Multi-agent system for microgrids: design, optimization and performance

Tazi, Khadija; Abbou, F. M.; Abdi, Farid

doi:10.1007/s10462-019-09695-7

Cited by 48 publications

(19 citation statements)

References 127 publications

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“…• Locally storage of user's energy information and decision-making will protect consumer's privacy and alleviate network usage, and therefore the communication bandwidth requirement will be more relaxed. 37 As an optimization problem, the nature of MGDC requires distributed optimization unlike the conventional form, which is used in MGCC. Generally, distributed optimization techniques can be categorized into four main divisions.…”

Section: Distributed Emsmentioning

confidence: 99%

Energy management system of microgrid: Control schemes, pricing techniques, and future horizons

et al. 2021

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The tremendous engagement of electric energy consumers and the advent of smart grids have left more complexity and challenges in terms of energy management system. Recently, microgrid is a preferable choice to cope with these challenges as small-scale power system and so close to consumers. However, there is a crucial need to find more compatible solutions to achieve economic, environmental, and reliable objectives of energy management system, since most current solutions are based on optimal scheduling of generating units at the supply side. Demand side management develops more opportunities to achieve these objectives by efficiency programs and demand response programs (pricing techniques). Therefore, this article reviews and assesses demand side management, particularly pricing techniques, in the light of energy management system as a part of control system of microgrid. Furthermore, the aspects of control schemes are discussed including centralized and distributed controls. Finally, this article identifies a number of shortcomings in the current research that concern demand side management and highlights future horizons of research.

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Section: Distributed Emsmentioning

confidence: 99%

Energy management system of microgrid: Control schemes, pricing techniques, and future horizons

et al. 2021

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“…Contrarily, having a relatively small number of agents results in a faster convergence and a lower system latency. 27 Some references use MAS in order to improve the reactive power sharing. The improvement occurs through the interaction between the primary and the secondary control by using an additional variable in the consensus, 17 an adaptive virtual impedance, 18 and an adaptive droop gain event-triggered.…”

Section: Literature Reviewmentioning

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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“…Besides, conventional mathematical modelling schemes are usually used for solving static problems, and in the case of dynamic problems, it is necessary to remodel once the problems change [10]. Accordingly, the conventional modelling method has some limitations and cannot fit the solution of such problems [11].…”

Section: Complex External Situationmentioning

confidence: 99%

“…e reusable resource conflict coordination mechanism can be used by multiple decision-making agents simultaneously to generate repeated resource conflicts. For these resource conflicts, the tasks exhibiting higher priority are reserved and the tasks with lower priorities are coordinated (1) Procedure MA-HTN (s, T, D) (2) set s, initial state (3) set T, initial task set (4) set D, domain (5) initial operator ActionID � 0 (6) set P � the empty plan (7) T 0 ⟵ t belongs to T : no other task in T is constrained to precede t (8) loop (9) if T is empty then return P (10) nondeterministically choose any t in T 0 (11) if t is a primitive task then (12) choose a ground instance a for t with the smallest cost among the available resources (13) ActionID � ActionID + 1 (14) opmessage(a) � (ID, Resourceused(a), st(a), et(a), cost(a)) (15) sendlist ⟵ opmessage(a) (16) send opmessage(a) to the other planners (17) if receivelist ≠ ∅ : (18) ResourceID � Resourceused(a) (19) conflict actions a′ ⟵ a′ belongs to the extopmessages of receivelist : (20) has Resource(a′) � ResourceusedID and has the smallest st(a′) (21) and duration(st, et) is overlapped with duration(st′, et′)}, then (22) if st < st′ (23) delete the rest extopmessage in receivelist (24) Resourcestate(r) � i, Resourcest(r) � st, Resourceet(r) � et (25) delete T in T 0 and add a into P (26) else if st > st′ (27) delete opmessage(a) in sendlist (28) Resourcestate(r) � i′, Resourcest(r) � st′, Resourceet(r) � et′ (29) Backtrack (30) else delete T in T 0 and add a into P (31) if t is a nonprimitive task then (32) choose a method to decompose t into subtasks t 1 , t 2 , . .…”

Section: Coordination Mechanism Of Local Conflicts For the Contradicmentioning

confidence: 99%

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Zhang

Gang

Song

et al. 2019

Mathematical Problems in Engineering

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For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.

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Multi-agent system for microgrids: design, optimization and performance

Cited by 48 publications

References 127 publications

Energy management system of microgrid: Control schemes, pricing techniques, and future horizons

Energy management system of microgrid: Control schemes, pricing techniques, and future horizons

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

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

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