Experimental Research and Numerical Simulation of Single Soil-Arc-Grounding-Fault in Distribution Networks

Among the distribution network faults, single-phase grounding faults have the greatest probability. The faults are often accompanied by arcs in the grounding point soil. This type of fault current has a small amplitude and seldom can obtain field record data. A soil arc grounding fault is tested on a realistic-distribution-network-experimental-platform (RDNEP), and it is concluded that the soil-arc-grounding-fault (SAGF) has three main characteristics: hysteresis, nonlinearity, and asymmetry. By comparing with the characteristics of common arc models, it is pointed out that common arc models cannot accurately fit the characteristics of SAGF. This paper proposes and establishes a double exponential function arc model. Through the comparison of simulation waveforms with experimental data, it is verified that the numerical simulation method proposed in this paper can simulate the development process of SAGF more accurately. Furthermore, the equivalence of RDNEP is verified on the real distribution network system (RDNS). On this basis, analyzed the arc characteristic changes of different SAGF development cycles. Finally, by studying the applicability of the proposed model in simulating ground faults in grass and gravel roads, it is verified that the model proposed in this paper has a strong generalization capability. The research has laid a theoretical foundation for a detection algorithm that is based on the characteristics of SAGF.

Effect of Electric Vehicles Charging Loads on Realistic Residential Distribution System in Aqaba-Jordan

In recent years, air pollution and climate change issues have pushed people worldwide to switch to using electric vehicles (EVs) instead of gas-driven vehicles. Unfortunately, most distribution system facilities are neither designed nor well prepared to accommodate these new types of loads, which are characterized by random and uncertain behavior. Therefore, this paper provides a comprehensive investigation of EVs’ effect on a realistic distribution system. It provides a technical evaluation and analysis of a real distribution system’s load and voltage drop in the presence of EVs under different charging strategies. In addition, this investigation presents a new methodology for managing EV loads under a dynamic response strategy in response to the distribution system’s critical hours. The proposed methodology is applied to a real distribution network, using the Monte Carlo method and the CYME program. Random driver behavior is taken into account in addition to various factors that affect EV load parameters. Overall, the results show that the distribution system is significantly affected by the addition of EV charging loads, which create a severe risk to feeder limits and voltage drop. However, a significant reduction in the impact of EVs can be achieved if a proper dynamic demand response programme is implemented. We hope that the outcomes of this investigation will provide decision-makers and planners with prior knowledge about the expected impact of using EVs and, consequently, enable them to take the proper actions needed to manage such load.

Polynomials and number sets associated with the probability distribution of the hyperbolic cosine type for even values of the parameter

The polynomials used in the formation of the probability distribution density function of the hyperbolic cosine type are investigated. Earlier, on the basis of a hyperbolic cosine distribution, the author obtained numerical sets, among which not only new ones, but also, for example, the triangle of Stirling numbers, the triangle of the coefficients of Bessel polynomials, sequences of coefficients in the expansion of various functions, etc. In this paper, depending on the natural parameter m and the real distribution parameter β , a new class of polynomials is obtained. For even and odd m , the polynomials are constructed using similar, but different formulas. The article presents polynomials for even values m . Structurally, polynomials consist of quadratic factors. The coefficients of the polynomials, ordered by m , form numerical triangles depending on β . Some relations are found between the coefficients. From the numerical triangles, a set of numerical sequences is obtained, which for integers β are integers. Also, polynomials with respect to x turn out to be polynomials with respect to β . With this interpretation the variable acts as a parameter. New numerical triangles and sequences for different x were found. The overwhelming majority of the obtained numerical sequences are new. The class of polynomials arising from problems of probability theory indicates the possibility of applying the results.

A Phase Generation Shifting Algorithm for Prosumer Surplus Management in Microgrids using Inverter Automated Control

Four-wire low voltage microgrids supply one-phase consumers with continuously changing electricity demand. For addressing climate change concerns, governments implemented incentive schemes for residential consumers, encouraging the installation of home PV panels for covering self-consumption needs. In the absence of sufficient storage capacities, the surplus is sold back by these entities, called prosumers, to the grid operator or in local markets, to other consumers. While these initiatives encourage the proliferation of green energy resources, and ample research is dedicated to local market designs for prosumer-consumer trading, the main concern of distribution network operators is the influence of power flows generated by prosumer surplus injection on the operating states of microgrids. The change in power flow amount and direction can greatly influence the economic and technical operating conditions of radial grids. This paper proposes a metaheuristic algorithm for prosumer surplus management that optimizes the power surplus injections using the automated control of three-phase inverters, with the aim of improving the active power losses and balancing the phase voltage profiles. A case study is performed on two real distribution networks with distinct layouts and load profiles and the algorithm shows its efficiency in both scenarios.

On horizontal immersions of discs in fat distributions of type (4,6)

In this paper, we discuss horizontal immersions of discs in certain corank-2 fat distributions on 6-dimensional manifolds. The underlying real distribution of a holomorphic contact distribution on a complex 3 manifold belongs to this class. The main result presented here says that the associated nonlinear PDE is locally invertible. Using this we prove the existence of germs of embedded horizontal discs.

A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids

The global climate change mitigation efforts have increased the efforts of national governments to incentivize local households in adopting PV panels for local electricity generation. Since PV generation is available during the daytime, at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of local generation to a later time. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation conditions in the network. This paper proposes a metaheuristic approach based on a genetic algorithm that considers three different scenarios of using energy storage for reducing the energy losses in the network. Two cases considers the battery placement and operation under the direct control of the network operator, with single and multiple bus and phase placement locations. Here, the aim was to maximize the benefit for the whole network. The third case considers selfish prosumer battery management, where the storage owner uses the batteries only for their own benefit. The optimal design of the genetic algorithm and of the solution encoding allows for a comparative study of the results, highlighting the important strengths and weaknesses of each scenario. A case study is performed in a real distribution system.

A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids

The global climate change mitigation efforts have increased the efforts of national government to incentivize local households in adopting individual renewable energy as a mean to help reduce the usage of electricity generated using fossil fuels and to gain independence from the grid. Since the majority of residential generation is made by PV panels that generate electricity at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of locally generated electricity at later times. On the other hand, the presence of distributed generation can affect negatively the operating conditions of low-voltage distribution networks. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation of the network. This paper proposes a metaheuristic approach based on a Genetic Algorithm that considers three different scenarios of using energy storage for reducing the losses in the network. Prosumer and network operator priorities can be considered in different scenarios inside the same algorithm, to provide a comparative study of different priorities in storage placement. A case study performed on a real distribution network provides insightful results.

Refinement of land use data for emission calculations in the CHIMERE chemistry-transport model: A case study for the Nizhny Novgorod region .

Refinement of land use data for emission calculations in the CHIMERE chemistry-transport model: A case study for the Nizhny Novgorod region / Borisov D.V., Shalygina I.U. // Hydrometeorological Research and Forecasting, 2021, no. 3 (381), pp. 150-161. The quality of calculating the concentration of pollutants in the chemistry-transport model largely depends on the reliability of used emission data. The possibility of updating the EMEP (European Monitoring and Evaluation Program) emission data using OpenStreetMap geodata for the CHIMERE chemistry-transport model calculations is discussed on the example of the Nizhny Novgorod region. The GlobCover land-use data refinement procedure based on OpenStreetMap information provides a 3.3% increase in the urban area and a more accurate configuration of the emission field as compared to the real distribution of sources of atmospheric emissions. Experimental CHIMERE chemistry-transport model calculations of pollutant concentrations based on the initial and updated emission fields demonstrated the efficiency of the proposed approach. Keywords: emissions, EMEP, land use, OpenStreetMap, CHIMERE chemistry-transport model, air quality

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.