Impacts of load pattern variation in iran power system on generation system planing

Salimi-beni, A.; Fotuhi-Firuzabad, M.; Gharagozloo, H.; Farrokhzad, D.

doi:10.1109/ccece.2005.1557429

Cited by 3 publications

(7 citation statements)

References 2 publications

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“…The produced power is calculated using the formula given in Equation (5). The mathematical model is explained by the objective function given in Equation (4). Figure 12 shows the energy generation and load profiles in the presence of RES.…”

Section: Resultsmentioning

confidence: 99%

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Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability.

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Section: Resultsmentioning

confidence: 99%

“…However, due to the integration of renewable energy, vehicle to grid (V2G) and grid to vehicle (G2V) options, the power system contingency may take economic benefits. However, the dynamic nature of renewable energy and power demand may lead to create system inflexibilities resulting in extra costs by turning on the peak power plants [4].…”

Section: Motivationmentioning

confidence: 99%

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

et al. 2018

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“…It can also be observed that except on January 6 and 7, all of the three loads were all together increasing or decreasing in MAPE values until base load has broken the uniformity on the last two days. Base load is generally steady as validated in the figure showing intermediate load adjusting with the performance of the base and the peak load [5], [14]. According to studies, acceptable error range for load prediction is between 3%-3.5% while the corporate tolerance error of the power utility being tested is below 5% [2], [11], [12], [15].…”

Section: B Ann Model Implementation Resultsmentioning

confidence: 99%

“…It can be beneficial for electric power system in economic dispatching, system planning, and reliability evaluation. Moreover, it helps to determine if there is a need to come up with replacement decisions due to an over or under loading condition [14]. With the three clustered data produced, it was then mapped out together with the load duration curve to effectively show how much base, intermediate, and peak load demand is required in the electric power system.…”

Section: A Load Data Preparation and K-means Clustering Resultsmentioning

confidence: 99%

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Day-ahead Base, Intermediate, and Peak Load Forecasting using K-Means and Artificial Neural Networks

Velasco¹,

Estoperez²,

Jayson³

et al. 2018

ijacsa

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Industries depend heavily on the capacity and availability of electric power. A typical load curve has three parts, namely, base, intermediate, and peak load. Predicting the three (3) system loads accurately in a power system will help power utilities ensure the availability of the supply and to avoid the risk for over-or under-utilization of generation, transmission, and distribution facilities. The goal of this research is to create a suitable model for day-ahead base, intermediate and peak load forecasting of the electric load data provided by a power utility company. This paper presents an approach in predicting the three (3) system loads using K-means clustering and artificial neural networks (ANN). The power utility's load data was clustered using K-means to determine the daily base, intermediate and peak loads that were then fed into an ANN model that utilized Quick Propagation training algorithm and Gaussian activation function. It was found out that the implemented ANN model generated 2.2%, 1.84%, and 1.4% as the lowest MAPE for base, intermediate, and peak loads, respectively, with highest MAPE below the accepted standard error rate of 5%. The results of this study clearly suggest that with the proper method of data preparation, clustering, and model implementation, ANN can be a viable solution in forecasting the day-ahead base, intermediate, and peak load demand of a power utility.

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Power development planning considering power plant response to load change

Vongsurapaitoon

Audomvongseree

2017

2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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Impacts of load pattern variation in iran power system on generation system planing

Cited by 3 publications

References 2 publications

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

Day-ahead Base, Intermediate, and Peak Load Forecasting using K-Means and Artificial Neural Networks

Power development planning considering power plant response to load change

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