Welfare Optimization Research Articles

Strategic Behavior and Social Welfare Optimization in a Batch-service Queueing System with Threshold Policy

Strategic Behavior and Social Welfare Optimization in a Batch-service Queueing System with Threshold Policy

Decentralized energy trading in microgrids: a blockchain-integrated model for efficient power flow with social welfare optimization

Decentralized energy trading in microgrids: a blockchain-integrated model for efficient power flow with social welfare optimization

How to achieve fair and efficient cooperative vehicle routing?

A cooperative is a business entity with the primary objective of providing benefits, services, and goods to its members, who both own and exercise democratic control over it. In the context of a cooperative, a fleet typically consists of vehicles owned by self-concerned individually rational owners who prioritize their own efficiency and the fairness of the system. This fairness refers to how their individual gain aligns with the gain of others. In this paper, we focus on the routing of such cooperative fleets. Considering only the fleet’s efficiency in terms of minimising its overall cost, the studied problem corresponds to the multiple Traveling Salesman Problem (mTSP). However, our interest lies in finding both efficient and fair solutions, so we propose two new variants of this problem that integrate and maximise the fleet’s egalitarian and elitist social welfare. Additionally, to enhance the balance between fleet efficiency and fairness, we propose the systematic elitist and systematic egalitarian social welfare optimisation algorithm. Through simulation results, we observe a wide diversity of routes depending on the approach considered. Therefore, a cooperative may choose a model that best balances its fleet’s efficiency and fairness based on its specific requirements.

Dynamic, fair, and efficient routing for cooperative autonomous vehicle fleets

This paper addresses challenges in agricultural cooperative autonomous fleet routing through the proposition, modeling, and resolution of the Dynamic Vehicle Routing Problem with Fair Profits and Time Windows (DVRP-FPTW). The aim is to dynamically optimize routes for a vehicle fleet serving tasks within assigned time windows, emphasizing fair and efficient solutions. Our DVRP-FPTW accommodates unforeseen events like task modifications or vehicle breakdowns, ensuring adherence to task demand, vehicle capacities, and autonomies. The proposed model incorporates mandatory and optional tasks, including optional ones in operational vehicle routes if not compromising the vehicles’ profits. Including asynchronous and distributed column generation heuristics, the proposed Multi-Agent-based architecture DIMASA for the DVRP-FPTW dynamically adapts to unforeseen events. Systematic Egalitarian social welfare optimization is used to iteratively maximize the profit of the least profitable vehicle, prioritizing fairness across the fleet in light of unforeseen events. This improves upon existing dynamic and multi-period VRP models that rely on prior knowledge of demand changes. Our approach allows vehicle agents to maintain privacy while sharing minimal local data with a fleet coordinator agent. We propose publicly available benchmark instances for both static and dynamic VRP-FPTW. Simulation results demonstrate the effectiveness of our DVRP-FPTW model and our multi-agent system solution approach in coordinating large, dynamically evolving cooperative autonomous fleets fairly and efficiently in close to real-time.

Composite Optimization With Coupling Constraints via Penalized Proximal Gradient Method in Asynchronous Networks

In this paper, we consider a composite optimization problem with linear coupling constraints in a multi-agent network. In this problem, the agents cooperatively optimize a strongly convex cost function which is the linear sum of individual cost functions composed of smooth and possibly non-smooth components. To solve this problem, we propose an asynchronous penalized proximal gradient (Asyn-PPG) algorithm, a variant of classical proximal gradient method, with the presence of the asynchronous updates of the agents and uniform communication delays in the network. Specifically, we consider a slot-based asynchronous network (SAN), where the whole time domain is split into sequential time slots and each agent is permitted to execute multiple updates during a slot by accessing the historical state information of the agents. By the Asyn-PPG algorithm, an explicit convergence rate can be guaranteed based on deterministic analysis. The feasibility of the proposed algorithm is verified by solving a consensus-based distributed regression problem and a social welfare optimization problem in the electricity market.

Social welfare optimisation in a Hotelling model with two double-sided queues

Social welfare optimisation in a Hotelling model with two double-sided queues

The labour market dilemma of young urban women in India: An outcome of family welfare optimization

This paper uses a theoretical model to explain the patterns and determinants of labour market participation behaviour of young urban women in India. Based on the National Sample Survey (NSS) data and through probit regression results, it argues that the labour market participation decision of young women is an outcome of the joint utility maximization behaviour of their family. The standard of living of the family, market wage, other family characteristics, including the number of children, adult women in the family, elderly members, and occupation, and gender of the head, etc., are significantly determining their labour market participation; apart from women’s individual characteristics like age (experience) and level of education and training. The empirical result also reflects that the “discouraged worker effect” is stronger than the “added worker phenomenon”. Hence, measures to create jobs in modern services could help boost the stagnant female labour force participation in urban India.

Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners Under Networking Constraints

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are not only heterogeneous but also not fully generated locally. In this paper, we propose an experimental design network paradigm, wherein learner nodes train possibly different Bayesian linear regression models via consuming data streams generated by data source nodes over a network. We formulate this problem as a social welfare optimization problem in which the global objective is defined as the sum of experimental design objectives of individual learners, and the decision variables are the data transmission strategies subject to network constraints. We first show that, assuming Poisson data streams in steady state, the global objective is a continuous DR-submodular function. We then propose a Frank-Wolfe type algorithm that outputs a solution within a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1-1/e$</tex-math> </inline-formula> factor from the optimal. Our algorithm contains a novel gradient estimation component which is carefully designed based on Poisson tail bounds and sampling. Finally, we complement our theoretical findings through extensive experiments. Our numerical evaluation shows that the proposed algorithm outperforms several baseline algorithms both in maximizing the global objective and in the quality of the trained models.

An Integrated Human–Cyber–Physical Framework for Control of Microgrids

In this paper, to jointly study the energy dynamic behavior of humans and the corresponding physical dynamics of the microgrid, we bridge two disciplines: systems & control and environmental psychology. Firstly, we develop second order motivation-behavior mathematical models inspired by opinion dynamics models for describing and predicting human activities related to the use of energy, where psychological variables and social interactions are considered. Secondly, based on these models, we develop a human-cyber-physical system framework consisting of three layers: (i) human, (ii) cyber and (iii) physical. The first one describes human behavior influenced by behavioral intervention and motivation, which in turn depend on contextual factors, personal values and social norms. The cyber layer solves an optimization problem and embeds load controllers, which are designed to automatically mimic human behavior. Finally, the physical layer represents an AC microgrid. Thus, we formulate a social-physical welfare optimization problem and solve it by designing a distributed primal-dual control scheme, which generates the optimal behavioral intervention (with respect to a given reference) and the control inputs to the microgrid.

Social Welfare-Based Task Assignment in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a novel data-collection paradigm in the internet of things. Social welfare is an important factor in the task allocation because it integrates the interests of all parties involved in MCS and represents societal satisfaction. The ultimate goal of task allocation is to maximize social welfare as much as possible. Existing social welfare optimization research does not consider the moral and psychological characteristics of people in the real world. In this study, the real-world situation is considered. A task allocation strategy, which includes two stages, is formulated for task allocation. A generalized shortest path algorithm and an optimal pricing algorithm are proposed for each stage. To evaluate the proposed algorithms, extensive simulation experiments are conducted on two real-world datasets. The experimental results demonstrate that the proposed algorithms produce the desired effects, and the proposed strategy significantly increases social welfare by 19% compared to another method.

Federated learning application in distributed energy trading in integrated energy system

Privacy protection in electricity market transactions is a long-term topic, and it must be considered in the application of any new power system project today. This paper explores a new welfare optimization method considering privacy protection in the field of energy trading based on federated learning (FL). First, this paper models an integrated energy system (IES) with one distributed network manager (DNM) and multiple load aggregators (LAs). This model adopts the idea of Stackelberg game, makes DNM dominate the trading game, and makes LAs as followers. Then, it uses the FL-Stackelberg method proposed for the first time to solve the established individual welfare optimization model participating in the game and compares the results with those of the traditional hierarchical optimization method and the Mathematical Program with Equilibrium Constraint optimization method. Numerical results at the end of the paper prove the good accuracy and calculation speed of the proposed FL-Stackelberg​ optimization algorithm, and demonstrate the reliability of FL in engineering practice of IES.

Analyzing congestion surplus distribution based on social welfare optimization

The distribution of congestion surplus affects not only the short-term economic interests of stakeholders in the power spot market, but the improvement of social welfare in the long run. At present, the congestion surplus in mature electricity spot markets of developed countries is usually returned to the power users or power generation enterprises according to established proportions. Although this method helps achieve rational distribution of congestion surplus, it does not offer a solution to the congestion problem and cannot achieve maximized social welfare. In light of the principles of welfare economics, we propose in this paper injecting the congestion surplus into the capital pool of the power grid as a transmission line expansion fund to fundamentally solve the power transmission congestion problem. Modeling results and case studies presented in our work verified that our proposed distribution model could achieve Pareto optimization. Finally, combining the power transmission and distribution (PTD) pricing and supervision method, this paper puts forward the method for use and supervision of congestion surplus on the power spot market to reach optimal allocation of resources.

Social Welfare Optimization in a Hotelling Model with Two Double-sided Queues

Social Welfare Optimization in a Hotelling Model with Two Double-sided Queues

Competitive Equilibriums of Multi-Agent Systems over an Infinite Horizon

In this paper, we investigate the properties of competitive equilibriums for dynamic multi-agent systems (MAS) over an infinite horizon. When there is no external resource supply, a group of dynamic agents with distributed resource allocations form a transactive market to share their resources at a specific price. Each agent makes decisions on the locally consumed resource and the traded resource. The system reaches a competitive equilibrium when each agent's payoff is maximized and the tradings are balanced. Firstly, we consider general utility functions and show that under feasibility assumptions, any competitive equilibrium maximizes the social welfare. Secondly, we prove that for sufficiently small initial conditions, the social welfare maximization solution constitutes a competitive equilibrium with zero price. We also prove for general feasible initial conditions, there exists a time instant after which the optimal price, corresponding to a competitive equilibrium, becomes zero. Finally, we specifically focus on quadratic MAS for which the system-level social welfare optimization becomes a classical constrained linear quadratic regulator (CLQR) problem. We construct explicitly a feasible set of initial conditions under which the price under a competitive equilibrium is zero for all time.

A Combinatorial Double Auction for Community Shared Distributed Energy Resources

The integration of distributed energy resources (DERs), such as renewable energy (RE) and energy storage systems, can have many environmental and economic benefits. The sharing of medium-scale DERs can enhance these benefits by ensuring efficient resource utilisation and can promote sharing economy and economies of scale opportunities. In this paper, we design a combinatorial double auction (CDA) for DER sharing between multiple DER providers and a community of consumers. The proposed DER market addresses the complementarity of DER services and supports the environmentally sustainable behaviour of consumers. The purpose of the proposed auction is to allocate the limited DER resources efficiently without compromising the privacy or the comfort of consumers. First, the combinatorial bidding rules are designed and the social welfare optimisation problem is formulated. Then, utility maximising strategies for consumers with both economic and environmental objectives are proposed. Additionally, three types of DER providers are introduced, and their revenue maximising strategies are investigated. A case study with real world RE generation and demand data from the UK is presented. Simulation results show that the proposed CDA can greatly reduce energy emissions (by 24%) while also enhancing the revenues of DER providers, with gains up to 82%, when compared with the case where DER energy is sold through a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -double auction.

Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called "preparation range" to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid.

Coded Distributed Computing With Predictive Heterogeneous User Demands: A Learning Auction Approach

Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers in distributed computing. In this paper, we focus on the auction design for efficient resource allocation in CDC. Specifically, we aim to design a learning auction mechanism to handle heterogeneous user demands and also to free users from the complexity of specifying valuations for resource combinations, which increases exponentially with the resource dimensions. The user demand type is heterogeneous according to different variation trends of the value with finish time and workload, which is modeled by deep learning. The platform would allocate resources according to the user value function. Then users do not need to consider the complex relationship between uncertain finish time and resource configuration in CDC. Due to the inference error of the learning model and the complexity of calculating uncertain finish time, the considered social welfare optimization problem is a non-linear and non-convex integer problem. Even worse, the typical VCG-based payment scheme cannot guarantee truthfulness with the inference error. In response to these difficulties, we transform the social welfare optimization problem into a mixed integer programming problem which already has efficient solutions. The social welfare gap caused by the inference error is analyzed theoretically. The relationship between the utility regret of reporting truthfully and the inference error is also analyzed. We prove that our mechanism satisfies incentive alignment and individual rationality. Extensive experiments show the superiority of our mechanism compared with existing ones.

From optimal to robust climate strategies: expanding integrated assessment model ensembles to manage economic, social, and environmental objectives

Cost-benefit integrated assessment models generate welfare-maximizing mitigation pathways under a set of assumptions to deal with deep uncertainty in future scenarios. These assumptions include socio-economic projections, the magnitude and dynamics of climate impacts on the economy, and physical climate response. As models explore the uncertainty space within the boundaries of their objective functions, they risk providing scenarios which are too narrow and not sufficiently robust. Here, we apply robust and multi-objective decision-making methods to extract relevant information from a large ensemble of optimal emissions-reduction pathways generated by a regionalized cost-benefit integrated assessment model under deterministic welfare optimization. We show that shifting the focus from optimal to robust solutions reduces the uncertainty in mitigation strategies and aligns them toward the Paris goals. Moreover, we analyze the trade-offs between climatic (temperature), social (inequality) and economic (welfare) objectives and illustrate four robust pathways under various decision-making criteria. We show that robust mitigation strategies can lead to regional emission-reduction strategies which are fair. Our results show how to extract more comprehensive climate strategies from available scenario ensembles and that the highest discrepancies at the local level policies are found in the developing and most-impacted regions.

Policy assistance for adoption of residential solar PV in India: A stakeholder-centric approach for welfare optimization

This paper presents a comprehensive analysis of the consumer-centric business model for rooftop solar PV installations in India. We explore areas where potential policy interventions may be introduced to improve collective stakeholder benefits and incentivize more domestic consumers to adopt rooftop solar power generation in their premises. The proposed policy framework optimizes Feed-in Tariff (FiT) rates, PV capacities and Average Billing Rates (ABRs) towards maximizing stakeholder benefits. The stakeholders considered are the consumers/prosumers and the utility. Case studies with three residential prosumers of different demand and generation profiles are presented. The models for utility profit and prosumer savings are developed, and a multi-objective problem is formulated with FiT, generation capacity (as a function of demand) and ABR as decision variables. The pareto-optimal front is identified for prosumer and utility benefits and suitable points with reasonable tradeoffs are selected based on sensitivity analysis of the impact on collective welfare. The suitability of prevailing tariff and FiT rates of two Indian utilities namely, MSEDCL and TATA POWER, Delhi are studied, and their impact on prosumer savings and utility profits is brought out. The workflow to fix tariff, FiT and local PV capacities in active residential distribution systems is devised, providing the policymakers an effective decision-making tool.

A Game-Theoretical Approach for Secure Crowdsourcing-Based Indoor Navigation System With Reputation Mechanism

At present, the crowdsourcing-based indoor navigation system (CINS) has attracted extensive attention from both industry and academia owing to its low-cost and high-accuracy performance. Unfortunately, the system that relies on crowdsourced data is vulnerable to the collusion attack, which leads to severe security issues. To address the security issues in the CINS, we propose to utilize a fully trusted fog server platform to advocate secure transactions between service requesters and responders. First, we propose a novel reputation incentive mechanism based on the behaviors of responders. Then, we employ the offensive and defensive game to model the interactions between the fog server platform and the responders, whereby a social welfare optimization problem is formulated to maximize the social welfare of the system. Next, the game equilibriums are found by using the replicator dynamic equation while the game stability is discussed. Finally, the simulation results show that the proposed mechanism can effectively encourage responders to provide positive navigation services and obtain more social welfare of the system compared with the conventional mechanisms.