Power Demand Of System Research Articles

Parameter optimization decision framework for transient process of a pumped storage hydropower system

The construction of Pumped Storage Hydropower Systems (PSHSs) has significance for improving the renewable energy accommodation capacity and meeting the peaking demand for future power systems. During the rapid construction of PSHS, whether the transient process meets the safety requirements is a core issue for decision-makers. The traditional methods to ensure the safety of PSHS transient process are based on Code for Design of PSHS (NB/T 10072–2018), which lacks a systematic theoretical framework to overcome the disadvantages of traditional methods, such as a large amount of calculation and strong subjectivity. Therefore, this study proposes a novel decision framework to determine the optimization parameters for PSHS transient process. The approach is based on the characteristics of PSHS design and transient process and an optimized evaluation system consisting of eleven influencing factors and five evaluation indexes are proposed and validated against experimental data from an actual PSHS. A comprehensive ranking of the influencing factors is also realized, which avoids the deviation caused by subjective experience. The results show that the change in pipe diameter has a greater influence on the surge tank than other factors. Besides, the available optimization parameters and priorities are different from various transient indexes. Finally, the feasibility of the optimization framework decision is verified through the case study in China and provides theoretical guidance to optimize the transient characteristics of PSHS according to the priorities.

Ensuring energy balance for sudden demand changes in smart grids

With the increasing diversity in the use of electrical energy and the inclusion of renewable electricity power plants in the system, more efficient and more controllable electrical power systems are provided by smart grid technologies. Load balance based on pricing in electrical power systems; It is a strategy that takes into account customer demands, enables customers to use electrical energy at the most attractive price, and provides suppliers with the desired profit, that is, in summary, it coordinates the efficient operation of energy systems. In this study; While load balance is created with smart grid technology, demand-supply-price balance is achieved by using an optimum Fuzzy Logic PI controller within a closed-loop electric power control system model. Within the scope of the study, the simulation results obtained by applying 6 different demand scenarios to the power system model consisting of different types of generation resources were given graphically and analyzed. The different demand scenarios applied include difficult demand conditions such as possible sudden increase or decreases in demand in the electrical power system.

Impact of Tourist Areas on the Electrical Grid: A Case Study of the Southern Dominican Republic

The growing integration of tourist areas and complexes increases the demand for electrical power systems. This increased demand may represent a vulnerability to voltage and frequency stability in electrical grids, where these parameters are essential for an optimal and continuous supply of electrical energy. The Dominican Republic has begun a tourist expansion process in areas that were previously not commercially exploited. Based on the factors mentioned above, this article’s objective was to analyze the impact caused by the increase in electricity demand due to the tourism sector, using the Enriquillo Region of the Dominican Republic as a case study. The impacts of this expansion on the voltage profiles and the system’s frequency were determined. The methodology consisted of obtaining information on the mathematical model of the system to evaluate the expansion plan for the study period and the projection of the demand of the grid. The complete system was modeled with this information, including expansion and possible renewable generators. Finally, the flow of charges was measured, and dynamic analysis was carried out. The quasi-dynamic and RMS/EMT simulations were carried out in the DIgSILENT software for this investigation. The results showed that the electrical system benefits stability and national standards. This is because the transmission lines reduced their loading by approximately 2.99% in 2032. As the years of study passed and the system load increased, the voltage in the bars of the 138 kV systems and generators did not exceed the range of ±5% established in the technical regulations of the Dominican electricity market.

Grid-forming control of doubly-fed induction generators based on the rotor flux orientation

The increasing penetration of renewable energies in power systems demands new services from renewable generation plants. System operators are concerned about system stability since renewable generators behave as constant power sources. Therefore, new requirements have been imposed to grid-following generators to improve their contribution to system stability acting as grid-supporting generators. Nevertheless, grid-supporting control can still compromise system stability for high penetration of renewables, and grid-forming control has arised to ensure proper operation. This paper proposes a novel grid-forming control scheme for doubly-fed induction generators, so they behave as real voltage sources. The proposed grid-forming control is based on the rotor flux orientation to a reference axis obtained from the emulation of the synchronous generator swing equation. The rotor flux is oriented to the reference axis by means of a flux controller that also controls the flux magnitude. The flux orientation in turn allows to control the doubly-fed induction generator torque, while the flux magnitude control allows to regulate the generator reactive power or terminal voltage. The proposed control system has been validated through a comprehensive real-time simulation with hardware in the loop, assessing its grid-forming capability. Moreover, small signal analysis has also been performed to assess system stability.

Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine

The underground mining industry is at the forefront when it comes to unsafe conditions at workplaces. As mining depths continue to increase and the mining fronts move away from the ventilation shafts, gas hazards are increasing. In this article, the authors developed a statistical polynomial model for nitrogen oxide (NOx) emission prediction of the LHD vehicle with a diesel engine. The best-achieved prediction accuracy by the 4th order polynomial model for 11 and 10 input variables is about 8% and 13%, respectively. It is comparable with the sensors’ accuracy of 10% at a stable regime of loading and 20% in the transient periods of operation. The obtained results allow planning of ventilation system capacity and power demand for the large fleet of vehicles in the deep underground mines.

Performance Analysis of Various Complementary Metaloxide Semiconductor Logics for High Speed Very Large Scale Integration Circuits

The demand for VLSI low voltage high-performance low power systems are increasing significantly. Today's deviceapplications necessitate a system that consumes little power and conserves performance. Recent battery-powered lowvoltagedevices optimize power and high-speed constraints. Aside from that, there is a design constraint with burst-modetype integrated circuits for small devices to scale down. Low voltage low power static CMOS logic integrated circuitsoperate at a slower rate and cannot be used in high performance circuits. As a result, dynamic CMOS logic is used inintegrated circuits because it requires fewer transistors, has lower parasitic capacitance, is faster, and enables pipelinedsystem architecture with glitch-free circuits. It has, however, increased power dissipation. Both types of CMOS circuits withlow power dissipation overcome their own shortcomings.This paper discusses dynamic CMOS logic circuits and their structures. Various logics are also discussed and on the basisof the results obtained, logic which is best suited for designing CMOS logic circuit will be found out. The logic on the basisof structure layout and design which gives best results for high-speed VLSI circuits, is found out.

Precise Modeling for Reducing Output Voltage Control Error of RX-Sensorless IPT System

This paper presents RX-Sensorless technologies required in a self-induced type of low power wireless power transfer system. Recently, apart from the fields of smartphone and electrical vehicles which have been arduously studied for application of wireless power transfer, wireless power transfer technologies are being applied to various fields. Among them, there is a very rapidly increasing demand for low power systems in displays, body implant medical devices, drones, electrical kickboards and so on. In order to acquire portability of RX side of low power systems, the most critical factors are power density and efficiency. For this purpose, there have been very much ongoing studies to improve the power density on the RX side. Among various research, most frequently used types of methods involve derivation of output voltage using the physical quantity on the TX side while removing sensors on the RX side. However, in the modeling process to derive output voltage, they fail to properly reflect the parasite components of wireless power transfer and also due to the lack of measures in case of being out of the range of resonance frequency, there tends to be a high error in the output voltage which raises an issue. Therefore, this paper aims to suggest a model which reflects the parasite resistance components and the permeability rate of medium for the TX/RX pads of wireless power transfer systems. By reflecting winding losses and eddy current losses of TX/RX pads to a model, it is possible to reflect the parasite resistance components and using the Neumann formula, it is possible to reflect it to the medium between RX/TX pads. As a result, based on the derived model, it is possible to control the output voltage. Prototypes of hardware are designed and implemented for verifications. According to the computer simulation results and test results, output voltage estimates yield an error in the range of less than or equal to 8% and model-based output voltage control also shows the control range with errors of less than or equal to 5%.

An approach to harmonic optimization for 25KVA auxiliary converter used in indian railway

As the Indian railway network expands, the demand for reliable and efficient power systems, especially auxiliary converters supporting various on-board systems, grows. By reducing power losses and fuel consumption, it is more crucial to design an energy-efficient and economic auxiliary converter for the railway system. This is partially accomplished by using a 25KVA auxiliary (Aux.) converter, which can accept input electrical energy from a 110V DC battery at Self-Generation (SG) coaches. Biogeography Based Optimization (BBO) method of harmonic elimination for a 25KVA converter is described in this study. The paper explores the 120-degree mode of conduction and compares the existing technique with a proposed 7-switching selective odd harmonic removal method. Results from Matlab simulations demonstrate the effectiveness of the BBO technique in reducing Total Harmonic Distortion (THD). The output voltage waveforms with and without BBO are compared, showcasing a significant improvement in harmonic suppression. The paper concludes by emphasizing the practical implementation of the optimized switching angles using an ATmega2560R3 microcontroller. The online application of stored switching angles on the microcontroller is calculated offline.

Reserve Demand Determination Method Considering the Different Time Periods Uncertainty of Wind and Solar

With the gradual realization of China’s 14th Five-Year Plan, the proportion of installed renewable energy capacity in all regions has increased significantly. The access of a high proportion of renewable energy has brought serious challenges to the safe and stable operation of the power system. The uncertainty of wind power and photovoltaic output makes it difficult for the traditional reserve demand determination method to economically and effectively calculate the reserve capacity required by the system, so it is urgent to propose a reasonable reserve capacity demand determination method. On the one hand, it ensures that the deserve capacity needed by the system is sufficient to cope with real-time active fluctuations, and on the other hand, it improves the economy of the reserve capacity. The reserve demand determination method considering the different time periods uncertainty of wind and solar in this paper is able to meet the reserve demand of new power system. At last, the article proves the benefits of Reserve Demand Determination Method through a numerical example.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition, the clean energy of hydropower, wind power and photovoltaic power generation has ushered in great development, but due to the randomness and volatility of its output, it has caused a certain waste of clean energy power generation resources. Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption, this paper establishes a quantitative model of clean energy restricted electricity from the perspective of power system supply and demand balance. Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid- to long-term electricity market and the day-ahead market. Through the multi-scenario analysis method, the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene, and the typical scene is obtained by using the hierarchical clustering algorithm. This paper establishes a electricity retailer’s risk decision model for purchasing and selling electricity in the mid- and long-term market and reduce-abandonment market, and takes the maximum profit expectation of the electricity retailer from purchasing and selling electricity as the objective function. At the same time, in the medium and long-term electricity market and the day-ahead market, the electricity retailer’s purchase cost, electricity sales income, deviation assessment cost and electricity purchase and sale risk are considered. The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies, on the premise of balancing profits and risks.

Power System Peak Regulation Demand Forecasting Based on LSTM Neural Network

In the context of a high proportion of renewable energy integrated to the power grid, the net load may has significant fluctuations, and it is necessary to quantify peak regulation demand of power system. This paper stablishes a peak regulation demand prediction model based on long short-term memory (LSTM) method by training historical data. The typical data such as load and renewable energy output are selected as the input vector, and correlation coefficients are used to process and simplify the input vector. The historical prediction errors are used to set margins for peak regulation demand prediction. The case study shows that the proposed model can effectively predict the peak regulation demand of the power system.

Minimum inertia demand estimation of new power system considering diverse inertial resources based on deep neural network

AbstractWith the high‐proportion integration of renewable energy and power electronic equipment, the inertia supporting ability of new power system continues to decline, which seriously threatens the frequency stability of power grids. In order to clarify the operation boundary, and realise the rapid analysis and prediction of the minimum inertia demand of new power systems, this study proposes a minimum inertia demand estimation method based on deep neural network (DNN). Firstly, this study establishes the system frequency response model of new power systems containing diverse inertia resources including renewable energy, induction machine and so on. Considering the constraints of rate of change of frequency and maximum frequency deviation, the minimum inertia demand estimation model is established to ensure the system frequency stability. DNN is introduced to effectively map non‐linear relations in complex situations, which can quickly estimate and predict the minimum inertia of new power systems. Adam algorithm is utilised to optimise the input weight matrix and hidden layer feature vector of the network to improve accuracy. Finally, the simulations and analysis are conducted in IEEE‐39 system to verify the accuracy and generalisation ability of the proposed method in this paper.

Assessing the demand side management potential in biofuel production; A theoretical study for biodiesel, bioethanol, and biomethane in Germany

AbstractMinimizing the emissions produced during the processing of biofuel, one aim is to reduce or completely replace the amount of the required fossil fuels used for internal process energy. For the transition of process energy from fossil to renewable energy sources, such as solar and wind, the energy demand of biomass processing must be adjustable to the fluctuating electricity supply. The flexible adjustment of a system's power demand to follow the current power generation is commonly referred to as demand side management (DSM). This contribution shows the results of a study on the implementation of DSM in biofuel biorefineries. By identifying reference concepts that could represent biofuel production plants, the specific mass and energy consumption for the individual process steps in these reference concepts was analyzed through a literature study. The annual throughput and energy consumption of process steps in biofuel production could then be calculated, enabling the identification of the most energy‐consuming process steps. Subsequently, possible flexible operating load ranges of the respective process steps in biofuel production were identified. These findings allowed an assessment of the potential for different process units of biorefinery systems concerning the quantitative adaptability of the electricity load – the theoretical DSM potential. An approximate theoretical DSM potential of 146 MW has been identified for biofuel production in Germany. This cumulated theoretical DSM potential in biofuel production was compared to that of other industrial processes, demonstrating the magnitude and importance of the implementation of DSM in biofuel production. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

Energy regeneration of active pendulum system in tall buildings subjected to wind and seismic loads

High power demand of active structural control systems in tall buildings and incapability of energy storage system (ESS) in supplying the demanded power results in a power shortage which, in turn, can adversely affect their performance. Energy regeneration in active structural control systems may contribute to efficient energy management and sustainable power supply in these systems. This paper explores various aspects of energy regeneration of active pendulum system (APS) in tall buildings. A regenerative hybrid passive-active pendulum system (HPAPS) is proposed to control the wind-induced vibrations in a benchmark tall building. The vibration control system includes an APS that incorporates a fuzzy logic controller (FLC) to calculate the stabilizing torque. The regeneration system comprises an energy management system (EMS), DC-DC convertor, AC boost rectifier, ESS, and their corresponding controllers which have been carefully modeled and included in the simulations. Furthermore, the system is implemented in a laboratory-scale structure, and extensive shake table tests are carried out to examine the system's performance in the presence of seismic disturbances. The findings of this research are as follows: the proposed APS can well reduce the transmitted vibrations to the structure; the top story’s displacements and accelerations are reduced by 47 and 50 percent respectively in the benchmark model, implying the effectiveness of the active structural control system in enhancing simultaneous structural safety and inhabitants’ comfort. Moreover, the energy regeneration scheme can restore 34 percent of the APS’s power. Experimental results also verify the simulation results where a considerable drop in both the top story’s displacement and acceleration is reported for the test structure under seismic load. Furthermore, 44 percent of the active pendulum power is restored in the battery of the laboratory-scale APS.

Optimal Peak Shaving Analysis of Power Demand Response Considering Unit Uncertainty

With the increasingly prominent environmental problems and the promotion of the “double carbon” goal, the participation of demand-side response resources in load peak shaving and valley filling is of great significance to the development of the power industry. In this paper, based on the background that demand-side response is being rapidly developed in China, we firstly consider the uncertainty of generating units and the operating constraints of power system and units, and construct the day-ahead output model of generating units by using stochastic production simulation and day-ahead optimal scheduling. Then, based on this model, we consider the impact of demand response on system peak shaving and build a demand response power system optimal scheduling model with unit uncertainty. Finally, the power supply and demand of the IEEE30 node system on two typical days of peak shaving considering demand response are analysed by MATLAB, and the minimum service cost of demand response and the amount of pull-out limit are derived, which clarify the important role of demand response in the process of power system peak shaving.

A Review of Auto-Regressive Methods Applications to Short-Term Demand Forecasting in Power Systems

The paper conducts a literature review of applications of autoregressive methods to short-term forecasting of power demand. This need is dictated by the advancement of modern forecasting methods and their achievement in good forecasting efficiency in particular. The annual effectiveness of forecasting power demand for the Polish National Power Grid for the next day is approx. 1%; therefore, the main objective of the review is to verify whether it is possible to improve efficiency while maintaining the minimum financial outlays and time-consuming efforts. The methods that fulfil these conditions are autoregressive methods; therefore, the paper focuses on autoregressive methods, which are less time-consuming and, as a result, cheaper in development and applications. The prepared review ranks the forecasting models in terms of the forecasting effectiveness achieved in the literature on the subject, which enables the selection of models that may improve the currently achieved effectiveness of the transmission system operator. Due to the applied approach, a transparent set of forecasting methods and models was obtained, in addition to knowledge about their potential in the context of the needs for short-term forecasting of electricity demand in the national power system. The articles in which the MAPE error was used to assess the quality of short-term forecasts were analyzed. The investigation included 47 articles, several dozen forecasting methods, and 264 forecasting models. The articles date from 1997 and, apart from the autoregressive methods, also include the methods and models that use explanatory variables (non-autoregressive ones). The input data used come from the period 1998–2014. The analysis included 25 power systems located on four continents (Asia, Europe, North America, and Australia) that were published by 44 different research teams. The results of the review show that in the autoregressive methods applied to forecasting short-term power demand, there is a potential to improve forecasting effectiveness in power systems. The most promising prognostic models using the autoregressive approach, based on the review, include Fuzzy Logic, Artificial Neural Networks, Wavelet Artificial Neural Networks, Adaptive Neurofuse Inference Systems, Genetic Algorithms, Fuzzy Regression, and Data Envelope Analysis. These methods make it possible to achieve the efficiency of short-term forecasting of electricity demand with hourly resolution at the level below 1%, which confirms the assumption made by the authors about the potential of autoregressive methods. Other forecasting models, the effectiveness of which is high, may also prove useful in forecasting by electricity system operators. The paper also discusses the classical methods of Artificial Intelligence, Data Mining, Big Data, and the state of research in short-term power demand forecasting in power systems using autoregressive and non-autoregressive methods and models.

The Influence of SMES Magnet Operation Parameters on Voltage Distribution Characteristic

The voltage distribution on the magnet of superconducting Magnetic Energy Storage (SMES) system are the result of the combined effect of system power demand, operation control of power condition system (PCS) and magnet parameters, which is a key issue that influence the stability and security of SMES magnet. This paper mainly focuses on the interaction between the different components and the influence factors on voltage distribution on the magnet. Firstly, the hybrid model which integrate the power system dynamics, the detailed device of PCS and the transient model of magnet is built. Then, from the perspective of power requirement of power system, operation mode and control strategy of PCS, the voltage distribution characteristics are comprehensively analyzed. Finally, the optimization design method of operation mode to reduce the unevenness of voltage distribution is proposed.

Machine Learning Approach for Short-Term Load Forecasting Using Deep Neural Network

Power system demand forecasting is a crucial task in the power system engineering field. This is due to the fact that most system planning and operation activities basically rely on proper forecasting models. Entire power infrastructures are built essentially to provide and serve the consumption of energy. Therefore, it is very necessary to construct robust and efficient predictive models in order to provide accurate load forecasting. In this paper, three techniques are utilized for short-term load forecasting. These techniques are deep neural network (DNN), multilayer perceptron-based artificial neural network (ANN), and decision tree-based prediction (DR). New predictive variables are included to enhance the overall forecasting and handle the difficulties caused by some categorical predictors. The comparison among these three techniques is executed based on coefficients of determination R2 and mean absolute error (MAE). Statistical tests are performed in order to verify the results and examine whether these models are statistically different or not. The results reveal that the DNN model outperformed the other models and was statistically different from them.

(1-x)[0.90NN-0.10Bi(Mg2/3Nb1/3)O3]-x(Bi0.5Na0.5)0.7Sr0.3TiO3 ceramics with core–shell structures: A pathway for simultaneously achieving high polarization and breakdown strength

Modern electronics and electrical power systems demand a large energy storage density (Wrec), high efficiency (η), and board operating temperature to deliver high performance. Nevertheless, the inverse correlation between polarization and dielectric breakdown strength hinders Pb-free dielectric ceramics from providing sufficient energy density and consequently limits their practical applications. This study demonstrates the effectiveness of (1-x)[0.90NN-0.10Bi(Mg2/3Nb1/3)O3]-x(Bi0.5Na0.5)0.7Sr0.3TiO3 (abbreviated as xBNST) ceramics with a core−shell structure in overcoming these challenges. Phase-field simulations reveal that the existence of a polar core in the grain effectively reduces the local electric field distribution of the weak polar shell, thereby significantly improving the breakdown strength. Further analyses of intrinsic electronic structure reveal the intrinsic mechanism for enhancing the breakdown strength via first-principles calculations based on density functional theory. Accompanied by the unique structure, grain inhomogeneity featuring the coexistence of strong polar and weak polar clusters contributes to the enhancement of the polarization difference. Consequently, the ceramics exhibit comprehensive energy storage performances [high Wrec = 7.72 J/cm3, large η = 83.78%, and broad usage temperature range (between −40 and 200 °C)]. These findings are expected to provide a feasible way to develop high-performance capacitors for advanced energy storage materials and applications.

Investigation of Existence of Steady States of a Solar Power Plant Operating in the Distribution Network of an Electric Power System

The paper presents the results of studies of the steady-state operating modes of solar photovoltaic stations. Using the data obtained, it is possible to significantly increase the efficiency of using solar power generating plants and significantly increase the service life of additional equipment. The relevance of this study is due to the increasing demand for low-power electric power systems, as well as for renewable energy technologies. For the research, materials were taken from relevant international departments and agencies, the Ministry of Energy of the Russian Federation. In addition, the sources of data are publications and studies of Russian and foreign scientists on the issues under study. The Federal Law No. 35-FZ “On Electricity”, adopted in December 2019 by the State Duma of the Russian Federation, introduces such a concept as “Micro-generation facility”, thereby simplifying the possibility of installing, connecting to the general network of an electrical system, for example, a small solar power plant (SPP) consumer. Small-scale electric power industry at this stage of its life is an energy-efficient tool in the restructuring of the energy sector of the Russian Federation, contributing to the abandonment of the traditional centralized system based on the use of large sources of electric power production, and the transition to alternative methods of generating electricity based on the use of energy sources, updated for specific natural conditions and requests of potential consumers. Reducing pollutants and emissions that are harmful to human health can reduce the level of the existing burden on the country's economy and give an impetus to the development of its potential due to the freed up resources. reduce the burden on the economy, thereby freeing up resources for its growth. Of course, the transition from traditional economic models to the model of “green growth” can be realized only if significant efforts are made to expand international cooperation in this area. At the same time, there is a need for consistent implementation of events at various levels on the part of all states, as well as maintaining the chosen political course for many years.