Academic literature on the topic 'Electricity network peak demands'
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Journal articles on the topic "Electricity network peak demands"
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Rokamwar, Kaustubh. "Feed- Forward Neural Network based Day Ahead Nodal Pricing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1029–33. http://dx.doi.org/10.22214/ijraset.2021.36352.
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An electricity locational marginal pricing prediction normally recognized by 24-hour day-ahead nodal price forecast. In this paper first collected all physical and technical data i.e. availability of generation and their cost characteristics, real and reactive demands at various buses, transmission capacity availability at various conditions like peak and off-peak conditions. All these input data are used as input for computation of optimal power flow. The nodal prices are calculated with AC-DC optimal power flow methodology for IEEE 30 bus system. The resulted optimal real electricity bus voltages, nodal prices, reactive and real demands, angles have been given as inputs to Artificial Neural Network (ANN) for predict day ahead nodal prices.
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Marwan, Marwan, and Pirman Pirman. "Mitigating Electricity a Price Spike under Pre-Cooling Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1281. http://dx.doi.org/10.11591/ijece.v6i3.9597.
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The growing demand for air-conditioning is one of the largest contributors to Australia overall electricity consumption. This has started to create peak load supply problems for some electricity utilities particularly in Queensland. This research aimed to develop a consumer demand side response model to assist electricity consumers to mitigate peak demand on the electrical network. The proposed model allows consumers to independently and proactively manage air conditioning peak electricity demand. The main contribution of this research is how to show consumers can mitigate peak demands by optimizing energy costs for air conditioning in a several cases such as no spike and spike considering to the probability spike cases may only occur in the middle of the day for half hour, one hour and one and half hour spikes. This model also investigates how air conditioning applied a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve energy savings and reducing electricity bills (costs) to the consumer. The model was tested with the Queensland electricity market data from Australian Energy Market Operator and Brisbane temperature data from Bureau statistic during hot days.
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Marwan, Marwan, and Pirman Pirman. "Mitigating Electricity a Price Spike under Pre-Cooling Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 1281. http://dx.doi.org/10.11591/ijece.v6i3.pp1281-1293.
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The growing demand for air-conditioning is one of the largest contributors to Australia overall electricity consumption. This has started to create peak load supply problems for some electricity utilities particularly in Queensland. This research aimed to develop a consumer demand side response model to assist electricity consumers to mitigate peak demand on the electrical network. The proposed model allows consumers to independently and proactively manage air conditioning peak electricity demand. The main contribution of this research is how to show consumers can mitigate peak demands by optimizing energy costs for air conditioning in a several cases such as no spike and spike considering to the probability spike cases may only occur in the middle of the day for half hour, one hour and one and half hour spikes. This model also investigates how air conditioning applied a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve energy savings and reducing electricity bills (costs) to the consumer. The model was tested with the Queensland electricity market data from Australian Energy Market Operator and Brisbane temperature data from Bureau statistic during hot days.
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Kim, Hyunsoo, Jiseok Jeong, and Changwan Kim. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network." Mathematics 10, no. 23 (November 28, 2022): 4486. http://dx.doi.org/10.3390/math10234486.
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Forecasting the electricity demand of buildings is a key step in preventing a high concentration of electricity demand and optimizing the operation of national power systems. Recently, the overall performance of electricity-demand forecasting has been improved through the application of long short-term memory (LSTM) networks, which are well-suited to processing time-series data. However, previous studies have focused on improving the accuracy in forecasting only overall electricity demand, but not peak demand. Therefore, this study proposes adding residual learning to the LSTM approach to improve the forecast accuracy of both peak and total electricity demand. Using a residual block, the residual LSTM proposed in this study can map the residual function, which is the difference between the hypothesis and the observed value, and subsequently learn a pattern for the residual load. The proposed model delivered root mean square errors (RMSE) of 10.5 and 6.91 for the peak and next-day electricity demand forecasts, respectively, outperforming the benchmark models evaluated. In conclusion, the proposed model provides highly accurate forecasting information, which can help consumers achieve an even distribution of load concentration and countries achieve the stable operation of the national power system.
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Ifaei, P., J. K. Park, T. Y. Woo, C. H. Jeong, and C. K. Yoo. "Leveraging media for demand control in an optimal network of renewable microgrids with hydrogen facilities in South Korea." IOP Conference Series: Earth and Environmental Science 1372, no. 1 (July 1, 2024): 012005. http://dx.doi.org/10.1088/1755-1315/1372/1/012005.
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Abstract In pursuit of a sustainable 2030 strategy in the Republic of Korea, this study addresses the oversight in recent optimal renewable energy microgrid designs, which, despite encompassing all feasible renewable sources, neglected the pivotal role of hydrogen as an energy carrier. This research explores the feasibility of reprogramming media platforms to dynamically shape energy consumption during peak intervals. It further proposes the retrofitting of microgrids with industrial hydrogen production and storage facilities, aligning with controlled electricity demand. A comprehensive social survey investigates the impact of media content on energy-conscious behaviour and cooperation, specifically targeting energy savings during peak hours. Utilizing a probabilistic model, the study quantifies responses from the surveyed sample and decomposes the energy demand time series to reveal three new consumption patterns: demand reduction by lowering residential electricity consumption at peak intervals without shifts, intense demand shifting by redistributing electricity consumption from peaks to valleys without human intervention, and moderate demand shifting achieved through cooperation with consumers. With these novel energy demand patterns in hand, the study optimally designs renewable microgrids in 17 sites in South Korea, comparing two strategies: Plan A, involving electrolysis-based hydrogen production and storage tanks, and Plan B, which excludes hydrogen facilities. Comparative results demonstrate that media content contributes to a 10.28% and 16.11% reduction in peak electricity consumption, with and without human intervention, respectively. In Plan B, a demand cut saves 937.3 MWh/yr, resulting in a 12.88% reduction in the levelized costs of electricity (LCOE) and a 4.67% reduction in net present costs (NPC) of optimal renewable microgrids in Korea. Conversely, in Plan A, intense demand reduction exhibits superior performance, leading to $981K less NPC, 1,046 MWh/yr less excess electricity, and a 3.76% smaller LCOE. The study recommends the implementation of smart gadgets to control residential electricity consumption, producing industrial hydrogen at Korean sites based on consumer attention and agreement with specific media content. However, it underscores the importance of studying the socio-psychological effects of this plan in future research.
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Gupta, Rajat, and Sahar Zahiri. "Examining daily electricity demand and indoor temperature profiles in UK social housing flats retrofitted with heat pumps." IOP Conference Series: Earth and Environmental Science 1363, no. 1 (June 1, 2024): 012093. http://dx.doi.org/10.1088/1755-1315/1363/1/012093.
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Abstract The UK Government has announced decarbonisation of domestic heating thorough low carbon heat pumps. This will result in the deployment of 5 million heat pumps by 2030, and over 25 million by 2050, thereby increasing seasonal and daily peak electricity demand and putting a strain on local electricity networks. Despite this, there is limited evidence on the impact of heat pump operation on daily electricity demand profiles. This paper empirically examines the impact of retrofitted ground source heat pump (GSHP) on daily electricity demand of six social housing flats co-located in a socially deprived housing estate in Oxford (UK) to understand the changes on daily electricity demand during the evening peak period (4-7pm). Concurrent time-series monitoring of electricity use, and indoor-outdoor temperatures was undertaken for one week before and after heat pumps installation during the heating season of 2020-2021. Contextual data about the flats was gathered using householder surveys and Energy Performance Certificates (EPCs). Pre-heat pump, electricity demand peaked during night time from 12am to 2am due to the use of night storage heaters. Post heat pump installation, mean indoor temperature and electricity demand profiles became more stable. While mean daily electricity use reduced by 42%, peak daily electricity use increased by 23%. Despite a small sample, the magnitude and timing of the peak period of electricity use varied. This reinforces the need for enabling flexible heat pump operation through time-of-use tariffs to bring value for local electricity network and householders.
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Nafkha, Rafik, Tomasz Ząbkowski, and Krzysztof Gajowniczek. "Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers." Energies 14, no. 8 (April 14, 2021): 2181. http://dx.doi.org/10.3390/en14082181.
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The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.
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Dejvises, Jackravut. "Energy Storage System Sizing for Peak Shaving in Thailand." ECTI Transactions on Electrical Engineering, Electronics, and Communications 14, no. 1 (November 30, 2015): 49–55. http://dx.doi.org/10.37936/ecti-eec.2016141.171094.
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This paper presents a mathematical model of energy storage systems (ESSs) to minimise daily electrical peak power demand in Thailand. A daily electrical load curve on a peak day obtained from Electricity Generating Authority of Thailand (EGAT) is used to analyse the capability of energy storage system for electrical peak power demand reduction with different ESS sizes. It is found that with power rate of 50 percent of the difference between the minimum and the maximum demands of the daily load curve and with energy capacity of 50 percent of the sum of each time step absolute energy difference between the demand and the average demand of the daily load curve, ESS can decrease daily electrical peak demand approximately 7.4 percent and increase daily load factor approximately 9.9 percent.
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Kauko, Hanne, Daniel Rohde, and Armin Hafner. "Local Heating Networks with Waste Heat Utilization: Low or Medium Temperature Supply?" Energies 13, no. 4 (February 20, 2020): 954. http://dx.doi.org/10.3390/en13040954.
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District heating enables an economical use of energy sources that would otherwise be wasted to cover the heating demands of buildings in urban areas. For efficient utilization of local waste heat and renewable heat sources, low distribution temperatures are of crucial importance. This study evaluates a local heating network being planned for a new building area in Trondheim, Norway, with waste heat available from a nearby ice skating rink. Two alternative supply temperature levels have been evaluated with dynamic simulations: low temperature (40 °C), with direct utilization of waste heat and decentralized domestic hot water (DHW) production using heat pumps; and medium temperature (70 °C), applying a centralized heat pump to lift the temperature of the waste heat. The local network will be connected to the primary district heating network to cover the remaining heat demand. The simulation results show that with a medium temperature supply, the peak power demand is up to three times higher than with a low temperature supply. This results from the fact that the centralized heat pump lifts the temperature for the entire network, including space and DHW heating demands. With a low temperature supply, heat pumps are applied only for DHW production, which enables a low and even electricity demand. On the other hand, with a low temperature supply, the district heating demand is high in the wintertime, in particular if the waste heat temperature is low. The choice of a suitable supply temperature level for a local heating network is hence strongly dependent on the temperature of the available waste heat, but also on the costs and emissions related to the production of district heating and electricity in the different seasons.
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Amin, Adil, Wajahat Ullah Khan Tareen, Muhammad Usman, Haider Ali, Inam Bari, Ben Horan, Saad Mekhilef, Muhammad Asif, Saeed Ahmed, and Anzar Mahmood. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network." Sustainability 12, no. 23 (December 4, 2020): 10160. http://dx.doi.org/10.3390/su122310160.
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This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.
Dissertations / Theses on the topic "Electricity network peak demands"
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Mullen, Christopher. "Interactions between demand side response, demand recovery, peak pricing and electricity distribution network capacity margins." Thesis, University of Newcastle upon Tyne, 2018. http://hdl.handle.net/10443/4170.
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The operation of the electricity system is subject to: charges comprised of energy, capacity, use of system, peak demand and balancing components; payments for services that influence the timing and magnitude of demand; and regulatory and physical network constraints. This work explores the interactions of these characteristics in the GB system. The revenue flows associated with energy demand, balancing and use of system charges are mapped for generators, transmission and distribution network operators (TNO and DNOs), system operator (SO), electricity retailers and electricity users. Triads are part of the transmission network use of service charges and are a form of peak demand pricing. The cost-benefit of Triad avoidance using emergency standby generation is evaluated. Demand Side Response (DSR) provision by commercial electricity users on the network is modelled and simulated. The research determines the impacts of DSR timing, location and penetration level, demand recovery and incidence of Triad periods. A suite of software models was developed including: network demand agents which can be populated with demand profiles and include a model of energy recovery; an interface to Matpower [1] to allow for time-domain based power flow calculations and a model of Short Term Operating Reserve (STOR) which synthesizes calls at representative dates and times. The network demand agents are linked to bus-bars on a network model. The software suite is used to investigate the impacts of STOR provision by demand reduction with and without energy recovery on Triad demand using a Monte Carlo simulation. The total cost benefit of participation in STOR is evaluated. It is also used to conduct timeaware power-flow analysis on a distribution network model with STOR provision by demand reduction. The impact on network capacity headroom is quantified. The cost effectiveness of using standby generation for Triad avoidance was found to depend on the cost of the grid compliant connection. For a payback time of 4 years or less, with the size of generator considered, the grid compliant connection would have to cost less than £5,600. The probability of decreased Triad demand due STOR provision by demand reduction with energy recovery is up to 4 % for the parameters considered. This compares to a probability of up to 1.6 % that the Triad demand would be increased. The most likely outcome is that Triad demand remains unaffected. The total cost benefit of STOR Abstract 2 provision by demand reduction for the 1st percentile may be negative compared to not participating. The impact of DSR provision by demand reduction with energy recovery on the distribution network capacity overhead varies significantly with time of day and with the distribution of DSR over the network. For evenly distributed DSR, demand recovery peaks greater than 40 kW cause a reduction in capacity overhead. However, for a case where the DSR is not evenly distributed the capacity overhead does not decrease for recovery peaks less than 800 kW.
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Morris, Peter J. "Improved residential electricity demand management through analysis of the customer perspective." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/83943/12/83943%28thesis%29.pdf.
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Peak electricity demand requires substantial investment to update transmission, distribution and generation infrastructure. A successful community peak demand reduction project was examined to identify residential consumer motivational and contextual factors involved in their decision to adopt/not adopt interventions. Energy professionals actively worked to achieve community 'peer' membership and by becoming a trusted information source, facilitated voluntary home energy assessment requests from over 80% of the residential community. By combining and tailoring interventions to the specific needs and motivations of individual householders and the community, interventions promoting energy conservation and efficiency can be effective in achieving sustained reduction in peak demand.
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CARON, MATHIEU. "Long-term forecasting model for future electricity consumption in French non-interconnected territories." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299457.
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In the context of decarbonizing the electricity generation of French non-interconnected territories, the knowledge of future electricity demand, in particular annual and peak demand in the long-term, is crucial to design new renewable energy infrastructures. So far, these territories, mainly islands located in the Pacific and Indian ocean, relies mainly on fossil fuels powered facilities. Energy policies envision to widely develop renewable energies to move towards a low-carbon electricity mix by 2028. This thesis focuses on the long-term forecasting of hourly electricity demand. A methodology is developed to design and select a model able to fit accurately historical data and to forecast future demand in these particular territories. Historical data are first analyzed through a clustering analysis to identify trends and patterns, based on a k-means clustering algorithm. Specific calendar inputs are then designed to consider these first observations. External inputs, such as weather data, economic and demographic variables, are also included. Forecasting algorithms are selected based on the literature and they are than tested and compared on different input datasets. These input datasets, besides the calendar and external variables mentioned, include different number of lagged values, from zero to three. The combination of model and input dataset which gives the most accurate results on the testing set is selected to forecast future electricity demand. The inclusion of lagged values leads to considerable improvements in accuracy. Although gradient boosting regression features the lowest errors, it is not able to detect peaks of electricity demand correctly. On the contrary, artificial neural network (ANN) demonstrates a great ability to fit historical data and demonstrates a good accuracy on the testing set, as well as for peak demand prediction. Generalized additive model, a relatively new model in the energy forecasting field, gives promising results as its performances are close to the one of ANN and represent an interesting model for future research. Based on the future values of inputs, the electricity demand in 2028 in Réunion was forecasted using ANN. The electricity demand is expected to reach more than 2.3 GWh and the peak demand about 485 MW. This represents a growth of 12.7% and 14.6% respectively compared to 2019 levels.I samband med utfasningen av fossila källor för elproduktion i franska icke-sammankopplade territorier är kunskapen om framtida elbehov, särskilt årlig förbrukning och topplast på lång sikt, avgörande för att utforma ny infrastruktur för förnybar energi. Hittills är dessa territorier, främst öar som ligger i Stilla havet och Indiska oceanen, beroende av anläggningar med fossila bränslen. Energipolitiken planerar att på bred front utveckla förnybar energi för att gå mot en koldioxidsnål elmix till 2028. Denna avhandling fokuserar på den långsiktiga prognosen för elbehov per timme. En metod är utvecklad för att utforma och välja en modell som kan passa korrekt historisk data och för att förutsäga framtida efterfrågan inom dessa specifika områden. Historiska data analyseras först genom en klusteranalys för att identifiera trender och mönster, baserat på en k-means klusteralgoritm. Specifika kalenderinmatningar utformas sedan för att beakta dessa första observationer. Externa inmatningar, såsom väderdata, ekonomiska och demografiska variabler, ingår också. Prognosalgoritmer väljs utifrån litteraturen och de testas och jämförs på olika inmatade dataset. Dessa inmatade dataset, förutom den nämnda kalenderdatan och externa variabler, innehåller olika antal fördröjda värden, från noll till tre. Kombinationen av modell och inmatat dataset som ger de mest exakta resultaten på testdvärdena väljs för att förutsäga framtida elbehov. Införandet av fördröjda värden leder till betydande förbättringar i exakthet. Även om gradientförstärkande regression har de lägsta felen kan den inte upptäcka toppar av elbehov korrekt. Tvärtom, visar artificiella neurala nätverk (ANN) en stor förmåga att passa historiska data och visar en god noggrannhet på testuppsättningen, liksom för förutsägelse av toppefterfrågan. En generaliserad tillsatsmodell, en relativt ny modell inom energiprognosfältet, ger lovande resultat eftersom dess prestanda ligger nära den för ANN och representerar en intressant modell för framtida forskning. Baserat på de framtida värdena på indata, prognostiserades elbehovet 2028 i Réunion med ANN. Elbehovet förväntas nå mer än 2,3 GWh och toppbehovet cirka 485 MW. Detta motsvarar en tillväxt på 12,7% respektive 14,6% jämfört med 2019 års nivåer.
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Hadjipaschalis, Constantinos. "An investigation of artificial neural networks applied to monthly electricity peak demand and energy forecasting." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286627.
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Paisios, Andreas. "Profiling and disaggregation of electricity demands measured in MV distribution networks." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28777.
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Despite the extensive deployment of smart-meters (SMs) at the low-voltage (LV) level, which are either fully operational or will be in the near future, distribution network operators (DNOs) are still relying on a limited number of permanently installed monitoring devices at primary and secondary medium-voltage (MV) substations, for purposes of network operation and control, as well as to inform and facilitate trading interactions between generators, distributors and suppliers. Accordingly, improved and sufficiently developed models for the analysis of aggregate demands at the MV-level are required for the correct assessment of load variability, composition and time-dependent evolution, necessary for: addressing issues of robustness, security and reliability; accomplishing higher penetration levels from renewable/distributed generation; implementing demand-side-management (DSM) schemes and incorporating new technologies; decreasing environmental and economic costs and aiding towards the realisation of automated and proactive ''smart-grid'' networks. The analysis of MV-demand measurements provides an independent source of information that can capture network characteristics that do not manifest in the data collected at the LV-level, or when such data is restricted or altogether unavailable. This information describes the supply/demand interactions at the mid-level between high-voltage (HV) transmission and LV end-user consumption and opens possibilities for validation of existing bottom-up aggregation approaches, while addressing issues of reliance on survey-based data for technical and economic power system studies. This thesis presents improved and novel methodologies for the analysis of aggregate demands, measured at MV-substations, aimed at more accurate and detailed load profiling, temporal decomposition and identification of the drivers of demand variability, classification of grid-supply- points (GSPs) according to consumption patterns, disaggregation with respect to customer-classes and load-types and load forecasting. The developed models are based on a number of traditional and modern analytical and statistical techniques, including: data mining, correlational and regression analysis, Fourier analysis, clustering and pattern recognition, etc. The approaches are demonstrated on demand datasets from UK and European based DNOs, thus providing specific information for the demand characteristics, the dependencies to external parameters and to socio-behavioural factors and the most likely load composition at the corresponding geographical locations, while the approaches are also intendent to be easily adaptable for studies at equivalent voltage and demand aggregation levels.
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Yen, Ta-Pin, and 顏大濱. "The Optimal Capacity Investigate to Suppress the Peak Loading of Micro-Grid Network with both of Wind Power and Solar Energy Electricity Generation." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/54712163890968264113.
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碩士和春技術學院
電機工程研究所
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The load characteristics of micro-grids in various areas are comprised by the typical industrial, commercial, agricultural and residential customers. By summation the power consumption of each typical customer to form the micro-grid network such as the industry-oriented, commerce-oriented, agriculture-oriented, resident-oriented and mixed type micro-grids. To suppress peak demand the distributed power supply has proven to be the best practice for micro-grid. Whereas main system is consist of many sub-systems and the loadings of main systems can be further categorized by micro-grids. Once the peak demand of micro-grids is suppressed by the distributed power supply of renewable energy, in the same view of point, the peak demand of the main systems is suppressed too. It proposed two kind of renewable energy distribution generation system to parallel into the micro-grid network to supply the power with Tai-power at the same time. Therefore, that can suppressed peak load to investigate the optimal timing strategy of the industrial-oriented, commercial-oriented, agricultural-oriented, residential-oriented and hybrid five combination demands.
Book chapters on the topic "Electricity network peak demands"
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Gorges, Tobias, Claudia Weißmann, and Sebastian Bothor. "Small Electric Vehicles (SEV)—Impacts of an Increasing SEV Fleet on the Electric Load and Grid." In Small Electric Vehicles, 115–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65843-4_9.
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AbstractHeading towards climate neutrality, the electrification of the transport sector has significant impact on the electric grid infrastructure. Among other vehicles, the increasing number of new technologies, mobility offers, and services has an impact on the grid infrastructure. The purpose of this case study therefore is to examine and highlight the small electric vehicle (SEV) impact on the electric load and grid. A data-based analysis model with high charging demand in an energy network is developed that includes renewable energy production and a charging process of a whole SEV fleet during the daily electricity demand peak for the city of Stuttgart (Germany). Key figures are gathered and analysed from official statistics and open data sources. The resulting load increase due to the SEV development is determined and the impact on the electric grid in comparison to battery electric vehicles (BEV) is assessed for two district types. The case study shows that if SEVs replace BEVs, the effects on the grid peak load are considered significant. However, the implementation of a load management system may have an even higher influence on peak load reduction. Finally, recommendations for the future national and international development of SEV fleets are summarized.
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Palmer, Graham. "Electricity Networks: Managing Peak Demand." In SpringerBriefs in Energy, 31–44. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02940-5_4.
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Balwant, Manoj Kumar, Sai Rohan Basa, and Rajiv Misra. "Reducing Peak Electricity Demands of a Cluster of Buildings with Multi-Agent Reinforcement Learning." In Springer Proceedings in Mathematics & Statistics, 307–17. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15175-0_25.
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Morgan, Roger. "Displacement of Conventional Domestic Energy Demands by Electricity: Implications for the Distribution Network." In Sustainability in Energy and Buildings, 149–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17387-5_16.
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Trenz, André, Christoph Hoffmann, Christopher Lange, and Richard Öchsner. "Increasing Energy Efficiency and Flexibility by Forecasting Production Energy Demand Based on Machine Learning." In Lecture Notes in Mechanical Engineering, 449–56. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_50.
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AbstractThe ability of manufacturing companies to compete depends strongly on the efficient use of production resources and the flexibility to adapt to changing production conditions. Essential requirements for the energetic infrastructure (EGI) result from the production itself, e.g., security of supply, efficiency and peak shaving. Since production always takes priority and must not be disturbed, the flexibility potential in terms of energy efficiency lies primarily in the EGI. Based on this, strategies will be developed that support companies in increasing their efficiency and flexibility by optimizing the configuration and operation of the EGI, while production processes are reliably supplied and not adapted. This is reached with intelligent operation strategies for the heating and cooling network based on forecasts, the use of energy storage systems, and the coupling of energy sectors. This paper presents an approach for energy forecasts used for the optimization of operation strategies. Hence, an energy-forecast-tool was developed, which is used for the prediction of electrical and thermal loads depending on the expected production. Therefore, machine learning models are trained with past weather, energy, and production data. Using production planning data and weather forecasts, the model can predict energy demands as input for an EGI optimization.
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Molla, Tesfahun. "Smart Home Energy Management System." In Research Anthology on Smart Grid and Microgrid Development, 1132–47. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3666-0.ch051.
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With the development of smart grid technology, residents can schedule their power consumption pattern in their home to minimize electricity expense, reducing peak-to-average ratio (PAR) and peak load demand. The two-way flow of information between electric utilities and consumers in smart grid opened new areas of applications. In this chapter, the general architectures of the home energy management systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient scheduling methods for home power usage are discussed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG.
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Molla, Tesfahun. "Smart Home Energy Management System." In Handbook of Research on New Solutions and Technologies in Electrical Distribution Networks, 191–206. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1230-2.ch011.
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With the development of smart grid technology, residents can schedule their power consumption pattern in their home to minimize electricity expense, reducing peak-to-average ratio (PAR) and peak load demand. The two-way flow of information between electric utilities and consumers in smart grid opened new areas of applications. In this chapter, the general architectures of the home energy management systems (HEMS) are introduced in a home area network (HAN) based on the smart grid scenario. Efficient scheduling methods for home power usage are discussed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG.
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Wang, Qing, Xiaohu Zhu, and Xiaozhuang Zhou. "Two-Layer Optimal Dispatching Strategy of Distribution Network Considering Demand Side Load." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231208.
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The traditional optimal dispatching of distribution network usually takes the demand load measurement as the rigid load. However, as the demand for electricity continues to rise, a large amount of distributed renewable energy generation is incorporated into the power grid. It is difficult to ensure the safe and reliable operation of the electrical system if only the optimal dispatching of the generating side is carried out. This paper proposes a two-layer optimal distribution network scheduling strategy that considers the demand side load. The upper layer model chooses controllable load output as the objective and aims to achieve the lowest overall operation cost of the distribution network, as well as the lowest comprehensive load fluctuation to reflect the power supply reliability of the distribution network. The adaptive particle swarm optimization algorithm based on genetic algorithms is implemented to solve the model based on its characteristics. Simulation verification is performed on the IEEE33-node distribution system, and the results depict that the proposed two-layer optimization method reduces the operating cost of the distribution network, effectively stabilizes the peak valley difference of load, and improves the overall stability of the distribution network.
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M., Maheswari, and Gunasekharan S. "Operation and Control of Microgrid." In Handbook of Research on Smart Power System Operation and Control, 412–33. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8030-0.ch018.
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The demand for electricity is increasing day by day due to technological advancements. According to the demand, the size of the grid is also increasing rapidly in the past decade. However, the traditional centralized power grid has many drawbacks such as high operating cost, customer satisfaction, less reliability, and security. Distribution generation has less pollution, high energy efficiency, and flexible installation than traditional generation. It also improves the performance of the grid in peak load and reliability of supply. The concept of micro-grid has been raised due to the advent of new technologies and development of the power electronics and modern control theory. Micro-grid is the significant part of the distribution network in the future of smart grid, which has advanced and flexible operation and control pattern, and integrates distributed clean energy.
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M., Maheswari, and Gunasekharan S. "Operation and Control of Microgrid." In Research Anthology on Smart Grid and Microgrid Development, 1437–58. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3666-0.ch065.
Full textAbstract:
The demand for electricity is increasing day by day due to technological advancements. According to the demand, the size of the grid is also increasing rapidly in the past decade. However, the traditional centralized power grid has many drawbacks such as high operating cost, customer satisfaction, less reliability, and security. Distribution generation has less pollution, high energy efficiency, and flexible installation than traditional generation. It also improves the performance of the grid in peak load and reliability of supply. The concept of micro-grid has been raised due to the advent of new technologies and development of the power electronics and modern control theory. Micro-grid is the significant part of the distribution network in the future of smart grid, which has advanced and flexible operation and control pattern, and integrates distributed clean energy.
Conference papers on the topic "Electricity network peak demands"
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Heidar Esfehani, Hamidreza, and Martin Kriegel. "Modeling and Analysis of Energy Load Management Using Advanced Off-Peak Controlled Heat Pump System With Thermal Storage Under Different Time and Weather Conditions." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52957.
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In this present paper, an advanced model of a ground source heat pump (GSH) system coupled with thermal energy storage (TES) is simulated and performed under a developed load management control strategy in order to supply a residential complex by taking the advantage of cheap excess electricity of the grid network during off-peak hours, where demand is in its lowest rate. Analyzing the performance of the system under different time and weather conditions leads to recognizing the effective parameters on thermal performance of the system which leads to achieve the optimal system efficiency and output energy. The proposed system with off-peak control scenario produces flexible heat supply profile which covers peak load consumptions whilst can smooth the electricity production peaks, caused by introduction of renewable energies, and prevents high rates of energy loss consequently.
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Bello, Olumide, and Landon Onyebueke. "Optimization of Smart Grid Solar Energy Application." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-36791.
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This paper presents an approach to modeling of renewable energy integration into Smart Grid for Electric Vehicle charging applications. Integration of renewable energy sources to smart grid is not only the key to smart Electric Vehicle charging but also the most efficient way to manage the distributed energy resources. It enables the ability to control, ease the peak load impacts, and protect distribution network components from being overloaded by Electric Vehicles. Thus, the electricity generation and consumption is managed in more cost effective way. The developed model is a grid connected solar-assisted Electric Vehicle charging station, with battery bank. It generates electricity using solar photovoltaic (PV) arrays to augment the electricity used to charge the electric vehicles. The battery bank stores electricity from the grid and discharges the stored energy during periods of peak charging demand. Optimization of the model was done by developing a program written in Visual Basic 2012. The computational results show the economic advantages of this model as well as the anticipated benefits of the smart grid for reduced peak loads, and increased efficiency.
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Liang, Yongtu, Jing Gong, Zhengling Kang, and Fafu Yang. "Research on Operation Optimization of Multi-Product Pipeline." In 2004 International Pipeline Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ipc2004-0597.
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It is a difficult task for pipeline operators to operate a long-distance complicated multi-product pipeline optimally, safely and efficiently. The off-line optimization simulation software for multi-product pipeline can assist to solve the problem rapidly and efficiently. Considering the operating characteristics of a complex multi-product pipeline with a variety of equipment configurations and demands for products along the pipeline, etc, the mathematical model for optimizing operations of multi-product pipeline was established, following the optimization theory, which is solved via the Dynamic Programming. In the model, the peak-to-valley ratio of electricity price is also taken into account. Almost all the governments make the policy of peak-to-valley electricity price to encourage users to consume more electricity during the valley load of electrical network. In China, the peak-to-valley ratio of electricity price is 3:1 generally, so the pipeline companies can obtain great profits via reasonable transportation plan. Based on the study, the “STROBER” software has been developed, which can support users to schedule and verify the off-take plans and control plans of the inlet pressure of pressure-reducing station, to propose the optimal pump configuration and to provide the optimization operation cost results. Input data and calculated results can be presented in kinds of forms users are accustomed to. The simulation software has been successfully applied to “LanZhou-ChengDu-ChongQing” multi-product pipeline, the most complicated one in China so far, including 13 off-take stations, 2 pressure-reducing stations and 4 pumping stations. The software simulates the operations on LanZhou-ChengDu-ChongQing multi-product pipeline, and provides the off-take plans and control project of pressures in the pipeline system, thus contributing to operators’ routine work. The feedback from the field indicates that it’s reliable for prediction and instruction.
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Manoharan, Yogesh, Alexander Headley, Keith Olson, Laurence Sombardier, and Benjamin Schenkman. "Energy Storage Versus Demand Side Management for Peak-Demand Reduction at the Hawaii Ocean Science and Technology Park." In ASME 2021 15th International Conference on Energy Sustainability collocated with the ASME 2021 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/es2021-63799.
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Abstract There is a growing interest in utilizing energy storage for behind-the-meter customers. Energy storage systems have many functions for behind-the-meter use such as energy time shifting, peak demand shaving, and backup power. However, demand side management of energy consuming systems can also provide similar energy shifting functionality often with a significantly lower upfront cost. Though energy storage systems and demand side management can both be applied, each option has strengths and weaknesses that can make the optimal selection of measures difficult in many cases. In this study, the tradeoff between energy storage and demand side management is investigated at the Hawaii Ocean Science and Technology (HOST) park of the Natural Energy Laboratory of Hawaii Authority (NELHA). The major energy consumption at the HOST park is for pumping the seawater that serves many functions at the park, including supplying temperature-controlled water for various agriculture applications and even building air conditioning measure. NELHA’s facilities are broken into two major load centers that are connected by the piping network, though they are electrically isolated and subject to different electricity price tariffs. This scenario is modeled to optimize the dispatch of the pump stations and potential battery systems to minimize the cost of electricity for both load centers. This scenario is a good example of the interplay between demand side management and energy-storage-based cost reduction measures.
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Gajowniczek, Krzysztof, Rafik Nafkha, and Tomasz Ząbkowski. "Electricity peak demand classification with artificial neural networks." In 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017. http://dx.doi.org/10.15439/2017f168.
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Nagah, Mostafa, and Mohamed Shaaban. "A Transactive Energy Microgrid Model using Blockchains." In International Technical Postgraduate Conference 2022. AIJR Publisher, 2022. http://dx.doi.org/10.21467/proceedings.141.31.
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The price of technology used in the production of renewable energy (RE) has come down significantly, and end users' roles have shifted from that of simple energy consumers to that of active participants in the creation of their energy. This new function is known as a prosumer, and it has led to the change in electricity markets by enabling prosumers to resell energy excess to electricity suppliers and other prosumers. As a result, there is a need for a peer-to-peer (P2P) energy trading network that makes use of the Ethereum blockchain and a smart contract mechanism to operate as an interface between prosumers and consumers. This paper develops a microgrid model incorporating Blockchain technology to simulate peer-to-peer energy transactions. In the simulation setup, solar panels are employed as the primary source of electrical energy. In addition, energy storage batteries when the sun sets, complement the energy provided by the solar PV in the simulation developed. Furthermore, an automated bidding system to facilitate energy transactions is implemented. The bidding system consists of a full interface that shows houses supply, demand, batteries, and the bid on the energy. The simulation is carried out for 20 days, with 15 houses connected to the grid. Full transaction simulation resulted in peak prices that were more than 25 percent lower than real-life energy tariff coming from the electric utility company.
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Tong, Zheng, Xiaoqi Wang, Dingwei Weng, Chunming He, Rui Yang, Zhigang Zhang, and Sun Qiang. "Unconventional Fields Recovery Enabled By Large-Scale Green Power Supply Based on Multi Micro-Grids and Energy Storage Sharing with National Data Centers." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215407-ms.
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Abstract Unconventional oil and gas reserves have drawn considerable attention for petroleum companies over the last two decades. Eco-friendly electric-powered drilling rigs and fracturing fleets have been commercially available instead of diesel-powered equipment. However, Operators were often confronted with challenges of the great demand on electrical power especially fracturing operation. Fossil-energy-powered electricity and existing expensive high-voltage power grids have negative effect on Environmental-Social-Governance (ESG) initiatives and CAPEX of shale-oil developing projects. Additionally, although operators concern multi-energy complementary with regard to green energy such as solar energy and wind power, economic and efficiency drawbacks of green electricity caused by power uncertainties could not be ignored. Unconventional reserves have been proved in Western China full of abundant green energy. As demonstrated in the national project of "Channels computing resources from east to west", computing-power data centers are constructed with electrochemical Energy-Storage System (ESS) in Western China and power source mainly stem from local green energy. In this paper one novel green-power supply solution based on capacity leasing and energy storage sharing with data centers was proposed due to electrical power surplus. One hybrid power network, combining 35kV grid with Multi Micro-grids (MMGs) between green-power stations and oil field pads, was planned to be constructed by operators. One ESS-based system integrating MMGs was set up in one case demonstration. Some mobile energy-storage units were used for cost-effective power supply and peak shaving on certain pads due to poor load curves. The solution feasibility was verified and compared with the traditional power supply mode. ESS-based system with MMGs is qualified for affordable green-powered well construction and sustainable development of unconventional oil fields.
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Khoir, Khoir Lazuardi, Ade Rafsanjani Ade, and Widi Hernowo Widi. "Technical and Economic Analysis of Mini LNG from the Utilization of Gas Flare by Optimization of Liquefaction Process." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/210883-ms.
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Abstract Nowadays, the world is facing global warming as one of its main issues. This problem can be caused by a rise in CO2, CH4, and other GHG emissions in the atmosphere. On the other hand, flared gas is very similar in composition to natural gas and is a cleaner source of energy than other commercial fossil fuels where is 142 bcm of natural gas was flared in 2020 globally. Around 265 Mt CO2, almost 8 Mt of methane (240 Mt CO2-eq), black soot, and other GHGs were being directly emitted into the atmosphere as the result. Currently, Mini-LNG facilities could have been eliminated from gas flaring. This study analyses the LNG chain concept which can be used for the monetization of small volumes (0.05 -10 MMscfd) of associated gas. The methodology of this study reviews both technical and economic aspects. Technically, two typical types of small-scale natural gas liquefaction processes in skid-mounted packages were designed and simulated. After simulating the LNG production unit, economic unit estimation was calculated and performed. Small Scale LNG has good prospects for its development, especially to eliminate gas flaring or reduce gas emission, providing advantage economic and social e.g. to address increasing demand for energy as a peak session (peak shaving), monetization of stranded gas, and overcome the distribution of fuel to remote areas that are not yet connected to the electricity grid and comprehensive gas pipeline network. The cost calculation uses two approaches, both levelized cost and cash flow. The gas price of levelized cost is US$ 2.49/MMBTU, while the cash flow is US$ 2.95/MMBTU. The cash flow method is higher in gas costs because it takes into account the cost of money, DMO for the State, inflation, and depreciation. While ROI and NPV were obtained at US$ 29,692,691 and US$ 29,692,691.
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Will, Adrian. "Autonomous demand-side management system for peak shaving and energy optimization in electricity distribution networks." In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). IEEE, 2017. http://dx.doi.org/10.1109/ictus.2017.8285986.
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Ristić, Leposava. "ENERGY OPTIMIZATION OF INDUSTRIAL PROCESSES THROUGH ADVANCED USE OF CONTROLLED ELECTRICAL DRIVES AND POWER ELECTRONICS." In IX Regional Conference Industrial Energy and Environmental Protection in the Countries of Southeast Europe, 340–70. Society of Thermal Engineers of Serbia,, 2024. http://dx.doi.org/10.46793/ieep24.340r.
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The paper investigates the role of advanced use of controlled electrical drives and power lectronics in optimizing energy consumption in industrial processes. These technologies are crucial for enhancing the efficiency, flexibility, and overall performance of industrial processes. The paper emphasizes the principle of minimizing energy losses as the basis of energy efficiency and discusses various strategies for achieving this goal. Variable speed drives (VSDs) are highlighted as essential components for regulating motor speed and torque according to specific application requirements. By adjusting the frequency and voltage supplied to the motor, VSDs ensure that the motor operates at its optimum efficiency point, thereby reducing power losses associated with motors operating at fixed speeds. Furthermore, the integration of power electronics and sophisticated control algorithms enables electrical drives to perform with exceptional precision and responsiveness, as well as to facilitate energy-saving strategies. The paper also discusses how power electronics devices can be employed to improve the power factor of industrial systems, ensuring more effective utilization of electrical power and reducing losses in the distribution network. Additionally, it highlights the role of advanced control systems and power electronics in managing peak loads efficiently, enabling industrial facilities to avoid costly demand charges and optimize energy usage during periods of high electricity prices. Integrating controlled electrical drives and power electronics with smart grid technologies enables demand response, grid interaction, and energy management strategies that contribute to overall energy efficiency and sustainability. The paper systematically analyzes the potential energy savings achievable through functional adjustment of drive components to meet technological requirements. It categorizes savings into two main groups: savings achievable through functional adjustment of drive components and savings at each individual component of the drive system. Selected examples are methodically discussed to illustrate these points.