Changes In Electricity Demand Research Articles

On the Increase of Electricity Prices in the Territory of Non-Price Zones of the Far Eastern Federal District and Their Impact on Industry

The economy of the Far Eastern Federal District of Russia is characterized by a significant share of industrial production in the overall GRP structure. For this reason, an important factor affecting the industrial and investment development of the macroregion is the price of electricity supplied to industrial consumers. This article is devoted to the study of the dynamics of a significant increase in electricity prices in 2023 in Khabarovsky Krai, Amurskaya Oblast, Primorsky Krai, Jewish Autonomous Oblast, included in the territory of non-price zones of the wholesale electricity market, as well as factors influencing the identified price increases that affect the magnitude and dynamics of changes in regional GRPs, which emphasizes the theoretical and the applied relevance of the presented research. The authors review the role of the Far Eastern Federal District economy in the formation of the country's GRP, the role of the industrial sector in the development of the economy of the macroregion, the share of electricity consumption by industry in the GRP structure, which emphasizes the high importance of the energy factor in the economic development of the studied territories. The materials present the results of calculating the dynamics of changes in electricity demand in the federal districts of Russia for the period of 2008-2022. The authors conclude that such a significant increase in electricity prices for the supplied industry will lead to an even greater lag in the industrial potential of the macroregion not only from the whole world, but also from the rest of Russia, which will negatively affect the entire sphere of socio–economic life of the macroregion, which determines the need to review government measures in the field of tariff containment in the territories of the Far Eastern Federal District.

Improving electricity demand forecasting accuracy: a novel grey-genetic programming approach using GMC(1,N) and residual sign estimation

PurposeThis paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.Design/methodology/approachThe research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.FindingsThe novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R2, yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.Originality/valueThis paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

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.

РОЗРОБКА СИСТЕМИ КЕРУВАННЯ ЕЛЕКТРОПОСТАЧАННЯМ В УМОВАХ ОБМЕЖЕННЯ ПОТУЖНОСТІ

Abstract. The article considers the problem of efficient distribution of electricity under conditions of limited production capacity. The specifics of production and energy consumption of various industries and classes of consumers are studied, which allows taking into account their needs and ensuring optimal distribution of electricity. Over the past decades, there has been an increase in demand for electricity in industry, the economy and the population. At the same time, existing sources of electricity generation may be limited in capacity, which leads to a situation where insufficient power cannot satisfy all consumer needs. The article presents an analysis of various methods of power supply management, including traditional and innovative approaches. The advantages and disadvantages of each method are discussed, and the author proposes his own control system that combines the best aspects of different approaches. The main results of the study are the development of an efficient power supply management system that ensures optimal distribution of electricity among different categories of consumers. The use of this system will reduce electricity losses and ensure a stable electricity supply for all consumers. In today's environment of technological development and growing electricity consumption, there is an urgent problem of optimising the management of limited sources of electricity supply to different categories of consumers. Solving this problem will help to increase the reliability and efficiency of electricity supply, as well as ensure the balanced operation of the system of conditionally limited electricity sources in the face of diverse consumer needs. Further research could consider the possibility of implementing the proposed power supply management system in practice and evaluate its effectiveness. Additional research can also be conducted on the impact of various factors, such as changes in electricity demand or increased production capacity, on the operation of the control system. Overall, this paper is an important contribution to the development of power management systems. It proposes a new approach to electricity distribution that allows for optimal resource utilisation and reduces energy losses. The results of the study may be useful for energy specialists and developers of power supply management systems. Keywords: autonomous power supplies, uninterrupted power supply, optimisation of energy consumption, renewable energy sources, power supply control system.

Have climate policies accelerated energy transitions? Historical evolution of electricity mix in the G7 and the EU compared to net-zero targets

Climate policies are often assumed to have significant impacts on the nature and speed of energy transitions. To investigate this hypothesis, we develop an approach to categorise, trace, and compare energy transitions across countries and time periods. We apply this approach to analyse electricity transitions in the G7 and the EU between 1960 and 2022, specifically examining whether and how climate policies altered the transitions beyond historical trends. Additionally, we conduct a feasibility analysis of the required transition in these countries by 2035 to keep the global temperature increase below 1.5°C. We find that climate policies have so far had limited impacts: while they may have influenced the choice of deployed technologies and the type of transitions, they have not accelerated the growth of low-carbon technologies or hastened the decline of fossil fuels. Instead, electricity transitions in the G7 and the EU have strongly correlated with the changes in electricity demand throughout the last six decades. In contrast, meeting the 1.5°C target requires unprecedented supply-centred transitions by 2035 where all G7 countries and the EU must expand low-carbon electricity five times faster and reduce fossil fuels two times faster on average compared to the rates in 2015–2020. This highlights the insufficiency of incremental changes and the need for a radically stronger effort to meet the climate target.

Detecting Power System Voltage Stability in Large Test System

The unpredictable changes in electricity demand, along with potential risks, require power system operators to act quickly to prevent voltage instability. To do this, real-time monitoring is crucial, especially in larger systems, but it is a challenging task for power system engineers. The existing methods for detecting voltage instability need to be tested and validated for complex systems. This study aims to validate a voltage instability detection index called SQLVIDI in a larger test system with 118 buses (IEEE 118 bus test system). The simulation results manifest that this index has the capability to detect potential voltage instability in the power system with larger number of buses.

Evaluating the household-level climate-electricity nexus across three cities through statistical learning techniques

As the climate crisis intensifies, rising temperatures and increased frequency of extreme events are likely to strain the electricity system. This will be particularly disastrous if the grid is unprepared for the climate-induced shifts in electricity demand that will result from increased temperatures. Recently, the use of data-driven modeling has emerged as a way to predict these climate-induced changes in electricity demand, however, much of the work has focused on entire sectors or regions. Here, we focus on the impact of climatic variables on hourly household electricity use for air conditioning. Our goal was to determine the best model for predicting the air conditioning use based on climate variables, as well as use that model to extract insights related to the household-level climate-electricity nexus. Using smart meter data from three US cities (Austin, Texas, Ithaca, New York, and San Diego, California), we tested seven different models of varying complexity. Ultimately, Bayesian additive regression trees (BART) was selected as the best model across all three cities (NRMSE ranged between 0.085 and 0.250). Additionally, we found that while the majority of the climate variables were important, relative humidity was the most important variable in each city. Given that air conditioning tends to drive non-base electricity demand in the summer, understanding these nuances in the climate-electricity nexus as it applies to air conditioning is critical for building a resilient grid.

Thermal performance of cascaded and combined sensible-latent heat storage tank under fluctuations in sunlight and electric demand

Based on its composite construction and usage of low-cost filler materials, thermocline thermal energy storage technology, which uses the molten as a heat transfer fluid, is more expensive than a conventional two-tank storage system. However, when integrated with concentrated solar power plants, the thermocline tank type experiences a significant disadvantage of low capacity and utilization ratio due to fluctuations in solar energy availability or an alteration in the electrical demand. The major concern of this subject is to look into, determine, and assess the charge and release cycle systems using various storage designs and under fluctuations in solar energy availability or a change in electricity demand. To examine the tank's cyclical thermal properties, a two-phase mathematical approach is developed. The current numerical formulas have been solved using the MATLAB program, and the findings of this analysis have been verified. The findings show that the combined sensible-latent heat storage scenario has the highest capacity ratio of 52.4%, the utilization ratio of 45.5%, retrieved energy of 181.06 MWh, and comprehensive efficiency of 67.3% than the cascaded layers heat storage scenario at a charging time of 11 h.

Energy Management System (EMS) Based on Model Predictive Control (MPC) for an Isolated DC Microgrid

Microgrids have become an alternative for integrating distributed generation to supply energy to isolated communities, so their control and optimal management are important. This research designs and simulates the three levels of control of a DC microgrid operating in isolated mode and proposes an Energy Management System (EMS) based on Model Predictive Control (MPC), with real-time measurement feedback for optimal energy dispatch, which ensures power flow distribution and operation at minimum cost while extending the lifespan of the BESS. The EMS can react to disturbances produced in the lower control levels. The microgrid’s performance is analyzed and compared in two scenarios without EMS, and with EMS against changes in irradiation and changes in electricity demand. The fulfillment of the power balance is evaluated by analyzing the power delivered by each generation unit, the operating cost, and the state of charge of the battery (SOC).

An evaluation of the impact of COVID-19 lockdowns on electricity demand

The COVID-19 pandemic has given rise to significant changes in electricity demand around the world. Although these changes differ from region to region, countries that have implemented stringent lockdown measures to curtail the spread of the virus have experienced the greatest alterations in demand. Within Australia, the state of Victoria has been subject to the largest number of days in hard lockdown during the COVID-19 pandemic. We conduct an exploratory data analysis to identify predictors of demand, and have built a time series forecasting model to predict the half-hourly electrical demand in Victoria. Our model distinguishes between lockdown periods and non-restrictive periods, and aims to identify a variety of patterns that we show to be influential on electricity demand. The model thereby provides a nuanced prediction of electrical demand that captures the shifting demand profile of intermittent lockdowns.

Assessment of hydroelectric potential under climate change and hydrological parameters based on soft-computing: A case study

Electricity is recognized as one of the most pervasive government services. Therefore, accurate forecasting of electricity demand has a great impact on electricity management. In this paper, the monthly electricity demand has been forecasted. For this objective, an integrated Artificial Neural Network (ANN) with a developed metaheuristic algorithm has been used to forecast short-term electricity demand. The improved algorithm reduces errors in the optimization process by preventing a fall into local optimization. This algorithm is called the Developed African Vulture Optimization (DAVOA) algorithm. The finding indicates the annual contribution of temperature, relative moisture, wind speed, and cloudiness to forecast short-term electricity demand by about 70%, 12%, 10%, and 8%, respectively. Therefore, temperature, with a correlation of 0.70%, is an important variable that affects short-term energy demand. Moreover, the finding of short-term energy demand based on the optimal ANN method indicates that the electricity demand change in January, February, July, August, and September is greater than in other months because the temperature changes more drastically in these months. Therefore, the use of heating and cooling tools will increase compared to other months, and consequently, the energy demand for the energy supply of cooling and heating tools will increase.

The Economic Influence of Energy Storage Construction in the Context of New Power Systems

The increase in the proportion of renewable energy in a new power system requires supporting the construction of energy storage to provide support for a safe and stable power supply. In this paper, the computable general equilibrium (CGE) quantitative assessment model is used coupled with a carbon emission module to comprehensively analyze the benefits and costs of energy storage construction from a macro perspective. Taking Zhejiang Province as an example, the investment in and construction of energy storage under the new power system of the 14th Five-Year Plan will slow down the economic growth of Zhejiang Province to a slight extent, but this investment and construction can improve the income level of residents and further redistribute the income structure. At the same time, energy storage investment and construction are conducive to building a clean, low-carbon, and efficient power system. The construction of energy storage can smooth out changes in electricity demand, while enhancing the electricity consumption of the residential sector, making the core sector’s electricity consumption more efficient. In addition, the construction of energy storage in the new power system has a positive effect on carbon emission reduction in Zhejiang Province, with the CO2 growth rate being significantly lower than the baseline scenario.

An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19

This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIMA) and the use of machine learning techniques, such as artificial neural networks, are the most covered in the literature review. All these approaches evaluate the prediction error when comparing the generated models with the data that fed the model, but they do not compare these values with the actual data of electricity demand once these are obtained, in addition, these techniques present high error values when there are unexpected changes in the trend of electricity consumption. This work proposes a methodology to generate an adaptive model for unexpected changes in electricity demand through the use of optimization in conjunction with SARIMA time series. The proposed case study is the electricity consumption in Quito, Ecuador to predict the electricity demand in the years 2019 and 2020, which are particularly challenging due to atypical electricity consumption attributed to COVID-19. The results show that the proposed model is capable of following the trend of electricity demand, adapting itself to sudden changes and obtaining an average error of 2.5% which is lower than the average error of 5.43% when using a non-adaptive approach (more than 50% or error improvement).

Response of China's electricity consumption to climate change using monthly household data.

Intensifying climate change significantly impacts residential electricity consumption, especially in developing countries, such as China, that are experiencing rapid income growth. By combining meteorological and monthly household consumption survey data, this study explores the response function of residential electricity consumption to temperature in China from a micro perspective. Future residential electricity demands and related CO2 emissions are then forecast under different climate scenarios. Overall, the response function is U-shaped, and one additional day above 34 °C will increase monthly residential electricity consumption by 2.11%. Global warming will more likely increase the electricity burden on low-income groups. There will be notable seasonal changes in electricity demand in the future, and the largest increase will occur in August. The total demand for residential electricity caused by temperature change will show a fluctuating growth trend, from 0.8% and 1% in 2025 to 2% and 2.9% in 2060 under the RCP4.5 scenario and RCP8.5 scenario, respectively; meanwhile, this demand will be accompanied by a cumulative increase in carbon dioxide emissions.

Impacts of the co-adoption of electric vehicles and solar panel systems: Empirical evidence of changes in electricity demand and consumer behaviors from household smart meter data

During the electrification of household energy consumption, there is an increasing number of consumers that purchase both electric vehicles (EV) and distributed solar photovoltaics (PV) systems. This study aims to examine the change in electricity demand from the power grid for EV owners when they add distributed solar panels to their homes. The impacts of the two technologies combined are different from the sum of two individual impacts because they may not be additive and EV consumers' behaviors may be subject to change. We apply a difference-in-differences model and compare consumers with or without EVs and also EV consumers with and without additional PVs. We use the hourly electricity demand data for 13,190 households in the Phoenix metropolitan area in Arizona. Our results show that EV consumers, without PV panels, use more electricity compared to non-EV consumers, and their average hourly demand is higher by 0.4 kWh. After adding PVs, EV consumers decrease the average hourly demand from the electric grid by 1.1 kWh. The co-adoption of PVs with EVs helps reduce the system peak hour loads. Besides, we also find evidence of behavior changes when EV consumers shift some of their EV charging from night to day so that they are charging their EVs with more cleaner electricity. The annual monetary savings for consumers after adding PVs are estimated to be ~$930, and the total social savings are estimated to be ~$925. Given the positive co-adoption effects, a policy implication is that incentives should be provided to promote the co-adoption of PVs with EVs.

Planning for the evolution of the electric grid with a long-run marginal emission rate

SummaryEmissions factors are widely used to estimate how various interventions would influence emissions from the electric sector. Both of the most commonly used metrics, however, neglect how changes in electricity demand can influence the structural evolution of the grid (the building and retiring of capital assets, such as generators). This omission can be significant when the factors are intended to comprehensively reflect the consequences of an intervention. In this work we evaluate a lesser known metric—the long-run marginal emission rate (LRMER)—which incorporates both the operational and structural implication of changes in electricity demand. We apply a modeling framework to compare the LRMER to the two near-ubiquitous metrics, and show that the LRMER can outperform the other two metrics at anticipating the emissions induced by a range of interventions. This suggests that adopting the LRMER could improve decision-making, particularly by better capturing the projected role of renewable generators in the evolution of the power sector.

The impact of COVID-19 containment measures on changes in electricity demand

Emergency measures imposed by governments around the world have had massive impacts on the energy sector, resulting in dramatic reductions in total energy demand. The New Zealand government introduced strict containment measures in response to the Covid-19 virus. We use an augmented auto-regressive-moving-average model to assess the impact of containment measures on wholesale electricity demand. The study spans the period 27 February 2020 to 23 February 2021. Results show that the Alert Level-4 lockdown had the largest, significant, and negative effect on electricity demand compared to other containment level measures. Specifically, Alert Level 4 resulted in a 12% reduction in wholesale electricity demand. Structural breaks in the data are evident as containment progressed to Alert Level 1. This unprecedented experiment provides insights into underlying patterns of electricity demand, therefore, projection of economic activity. Furthermore, the analysis offers insights into the performance of the electricity market when both aggregate demand and the pattern of demand change in response to exogenous constraints. Finally, the outcomes of this analysis also provide a robust reference for other countries on how the New Zealand market performed.

Measuring aggregate electricity savings from the diffusion of more efficient lighting technologies

Increasing concerns about sustainability and energy conservation, coupled with the proliferation of incentives in the EU to achieve energy savings, suggest that significant improvements in energy efficiency should be realized. A policy measure that should have a direct impact on energy savings is the replacement of incandescent and halogen light bulbs by more efficient lighting technologies, which was implemented in 2009. Due to the lack of detailed data, it is not feasible to measure the effect of energy-efficient improvements on electricity consumption at the aggregate level using a bottom-up approach. To overcome this limitation, this paper analyzes hourly electricity demand in a very specific period of the day: the transition from day to night. In this short period, it is plausible that lighting is the main driver of changes in electricity demand, thus making it possible to estimate the increase in electricity consumption when lights are switched on and to analyze the effects of higher energy efficiency in lighting, if any. The results of the analysis for Spain show that during the periods 2009–2011 and 2015–2016, an estimated energy savings of 251 GWh can be attributed to a reduction in the magnitude of the lighting effect, which accounts for 20.3% of the observed decrease in electricity consumption during these two periods.

The Impact of COVID-19 on Electricity Demand Profiles: A Case Study of Selected Business Clients in Poland

The COVID-19 pandemic has caused changes in electricity demand and, consequently, electricity consumption profiles. Given the rapid changes in energy prices, it is significant from the perspective of energy companies, and forecasting consumed energy volume. A necessity for accurate energy volume planning forces the need for analyzing consumers’ behaviors during the pandemic, especially under lockdowns, to prepare for the possibility of another pandemic wave. Many business clients analyzed in the paper are economic entities functioning in sectors under restrictions. That is why analyzing the pandemic’s impact on the change in energy consumption profiles and volume of these entities is particularly meaningful. The article analyzes the pandemic and restrictions’ impact on the total change of energy consumption volume and demand profiles. The analysis was conducted basing on data collected from a Polish energy trading and sales company. It focused on the energy consumption of its corporate clients. Analyzed data included aggregated energy consumption volumes for all company’s customers and key groups of economic entities under restrictions. The analysis demonstrates the influence of pandemic restrictions on energy consumption in the group of business clients. Significant differences are observable among various sectors of the economy. The research proves that the largest drops in energy consumption are related to shopping centers and offices. Altogether, the restrictions have caused a 15–23% energy consumption drop during the first lockdown and a maximum 11% during the second against expected values.

Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization

Accurate forecast of mid-term power demand ensures the stable and efficient operation of power systems, and is essential for the construction of energy interconnections and renewable energy microgrids. However, the implementation of strategies aimed at reducing carbon emissions such as electric energy substitution increases the uncertainty of power demand. In order to effectively extract the changing characteristics of electricity demand, this paper firstly proposes a secondary decomposition model based on a seasonal-trend decomposition procedure based on Loess (STL) and variational mode decomposition (VMD) to reduce sequence complexity. Then, different models such as grey wolf optimized support vector regression (GWO-SVR) for different sequences were used to achieve the best prediction effect. In addition, this study used the Markov chain model to further improve the prediction accuracy based on interval optimization. To verify the effectiveness of the hybrid model, a case study was conducted on the monthly electricity consumption in Zhejiang Province, China. The results show that the proposed model effectively extracts the characteristics of changes in electricity demand and greatly improves the forecast accuracy.