Fluctuations In Electricity Demand Research Articles

Analysing the impact of diverse factors on electricity generation cost: Insights from Sri Lanka

The primary responsibility of a power system utility is to provide continuous, high-quality electricity at a fair and competitive price. To maintain low generation costs while utilising diverse energy sources, optimisation methodologies are crucial. Water value and generation costs exhibit significant variability on seasonal, daily and timely basis and reflect dynamic interactions between electricity demand and power plant availability. Currently, power system planners often overlook these seasonal and time-of day dependent variations in water value and generation costs. This research paper addresses this gap by analysing how fluctuations in electricity demand and power plant availability affect generation costs in the Sri Lankan power system. It introduces a novel economic dispatch model and presents three methods for estimating water value to calculate seasonal and time-of-day electricity costs. The discussion highlights how these findings can utilise policymaking, particularly in the context of electricity pricing debates and water management for agriculture versus electricity generation, as well as in calculating the water value for each reservoir.

Capacity investment decisions of energy storage power stations supporting wind power projects

PurposeRapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.Design/methodology/approachBased on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.Findings(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.Originality/valueThis paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.HighlightsElectricity pricing and capacity of energy storage power stations in an uncertain electricity market.Investment strategy of energy storage power stations on the supply side of wind power generators.Impact of pricing method on the investment decisions of energy storage power stations.Impact of pricing method, energy storage investment and incentive policies on carbon emissions.A two-stage wind power supply chain including energy storage power stations.

Neural network-based adaptive control and energy management system of a direct methanol fuel cell in a hybrid renewable power system

Due to fluctuations in electricity demand and weather patterns, the solar energy-based system should be integrated with energy storage for efficient energy management. However, using only a battery cannot cope with the long-term power demand. Direct methanol fuel cells (DMFCs) have been considered one of the most promising backup power supplies for household applications. Therefore, the photovoltaic and solar thermal collector system incorporated with battery and DMFC is proposed along with the energy management system. In this study, a neural network-based adaptive control is developed to control the DMFC to meet persistent residential power demand and follow the technical requirements. Simulation results show that the neural network-based control outperforms the proportional-integral-derivative control in set-point change, disturbance, and model mismatch cases. Implementing the controlled DMFC in the hybrid system results in lower power requirements from the grid, which is less than twice that of a hybrid system without the DMFC. Furthermore, when solar power was insufficient to supply the load demand, the battery lifecycle in the system integrated with the DMFC seems more extended than that of a system without the DMFC.

Inefficient Building Electrification Will Require Massive Buildout of Renewable Energy and Seasonal Energy Storage

Building electrification is essential to many full-economy decarbonization pathways. However, current decarbonization modeling in the United States (U.S.) does not incorporate seasonal fluctuations in building energy demand, seasonal fluctuations in electricity demand of electrified buildings, or the ramifications of this extra demand for electricity generation. Here, we examine historical energy data in the U.S. to evaluate current seasonal fluctuation in total energy demand and management of seasonal fluctuations. We then model additional electricity demand under different building electrification scenarios and the necessary increases in wind or solar PV to meet this demand. We found that U.S. monthly average total building energy consumption varies by a factor of 1.6×—lowest in May and highest in January. This is largely managed by fossil fuel systems with long-term storage capability. All of our building electrification scenarios resulted in substantial increases in winter electrical demand, enough to switch the grid from summer to winter peaking. Meeting this peak with renewables would require a 28× increase in January wind generation, or a 303× increase in January solar, with excess generation in other months. Highly efficient building electrification can shrink this winter peak—requiring 4.5× more generation from wind and 36× more from solar.

Negawatt Planning for Aggregation Coordinator Considering Fluctuations in Electricity Demand of Consumers

Power suppliers generate electricity to ensure that the power generated equals demand. The vast daily fluctuations in electricity demand result in very high generation costs. Demand response is attracting attention as a solution to these problems in recent years with the spread of renewable energy, energy resources such as storage batteries, and the development of IoT technologies. Demand response means changing the power demand pattern by controlling demand side energy resources and distributed energy resources. In recent years, the use of demand response and the construction of mechanisms for it are being promoted. In this study, we focus on negawatt trading that is one of the demand response policies. We formulate a stochastic programming model for negawatt planning operation and the efficient solution method are shown. From experiment results, it is shown that the worst situation can be avoided using CVaR model. We also showed the efficiency of solution.

A novel method for acquiring rigorous temperature response functions for electricity demand at a regional scale

The demand for electricity affects the future climate through its effect on greenhouse gas emissions in the electricity generation process, but climate change also impacts electricity demand by changing the need for heating and cooling. Developing reliable temperature response functions (TRFs) that illustrate electricity demand as a function of temperature is key for decreasing uncertainty in future climate projections under a changing climate and for impact assessments of climate change on energy systems. However, this task is challenging because electricity demand is determined by multiple factors that interact in complicated ways because demand fluctuations represent timely human responses to given meteorological conditions. We propose a novel method to acquire reliable TRFs at a regional scale based on comprehensive modeling of electricity demand fluctuations. Six candidate algorithms were examined, and multivariate adaptive regression splines (MARS) was selected as the best algorithm with the dataset used. Using MARS, we constructed models with the capacity to precisely reproduce complex electricity demand patterns based on multiple predictors and simulated the impact of temperature on electricity demand while controlling for the effects of other factors. The temporal segments in TRFs are detected and parameters and functional forms of TRFs for 10 regions in Japan were presented.

Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

Drivers behind Residential Electricity Demand Fluctuations Due to COVID-19 Restrictions

The COVID-19 pandemic rapidly reoriented the lives of billions of people across the globe toward working, learning, and subsisting from home. This paper examines the consequences of this disruption of electricity use in Australian households. Using high-frequency electricity monitoring from 491 houses and per-circuit monitoring and in-depth interviews with 17 households, the paper (1) compares changes in energy use before and during COVID-19 lockdown, (2) quantifies the key drivers of changes in energy use experienced by households during lockdown, and (3) tracks households’ interactions with energy use feedback. The findings identify significant increases in certain aspects of household electricity use directly related to COVID-19, including increased cooking and digital device use. Yet despite the government mandate requiring a large proportion of the population to remain at home, overall energy use among the majority of Queensland households monitored actually decreased during lockdown versus prior, driven primarily by a reduction in air conditioner use during lockdown as the weather cooled. Further, despite significant quantified and self-reported changes in energy use, users who had energy use feedback installed accessed their dashboards less during lockdown than they did prior. The paper discusses these results in the context of statistics on COVID-19 related energy demand fluctuations elsewhere, and the implications for the provision of energy use information to residents during significant disruptions such as lockdown.

Negawatt planning via stochastic programming

Power suppliers generate electricity constantly to ensure that the power generated equals demand. The vast daily fluctuations in electricity demand result in very high generation costs. One of the solutions to this problem is known as negawatt trading. This is a contract between power suppliers and customers that involves the promise of a fixed amount of power demand reduction in advance. In this study, a stochastic programming model is formulated for a negawatt planning operation, considering the uncertainty of the power demand and the probability of the customer's failure to reduce it. The stochastic programming method was used to optimise the operation, maximising the profit for customers, and its effect was verified. The experiment results show that customers can choose an operation method tailored to their strategy while controlling the value of the failure probability. Compared to using a deterministic model, this stochastic programming model ensures high profits and a stable supply to consumers.

Negawatt planning via stochastic programming

Power suppliers generate electricity constantly to ensure that the power generated equals demand. The vast daily fluctuations in electricity demand result in very high generation costs. One of the solutions to this problem is known as negawatt trading. This is a contract between power suppliers and customers that involves the promise of a fixed amount of power demand reduction in advance. In this study, a stochastic programming model is formulated for a negawatt planning operation, considering the uncertainty of the power demand and the probability of the customer's failure to reduce it. The stochastic programming method was used to optimise the operation, maximising the profit for customers, and its effect was verified. The experiment results show that customers can choose an operation method tailored to their strategy while controlling the value of the failure probability. Compared to using a deterministic model, this stochastic programming model ensures high profits and a stable supply to consumers.

Natural disasters and electricity consumption behavior: a case study of the 2011 Great East Japan Earthquake

A better understanding of the price mechanisms behind electricity demand fluctuations could help develop effective tools to forecast energy demand after natural disasters. To this end, this study empirically analyzed the price elasticity of residential electricity demand in Japan over the 1990–2015 period using regional data. In particular, it attempted to shed light on the price mechanisms behind demand fluctuations by analyzing the effects of both price increases and decreases on power demand. The results showed that households tend to consume electricity according to past patterns. Given this electricity consumption behavior, once the habit of saving electricity is established, it persists. The results also show that changes in the power supply configuration associated with natural disasters may alter people’s electricity consumption behaviors. Consumer responses seem to be more sensitive to price rises than to price falls. This suggests the possibility that residential electricity demand will further decrease if the trend of rising electricity rates continues in the future.

Climate Change, Operating Flexibility, and Corporate Investment Decisions

Extreme temperatures lead to large fluctuations in electricity demand and wholesale prices of electricity, which in turn affects the optimal production process for firms to use. Using a large international sample of planned power plant projects, we measure the way that electric utilities’ investment decisions depend on the frequency of extreme temperatures. We find that they invest more in regions with more extreme temperatures. These investments are mostly in flexible gas and oil-fired power plants that can easily adjust their output to improve their operating flexibility. Our results suggest that climate change is becoming a meaningful factor affecting firms’ behavior.

Real option valuation of a decremental regulation service provided by electricity storage.

This paper is a quantitative study of a reserve contract for real-time balancing of a power system. Under this contract, the owner of a storage device, such as a battery, helps smooth fluctuations in electricity demand and supply by using the device to increase electricity consumption. The battery owner must be able to provide immediate physical cover, and should therefore have sufficient storage available in the battery before entering the contract. Accordingly, the following problem can be formulated for the battery owner: determine the optimal time to enter the contract and, if necessary, the optimal time to discharge electricity before entering the contract. This problem is formulated as one of optimal stopping, and is solved explicitly in terms of the model parameters and instantaneous values of the power system imbalance. The optimal operational strategies thus obtained ensure that the battery owner has positive expected economic profit from the contract. Furthermore, they provide explicit conditions under which the optimal discharge time is consistent with the overall objective of power system balancing. This paper also carries out a preliminary investigation of the 'lifetime value' aggregated from an infinite sequence of these balancing reserve contracts. This lifetime value, which can be viewed as a single project valuation of the battery, is shown to be positive and bounded. Therefore, in the long run such reserve contracts can be beneficial to commercial operators of electricity storage, while reducing some of the financial and operational risks in power system balancing.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.

Optimisation techniques for planning automatic under frequency load shedding in New Zealand's power system

Electricity generation is vital in developed countries to power the many mechanical and electrical devices that people require. Unfortunately electricity generation is costly. Although electricity can be generated easily but not normally stored. Electricity generation is also difficult to manage because exact demand is unknown from one instant to the next. A number of services are required to manage fluctuations in electricity demand, and to protect the system when frequency falls below acceptable levels. A current approach is called automatic under frequency load shedding. This article proposes new methods for optimising this approach in New Zealand's power system. The core ideas were developed during the 2015 Maths and Industry Study Group in Brisbane, Australia. The problem was motivated by Transpower Limited, a company that manages New Zealand's power system and transports bulk electricity from where it is generated to where it is needed. The approaches developed in this article can be used in electrical power systems anywhere in the world. References Arroyo J. M. and Galiana F. D. Energy and reserve pricing in security and network constrained electricity markets. IEEE Transactions on Power Systems, 20(2):C634–C643, 2005. Burdett R. and Kozan, E. Performance profiling for predictive train schedules. Journal of Rail Transport Planning and Management, 4:C98–C114, 2014. doi:10.1016/j.jrtpm.2014.11.002. Hiscock J., Hiscock N. and Kennedy A. Advanced voltage control for networks with distributed generation. 19th International Conference on Electricity Distribution. Vienna, 21–24 May, 2007. Just S. and Weber C. Pricing of reserves. Energy Economics, 30:C3198–C3221, 2008. El-Khattam W., Bhattacharya K., Hegazy Y. and Salama M. M. A. Optimal investment planning for distributed generation in a competitive electricity market. IEEE Transactions On Power Systems, 19(3). Kusano N. New trends in protection relays and substation automation systems in Japan. Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, 1, 624–628. Lopes J. A., Hatziargyriou N., Mutale J., Djapic P. and Jenkins, N. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electric Power Systems Research, 77, 1189–1203. Thomson M. Automatic voltage-control relays and embedded generation. Power Engineering Journal, April, 71–76. Wong S. and Fuller D. J. Pricing energy and reserves using stochastic optimization in an alternative electricity market. IEEE Transactions on Power Systems, 22(2), 631–638. Transpower AUFLS Summary Card. WL 13309 https://www.systemoperator.co.nz/sites/.../AUFLS_Summary_Card.pdf Transpower Electricity reserve technical requirements schedule–-ERTWG https://www.systemoperator.co.nz/sites/default/files/bulk-upload/documents/ERTWG%20Trial%20Procurement%20Schedule%20Guide.pdf AIMMS AIMMS 3.13 - AIMMS Modeling Guide - Integer Programming Tricks. http://www.aimms.com

Academic landscape of hydropower: citation-analysis-based method and its application

Hydropower has superior storage capacity and speedy response to meet sudden fluctuations in electricity demand. According to a recent estimate by International Energy Agency (IEA), its generation capacity could double by 2050. Nevertheless, no great picture of academic knowledge exists in relation to hydropower. This study was undertaken to reveal the academic landscape in this field by analysing the citation network of papers published in academic journals. We collected 7,521 target papers from the Web of Science using a specific search query. Using a topological-based method, all the papers were categorised into clusters according to their own characteristic topics. Results show the existence of six principal research clusters: renewable energy, optimisation of system operation, environmental impact, fish management, water governance and hybrid solutions. Combining the analysis results, clusters were found related to 'small hydropower', 'Mekong Basin hydropower' and 'pumped-storage' are developing, as indicated by the recent increasing trend. Even in developing countries, policymakers should make policy with full access to that knowledge. Governments must prepare environments in which policymakers can access the latest knowledge and information related to their own countries.

Demand for industrial and commercial electricity: evidence from Japan

This study aims to estimate electricity demand functions in Japan’s industrial and commercial sectors. We adopt data from the Energy Consumption Statistics by Prefecture by the Ministry of Economy, Trade and Industry, Japan, to delineate the demand between the industrial and commercial sector. The results reveal that in both sectors, the price elasticity of demand is extremely low in the short and long run and production elasticity is greater than price elasticity. Thus, price elasticity is not a key determinant in electricity demand fluctuations. Furthermore, an analysis of the factors influencing changes in electricity demand in the industrial sector suggests that the declining demand growth in large metropolitan areas is mainly attributable to declining production factors, not increasing electricity rates. By contrast, the commercial sector is experiencing an increasing demand for electricity and significant growth nationwide, which can be attributed to an increase in commercial floor space and advances in office automation.

Life-cycle comparison of greenhouse gas emissions and water consumption for coal and shale gas fired power generation in China

China has the world's largest shale gas reserves, which might enable it to pursue a new pathway for electricity generation. This study employed hybrid LCI (life cycle inventory) models to quantify the ETW (extraction-to-wire) GHG (greenhouse gas) emissions and water consumption per kWh of coal- and shale gas-fired electricity in China. Results suggest that a coal-to-shale gas shift and upgrading coal-fired power generation technologies could provide pathways to less GHG and water intensive power in China. Compared to different coal-fired generation technologies, the ETW GHG emissions intensity of gas-fired CC (combined cycle) technology is 530 g CO2e/kWh, which is 38–45% less than China's present coal-fired electricity. Gas-fired CT (combustion turbine) technology has the lowest ETW water consumption intensity at 960 g/kWh, which is 34–60% lower than China's present coal-fired electricity. The GHG-water tradeoff of the two gas-fired power generation technologies suggests that gas-fired power generation technologies should be selected based on regional-specific water resource availabilities and electricity demand fluctuations in China. However, the low price of coal-fired electricity, high cost of shale gas production, insufficient pipeline infrastructures, and multiple consumers of shale gas resources may serve as barriers to a coal-to-shale gas shift in China's power sector in the near term.

Experimental Study of the Dynamic Behavior of the Stripping Column for Post-combustion CO2 Capture with Monoethanolamine

CO2 capture from coal-fired power plants using a monoethanolamine (MEA) solution is one of the most promising technologies for CO2 abatement. The MEA absorption process for CO2 capture from power plants is an inherently dynamic system that is affected by the load variations in the upstream power plant because of fluctuations in electricity demand. This paper presents an experimental study of the dynamic behavior of a stripping column after various disturbances. Tests of negative and positive steps of different magnitudes in the reboiler heat duty were conducted to determine the process gain and time constant. The dynamic behavior of the desorption process was also investigated using ramp tests of different magnitude in the reboiler heat duties and feed solvent temperatures. The results show that the desorption process response to the heat duty could be approximated as a first-order time delay model. The solvent holdup time in the reboiler was the dominant factor controlling the system response time. The results also show that the CO2 capture is a nonlinear process.

Multiscale Markov models with random transitions for energy demand management

A stochastic model is proposed for fluctuations in electricity demand that are associated with individual user's consumption choices. Electricity consumption is modeled as a function of social activities of consumers. The dynamics of these activities are modeled as a Markov chain. Markov models are simplified models that capture the stochasticity to the unmodeled dynamics typically attributed to white noise disturbances. Additional uncertainties are also accrued in the process of calibrating the transition rates of these chains from finite samples. In this paper, these uncertainties are accounted for by considering random transition matrices. Such formalism can also reflect the fluctuations in the environment in which the chain evolves. We also discuss a third interpretation where uncertain transitions, in a multiscale setting, reflect the fine-resolution information that is lost in the process of state aggregation. As numerical demonstration, we study the activity modeling of a heterogeneous population. Activity uncertainties are propagated onto the energy demand. Demand uncertainties, in turn, are propagated onto a global performance metric. Such uncertainty management framework bridges between the actual drivers of the energy consumption and the system health. Subsequent decisions can be robustly supported based on the results of the quantitative model proposed in this paper.

An empirical study on seasonal fluctuations in electricity demand in China

Seasonal fluctuations in electricity demand have resulted in power cuts in peak periods in China. This paper finds that the main component of fluctuations in electricity demand of secondary industry consists of four high energy consumption industries. The fluctuations in electricity demand of steel are relevant to the fluctuations in automobile sales. The energy consumption of tertiary industry is highly consistent with the seasonal fluctuations in residential electricity consumption, which is mainly affected by extreme weather conditions. In view of the transmission mechanism of the seasonal fluctuations in electricity demand from the demand side, to improve energy efficiency, electricity companies can adopt the strategy of price discrimination to smooth the seasonal fluctuations in demand.