Industrial Electricity Demand Research Articles

Analysis of the Impact of Policies and Meteorological Factors on Industrial Electricity Demand in Jiangsu Province

Analysis of the Impact of Policies and Meteorological Factors on Industrial Electricity Demand in Jiangsu Province

Air Quality and Health Implications of Coal Power Retirements Attributed to Industrial Electricity Savings in China.

The coal-dominated electricity system, alongside increasing industrial electricity demand, places China into a dilemma between industrialization and environmental impacts. A practical solution is to exploit air quality and health cobenefits of industrial energy efficiency measures, which has not yet been integrated into China's energy transition strategy. This research examines the pivotal role of industrial electricity savings in accelerating coal plant retirements and assesses the nexus of energy-pollution-health by modeling nationwide coal-fired plants at individual unit level. It shows that minimizing electricity needs by implementing more efficient technologies leads to the phaseout of 1279 hyper-polluting units (subcritical, <300 MW) by 2040, advancing the retirement of these units by an average of 7 years (3-16 years). The retirements at different locations yield varying levels of air quality improvements (9-17%), across six power grids. Reduced exposure to PM2.5 could avoid 123,100 pollution-related cumulative deaths over the next 20 years from 2020, of which ∼75% occur in the Central, East, and North grids, particularly coal-intensive and populous provinces (e.g., Shandong and Jiangsu). These findings provide key indicators to support geographically specific policymaking and lay out a rationale for decision-makers to incorporate multiple benefits into early coal phaseout strategies to avoid lock-in risk.

Modeling and forecasting industrial electricity demand for Saudi Arabia: Uncovering regional characteristics

This study aims to investigate and forecast Saudi Arabia's industrial electricity consumption at the regional level. By applying structural time series modeling to annual data from 1990 to 2019, we find the electricity demand's long-run income and price elasticities to vary across regions. Also, the underlying energy demand trend analysis indicates efficiency improvements in industrial electricity consumption patterns in all regions. In the low-case scenario, we project the 2030 demand to be 11.6 terawatt-hours (TWh), 63.5 TWh, 0.7 TWh, and 6.8 TWh for the central, eastern, southern, and western operation areas, respectively, with the total demand forecast to be 82.5 TWh. The study's findings can help policymakers understand the effect of future energy policies and economic growth trajectories.

Decarbonizing integrated chlor-alkali and vinyl chloride monomer production: Reducing the cost with industrial flexibility

Industrial demand response will become increasingly important in power grids with high shares of variable renewables, yet the existing knowledge on how the industrial electricity demand and flexibility will change with the decarbonization of chemical processes is limited. Here we develop a mixed-integer linear optimization model, which we use to compare the cost and flexibility of the most relevant decarbonization options for the combined chlor-alkali electrolysis (CAE) and vinyl chloride monomer (VCM) production process. We combine product and energy storage to enable the full flexibility potential of the decarbonized process. Our results show that flexible operation of the CAE process is deemed technically possible but limited by internal process dependencies due to decarbonization of the VCM production. Combining energy and product storage for demand response enables up to 4% operational cost reduction by shifting loads during peak price hours. High overcapacity of PEM electrolyzers is required to release the full flexibility potential in the hydrogen based decarbonization option, while the less flexible direct electrification option shows a potential for OPEX reduction. Full decarbonization of the combined CAE and VCM process without increasing operational cost significantly appears difficult. Our study emphasizes demand response through product and energy storages as a viable pathway for minimizing the added cost, and also enables a significant reduction of electric demand in high-price hours.

The Role of Cogeneration in the Electrification Pathways towards Decarbonization

The global call for an environmentally friendly, sustainable, and reliable energy system looks for the optimal integration of different technologies to allow a smooth and economically viable transition towards electrification. In this context, small, medium, and large industrial processes are relevant contributors to global CO2 emissions production due to the simultaneous requirement of electricity, heating, and cooling power generally obtained through fossil fuel combustion. In this context, Combined Heat and Power Energy converters based on internal combustion engines, such as reciprocating engines, gas turbines, and gas turbine combined cycles, and external combustion, such as backpressure and condensing steam power plants, are the most suitable solutions for the efficient and reliable generation of the above-mentioned assets. Typically, the industrial demand for heat and electricity differs in terms of heat-to-power ratio when compared to the heat-to-power ratio of the CHP plant, and this has led to requiring the selection of a control strategy to follow, partially or fully, the heat load or the electric load. In this paper, the authors propose an operating and design strategy addressed to fully covering the heat load demands by the heat generated by the CHP, allowing the system to have an excess of electricity generated. This electricity can be used for different purposes, as regards the novel electrification roadmap. Indeed, the authors have explored four configurations in which the excess of the CHP-generated electricity can be exported to the national grid, used for high-tension fast-charging electromobility systems, for running reverse osmosis desalination plants, and for the production of alternative fuels such as hydrogen. The authors propose a methodology for providing an extensive environmental techno-economic assessment that looks at 2050 CO2 targets. Accordingly, the environmental techno-economic assessment results are presented and discussed by considering the Net Present Value, payback period, and CO2 emission savings.

Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands

Achieving emissions reduction targets requires improved energy efficiency to avoid an oversized and excessively expensive electricity network. This can be analysed using hourly demand modelling that captures behaviour profiles, technology types, weather factors and building typologies. Numerous domestic models exist, but whole systems energy modelling, including commercial and industrial demand, are limited by data availability. Time-use survey data has typically been used to model domestic demand- in this work is expanded to also model commercial and industrial electricity-heating for the Scottish islands at an hourly and individual building level. This method is widely applicable for modelling whole system energy demand wherever time-use survey data are available. Combinatorial optimisation has been applied to generate a synthetic population, match individuals to properties and apply construction types to building polygons. SimStock is used for heating and lighting modelling. Validation of the model with 2016 data shows that it reflects longer term trends, with a monthly mean absolute percentage error (MAPE) of 1.6% and an R2 of 0.99. At the hourly level, the MAPE of 6.2% and R2 of 0.87 show the model captures variability needed to combine it with a supply-side model. Dataset accuracy, variability in the date recorded, missing data and unknown data correlations are discussed as causes for error. The model can be adapted for other regions and used to analyse the costs and benefits of energy efficiency measures with a supply-side generation model.

Price and income elasticities of industrial electricity demand in Brazil: estimates and implications of COVID-19

PurposeThis study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.Design/methodology/approachDynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.FindingsThe short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.Originality/valueThe research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

Asymmetric price response of industrial electricity demand in India

Asymmetric price response of industrial electricity demand in India

Analysis of the Influencing Factors of Power Demand in Beijing Based on the LMDI Model

Since the reform and opening-up, under the new economic situation and policy, the rapid growth of power demand in Beijing is threatening the sustainable development of China’s economy and environment. To recognize the driving factors of electricity consumption growth and offer policy implications, based on the data of electricity consumption, the Gross Domestic Product (GDP) and the resident population in Beijing from 1990 to 2021, this research used the Kaya-equation and logarithmic mean divisia index (LMDI) model to decompose the growth of power demand in Beijing into the quantitative contribution of each driving factor from the perspective of industrial electricity consumption and residential electricity consumption. The results of the decomposition analysis show that, as far as industrial electricity consumption is concerned, the contribution rates of economic growth, electricity consumption intensity and output value structure to industrial electricity growth are 234.26%, −109.01% and −25.25%, respectively, which shows that economic growth is the primary driving force promoting the growth of industrial electricity demand. Power consumption intensity is the main reason for restraining the growth of industrial power demand, the growth rate is sliding and the contribution of the industrial structure is relatively small; as far as residential power consumption is concerned, the contribution rates of per capita power consumption and population size to residential power growth are 68.13% and 31.87%, respectively, which indicates that per capita power consumption is the main factor promoting the growth of residential power demand, followed by the total population. The study results show that the consumption of electric power would increase if Beijing’s economy and urbanization keep developing, and optimizing the industry structure, improving the efficiency of electric energy utilization and adopting clean power energy are the main approaches to making Beijing’s consumption of electric power decrease.

Carbon-neutral power system transition pathways for coal-dominant and renewable Resource-abundant regions: Inner Mongolia as a case study

Under the vision of carbon neutrality, reaching carbon peaking and neutrality targets in the power industry in coal-dominated, renewable energy-rich provinces is facing unprecedented development pressure. This study used the optimization model to research the deeper decarburisation path with the lowest cost to the Inner Mongolian power industry. Then, three scenarios were set based on the gradual increase of carbon emission constraints and the increased renewable energy target. The electricity demand, installed capacity and generation, cost-effectiveness, and carbon emissions were evaluated under the three scenarios. Results showed that (1) electricity demand will grow steadily in all three scenarios by 2060, to achieve carbon peak and carbon neutrality targets, with industrial electricity demand falling by an average of 41.15%, while the ‘transport, storage & postal’ needs to rise by 88.82%. (2) Inner Mongolia needs to fully tap the renewable energy potential, establish a renewable energy storage system, diversify its power supply mode, and achieve the 2060 carbon neutrality target. (3) Achieving a profound emission reduction at minimum cost is feasible. Compared with keeping 13.01% of coal power as a flexible power supply in the nationally determined contributions scenario, completely withdrawing coal power in the carbon neutrality scenario in 2060 will reduce carbon emissions by 148.10 million metric tons and costs by 1375.92 billion yuan. This method can analyse the carbon emission pathways and emission reduction measures of the resource-based provincial power sector, including setting boundaries, scenario analysis, forecasting future emissions, analysing results, and providing policy recommendations. Providing relevant countermeasures for government departments, industry organisations, power companies, and individuals to conduct carbon neutrality-related work.

Air pollution and cardiovascular heath in a changing climate

Air pollution and cardiovascular heath in a changing climate

Dynamics of Industrial Electricity Demand in the State of Bahia (Brazil): Evolution of Price and Income and COVID-19 Implications

The growth in demand for electricity is a determining factor for the regional development. This research aimed to estimate the price and income elasticity parameters of the industrial demand for electricity in the State of Bahia, Brazil, from January 2003 to June 2022, in addition to making forecasts for the period from July to December 2022. After verifying that some of the analyzed data were non-stationary, we chose to use the cointegration method, estimating the econometric model through the Error Correction Mechanism (ECM). The ECM considers the model’s variables and their lags, relating the series’ short and long-run trends. The estimated parameters were inelastic and presented the following values: 0.501 and 0.762 (price and income, long-run) and 0.482 and 0.702 (price and income, short-run). The adjustment coefficient between the short and long-run was also statistically significant and indicated that approximately 8% of the difference between the effective value and the long-run value (balance value) is corrected in each period, demonstrating the rigidity of the consumption structure of electricity in the industrial sector in Bahia. The model also demonstrated an 8.1% reduction in consumption during COVID-19 incidence period. Regarding the forecasts, they proved to be robust and with an average difference of 5.4% in relation to what actually happened in the months of July and August 2022. Thus, the calculated parameters are configured as another source of information for public policymakers and private investors interested in the electricity sector in the State of Bahia.

Technical feasibility of powering U.S. manufacturing with rooftop solar PV

The use of renewable electricity is vital for the decarbonization of industry. Industrial firms source renewables through off-site power purchase agreements or on-site installations, though the latter currently supplies <0.1% of industrial electricity demand in the U.S. Manufacturing buildings typically have large, flat rooftops that are ideal for solar photovoltaic (PV) arrays. This study investigates the feasibility of using rooftop solar PV to cover the net annual electricity needs of industry across all U.S. states and manufacturing sectors. Modeled electricity supply intensity for solar PV arrays is compared with the electricity demand per unit of floorspace for average manufacturing buildings derived from the U.S. Department of Energy Manufacturing Energy Consumption Survey. Results show that, depending on geographical location, rooftop solar PV can completely fulfill the electricity requirements of 5%–35% of manufacturing sectors considered on a net annual basis (assuming net metering). Furniture, textiles, and apparel manufacturing can be powered through on-site means in nearly every location, representing 2% of U.S. manufacturing electricity use and 6% of floorspace. Considering seasonal potential during summer months expands the list of feasible sites, particularly in the U.S. Southwest. Compared to off-site sourcing of renewable energy, pursuing on-site PV installations can also enable manufacturers to maintain limited operations during periods of grid disruption, especially when coupled with on-site energy storage. Overall, the results indicate a substantial physical opportunity for industrial firms to expand rooftop solar PV from currently low levels to help meet decarbonization goals.

An experiment in own-price elasticity estimation for non-residential electricity demand in the U.S.

This paper uses an experiment to identify what modeling decisions significantly affect estimates of own-price elasticity for non-residential (commercial and industrial) electricity demand in the United States (U.S.). Based on 174,240 panel data model runs involving 10,944 monthly state-level observations from the Energy Information Administration for 2001–2019, these decisions are parametric specification, estimation method, and treatment of cross-section dependence. As most of the many generated elasticity estimates are between 0.0 and −0.2, price-induced conservation is likely modest, thus justifying continued policy support for energy efficiency standards and demand-side management in the U.S. path to deep decarbonization.

An Optimization Scheme of Water Resources Allocation Based on the Particle Swarm Algorithm

Climate change will exacerbate water scarcity, as warmer temperatures will increase evaporation from soil and water surfaces, raising crop water requirements, and consumers’ demand for energy will increase at higher temperatures, with a concomitant increase in demand for cooling water. Therefore, water scarcity has evolved into a global issue, most notably in the Colorado Basin. As the climate changes, the future of the Colorado River becomes even more tragic. The river’s water levels are dropping year after year, while the water needs of the surrounding urban development are increasing, in what seems to be an endless cycle. At the same time, the entire southwestern United States may be in an unprecedented resource crisis as a result. Therefore, we have modeled water supply, demand, and allocation scenarios and constructed a water allocation model using the Colorado Basin water allocation as an example, combining Lake Mead, Lake Powell, and the agricultural, industrial, and residential demand for water and electricity in five states. To better achieve the optimal balance between the economy and the environment, we set three objectives: economic efficiency, regional surplus water, and total regional electricity generation, and solved the problem using a particle swarm algorithm. By solving the Colorado Basin water allocation problem, we explore a feasible solution with high generalization capability that can be applied in the future to scenarios of rational water allocation in multiple regions around the world.

Assessment of the wind power plants role in CO2 emissions reducing and energy supply in the regions of Russia

The article contains an analysis of the share of mayor wind power plants in regional energy mixes and evaluation of their contribution to CO2 emission reducing, basing on the data for wind capacities construction and launch. The estimations showed that wind plants in Republic of Kalmykia could cover more than 94% of industrial and household electricity demand. Forecast evaluations for the wind plants planned to be put into operation until 2024 demonstrate a high wind potential for satisfaction of energy demand in Republics of Kalmykia and Adygeya as well as in Ulyanovsk and Astrakhan regions. The decrease in CO2 emissions in the regions of Russia was estimated by 2024 due to the absence of emissions at wind farms in the amount of 4.5 million tons per year.

Thermal energy storage for industrial thermal loads and electricity demand side management

Thermal energy storage offers a solution to mitigate the intermittency of electricity generation in a grid powered by renewable energy and an opportunity to arbitrage on the temporal variability in the cost of the grid supplied energy. This study investigates the potential of a thermal energy storage system used for thermal load and electricity demand management at the industrial scale. A latent heat thermal energy storage system in conjunction with an advanced control and forecast algorithm have been integrated into an existing industrial food processing plant. This demonstration system also incorporates electricity from onsite PV and the grid to power the refrigeration system. The control and forecast code provide a schedule to charge the thermal energy storage from the grid during low or negative prices; otherwise, it instructs to charge from solar PV or discharge to cover the refrigeration load. The data collected from the demonstration system clearly showed the role of the integrated system in demand side management leading to electricity cost reduction during the ten months of operation. The net estimated electricity cost savings was 10,435 AUD in ten months, while doubling the capacity of thermal energy storage could save about 48,700 AUD per annum. Data analysis of energy demand acquired from all the modules of the plant provided valuable insight into further optimization of the control code for higher realization of its potential in the reduction of electricity demand charges. Moreover, such a control system combined with thermal energy storage can be used for the management of any variable load and renewable energy sources.

Linear time-varying regression with copula–DCC–asymmetric–GARCH models for volatility: the co-movement between industrial electricity demand and financial factors

ABSTRACT This paper examines the dependence structure of industrial electricity demand and financial indicators (the Korea Composite Stock Price Index [KOSPI], Korean Securities Dealers Automated Quotations [KOSDAQ], exchange rate, government bonds and exports) using the copula dynamic conditional correlation with symmetric and asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models to forecast volatility. We investigated symmetric and asymmetric GARCH types, such as the standard, exponential, Glosten–Jagannathan–Runkle and asymmetric power models to fit the marginal distribution. The two types of elliptical copula, Gaussian and Student’s t-distributions, were also considered to investigate the tail dependence between financial and electricity time series. We analysed the monthly log returns for January 2002 to April 2020. The empirical results reveal that the best-fit models for the Akaike information criteria are the asymmetric GARCH models, specifically the exponential-GARCH (E-GARCH). Moreover, the asymmetric GARCH model is superior to the symmetric GARCH in terms of forecast volatility. Extreme tail dependence exists for the KOSDAQ and exports indicators with the electricity demand. The KOSPI, Korea’s primary stock market, is the best-fit financial variable and presents the highest forecasting accuracy.

How price responsive is industrial demand for electricity in the United States?

We use five parametric specifications and monthly data for the lower 48 states in 2001–2019 to document that industrial demand for electricity in the United States (US) is price inelastic, with statistically significant (p-values ≤ 0.05) estimates of − 0.029 to − 0.130 for the static own-price elasticity, − 0.021 to − 0.133 for the short-run own-price elasticity, and − 0.043 to − 0.214 for the long-run own-price elasticity. Absent significant price escalation (e.g., 10% per year), the US industrial customer class’s price-induced conservation of electricity is likely modest, justifying the continued use of energy efficiency standards and demand side management programs to achieve deep decarbonization.

Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights

The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate change nexus. The ANN model adopted in this study is a multilayer perceptron (MLP) with a feedforward backpropagation neural network. This study compares the outcomes of three ANN activation functions with multiple linear regression (MLR). According to the estimation results, ANN with a hidden layer and hyperbolic tangent activation function outperforms other techniques and has statistical solid performance values. The estimation results indicate that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23, with climate change positively influencing energy demand. The relationship between manufacturing output and energy consumption is relatively diverse at the disaggregated level.