Game-Theoretic Multi-Agent Control and Network Cost Allocation Under Communication Constraints

Abstract

Multi-agent networked linear dynamic systems have attracted attention of researchers in power systems, intelligent transportation, and industrial automation. The agents might cooperatively optimize a global performance objective, resulting in social optimization, or try to satisfy their own selfish objectives using a noncooperative differential game. However, in these solutions, large volumes of data must be sent from system states to possibly distant control inputs, thus resulting in high cost of the underlying communication network. To enable economically-viable communication, a gametheoretic framework is proposed under the communication cost, or sparsity, constraint, given by the number of communicating state/control input pairs. As this constraint tightens, the system transitions from dense to sparse communication, providing the trade-off between dynamic system performance and information exchange. Moreover, using the proposed sparsity-constrained distributed social optimization and noncooperative game algorithms, we develop a method to allocate the costs of the communication infrastructure fairly and according to the agents' diverse needs for feedback and cooperation. Numerical results illustrate utilization of the proposed algorithms to enable and ensure economic fairness of widearea control among power companies.

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

“…Thus, under this circumstance, a non-cooperative game model based on the distributed energy system for coordinated operation in the MGC is presented in [53,54], the optimal balanced state of each agent can be achieved. Moreover, the non-cooperative game model cannot be applied to the large-scale computing and simulation analysis scenario for the dynamic game process of the MGC.…”

“…Graph theoretic topology model [45][46][47][48][49][50][51] ·Simple model structure ·High redundancy and easy to expand ·Robustness is greatly affected by graph Non-cooperative dynamic game model [52][53][54] ·Each agent can achieve the optimal balanced state ·Algorithm is complex and time-consuming Genetic algorithm [55][56][57] ·High prediction accuracy ·Fast convergence ·Scalability and parallelism operation ·Most of the parameters depend on experience ·Slow dynamic response PSO algorithm [58][59][60][61] ·Simple model structure ·Fast computation speed ·Efficient economic scheduling ·Improve the frequency and voltage of MG ·Not handling the discrete optimization problems…”

“…Distributed multi-agent control method has been widely used to establish optimal model to enhance reliability and energy management, optimization, and improve the performance of ancillary services [44]. Various methods for system modelling, including topology models and mathematic models, in MAS have been reviewed in this paper, such as the graph topology model [45][46][47][48][49][50][51], non-cooperative game model [52][53][54], genetic algorithm [55][56][57] and particle swarm optimization (PSO) [58][59][60][61], etc. Moreover, consensus protocol is the rule of interaction among multiple agents in the complex systems, which describes the process of information interaction among agents and their neighbors.…”

Incentive Design in a Distributed Problem with Strategic Agents

“…In [20], [21] the authors use cost allocation based on standalone cost principle and cost causation principle respectively. A different, but related, line of work is that in [22], [23] that develops a distributed algorithm based on the Nash Bargaining Solution to fairly allocate the costs of the communication infrastructure in [22] and power management in [23] among the agents. However, to the best of our knowledge, the literature on cost allocation assumes the agents to be not cost anticipatory, in the sense that they do not optimize the decision that they take with respect to both their a priori utility function and the cost that will be allocated to them.…”

Joint Frequency Regulation and Economic Dispatch Using Limited Communication

“…Using power line measurement, a control policy was developed in [11] to withstand any single communication link failure. The role of communication network topology on power grid control has been studied in [12], [13].…”