Investigation of the trends of electricity demands in Jordan and its susceptibility to the ambient air temperature towards sustainable electricity generation

Abstract

Background: Efficient production and reliable availability of electricity requires comprehensive understanding of load demand trends to plan and match production with consumption. Although electricity demand depends on a combination of cultural and economic conditions, weather conditions remain as the major driver. With increased capabilities of accurate predictions of weather, the importance of investigating and quantifying its impact on electricity demand becomes obvious. The electrical system in Jordan has been facing several challenges including the failure to respond to increased demands induced by extreme temperatures. This paper covers a clear gap in literature through presenting a detailed investigation of the electricity consumption trends and in identifying the susceptibility of these trends to weather. Methods: This study relies on the statistical processing and analysis, through modeling of hourly electricity demands in Jordan in the period of 10 years between 2007 and 2016. Actual weather data was used employing the degree-day approach. The monthly, daily, and hourly seasonal variation indices were determined. Optimally formulated piecewise functions were used to track the thermal comfort zone and rate of increase in electricity demand for temperatures beyond it for each year. Moreover, the elasticity of polynomial functions was adopted to identify saturation points to thermally map the electricity consumption. Results: The developed models successfully described the relationship between the daily electricity demand and the mean daily ambient temperature. The average comfort zone width was 4°C and the average mean base temperature was 17.9°C. The sensitivity of electricity demand to both high and low temperatures has increased on average, with 11% and 16.4% to hot and cold weather, respectively. Finally, the electricity demand in cooling was found to saturate at 32.9°C, whereas it saturates for heating at 4.7°C. Conclusions: The electricity demand in Jordan observes seasonal trends in a consistent and predictable manner. An optimally formulated piecewise function successfully tracked the thermal comfort zone and the rate of increase in electricity demand for temperatures beyond it for each year of the study period. Finally, saturation heating and cooling temperatures were acquired from the elasticity of the daily electricity demands modeled against daily HDD and CDD.

Analysis of Cost-Optimal Renewable Energy Expansion for the Near-Term Jordanian Electricity System

“…Within the National Energy Masterplan for 2007-2020 [10] a reduction of energy dependency from 82% to 40% in 2020 was envisioned, which was not achieved, as Jordan is still importing 94% of its oil and gas to meet energy needs [11]. In 2018, 15% of the total electrical power consumption was used for water pumping, 45% in the residential sector, 22% in the industrial sector, 15% for commercial purposes and 2% for street lighting [12]. However, Jordan holds large renewable energy potential [5].…”

“…In 2018 the Jordanian peak load amounted to 3205 MW, which meant a decrease by about 3.4% from 3320 MW in 2017 [7]. NEPCO (National Electric Power Company) expects an increase of 1.9% by 2019 and a further 3% increase annually between 2019 and 2040 [7,12]. This means an electricity demand increase from 20,143 GWh to 38,261 GWh in 2040.…”

“…Earlier studies examining potential future energy systems for Jordan, such as [14], expected an electricity demand of 106 TWh in 2050. This is due to the demand development between 2007 and 2013, showing a steady growth of electricity loads with an actual increase in consumption of 6.8% on average per year in the mentioned time period [8,12,15]. This was predicted to resume with a projected growth rate of 7.4% annually between 2014 and 2020 within the Master Strategy for Energy in Jordan [10], leading to an overestimation of the current demand.…”

“…Analogously to Equations (5) and (6), the energy storage level and its maximum investment level are bounded as shown in Equations (10) and (11). For all conventional units c ∈ C, upper and lower limits for the total energy supply over the time horizon T can be bounded with Equations (12) and (13).…”

Power load forecasting in energy system based on improved extreme learning machine

“…With the rapid development of social economy, people’s demand for electricity is also increasing (Abdel-Aal, 2004; Almuhtady et al., 2019; Li et al., 2017a, 2018b; Pai and Hong, 2005). Power load forecasting is based on the operating characteristics of the energy system, capacity expansion decisions, and other factors, and on the premise that certain forecast accuracy is met, load data for the future moment are determined (Caro et al., 2020; Che et al., 2012; Li et al., 2019b).…”