Python‐based multi‐agent platform for application on power grids

Melo, Lucas Silveira; Sampaio, Raimundo Furtado; Leão, Ruth Pastôra Saraiva; Barroso, Giovanni Cordeiro; Bezerra, José Roberto

doi:10.1002/2050-7038.12012

Cited by 34 publications

(12 citation statements)

References 30 publications

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“…Communication between agents in this case is based on a standard language called FIPA-Agent Communication Language (ACL), which uses protocols FIPA-Request Protocol, FIPA-ContractNet and FIPA-Subscribe. 29 The agents are developed and executed on the PADE platform, which is hosted on the GitHub repository. 28 The co-simulation between PSCAD TM and PADE is performed using the automation library provided by PSCAD TM , which uses TCP/IP communication network to send and receive data on a real-time basis.…”

Section: Secondary Control Based On a Multi-agent Systemmentioning

confidence: 99%

Microgrid distributed secondary control and energy management using multi‐agent system

Almada

Leão

Almeida

et al. 2021

Int Trans Electr Energ Syst

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This paper is concerned with the two-stage hierarchical control of microgrids (MG) that can operate in both grid-connected and off-grid modes. The procedure consists in the segmentation of the control scheme in the primary and secondary levels, thus enabling the application of faster and slower dynamics according to the process. A decentralized droop control-based method is used in the primary level, while a distributed multi-agent coordination scheme is applied in the secondary one. The multi-agent system is run on Python Agent DEvelopment (PADE) environment, a FIPA-compliant agent framework. The key contributions this paper provides are the control strategies of reactive power output of nondispatchable sources, dynamic sharing of energy storage systems, and the management of loads of microgrids, all of them based on multi-agent system. Cosimulations using PSCAD TM and PADE are performed considering several operating scenarios of the MG resources under different load conditions. Simulation results validate the accurate performance of the proposed control strategies. K E Y W O R D Sdistributed energy resources, distributed generation, droop control, energy storage system, hierarchical control, microgrid, multi-agent systems List of abbreviations: ω*, frequency for P*; V*, voltage amplitude for Q*; ω, measured frequency; V, measured voltage amplitude; P*, active power reference; Q*, reactive power reference; P, measured active power; Q, measured reactive power; G P (s), active power controller; G Q (s), reactive power controller; m p , proportional gain of active power; m d , derivative gain of active power; m i , integral gain of active power; n p , proportional gain of reactive power; n i , integral gain of reactive power; MOP, MG status; α, coeficiente modifica a potência BT com seu SoC; Δδ, angular difference between the MG and the main network voltages; k, gain for achieve synchronism; f nom and ω nom , nominal frequency [Hz and rad/s]; S nom , rated power; V nom , nominal voltage; Z cable , cable impedance; E max , maximum energy of battery bank; P MPP , optimal instantaneous active power of the nondispatchable source; β, adjustment factor for reactive power; Q max , maximum active power generated by intermittent sources; Q L , reactive power requested by the load; Q int *, reference reactive power for intermittent resources; Q disp *, reference reactive power for dispatchable resources; CX x , the availability of the resource (0 = unavailable, 1 = in operation); nd, number of dispatchable DERs; ni, number of intermittent DERs; ACL, agent communication language; AgDERdisp, agent of dispatchable DER; AgDispG, agent of dispatchable generation; AgESS, agent of ESS; AgIG, agent of intermittent generation; AgIGB, agent of intermittent generation balance; AgL, agent of load; AgPCCandB, agent of pcc and balance; AgTL, agent of total load;

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Section: Secondary Control Based On a Multi-agent Systemmentioning

confidence: 99%

Microgrid distributed secondary control and energy management using multi‐agent system

Almada

Leão

Almeida

et al. 2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

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“…The use of multi-agent systems on a particular area begins by partitioning the problem into smaller components. A particular agent is assigned to each of these components to perform particular tasks and reach its goals, thus making problem-solving process easier [23].…”

Section: Openflow Protocolmentioning

confidence: 99%

“…For simulating a multiagent system, we used PADE [23] which is a framework for developing, running, and managing multiagent systems in distributed computing environments. PADE is an open-source platform implemented in Python language and uses the twisted libraries for implementing the communication between the network nodes.…”

Section: Multiagent Systems Frameworkmentioning

confidence: 99%

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

2021

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“…As an interaction-oriented autonomous learning paradigm, Multiagent Reinforcement Learning (RL) allows robots to learn the mapping from state to action through the reward obtained by interaction with the environment, so as to cooperate with robot behavior and complete specific tasks, which is widely used in multirobot systems [15][16][17]. In reinforcement learning, each agent learns the optimal strategy by interacting with its dynamic environment [18]. When single agent reinforcement learning is applied to a multiagent system, reinforcement learning faces some challenges.…”

Section: Introductionmentioning

confidence: 99%

Semantic Interaction Strategy of Multiagent System in Large-Scale Intelligent Sensor Network Environment

2022

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In a multiagent system, the semantic interaction between agents is an important aspect affecting multi-intelligence. The purpose of interaction is to reasonably arrange task objectives and behaviors through information sharing and communication interaction, so as to maximize the overall performance of multiagent system. This paper analyzes the communication and interaction process between agents from the perspective of semantic layer and introduces the BDI (belief, desire, intention) model of agent’s thinking state into the communication and interaction process. Furthermore, we propose a multiagent semantic interaction strategy model based on a large-scale intelligent sensor network, which supports various types of negotiation and interaction on the basis of basic interaction behavior to solve the problem of information operational conflicts. In addition, this paper limits the scale of historical information through the definition of equivalence and the merging theorem of history, and it uses reinforcement learning algorithm to detect possible conflicts and delay communication and makes rational use of limited resources to improve system revenue and coordination efficiency. The experimental results show that compared with the previous methods such as debate and negotiation, the strategy model can realize the flexible interaction based on scene and is more practical. At the same time, the existence of reinforcement learning improves the efficiency analysis and the convergence performance of semantic interaction strategy.

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Python‐based multi‐agent platform for application on power grids

Cited by 34 publications

References 30 publications

Microgrid distributed secondary control and energy management using multi‐agent system

Microgrid distributed secondary control and energy management using multi‐agent system

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

Semantic Interaction Strategy of Multiagent System in Large-Scale Intelligent Sensor Network Environment

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