Integrated hydrological, power system and economic modelling of climate impacts on electricity demand and cost

Nature Energy ◽  
2022 ◽  
Author(s):  
Mort Webster ◽  
Karen Fisher-Vanden ◽  
Vijay Kumar ◽  
Richard B. Lammers ◽  
Joseph Perla
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Changyu Zhou ◽  
Guohe Huang ◽  
Jiapei Chen

In this study, an inexact two-stage stochastic linear programming (ITSLP) method is proposed for supporting sustainable management of electric power system under uncertainties. Methods of interval-parameter programming and two-stage stochastic programming were incorporated to tackle uncertainties expressed as interval values and probability distributions. The dispatchable loads are integrated into the framework of the virtual power plants, and the support vector regression technique is applied to the prediction of electricity demand. For demonstrating the effectiveness of the developed approach, ITSLP is applied to a case study of a typical planning problem of power system considering virtual power plants. The results indicate that reasonable solutions for virtual power plant management practice have been generated, which can provide strategies in mitigating pollutant emissions, reducing system costs, and improving the reliability of power supply. ITSLP is more reliable for the risk-aversive planners in handling high-variability conditions by considering peak-electricity demand and the associated recourse costs attributed to the stochastic event. The solutions will help decision makers generate alternatives in the event of the insufficient power supply and offer insight into the tradeoffs between economic and environmental objectives.


2020 ◽  
Vol 12 (8) ◽  
pp. 3103 ◽  
Author(s):  
Hyojoo Son ◽  
Changwan Kim

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.


2016 ◽  
Author(s):  
Jordan Macknick ◽  
Ella Zhou ◽  
Matthew O'Connell ◽  
Gregory Brinkman ◽  
Ariel Miara ◽  
...  

Author(s):  
Viết Cường Võ ◽  
Phuong Hoang Nguyen ◽  
Luan Le Duy Nguyen ◽  
Van-Hung Pham

An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the world, and its electricity demand has been increased dramatically high of about 15%/y for the last three decades. Contribution of industry and construction sectors in GDP has been increasing year by year, and are currently holding the leading position of largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate the electricity consumption of these sectors correctly makes a crucial contribution to the planning of the power system. This paper applies an econometric model with Cobb Douglas production function - a top-down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are the value of the sectors in GDP, income per person, the proportion of electricity consumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for long-term forecasting. Variable of electric price does not affect the demand. This is a very critical result of the study for authority governors in Vietnam. In the base scenario of the GDP and the income per person, the forecasted electricity demands of the sectors are 112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high scenario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above values in the high scenario are less than from 9.0% to 15.8 % of that of the based on in the Revised version of master plan N0. VII.


Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4132 ◽  
Author(s):  
António Couto ◽  
Ana Estanqueiro

Understanding the spatiotemporal complementarity of wind and solar power generation and their combined capability to meet the demand of electricity is a crucial step towards increasing their share in power systems without neglecting neither the security of supply nor the overall cost efficiency of the power system operation. This work proposes a methodology to exploit the complementarity of the wind and solar primary resources and electricity demand in planning the expansion of electric power systems. Scenarios that exploit the strategic combined deployment of wind and solar power against scenarios based only on the development of an individual renewable power source are compared and analysed. For each scenario of the power system development, the characterization of the additional power capacity, typical daily profile, extreme values, and energy deficit are assessed. The method is applied to a Portuguese case study and results show that coupled scenarios based on the strategic combined development of wind and solar generation provide a more sustainable way to increase the share of variable renewables into the power system (up to 68% for an annual energy exceedance of 10% for the renewable generation) when compared to scenarios based on an individual renewable power source. Combined development also enables to reduce the overall variability and extreme values of a power system net load.


2017 ◽  
Vol 1 (3) ◽  
Author(s):  
Peng Qingming

At this stage, influenced by the rapid economic, China's electricity demand is also increasing at an average annual rate of more than 10% which has posed great challenges to the power industry, so ensuring the stability and reliability of the power system has gradually become a common requirement of society. The characteristics of 10kV distribution network which is as the basis of power system structure features wide coverage, long lines, high risk of fault in the operation, therefore, a good daily maintenance work and high reliability of its operation are becoming one of the most urgent task.


2021 ◽  
Author(s):  
Salaheddine Soummane ◽  
Frédéric Ghersi

Projecting future demand for electricity is central to power sector planning, as these projections inform capacity investment requirements and related infrastructure expansions. Electricity is not currently economically storable in large volumes. Thus, the underlying drivers of electricity demand and potential market shifts must be carefully considered to minimize power system costs.


2012 ◽  
Vol 512-515 ◽  
pp. 2643-2649
Author(s):  
Gui Ping Zhu ◽  
Zong Xiang Lu

Battery Electric vehicle (BEV) has been set as one of the most prominent sectors of automobile industry in China in the future due to its significant contribution to energy safety, low carbon emission and leading technology status in vehicle driven by new energy. High penetration of BEV will have obvious impacts on power systems, and its load characteristics are quite different from those traditional loads. Therefore with an eye on the safe, stable and economic operation of power system, this paper studied the impacts of EV on power systems from four aspects: total electricity demand, power rush in short period, power quality and vehicle to grid (V2G) technology. Total electricity demand by BEV charging in 2020 in China was firstly estimated in the paper, and it is sure that power system has the capability to meet this demand. However uncontrolled massive BEV charging will probably results in higher peak load and upgrading requirement of power systems, so orderly charging is required. EV battery is charged through rectifier, which will decrease power quality by harmonic current, therefore power electronic equipment is required to ensure power quality. Finally possibility of scheme of vehicle to grid (V2G) application is discussed when the scale of BEV is large enough and performance of EV battery is greatly improved.


2019 ◽  
Vol 32 (11) ◽  
pp. 6857-6875 ◽  
Author(s):  
Ping Jiang ◽  
Ranran Li ◽  
Haiyan Lu ◽  
Xiaobo Zhang

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