Social Welfare Maximization Research Articles

Fair Multi-Resource Allocation in Heterogeneous Servers With an External Resource Type

This paper considers the problem of fair allocation of multiple types of resources in heterogeneous servers, along with a resource type external to those servers. Our work is motivated by the need for fair multi-resource allocation in mobile edge computing (MEC), where the users must upload their tasks over a single dedicated wireless communication link that exists outside the computing servers. We propose a fair multi-resource allocation mechanism for this environment, termed Task Share Fairness with External Resource (TSF-ER), which finds the Kalai-Smorodinsky bargaining solution satisfying important fairness properties. We show that TSF-ER is envy-free, Pareto optimal, and strategy-proof, and it satisfies the property of sharing incentive. Large-scale simulation driven by Google and Alibaba cluster trace further shows that TSF-ER significantly outperforms the existing utilitarian, Nash social welfare maximizer, and egalitarian solutions, leading to fairer resource allocation while maintaining a high level of resource utilization.

Community-aware empathetic social choice for social network group decision making

To study how interdependent and self-interested decision makers collectively make a choice in highly complex social environments, this paper presents a novel and powerful Community-Aware Empathetic Social Choice (CAESC) model, which considers decision makers in social networks as autonomy-oriented agents, who derive utility based on both their own intrinsic preferences, and empathetic preferences determined by the satisfaction of their intra-community neighbors. We devise optimization approaches for identifying community structures with local (global) social welfare maximizing under two varieties of CAESC models: The local CAESC model first identifies the non-cooperative behaviors by calculating the structure-preference coordination coefficients between pairwise decision makers, then employs the Logit response like dynamics process in a carefully defined potential game to find the optimal community structure that maximizes the local social welfare. To find the optimal community structure that maximizes the global social welfare, the global CAESC model is composed of three coupled phases during each stage: (i) opinion evolution within each community using the classic Jacobi method; (ii) structure-preference coordination matrix updating based on the current opinion vector; and (iii) local optimal community structure updating based on the local CAESC model. Extensive experiments on both randomly generated and real-world social networks with synthetic and real-world preferences confirm that neglecting the empathetic effect and community structure usually yields sub-optimal group decisions which degrade the social welfare of network members. Our experiments also show that, the higher societal empathy, the rougher preference distribution and the denser network structure will facilitate the maximal social welfare reaching in both local and global CAESC models; meanwhile, by comparing with the typical empathetic decision making models and some recent proposed consensus reaching methods, our proposed approaches show relatively significant advantages in terms of candidate ranking and decision efficiency.

Distributed discrete-time optimization over directed networks: A dynamic event-triggered algorithm

Distributed optimization has a wide variety of applications, such as economic dispatch in power systems, social welfare maximization, optimal power flow, where distributed nodes work cooperatively to achieve a minimized global objective function, where the information exchanges among distributed and networked nodes play an important role. However, such information exchanges often suffer from inevitable energy and bandwidth constraints due mainly to their distributed and networked nature. Event-triggered distributed optimization provides an effective means to address this issue. However, designing an event-triggered distributed optimization algorithm with both fast convergence and resource efficiency is challenging. In this paper, a distributed zero-gradient-sum algorithm with a dynamic event-triggered scheme is presented for distributed discrete-time optimization. The triggering threshold is composed of an exponential decaying term and a positive dynamic auxiliary variable. The linear convergence is established theoretically. Finally, this algorithm is applied to an economic dispatch problem to verify the theoretical findings and to demonstrate the effectiveness and superiority of the proposed algorithm.

Strategy selection of ride-sourcing platform supply chain under the targets of “double carbon”: Integration or cooperation

Since the targets of “double carbon” (peak carbon dioxide emissions and carbon neutrality) was proposed, more and more ride-sourcing platform supply chains (RPSC) are committed to sustainable development. We consider a RPSC consisting of a ride-sourcing platform (platform), a new energy vehicle manufacturer (manufacturer), and a charging station service provider (service provider), where the government provides subsidies for building charging stations. Considering the integration and cooperation among the platform, the manufacturer and the service provider, we propose four strategies in combination with practical business practices. We find that: when the government provides the same subsidy level to the RPSC under each strategy, the platform is more inclined to select the strategy with a higher integration degree. Whether the service provider selects to cooperate with the platform in the integrated state or the cooperative state is related to the government subsidy. In the extension, based on the fairness among decision-makers, we investigated their optimal strategy selections. In addition, we discuss the optimal subsidy case of the government in terms of social welfare maximization, and the feasible range of the cooperative strategy is larger than other three strategies for three decision-makers. Robust results can still be obtained when considering private car drivers joining the platform and the electricity cost of the charging station.

Coordinative Optimization Between Multiple Data Center Operators and a System Operator Based on Two-Level Distributed Scheduling Algorithm

Data Centers (DCs) have been playing a significant role in demand response (DR) programs in recent years due to their considerable DR capability. DCs are in the charge of the data center operator (DCO), who is responsible for making a reasonable allocation of computing tasks to provide DR resources. To relieve the transmission pressure of power systems, the system operator (SO) encourages DCOs to participate in the DR programs. To maximize the total welfare, a detailed DR scheduling model of DCOs and SO is proposed for the coordinative optimization. Considering the privacy issue of DCOs, a two-level distributed scheduling algorithm based on the alternating direction multiplier method (ADMM) is designed for privacy protection and distributed autonomy. Simulation results show that the proposed coordinative optimization algorithm can effectively realize the maximization of total social welfare with data privacy protection. For a power system with multiple DCOs, reasonable scheduling of DCO’s DR resources can reduce the peak-valley difference of system loads reliably and economically.

Optimal Power Flow Using a Hybrid Improved Harris Hawks Optimization Algorithm-Pattern Search Method

The optimal power flow (OPF) problems for effective power system planning and operations are solved using a hybrid Improved Harris Hawks Optimization and Pattern Search (PS) (hIHHO-PS) algorithm in this research. The OPF problem is considered minimization of non-convex active power generation fuel cost (NAPGFC), total active power losses (TPL), shunt capacitor cost (SCC), voltage variation (VD), and maximization of social welfare (SW) and loadability factor (LF) objective functions. The proposed hIHHO-PS algorithm was tested on different benchmark test functions (unimodal (UM), multi-modal (MM), and fixed-dimension MM functions) and IEEE 30 bus test system, and obtained results are validated with existing algorithms. These results show the proposed hIHHO-PS algorithm outperformed the others. Further, to enhance the OPF results, optimally place the multi-line Flexible Alternating Current Transmission System (FACTS) devices (unified power flow controller (UPFC) and generalized unified power flow controller (GUPFC)) using severity index. Furthermore, test the effects of control modes of multi-line UPFC and GUPFC devices on OPF results. These results show the multi-line FACTS devices with control mode enhance the OPF results, especially GUPFC in control mode operation.

Carbon allowance approach for capital-constrained supply chain under carbon emission allowance repurchase strategy

PurposeThe purpose of this study is to investigate which of the two carbon allowance allocation methods (CAAMs), i.e. grandfathered system carbon allowance allocation (GCAA) and baseline system carbon allowance allocation (BCAA), is more beneficial to capital-constrained supply chains under the carbon emission allowance repurchase strategy (CEARS).Design/methodology/approachAdopting CEARS to ease the capital-constrained supply chains, this study develops two-period game models with manufacturers as leaders and retailers as followers from the perspective of profit and social welfare maximization under two CAAMs (GCAA and BCAA), where the first period produces normal products, and the second period produces low-carbon products.FindingsFirst, higher carbon-saving can better use CEARS and achieve a higher supply chain profit under the two CAAMs. However, the higher the end-of-period carbon price is, the lower the social welfare is. Second, when carbon-saving is small, GCAA achieves both economic and environmental benefits; BCAA reduces carbon emissions at the expense of economic benefit. Third, the supply chain members gain higher profits and social welfare under GCAA, so the government and supply chain members are more inclined to choose GCAA.Originality/valueBy analyzing the profits and total carbon emissions of capital-constrained supply chains under GCAA and BCAA, this study provides theoretical references for retailers and capital-constrained manufacturers. In addition, by comparing the difference in social welfare under GCAA and BCAA, it provides a basis for the government to choose a reasonable CAAM.

Product‐line pricing with dual objective of profit and consumer surplus

In many settings, consumer surplus directly impacts a firm or organization's objective, and the profit‐only objective becomes inadequate. Our paper is the first to consider the dual objective of profit and consumer surplus in multi‐product pricing under the multinomial logit demand, where the prices are continuously set. We define a firm's marginal consumer surplus as its marginal contribution to the expected customer utility transformed into monetary unit. Although the profit is concave in the choice probability space, the marginal consumer surplus is convex, complicating the analysis. We identify the optimal monopoly pricing solution and develop solution approaches for the equilibrium solutions of both price‐competition and quantity‐competition oligopolies. In the monopolistic setting, we solve the optimal solution in a near‐closed‐form expression, and show that the firm's markup and profit decline as its emphasis for marginal consumer surplus increases and eventually drops to zero when the firm turns into a social welfare maximizer. Therefore, social welfare is maximized only when the monopolist is willing to endure zero profit. In competitive settings, if one firm's emphasis on marginal consumer surplus increases, then the improvement in marginal consumer surplus can magnify through competitive forces, reflected in reduced equilibrium prices for all firms. Moreover, we find that, while the overall marginal consumer surplus always increases with any firm's weight on marginal consumer surplus in its objective (referred to as the CS weight), a firm's marginal consumer surplus increases in its own CS weight but decreases in other firms' CS weight. Finally, we prove that, all else equal, a firm with a higher CS weight can earn a higher profit than its profit‐maximizing competitors, which is counterintuitive. In the quantity competition, a firm may even increase its absolute profit level by increasing its CS weight.

A Truthful Combinatorial Auction Mechanism Towards Mobile Edge Computing in Industrial Internet of Things

Mobile edge computing (MEC) shows prominent application prospects in the Industrial Internet of Things (IIoT) by allowing resource-restricted IIoT mobile devices (MDs) to offload their tasks to geographical proximity edge clouds. An efficient incentive mechanism should be designed jointly addressing resource allocation and pricing to incentivize MDs (i.e., buyers) and edge clouds (i.e., sellers) to participate in offloading service trading. This paper aims to solve the social welfare maximization problem of a personalized MEC computation offloading service market where each edge cloud can allocate different computing and wireless resources to each MD according to the MDs' delay and energy consumption constraints, and each MD submits bids to edge clouds differently based on the resource allocation of the edge clouds. We propose a truthful combinatorial auction (TCA) mechanism which involves three phases of resource allocation, buyer-seller matching, and payment determination. It should be highlighted that our proposed buyer-seller matching algorithm combines optimal matching and heuristic matching, so it greatly improves the auction effect while ensuring computational efficiency. Considerable theoretical analysis and experimental results prove that the performance of the proposed TCA mechanism is significantly superior to that of other auction mechanisms while holding the desirable properties.

분산형전원 사업자와 소비자 간 전력거래에서 전력도매가격과 계시별 요금제를 고려한 사회후생 최대화 전략에 대한 연구

Aggregators of distributed energy resources and consumers in the distributed energy system can transact electricity with various ways. The aggregators and the consumers can contract the appropriate power generation or power demand due to advances in the power communication. Although the aggregators and the consumers would not contract others in the conventional power system, the necessity of transaction between them will increase in the decentralized power system. The adequate operation strategies for aggregators and consumers are required to enhance their contracts and increase their benefit. In this study, we propose an operation strategy for distributed energy resources aggregators and consumers in order to maximize the social welfare. The proposed model considers the transaction between the aggregator and the consumer. The results demonstrate that the maximum social welfare can be achieved by considering the transaction between the aggregator and the consumer.

Competition among parking platforms in the presence of indirect network effects

A parking market including conventional public parking and novel shared parking is considered for matching parking demands and supplies. To investigate the competition between the public parking platform and the shared parking platform, a market equilibrium approach is proposed to investigate the interactions among the parking platforms, parking demanders, and parking owners, where indirect network effects referring mutual attractions between demands and supplies are incorporated. Targeted for profit maximization, we first formulate a basic model and derive the equilibrium results of parking platforms’ profits. Results show that indirect network effects positively affect the two parking platforms’ profits when the equivalent gains from alternative activities for parking owners are large, while negatively affect the profits otherwise in certain cases. Extensions regarding some considerations are then discussed, where Model D is proposed for dual decision of the shared parking platform and Model S is proposed for the social welfare maximization objective of the public parking platform. Comparisons of the three models in terms of equilibrium results are simulated consequently. Managerial implications to enhance the profits of the two parking platforms are further discussed.

From profit maximization to social welfare maximization: Reclaiming the purpose of American business education

Business schools were founded for the purpose of providing prosocial professional training that would counterbalance profit motives. Recent events in global society, including major social movements, a global pandemic, and the effects of climate change, have accelerated a reorientation of business practice toward socially responsible practice. We identify ways in which American business education can--and normatively should--return to the social welfare maximizing approach that formed its genesis. We thus envision a normative future for American business schools in which social welfare maximization, not profit maximization, exists at the center of pedagogy and practice. Inspired by the backcasting technique, we examine how the future for American business schools (and, as a result, American business practice) may in fact result from recapturing the trajectory of its past. These natural shifts are accelerated by the influence of key business associations including the UN PRME and the Business Roundtable, as well as the accrediting body for American business schools, the AACSB. But only with the self-regulatory participation of business schools in reorienting toward social welfare maximization can the normative goal of prosocial American business schools be achieved.

Demand Response Management via Real-Time Pricing for Microgrid with Electric Vehicles under Cyber-Attack

The initiative of users to participate in power grid operation is a key factor in realizing the optimal allocation of power. Demand response (DR) management mechanisms based on real-time pricing (RTP) can effectively promote the enthusiasm of users, stimulate the efficiency of microgrids for power dispatch, and achieve the goasl of power peak shifting and valley filling. In this paper, we consider a microgrid composed of several energy providers (EPs) and multiple users, and each user is equipped with several electric vehicles (EVs). It should be noted that EVs may be attacked by networks in the process of data exchange when EVs connect to the MG. In this environment, we establish a multi-time slots social welfare maximization model that reflects the common interests of EPs and users. To simplify the problem, we decompose this multi-time slots model into a set of single-time slot optimization problems by the relaxation method. Furthermore, the mechanisms of identification and processing (MIP) for EVs under cyber-attack are proposed. The problem is decoupled to EPs and users by duality decomposition. Then, through integration with MIP, a distributed RTP algorithm based on the dual subgradient algorithm is designed to obtain the optimal electricity price. Finally, the simulation results verify the feasibility of the model and the effectiveness of the proposed algorithm. Through comparative analysis, the necessity of identifying EVs under cyber-attack is fully embodied.

The optimal carbon tax mechanism for managing carbon emissions

Along Pigouvian lines, the carbon tax not only exceeds the carbon emission damage imposed on society, but ignores the potential cost from deliberate carbon abatement, which in turn challenges the stability and optimality of the Pigouvian solution. For correcting these distortions, this paper amends the standard Pigouvian version by using piecewise tax functions to approximate the social damage curve of carbon emissions. An optimal carbon tax mechanism is designed, where the tax is endogenously determined from social welfare maximization. With the help of a modification instrument, the carbon tax corrects emitters’ non-optimal individual decision and the social optimum is implemented efficiently. How to put the carbon tax into practice is examined under both the deterministic and stochastic modeling settings. In both cases, we demonstrate the structure and effectiveness of the carbon tax in detail. Moreover, a flexible adjustment tax scheme is proposed, which may produce the double-dividend effect that reduces carbon emissions and relieves financial burden of carbon abatement simultaneously. These may improve the application of market-based carbon-reducing tools in public management and pollution regulation.

P2P Multigrade Energy Trading for Heterogeneous Distributed Energy Resources and Flexible Demand

This paper proposes a novel peer-to-peer (P2P) multi-grade energy trading design to encourage demand side flexibility to locally absorb the uncertainty of renewable distributed energy resources (DERs) in distribution networks. In particular, a reliability credit (RC) assignment method is developed for customers to differentiate the energy grades considering the heterogeneity in energy supplying reliability of DERs and the consumption preferences of customers. Later, an innovative P2P multi-grade energy trading model is introduced where different types of demand are matched up with the corresponding grades of energy. The market clearing is modeled as a social welfare maximization problem considering the net utilities of customers and the profits of DER producers. Furthermore, a fast decentralized pricing algorithm is developed that achieves the maximum of social welfare and also preserves the privacy of individual participants. To substantially improve the computational efficiency, a fast solution method is devised for customers to solve the optimal energy procurement combination problem, and closed-form solutions are derived for DER producers to allocate energy optimally. Numerical tests on a 69-node distribution feeder validate the superiority of the proposed P2P multi-grade energy trading scheme compared to the benchmark scheme. The high efficiency and scalability of the proposed fast pricing algorithm are corroborated by the simulation on the IEEE 8500-node distribution system.

A multi-rate hybrid model for real-time iterative bidding coupled with power system dynamics

To maintain the system's real-time balance, mature electricity markets generally tend to raise the frequency of market clearing. Given the shrinking time lag between the price update process and underlying electromechanical dynamics, it inevitably considers the two in a unified, dynamical framework. However, the relationship between market activities and the evolution of electricity networks has received scant attention in pertinent research. Inspired by the primal-dual gradient method and modern control theory, we propose a novel multi-rate hybrid model that simultaneously accounts for the continuous nature of physical system dynamics and the discrete nature of market clearing. To maintain the asymptotical convergence of the proposed model, we derive the explicit constraints on the upper bounds of market clearing and bidding intervals. These bounds can be determined without prior knowledge of the Nash equilibrium, and even the time schedules don't have to be periodic, which dramatically improves the applicability of the proposed model in practical market operation. The whole model provides a new perspective to study economic-physical problems, which can be readily implemented in a distributed way and achieve social welfare maximization along with frequency regulation.

On the effects of airport capacity expansion under responsive airlines and elastic passenger demand

This paper investigates the effect of airport expansion on air traffic and its implications on airport congestion, airline competition and the social welfare, considering various airport administrative regimes (i.e., profit-maximization, social welfare-maximization, and budget-constrained social welfare-maximization), airline market structures (i.e., perfectly competitive, oligopoly, monopoly, leader–follower), and passenger demand patterns (i.e., regard airlines as perfect substitutes or imperfect substitutes). We develop an analytical tri-level model to examine the air traffic equilibrium in the airport–airline–passenger system, the effect of airport capacity expansion on the traffic equilibrium, and the decisions of different stakeholders. Specifically, we examine the airport’s decisions on capacity investment and airport charge in the first level, airlines’ decisions on flight volume and airfare in the second level, and the passenger choice equilibrium in the third level. The analysis in this paper suggests that (i) airport capacity expansion may induce the airline market to over schedule flights which leads to a more congested airport (i.e., capacity paradox); (ii) with a given airport charge, the capacity paradox is more likely to occur in an airline market with fewer competitive airlines; (iii) given the same airport capacity and traffic, the capacity paradox is more likely to occur when the airport operator’s objective is social welfare-maximization (compared with profit-maximization and budget-constrained social welfare-maximization); (iv) airlines with market power will internalize a portion of airport congestion based on their market share, while a leader airline with the knowledge of the follower’s response will scale up or down their airfare in order to maximize its profit; (v) under different market structures, increasing airport charge always reduces the aggregate traffic volume when the airport capacity is fixed.

Managing parking with progressive pricing

Parking supply and demand are often imbalanced in urban areas, causing adverse consequences such as excessive search times and long walking distances. Many parking authorities price parking as a demand management strategy by charging either a fixed daily fee or an hourly price for parking. An emerging alternative is progressive pricing, whereby drivers pay an hourly price that increases if their tracked parking duration is longer than a predetermined threshold. This study investigates the optimal design of progressive pricing for revenue and social welfare maximization when there are two market segments. We study equilibrium properties of progressive pricing and show that its optimal design for revenue maximization segmentizes the demand. In contrast, progressive pricing does not improve social welfare over hourly pricing, because any additional surplus accrued by drivers is offset by the revenue of the parking authority. We develop a micro-simulation model of the City of Toronto’s downtown core with parking capability, and show that progressive pricing lowers average parking occupancy and search time in high demand parking clusters by 5.6% and 12.5%, respectively, compared to hourly pricing.

Optimal microgrid programming based on an energy storage system, price-based demand response, and distributed renewable energy resources

Microgrids (MGs) in distribution systems can be operated in far regions at lower investment costs using renewable distributed energy resources (DERs). The present paper introduces a stochastic model for optimal energy-heat programming and the daily storage of an MG. Bi-level stochastic programming is presented for integrated energy-heat scheduling and storage in the presence of an energy storage system (ESS) and demand response (DR) based on social welfare maximization. Out of the incentive-based DR programs, the tender and redemption and the ancillary services market programs were selected and applied to the given model. Besides, the time of use (TOU) -based DR and real-time pricing (RTP) were considered as the price-based demand response (PBDR) programs in optimal programming. The PBDR programs have been included in the objective function using a linear function based on consumer benefits. The proposed bi-level stochastic model was solved using a developed metaheuristic optimization algorithm called the lightning search algorithm (LSA) in the present work. The Latin hypercube sampling (LHS) and KMEANS methods were used to produce and reduce the scenario. The proposed framework was investigated in a 33-bus test model. The obtained simulation results were evaluated from different aspects. The TOU and RTP effects and ESS are shown in obtained numerical analysis by considering the operating cost, total social welfare, and the client's utility function.

Carbon emission reduction policy with privatization in an oligopoly model.

This study constructs a mixed oligopoly model that includes a public enterprise and two private enterprises. Game theory was adopted to explore the effects of carbon emission reduction policies. In addition, this study analyzes the optimal carbon emission trading prices and privatization decisions. The results show that the proportion of state-owned shares and the equity efficiency gap affect the equilibrium results for different carbon emission policies. Privatization increases the profits of public firms but does not necessarily increase social welfare. Different carbon emission reduction policies have different effects on the equilibrium results. Moreover, the emission reduction target is not completely consistent with the maximum social welfare target and should be comprehensively considered. The government can intervene by setting carbon emission trading prices and making privatization decisions. Full and partial privatization may be optimal decisions.