Demand Curve Research Articles

Multi-Agent Reinforcement Learning for Smart Community Energy Management

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

Robust lower bounds on monopoly profit with [formula omitted]-concave demand

I extend Condorelli’s lower bound on monopoly profit from log-concave demand to a broader class of α-concave demand, with α=0 corresponding to log-concavity and α=1 to concavity. The monopoly profit is at least 1(1+α)1/α of the area under the demand curve. I further derive upper bounds for consumer surplus and deadweight loss relative to monopoly profit and show all three bounds are sharp.

Nudging consumers’ choices for niche milk: A real purchase experiment

Changing behaviours causing poor health is an ongoing global health challenge that requires developing adequate food policies and strengthening their effectiveness. Policies based on nudging have become increasingly popular internationally, but the literature provides mixed evidence of their effectiveness, with an intense academic debate. This study contributes to the debate by investigating the effect of different nudging techniques (availability enhancement, visibility enhancement and healthy eating calls) via a framed field experiment involving purchases of milk types. Participants were split into different nudging treatment groups and were endowed with a cash amount to purchase any desired quantity of the different products. We modelled observed choices via the multiple discrete–continuous nested extreme value model, which has yet to receive attention for the analysis of nudging effects on food choices. Results show that only the healthy eating call nudge effectively drove participants towards healthier milk choices, while the effect of other nudges is statistically insignificant. We provide simulated demand curves conditional on nudging treatments, which measure the effect of the latter on consumers’ consumption levels of the different milk types available.

Bid-Aggregation Based Clearing of Day-Ahead Electricity Markets

In this work we propose a heuristic clearing method for a simplified model of European day-ahead electricity auction markets, considering divisible standard bids and block orders. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clearing prices of individual periods are determined. In the final step, the clearing for the original bid set is solved, taking into account the price ranges determined previously as constraints, reducing the feasibility region of the clearing problem. By removing bids whose acceptance or rejection is already determined by the assumed price range constraints, the size of the problem is also significantly reduced. Via simple examples, we show that due to the possible paradox rejection of block orders the proposed bid-aggregation based approach may result in a suboptimal solution or in an infeasible problem, but we also point out that these pitfalls of the algorithm may be avoided by using different aggregation patterns. We propose to run multiple threads of the algorithm, using different aggregation patterns. We evaluate the performance of the algorithm via numerical tests.

Seismic Performance of Multistory Reinforced Concrete Buildings by Pushover Analysis

Abstract: In this investigation the analysis of G+4, G+11 and G+21 building is situated in New Delhi (Zone IV. The seismic analysis of of G+4, G+11 and G+21 building as per IS 1893-2002. In this research pushover analysis was performed in SAP 2000 and after that is designed as per IS 456-2000 for different loads. This analysis gives better understanding seismic performance of building & also shows damage or failure of buildings. The building performance level is determined by pushover curve & demand curve. The result shows that the failure is noticed in the column of ground storey of the building. After that enlarged amount of reinforcement in the ground story the buildings have reached life safety performance level.

Behavioral economic analysis of the comorbidity of alcohol problems and posttraumatic stress: Findings from high-risk young adults and general community adults.

A behavioral economic reinforcer pathology model theorizes that alcohol problems are influenced by steep delay discounting, overvaluation of alcohol reinforcement, and low reinforcement from alcohol-free activities. Extending this account to the comorbidity of alcohol problems and posttraumatic stress disorder (PTSD), the present study tested the hypothesis that alcohol problems and PTSD symptom severity would interact and be positively associated with indicators from these three domains. High-risk emerging adults from North America (Study 1, n = 1,311, Mage = 22.13) and general community adults from Canada (Study 2, n = 1,506, Mage = 36.80) completed measures of alcohol problems, PTSD symptoms, delay discounting, alcohol demand, and proportionate alcohol-related reinforcement. Across studies, regression analyses revealed significant main effects of alcohol problems and PTSD symptoms in relation to selected reinforcer pathology indicators, but no significant interactions were present for delay discounting or proportionate alcohol-related reinforcement. Interactions were observed for alcohol consumption at $0 (intensity) and the rate of change in consumption across the demand curve (elasticity; Study 1) and for elasticity and maximum alcohol expenditure (Omax; Study 2), but not in the predicted directions. Higher synergistic severity was associated with lower alcohol reinforcing value in each case. These findings reveal expected relations between reinforcer pathology indicators and both alcohol problems and PTSD symptomatology in general but did not support the hypothesized synergistic relationship. The relation between alcohol problems and PTSD is more complex than predicted by existing extensions of the reinforcer pathology model, warranting further investigation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mode selection and combination rules of energy-based multimodal pushover method for simply supported beam bridges

The energy-based method (EBM) has been improved as Energy-based Multimodal Pushover Method (EMPM), and is introduced and verified to be effective and convenient for evaluate the seismic performance of the simply supported beam bridges. This paper mainly focuses on the research of vibration mode selection and combination rules of EMPM. Firstly, the longitudinal vibration modes of simply supported beam bridge are categorized into five cases based on mode shapes, and the case definition is emphasized. Then, corresponding to each case, the Pushover loading force are established, and the modal energy, modal displacement, and modal energy capability curve are proposed. After that, the energy-based demand curve and the corresponding energy coefficient for each case is recommended. Finally, only the mode with maximum mode participation factor (MPF) of each case is suggested to take part in the calculation of the seismic response with the proposed mode participation factor combination rule. Case studies of a practice bridge under Chi-Chi and El Centro earthquake waves are used to illustrate the effectiveness and accuracy of the proposed mode selection and combination rules. The study found that the EMPM with the proposed rules could effectively and rapidly assessing the seismic performance of the simply supported beam bridge, resulting in a 70 % decrease in time consumption compared with the Nonlinear Time History Analysis Method (NTHAM). Compared with the CQC and SRSS combination results, the proposed Mode Participation Factor (MPF) combination rule reduced the maximum relative deviation from 39.4 % to 21.09 % between EMPM and NTHAM for peak displacement. Further research showed that the proposed mode selection approach only uses modes with the highest MPF in each case, decreased the number of modes involved more than 50 % and the result accuracy remained almost identical.

EXPRESS: The Heterogeneity of Hotel Demand Curves across Consumers and Contexts

This study constructs hotel demand curves at the disaggregate level to uncover the heterogeneity of demand curves across consumers and during both normal periods and times of crisis, exemplified by the pandemic. The novel demand modeling technique fits nonlinear demand curves, parameterizes elasticity dynamics, and enables the comparison of demand curves by essential value. The demand curves for three hotel types in normal and pandemic situations are fitted and decomposed by consumers’ sociodemographics, preferences, and risk tolerance. A pandemic makes the demand curve for midscale [upscale] hotels more inelastic [elastic] and mitigates [amplifies] the influence of individual differences on the demand curve, whereas the demand curve for economy hotels is unaffected by the pandemic. The findings offer insights into the business operations of different hotels, including optimal pricing, customized marketing across consumer segments, and business strategies in case of a health crisis.

Sizing of storage‐based renewable off‐grid system according to supply of electrical and thermal energies considering unscented transformation‐based stochastic optimization: A case study

AbstractThe paper presents the planning (sizing) of a hybrid islanded system containing only renewable sources including wind turbines, photovoltaics, and bio‐waste energy units for the simultaneous supply of electrical and thermal energy. The mentioned renewable sources are used to supply electrical energy. The bio‐waste unit (BEU) is equipped with combined electrical and thermal technology. It is used along with heat pumps to supply thermal energy. Electrical and thermal storage are employed to make the renewable power output as close as possible to the demand level. Electric storage can be either stationary (e.g., battery) or mobile (e.g., electric vehicles), but thermal storage is as stationary type of storage. In the following, the proposed scheme minimizes construction and maintenance costs imposed by power sources, storage devices, and power electronic converters, and expected storage degradation cost. This plan is bound to the model of operation of sources, storage devices, and power electronic converters. In this model, renewables are the main source of power to supply consumers, where storage is adopted to increase the generation level to make it as close as possible to the demand curve. The uncertain parameters are imposed by the load, renewable phenomena, and mobile storage parameters. To model these uncertainties, stochastic optimization based on unscented transformation is used. Finally, the findings of the paper demonstrate that the suggested scheme succeeds in the economic planning of the system with a simultaneous supply of electrical and thermal energy. The BEU equipped with a combined electricity and heat system along with thermal storage and a heat pump to supply thermal load besides providing electric energy can reduce the planning cost by 2.9% compared with the case with only electrical energy. Also, the mobile storage presence in the hybrid system can reduce the number of stationary storage devices, which alone results in a 7.7% decrease in the planning cost of the hybrid system.

Do Demand Curves for Corporate Bonds Slope Down?

Do Demand Curves for Corporate Bonds Slope Down?

Los vehículos eléctricos y su impacto en el sistema eléctrico de distribución: Caso de estudio “Alimentador urbano de la ciudad de Portoviejo”

In Portoviejo, the current use of electric vehicles (EVs) is limited compared to conventional vehicles. However, due to the implementation of laws, regulations, and policies promoting electric mobility in Ecuador, a significant increase in the integration of EVs into the city's electrical system is anticipated in the coming years. To anticipate the impact on the electrical infrastructure, a simulation is conducted using CYMDIST software on an electrical distribution feeder operated by the Public Company Corporación Nacional de Electricidad (CNEL EP), Manabí Business Unit (Portoviejo). The simulation considers three scenarios projected for 2030: 1. Baseline scenario without EV integration, 2. Unrestrained EV integration, and 3. Managed EV integration. This research aims to simulate the integration of up to 230 EVs into the network to provide benchmark data for understanding the potential impacts on the feeder as EV adoption increases, with vehicles being charged over extended periods. The investigation will highlight the importance of demand management with EV integration, demonstratingsignificant effects on the demand curve, voltage profile, and total harmonic distortion rate (THD%) of a 13.8 kV distribution feeder.

Moderating Effect of Investor Demand on the Relationship between Institutional Investors Participation and Flipping Activity

This paper examines the moderating effect of investor demand on the relationship between institutional investors’ participation and flipping activity of Malaysian IPOs. Measured with proxy, flipping activity seems to be the least risky way to optimize returns from IPOs. If done excessively, flipping activity could produce a damaging effect on the aftermarket performance of the new shares. Issuers and underwriters could restrict availability of shares to flip by strategically allocating a larger proportion of IPOs to institutional investors. This strategy could work because this group of investors is perceived to be “loyal” as they tend to keep their shares for a long-term investment. However, the impact of restricting supply of IPOs in the immediate aftermarket could aggravate the situation if the IPOs are highly demanded. The shift of the demand curve to the right when supply curve has shifted to the left induces a new equilibrium at a higher price level. Further price appreciation will make investors be more tempted to flip to optimize their returns, pushing flipping activity to its maximum level. Using 247 Malaysian IPOs covering the period from January 2000 to December 2012, this study finds that investor demand does moderate the negative relationship between institutional investors’ participation and flipping activity.

Non-intrusive demand response management strategy to mitigate the impacts of residential electric vehicle charging on distribution systems

The proliferation of electric vehicles (EVs) is expected to significantly increase electricity demand. However, if left uncontrolled, the aggregated demands from EVs have the potential to affect the power balance and quality of distribution systems adversely. Given this complex framework, this work presents a non-intrusive demand response management (DRM) approach to mitigate the impacts of residential EV charging on distribution systems. After analyzing a database comprising 157,250 operations, 74,316 charging patterns described by 17 attributes are obtained. These patterns are used for sampling EV fleets distributed in the Institute of Electrical and Electronics Engineers (IEEE) 33-bus test system. From scenarios with different levels of EV penetration, critical regions in the demand curve are identified, while actions for demand response (DR) are taken. Despite the increase in total energy consumption, larger EV fleets have greater potential for participation in DR. At a penetration level of 5%, it is not possible to shift excess energy consumption during the peak period, even with the participation of about 47% of the fleet, which corresponds to all users involved in EV charging during the peak period. At a penetration level of 50%, the energy goal can be achieved with the participation of about 15% of the fleet. In this latter scenario, the proposed management strategy can achieve a peak demand reduction of 9–11%.

PV/PV-Battery hosting capacity estimation method based on hidden Markov model for effective stochastic computation

Monte Carlo is commonly applied to model uncertainties in the penetration of photovoltaic (PV) systems as random processes. However, Monte Carlo simulations require a large number of stochastic calculations to obtain the desired accuracy. This paper develops a hosting capacity estimation model using hidden Markov to provide an effective stochastic calculation of PV/PV-battery penetration. To improve the representative in modeling actual penetration scenarios, the proposed model considers the probabilities among candidates on the basis of the customer types, the customer with PV-only or the customer with PV-battery, and the size of the PV/PV-battery. To be used in the simulations, a technique is proposed to calculate the min–max load demand and PV generation curves. To assess the computational load of the proposed model, this work provides an accuracy evaluation with respect to the number of stochastic simulations. The findings indicate that the proposed solution can achieve a cost-effective calculation of the hosting capacity. In practice, this work can provide the distribution planner with useful direction to help make informed decisions about the distribution network reinforcement strategy to deal with high PV/PV-battery penetration.

Mapping the Wholesale Day-Ahead Market Effects of the Gas Subsidy in the Iberian Exception

Amidst the global energy crisis in 2022, the Spanish and Portuguese governments introduced a subsidy to natural gas (“the Iberian exception”), attempting to lower the wholesale electricity market prices, with the understanding that gas-fired-combined cycle gas turbines (CCGTs) are price-setting technologies most of the time, directly or indirectly. The subsidy succeeded in lowering the market price but induced several other effects, such as (1) the increase in cleared energy in the Spanish market (mostly produced with gas), (2) the bias in the import/export cross-border position between Spain and France (Spain became a net exporter to France immediately), or (3) the consequent increase in congestion rents, which serve to lightly finance the subsidy, among other effects. This paper provides a framework for clustering the different effects based on the market participation phases: the subsidy, the market bidding, the market results, and surplus and rents. Moreover, this paper builds on the theoretical market models, with and without subsidies, and with and without cross-border exchanges. Based on the real market bids, the subsidies, and the generators’ data, we reconstruct the supply and demand curves and simulate the counterfactual market scenarios in order to illustrate and quantify the effects. We highlight the quantification of the theoretical effect of the transfer of rents, from non-fossil to fossil fuel producers, induced by the gas subsidy.

On natural interest rate volatility

Episodes of low natural interest rates, even transitory, pose a challenge to monetary policy, by possibly causing the effective lower bound (ELB) on the policy rate to bind. Those episodes are more likely to occur not only when the natural rate is low on average but also when fluctuations around its average level are large. We study the responsiveness of the natural interest rate to structural aggregate shocks affecting the aggregate supply of and demand for savings. Using a quantitative overlapping-generations model, we trace back this responsiveness to the slopes of aggregate savings supply and demand curves and argue that both curves have likely flattened over the past four decades in the US This implies a greater sensitivity of the natural interest rate to structural shocks affecting the supply of and demand for aggregate savings — making it more likely, all else equal, that it fall into negative territory.

Structural Estimates of Supply and Demand Elasticity for Houses in Sydney

We report estimates of supply and demand elasticities for houses (i.e., non-strata properties) in three geographic locations of Sydney. In the Inner Ring of Local Government Areas (LGAs)—those closest to the Central Business District (CBD)—our estimates indicate that the supply curve for houses is perfectly inelastic. This finding allows us to condition on the stock of houses and estimate the corresponding Inner Ring demand curve using ordinary least squares. In the Middle and Outer Rings—where the supply curve for houses has positive elasticity—we use instrumental variables to estimate the demand curve for houses. For all three locations, we obtain theoretically reasonable point estimates of standard demand elasticities, although the degree of uncertainty surrounding the Outer Ring estimates is relatively large. Averaging across the three regions of Sydney, the price elasticity of demand for houses is −1.3, cross-price elasticity with units is 1.1, and income elasticity is 2.1. Based on our elasticity estimates, only in the Outer Ring are any of the direct burdens of stamp duty born by buyers (about 40 percent).

Modeling the potential effects of rooftop solar on household energy burden in the United States

Policymakers at the federal and state level have begun to incorporate energy burden into equity goals and program evaluations, aiming to reduce energy burden below a high level of 6% for lower income households in the United States. Pairing an empirical household-level dataset spanning United States geographies together with modeled hourly energy demand curves, we show that rooftop solar reduces energy burden across a majority of adopters during our study period from a median of 3.3% to 2.6%. For low- and moderate-income adopters (at or below 80% and 120% of area median income, respectively), solar reduces median 2021 energy burden from 7.7% to 6.2%, and 4.1% to 3.3%, respectively. Importantly, solar reduces the rate of high or severe energy burden from 67% of all low-income households before adoption to 52% of households following adoption, and correspondingly from 21% to 13% for moderate-income households. Here, we show rooftop solar can support policy goals to reduce energy burden along with strategies such as weatherization and bill assistance.

The impact of photovoltaic self-consumption on the daily electricity demand in Spain: Definition of a model to estimate it

IntroductionA radical shift in energy production is underway worldwide, replacing fossil fuels with renewable sources and causing structural changes in power generation systems. Problem statementPhotovoltaic installations for self-consumption have experienced a steep increase in recent years. They have reached a significant installed capacity to cause a noticeable reduction in consumption from the national grid, which can cause serious management problems. ObjectivesIn this work, the evolution of the Spanish demand in the last years is analyzed to identify the influence of self-consumption in the overall demand. In addition, a mathematical model is defined to estimate this influence. MethodologyThe demand curves of equivalent days in years with high and low installed self-consumption photovoltaic systems have been compared. Then, an estimation of the electricity generated with this source is proposed, with a mathematical model that takes into account data on solar radiation, installed photovoltaic power for self-consumption and other relevant factors. ResultsThe analysis of the demand has shown a significant reduction of the electricity demand in daylight hours when the number of self-consumption photovoltaic systems increases. Moreover, the proposed model has been able to provide an estimation of the electricity generated with this source. The addition of these estimates to the actual consumption curves of years with a high number of self-consumption installations gives profiles close to those obtained when self-consumption was low. RecommendationNew storage systems need to be implemented and grid management need to be improved to take advantage of the surpluses produced by photovoltaic systems.

Hotelling and Wilson on Statistical Inference: Local Attitudes and Universal Dreams

During the early 1920s, a scattered group of American scholars, among whom were Harold Hotelling and E. B. Wilson, started a joint effort to develop statistical instruments in economics. They became interested in disparate topics such as the statistical determination of demand curves, business cycles, yield forecasting, population growth, and many others. Hotelling and Wilson shared much: both were trained in mathematics, both were convinced of the importance of developing inferential statistical tools based on probability theory, and both were at odds with the common sentiment of the community of statisticians contemporary with them, a community that was highly skeptical about statistical inference grounded on probabilities. Their trajectories, however, differed, diverted by their entanglement with Stanford for Hotelling and Harvard for Wilson. Confronted with different local realities, they came to develop profoundly different approaches that characterized the debates of the 1920s and 1930s on statistical inference.