BP-WO3-based micro-electromechanical system gas sensors were fabricated. The characterization of the as-prepared BP-WO3 composite through X-ray diffraction scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the successful formation of the sandwich-like nanostructures. The result of gas-sensing tests with 2–14 ppm NO2 indicated that the sensor response was 1.25–2.21 with response–recovery times of 36 and 36 s, respectively, at 190 °C. In contrast to pure WO3, which exhibited a response of 1.07–2.2 to 0.3–5 ppm H2S at 160 °C, BP-WO3 showed almost no response to H2S. Thus, compared with pure WO3, BP-WO3 exhibited significantly improved NO2 selectivity. Overall, the BP-WO3 composite with sandwich-like nanostructures is a promising material for developing highly selective NO2 sensors for practical applications