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Simultaneous deployment of the electric vehicle charging stations (EVCSs) and distributed photovoltaic stations (DPVSs) in the distribution systems is an effective way to reduce greenhouse gas emissions, promote renewable power adoption, and achieve sustainable development in energy utilization. In this context, how to deploy the EVCSs and DPVSs in the distribution systems with a reasonable scheme is of great importance. In this article, a joint planning model is developed to optimize locations and capacities of the EVCSs and DPVSs simultaneously to reduce energy losses in the distribution systems. In the joint planning model, constraints on bus voltage deviations and line currents are both formulated as chance constraints to ensure that the distribution systems are in reasonable operating statues. To quantify these two chance constraints, a scenario-based method is developed to calculate the probabilistic power flow of the distribution systems during a typical planning day, in which random characters of the DPVS generations and the EVCS charging loads are both considered. The joint planning model of the EVCSs and DPVSs developed in this article is difficult to be solved by mathematical optimization methods. Therefore, genetic algorithm (GA) is customized and utilized to solve the joint planning model of the EVCSs and DPVSs. Finally, a case study based on IEEE 33-bus distribution systems validates the joint planning model and its solving algorithm.

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.

Commercial distributed photovoltaic (DPV) inverters need to comply with standards and guidelines defined by local utilities and distribution system operators. However, due to lack of clear guideline in standards about the performance of inverters under certain types of grid disturbances, these inverters can exhibit a diverse range of responses. As power systems begin to accommodate larger quantities of generation from DPVs their response becomes critical to system security. Power hardware-in-the-loop (PHiL) testing is used in this paper to analyze the behavior of commercial DPV inverters under various grid disturbances. One of the key advantages of this approach is the integration of detailed distribution network models in the testing. The voltage on the point of common coupling (PCC) of the DPV inverter can thus be similar to practical conditions. Experimental tests on multiple commercial DPV inverters are performed to validate the proposed approach and its importance in identifying the interaction of inverters with grid disturbances. The results also demonstrate the need for extensive network-oriented testing of DPV inverters beyond standards compliance.

In order to tackle the energy crisis, plenty of researches have focused on the integration of renewable energy, such as the wind power and the distributed photovoltaic (DPV). However, the penetration of DPVs in the individual power system is restricted due to the operational limits. Hence, the integrated electricity and heat system (IEHS) has been developed to improve the system flexibility and enlarge the penetration of DPV. This paper presents an optimal planning method of DPV in IEHS, which formulates the optimization model of DPV planning according to the modeling of the multi-energy networks. In order to reduce the curtailment of DPVs, the penalty cost of DPVs is integrated to the objective of the proposed planning model. Numerical simulation results demonstrate the effectiveness of the proposed planning method.

One of the most common applications of time-domain reflectometry (TDR) is the localization of dielectric permittivity variations (DPVs) along cable systems. However, signal attenuation and dispersion phenomena limit the usability of TDR when used on long cable systems. Some TDR measuring instruments offer the possibility of modifying the amplitude and the width of the TDR test pulse signal, along with the gain of the input stage amplifier. Increasing these parameters generally allows to investigate longer distances; nevertheless, increasing the gain may saturate the instrument analog to digital converter (ADC) and increasing the pulse width leads to worse spatial resolution. For this reason, it is customary for the operator to repeat TDR measurements several times, with different settings, and proceed by focusing each time on a different TDR reflectogram; however, this procedure is time-consuming and, eventually, it does not guarantee optimal performance. Starting from these considerations, in this work, a new measurement algorithm for facilitating and making more effective the TDR-based localization of large DPVs on long cable systems is proposed. The algorithm automatically acquires several TDR reflectograms obtained by setting different values of the gain of the input stage amplifier and the values of the pulse width of the test signal. The reflectograms are then automatically processed, in an effort to balance the short-distance resolution and long-distance gain in a single composite reflectogram (CR), which can be conveniently used also by non-skilled operators. The developed algorithm was implemented and validated through experiments on a practical case study to localize DPVs on a 300 m-long cable system.

The three-step rule of distributed photovoltaic (DPVs) connecting to a distribution network in the planning of practical photovoltaic projects is modeled in this paper. On this basis, a method based on large sample historical generation-load data is proposed to comprehensively evaluate the DPV limit penetration level of the low voltage&ndash, medium voltage (LV-MV) distribution network, which is the premise of DPV connection planning. Considering the fluctuations of generation-load data in time and space, the DPV limit penetration level of the LV network is evaluated based on kernel density estimation. On this basis, considering the uncertainties of DPVs connecting node schemes and connection capacity, the limit penetration level of DPVs continuously connecting to the MV network is evaluated based on the dichotomy method and stochastic simulation. Finally, the feasibility of evaluating the DPV limit penetration level by the proposed method is verified based on an example of a real distribution network, which is of great importance for the planning of a distribution network with high DPV permeability to continuously connect to DPVs.

In this paper, we present an interpolation algorithm for numerical solution to optimal control problems, which is based on the dynamic programming viscosity solution (DPVS) approach. An upwind finite-difference method is adopted to solve the HJB equation. Several simulations are executed and both of them, without exception, output the accurate numerical results. This design can avoid solving the HJB equation repeatedly, thus efficaciously promote the computation efficiency and save memory.

Accurate customer baseline load (CBL) estimation is critical for implementing incentive-based demand response (DR) programs. The increasing penetration of grid-tied distributed photovoltaic systems (DPVS) complicates customers’ load patterns, making the CBL estimation more difficult because the volatile actual load and the intermittent PV output power are coupled together. A PV-load decoupling framework is proposed in this paper to address the above issue. The basic idea is to decouple the actual load power and the PV output power, then estimate them separately. To this end, historical PV output power data of each individual DPVS is required. However, pure historical PV output power data is usually unavailable for small-scale DPVSs, since they are normally located behind the meter, thus only the net load (i.e. actual load power minus PV output power) data is metered. Therefore, this paper proposes a machine learning approach to disaggregate the output power of each individual DPVS from net load data. The proposed approach includes two stages: DPVS capacity estimation and PV output power estimation. The first stage consists of two steps. First, a net load curve optimal pairing-based feature extraction method is proposed to extract features from the discrepancy between two different net load curves of the customers under heterogeneous weather conditions. Second, a multiple support vector regression-based ensemble model with the input features extracted in the first step is established to estimate the DPVS capacity. In the second stage, the output power of each DPVS is estimated by its capacity multiplied by the output power of a standard DPVS. Case studies using a real dataset from Sydney indicate that the proposed approach shows a promising performance on PV output power estimation and can significantly improve the CBL estimation accuracy for customers with DPVSs.

Diver propulsion vehicles (hereinafter referred to as DPV) are a kind of small vehicle with underwater high-speed used by divers, who are able to grasp or ride on, and operate the volume switch to change the speed. Different from unmanned underwater vehicles (UUVs), the interference caused by diver&rsquo, s posture changing is a unique problem. In this paper, a Diver&ndash, DPV multi-body coupling hydrodynamic model considering rigid body dynamics and fluid disturbance is established by analyzing the existing DPV related equipment. The numerical simulation of multi-body articulated motion is realized by using Star-CCM+ overlapping grid and DFBI 6-DOF body motion method. Five cases of DPVs underwater cruising in a straight-line when restraining diver movement is simulated, and five cases with free diver movement are simulated too. Finally, the influence of the diver&rsquo, s posture changing on the cruising speed resistance is analyzed, and the motion equation including the disturbance is solved. The final conclusion is that, the disturbance is favorable at high speed, which can reduce the cruising resistance, and unfavorable at low speed, which increases the cruising resistance. The friction resistance Ff always accounts for the main part in all speed cases.

In this paper, we present a feedback design for numerical solution to optimal control problems, which is based on solving the corresponding Hamilton-Jacobi-Bellman (HJB) equation. An upwind finite-difference scheme is adopted to solve the HJB equation under the framework of the dynamic programming viscosity solution (DPVS) approach. Different from the usual existing algorithms, the numerical control function is interpolated in turn to gain the approximation of optimal feedback control-trajectory pair. Five simulations are executed and both of them, without exception, output the accurate numerical results. The design can avoid solving the HJB equation repeatedly, thus efficaciously promote the computation efficiency and save memory.

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.

The traditional Instrumental observation (or traditional optical instrument) is difficult to obtain correct visibility information in a complicated atmospheric (e.g. rainy and foggy) environment. We developed a new visibility instrument, digital photography visiometer system (DPVS), equipped with advanced digital photographic technology. We compared the results of the new DPVS with those from a forward-scattering visibility instrument (FD12) and manual visibility observations in various (rainy, non-rainy, foggy) weather conditions. The comparative results show that the new DPVS, FD12, and manual observation have the same trend of change, but the observation from the new DPVS is closer to that from the manual observations in rainy days or complicated weather conditions. Our study demonstrates that the new DVPS is superior to the optical visibility instrument and can be used for automated visibility observations under all weather conditions.

Digital photography visiometer system (DPVS), that is video visibility instrument, is a new visibility instrument. It adopts the advanced digital photographic technology, and entirely imitates the principle of manual visibility observation with human eye. It carries on the manual visibility observation method, and overcomes the defects of the optical visibility instrument. This paper introduces the basic principles of DPVS, and shows the comparative results and observation differences between DPVS, forward scattering visibility instrument (Vaisala FD12) and manual visibility observation in complicated weather conditions. The comparative results show that DPVS, Vaisala FD12, and manual observation share the same change trend. However, in rainy days and other complicated weather conditions, the observation results of DPVS differ from that of the FD12 to some extent, while are much closer to that of manual observation. In the end of the paper, a concrete theoretical analytic conclusion on observation differences is provided.

Digital photographic visibility system (DPVS) based on binocular targets is a method using digital camera and image processing technology for visibility detection. In order to obtain accurate results, the function and installation methods for main components of video visibility detection system including artificial objects, cameras and other parts are designed delicately. Also the article explains the image processing algorithm. From this analysis, the detection system engineering and implementation are not complicated. In 2013, this video visibility detection method has been applied to enhance foggy area security system in Anhui Province, Bengbu - Huainan expressway, and achieved good results.

Visibility information is fundamental in aviation, navigation, land transportation, air quality and dust storm monitoring, and military activities which often require frequent and accurate real-time observation of visibility. The traditional manual observation, the primary means to obtain visibility information by human eyes, is subjective, inconsistent and costly. Instrumental observation (or traditional optical instrument) has overcome some of these limitations, but it is difficult to obtain correct visibility information in a complicated atmospheric (e.g. rainy and foggy) environment. We developed a new visibility instrument, digital photography visiometer system (DPVS), equipped with advanced digital photographic technology including high-resolution charge-coupled-device camera and computer. The new DPVS imitates the human eye observation and accurately calculates the visibility based on its definition and observational principles. We compared the results of the new DPVS with those from a forward scattering visibility instrument (FD12) and manual visibility observations in various (rainy, non-rainy, foggy) weather conditions. The comparative results show that the new DPVS, FD12, and manual observation have the same trend of change, but the observation from the new DPVS is closer to that from the manual observations in rainy days or complicated weather conditions. Our study demonstrates that the new DVPS is superior to the optical visibility instrument and can be used for automated visibility observations under all weather conditions.

In this paper, we present a new numerical algorithm to find the optimal control for the general nonlinear lumped systems without state constraints. The dynamic programming-viscosity solution (DPVS) approach is developed and the numerical solutions of both approximate optimal control and trajectory are produced. To show the effectiveness and efficiency of new algorithm, we apply it to an optimal control problem of two types of drug therapies for human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS). The quality of the obtained optimal control and the trajectory pair is checked through comparison with the costs under the arbitrarily selected different controls. The results illustrate the effectiveness of the algorithm.

Safety property checking is mandatory in the validation process of critical software. When formal verification tools fail to prove some properties, the automatic generation of counterexamples for a given loop depth is an important issue in practice. We investigate in this paper the capabilities of constraint-based bounded model checking for program verification and counterexample generation on real applications. We introduce DPVS (Dynamic Post-condition Variable driven Strategy), a new backjumping strategy we developed to handle an industrial application from a car manufacturer, the Flasher Manager. This backjumping strategy is used to search a faulty path and to collect the constraints of such a path. The simplified control flow graph (CFG) of the program is explored in a backward way, starting from the post-condition and jumping to the most promising node where the variables of the post-condition are defined. In other words, the constraints are collected by exploring the CFG in a dynamic and non-sequential backward way. The Flasher Manager application has been designed and simulated using the Simulink platform. However, this module is concretely embedded as a C program in a car computer, thus we have to check that the safety properties are preserved on this C code. We report experiments on the Flasher Manager with our constraint-based bounded model checker, and with CBMC, a state-of-the-art bounded model checker. Experiments show that DPVS and CBMC have similar performances on one property of the Flasher Manager; DPVS outperforms CBMC to find a counterexample for two properties; two of the properties of the Flasher Manager remain intractable for CBMC and DPVS.

Facing the grim situation that so far at home and abroad there has been no automatic visibility observing instrument coming out yet that truly meets the definition of visibility, this paper, adopting digital photographic technology, based on the principles of manual visibility observation, delves into a new automatic digital photographic visibility detection system that completely conforms to the definition of visibility. This thesis introduced the work theory of system, compose of system, structure of hardware and software flow, in the end some contrasted experiment results between DPVS and traditional optical visibility as well as manual visibility observation were given. The results show that the three ways share the same change trend with kind of differences. And in complicated weather conditions like rainfall, the differences are more obvious.

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.

An automatic digital photography visibility system (DPVS) is presented, which can directly measure and monitor horizontal visibility during daytime through digital shooting. After shooting a scene of given black object and background via its CCD digital camera, the system can capture and transfer photographic images in the camera to a computer, as well as process the digital images and calculate the value of visibility. The formulas for calculating visibility with this method and analysing the effect of non-black object on measured results have also been derived. And then the comparison of DPVS observations with a Lidar-visibility measurements has been carried out. The results show that the average deviation and relative average deviation of visibility acquired by DPVS from lidar-visibility measurements are respectively within 0.927 km and 6.68%.

The observation automation of cloud, energy, and sky is a principal issue to solve in the modern meteorological observation field. The results of most of the research yet done show that: it is possible to achieve the observation automation of cloud, energy, and sky, since the theory and technology is basically prepared, and some of the technology is actually very mature. In the late 20th century, scientists have started to develop a kind of digital visibility automatic monitor, which has adopted advanced digital photographic technology and image recognition technology, based on the definition of visibility and fully imitating the human eye observing visibility principle. Undoubtedly, the digital visibility automatic monitor is the best instrument to replace the manual observation of visibility. Digital photography visibility automation observation instrument is made according to manual visual observation visibility's theory, more objective than traditional transmission, dispersion visibility instrument. This thesis introduced the work theory of digital photographic visibility system (for short is DPVS), compose of system, structure of hardware and software flow, in the end communication between host and outdoor cell was given.