dPVS

Abstract: Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

Abstract: Customer baseline load (CBL) estimation is very important in demand response (DR) program. Due to the increasing installation of distributed photovoltaic system (DPVS), the load patterns of residential customers become more complex and random. The actual load power of the customer is coupled with the DPVS output power, which makes it more difficult to estimate CBL. Since the electricity meter can only measure the net load data, this article proposes a PV-load decoupling approach to improve the CBL estimation accuracy in the presence of DPVS. CBL is the difference between actual load power and DPVS output power, so the CBL estimate is converted into two sub-problems: the estimation of actual load power and the estimation of DPVS output power. First, the actual load power of DR customers is estimated based on the load power of the control group customers. Then, the DPVS output during DR period is obtained based on the DPVS output estimation model. Finally, CBL is estimated based on the actual load power and DPVS output power. In order to verify the effectiveness and feasibility of the approach, two real datasets from Sydney and Austin are used to simulate the CBL estimation. Compared with the net load directly estimating the CBL, the comparison results indicate that the proposed method shows better accuracy performance.

Abstract: An efficient and readily scalable thioetherification between 1,1-diphenylethene (DPE) and sodium arylsulfinate was developed for the synthesis of 1,1-diphenylvinylsulfide (DPVS) with the yield up to 99 %. The photophysical properties of DPVS show that the introduction of arylsulfenyl groups onto the parent molecule DPE makes DPVS a novel type of aggregation-induced emission (AIE) luminogen (AIEgen) with large Stoke's shift (up to 188 nm). These DPVS possess AIE properties due to restriction of intramolecular motions (RIM), as demonstrated by crystal structure analysis. Importantly, the AIE performance of DPVS can be applied to sense the nitroaromatic explosive picric acid in aqueous systems through a "turn-off" response.

Abstract: A platform-independent occlusion culling library for dynamic environments, dPVS, can benefit such applications as CAD and modeling tools, time-varying simulations, and computer games. Visibility optimization is currently the most effective technique for improving rendering performance in complex 3D environments. The primary reason for this is that during each frame the pixel processing subsystem needs to determine the visibility of each pixel individually. Currently, rendering performance in larger scenes is input sensitive, and most of the processing time is wasted on rendering geometry not visible in the final image. Here we concentrate on real-time visualization using mainstream graphics hardware that has a z-buffer as a de facto standard for hidden surface removal. In an ideal system only the complexity of the geometry actually visible on the screen would significantly impact rendering time - 3D application performance should be output sensitive.