Regional Electricity Demand Research Articles

Modelling and projecting regional electricity demand for Saudi Arabia

AbstractWe address the shortcomings associated with modelling national aggregate electricity consumption in a large and diverse country with unique regional characteristics. Focusing on Saudi Arabia, we employ an econometric approach to analyse the distinct responses of its regions to electricity prices and income levels. The study employs a general to specific modelling approach, utilizing data from 1990 to 2019 within the conventional energy demand theoretical framework. Our region‐specific estimations reveal income and price as the primary drivers, with the southern region exhibiting a higher sensitivity to income, while the eastern region shows a comparatively smaller response to prices attributed to its significant industrial presence. Elasticities for the central and western regions align with previous research, while eastern and southern regions exhibit larger elasticities. Weather impacts are observed only in the warmest western region, characterised by a substantial share of residential electricity consumption. Furthermore, we utilise our estimated model to project a regional baseline demand for electricity in Saudi Arabia and demonstrate how prices affect regions differently. This information is important for an oil‐exporting country like Saudi Arabia, considering the diverse fuel mix used for electricity generation across regions. Assuming moderate economic growth and no price change, our baseline projections indicate a total electricity demand of 366 terawatt hours (TWh) by 2030.

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation’s energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events.

Lessons from Regional Electricity Demand Change following Natural Disasters in Japan

Analyzing the impact of electricity price changes on electricity demand helps in energy supply planning. This study estimated the electricity demand functions of households and industries, as well as the impact of electricity price hikes on electricity demand following natural disasters. We found that structural changes occurred in various regions' electricity demand's price elasticity following natural disasters. Furthermore, the results showed that the decrease in consumer surplus associated with higher electricity prices was more significant in regions that depended on nuclear power. These results have important implications for regional energy policies.

Load Forecasting and Operation Optimization of Ice-Storage Air Conditioners Based on Improved Deep-Belief Network

The prediction of cold load in ice-storage air conditioning systems plays a pivotal role in optimizing air conditioning operations, significantly contributing to the equilibrium of regional electricity supply and demand, mitigating power grid stress, and curtailing energy consumption in power grids. Addressing the issues of minimal correlation between input and output data and the suboptimal prediction accuracy inherent in traditional deep-belief neural-network models, this study introduces an enhanced deep-belief neural-network combination prediction model. This model is refined through an advanced genetic algorithm in conjunction with the “Statistical Products and Services Solution” version 25.0 software, aiming to augment the precision of ice-storage air conditioning load predictions. Initially, the input data undergo processing via the “Statistical Products and Services Solution” software, which facilitates the exclusion of samples exhibiting low coupling. Subsequently, the improved genetic algorithm implements adaptive adjustments to surmount the challenge of random weight parameter initialization prevalent in traditional deep-belief networks. Consequently, an optimized deep-belief neural-network load prediction model, predicated on the enhanced genetic algorithm, is established and subjected to training. Ultimately, the model undergoes simulation validation across three critical dimensions: operational performance, prediction evaluation indices, and operating costs of ice-storage air conditioners. The results indicate that, compared to existing methods for predicting the cooling load of ice-storage air conditioning, the proposed model achieves a prediction accuracy of 96.52%. It also shows an average improvement of 14.12% in computational performance and a 14.32% reduction in model energy consumption. The prediction outcomes align with the actual cooling-load variation patterns. Furthermore, the daily operational cost of ice-storage air conditioning, derived from the predicted cooling-load data, has an error margin of only 2.36%. This contributes to the optimization of ice-storage air conditioning operations.

Price Elasticity of Electricity Consumption in Japan, 1990 to 2015

This study examines regional price elasticities in electricity consumption to offer valuable insights for policymaking in electricity demand-side management in Japan. By employing a partial adjustment model and utilizing data from regional electric utilities, the study estimates regional electricity demand functions. The analysis reveals that, within the residential sector, the size of houses plays a significant role in explaining regional variations in price elasticity. Likewise, in the industrial sector, the presence or absence of private power generation facilities in firms significantly influences regional disparities in price elasticity. These findings hold substantial implications for enhancing the cost-effectiveness of electricity consumption regulations and policies. JEL Classification: Q41, C01, R11.

Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway

In this article, we investigate the mismatch of renewable electricity production to demand and how flexibility options enabling spatial and temporal smoothing can reduce risks of variability. As a case study we pick a simplified (partial) 2-region representation of the Norwegian electricity system and focus on wind power. We represent regional electricity production and demand through two stochastic processes: the wind capacity factors are modelled as a two-dimensional Ornstein–Uhlenbeck process and electricity demand consists of realistic base load and temperature-induced load coming from a deseasonalised autoregressive process. We validate these processes, that we have trained on historical data, through Monte Carlo simulations allowing us to generate many statistically representative weather years. For the investigated realisations (weather years) we study deviations of production from demand under different wind capacities, and introduce different scenarios where flexibility options like storage and transmission are available. Our analysis shows that simulated loss values are reduced significantly by cooperation between regions and either mode of flexibility. Combining storage and transmission leads to even more synergies and helps to stabilise production levels and thus reduces likelihoods of inadequacy of renewable power systems.

How to size regional electrolysis systems - Simple guidelines for deploying grid-supporting electrolysis in regions with renewable energy generation

Our energy system is facing major challenges in the course of the unavoidable shift from fossil fuels to fluctuating renewable energy sources. Regional hydrogen production by electrolysis, utilizing regional available excess energy, can support the expansion of renewable energy by converting surplus energy into hydrogen and supplying it to the end energy sectors as a secondary energy carrier or process media. We developed a methodology which allows the identification of the regional optimal electrolysis scaling, the achievable Levelized Costs of Hydrogen (LCOH) as well as the annually producible amount of hydrogen for Central European regions, using renewable surplus energy from PV and wind production. The results show that as best case currently LCOH of 4.5 €/kg can be achieved in regions with wind energy and LCOH of 5.6 €/kg in regions with PV energy at 1485 €/kW initial investment costs for the hydrogen production infrastructure. In these cases, regions with wind energy require electrolysis systems with a capacity of 60% of the wind peak power. Regions with PV energy require a scaling factor of only 45% of the PV peak power. However, we show that the impact of regional electricity demand and grid expansion has a significant influence on the LCOH and the scaling of the electrolysis. These effects were illustrated in clear heatmaps and serve as a guideline for the dimensioning of grid-supporting electrolysis systems by defining the renewable peak power, the regional electricity demand as well as the existing grid capacity of the region under consideration.

Environmental temperature variation and electricity demand instability: A comprehensive assessment based on high-frequency load situation

Climate change is causing profound changes in energy and power systems around the world. Although many papers have confirmed the impact of environmental weather on electricity demand, the complex nexus of regional environmental temperature and electricity demand instability remains incomplete, especially neglecting the understanding from the perspectives of multi-quantiles. This paper uses a unique dataset of 15-min the high-frequency grid load situation of Datong, a northern city in China, to construct a comprehensive indicator to measure the regional electricity demand instability. Then, employing the Quantile-on-Quantile approach, this paper evaluates the association between intraday temperature difference and both the composite and multidimensional electric instability indicators. According to the empirical analysis, a larger environmental temperature difference always rises the instability of the electricity demand, which is stronger when both two variables are at higher quantile states. There are heterogeneities among the effects on multi-dimensional indicators of electricity demand instability. Environmental temperature difference provides a negative influence on the load rate, while it will magnify other instability indicators. Furthermore, we also assess the impact of daily extreme temperatures and divide the realized volatility of load into two directions for discussion. Our research presents important knowledge about responding the climate change-induced electric system risk.

Machine-Learning-Based Electric Power Forecasting

The regional demand for electric power is influenced by a variety of factors, such as fluctuations in business cycles, dynamic linkages among regional development, and climate change. The valid quantification of the impacts of these factors on the demand for electric power poses significant challenges. Existing methods often fall short of capturing the inherent complexities. This paper addresses these limitations by proposing a framework, which integrates machine-learning techniques into regional electricity demand forecasting. Regional electricity generation firms could then leverage the power of machine learning and improve the accuracy and robustness of electric power forecasting. In this paper, we conduct extensive numerical experiments using an actual dataset from a large utility firm and other public data sources. The analysis indicates that the support vector regression model (the SVR model) has high accuracy in predicting the demand. The results show that socio-economic development is the major driver of growth in electricity demand, while weather variability is a key contributor to the seasonal fluctuations in electricity use. Furthermore, linkages among regional development and the status of development of the green economy become increasingly important influencing factors. The proposed forecasting approach helps the regional electricity generation firms reduce a large amount of carbon dioxide emissions.

Agrivoltaic: How much electricity could photovoltaic greenhouses supply?

In regions where the land available is scarce it is of special interest to deploy agrivoltaic systems. The combined use of greenhouses to produce food and energy at the same time increases farmers’ income, converting farming into a more attractive sector. The farming sector could benefit from agrivoltaic, since farmers could profit from a double source of incoming: vegetables and energy. The aim of this research is to establish how relevant agrivoltaic can be in terms of energy production at regional scale. For this purpose, a methodology is developed to: (i) identify greenhouses using cartographic information systems, (ii) estimate how much of these areas could be covered by solar photovoltaic panels without decreasing the crops production, thus, estimating the optimal photovoltaic cover ratio for different type of crops under different solar conditions by developing a novel set of equations and (iii) evaluate the corresponding photovoltaic power and production. This methodology has been applied to one regional practical case, the Canary Islands, and the results are surprising in terms of the potentiality of agrivoltaic, which could cover rates as high as 30% of the annual regional electricity demand depending on, among others, the transmittance value of the greenhouse material and the adequate determination of the cover ratio.

A comprehensive evaluation of wind-PV-salt cavern-hydrogen energy storage and utilization system: A case study in Qianjiang salt cavern, China

With global energy consumption increasing year by year, the industrial sectors of the world’s countries have never been more urgent in achieving carbon reduction. Renewable energy plays a crucial role in the industrial decarbonization process. As the most mature renewable energy technology, wind and photovoltaic power generation have made significant progress in recent years. However, the intermittent characteristics hinder the efficient use of renewable energy. Existing research provides sufficient support for the flexible scheduling of large-scale renewable energy. Hence, this paper proposes a combined energy system composed of wind power-photovoltaic-energy storage salt cavern with hydrogen as the energy scheduling carrier. The system mainly realizes energy conversion through electrolytic water equipment and fuel cells. Then, Qianjiang City, Hubei Province, is taken as the analysis object, and the working conditions of each component in the system are optimized with the help of an improved particle swarm optimization algorithm. According to the results, the system’s contribution to the energy system is discussed, and the system's economy and carbon reduction effect is studied. The analysis proves that the improved particle swarm optimization algorithm has a stronger solving ability. The combined energy system can effectively improve the economy and renewable energy utilization rate, meet the regional electricity demand, and significantly reduce carbon emissions. The economic analysis of the system shows that the operation and maintenance cost of hydrogen storage salt caverns accounts for the largest proportion of the total cost, about 60 %. Compared with several existing large-scale energy storage technologies, it is found that the energy efficiency of the whole system is only about 40 % due to the limitation of technology maturity. Nevertheless, the flexibility of the hydrogen storage system layout is relatively high. The required underground space volume is only equivalent to 1/6 and 1/2 of pumped storage and compressed air storage with the same scheduling capacity. The system will provide a theoretical support for the optimization of energy pattern and layout.

Coupling Hydrodynamic and Energy Production Models for Salinity Gradient Energy Assessment in a Salt-Wedge Estuary (Strymon River, Northern Greece)

Salinity gradient energy (SGE) plants generate power from the mixing of salt water and fresh water using advanced membrane systems. In the Strymon River, under low-flow conditions, a salt wedge is formed, developing a two-layer stratified system, which could be used to extract SGE. In this paper, a novel study was implemented by coupling a 3D hydrodynamic model simulating the salt wedge flow, with the SGE model which assesses the net energy produced by a 1 MW SGE plant. Two scenarios were followed: (a) the optimal scenario, operating throughout the year by mixing salt water from the sea (38.1 g/L) and fresh water (0.1 g/L) from the river to produce 4.15 GWh/yr, and (b) the seasonal scenario, utilizing the salinity difference of the salt wedge. Results show that the daily net SGE production varies between 0.30 and 10.90 MWh/day, in accordance with the salinity difference (ΔSsw ~15–30 g/L). Additionally, a retrospective assessment (from 1981 to 2010) of the annual and seasonal net energy production was conducted. This analysis illustrates that the salt-wedge formation (spring to late summer) coincides with the period of increased regional electricity demand. In the future, the emerging SGE could serve as a decentralized renewable energy source, enhancing energy security in the region.

A bottom-up GIS-based method for simulation of ground-mounted PV potentials at regional scale

Solar photovoltaic (PV) is a key technology for any renewable energy system. As subsidy-free PV becomes more and more economically feasible, region-specific planning tools that define areas suitable for ground-mounted PV are needed. While many top-down studies have assessed suitable areas at a national scale, an accurate scalable bottom-up assessment of regional ground-mounted PV potentials in high spatial and temporal resolution that goes further than a mere identification of appropriate land areas is missing. This work introduces such a method based on digital landscape models that consider terrain slope, orientation, location-specific irradiation, and land use type, and combines this geoinformatical information with a PV yield model that allows to assess hourly PV generation potential on suitable areas. The method is validated with three existing ground-mounted PV plants in Germany, where a comparison of real and simulated annual electricity yields shows average deviations of 5%. Subsequently, ground-mounted PV potentials in three German counties with varying settlement structures as well as topographic and weather patterns are assessed and a comparison of yearly and hourly simulated generation potentials with regional electricity demand is performed. While the yearly analysis demonstrates the substantial overall potentials of local ground mounted-PV in all regions, with demand coverages ranging from 80% to hypothetically more than 40 times of current electricity demand according to current regulations, the hourly autarky ratio, defined as the share of hours of a year where ground-mounted PV can satisfy demand, ranges from 25% to 40%, without consideration of storage or demand side management. A subsequent investigation of the ability to export excess electricity generation from ground-mounted PV shows that the two regions with highest ground-mounted PV potentials have less-developed grid infrastructures, thus restricting excess electricity generation export potentials.

A methodology to estimate the photovoltaic potential on parking spaces and water deposits. The case of the Canary Islands

In regions where the land available is scarce it is of special interest to deploy solar photovoltaic energy without occupying additional land. Besides solar roofs, which have already been extensively studied, there are other type of constructions that could be used for the deployment of solar photovoltaic such as car parking spaces and big water deposits. No much attention has been given so far to these type of elements that could potentially been used to deploy solar photovoltaic energy. So the aim of this research is to establish how relevant can these type of surfaces be in terms of solar energy production. For this purpose a methodology has been developed to identify the surfaces corresponding to uncovered parking spaces and water deposit at regional level using cartographic information, first, and, secondly, to estimate how much of these areas could be used to deploy solar photovoltaic energy. This methodology has been applied to one insular region, the Canary Islands, and the results are surprising in terms of the potentiality of these type of elements for the deployment of solar photovoltaic energy which could cover around 9% of the whole regional electricity demand.

Deep decarbonization impacts on electric load shapes and peak demand

The existing literature has shown the important role of electrification in deep decarbonization pathways, increasing electricity demand as end uses decarbonize. However, studies have not focused on the effects of electrification on aggregate load shapes and peak demand, which influence power sector investments, operations, and costs. Here we investigate potential impacts of deep decarbonization on regional load shapes and peak electricity demand using a detailed end-use simulation model linked to an electric sector capacity planning model. Scenario results suggest that electrification may contribute to peak load increases and shifts from summer peaks to winter ones, especially in cooler climates due to space heating electrification. We illustrate how net-zero emissions goals can amplify electrification and may entail 120%–165% increases in electric system capacity by 2050 due to a combination of electrification and high renewables deployment. The intensity and frequency of peak demand can be limited by load flexibility (providing incentives for electric end uses to shift away from periods of high demand, e.g. through deferrable electric vehicle charging), alternate end-use technology configurations (deploying higher efficiency end-use equipment to lower electricity consumption during peaks or using dual-fuel systems such as heat pumps paired with gas furnaces), and carbon removal (displacing higher marginal abatement cost electrification while reaching an equivalent emissions cap). This analysis is a first step toward systematically exploring load curves for electrified and decarbonized energy systems, and the results highlight opportunities for future research to better understand load shape impacts and flexibility.

LEAP simulated economic evaluation of sustainable scenarios to fulfill the regional electricity demand in Pakistan

Some planning problems in the power sector of Pakistan have generated several issues ranging from gradual reliance on thermal-based expensive energy mix, supply shortages, subsidization, inefficiencies at different levels of the value chain, and resultantly, the circular debt. This situation in the power sector has also hampered the growth of allied economic sectors. This study uses the Long-range Energy Alternative Planning (LEAP) system for simulating Pakistan’s electrical system from 2016 to 2040. It has forecasted the electricity demand at the provincial level. At the supply side, five scenarios Business as Usual Scenario (BAUS), Coal Scenario (CS), Gas Scenario (GS), Nuclear Scenario (NS), and Renewable Scenario (RS) have been analysed based on the techno-economic and environmental parameters. The study has also included environmental externality costs for evaluating the actual costs and benefits of different scenarios. Contrary to the expectations, RS has a lower generation than even BAUS. The study has concluded that the generation from RS has five times lesser costs than BAUS, CS, and GS. NS can also be an alternative for the sustainable future of Pakistan. Generation from imported coal is not a good option, however, indigenous coal with clean coal technologies should be promoted. This paper proposes energy planners of the country to devise incentives for the utilization of indigenous energy resources including renewables on priority and then clean coal to reduce the energy crises of Pakistan.

Peramalan Permintaan Pasokan Energi Berdasarkan Intensitas Konsumsi Listrik dan Kapasitas Pembangkit Listrik Terpasang

This research is an important step in energy planning which is the main in electricity planning in the long-term forecast for the demand and supply of energy supply. A simple model is presented using LEAP (Long Term Alternative Energy Planning System) as a broadcasting tool and Central Java as a case study case for this research. This research discusses future energy needs. Electricity needs in the household, industrial, business, and community sectors are calculated based on data on participation, electricity consumption, installed electricity generation capacity, electrification ratio, and electricity strengthening. The base year of this research is 2020 and 2045 is the final period of this forecast. The results of this study indicate that energy needs in Central Java will increase by an average of 23.44% in 2045 or five times compared to 2020 by taking into account various factors of regional economic growth and electricity demand each year

Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany

Decentralized systems seeking to balance electricity supply and demand regionally are increasingly being discussed. Regional flexibility concepts, however, require spatially and temporally highly resolved knowledge. To provide this, we investigate the 2030 demand, supply, and demand-side flexibility for the German NUTS-3 regions. We (1) model the hourly regional electricity demand and supply, (2) cluster regions with regard to the regional demand and supply fit and (3) evaluate demand response for the identified clusters. While in windy or industrialized regions, the impact of demand response is low, in urban regions on the other hand, the effectiveness of flexible end uses is higher and residual load is flattened. Regions with medium-sized cities or those with smaller energy-intensive industries show a good regional fit. Here, demand response can be used very effectively to increase the regionally consumed share of electricity.

Regional heterogeneous drivers of electricity demand in Saudi Arabia: Modeling regional residential electricity demand

This study investigates the drivers of residential electricity demand in Saudi Arabia at a regional level for the period 1990–2018 using Structural Time Series Modeling. We find that Saudi Arabia's two waves of energy price reforms in 2016 and 2018 have had different impacts on residential electricity consumption across its regions. The empirical estimation results show that the long-run price responses of residential electricity demand vary across regions: from −0.20 in the Central region to −0.46 in the Eastern region. The short-run elasticities are −0.10 for the Central and Western regions, −0.15 in the Southern region, while the Eastern region's demand does not respond to price changes in the short run. The long run income elasticities of the regions' residential electricity demand also differ considerably: from 1.02 in the Western region to 0.27 in the Eastern region. The short-run income elasticities are 0.14 and 0.43 for the Eastern and Western regions, respectively while the residential electricity demand in the Central and Southern regions' does not react to income changes. We further estimate that hot weather conditions significantly impact all regions' residential electricity demand. Finally, we find that all regions saw some efficiency improvements in light of the energy price reforms, although there is a room for further improvements. The findings of the study can be useful for policymakers through the ways that we discussed in the paper.

Regional electricity demand and economic transition in China

A structural shift to the tertiary sector and production reduction in heavy industries slow growth in electricity consumption in China. GDP remains as the leading factor driving electricity demand. It is projected an annual growth rate of 3.1 percent to 5.1 percent for electricity consumption in China by 2020, given that key features in China's economic transition are likely to continue in the foreseeable future. There exist regional patterns in electricity demand growth, separating the more developed regions along the eastern coast and the less-developed inland regions, due to different economic and demographic trends.