High Electricity Demand Research Articles

Diurnal Pumped‐Storage Operation Minimizes Methane Ebullition Fluxes From Hydropower Reservoirs

AbstractHydropower is considered green energy and promoted to reduce greenhouse warming. However, hydropower is typically generated using reservoirs and reservoirs are known to emit substantial amounts of the greenhouse gas methane (CH4) to the atmosphere. In many reservoirs ebullition is the dominant pathway of CH4 emission. We show that continuous diurnal pumped‐storage operation, which combines water pumping into the reservoir typically during the night and water drawdown during high demand of electricity, is beneficial for reducing CH4 ebullition associated with hydropower generation. This conclusion is based on ebullition fluxes and water levels measured over 3 months in Schwarzenbach reservoir located in Germany. The reservoir was managed using three modes of operation: (1) diurnal pumping and turbination, (2) no pumping and no turbination, and (3) diurnal turbination. Cross‐correlation analysis indicates that ebullition fluxes predominantly occur during diurnal water level decrease associated with turbination. Consistently, average ebullition fluxes of CH4 were negligible during Mode (2) and substantial during Modes (1) and (3). During Mode (3) the average CH4 ebullition flux was ~197 mg m−2day−1, ~12 times larger than during Mode (1) (16 mg m−2day−1). Our data indicate that overall CH4 ebullition is about 3 times larger during 51 days of operation consisting of 38 days of no turbination followed by 13 days of diurnal turbination than during 51 days of continuous diurnal pumped‐storage operation. This suggests that continuous diurnal pumped‐storage operation leads to reduced CH4 ebullition from reservoirs and is therefore advantageous compared to modes of operations involving long‐term, large‐amplitude turbination cycles.

Opportunities and Challenges of Flexible Electricity-Based Fuel Production for the European Power System

To mitigate global warming, the European Union aims at climate neutrality by 2050. In order to reach this, the transportation sector has to contribute especially, which accounts for about a quarter of the European greenhouse gas emissions. Herein, electricity-based fuels are a promising approach for reducing emissions. However, a large-scale deployment of electricity-based fuels has a significant impact on the power system due to high electricity demand and the requirement to use renewable energy sources in order to be sustainable. At the same time, this fuel production could offer additional flexibility for the power system. This article investigates the opportunities and challenges of electricity-based fuels and flexible electricity-based fuel production for the European power system. In a literature analysis, the pivotal role of electricity-based fuels for climate neutrality is confirmed. To analyze the impact of flexible fuel production, European power market simulations for the year 2035 are conducted. Results indicate that flexibilization leads to an increased integration of electricity based on renewable energy sources as well as reductions in both carbon dioxide emissions and total operational costs of the power system. However, very high flexibility levels also benefit high-emission power plants and may even lead to increased emissions.

Nanostructured metallic FeNi2S4 with reconstruction to generate FeNi-based oxide as a highly-efficient oxygen evolution electrocatalyst

Abstract FeNi-based nanomaterials with bimetallic alloying effect deliver a great promise as an efficient electrocatalyst for oxygen evolution reaction (OER). However, the reconstruction to generate in-situ catalytically FeNi-based oxides or hydroxides during OER process and high electrical conductivity demand foster the great pursuit of the rational design of FeNi-based OER electrocatalyst a big challenge. Herein, we design and fabricate a nanostructured metallic FeNi2S4 as a highly efficient OER electrocatalyst. This catalyst exhibits an overpotential of (250 mV) and a Tafel slope (62 mV dec−1), that outperforms the benchmark of commercial RuO2. The experimental results demonstrate that the alloying effect and the superior electrical conductivity, endow it a great electrocatalytic performance. Such a nanostructured metallic OER electrocatalyst shows a great promise in water splitting.

Liquid air energy storage coupled with liquefied natural gas cold energy: Focus on efficiency, energy capacity, and flexibility

A novel power-management-system design coupling liquid air energy storage (LAES) with liquefied natural gas (LNG) regasification is proposed that combines flexibility in responding to power demand, presented high energy efficiency and capacity. The proposed liquefied natural gas-thermal energy storage-liquid air energy storage (LNG-TES-LAES) process uses LNG cold energy via two different mechanisms. During on-peak times, when the proposed process requires no power consumption to meet the relatively higher electricity demand, LNG cold energy is recovered and stored via liquid propane. During off-peak times, the proposed process uses both cold energy from LNG and liquid propane, effectively doubling the cold energy available and enhancing the process flexibility. The liquid propane cold energy is used for air compression to reduce the power input requirement, while LNG cold energy is used mainly to liquefy air. These unique features afforded an electrical round-trip efficiency of 187.4% and an exergy efficiency of 75.1%, which are the highest among recently reported values. The energy capacity for the regasification of 1 MTPA LNG was 12.14 MW, which is adequate for bulk power management systems. By adopting flexibility, LNG cold energy has been distributed efficiently, and where LNG could be continuously regasified in the energy storage/release processes.

Life Cycle Assessment of Power-to-Syngas: Comparing High Temperature Co-Electrolysis and Steam Methane Reforming

To achieve the European Union's ambitious climate targets, not only the energy system must be transformed, but also other sectors such as industry or transport. Power-to-X (PtX) technologies enable the production of synthetic chemicals and energy carriers using renewable electricity, thus contributing to defossilization of economy. Additionally, they provide storage capacity for renewable energy. Detailed life cycle assessments (LCA) of PtX is required, to prove the environmental advantages to fossil-based benchmark technologies. An emerging PtX technology for syngas production is the high temperature co-electrolysis (HT-co-electrolysis), which produces syngas. Aim of this LCA is the evaluation of syngas production by HT-co-electrolysis at its early stage of development to derive incentives for further research. For comparison, a small-scale steam methane reforming process (SMR) serves as today’s fossil-based benchmark. The required CO2 is obtained via direct air capture (DAC). The by-far most important input for the HT-co-electrolysis is electricity. Hence, several future electricity mixes are considered, representing two different climate protection targets (CPT80, CPT95) for the energy system in 2050. For each CPT, an additional distinction is made regarding full load hours, which depend on the availability of renewable energy. The results show lower global warming potential (GWP) and fossil fuel depletion for HT-co-electrolysis compared to SMR if mostly renewable power is used. Exclusively renewable operated HT-co- electrolysis even achieve negative net GWPs in cradle-to-gate LCA without considering syngas use. If HT-co-electrolysis shall operate continuously (8760 h) additional fossil electricity production is needed. For CPT80, the share of fossil electricity is too high to achieve negative net GWP in contrast to CPT95. Other environmental impacts such as human toxicity, acidification, particulate matter or metal depletion are worse in comparison to SMR. The share of DAC on the total environmental impacts is quite noticeable. Main reasons are high electricity and heat demands. Although plant construction contributes to a minor extent to most impact categories, a considerable decrease of cell lifetime due to higher degradation caused by flexible operation, would change that. Nevertheless, flexibility is one of the most important factors to apply PtX for defossilization successfully and reinforce detailed research to understand its impacts.

Study on Household Energy Usage Patterns in Urban and Rural Areas of Nepal

In the last decades, the household’s energy demand has increased significantly in various countries including Nepal. In the case ofNepal, 94% of energy use is in the domestic sector. There is a possibility of a huge increase in electricity production, but we are stillsuffering from load shedding due to the high electricity demand. Electricity use is an important factor for the quality of life anddevelopment of a nation. There is not a sufficient number of researches done about electricity consumption in different climaticregions of Nepal which are analyzed by the income level of residents. This study gives descriptive information on the household’senergy uses patterns and investigates the electricity use rate, using electrical appliances in households. This study also identifies themajor source of energy use and awareness of energy use. The data were collected from 442 households in three regions in the winterseason of 2018. Kalikot is a rural area, Chitwan is a semi-urban, and Kathmandu is an urban area. We have collected electricity bills,family income, and family size, electricity using appliances, expenditure for energy and energy use for heating/cooling, cooking, andlighting. The electricity was used only for lighting purposes in the rural area, but other electrical appliances were used in semi-urbanand urban areas. The amount of electricity use has not affected by household income level in the rural area, but it has affected in semi-urban and urban areas. The level of education affects the use of the LED significantly. This study would be helpful to know theelectricity use patterns which is useful for energy saving and energy management of the rural and urban areas of Nepal.

Experimental investigation of demand response potential of buildings: Combined passive thermal mass and active storage

Heating, ventilation, and air conditioning (HVAC) systems, combined with the internal thermal mass of buildings, have been deemed to be promising means of providing demand response (DR) resources, particularly for buildings with active energy storage systems. DR resources, such as peak-load reduction potential, can provide grid-responsive support resulting in a high degree of grid involvement and high flexible electricity demand. In the DR field, the potential of HVAC load flexibility has been considered in buildings. In the future smart buildings, it is important to take advantage of demand-side resources to achieve real-time energy supply–demand balance sustainably. In this context, DR potential and characteristics of buildings play a pivotal role in DR programs. However, few studies have investigated the internal thermal mass’s heat release and DR characteristics of buildings. Thus, a systematic experiment is conducted to study the DR potential and characteristics of internal thermal mass and active storage systems. The DR resources include the passive cooling storage from furniture, building envelope and an active water storage tank. Two DR control strategies, including pre-cooling and temperature resetting, are analyzed in this study. The experimental results show that the strategies are effective for short-term (0.5 h) and intermediate-term (2 h) DR programs. For a long-term DR program, active energy storage technology such as a water storage tank is required to satisfy the occupant's comfort requirements. Hence, we conclude that passive thermal mass and active storage systems should be simultaneously considered in practical DR programs for better DR implementation.

A dynamic network analysis of spot electricity prices in the Australian national electricity market

Abstract This study applies dynamic network analysis to the power sector, examining the relationship between regional spot electricity prices in the Australian National Electricity Market (NEM). In particular, we employ principal component analysis and generate Granger causality networks to examine the degree of interconnectedness of the NEM in a time-varying setting. We find that the derived measures of interdependence can be related to actual market events such as price spikes, unexpected high demand for electricity, sudden increases in price volatility, rebidding of dominant generators, the temporary or permanent outage of major power stations, and upgrades and limitations in transmission capacity. In the analysed network, we find that stronger dependence is exhibited by regional markets that are linked through interconnectors, while the direction of Granger causality can be related to interregional trade. We further examine the usefulness of the derived measures for forecasting distributional characteristics of spot prices such as the maximum price, volatility, price spreads, or upcoming periods of price spikes. Our results suggest that the derived network measures have predictive power, albeit limited, for the behaviour of spot electricity prices in the NEM.

High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications

This work relates to a new stochastic and probabilistic tool allowing to model dwellings specific electricity load profiles (in the European context) for 39 different electrical appliances with a high temporal resolution (2-min time step). This tool was developed by using probabilistic (Monte Carlo) and statistic (Time Of Use curves) methods as well as transition probabilities (Markov chains), seasonal, daily and hourly effects coefficients to obtain the more realistic as possible electricity load curves. The tool distinguishes the flexible part which can be shifted during power self-production operation (micro combined heat and power µCHP or photovoltaic panels PV) periods and the no flexible part (lighting for example). A sensitivity analysis shows the impact of the time step and the repeatability level of the tool on a µCHP system coupled with a building to prove the need to use high resolution electricity demand load curves at low scale (a building) in comparison with hourly data which can overestimate µCHP electrical generation self-consumption rate up to 20%. Time step of maximum 2 min is required for the electricity demand profiles to obtain reliable results on energetic indicators. Finally, a validation procedure based on literature review profiles, high resolution in situ measurements and expansion national load curves is carried out.

Powering electric tower cranes by solar energy for sustainable construction

The construction industry is responsible for many negative environmental impacts that include emissions, waste generation, and the use of land, water, and energy. Most previous construction research attention has focussed on the environmental impacts only for material selection and building operation. Nevertheless, the high electrical demand that comes from the use of construction equipment on-site, mainly tower cranes, is considered a critical component of construction sites that is responsible for a significant volume of CO2 emissions and energy consumption. This research aims to study the feasibility of using photovoltaic (PV) systems to power the electric tower cranes used on construction sites, using as a case study The Mall of the Emirates’ expansion project in Dubai. Two concepts of PV systems were designed to power the seven cranes used on site; one connected (on-grid) and the other as a hybrid PV/Diesel system (off-grid). The results indicated that for both cases the solar systems supplied electricity with competitive costs, and reduced the greenhouse gases by substantial amounts. The results are then generalized for different climatic regions; especially for less solar blessed regions. Finally, managerial and policy implications of the study are discussed.

Environmental Degradation in Indonesia and Malaysia: The Effect of Energy Consumption, Economic Growth, Population, and Foreign Direct Investment (FDI)

The main objective of this study is to examine how energy consumption, economic growth, population, and foreign direct investment (FDI) affects CO2 emissions in Indonesia and Malaysia. This study uses the longest and most updated annual data during the period 1960-2018. To get a deeper analysis, this study employs disaggregate of CO2 emissions and energy consumption data namely, oil, coal and natural gas. The ordinary least square which preceded by unit root test and classical assumption test are employed. The results show that all type of energy consumption affect positively to CO2 emission. Economic growth is identified as the variable with greatest influences on CO2 emissions in oil and natural gas model, while CO2 emissions from coal consumption are mainly affected by populations. The study concludes that economic growth of both countries relies heavily on fossil fuel. CO2 emission sourced from coal mostly affected by population due to the high demand of electricity from household fulfilled by power generation which use coal as the fuel. The EKC hypothesis is confirmed in the model of gas, indicate that natural gas is the most appropriate source of energy to be used at the certain level. Using natural gas is effectively decrease the CO2 emission while in the same time increase the economic growth. Natural gas is also found as the most environmentally friendly fossil fuel due as it produces less CO2 emission compared to oil and coal. The findings have important implications for policy makers in determining policy and business decisions especially to enhance environmentally friendly energy uses for the benefit of the economy.

Economic analysis of cross-breed power arrangement for Salalah region in the Al-Khareef season

ABSTRACT Salalah is known for its Al-Khareef unique weather that is marked by influx of tourists into the community resulting in high electrical load demand during the Al-Khareef season. This paper investigates the viability of two hybridised energy systems for application in Salalah community. The result demonstrated that the Photovoltaic, Wind and Battery combination is more cost effective in terms of energy consumption, initial cost and renewal price, in comparison to the other hybrid energy system. The Homer Pro software, return on investment and internal rate of return analysis showed that the Photovoltaic/Wind/Battery scheme commands higher economic and environmental viability, presenting the Photovoltaic, Wind and Battery scheme as a better option to meet the power demand for Salalah city.

Insights on Germany’s Future Congestion Management from a Multi-Model Approach

In Germany, the political decision to phase out nuclear and coal-fired power as well as delays in the planned grid extension are expected to intensify the current issue of high grid congestion volumes. In this article, we investigate two instruments which may help to cope with these challenges: market splitting and the introduction of a capacity mechanism. For this purpose, we carry out a comprehensive system analysis by jointly applying the demand side models FORECAST and eLOAD, the electricity market model PowerACE and the optimal power flow model ELMOD. While a German market splitting has a positive short-term impact on the congestion volumes, we find the optimal zonal delimination determined for 2020 to become outdated by 2035 resulting in new grid bottlenecks. Yet, readjusting the zonal configuration would lower the ability of the market split to provide regional investment incentives. Introducing a capacity mechanism with a congestion indicator allows allocating new power plants in regions with higher electricity demand. Consequently, we find the required congestion management to be substantially reduced in this setting. However, given the large amount of design parameters, any capacity mechanism needs to be carefully planned before its introduction to avoid new inefficiences on the market side.

Modelling energy demand response using long short-term memory neural networks

We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAPE ≈ 1.6% and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions.

Estimation Analysis of Long-Term Electrical Energy Needs In PT. PLN (Persero) P2B REGION MERAUKE Area Using Linear Regression Method

Electrical energy is one of the important parts in the development of an area. The development of sustainable development and improvement in living standards can cause electricity consumption to continue to increase high, not least for Merauke Regency. In meeting the future electrical energy needs, it is necessary to prepare the development of the electrical system so that it is able to serve the demand for electrical energy needs in the future. One of the ways is to make predictions or estimates the electrical energy requirements.The estimation of the need for electrical energy is needed to answer the high demand for electricity that continues to increase, so that we see the development of electricity consumption in Merauke Regency that is linear. The method used in this research is analyzing the calculation of electricity needs using the Linear Regression Method.The estimation results of electrical energy needs in 2018 electrical energy needs amounted to 127,035,341 KWh, while in 2022 amounted to 17,139,908 KWh with an average increase per year of 8,747,989 KWh or 5.84%. From the calculation results, the household sector becomes the sector that has the highest electrical energy needs up to 2022 amounting to 114,294,938 KWh with an average percentage increase per year of 5.82%

Use of carbon dioxide as raw material to close the carbon cycle for the German chemical and polymer industries

This article explores how far the use of CO2 as raw material could enable the German chemical and polymer industries to contribute to a circular economy. Material Flow Analysis was conducted for all carbon flows for material use in Germany, comprising chemical production, polymer production, domestic use and waste management. For scenario modelling, Carbon Capture and Utilization technologies were included, and key parameters determining carbon flows were altered to show potential corridors for the future development. The results show that current carbon flows are dominated by fossil sources and are highly linear, with a secondary input rate of only 6%. Additionally, 12% (2 Mt/a) of the primary carbon input is lost due to dissipation. Currently available Carbon Capture and Utilization technologies would allow reaching a secondary input rate of 65% for the chemical industry. However, to achieve this rate between 80% (processes of direct synthesis) and 103% (methanol-based processes) of the total net supply for renewable electricity in Germany would be required in 2030 and between 41% and 50% in 2050. In contrast, the unavoidable substance related CO2-point sources in Germany could probably fulfill the carbon requirement for material use of the chemical industry in 2050. The authors conclude that the utilization of CO2 as a carbon source is necessary to close the carbon cycle where material or chemical recycling is technically not feasible or reasonable. The very high demand for renewable electricity indicates that the required production facilities for CO2-based chemicals will probably not be completely based in Germany.

Techno-economic potential of low-temperature, jacket-water heat recovery from stationary internal combustion engines with organic Rankine cycles: A cross-sector food-retail study

We examine the opportunities and challenges of deploying integrated organic Rankine cycle (ORC) engines to recover heat from low-temperature jacket-water cooling circuits of small-scale gas-fired internal combustion engines (ICEs), for the supply of combined heat and power (CHP) to supermarkets. Based on data for commercially-available ICE and ORC engines, a techno-economic model is developed and applied to simulate system performance in real buildings. Under current market trends and for the specific (low-temperature) ICE + ORC CHP configuration investigated here, results show that the ICE determines most economic savings, while the ORC engine does not significantly impact the integrated CHP system performance. The ORC engines have long payback times (4–9 years) in this application, because: (1) they do not displace high-value electricity, as the value of exporting electricity to the grid is low, and (2) it is more profitable to use the heat from the ICEs for space heating rather than for electricity conversion. Commercial ORC engines are most viable (payback ≈ 4 years) in buildings with high electrical demands and low heat-to-power ratios. The influence of factors such as the ORC engine efficiency, capital cost and energy prices is also evaluated, highlighting performance gaps and identifying promising areas for future research.

Power control and simulation of a building integrated stand-alone hybrid PV-wind-battery system in Kasuga City, Japan

With buildings accounting for high electrical demand and GHG emissions in Japan, enabling higher penetration of hybrid renewable energy systems can significantly reduce their load demand. This paper proposes an efficient power control scheme and design for a hybrid renewable standalone system installed on the roof of a building in Kasuga-City, Japan. The proposed system consists of three PV modules with a total power of (480 W), a wind turbine (400 W), a lead–acid battery (30 Ah), an inverter, and controllers. The main purpose of this research is to implement a well-defined control scheme for standalone Hybrid Renewable Energy Systems (HRES), that has a low level of complexity in terms of Maximum Power Point Tracking (MPPT) with a limited number of components in order to reduce costs and provide high power quality output. The main control layers of the model include the Perturb and Observe (P&O) controller used for MPPT of both the wind turbine and PV systems and a PI controller used to control both the battery charging and inverter of the system. The proposed control scheme also includes two charging modes for the battery when there is no load; Bulk mode for faster charging and Float mode for slower charging to keep the battery fully charged, as its charge decrease with time Dynamic modeling and simulation are accomplished using MATLAB-SimulinkTM 9.3, to evaluate the performance and system’s dynamics, considering two scenarios of (1) variable weather conditions and (2) different charging modes of battery. The results of the simulation reveal that the proposed system can utilize an efficient battery charging control configuration, eliminating the need for a specific battery converter, providing high output power quality with the Total Harmonic Distortion(THD) below 1% in steady-state, and sustaining output voltage magnitude and frequency under changing system dynamics.

Forecasting high resolution electricity demand data with additive models including smooth and jagged components

Short-Term Load Forecasting (STLF) is a fundamental instrument in the efficient operational management and planning of electric utilities. Emerging smart grid technologies pose new challenges and opportunities. Although load forecasting at the aggregate level has been extensively studied, electrical load forecasting at fine-grained geographical scales of households is more challenging. Among existing approaches, semi-parametric generalized additive models (GAM) have been increasingly popular due to their accuracy, flexibility, and interpretability. Their applicability is justified when forecasting is addressed at higher levels of aggregation, since the aggregated load pattern contains relatively smooth additive components. High resolution data are highly volatile, forecasting the average load using GAM models with smooth components does not provide meaningful information about the future demand. Instead, we need to incorporate irregular and volatile effects to enhance the forecast accuracy. We focus on the analysis of such hybrid additive models applied on smart meters data and show that it leads to improvement of the forecasting performances of classical additive models at low aggregation levels.

Intra-day Electricity Demand and Temperature

The objective of this paper is to explain the relationship between high frequency electricity demand, intra-day temperature variation and time. Using the Generalised Additive Model (GAM) framework we link high frequency (5-minute) aggregate electricity demand in Australia to the time of the day, time of the year and intra-day temperature. We document a strong relationship between high frequency electricity demand and intra-day temperature. We show a superior model fit when using Daylight Saving Time (DST), or clock time, instead of the standard (solar) time. We introduce the time weighted temperature model that captures instantaneous electricity demand sensitivity to temperature as a function of the human daily activity cycle, by assigning different temperature signal weighting based on the DST time. The results on DST and time weighted temperature modelling are novel in the literature and are important innovations in high frequency electricity demand forecasting.