Demand-side Decisions Research Articles

Evolutionary game analysis of forestry carbon sink trading model under blockchain technology

As a new balanced economy, the green economy guides the upgrade and transformation of industrial structures based on the original concept of sustainable development. Reducing carbon emissions is indispensable for achieving better and faster green economic development. From a functional perspective, forestry carbon sinks have good carbon sequestration potential and have positive attributes, such as climate regulation and biodiversity maintenance, for the entire ecosystem. To promote the continuous development of forestry carbon sink trading in China, blockchain, a new technology with several advantages such as traceability, supervision, and tamper-proofness, has emerged, which creates a good market trading environment and has made a breakthrough in the trading mode. If forestry carbon sink trading is effectively supported, organic combinations and simultaneous synergistic development can be conducted, which will open new avenues for the carbon sink economy. In this study, a game model of the forest carbon sink trading mode under blockchain technology was constructed from the perspective of traders, and influential factors, such as the carbon sink available for sale by the supply side and the demand side's perspective towards emission reduction, were considered. Subsequently, the MATLAB R2020b software was used to conduct a simulation analysis of the transactions, thereby showing the evolutionary path of the game between the two sides and the influence mechanism of the addition of blockchain technology on the evolution trajectory of the demand-side decision. The findings indicate that, from the perspective of participating traders, the inclusion of blockchain technology in forestry carbon sink trading resulted in positive effects. Moreover, the greater the probability that the carbon sinks available for sale in the market can meet the demand, the more likely the demand side is to participate in the transaction.

Data-oriented distributed overall optimization for large-scale HVAC systems with dynamic supply capability and distributed demand response

Operational optimization of heating, ventilation, and air-conditioning (HVAC) systems is crucial to achieving economic and environmental benefits. However, owing to the dynamic supply capability (DSC) and distributed demand response (DDR), large-scale HVAC systems that integrate multiple distributed sources with demanders are increasingly being developed to resemble distributed energy systems (DESs); thus, the traditional centralized strategies are now facing unprecedented challenges. With the application of wireless communication and sensor network technology, distributed schemes that allow the modular integration of an arbitrary number of suppliers and demanders have attached widespread interest of researchers. However, existing distributed optimization schemes that focus on either multi-zone temperature or equipment group efficiency are unilateral and non-interfering. In this work, we developed a data-oriented distributed overall optimization strategy for large-scale HVAC systems with DSC and DDR. We first suggested an agent-based distributed overall optimization framework that defines the roles of participants in distributed optimization and clarifies their duties, mechanisms, and relationships with each other, thus bridging the supply-side and demand-side decisions. Then, we exploited state-of-the-art data-driven models and designed a bi-level optimization algorithm, where the heuristic algorithm works as the lower-level scheme and the Nash-optimization algorithm acts as the upper-level scheme, to implement the distributed overall optimization. Last, a hardware-in-the-loop simulation was performed, and time-series data were collected to validate our proposed optimization strategy. Compared with centralized strategies, it can obtain better operational performance by paying a lower computational cost. This work provides a reference for optimizing increasingly complex HVAC systems.

Determinants of online hotel room prices: comparing supply-side and demand-side decisions

Purpose This study aims to examine the effect of hotels’ star ratings and customer ratings on online hotel prices from both supply- and demand-side perspectives. Design/methodology/approach To compile the supply-side data, a Web crawler was designed and implemented to read online prices and characteristics of available hotels from Trivago. Demand-side data were compiled from surveys conducted using the Amazon Mechanical Turk portal. Data were analyzed with an array of advanced machine learning regression models. Findings The results show that while a star rating is the most important predictor of price from both supply- and demand-side perspectives, customer rating influences the price much more significantly on the demand-side. Customers showed a tendency to overestimate the room price of three- and four-star hotels and underestimate the price of five-star hotels. Customers placed a heavier weight on customer ratings when estimating prices particularly when the average rating was above 7.5 (out of 10). The study also confirms the strong effect of price adjustment for customers when they were exposed to the prices of other similar hotels. Finally, the study examines the impact of demographics on the perceived hotel value. Age, ethnicity, education and income are shown to be the most significant demographic characteristics. Originality/value The results are valuable from a research perspective because they demonstrate how to price rooms more effectively based on their perceived value from consumers’ perspectives. From a practical standpoint, the findings provide useful managerial tools for pricing in competitive environments.

A Stochastic Resource-Planning Scheme for PHEV Charging Station Considering Energy Portfolio Optimization and Price-Responsive Demand

Plug-in hybrid electric vehicle (PHEV) charging station is playing a critical role in the rapid development of PHEVs. The unique characteristics of charging demands provide flexibility for the resource planning of PHEV charging station, while its internal generation resources and procurement decisions from utility grid offer various options to meet the charging demand. To achieve the maximum benefits while managing the associated risk, the operator of PHEV charging station should optimally schedule those resources on both supply and demand sides. In this paper, a stochastic resource-planning scheme for PHEV charging stations is proposed, while two types of PHEV charging loads, including price-responsive commercial charging customers and the contracted controllable charging fleets, are taken into the account. Energy supply decisions on energy procurement in multiple markets and internal generation scheduling are co-optimized with demand-side decisions on charging service pricing and controllable demands allocation. The uncertainties from spot market price and availability of renewable generations are considered in the proposed model. A numerical case study is also provided to illustrate the effectiveness of the proposed scheme.

Locational marginal emissions: Analysis of pollutant emission reduction through spatial management of load distribution

Environmental concerns associated with power generation drive an increasing interest in developing load management strategies to reduce pollutant emissions. Currently, no mechanism exists to directly influence pollutant emissions based on demand-side decisions. This shortcoming is addressed through the exploration of an alternative load distribution management paradigm based on the use of locational marginal emissions (LMEs). LMEs present a novel mechanism for optimizing load based on pollutant emissions. To demonstrate the application of LMEs, simulation studies using the IEEE 14-bus system and a large regional transmission system in the US (PJM) were performed and changes in CO2, SO2, and NOx emissions were quantified for varying levels of spatial load flexibility. The simulation results confirm that the proposed LME-based load management method is effective in reducing pollutant emissions in comparison to the traditional economic load distribution management method based on the locational marginal price (LMP). Emission reductions were found to become more significant as the proportion of spatially controllable loads increased. Adoption of LMEs by independent system operators (ISOs) or Regional Transmission Organizations (RTOs) would empower demand-side clients to reduce pollutant emissions based on their own load management decisions and enhance the sustainability of free-market power systems. Alternately, the LME management scheme could be automated by utilities through connections to Smart Grid compatible appliances.

Evaluation of a rapid LMP-based approach for calculating marginal unit emissions

To evaluate the sustainability of systems that draw power from electrical grids there is a need to rapidly and accurately quantify pollutant emissions associated with power generation. Air emissions resulting from electricity generation vary widely among power plants based on the types of fuel consumed, the efficiency of the plant, and the type of pollution control systems in service. To address this need, methods for estimating real-time air emissions from power generation based on locational marginal prices (LMPs) have been developed. Based on LMPs the type of the marginal generating unit can be identified and pollutant emissions are estimated. While conceptually demonstrated, this LMP approach has not been rigorously tested. The purpose of this paper is to (1) improve the LMP method for predicting pollutant emissions and (2) evaluate the reliability of this technique through power system simulations. Previous LMP methods were expanded to include marginal emissions estimates using an LMP Emissions Estimation Method (LEEM). The accuracy of emission estimates was further improved by incorporating a probability distribution function that characterize generator fuel costs and a membership function (MF) capable of accounting for multiple marginal generation units. Emission estimates were compared to those predicted from power flow simulations. The improved LEEM was found to predict the marginal generation type approximately 70% of the time based on typical system conditions (e.g. loads and fuel costs) without the use of a MF. With the addition of a MF, the LEEM was found to provide emission estimates with errors typically less than 25% for CO2, and less than 50% for SO2 and NOX. Overall, the LEEM presented provides a means of incorporating pollutant emissions into demand side decisions.

Jeffrey Rohlfs' 1974 Model of Facebook: An Introduction

This short essay, forthcoming in Competition Policy International, summarizes and evaluates Jeffrey Rohlfs' 1974 Bell Journal paper, A Theory of Interdependent Demand for a Telecommunications Service. Rohlfs' work helped create a large literature on markets with network externalities in which demand decisions have long-lasting consequences, a literature that has informed competition policy. But Rohlfs assumed that demand-side decisions did NOT have long-lasting consequences. Social networking and internet-based markets of this sort are increasingly important but have not been extensively studied. While they may post interesting antitrust challenges, they are almost certainly not the challenges to which the post-Rohlfs literature pointed.

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This study evaluates the characteristics of the men who served in the volunteer military in combat occupations. It examines whether these characteristics stem from supply-side or demand-side decisions, or reflect class bias. The findings suggest that, on the supply side, men who had greater academic abilities were more likely to go to college, thereby avoiding military service and the possibility of serving in a combat occupation. On the demand side, the armed forces were more likely to exclude men with lower academic abilities but were more likely to assign such men in the military to combat occupations. Net of the impacts of these supply-side and demand-side decisions, men who served in combat occupations still differed from those who did not in terms of their family background. The impact of family background was stronger on entering the military than on being assigned to combat occupations once in the military.

Unequal Risk: Combat Occupations in the Volunteer Military

This study evaluates the characteristics of the men who served in the volunteer military in combat occupations. It examines whether these characteristics stem from supply-side or demand-side decisions, or reflect class bias. The findings suggest that, on the supply side, men who had greater academic abilities were more likely to go to college, thereby avoiding military service and the possibility of serving in a combat occupation. On the demand side, the armed forces were more likely to exclude men with lower academic abilities but were more likely to assign such men in the military to combat occupations. Net of the impacts of these supply-side and demand-side decisions, men who served in combat occupations still differed from those who did not in terms of their family background. The impact of family background was stronger on entering the military than on being assigned to combat occupations once in the military.