Demand Spikes Research Articles

Air-conditioning demand response resource assessment for Australia

It is widely acknowledged that demand response must play an increasingly important role in future electricity markets. This is as much due to increasing demand for air-conditioning as it is due to the changing sources of generation. In the National Electricity Market (NEM) in Australia, for just 3% of the year demand increases by 20%, or the equivalent of the generation from the two largest coal fired power stations combined. Over smaller regions and time intervals demand spikes are larger. Air-conditioning demand response takes advantage of a buildings inherent thermal storage to reduce electricity demand at peak times with no discernible impact on the indoor operating environment. Here we estimate the existing potential of commercial and residential air-conditioning systems to provide demand response across Australia. We use a top-down approach based on disaggregation of electricity substation half-hourly data. Results are that peak (i.e. 99.5th percentile) demand on the NEM could be reduced by up to 5.8% or 1.2 GW with the time of day at which the peak occurs delayed by approximately 2 hours. Based on the timing of the available capacity, both residential and commercial buildings are suited to providing air-conditioning demand response.

The effect of mass shootings on the demand for guns

AbstractUnfortunately, mass shootings are common occurrences in the United States. When one occurs, it makes national news, becomes fodder for the ongoing national gun debate among politicians and activists, and may impact the demand for guns through fears of violence and future gun regulation. We attempt to estimate the overall effect of mass shootings on gun demand in the United States and how this effect varies across the nation, time, and other factors. Mass shootings are found to increase the national demand for firearms, with the effect lasting up to 2 months. Stronger effects are found in gun‐heavy and/or Republican states but not necessarily near where the shooting occurred. Demand spikes are also larger if the shooter was White, female, or if the event took place in a rural setting, but seemingly less related to the specific venue, the shooter's age or known mental health, the number of fatalities, or weapon characteristics.

Multi-stage stochastic programming models for provisioning cloud computing resources

We focus on the resource provisioning problem of a cloud consumer from an Infrastructure-as-a-Service type of cloud. The cloud provider offers two deployment options, which can be mixed and matched as appropriate. Cloud instances may be reserved for a fixed time period in advance at a smaller usage cost per hour but require a full commitment and payment for the entire contract duration. In contrast, on-demand instances reflect a pay-as-you-go policy at a premium. The trade-off between these two options is rooted in the inherent uncertainty in demand and price and makes it attractive to complement a base reserved capacity with on-demand capacity to hedge against the spikes in demand. This paper provides several novel multi-stage stochastic programming formulations to enable a cloud consumer to handle the cloud resource provisioning problem at a tactical level. We first formulate the cloud resource provisioning problem as a risk-neutral multi-stage stochastic program, which serves as the base model for further modeling variants. In our second set of models, we also incorporate a certain concept of system reliability. In particular, chance constraints integrated into the base formulation require a minimum service level met from reserved capacity, provide more visibility into the future available capacity, and smooth out expensive on-demand usage by hedging against possible demand fluctuations. An extensive computational study demonstrates the value of the proposed models by discussing computational performance, gleaning practical managerial insights from the analysis of the solutions of the proposed models, and quantifying the value of the stochastic solutions.

Continuous Production of Five Active Pharmaceutical Ingredients in Flexible Plug-and-Play Modules: A Demonstration Campaign

Traditional pharmaceutical manufacturing is based on a complex supply chain that is vulnerable to spikes in demand and interruptions. Continuous pharmaceutical production in compact modules is a po...

Rebound behaviour of uncoordinated EMS and their impact minimisation

In this paper, the impacts of uncoordinated energy management systems (EMS), with a rebound effect, on a renewable energy-dependent microgrid are discussed and feasible solutions are presented. Two different approaches, i.e. load-based and price-based EMS are modelled which consider PV units, battery energy storage systems (BESS), and electric vehicles (EVs). Taking account of each component's boundary conditions, the load-based approach intelligently charges the EV and BESS from the grid/PV during off-peak hours, and provides a combined discharge response during peak load hours. In the price-based approach, the charging-discharging of BESSs and EVs from/to grid and PV depends on the time-of-use tariff signal. The primary objective of both models is to minimise the customers' peak electricity consumption and the saturation issues of distribution transformers. It is observed that the simultaneous response of the EMS due to the identical behaviour of load or price curves, and the rebound effect after mode switching transition create large power demand spikes. To mitigate its negative consequence, an improved locking and randomisation technique is designed and implemented. Additionally, the impact of the PV power fluctuations on the load-support systems due to fast-moving clouds and their consequences to the behaviour of the EMS response are investigated.

Characterization of antifungal metabolites produced by Lactobacillus plantarum and Lactobacillus coryniformis isolated from rice rinsed water.

A recent spike in demand for chemical preservative free food has derived the scientific community to develop natural ways of food preservation. Therefore, bio-preservation could be considered as the great alternative over chemical ones owing to its potential to increase shelf-life and nutritional values of foodstuffs. In the present study, lactic acid producing bacterial species were isolated from rice rinsed water and identified by 16S rRNA gene sequencing as Lactobacillus plantarum BCH-1 (KX388380) and Lactobacillus coryniformis BCH-4 (KX388387). Antifungal metabolites from both Lactobacillus species were extracted by polarity-based solvents in which ethyl acetate showed remarkable antifungal activity against Aspergillus flavus and Aspergillus fumigatus by disc diffusion assay. Different organic acids and fatty acids have been identified by reversed-phase high-performance liquid chromatography (RP-HPLC) and gas chromatography-mass spectrometry (GC-MS) analysis, respectively. Lactic acid and citric acid were the major organic acids found in ethyl acetate fractions of L. plantarum and L. coryniformis, respectively. Similarly, 9,12-otadecadienoic acid (Z,Z)-methyl ester and hexadecanoic acid, methyl ester were the major fatty acids found in n-hexane fractions of L. plantarum and L. coryniformis respectively. Moreover, the isolation of novel antifungal metabolites from locally isolated Lactobacillus species was focused and it was revealed that organic acids are important contributors towards antifungal potential. A novel fatty acid (i.e. 12-hydroxydodecanoic acid) has also been explored and found as potential metabolite against filamentous fungi. Conclusively, various metabolites isolated from non-dairy source showed antifungal activity especially against Aspergillus species. Hence, these metabolites have been considered as a good choice for bio-preservation.

Experimental study of polymer solar collectors performance characteristics in development of clean and more efficient energy-conversion systems

In the heating of water demand for electricity comprises a substantial part of more general household electrical consumption, where electrical and gas boilers are among the most widely used. Solar water heating systems have been around for some time in many countries, becoming more common nowadays to mitigate peak loads, and they can in some cases fully replace gas boilers and electrical heaters if a heat-accumulation tank is to be used. The solar collector is apparently the most complex and expensive element of such a system, which cumulative cost largely depends on the choice of materials and system size. The solar polymer flat plate collectors are of particular interest mainly because of their low weight and cost, resilience to corrosion, with an ultimate goal to replace metal-made parts altogether. Recent decades witnessed a steady rise of interest in solar energy conversion collector designs. In this paper we re-evaluated several proposed solutions in this field to compare our experimental results with that of gathered by other researchers, paying particular efforts to standardized methodology compliance. It could be demonstrated that polymeric materials with additives used in the construction of a heat sink (absorber), and in the transparent coating of a particular solar collector, are able to sustain high temperature levels yet retain its thermal stability, and possibly improving also its service life. The efficiency of the solar collector was further assessed with and without the effect of an air gap which manifested itself as being not significant at low wind speeds. Use of polymeric materials in solar polymer collectors makes an important step towards development of a more clean energy-conversion system to cope with global energy demand spikes.

Deep learning for energy markets

AbstractDeep Learning (DL) is combined with extreme value theory (EVT) to predict peak loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose a deep temporal extreme value model to capture these effects, which predicts the tail behavior of load spikes. Deep long‐short‐term memory architectures with rectified linear unit activation functions capture trends and temporal dependencies, while EVT captures highly volatile load spikes above a prespecified threshold. To illustrate our methodology, we develop forecasting models for hourly price and demand from the PJM interconnection. The goal is to show that DL‐EVT outperforms traditional methods, both in‐ and out‐of‐sample, by capturing the observed nonlinearities in prices and demand spikes. Finally, we conclude with directions for future research.

Asthma inhaler stocks low after COVID-19 linked demand spike, government says

Asthma inhaler stocks low after COVID-19 linked demand spike, government says

Predictable End-of-Month Treasury Returns

We document a distinct pattern in the timing of excess returns on coupon Treasury securities. Average returns are positive and highly significant in the last few days of the month, and are not significantly different from zero at other times. A long Treasury position for just the last few days of each month gives a high annualized Sharpe ratio of around 1. We attribute this pattern to temporary spikes in investor demand for specific securities due to window dressing and portfolio rebalancing. We find evidence in quantities that aggregate insurer transactions contribute to the end-of-month price pattern. In particular life insurers are large net buyers of Treasury securities on benchmark index rebalancing dates.

A Trust-Aware Mechanism for Cloud Federation Formation

Cloud providers can form cloud federations by pooling their resources together to balance their loads, reduce their costs, and manage demand spikes. However, forming cloud federations is a challenging problem, especially when considering the incentives of the cloud providers making their own decisions to participate in cloud federations. In this paper, we model the formation of cloud federations necessary to provide resources to execute Map-heavy/Reduce-heavy programs while considering the trust and reputation among the participating cloud providers. The objective is to form cloud federations with highly reputable cloud providers that achieve maximum profit for their participation. This is an NP-hard bicriteria optimization problem. We introduce a coalitional graph game, called trust-aware cloud federation formation game, to model the cooperation among cloud providers. We design a mechanism for cloud federation formation that enables the cloud providers with high reputation to organize into federations reducing their costs. Our proposed mechanism guarantees the highest profits for the participating cloud providers in the federations, and ensures high reliability of the formed federations in executing the applications. We perform extensive experiments to characterize the properties of the proposed mechanism. The results show that our proposed mechanism produces Pareto optimal and stable cloud federations that not only guarantee that the participating cloud providers have high reputation, but also high individual profits.

Residential income segregation: A behavioral model of the housing market

We represent the functioning of the housing market and study the relation between income segregation, income inequality and house prices by introducing a spatial Agent-Based Model (ABM). Differently from traditional models in urban economics, we explicitly specify the behavior of buyers and sellers and the price formation mechanism. Buyers who differ by income select among heterogeneous neighborhoods using a probabilistic model of residential choice; sellers employ an aspiration level heuristic to set their reservation offer price; prices are determined through a continuous double auction. We first provide an approximate analytical solution of the ABM, shedding light on the structure of the model and on the effect of the parameters. We then simulate the ABM and find that: (i) a more unequal income distribution lowers the prices globally, but implies stronger segregation; (ii) a spike in demand in one part of the city increases the prices all over the city; (iii) subsidies are more efficient than taxes in fostering social mixing.

Optimal Overbooking Policy for Cloud Service Providers: Profit and Service Quality

A cloud federation is a current paradigm that enables partnered cloud providers to share idle capacities during low demand periods and to purchase spare resources during demand spikes. In this research, we propose an optimal overbooking policy to maximize federation members’ profits and enhance cloud users’ experiences. The proposed policy overcomes cloud providers’ low utilization and increases their profits. Under the market-oriented cloud federation system, we use the number of idle resources in the cloud federation and the operational costs of those resources to help the provider decide on its instance exchange price. Under such a price mechanism, we develop an optimal overbooking model and identify the conditions necessary for optimal solutions. Through implementing the optimal mechanism, we observe that the proposed overbooking policy can improve a federated provider’s profits and decrease the probability of service level agreement (SLA) violation. When a provider’s capacity is relatively large and the provider adopts the proposed overbooking policy, it could achieve maximum profits and decline its SLA violation when it has unmet customer demands and there are idle resources in the cloud federation. Through establishing the cooperative game model of the cloud federation, we make a reasonable profit distribution based on Shapley value. The cloud provider’s profits and the probability of the SLA violation change as the instance price, the distribution of unserved customers, the number of federation members and penalty cost change. Compared with the other overbooking policies and no overbooking mechanisms, our research improves profits and reduces cloud provider’s overbooking risks, thereby presenting a win-win situation for both the individual providers and the cloud federation.

Combining simulation with genetic algorithm for solving stochastic multi-product inventory optimization problem

All companies are challenged to match supply and demand, and the way the companytackles this challenge has a tremendous impact on its profitability. Due to the fact that markets are rapidlyevolving and becoming more complex, flexible, and information-intensive, notorious binging-andpurgingapproach is inappropriate. Scuh an approach, in which product is, firstly, overpurchased or overproducedin order to prepare for expected demand spikes and then discarded by sharp decline in price.Thus, in order to tailor inventory control to urgent industrial needs, the discrete-event simulation modelis proposed. The model is stochastic and operates with multiple products under constrained total inventorycapacity. Besides that, the model under consideration is distinguished by uncertain replenishmentlags and lost-sales. The paper contains both mathematical description and algorithmic implementation.Besides that, an optimization framework based on genetic algorithm is proposed for deriving an optimalcontrol policy. The proposed approach contributes to the field of industrial engineering by providing asimple and flexible way to compute nearly-optimal inventory control parameters.

Product Availability, Consumer Stockpiling, and Hurricane Events: Empirical Evidence From a Natural Experiment

The provision of essential supplies is a key service provided by retailers when demand spikes due to consumer stockpiling during environmental emergencies. Moreover, it is important for retailers to quickly recover from these events by replenishing the stock of essential supplies to meet the continuing needs of local residents. As exogenous events, hurricanes provide natural experiments to test retail operational performance in the face of environmental emergencies. The main purpose of this research is to determine how consumer stockpiling (or precautionary buying) behavior, as well as storm and retail characteristics, impact the availability of essential supplies at retailers following a hurricane. We study consumer stockpiling behavior prior to the onset of hurricane landfalls, and determine the impact of this behavior on in-store product availability for various formats of retail store outlets. Specifically, we focus on the bottled water product category, an essential emergency category in hurricane preparedness. On average, consumers stock 58% more bottled water than regular purchases before hurricanes approach. This study combines an event analysis methodology with econometric models using archival retail scanner data from 60 U.S. retail chains located in 963 counties and real-time data from four recent U.S. continental hurricanes. We find that supply-side characteristics (retail network and product variety), demand-side characteristics (hurricane experience and household income), and disaster characteristics (hazard proximity and hazard intensity) significantly affect consumer stockpiling propensity as the hurricanes approach. The increased consumer stockpiling has immediate and longer-term impacts on retail operations, namely, in-store product availability. Among various retail formats, drug stores are associated with the highest consumer stockpiling propensity before hurricanes, while dollar stores and discount stores are associated with the lowest in-store product availability following hurricanes. Our study points to the need for retailers and policymakers to carefully monitor factors affecting consumer stockpiling behavior that will allow for better allocation of critical supplies during the hurricane season.

Stakeholder mapping and disruption scenarios with application to resilience of a container port

Port infrastructure and port operations are highly impacted by disruptive scenarios that stem from several different types of causes, including adverse weather events, economic crises, traffic congestion, and spikes in demand, among others. Port intermodal transportation systems are characterized by high complexity and uncertainty since they serve and impact multiple groups of stakeholders, including exporters, importers, operators, organizations, port workers, port authorities, residents, and others. The influences of the disruptive scenarios combined with the influences of possible conflicting interests among groups of stakeholders make significant impacts to port operations and planning programs. The innovation of this study is that it integrates two existing approaches to resilience analytics addressing the influences of scenarios and stakeholders to priorities: a stakeholder classification approach called stakeholder mapping, and an approach known as scenario-based preferences modeling. The framework that is developed in this paper finds the joint influences of disruptive scenarios and groups of stakeholders to disrupt the importance ranking of initiatives. The results emphasize that the most disruptive scenario to the multiple groups of stakeholders is s1, Traffic Congestion, the least disruptive scenario is s3, High Operation Cost, and the most robust and highly prioritized initiative is x18, Constructing the CIMT marine terminal.

Assortment Optimization for Parallel Flights Under a Multinomial Logit Choice Model With Cheapest Fare Spikes

Airline booking data have shown that the fraction of customers who choose the cheapest available fare class often is much greater than that predicted by the multinomial logit choice model calibrated with the data. For example, the fraction of customers who choose the cheapest available fare class is much greater than the fraction of customers who choose the next cheapest available one, even if the price difference is small. To model this spike in demand for the cheapest available fare class, a choice model called the spiked multinomial logit (spiked-MNL) model was proposed. We study a network revenue management problem under the spiked-MNL choice model. We show that efficient sets, i.e., assortments that offer a Pareto-optimal trade-off between revenue and resource usage, are nested-by-revenue when the spike effect is nonnegative. We use this result to show how a deterministic approximation of the stochastic dynamic program can be solved efficiently by solving a small linear program. The solution of the small linear program is used to construct a booking limit policy, and we prove that the policy is asymptotically optimal. This is the first such result for a booking limit policy under a choice model, and our proof uses an approach that is different from those used for previous asymptotic optimality results. Finally, we evaluate different revenue management policies in numerical experiments using both synthetic and airline data.

Planning for variable renewable energy and electric vehicle integration under varying degrees of decentralization: A case study in Lusaka, Zambia

High urbanization rates, decentralized solar photovoltaic growth, and transportation electrification are changing the electricity planning landscape across Sub-Saharan Africa. This paper explores the operational implications of variable renewable energy and electric vehicle integration at the city scale. A production cost dispatch model is applied to Lusaka, Zambia's capital, whose largely hydro-based electricity system is currently facing shortfalls due to population and economic growth and climate change. The results demonstrate that variable renewable energy penetration and decentralization are important levers for managing greenhouse gas emissions, while the choice of electric vehicle charging policy has significant cost implications. Due to Zambia's flexible hydro assets and potential pumped hydro storage capacity, large penetrations of centralized solar photovoltaic energy can be integrated with low curtailment rates, regardless of electric vehicle charging policy. The high curtailment rates (>10%) and increased greenhouse gas emissions associated with non-export solar PV penetrations greater than 35% can be mitigated with flexible loads, such as electric vehicle charging, that is strategically shifted to coincide with high-resource periods of the day. Without active electric vehicle charging management, large peaking capacity is required to satisfy demand spikes. Conversely, system-optimized vehicle-to-grid charging eliminates the need for additional storage infrastructure thereby mitigating system costs.

Sulfur-based battery firms secure cash

Two firms vying to develop lithium-sulfur batteries, which could be more energy-dense and cheaper to make than today’s lithium-ion batteries, have received major boosts to their funding. Baseload Renewables, a 2017 spin-off from MIT, has raised $7.4 million from an undisclosed investor. Baseload will use the money to advance its lithium-sulfur flow batteries, which are designed to meet demand spikes on electricity grids. Baseload’s batteries feature cheap, readily available sulfur dissolved in water as the anode. The firm anticipates that its technology will enable batteries to be made for between 10 and 20% of the cost of equivalent lithium-ion batteries. The firm raised almost $2 million from MIT’s technology incubator, the Engine, when it launched last year. Meanwhile, U.K.-based Oxis Energy, a developer of batteries featuring a lithium anode and a sulfur-based cathode, says it will receive $5 million from Aerotec, a Brazilian private equity fund. The Aerotec investment will

Additive manufacturing in the mechanical engineering and medical industries spare parts supply chain

PurposeThe purpose of this paper is to approximate the impact that additive manufacturing (AM) will have on firm inventory performance (IP) and customer satisfaction (CS) when it is applied within the spare parts (SP) supply chain of manufacturing organisations. This research also explores the influence of customer sensitivity (CSy) to price and delivery lead time and supply risk (SR) within those approximations.Design/methodology/approachAn online survey was used to collect the primary data for this research. Data were collected from 69 respondents working for organisations in two industrial segments within the UK manufacturing sector: “Industrial and Commercial Machinery and Computer Equipment” and “Measuring, Analysing and Controlling Instruments, Photographic, Medical and Optical Instruments”. The respondents worked for entities that were categorised in three groups: customers, suppliers, and entities that were both customers and suppliers. The groups that were self-identified as “customers” or “suppliers” answered 20 survey items each and the group that was identified as both “customers” and “suppliers” answered 40 survey items.FindingsThe results revealed that AM was considered a suitable vehicle for the fulfilment of SP demand. However, AM appeared to make no material difference to CS; the scenario used improved delivery time of SP but increased price. Also, AM was thought to improve IP through less reliance on buffer stock to manage SR and spikes in demand and less carrying of SP at risk of obsolescence.Research limitations/implicationsThe respondents worked for entities within two manufacturing industry segments within the UK and the insights garnered may not be indicative of similar organisations competing in other manufacturing industry segments within the UK or in other countries. In addition, approximately 82 per cent of the surveyed respondents worked for small organisations with fewer than 100 employees and the results may differ for larger organisations. Further limitations were the relatively small sample size and lack of open-ended questions used in the survey. Larger sample size and the usage of open-ended survey questions may lead to more reliable and valuable responses and feedback.Practical implicationsThe findings from this research are considered to be of interest to practitioners contemplating adoption of AM and to developers of AM wishing to increase market share due to the positive reaction of entities within the industrial and commercial machinery and computer equipment, and measuring, analysing and controlling instrumentation industrial segments. This research raises awareness to the possible risks and rewards – from a range of perspectives, of AM to practitioners considering its adoption in the spare parts supply chain (SPSC).Originality/valueThe paper takes a novel perspective on AM in SPSCs by illuminating the supplier and buyer perspective based on empirical data. This research provides new insights about the appreciation of the use of AM in SPSCs of mostly small sized manufacturing companies located in the UK. This paper also gives new insights about the willingness/conditions of manufacturing companies in the UK to adopt AM for the provision of SP. The originality of this research is twofold: it broached the applicability of AM in the supply chains of the two targeted industrial segments, and as far as the authors are aware, the influence of CSy (e.g. to price or lead time) and SR on SPSC players’ attitude to AM is yet to be considered. Finally, this research adopted a systems theory lens and considered system-wide impact of AM introduction.