Welfare Maximization Research Articles

Two-Phase on-Line Joint Scheduling for Welfare Maximization of Charging Station

Two-Phase on-Line Joint Scheduling for Welfare Maximization of Charging Station

Theoretical study on demand-side management to reduce imbalance between electricity supply and demand

The gap between electricity supply and demand, which is the electricity imbalance, can have negative economic impacts on retail electricity suppliers. This paper presents a methodology for designing demand response (DR) programs while mitigating the negative impacts. The DR programs require the retailers to set target electricity consumption and prices of economic incentives for customers who cooperate with the DR programs. First, the target electricity consumption is determined by solving a problem of social welfare maximization in which the objective function is the sum of economic surplus of the retailers and their customers and impacts of electricity imbalance. The optimal electricity prices or the optimal rebate levels are then calculated under the principle that rational customers act to maximize their economic surplus. Through numerical simulations, the authors confirmed that the proposed methodology mitigated the impacts of electricity imbalance. Meanwhile, results of the numerical simulations showed that in practical situations, there were cases where the retailers unable to reduce the electricity imbalance profitably. Therefore, the authors additionally discuss ways to assess the electricity imbalance, which motivates the retailers to reduce the imbalance, in such cases.

An iterative price-based combinatorial double auction for additive manufacturing markets

The increasing adoption of additive manufacturing (AM) in the industrial sector is leading to an imbalance between supply and demand of additively manufactured subcomponents: companies demanding AM services require very specific products and AM suppliers differ widely in their capabilities. Some existing proposals aim to help match supply and demand by merely making customer–supplier allocations. Only a few recent works go beyond allocation issues and propose market mechanisms to also address pricing aspects. However, we observe that these mechanisms do not fully exploit the potential of additive manufacturing techniques. The aim of this paper is to design a market mechanism that considers the particularity of AM techniques, wherein suppliers can benefit from manufacturing multiple heterogeneous parts from multiple customers in the same build area to increase production throughput. This market mechanism has been implemented as an iterative combinatorial double auction that adapts to this feature of the AM market: customers will bid to get their orders produced and suppliers will submit asking quotes to win the production of combinations of those orders. The mechanism solves the allocation and pricing of AM orders while seeking the maximization of social welfare. The procedure is simulated in a theoretical environment to evaluate its performance and to identify the most appropriate conditions for its implementation in a real environment. Unlike other existing proposals for client-supplier allocation mechanisms in additive manufacturing, the proposed mechanism allows a single supplier to produce a combination of orders from different clients, leading to a pricing system that maximizes social welfare without participants disclosing sensitive information.

Endogenous innovation scale and patent policy in a monetary schumpeterian growth model

Abstract This paper develops a monetary R&D-driven endogenous growth model featuring endogenous innovation scales and the price-marginal cost markup. To endogenize the step size of quality improvement, we propose a tradeoff mechanism between the risk of innovation failure and the benefit of innovation success in R&D firms. Several findings emerge from the analysis. First, a rise in the nominal interest rate decreases economic growth; however, its relationship with social welfare is ambiguous. Second, either strengthening patent protection or raising the professional knowledge of R&D firms leads to an ambiguous effect on economic growth. Third, the Friedman rule of a zero nominal interest rate fails to be optimal in view of the social welfare maximum. Finally, our numerical analysis indicates that the extent of patent protection and the level of an R&D firm’s professional knowledge play a crucial role in determining the optimal interest rate.

WELFARE ANALYSIS VIA MARGINAL TREATMENT EFFECTS

We consider a causal structure with endogeneity, i.e., unobserved confoundedness, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization rules. Focusing on the application of the empirical welfare maximization framework, we provide convergence rates of the worst-case average welfare loss (regret).

A nonsmooth Levenberg–Marquardt method based on KKT conditions for real-time pricing in smart grid

The real-time pricing of smart grid, which is an important mean of demand side management, is an ideal method to adjust the power balance between the supply and demand in smart grid system. By transforming the social welfare maximization model into a nonsmooth system of equations based on KKT conditions and investigating the related nonsmooth-function’s generalized Jacobi, a nonsmooth Levenberg–Marquardt method with global convergence is proposed in this paper. Also, the local convergence rate is obtained under the local error bound, which is weaker than the nonsingular condition. Finally, numerical simulations show that the smaller the approximate parameter in smoothing Newton’s method, the closer the electricity price is to the value obtained by the proposed algorithm. And the price of the multi price based on the method is lower than that of the single price algorithm that does not consider user types. Meanwhile, compared with the distributed algorithm with different scales, it is found that the price is lower and the social welfare is higher, which shows that the algorithm is robust and effective.

On market clearing of day ahead auctions for European power markets: Consumer payment minimisation versus social welfare maximisation

For inflexible demand and considering network constraints, we introduce a Consumer Payment Minimisation (CPM) market clearing mechanism and compare it with the standard Social Welfare Maximisation (SWM) used in European Day Ahead Electricity Markets. CPM presents a challenging optimisation problem, which we addressed by four numerical algorithms with varying computational costs and convergence guarantees, evaluated on synthetic data. We derive strong, partial analytical results and the concept of optimal zonal stack curves to facilitate efficient CPM solutions. Evaluations using 2019–2020 data from the Central Western European Day Ahead Power Market under the assumption of truthful bidding show that SWM models historical prices well, while CPM reduces producers’ market power and decreases zonal prices and total electricity procurement costs compared to SWM.

Edwin Chadwick: A Pioneer of Public Health Reform and His Role in Sanitary Awakening.

Edwin Chadwick (1800-1890) was a central figure in the 19th-century public health reform movement in Britain. His work was instrumental in the sanitary awakening, a movement that revolutionized public health through the systematic improvement of urban sanitation and hygiene. As a lawyer by training, Chadwick was deeply influenced by Jeremy Bentham's welfare maximization theory, which emphasized the greatest good for the greatest number. His most significant contribution was his 1842 publication, "The Report on the Sanitary Condition of the Labouring Population," in which he documented the deplorable conditions faced by the working class and the link between poor sanitation and disease. Chadwick's advocacy led to the passage of the Public Health Act of 1848, which established local health boards and marked the beginning of modern public health systems. His focus on clean water, efficient sewage systems, and waste management not only reduced the spread of diseases like cholera and typhoid but also set the stage for future public health initiatives globally. The sanitary awakening, largely driven by Chadwick's efforts, highlighted the critical connection between environment and health, a principle that continues to underpin public health practices today. Chadwick's legacy lives on in the ongoing efforts to improve urban living conditions and prevent disease through public health infrastructure.

Real-time pricing considering carbon constraints for smart grid with multiple energy sources

With the escalation of fossil fuel pollution and the challenges of climate change, the smart grid is forced to address the carbon emissions associated with fossil fuel use. Real-time pricing of smart grid is an effective means to regulate the balance of supply and demand in the grid as well as to achieve energy conservation and environmental protection. This paper takes traditional fossil energy and clean energy power supply as the background, takes social welfare maximization as the goal, combines carbon emission cost with traditional power generation cost, and constructs a real-time pricing model that takes into account carbon emission cost, so that thermal power enterprises participate in the power market and carbon market at the same time. By using duality methods, we transformed the original optimization problem into an equivalent one. Furthermore, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. The simulation results verify the feasibility and effectiveness of the proposed real-time pricing strategy.

Risk alleviation and social welfare maximization by the placement of fuel cell and UPFC in a renewable integrated system

Risk alleviation and social welfare maximization by the placement of fuel cell and UPFC in a renewable integrated system

A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients’ volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets.

Demand-side energy sharing mechanism with residential energy storage in active distribution networks

Abstract With the widespread adoption of distributed new energy and residential energy storage (RES), demand-side users in active distribution networks (ADNs) are transitioning from traditional consumers to prosumers. The flexible resources of demand-side prosumers require an appropriate trading mechanism to achieve optimal resource allocation. A demand-side energy sharing mechanism based on residential energy storage is proposed. Firstly, a trading decision model for prosumer units is established by integrating the charge-discharge utility of RES devices with production-consumption characteristics. Then, a decentralized social welfare maximization energy sharing problem for ADNs is constructed using the Alternating Minimization Algorithm (AMA). Furthermore, a 10-node active distribution network is analyzed as an example. The proposed mechanism can maximize social welfare and increase the adaptation level of new energy from 64.58% to 66.83%.

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Optimised break scheduling vs. rest breaks in collective agreements under fatigue and non preemption

Collective agreements provide all workers with identical break schedules, irrespective of their individual differences in susceptibility to fatigue and recovery speed. In addition, companies tend to limit breaks which is also detrimental to productivity. We consider workers performing a repetitive task, each endowed with an individual fatigue and recovery function. Fatigue slows down the unit processing time which can be restored by appropriate breaks, therefore allowing for the daily production amount to be optimised. The optimised production of each worker can actually be reached for a multitude of break schedules. We therefore develop an algorithm that lists all possible break schedules (enumeration), and selects those leading to the maximum daily output, and the maximum welfare. Welfare is defined by the break duration and their timing flexibility, since we allow breaks to be also taken at the beginning or end of the workday, enabling workers to spend less time in the workplace. Having a variety of schedules would help in meeting at best the workers’ preferences and facilitate a consensus on a specific schedule between managers and workers. The resulting optimised break schedules are compared with common break practices in collective agreements so as to assess their impact on productivity and on worker’s welfare. None of the break scheduling models in the literature considers the option of leaving the workplace earlier, which is nevertheless highly appreciated by workers, nor do these models provide a variety of optimised break schedules to best accommodate workers’ preferences.

Reflections on the Kerala Public Health Act 2023: A response to Dr Karpagam's commentary.

In this response, we seek to analyse and rebut the observations of Karpagam S using an understanding of the Kerala Health system and the general purpose of the Kerala Public Health Act (KPHA). The KPHA was crafted with a greater focus on a one-health and preventive approach. It does not seek to interfere in an individual's preferred choice of treatment, except in the case of public health emergencies. KPHA is not a standalone document, but a supporting tool to the existing Kerala Health Policy and various other health policies and programmes instrumental in improving the health and quality of life of the people of the State. The KPHA is intended to be an enforcement tool for legal provisions to ensure welfare maximisation of the society at large, and thus detailed discussions regarding actions to be taken beyond these legal provisions do not fall within the purview of the Act.

Internalization of External Cost in the Geothermal Power Generation on Social Welfare Maximization

Internalization of External Cost in the Geothermal Power Generation on Social Welfare Maximization

Trading mechanism for social welfare maximization in integrated electricity and heat systems with multiple self-interested stakeholders

Cooperation across multiple energy sectors is capable of maximizing social welfare as revealed by existing research, but such holistic dispatch can be hardly implemented in a reality where energy sectors are run by different stakeholders respectively. To fill this gap, energy trading among multiple self-interested stakeholders is investigated in this paper. To maximize the social welfare of the trading results, an effective pricing method, referred to as network-constrained generalized locational marginal prices, is proposed. A trading mechanism is then carefully designed to encourage stakeholders to trade at network-constrained generalized locational marginal prices, while economic properties of the resultant generalized Nash equilibrium are analyzed in depth. The effectiveness of the mechanism is validated in two integrated electricity and heat systems with different scales. Numerical comparison demonstrates that existing non-cooperative mechanisms may result in undesirable deadweight losses, while surplus sharing is a tricky issue in cooperative mechanisms, making the cooperation unstable and infeasible. On the contrary, each stakeholder may optimize its subsystem in a self-interested manner in the proposed mechanism, but the overall outcome strikingly coincides with social welfare maximization.

Social welfare maximization of tourist bus service system in the M/M/1 queue with strategic tourists

Social welfare maximization of tourist bus service system in the M/M/1 queue with strategic tourists

An incentive regulation approach for balancing stakeholder interests in transmission merchant investment

The merchant-regulatory mechanism represents a promising tool that combines the benefits of merchant investment and regulated investment, thereby providing efficient incentives for merchant Transmission Companies (Transcos) subject to regulatory compliance. Taking the H-R-G-V mechanism as a foundational example of this approach, it permits Transcos to receive the total surplus increase from investments, and the profit-maximizing Transco will perform social welfare maximum investment under this mechanism. However, one drawback of this mechanism is that it allows the Transco to receive the whole benefit created by the Transco, while excluding consumers and generators from the resultant economic benefits. To address this issue, we propose an incentive tuning parameter, which is incorporated into the calculation of the incentive fee for the Transco. Accordingly, the regulatory framework can effectively manage the Transco’s profit and allow market participants to access economic benefits, thus ensuring a fair distribution of economic advantages among the stakeholders, while the impact on overall social welfare remains relatively modest. The results on the case study demonstrate that this careful balancing act maintains the essence of the H-R-G-V mechanism while addressing its critical gap—the equitable sharing of economic gains.

Evaluation of cortisol levels and behavior in dogs during animal-assisted interventions in clinical practice

Animal-assisted intervention (AAI) is designed to improve the physical or psychological condition of clients through targeted work that involves animals. For the long-term high-quality implementation of AAI, the maximum welfare of the participating animals must be ensured. The most frequently involved type of animal is the dog and therefore the aim of this study was the assessment of the cortisol level and the behavior of dogs during AAI. Fifteen dogs with long-term working experience in clinical practice with AAI were involved in the study. Cortisol was evaluated from a saliva sample during 4 experimental and 4 control days. On each day, the samples were taken three times in total - before the therapy, after its end, and with a half-hour interval. At the same times, samples were taken on control days as well. The ethogram questionnaire was used. After each session, the handler completed a questionnaire consisting of 25 behaviors. For each type of behavior, the handler indicated whether it occurred during the session and, if so, added the frequency of the behavior. Statistical analysis of the baseline data of the measured cortisol concentrations showed no statistically significant difference between the sampling times, on both experimental and control days. There was also no significant difference when comparing the data from the experimental and control days. The ethogram questionnaires recorded a difference between the different categories of behavior. Results indicate that when AAI is properly guided and respects the individual needs of the dog, AAI can be a meaningful and fulfilling activity that creates fun and active contact for the dog with its handler and the client.