Social Welfare Maximization Research Articles

The impact of government subsidy on new vaccine R&D and production with capacity sharing

ABSTRACT New vaccine research and development (R&D) and production is of importance in combating against prevalent infectious diseases, as vaccines have prevented countless cases of disease and saved millions of lives. We formulate a multi-stage game comprised by one developer, one manufacturer and one government in the new vaccine R&D and production, considering R&D failure risk and capacity sharing. This paper examines the effects of different subsidy forms and two alliance modes. The result reveals that: (1) The government should consistently provide a combo of subsidy to achieve maximum social welfare. The developer has no preference for the form of subsidy in the manufacturer-initiated mode, whereas the manufacturer desires to get subsidised by the per-unit cost form regardless of alliance modes. (2) Being the initiator inside the alliance may not always be the optimal decision for players. The manufacturer should relinquish the role as the initiator if certain requirements are fulfilled. (3) When capacity sharing revenue is substantial or the failure risk is sufficiently low, the government should give priority to using per-unit cost subsidy instead of quality improvement subsidy, and the manufacturer would like to be the follower if per-unit cost subsidy is offered.

Demand side management programs in smart grid through cloud computing

The development of power systems requires smart grids to facilitate real-time control and monitoring using bilateral communications across the power grid. With the implementation of a distributed structure, smart grids will be highly reliable, efficient, and secure, as well as affordable in terms of power management. With the expansion of the power grid at the level of distribution and the smarter equipment used by the shareholders, it is necessary to consider measures to balance production and consumption and to optimize the consumer pattern. One of these approaches is the use of demand-side management programs, in which the system operator seeks to optimize the coordination between production units, storage units, and consumption loads in a way that achieves maximum social welfare. This article attempts to present models for planning a demand-side management program that is presented in the presence of new cloud computing technologies. The model proposed is based on the use of storage units and will be able, in addition to covering uncertainty in renewable production, to customize energy reserves and computational capacity in a way that brings the lowest operating costs.

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.

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.

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.

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.

Integrated Sensing, Communication, and Computing for Cost-effective Multimodal Federated Perception

Federated learning (FL) is a prominent paradigm of 6G edge intelligence (EI), which mitigates privacy breaches and high communication pressure caused by conventional centralized model training in the artificial intelligence of things (AIoT). The execution of multimodal federated perception (MFP) services comprises three sub-processes, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately competitive on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is vital to the MFP networks. To address the above issues, this article explores service-oriented resource management with integrated sensing, communication, and computing (ISCC). Specifically, employing the incentive mechanism of the MFP service market, the resources management problem is defined as a social welfare maximization problem, where the concept of “expanding resources” and “reducing costs” is used to enhance learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms.

Strategic Queueing Behavior of Two Groups of Patients in a Healthcare System

Long waiting times and crowded services are the current medical situation in China. Especially in hierarchic healthcare systems, as high-quality medical resources are mainly concentrated in comprehensive hospitals, patients are too concentrated in these hospitals, which leads to overcrowding. This paper constructs a game-theoretical queueing model to analyze the strategic queueing behavior of patients. In such hospitals, patients are divided into first-visit and referred patients, and the hospitals provide patients with two service phases of “diagnosis” and “treatment”. We first obtain the expected sojourn time. By defining the patience level of patients, the queueing behavior of patients in equilibrium is studied. The results suggest that as long as the patients with low patience levels join the queue, the patients with high patience levels also join the queue. As more patients arrive at the hospitals, the queueing behavior of patients with high patience levels may have a negative effect on that of patients with low patience levels. The numerical results also show that the equilibrium behavior deviates from a socially optimal solution; therefore, to reach maximal social welfare, the social planner should adopt some regulatory policies to control the arrival rates of patients.

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

A novel residential demand response (DR) program is proposed to encourage microgrid customers to participate in demand reduction using a house energy consumption scheduler (HECS) to maximize social welfare by optimally reducing the cost of electricity and to receive maximum incentives. In this research, both Price-based and incentive-based DR programs are considered, and the demand-price elasticity approach is implemented to model them. To make the DR model more effective, customer behavioral response is taken into account, by leveling the customers according to their price elasticity. An optimized non-cooperative energy game theory framework and a Nash equilibrium strategy are employed to model customer response under a multi-time of use (TOU) pricing and incentive scheme, with the aim of achieving the best scheduling of home-controlled appliances while preserving customers’ privacy. This approach maximizes social welfare by minimizing the electricity consumption cost for customers while simultaneously saving on generation costs. The negative externality effect on price rate is internalized between the customers of different consumption levels using multi-dynamic consumption-level pricing and incentive scheme (MDCLPIS). The suggested model distinguishes between consumers’ behavior in response to changes in energy prices and their behavior in response to changes in incentives. Finally, numerical and simulation results demonstrate the suitability and effectiveness of the proposed model and its advantages in terms of customer saved cost by up to 21.26%, microgrid saved cost by up to 29.44%, and getting benefits by optimal scheduling of their load.

Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach

In this paper, an equilibrium-heuristic online prediction optimization approach is proposed for multi-market peer-to-peer (P2P) electricity–hydrogen trading of integrated energy systems (IESs) with uncertainties. First, the IES, consisting of a hydrogen energy storage subsystem and a combined cooling, heating, and power subsystem, is constructed in the distribution network to improve energy utilization and market efficiency. Then, a bi-level optimization model for IESs, participating in the P2P electricity–hydrogen energy trading, the real-time electricity, and the ancillary service markets, is proposed, in which the top-level model can be formulated as a P2P electricity–hydrogen trading pricing model through the social welfare maximization problem, and the lower-level model is used to maximize the IES operating profit. To effectively solve the bi-level model, the game equilibrium-based ADMM distributed algorithm is used to obtain the P2P trading volume and prices of the top-level model, and a new hybrid heuristic algorithm, called hybrid sand cat swarm optimization and improved honey badger algorithm (SCIHB), is proposed to solve the lower level model. SCIHB utilizes a hybrid mechanism and an adaptive learning rate parameter to balance local exploitation and global exploration. Furthermore, a new online learning probabilistic prediction method based on the hidden Markov model and wavelet transform is introduced to describe the uncertainties of wind power, electricity prices, and frequency regulation prices. Finally, case studies are conducted on an IEEE 33-bus test system, and the numerical results verify the effectiveness of the proposed P2P trading model and solution approach.