Managing the Uncertainties Inherent in Prohibitive Voice: How Leadership Interacts With Employee Political Skill

Drawing from the uncertainty management theory, we examine how authoritarian leadership and humble leadership interact with employee political skill to predict prohibitive voice. We conducted a two-wave survey study of 43 managers and 176 subordinates in a power company in China. Our findings indicate that authoritarian leadership has a minimal negative effect on the psychological safety of employees with higher political skill, which in turn leads to a minimal negative effect on their prohibitive voice. Moreover, humble leadership is positively associated with prohibitive voice for employees with lower political skill. For employees with higher political skill, no type of leadership behavior has a significant influence on their prohibitive voice. We outline the implications of these findings for both theoretical and managerial practices.

A global system for mobile communications-based electrical power consumption for a non-contact smart billing system

<span>This paper proposes a cheap solution to be a starting point for building smart electrical power billing systems. Electrical power has many challenges issues in Iraq, one of these challenges and the most important is the Billing system. The smart power meter uses an Arduino Uno as the core for controlling the sensed data and transmits it to the electrical power unit for billing services. The system is constructed with two parts: the transmitting unit measures the current, voltage, power and power factor through the compact new sensor PZEM-004T. The data sensed and processed by the microcontroller which displayed the KWh on (2*16 LCD) display. This data also transmitted to the electrical power company unit via the global system for mobile communications (GSM) module (SIM900A) which is a dual band GSM/GPRS-</span><span>general packet radio service</span><span> modem. The receiving part is mainly dedicated for collecting the required consumed power data via the same (SIM900A) module and also can display it using (2*16 LCD) display. For the sake of saving these data, the receiver part uses SD ram for such purposes. With such proposed system the electrical power company can control and collect their fees monthly without loss and with minimum cost.</span>

Shapley value cooperative game theory-based locational marginal price computation for loss and emission reduction

An iterative method based on Shapley Value Cooperative Game Theory is proposed for the calculation of local marginal price (LMP) for each Distributed Generator (DG) bus on a network. The LMP value is determined for each DG on the basis of its contribution to reduce loss and emission reduction, which is assessed using the Shapley Value approach. The proposed approach enables the Distribution Company (DISCO) decision-maker to operate the network optimally in terms of loss and emission. The proposed method is implemented in the Taiwan Power Company distribution network 7 warnings consisting of 84 buses and 11 feeders in the MATLAB environment. The results show that the proposed approach allows DISCO to operate the network on the basis of its priority between the reduction of active power loss and emission in the network

Modeling of Seasonal Variations of Thermal Energy Production by an Electric Power Company based on Neural Network Technology

The paper considers the problem of modeling the dependence of the value of thermal energy production by an electric power company on the air temperature using neural network technology. As an example of an electric power company producing thermal energy, the Public Joint-Stock Company (PJSC) Yakutskenergo. As consumers of thermal energy, organizations, enterprises and the population of the city of Yakutsk, are located at latitude 62 and characterized by a cold northern climate. The numerical experiments carried out in this paper have shown that the general trend of the temperature dependence of thermal energy production, observed empirically, is well described by a neural network

Research on Early Warning Technology of Lightning Arrester Defects Based on Multi-stage Information and Bayesian Inference

The differences in the probability of occurrence of different equipment and defects lead to the small sample characteristics of the defect of the arrester, which makes it difficult to train an accurate prediction model. It is difficult to identify the abnormal state when the arrester monitoring data does not exceed the limit and increase steadily relying on the arrester monitoring index and threshold to judge the defect. Therefore, a lightning arrester defect early warning method based on multi-stage information and Bayesian inference is proposed. The Bayesian inference algorithm is used to calculate the probability of defect cause categories under different feature quantities. According to the new test evidence, the probability of the defect cause category under different feature quantities is updated to identify the defect cause. The algorithm automatically adjusts the prior probability indicators of equipment defects and causes in the model based on the new detection data and annotation conclusions to ensure the accuracy of defect cause classification. The lightning arrester operation and maintenance data and online monitoring system of a power company is used to analyze and verify the effectiveness and correctness of the method proposed in this paper, which provides effective supportfor the lightning arrester operation and maintenance.

When the Complexity of the Large-Scale Development Project Becomes a Roadblock to Access to Justice

On 23 July 2018, when the villagers gathered around the porch to wrap up the day with a good chat, one of the five auxiliary dams of the Xe-Pian Xe-Namnoy hydropower dam in Attapeu province, the southeastern state of Laos, collapsed. Four days before the collapse, reports of cracks and subsidence started to come through. It should have been enough to prompt evacuation warning issuance by the Xe-Pian Xe-Namnoy Power Co. Ltd (PNPC), a consortium of South Korean companies SK Engineering and Construction (SK E&C) and Korea Western Power Company (KOWEPO), Thailand-based RATCH Group, and Lao Holding State Enterprise (LHSE). PNPC has a Concession Agreement with the Laos government ‘to plan, design, finance, construct, own, operate and maintain’ the Xe-Pian Xe-Namnoy hydropower dam. The warning was issued, but it came too late.

A Fuzzy Embeddedness

Corporate Social Responsibility (CSR) in the extractive industries is a relatively new but growing topic for anthropology. To study CSR, anthropologists often conduct ethnography in corporations, which provides a unique perspective to discern the functioning of corporate power, company–community relations, and the mainstream discourses of global governance. However, ethnography in corporations requires further reflexivity about the anthropologist’s positionality and what it can tell us about the functioning of CSR. I build on my experience conducting an ethnography of the Cerrejón mine in Colombia, one of the biggest in the world, and a dialoguewith other anthropologists’ methodological and theoretical reflections about CSR. I elaborate on the conceptualization of ethnographers’ work in corporations as fuzzy embeddedness, to explore the temporary, ambiguous, and often unacknowledged ways in which the ethnographer is immersed in corporate logics, and becomes part of the hierarchies and power relations that corporations enact in the extractive territories. The article develops two main arguments. First, mining corporations see ethnographers as stakeholders in their performance of transparency, therefore turning the relation into an enactment of CSR. Second, empathizing with corporate officials is a productive avenue to understand the functioning and reproduction of CSR. Through the text, I present some methodological considerations and hints about the overall functioning of CSR.

Optimal Time-of-Use Electricity Price for a Microgrid System Considering Profit of Power Company and Demand Users

With high proportions of renewable energy generation in power systems, the power system dispatch with renewable energy generation has currently become a popular research direction. In our study, we propose a multi-objective dispatch model for a hybrid microgrid comprising a wind generator, photovoltaic (PV) generator, and an energy storage system to optimize the time-of-use (TOU) electricity price. The objective of the proposed multi-objective dispatch model is to maximize the profit of the power company and demand users, and minimize the proportion of users abandoning PV power and wind power. The elastic price of the load demand with a linear function is employed to optimize the TOU electricity price. Finally, we applied five test cases to validate the practicability of the multi-objective dispatch model.