Demand Intensities Research Articles

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m2), 10.0 W/m2, 11.1 W/m2, 7.1 W/m2, and 4.7 W/m2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. The comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

Dynamic vehicle routing problem for flexible buses considering stochastic requests

Flexible buses provide on-demand services to one or more local communities in a specific geographical area. Bus routes can be adjusted dynamically according to real-time passenger demand in a cost-effective manner. This study investigated the dynamic bus-routing problem considering stochastic future passenger demand. A two-stage stochastic programming model was formulated to minimise the total vehicle travel time cost and penalty for rejecting requests. A rolling horizon scheme was adopted to handle the dynamic changes in passenger requests and vehicle routes. A vector-similarity-based clustering and adaptive large neighbourhood searching (VSC-ALNS) algorithm was developed to solve this problem. Vehicles and passengers were matched and clustered into groups based on vector similarity, and vehicle routes were generated using an adaptive large-neighbourhood search algorithm for each cluster. The effectiveness of the proposed method was evaluated in four cases with different demand intensities using Shanghai taxi order data. The results indicate that flexible buses are more suitable for moderate demand cases, ranging from 20 to 50 requests per square kilometre per hour.

A Randomized Controlled Pilot Trial Comparing Effects of Qigong and Exercise/Nutrition Training on Fatigue and Other Outcomes in Female Cancer Survivors.

Cancer-related fatigue (CRF) is a common and burdensome, often long-term side effect of cancer and its treatment. Many non-pharmacological treatments have been investigated as possible CRF therapies, including exercise, nutrition, health/psycho-education, and mind-body therapies. However, studies directly comparing the efficacy of these treatments in randomized controlled trials are lacking. To fill this gap, we conducted a parallel single blind randomized controlled pilot efficacy trial with women with CRF to directly compare the effects of Qigong (a form of mind-body intervention) (n = 11) to an intervention that combined strength and aerobic exercise, plant-based nutrition and health/psycho-education (n = 13) in a per protocol analysis. This design was chosen to determine the comparative efficacy of 2 non-pharmacologic interventions, with different physical demand intensities, in reducing the primary outcome measure of self-reported fatigue (FACIT "Additional Concerns" subscale). Both interventions showed a mean fatigue improvement of more than double the pre-established minimal clinically important difference of 3 (qigong: 7.068 ± 10.30, exercise/nutrition: 8.846 ± 12.001). Mixed effects ANOVA analysis of group × time interactions revealed a significant main effect of time, such that both groups significantly improved fatigue from pre- to post-treatment (F(1,22) = 11.898, P = .002, generalized eta squared effect size = 0.116) There was no significant difference between fatigue improvement between groups (independent samples t-test: P = .70 ), suggesting a potential equivalence or non-inferiority of interventions, which we could not definitively establish due to our small sample size. This study provides evidence from a small sample of n = 24 women with CRF that qigong improves fatigue similarly to exercise-nutrition courses. Qigong additionally significantly improved secondary measures of mood, emotion regulation, and stress, while exercise/nutrition significantly improved secondary measures of sleep/fatigue. These findings provide preliminary evidence for divergent mechanisms of fatigue improvement across interventions, with qigong providing a gentler and lower-intensity alternative to exercise/nutrition.

Data driven scalability and profitability analysis in free floating electric car sharing systems

In this paper, we analyse the impact of system design options with different demand intensities for electric vehicle free-floating car sharing systems (EV-FFCS). We consider three different cities for which we collected rental data from a car sharing system. Using these data, we build demand and supply models of an EV-FFCS. We evaluate the performance of different design options from both the customers’ and the operators’ perspectives, i.e., quality of service and profitability. We study the number of chargers, their placement and the size of the fleet. We observe the impact on the system when demand is constant and then when demand increases. The results show that it is critical to scale the capacity of the charging infrastructure proportionally to the mobility demand. Conversely, the same fleet size can accommodate a 300% increase in demand, not satisfying less than 15% of it. Moreover, the observed demand and supply would likely not generate profits for the EV system. This is due to the high cost of electric vehicles and the need to manage the fleet for charging operations. The figure changes with at least a 5-fold increase in demand, with the current fleet size becoming profitable.

Predicting the matching probability and the expected ride/shared distance for each dynamic ridepooling order: A mathematical modeling approach

The popularity of smartphones and the advent of GPS positioning and wireless communication technologies in the recent decade have facilitated large-scale implementations of dynamic ridepooling services, such as Uber Pool, Lyft Line, and Didi Pinche. As in such services trips usually start before the appearance of pooling partners, knowing the probability of getting matching with another order (i.e., matching probability), the expected detour distance, and the expected shared distance before the start of each trip is essential for passengers to evaluate their willingness to pool and for ridepooling platforms to offer attractive discounts. In this paper, assuming that every ridepooling passenger shares vehicle space with at most one another during the entire trip, and ridepooling orders in each (origin-destination) OD pair appear following a Poisson process with a given rate, we propose a mathematical modeling approach to predict the matching probability, the expected ride distance, and the expected shared distance of each order under a first-come-first-serve strategy in dynamic ridepooling service. The method defines unmatched passengers at different locations along the exclusive-riding path of each OD pair into different seeker- and taker-states, formulates the complex interdependency of the matching probabilities, matching rates and arrival rates of (unmatched) passengers in different states into a system of nonlinear equations, and generates the matching probabilities and expected ride/shared distances of all OD pairs simultaneously. Under the same first-come-first-serve strategy, we simulated the occurrence, movements and state transitions of ridepooling orders based on a 30*30 grid network and the real network of Haikou City in China. In comparison with simulation results, we show that the method proposed in this paper can generate fairly satisfactory predictions under diverse matching conditions and demand intensities.

Development of Generalized Fragility Functions for Seismically Induced Content Disruption

This paper reports on the evaluation of the response of unanchored building contents in two separate shaking experiments, with the goal of developing simplified seismic fragility functions to be used for content disruption. The content response was evaluated collectively, by defining qualitative categorical ratings of overall content disruption based on observation of specific behaviors, such as sliding, toppling, rolling, or falling. Next, these disruption ratings were correlated to demand parameters, such as peak floor acceleration and peak floor velocity. Fragility functions were developed by combining two alternative implementations of the bounding demand data method. Among the demand parameters evaluated, peak floor velocity is shown to be a consistent indicator of disruption when comparing results from the two independent data sets. Vertical acceleration is shown to also influence the demand intensities that trigger disruption.

Competitive Multi-Product Pricing with Demand Learning and Substitution Effects

Many firms are selling different types of products. Typically sales applications are characterized by competitive settings, limited information and substitution effects. The demand intensities of single types of products are affected by the own products as well as the products of competitors. Due to the complexity of such markets, smart pricing strategies are hard to derive. We analyze stochastic dynamic multi-product pricing models under competition for the sale of durable goods. In a first step, a data-driven approach is used to measure substitution effects and to estimate sales probabilities in competitive markets. In a second step, we use a dynamic model to compute powerful heuristic feedback pricing strategies, which are even applicable if the number of competitors’ offers is large and their pricing strategies are unknown. Moreover, our approach allows taking additional features, such as customer ratings or shipping times into account. Adaptive estimations are used to update the estimation of sales probabilities and to further improve the strategy.

Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework

In this paper, we assess future water demands for the agricultural (irrigation and livestock), energy (electricity generation, primary energy production and processing), industrial (manufacturing and mining), and municipal sectors, by incorporating water demands into a technologically-detailed global integrated assessment model of energy, agriculture, and climate change — the Global Change Assessment Model (GCAM). Base-year water demands – both gross withdrawals and net consumptive use – are assigned to specific modeled activities in a way that maximizes consistency between bottom-up estimates of water demand intensities of specific technologies and practices, and top-down regional and sectoral estimates of water use. The energy, industrial, and municipal sectors are represented in fourteen geopolitical regions, with the agricultural sector further disaggregated into as many as eighteen agro-ecological zones (AEZs) within each region. We assess future water demands representing six socioeconomic scenarios, with no constraints imposed by future water supplies. The scenarios observe increases in global water withdrawals from 3710 km3 year− 1 in 2005 to 6195–8690 km3 year− 1 in 2050, and to 4869–12,693 km3 year− 1 in 2095. Comparing the projected total regional water withdrawals to the historical supply of renewable freshwater, the Middle East exhibits the highest levels of water scarcity throughout the century, followed by India; water scarcity increases over time in both of these regions. In contrast, water scarcity improves in some regions with large base-year electric sector withdrawals, such as the USA and Canada, due to capital stock turnover and the almost complete phase-out of once-through flow cooling systems. The scenarios indicate that: 1) water is likely a limiting factor in meeting future water demands, 2) many regions can be expected to increase reliance on non-renewable groundwater, water reuse, and desalinated water, but they also highlight an important role for development and deployment of water conservation technologies and practices.

Influence of climate change mitigation technology on global demands of water for electricity generation

Globally, electricity generation accounts for a large and potentially growing water demand, and as such is an important component to assessments of global and regional water scarcity. However, the current suite—as well as potential future suite—of thermoelectric generation technologies has a very wide range of water demand intensities, spanning two orders of magnitude. As such, the evolution of the generation mix is important for the future water demands of the sector. This study uses GCAM, an integrated assessment model, to analyze the global electric sector's water demands in three futures of climate change mitigation policy and two technology strategies. We find that despite five- to seven-fold expansion of the electric sector as a whole from 2005 to 2095, global electric sector water withdrawals remain relatively stable, due to the retirement of existing power plants with water-intensive once-through flow cooling systems. In the scenarios examined here, climate policies lead to the large-scale deployment of advanced, low-emissions technologies such as carbon dioxide capture and storage (CCS), concentrating solar power, and engineered geothermal systems. In particular, we find that the large-scale deployment of CCS technologies does not increase long-term water consumption from hydrocarbon-fueled power generation as compared with a no-policy scenario without CCS. Moreover, in sensitivity scenarios where low-emissions electricity technologies are required to use dry cooling systems, we find that the consequent additional costs and efficiency reductions do not limit the utility of these technologies in achieving cost-effective whole-system emissions mitigation.

Coordinating the tourism supply chain using bid prices

A bid price control method for coordinating a decentralised tourism supply chain is presented in this paper. The method is applied to a supply chain channel involving a hotel and a retailer of tourism services. Information is shared between the hotel and the retailer by incorporating the opportunity costs of inputs purchased by the retailer from the supplier into the retailer's linear programming model (LP). Proxies for the opportunity costs, which are based on the dual prices of the hotel's LP and the rack rates are utilised. The hotel and the retailer use the dual prices from their own LPs as bid prices for independently making acceptance and rejection decisions concerning bookings requested; any booking request that is accepted by the retailer, but not by the hotel, is rejected. Simulation is used to compare the revenue of the supplier from the coordinated supply channel with that when the supplier acts alone. It was found that the proposed method can yield improvements in revenue, but the results depend on the demand intensity and the method used for computing the opportunity costs of the hotel's resources. The greatest improvements in revenue were achieved at low demand intensities. At high demand intensities, there may not be any improvements in revenue and reductions in revenue are even possible. The major limitation of the study is that the scenario depicted is a simplification of what occurs in practice. The paper extends the revenue management methodology to coordinating inventory decisions among suppliers and retailers in the tourism industry. It also gives insights into the possibility of further improving the revenue of tourism supply chain partners through this type of cooperation.

An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria

To the best of our knowledge, this paper describes the first ant colony optimization (ACO) approach applied to nurse scheduling, analyzing a dynamic regional problem which is currently under discussion at the Vienna hospital compound. Each day, pool nurses have to be assigned for the following days to public hospitals while taking into account a variety of soft and hard constraints regarding working date and time, working patterns, nurses qualifications, nurses’ and hospitals’ preferences, as well as costs. Extensive computational experiments based on a four week simulation period were used to evaluate three different scenarios varying the number of nurses and hospitals for six different hospitals’ demand intensities. The results of our simulations and optimizations reveal that the proposed ACO algorithm achieves highly significant improvements compared to a greedy assignment algorithm.

Assessing the Risk of Liquidity Suppliers on the Basis of Excess Demand Intensities

In this article we introduce the concept of excess volume durations, which are defined as the time until a given amount of buy or sell excess volume is traded on the market. Excess volume durations indicate the one-sided intensity of liquidity demand and characterize the risk of a market maker with respect to asymmetric information and inventory problems. By modeling excess volume durations based on Box–Cox-type autoregressive conditional duration (ACD) models, it is shown that market microstructure variables are predictors for the expected liquidity demand intensity. Moreover, the length of excess volume durations is found to be positively correlated with the magnitude of the corresponding price impact and thus the market depth.

Refillable versus non-refillable containers: the impact of regulatory measures on packaging mix and quality choices

With the continually declining percentage of soft drink sales in refillable bottles in favour of cans and PET bottles, despite a growing soft drink market, governments have become increasingly concerned with the alleged more environmentally harmful impacts of throw-away convenience packaging and tried to enact policies to induce consumers to switch to refillable glass bottles. In many cases, fully or partially refundable deposits have been opted for to provide consumers with the incentive to properly dispose of packaging, but not to switch between different container types, and thus, they may not constitute the most desirable solution. The effects of various regulatory measures on producers’ choices of packaging quality and mix in the presence of consumers with differing demand intensities are analyzed to discern the least distortionary alternative.

Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices

Consider a continuous-time inventory problem in which a retailer sets the price on a fixed number of a perishable asset that must be sold prior to the time at which it perishes. The retailer can dynamically adjust the price between any of a finite number of allowable prices. Demand for the product is Poisson with an intensity that is inversely related to the price. The optimal policy is piecewise-constant. The maximum expected revenue is nondecreasing and concave in both the remaining inventory and the time-to-go. For a given inventory level the optimal price declines as the time at which the products perish approaches. At any given time the optimal price is nonincreasing in the number of items remaining unsold. These results are extended to (i) the case in which the prices and corresponding demand intensities depend on the time-to-go; and (ii) the case in which the retailer can restock to meet demand at a unit cost after the initial inventory has been sold.

The Urban Hierarchy and Urban Area Production Function: A Synthesis

Urban production functions, often used to indicate optimal city size, usually deal with one city in isolation. This paper introduces differing production and transport costs and demand intensities in order to generate an urban hierarchy of differing urban sizes and market areas. It then distinguishes between continuous hierarchies with a single production function and discretely stepped hierarchies with differing production functions. The model is then applied in order to generate a seven-level hierarchy for 464 urban centres in Saskatchewan, but this is outperformed by the corresponding continuous hierarchy model. The policy implications for self-sustaining urban growth and the potential non-viability of small communities are then discussed.