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Due to the integration of more intermittent renewable energy into the power grid, the demand for frequency control in power systems has been on the rise, and primary frequency control of hydropower units plays an increasingly important role. This paper proposes an improved frequency dead zone with feed-forward control. The aim is to achieve a comprehensive performance of regulating rapidity, an assessment of integral quantity of electricity, and the wear and tear of hydropower units during primary frequency control, especially the unqualified performance of integral quantity of electricity assessment under frequency fluctuations with small amplitude. Based on a real hydropower plant with Kaplan units in China, this paper establishes the simulation model, which is verified by comparing experimental results. After that, based on the simulation of real power grid frequency fluctuations and a real hydropower plant case, the dynamic process of primary frequency control is evaluated for three aspects, which include speed, integral quantity of electricity, and wear and tear. The evaluation also includes the implementations of the three types of dead zones: common frequency dead zone, the enhanced frequency dead zone, and the improved frequency dead zone. The results of the study show that the improved frequency dead zone with feed-forward control increases the active power output under small frequency fluctuations. Additionally, it alleviates the wear and tear problem of the enhanced frequency dead zone in the premise of guaranteeing regulation speed and integral quantity of electricity. Therefore, the improved frequency dead zone proposed in this paper can improve the economic benefit of hydropower plants and reduce their maintenance cost. Accordingly, it has been successfully implemented in practical hydropower plants in China.

Tajikistan's electricity system is in a state of crisis. Approximately 70 percent of the Tajik people suffer from extensive shortages of electricity during the winter. These shortages, estimated at about 2,700 GWh, about a quarter of winter electricity demand, impose economic losses estimated at over United States (US) 200 million dollars per annum or 3 percent of Gross Domestic Product (GDP). The electricity shortages have not been addressed because investments have not been made in new electricity supply capacity and maintenance of existing assets has not improved. The financial incentive for electricity consumers to reduce their consumption is inadequate as electricity prices are among the lowest in the world. Without prompt action to remedy the causes of Tajikistan's electricity crisis and with growing demand, the shortages could increase to about 4,500 GWh by 2016 (over a third of winter electricity demand) or worse. The World Bank undertook this study to assist the Government of Tajikistan (GoT) in finding ways to overcome the current electricity shortages and establish a sound basis for meeting the growing electricity demand in Tajikistan. The study focuses on the investments and policy reforms needed between now and 2020 to strengthen the financial, technical and institutional capacity of the Tajik power sector and prepare the GoT for undertaking a major expansion of power supply capacity. The study excludes large hydropower plants with storage, given their complexity and global experience that such projects are subject to delays. The winter electricity shortages are caused by a combination of low hydropower output during winter when river flows are low and high demand driven by heating needs. The GoT should focus its immediate attention on three ways to eliminate the current winter power shortages: 1) ambitious energy efficiency plans to reduce uneconomic power usage; 2) new dual-fired thermal power supply to complement the existing hydropower supply during winter; and 3) increased energy imports to leverage surplus electricity supply in neighboring countries.

In response to the COVID-19 emergency, many countries have introduced a series of social-distancing measures including lockdowns and businesses’ shutdowns, in an attempt to curb the spread of the infection. Accordingly, the pandemic has been generating unprecedented disruption on practically every aspect of society. This paper demonstrates that high-frequency electricity market data can be used to estimate the causal, short-run impacts of COVID-19 on the economy, providing information that is essential for shaping future lockdown policy. Unlike official statistics, which are published with a delay of a few months, our approach permits almost real-time monitoring of the economic impact of the containment policies and the financial stimuli introduced to address the crisis. We illustrate our methodology using daily data for the Italian day-ahead power market. We estimate that the 3 weeks of most severe lockdown reduced the corresponding Italian Gross Domestic Product (GDP) by roughly 30%. Such negative impacts are now progressively declining but, at the end of June 2020, GDP is still about 8.5% lower than it would have been without the outbreak.

In the rapid promotion of China’s electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves.

Operating Statuses Identification (OSI) can help operators to find fault timely and minimize the loss. So it is of great significance for optimal operation of photovoltaic (PV) plants. The loss quantity of electricity (LQOE) is defined as that should have been generated but actually not, which is caused by inverter fault, PV modules fault, dust stratification or combinations of them. Firstly, mathematical models of PV cells are proposed to figure out the LQOE of each PV module. Secondly, after analysing the relation between LQOE and operating statuses, an index set of LQOE describing the distinction of different operating statuses are defined including four statistical feature parameters and a user-defined index. Thirdly, Support Vector Classification models for OSI (OSI-SVC) are built with input features extracted from the index set. Lastly, simulations are carried out to verify the effectiveness and evaluate the performance of the OSI-SVC models. The results indicated that the operating statuses can be effectively recognized by the proposed model.

We consider a day-ahead electricity market that consists of multiple competing renewable firms (e.g., wind generators) and conventional firms (e.g., coal-fired power plants) in a discrete-time setting. The market is run in every period, and all firms submit their price-contingent production schedules in every day-ahead market. Following the clearance of a day-ahead market, in the next period, each (renewable) firm chooses its production quantity (after observing its available supply). If a firm produces less than its cleared day-ahead commitment, the firm pays an undersupply penalty in proportion to its underproduction. We explicitly characterize firms’ equilibrium strategies by introducing and analyzing a supply function competition model. The purpose of an undersupply penalty is to improve reliability by motivating each firm to commit to quantities it can produce in the following day. We prove that in equilibrium, imposing or increasing a market-based undersupply penalty rate in a period can result in a strictly larger renewable energy commitment at all prices in the associated day-ahead market, and can lead to lower equilibrium reliability in all periods with probability 1. We also show in an extension that firms with diversified technologies result in lower equilibrium reliability than single-technology firms in all periods with probability 1. The electronic companion is available at https://doi.org/10.1287/mnsc.2017.2961 . This paper was accepted by Serguei Netessine, operations management.

The rapid increase in electric vehicles (EVs) has led to a continuous expansion of electric vehicle (EV) charging stations, imposing significant load pressures on the power grid. Implementing orderly charging scheduling for EVs can mitigate the impact of large-scale charging on the power grid. However, the charging behavior of EVs significantly impacts the efficiency of orderly charging plans. By integrating user portrait technology and conducting research on optimized scheduling for EV charging, EV users can be accurately classified to meet the diverse needs of various user groups. This study establishes a user portrait model suitable for park areas, providing user group classification based on the user response potential for scheduling optimization. First, the FCM and feature aggregation methods are utilized to classify the quantities of features of EV users, obtaining user portrait classes. Second, based on these classes, a user portrait inventory for each EV is derived. Third, based on the priority of user response potential, this study presents a method for calculating the feature data of different user groups. The individual data information and priorities from the user portrait model are inputted into the EV-optimized scheduling model. The optimization focuses on the user charging cost and load fluctuation, with the non-dominated sorting genetic algorithm II utilized to obtain the solutions. The results demonstrate that the proposed strategy effectively addresses the matching issue between the EV user response potential and optimal scheduling modes without compromising the normal use of EVs by users. This classification approach facilitates the easier acceptance of scheduling tasks by participating users, leading to optimized outcomes that better meet practical requirements.

Apparent power and power factor are crucial metrics for evaluating the energy transmission efficiency and reactive power management in power systems. The increasing complexity of power load structures, driven by evolving energy production and consumption models, has intensified the nonlinear and unbalanced characteristics of circuits, presenting significant challenges to accurate apparent power measurement. The IEEE 1459-2010 standard introduces the concept of effective apparent power to enhance the assessment of energy transmission efficiency under non-sinusoidal and unbalanced conditions. However, the absence of a physical standard and a standardized traceability method for effective apparent power results in inconsistent measurement outcomes across instruments. This study proposes a novel method to trace effective apparent power measurements to the International System of Units (SI) benchmarks, based on the loss characteristics of transmission lines. The method includes a comprehensive analysis of measurement uncertainty. Simulation and experimental validation confirm that the proposed traceability circuit can achieve a measurement uncertainty of 0.0110% (coverage factor k = 2), satisfying the engineering requirement of expanded uncertainty U approximately 0.02% (k = 2). These results demonstrate the method’s practical suitability for engineering applications.

This study aims to develop a novel Economic Production Quantity (EPQ) model that integrates important sustainability and operational factors reorders, rework, imperfect quality, emission taxes, and variable electricity tariffs- by minimizing the total inventory cost while considering environmental and energy-related constraints. The model is formulated as an Integer Non-Linear Programming (INLP) problem, with two main decision variables: the total number of products produced in a cycle (y) and the maximum allowable reorder level (w). To solve this complex optimization problem, the Genetic Algorithm (GA) is used for its efficiency in handling non-linear and combinatorial problems. In addition, a sensitivity analysis is performed to assess the impact of various parameters on the total cost. Numerical experiments show that increasing emission taxes, electricity tariffs, and installation costs significantly increase the total inventory and production costs. In particular, higher emission taxes and electricity tariffs amplify the financial burden on manufacturers, underscoring the economic implications of environmental regulations and energy use. These findings emphasize integrating operational and ecological considerations into production planning. This study contributes to the field by offering a comprehensive framework that supports sustainable manufacturing practices through cost-effective inventory management. The proposed EPQ model enables manufacturers to balance economic performance and ecological responsibility, aligning operational strategies with sustainability goals and regulatory compliance.

This paper addresses the mechanisms needed to coordinate vertically and horizontally disaggregated actors in electricity distribution systems. The mechanisms designed to coordinate planning, investments, and operations in the electric power sector were designed with minimal participation from either the demand side of the market or distributed energy resources (DERs) connected at distribution voltages. The emergence of DERs is now animating consumers and massively expanding the number of potential investors and participants in the provision of electricity services. We highlight how price signals—the primary mechanism for coordinating investments and operations at the transmission level—do not adequately coordinate investments in and operations of DERs with network infrastructure. We discuss the role of the distribution system operator in creating cost-reflective prices, and argue that the price signals governing transactions at the distribution level must increasingly internalize the cost of network externalities, revealing the marginal cost or benefit of an actor’s decisions. Price signals considered include contractual relationships, organized procurement processes, market signals, and regulated retail tariffs. This paper is the second part of a two-part series on competition and coordination in rapidly evolving electricity distribution systems.