Electric Power Load Research Articles

A Stochastic Transmission Planning Model With Dependent Load and Wind Forecasts

This paper introduces a two-stage stochastic program for transmission planning. The model has two dependent random variables, namely, total electric load and available wind power. Given univariate marginal distributions for these two random variables and their correlation coefficient, the joint distribution is modeled using a Gaussian copula. The optimal power flow (OPF) problem is solved based on the linearized direct current (DC) power flow. The Electric Reliability Council of Texas (ERCOT) network model and its load and wind data are used for a test case. A 95% confidence interval is formed on the optimality gap of candidate solutions obtained using a sample average approximation with 200 and 300 samples from the joint distribution of load and wind.

Hydrothermal Carbonization and Gasification Technology for Electricity Production Using Biomass

The concept uses agricultural residues as rice husk and rice straw as a renewable energy resource. These rice residues typically exhibit low energy density which limits an economic transportation for electrical power generation. In addition usually combustion does not make sense due to the transportation restrictions and the high contents of ash and dust. To master this challenge, the process of hydrothermal carbonization (HTC) is applied to produce bio coal as a transportable value added product with high energy density and the same caloric value as lignite. The produced bio coal can be transported to gasification units in remote villages to generate electrical base load power for mini-grids in rural communities. Furthermore the usage of the bio coal for gasification has the advantage of a clean gasification process with a very low level of ash and dust pollution. This approach could be the key to a profitable generation of electricity as the HTC carbonization facility produces enough bio coal to achieve economic efficiency while supplying remote gasification units to produce electricity for mini-grids on a reliable and steady level.

Weather impacts on electric power load: partial phase synchronization analysis

ABSTRACTElectricity demand is influenced by atmospheric conditions, and, therefore it is important to quantify their relationships suitably for accurate electricity demand forecasting and the implementation of power‐saving policies. However, interdependencies and characteristics of covariance among meteorological variables within the same periodicities hinder the quantification of their direct and indirect impacts on electric power load. To investigate the strength of the direct correlation between atmospheric conditions and electric power load, this study harnessed a new partialization analysis method based on a partial phase synchronization index combined with wavelet transformation. The advantage of the proposed method is that it can be used to evaluate the degree of independent contribution of the variables over different spatiotemporal scales. Compared with traditional statistical analyses, this new partialization analysis shows that air temperature is the principal variable associated directly with electricity demand, but that the strength of the relationship varies with season and time scale. Relative humidity and wind speed have strong direct correlations with electricity in summer and winter, respectively. Insolation is directly coupled to the electric power load only on sub‐diurnal time scales. This investigation indicates that for accurate forecasting of electricity demand, changes in the coupling strengths of different atmospheric variables should be incorporated into the electric power load forecasting process. The study shows that a partial phase synchronization index, combined with wavelet transformation, is a useful tool that could be used in other studies to assess complex interacting atmospheric oscillations that cannot be assessed properly by traditional approaches.

Study on the dynamic characteristics of an integrated coal gasification fuel cell combined cycle

In this study, a dynamic analysis model for integrated gasification fuel cell (IGFC) power generation was developed and the dynamic characteristics of electric power output by the system were investigated. Using the proposed analytical model, the study addressed the frequency deviation of electric power output, the inertia system of gas and steam turbines, the air–fuel ratio of the solid oxide fuel cell (SOFC), and load patterns. Moreover, using the model, an example of an electric power load pattern in Japan was examined. Because the supply and demand difference in electric power can be monitored as frequency deviation in a power network, the frequency of each power generator was analyzed. Consequently, the output of IGFC gas and steam turbines could not be controlled in the target range. Therefore, it was necessary to stabilize the quantity of exhaust heat by improving the heat transfer speed of the SOFC.

Exergetic Analysis of an Integrated Tri-Generation Organic Rankine Cycle

This paper reports on a study of the modelling, validation and analysis of an integrated 1 MW (electrical output) tri-generation system energized by solar energy. The impact of local climatic conditions in the Mediterranean region on the system performance was considered. The output of the system that comprised a parabolic trough collector (PTC), an organic Rankine cycle (ORC), single-effect desalination (SED), and single effect LiBr-H2O absorption chiller (ACH) was electrical power, distilled water, and refrigerant load. The electrical power was produced by the ORC which used cyclopentane as working fluid and Therminol VP-1 was specified as the heat transfer oil (HTO) in the collectors with thermal storage. The absorption chiller and the desalination unit were utilize the waste heat exiting from the steam turbine in the ORC to provide the necessary cooling energy and drinking water respectively. The modelling, which includes an exergetic analysis, focuses on the performance of the solar tri-generation system. The simulation results of the tri-generation system and its subsystems were produced using IPSEpro software and were validated against experimental data which showed good agreement. The tri-generation system was able to produce about 194 Ton of refrigeration, and 234 t/day distilled water.

Dynamic numerical analysis of cross-, co-, and counter-current flow configuration of a 1 kW-class solid oxide fuel cell stack

Alternative designs of planar solid oxide fuel cell stack have been developed over the last decade. Depending on the application, different geometries, materials and flow configurations were adopted. This study investigates a generic solid oxide fuel cell stack with nominal power output above 1.3 kWel. The fuel cell stack is a key component of a highly integrated micro-cogenerator for domestic application. As daily, weekly and monthly variations in the electrical power load are expected, there is a need to develop numerical tools to predict the unit's performance with high accuracy. Hence, a fully physical dynamic model of a SOFC stack was developed and implemented in Aspen HYSYS 7.2 modeling software to enable predictive analysis of the off-design operation. The presented model exhibits high accuracy and accounts for thermal and electrochemical processes and parameters. The authors present the methodology used in the study to analyze the sensitivity of key parameters to load changes in cross-, co- and counter current flow configuration in the stack. Further functionality of the model, which was validated using manufacturer's data is discussed.

IT2TSK 퍼지논리 기반 전력부하 예측 모델 설계에 관한 연구

In most cases, the use of electric power is associated with the economic scale of a nation closely. Thus, the electric power load forecasting plays an important role for the national economic plan. This paper deals with the design method for the electric power load forecasting system. In this paper, RCR-MA data processing, which can make the complex properties of the original data form simple, is proposed. Next, IT2TSK FLS, which can reflect the uncertainty of data more than T1TSK FLS, is applied. Consequently, the structural advantage of the proposed system can improve the forecasting accuracy, and is verified by using two types of electric power data.

Electromagnetic interference caused by electric load of more electric aircraft

As an increasing number of power electronic converters are used in the electrical system of more electric aircraft( MEA),electro-magnetic interference( EMI) problems are becoming more and more severe both within the electrical systems and between different systems. Based on the electrical load characteristics of MEA,electrical load was classified into rectification,chopping and inverter. The conduction EMI current of the three major electrical load during the stable operation and the load surge of electrical loads were studied by modeling and simulations. EMI characteristics caused by the inverter load obtained was verified by experiments. The research results show that the conducted EMI to the power line is concentrated in the switch frequency of the power electronic device and the multiple frequency or so; the step change of the high power electrical load after the Buck chopper circuit makes the conduction EMI characteristics change significantly,which reflected in the frequencies of local maximum of amplitude spectrum. The study results provide the guidance for the EMI identification and the filter design of an independent electrical system.

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.

English

Short-term load forecasting (STLF) plays an essential role in the economic system and save the country's electricity supply. In this paper, are used a neural network with genetic learning algorithm for forecasting the electric power load of Khorasan area in Iran. Because the importance of neural network inputs, select the optimal inputs is deducted errors system. Consumption load is a nonlinear function of various factors such as weather conditions and periodic changes. This paper proposed a new variable together with the data load and temperature parameters for the problem of STLF. The variable obtained from the load curves and effect of periodic changes. The obtained results indicate that the proposed variable is effective for forecasting the short term load in electric power systems Keywords—Short-term load forecast, Neural Network, Genetic algorithm. I. I ntro d uct io n Load forecasting is an important part of management of electric power systems. Power supply planning is a major objective of short-term load forecasting. High forecast error is concluded large loss in safe economical operation in the power system.

Analysis for Evaluating the Impact of PEVs on New-Town Distribution System in Korea

This paper analyzes the impact of Plug-in Electric vehicles(PEVs) on power demand and voltage change when PEVs are connected to the domestic distribution system. Specifically, it assesses PEVs charging load by charging method in accordance with PEVs penetration scenarios, its percentage of total load, and voltage range under load conditions. Concretely, we develop EMTDC modelling to perform a voltage distribution analysis when the PEVs charging system by their charging scenario was connected to the distribution system under the load condition. Furthermore we present evaluation algorithm to determine whether it is possible to adjust it such that it is in the allowed range by applying ULTC when the voltage change rate by PEVs charging scenario exceed its allowed range. Also, detailed analysis of the impact of PEVs on power distribution system was carried out by calculating existing electric power load and additional PEVs charge load by each scenario on new-town in Korea to estimate total load increases, and also by interpreting the subsequent voltage range for system circuits and demonstrating conditions for countermeasures. It was concluded that total loads including PEVs charging load on new-town distribution system in Korea by PEVs penetration scenario increase significantly, and the voltage range when considering ULTC, is allowable in terms of voltage tolerance range up to a PEVs penetration of 20% by scenario. Finally, we propose the charging capacity of PEVs that can delay the reinforcement of power distribution system while satisfying the permitted voltage change rate conditions when PEVs charging load is connected to the power distribution system by their charging penetration scenario.

Algorithms for the Implementation of a Dynamic Electrical Energy Pricing Policy

This paper addresses the implementation of electrical power load scheduling, given a model for predicting energy price fluctuations during a single day. We present a realistic model that utilities may use for implementing dynamic pricing, and discuss how this can be profitable for the utilities and their customers. We provide a dynamic programming algorithm and a greedy algorithm to partition the total demand over a 24-hour period into intervals, minimizing the total cost. The outputs of these algorithms provide ideal load distribution curves for the utility. Using these, each subunit of the grid, e.g., an individual consumer, can create its own ideal load curve which would be a scaled version of the global load curve. Each grid subunit can then find a schedule for its flexible loads so that its load profile is as similar to its ideal load curve as possible. The optimization problem is NP-hard, hence we have explored several algorithms to traverse through the search space of possible schedules, including a greedy algorithm, a randomized greedy algorithm with restarts, the Metropolis algorithm, Tabu search, and finally, a randomized Tabu search with random restarts. Best performance in simulations was obtained with the randomized algorithms.

Thermodynamic Analysis of a Novel Liquid Air Energy Storage System

In this study, a novel liquid air energy storage system for electrical power load shifting application is introduced. It is a combination of an air liquefaction cycle and a gas turbine power generation cycle without fuel combustion. Thermodynamic analysis is conducted to investigate the performance of this system. The results show that liquid air energy storage systems could be very effective systems for electrical power storage with high efficiency, high energy density and extensive application prospects.

Factors Contributing to Haze and the Influence on External Insulation of Power Equipments

Haze-fog has been a severe pollution weather phenomenon in China due to a large number of emissions of pollutants with the rapid development of economy. The areas burst haze are usually coincidence with high density of electricity transmission line corridor or power load areas, and so the pollution flashover accidents happen frequently. In this paper the haze pollution situations and factors contributing to haze are introduced, and the mechanisms of haze inducing and aggravating the pollution flashover accidents are explained by analyzing the temporal and spatial distribution of PM2.5 and size characteristics of dust deposited transmission. Moreover, the influences of haze on power transmission and transformation equipment external insulation are discussed with combining the simulation pollution flashover experiments of high conductivity fog.

A hybrid dynamic and fuzzy time series model for mid-term power load forecasting

• We present a hybrid model for mid-term power loads forecasting. • The model combines a dynamic model and fuzzy time series. • The model reduces forecasting error resulted from bias of weather forecasting. • The model predicts four demand sectors. • Load forecast error is less than 3% for consecutive four-month load forecasting. A new hybrid model for forecasting the electric power load several months ahead is proposed. To allow for distinct responses from individual load sectors, this hybrid model, which combines dynamic (i.e., air temperature dependency of power load) and fuzzy time series approaches, is applied separately to the household, public, service, and industrial sectors. The hybrid model is tested using actual load data from the Seoul metropolitan area, and its predictions are compared with those from two typical dynamic models. Our investigation shows that, in the case of four-month forecasting, the proposed model gives the actual monthly power load of every sector with only less than 3% absolute error and satisfactory reduction of forecasting errors compared to other models from previous studies.

Comparative Study on Short-term Electric Load Forecasting Techniques

In this paper, the problem of short-term load forecasting is divided into load classification and forecasting. Load classification is needed to obtain meaningful load data as input to train forecasting models. To this end, k-NN and K-mean algorithms are presented. K-mean and kNN algorithms can handle seasonal load classification and daily load classification, respectively. The classified load data are used to train forecasting models, which are Artificial Neural Networks, Simple Exponential Smoothing, and ARIMA models. As a real case study, we tried to forecast the electric power load of the Republic of Korea. A comparison between the classified and non-classified load forecasts demonstrates the efficiency of the proposed method.

Applied-Information Technology in Power System with Short-Term Load Forecasting Based on SPSS and BP Neural Network

In the study of power load forecasting, the factors influencing power load have data redundancy and data nonlinearity. The traditional load forecasting methods can’t eliminate redundant or nonlinear law between data, which result in reduced accuracy. In order to improve the predictive accuracy of power load, a prediction model based on BP neural network and SPSS (SPSS-BP) is established. The paper first analyzes the correlation and principal component of influence factors of electric power load, which eliminates the redundancy between various factors, accelerates the speed of BP neural network forecasting and improves predictive accuracy; then model the processed data and forecast through the BP neural network model. One-month weather data and load data of Yichang city have been confirmatory tested and analyzed through application of SPSS-BP model. The results show that SPSS-BP model significantly improves the accuracy, verify the feasibility and effectiveness of the model.

OWA filters and forecasting models applied to electric power load time series

In this paper we propose a novel approach for time series forecasting based on ordered weighted averaging operators (OWA) as linear filter and forecasting models. The OWA operators describe a family of averaging operators parameterized by the choice the weights or filter coefficients. Starting with a nonstationary time series of a given phenomenon, we evaluate the use of linear decaying and constant weights as filtering processes of time series. Moreover, we investigate the effectiveness of using exponential weighted moving average process as a filter linear. After the application of the linear operators, we formulate the best possible forecasting models, ARIMA and neural network models, for short-term forecasting for each of the new structured time series using the usual optimal procedures for a real load data from the Southest Brazilian Company. A residual analysis of these forecasting models is given. In addition, the classical ARIMA and neural network models are also developed for the subject data, and the results are compared with the proposed models that we have introduced. In all cases the new models give better short-term forecasting results than the classical models.

A Forecasting Method of Short-Term Electric Power Load Based on BP Neural Network

Described the meaning of the Short-Term Power forecasting firstly, then gives summary of the basic principles and steps of the power load forecasting, analyses the disadvantages of traditional forecasting methods, and proposing the load analysis plan base on BP neural network theory. Taking full account of the relationship between the daily load and weather factors, establishes a short-term load forecasting model. Results of the prediction are verified highly precise and stable, which makes it suitable for different forecasting conditions.

Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods

Predicting full load electrical power output of a base load power plant is important in order to maximize the profit from the available megawatt hours. This paper examines and compares some machine learning regression methods to develop a predictive model, which can predict hourly full load electrical power output of a combined cycle power plant. The base load operation of a power plant is influenced by four main parameters, which are used as input variables in the dataset, such as ambient temperature, atmospheric pressure, relative humidity, and exhaust steam pressure. These parameters affect electrical power output, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected over a six-year period. First, based on these variables the best subset of the dataset is explored among all feature subsets in the experiments. Then, the most successful machine learning regression method is sought for predicting full load electrical power output. Thus, the best performance of the best subset, which contains a complete set of input variables, has been observed using the most successful method, which is Bagging algorithm with REPTree, with a mean absolute error of 2.818 and a Root Mean-Squared Error of 3.787.