Multi-agent based distributed control architecture for microgrid energy management and optimization

Khan, M. Reyasudin Basir; Jidin, Razali; Pasupuleti, Jagadeesh

doi:10.1016/j.enconman.2016.01.011

Cited by 167 publications

(36 citation statements)

References 31 publications

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“…Energy supply in this system is a balance between electric power provided by a power line and that obtained from domestic loads generated by user sources [5][6][7]. Supervision and control of energy flow is managed by a Microgrid Central Control(MGCC) [8][9][10], which manages the balance between energy consumption, the main supply, and energy from microgrid components [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, in the legacy LV system [15] the phase-load imbalance is a drawback, especially because domestic loads generated by single-phase consumers affect grid phase stability, and the energy quality supplied [16,17]. Thus, some methods of solving this problem are highlighted in the the electrical current injection from distributed generation microgrids [18][19][20], the coordinated load balance [16,21], the integrated multimicrogrid control [12,[22][23][24] and the load phase balance [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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A Load-Balance System Design of Microgrid Cluster Based on Hierarchical Petri Nets

et al. 2018

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In the new paradigm of urban microgrids, load-balancing control becomes essential to ensure the balance and quality of energy consumption. Thus, phase-load balance method becomes an alternative solution in the absence of distributed generation sources. Development of efficient and robust load-balancing control algorithms becomes useful for guaranteeing the load balance between phases and consumers, as well as to establish an automatic integration between the secondary grid and the supervisory center. This article presents a new phase-balancing control model based on hierarchical Petri nets (PNs) to encapsulate procedures and subroutines, and to verify the properties of a combined algorithm system, identifying the load imbalance in phases and improving the selection process of single-phase consumer units for switching, which is based on load-imbalance level and its future state of load consumption. A reliable flow of automated procedures is obtained, which effectively guarantees the load equalization in the low-voltage grid.Energies 2018, 11, 3245 2 of 30In the case of urban microgrids with distributed generation, the load-balancing method is based on the "electric current injection" in consumer unit phases, as well as in the phases of the LV grid, compensating for the imbalance of load and voltage. However, it is necessary to use a complex AC/DC-DC/AC signal converter control architecture called Microgrid Central Control (MGCC) [28], frequency inverters [29] and, in particular, supervision and control algorithms that optimize power and electric current flow [20]. The MGCC usually manages this automated solution flow, which does not always guarantee the efficient control of the phase shift effects between the main electrical current and the injected electrical current [30].The load-balance procedure based on the "coordinated load balance" offers a wide range of control features for current injection, working synchronously with the grid transformer [16], with frequency compensation between the grid phases and consumer units, along with phase compensation between the grids' electrical current and the electric current injected [31]. Ensuring robustness and load balancing, however, requires a complex central control and supervision structure with local (distributed) controllers with high-reliability algorithms [32] that ensure automated operational integration at all control and supervisory levels.Another method of load balance based on "integrated multimicrogrids control" is being widely used because of the large mix of micro-sources of energy to be applied for load-balancing [22,29,33], along with frequency and phase compensation in the grid and consumer units [34], also requiring a complex architecture with control and supervision algorithms that efficiently coordinate current injection and frequency and phase compensation in the LV grid [9,11,35], as well such as a large number of distributed generation units [36], which in fact means a great limitation for a large-scale implementation in developing countries [7,3...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Load-Balance System Design of Microgrid Cluster Based on Hierarchical Petri Nets

et al. 2018

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“…x PI EG ∈ R Imported active power from the EG (kW) x QI EG ∈ R Imported reactive power from the EG (kvar) (31) and (32).…”

Section: Variable Descriptionmentioning

confidence: 99%

Optimization of the Operation of Smart Rural Grids through a Novel Energy Management System

2017

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Abstract:The paper proposes an innovative Energy Management System (EMS) that optimizes the grid operation based on economic and technical criteria. The EMS inputs the demand and renewable generation forecasts, electricity prices and the status of the distributed storages through the network, and solves with an optimal quarter-hourly dispatch for controllable resources. The performance of the EMS is quantified through diverse proposed metrics. The analyses were based on a real rural grid from the European FP7 project Smart Rural Grid. The performance of the EMS has been evaluated through some scenarios varying the penetration of distributed generation. The obtained results demonstrate that the inclusion of the EMS from both a technical point of view and an economic perspective for the adopted grid is justified. At the technical level, the inclusion of the EMS permits us to significantly increase the power quality in weak and radial networks. At the economic level and from a certain threshold value in renewables' penetration, the EMS reduces the energy costs for the grid participants, minimizing imports from the external grid and compensating the toll to be paid in the form of the losses incurred by including additional equipment in the network (i.e., distributed storage).

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“…In [18], the authors described the control algorithm of a utility connected microgrid, based on independent control of active and reactive power and operating in centralized and distributed Energies 2017, 10, 806 3 of 14 operation mode. In addition to these works, a significant number of published works have been focused on the multi-agent based and distributed control models for the energy management of the microgrids [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Real-Time Microgrid Simulation Platform Based on Centralized and Distributed Management

et al. 2017

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Demand response and distributed generation are key components of power systems. Several challenges are raised at both technical and business model levels for integration of those resources in smart grids and microgrids. The implementation of a distribution network as a test bed can be difficult and not cost-effective; using computational modeling is not sufficient for producing realistic results. Real-time simulation allows us to validate the business model's impact at the technical level. This paper comprises a platform supporting the real-time simulation of a microgrid connected to a larger distribution network. The implemented platform allows us to use both centralized and distributed energy resource management. Using an optimization model for the energy resource operation, a virtual power player manages all the available resources. Then, the simulation platform allows us to technically validate the actual implementation of the requested demand reduction in the scope of demand response programs. The case study has 33 buses, 220 consumers, and 68 distributed generators. It demonstrates the impact of demand response events, also performing resource management in the presence of an energy shortage.

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Multi-agent based distributed control architecture for microgrid energy management and optimization

Cited by 167 publications

References 31 publications

A Load-Balance System Design of Microgrid Cluster Based on Hierarchical Petri Nets

A Load-Balance System Design of Microgrid Cluster Based on Hierarchical Petri Nets

Optimization of the Operation of Smart Rural Grids through a Novel Energy Management System

Implementation of a Real-Time Microgrid Simulation Platform Based on Centralized and Distributed Management

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