Seasonal Variations In Demand Research Articles

Seasonal Variation in Electricity Demand of Solar Powered Net-Zero Energy Housing in Temperate Climates

Seasonal Variation in Electricity Demand of Solar Powered Net-Zero Energy Housing in Temperate Climates

Reviewing Mini-lysimeter Controlled Irrigation in Container Crop Systems

This study reviews how mini-lysimeters have been used effectively to optimize irrigation control in container horticulture production. Lysimeters are devices that measure evapotranspiration (ET) from the water balance of a fixed soil volume. The primary components of lysimeter-controlled irrigation are load cell sensors, a multiplexer, a data logger, a controller, and solenoid valves. The two common mini-lysimeter systems are platform lysimeters and suspension lysimeters. In these systems, a bending-beam single-point load cell is fastened between two plates, and a container is placed directly on the top platform. Platform lysimeters are commonly used for smaller pot sizes, and suspension lysimeters have been used for large shade trees up to 2.8 m tall and weighing 225 kg. Mini-lysimeters have been used for decades to calibrate ET models and create on-demand irrigation control programs that replenish plant daily water use or maintain deficit conditions. Research has demonstrated that lysimeter-based irrigation can respond more effectively to seasonal and diurnal variations in water demand, increasing irrigation cycles when evaporative demand is high, and decreasing irrigation cycles when demand is low. A strength of these systems is that for containerized plants, such as nursery production systems, mini-lysimeters capture whole-plant water use, which presents a more holistic measure compared with soil moisture sensors or leaf moisture sensors.

Exceptional cold-crystallization kinetics of erythritol-polyelectrolyte enables long-term thermal energy storage

Long-term thermal energy storage balances the seasonal variations in renewable energy supply and demand, but applied storage concepts require improved performance in efficiency, reliability and capacity. In principle, supercooling and cold-crystallization offer a way to store heat for an extensive amount of time. In this approach, crystallization behaviour of the material governs the storage performance, as it directly relates to optimal efficiency, length of the storage period and heat release properties. This work explains the unique cold-crystallization behaviour of erythritol in cross-linked sodium polyacrylate. To this end, isothermal cold-crystallization was measured experimentally and analysed with the Avrami equation. Although the cold-crystallization rate constant follows the Arrhenius equation, it drastically decreases near the glass transition region and diverges from the equation. Thermal history also influences the cold-crystallization behaviour. Increases in cooling end-temperature reduce the subsequent crystallization time and promote metastable polymorph formation. These findings stem from the peculiar energy landscape of erythritol in cross-linked sodium polyacrylate. The landscape is classified as kinetically strong and thermodynamically fragile, which facilitates long-term thermal energy storage. Consistent supercooling and cold-crystallization behaviour of the material enables predicting the time-dependent crystallization rate at different temperatures. This confirms applicability of the two-stage Arrhenius-VFT model for temperature dependence and supports storage design in real-life applications. • Cold-crystallizing PCM for long-term TES with 230–350 MJ/m 3 storage capacity. • Spherical cold-crystallization enables modelling and predicting the kinetics. • Cold-crystallization follows Avrami equation and combined Arrhenius-VFT dependency. • Crystallization rate decreases drastically when approaching glass transition region. • Reducing cooling temperature increases following isothermal crystallization rate.

Hourly potential evapotranspiration at 0.1\xb0 resolution for the global land surface from 1981-present

Challenges exist for assessing the impacts of climate and climate change on the hydrological cycle on local and regional scales, and in turn on water resources, food, energy, and natural hazards. Potential evapotranspiration (PET) represents atmospheric demand for water, which is required at high spatial and temporal resolutions to compute actual evapotranspiration and thus close the water balance near the land surface for many such applications, but there are currently no available high-resolution datasets of PET. Here we develop an hourly PET dataset (hPET) for the global land surface at 0.1° spatial resolution, based on output from the recently developed ERA5-Land reanalysis dataset, over the period 1981 to present. We show how hPET compares to other available global PET datasets, over common spatiotemporal resolutions and time frames, with respect to spatial patterns of climatology and seasonal variations for selected humid and arid locations across the globe. We provide the data for users to employ for multiple applications to explore diurnal and seasonal variations in evaporative demand for water.

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids

Techno-economic analysis of long-duration energy storage and flexible power generation technologies to support high-variable renewable energy grids

Urban greening based on the supply and demand of atmospheric PM2.5 removal

Faced with various urban environmental problems, cities are implementing greening plans to satisfy the demands of residents for a more habitable environment. Because the relationship between the supply and demand of ecosystem services (ESs) often changes spatially and seasonally, identifying the priority space and suitable species for greening are critical. Taking the removal of particulate matter less than 2.5 µm (PM2.5) in Beijing, China, as an example, a Greening Demand Index was proposed by combining seasonal nongreen coverage, PM2.5 concentration, and population density. The results show that, the greening demand (GD) increases along the suburban-urban gradient and is higher in the impervious areas than in other land-cover types. Without considering the seasonal variation in ES supply and demand, the GD will be underestimated in areas with deciduous vegetation coverage. On the one hand, demand comes from scarcity, and on the other hand, demand also comes from inequity. To alleviate the urban-suburban greening demand difference (GDD), greening in impervious areas is the key. To alleviate the seasonal GDD, evergreen greening in the forest and impervious areas is crucial. Forest evergreen greening, which has often been overlooked in the past, should also be considered. Three tree planting scenarios with different species compositions were simulated to evaluate the effects on PM2.5 and green distribution. The results indicate that evergreen trees are more efficient in removing atmospheric PM2.5 and are indispensable in alleviating the seasonal variation in PM2.5 concentration and the spatiotemporally uneven distribution of green. Therefore, they are recommended for greening. This research will provide help for establishing tree planting schemes in urban areas.

Seasonal Variation in Demand for Smoking Cessation Treatment and Clinical Outcomes.

Smoking behaviour shows seasonal variation, with cigarette consumption and youth smoking onset highest in summer and smoking-related web searches and sales of nicotine replacement products highest in winter. Variation in demand for clinical care and in outcomes has not been explored. We measure seasonal variation in enrolments, total clinical visits, visits per enrolment, and treatment outcome (7-day abstinence at 6-month follow-up) from 2015 to 2018 in a large (n = 85 869) clinical cohort from 454 clinics across Ontario, Canada. We model seasonality using harmonic logistic and negative binomial regression. For individual-level outcomes, we adjust for variables, selected a priori, known to be associated with treatment use or outcomes. Data are nearly complete for 3 outcomes, but 6m abstinence is missing for 45% of participants. We use multiple imputation to adjust for missing data. All four outcomes showed significant seasonal variation (all p <.001). Total enrolments and visits were 20%-25% higher in January-April than in June-September. Visits per enrolment varied slightly, with lowest levels from May-July. Abstinence at 6 months was lowest among individuals enrolled from February-May and highest for those enrolled from July-November, with an absolute peak-trough difference of 4.3% (95% CI = 3.2% to 5.5%). There is meaningful seasonal variation in demand for, and outcomes of, smoking cessation treatment. Climate and weather may be indirectly responsible. Seasonal differences underscore the general importance of contextual factors in smoking cessation, may be useful in program promotion, and may explain some variability in outcomes in evaluation and research. Demand for tobacco cessation treatment and clinical outcomes vary seasonally. This underscores the importance of context in substance-related problems, and implies that some variability in research and evaluation results may be due to the time of year data were collected. Promotion efforts might usefully consider seasonal effects to smooth out demand and possibly improve outcomes.

An empirical analysis of domestic electricity load profiles: Who consumes how much and when?

With the increased share of renewables in power generation, end users play a key role in keeping the demand at levels that better match variable supply, maintaining lower overall system costs, and reducing carbon dioxide emissions. To increase the potential for demand-side flexibility, a deeper understanding of domestic electricity load profiles is needed. Informed by customer grouping based on similar consumption patterns and drivers, targeted interventions can be better designed to time-shift peak loads and reduce overall demand. Thus, the objectives of this study are (i) to segment households in relation to their electricity load patterns using K-means clustering and (ii) to investigate household characteristics that have an influence on electricity load patterns by employing multinomial probit regression. This study uses hourly electricity consumption for 2017, combined with population-based register data for a large sample of Danish households. The results indicate that four distinct Danish household groups are characterized by different timing and magnitudes of electricity consumption, which are influenced by specific sociodemographics and dwelling characteristics. Similarities between the groups emerge with respect to the evening peak consumption, seasonal variation in electricity demand, and weekend morning demand ramp-up. Challenges and opportunities for domestic load profiling in the power industry and policymaking are discussed.

Fuel quality of stored spruce bark – Influence of semi-permeable covering material

Bark is a by-product of the sawmilling and pulp industries that is produced year-round. It is mainly used as a fuel, making its storage unavoidable due to seasonal variation in energy demand. Storing bark can lead to high energy losses but can also increase its fuel quality, e.g. by reducing its moisture content (M) and increasing its net calorific value (Q). Covering wood chip stacks with semi-permeable fabric sheets can improve fuel quality and reduce dry matter losses (DML) during storage. The objective of this study was, therefore, to evaluate the effect of similar coverage during storage on the fuel quality and energy recovery of stored bark. Two bark stacks of 1000 and 1400 m3 were established, with half of each stack being covered with a water-resistant, vapour-permeable fabric. The temperature, M, and Q were measured in each stack immediately after construction and after three and seven months of storage. Additionally, the DML, total accessible energy, and economic value of each stack were calculated at the same time points.The combined changes in fuel quality and DML reduced the amount of accessible energy by 13.7% in the uncovered parts but increased that in the covered parts by 0.6%. The economic value of the covered parts was unchanged after storage whereas that of the uncovered parts was reduced by 14%. Covering bark stacks with semi-permeable fabric during storage could thus increase the profitability of bark fuel supply chains.

A mass balance approach to urban water analysis using multi‐resolution data

AbstractWith a growing urban population and increasing climate uncertainty, it is necessary to quantitatively understand the flux of resources through cities, specifically energy and water resources. Many methods exist to analyze the urban energy–water nexus based on either physical characteristics of the system or using available data. While data‐driven approaches can be valuable, they are often challenging to duplicate on a large scale due to data availability, or they do not allow for drawing specific, detailed conclusions. Our work seeks to remedy these challenges by using multi‐resolution data and a mass balance approach to provide a reproducible and scalable method for analyzing water and embedded energy systems in cities with varying levels of infrastructure and technological advancements. Using a combination of utility‐scale and meter‐level data, we provide several quantitative performance gauges on a monthly and annual time scale. This process reveals notable seasonal variation in water demand, non‐revenue water, and embedded energy, providing a holistic understanding of water and its embedded resources. Our work further confronts the challenges of integrating disparate data sets into a uniform format to allow for accurate analysis. These particular data, coupled with a mass balance methodology, facilitate the examination of an urban area from top‐down and bottom‐up perspectives, yielding opportunities to quantify additional performance metrics beyond a single approach.

Information in Electricity Forward Prices

We examine forward prices in a market where nonstorable inventory exacerbates the influence of seasonal and hourly variation in supply and demand, expected and unexpected, on the level and volatility of spot prices. We find strong evidence, unusual for a commodity, that the difference between contemporaneous forward and spot prices has power to forecast both the spot price change and the risk premium realized at delivery. Our evidence of a time-varying risk premium is consistent with expected hourly and seasonal variation in the needs of producers and retailers of electricity to hedge against extreme spot price decreases and increases, respectively.

Design and Development of Reactive Material Ordering System using E-Kanban and Taguchi’s Technique for a warehouse of Tractor Assembly Plant

In the last decade, information technology has proliferated in all sectors and it has become essential tool to survive in the 21st century for all industries. In this endeavour the efforts taken by authors to propose a virtual kanban system with the help of e-kanban database to react to the seasonal variations in demand. To react in stochastic conditions in JIT system, is a challenge and hence a reactive ordering system is developed to operate the e-kanban system. The reactive ordering system consists of a simulation and Taguchi technique module to analyse the various parameters governing kanban system which can be changed to react to the variation in demand, in the database of e-kanban. Virtual e-kanban system can be established just by changing the parameters in database, governing the operation of kanban without physically changing the number of kanban cards on shop floor.

The role of storage technologies throughout the decarbonisation of the sector-coupled European energy system

We use an open, hourly-resolved, networked model of the European energy system to investigate the storage requirements under decreasing CO2 emissions targets and several sector-coupling scenarios. For the power system, significant storage capacities only emerge for CO2 reductions higher than 80% of 1990 level in that sector. For 95% CO2 reductions, the optimal system includes electric batteries and hydrogen storage energy capacities equivalent to 1.4 and 19.4 times the average hourly electricity demand. Coupling heating and transport sectors enables deeper global CO2 reductions before the required storage capacities become significant, which highlights the importance of sector coupling strategies in the transition to low carbon energy systems. A binary selection of storage technologies is consistently found, i.e., electric batteries act as short-term storage to counterbalance solar photovoltaic generation while hydrogen storage smooths wind fluctuations. Flexibility from the electric vehicle batteries provided by coupling the transport sector avoid the need for additional stationary batteries and reduce the usage of pumped hydro storage. Coupling the heating sector brings to the system large capacities of thermal energy storage to compensate for the significant seasonal variation in heating demand.

Transmission Congestion Management Considering Modeling of Solar Photovoltaic Distributed Generator in Deregulated Power System

This paper presents an effective methodology for transmission congestion management (TCM) in deregulated power system considering random nature of solar photovoltaic distributed generator (SPVDG). Solar photovoltaic power generation has gained popularity worldwide. Its’ optimal sitting in the grid can provide congestion relief and reduce line losses etc. However, to maximize the potential benefits of this renewable energy source, its’ stochastic power output which mainly depends on solar irradiance needs due consideration. In this paper, seasonal variations of solar irradiance have been modeled using beta probability density function to determine expected power output of SPVDG over various seasons of one year. TCM problem has been formulated as a non-linear programming with the objective of social welfare maximization of electricity market subject to equality and inequality constraints incorporating seasonal load demand variations. The optimal siting of SPVDG integration in the grid has been discussed. The proposed methodology has been simulated by incorporating practical data of a real-life SPVDG in standard IEEE 30-bus, IEEE 118-bus and practical Indian Utility 62-bus systems. Simulation results show the benefits of proposed methodology on market indices. The effectiveness of proposed approach is also discussed in comparison with existing methodology of distributed generation modeling

Transition towards 100% renewable power and heat supply for energy intensive economies and severe continental climate conditions: Case for Kazakhstan

Transition towards 100% renewable energy supply is a challenging aim for many regions in the world. Even in regions with excellent availability of wind and solar resources, such factors as limited availability of flexible renewable energy resources, low flexibility of demand, and high seasonality of energy supply and demand can impede the transition. All these factors can be found for the case of Kazakhstan, a mostly steppe country with harsh continental climate conditions and an energy intensive economy dominated by fossil fuels. Results of the simulation using the LUT Energy System Transition modelling tool show that even under these conditions, the power and heat supply system of Kazakhstan can transition towards 100% renewable energy by 2050. A renewable-based electricity only system will be lower in cost than the existing fossil-based system, with levelised cost of electricity of 54 €/MWh in 2050. The heat system transition requires installation of substantial storage capacities to compensate for seasonal heat demand variations. Electrical heating will become the main source of heat for both district and individual heating sectors with heat cost of about 45 €/MWh and electricity cost of around 56 €/MWh for integrated sectors in 2050. According to these results, transition towards a 100% renewable power and heat supply system is technically feasible and economically viable even in countries with harsh climatic conditions.

Review of energy storage and transportation of energy

AbstractEnergy storage and transportation are essential keys to make sure the continuity of energy to the customer. Electric power generation is changing dramatically across the world due to the environmental effects of Greenhouse gases (GHG) produced by fossil fuels. The unpredictable daily and seasonal variations in demand for electrical energy can be tackled by introducing the energy storage systems (ESSs) and hence mitigating the extra GHG emission in the atmosphere. Energy storage techniques can be mechanical, electro‐chemical, chemical, or thermal, and so on. The most popular form of energy storage is hydraulic power plants by using pumped storage and in the form of stored fuel for thermal power plants. The classification of ESSs, their current status, flaws and present trends, are presented in this article. The present state of fossil fuel reserves, their production, consumption, and as a consequence of these the CO2emissions are also discussed. The primary energy carriers coal, oil and gas are not evenly distributed along the globe. Long distances are involved in transporting these energy carriers and transportation and delivery of these key resources to the prime customers is always necessary. The different methods to transport the energy from the source end to demand end is also discussed in this article. The assessment of various energy storage methods on the basis of several factors and present status and development of storage and transportation of energy in Pakistan is discussed.

Managing clinic variability with same-day scheduling, intervention for no-shows, and seasonal capacity adjustments

This study investigates demand and capacity strategies for managing clinic variability. These include (i) same-day scheduling to control random walk-ins, (ii) no-show intervention, where the clinic calls advance-booked patients a day before to identify and release cancelled slots to same-day patients, and (iii) adjustments to daily number of appointments for advance-booked patients to match seasonal variations in same-day demand. These strategies are tested over the individual-block/fixed-interval (IBFI) and the Dome appointment rules. Our results show that choosing the appropriate refinements in the order of appointment rules, same-day scheduling, no-show intervention, and capacity adjustment provides maximum improvement. The total cost benefit of demand strategies (i) and (ii) is 7 to 21%, whereas the benefit of capacity strategy (iii) is as high as 6%. Our study affirms the universality of the Dome rule to perform well when combined with the demand and capacity strategies across different environments.

Optimal Staffing Policy in Commercial Banks Under Seasonal Demand Variation

According to evidence from research and practice, the seasonal demand variation exists in the banking sector and significantly affects the commercial bank's operation. Thus, the bank needs to adopt a seasonal staffing policy to ensure that the human resources can match the customer demands in different seasons. Otherwise, a shortage or surplus of human resource will occur and negatively affect bank operations. In this paper, we develop a seasonal staffing method to help the bank find the optimal staffing policy under seasonality. To capture the characteristics of bank operations, we model the service systems of n branches in a bank as a n-dimensional M/M/c/N queueing system with balking and reneging. Then a profit maximizing model based on the queueing system is constructed, and it is further simplified through linearization so that the model can be solved in a short time period. In addition, we conduct a set of numerical experiments that not only prove the superiority of our method compared with the traditional methods, but also explore the effects of some key factors on the optimal seasonal policy.

Analyzing the Impact of Climatic and Economic Variables on Tourism Demand Fluctuation in Japan

The aim of this study is to analyze the influence of climatic and economic variables on seasonal tourism demand variations in Japan’s major western areas. Seasonal tourism fluctuations can present a serious problem, since the profit and daily administration of stakeholders, as well as tourism policy design, are affected. Considering these aspects, this study investigated the influence of climatic and economic variables on tourism demand fluctuations in typical tourist destinations in western Japan. GMM (Generalized Method of Moments) estimations, based on two models, are implemented in an empirical study. The estimated parameters of the first model imply that rainfall is a negative factor in Kyoto’s tourism, while temperature and price levels are positive elements. In spite of the presence of these positive factors in Hiroshima, we could not determine the effects of rainfall and vitalization of the regional economy on tourism in this area. In Naha, temperature, price level, and vitalization of the regional economy may increase the number of visitors. The second model’s estimates for Kyoto imply that temperature, sunshine duration, and price level are positive influences. For Osaka, Fukuoka, and Hiroshima, we find that sunshine duration, and price level are positive factors. In Hiroshima, temperature is an additional positive factor, while the effects of vitalization of the regional economy cannot be determined. For Naha, temperature, price level, and vitalization of regional economy may increase the number of visitors, but no significant implication can be drawn about the effect of sunshine duration.

Hydrochemistry and heavy metals concentrations in sediment of Woji creek and Bonny estuary, Niger Delta, Nigeria

Physicochemical characteristics of the surface water and heavy metals levels in sediment of Woji creek and upper reaches of the Bonny estuary, Niger Delta, Nigeria were investigated between October, 2012 and September, 2014. Ten sampling stations were established along the main creek course based on accessibility and proximity gradient from expected pollution sources. Water and sediment samples were collected monthly at each sampling station for twenty four months, using Hydrobios Water Sampler and Van-veen grab respectively. Water samples were analysed in the laboratory following standard procedures. Heavy metals (Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd and Pb) levels in sediment were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The surface water temperature showed seasonal changes, pH (6.68 ± 0.7) showed spatio-temporal uniformity throughout the study period and Total Organic Carbon (TOC) was generally low (0.23 ± 0.06). There was no seasonal variation in Biochemical oxygen demand (BOD5). Salinity showed a positive correlation with pH and conductivity but negative correlation with temperature. Physicochemical properties of the water body were however, within Federal Ministry of Environment set limits for coastal water. The levels of heavy metals in sediments were in the order: Fe > Mn > Cu > Ni > Co > Zn > As > Cr > Pb > Cd. Sediments were found to have low levels enrichment of heavy metals from anthropogenic sources. This could be as a result of high tidal flushing in the study area. Pollution load index (PLI) and the degree of contamination (Cd) evaluated showed that sediment of the study area has low level of contamination from heavy metals and can be said to be relatively unpolluted.