Cooperative Control of Distributed Energy Storage Systems in a Microgrid

Xu, Yinliang; Zhang, Wei; Hug, Gabriela; Kar, Soummya; Li, Zhicheng

doi:10.1109/tsg.2014.2354033

Cited by 222 publications

(124 citation statements)

References 27 publications

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“…As noted in [21], the efficiency of the storage unit reduces when its power increases. Therefore, the storage efficiency depends on its output power, as given in (10).…”

Section: Cooperative Voltage Control Strategymentioning

confidence: 92%

“…For example, in [2], this approach is used to provide fair sharing of active power among storage units to perform load management in a power system; in [20], this approach is adopted for load sharing among photovoltaic (PV)-storage system of a low-voltage network. In recent literature, this approach is also used to provide optimal utilization of resources [21]. Based on the noted literatures, the proposed distributed control structure for a storage unit is implemented in this paper, as shown in Figure 4.…”

Section: Cooperative Voltage Control Strategymentioning

confidence: 99%

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Optimal Cooperative Management of Energy Storage Systems to Deal with Over- and Under-Voltages

et al. 2017

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This paper presents an optimal cooperative voltage control approach, which coordinates storage units in a distribution network. This technique is developed for storage systems' active power management with a local strategy to provide robust voltage control and a distributed strategy to deliver optimal storage utilization. Accordingly, three control criteria based on predefined node voltage limits are used for network operation including normal, over-voltage, and under-voltage control modes. The contribution of storage units for voltage support is determined using the control modes and the coordination strategies proposed in this paper. This technique is evaluated in two case studies to assess its capability.

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“…As noted in [21], the efficiency of the storage unit reduces when its power increases. Therefore, the storage efficiency depends on its output power, as given in (10).…”

Section: Cooperative Voltage Control Strategymentioning

confidence: 92%

Section: Cooperative Voltage Control Strategymentioning

confidence: 99%

Optimal Cooperative Management of Energy Storage Systems to Deal with Over- and Under-Voltages

et al. 2017

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“…Many works have focused on hybrid microgrids and have proposed a number of control schemes for different mode of operations [2]- [8]. A multiagent-based energy management system to optimizes the economic operation of a MG is presented in [2].…”

Section: Index Termsmentioning

confidence: 99%

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Chettibi

Mellit

Sulligoi

et al. 2016

IEEE Trans. Smart Grid

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-In this paper, the behavior of a grid-connected hybrid AC/DC Microgrid has been investigated. Different Renewable Energy Sources -photovoltaics modules and a wind turbine generator -have been considered together with a Solid Oxide Fuel Cell and a Battery Energy Storage System. The main contribute of this work is the design and the validation of an innovative online-trained artificial neural network based control system for a hybrid microgrid. Adaptive Neural Networks are used to track the Maximum Power Point of renewable energy generators and to control the power exchanged between the Front-End Converter and the electrical grid. Moreover, a fuzzy logic based Power Management System is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the Matlab/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system. OWADAYS, the wide diffusion of distributed RES presents a new scenario for the regulation of distribution networks and the availability of new technologies for storage systems encourages their use in power systems [1]. In general, a hybrid AC/DC MG integrates different Distributed Generators (e.g. solar power sources, wind power generators, cogenerators, etc.), a energy storage system and a number of AC and DC loads. A FEC can interface the MG with the electric grid and can operate either in a grid-connected or islanded mode. The use of a PMS is crucial to optimize the power flow through the different components of the MG and the exchange of energy with the electric grid. Moreover, since the power produced by RESs depends on the climatic conditions, MPPT algorithms are needed in order to harvest the maximum available energy. The intermittent nature of RESs with the time-varying loads demand make the use of advanced control structures fundamental in order to make the operation of the MG reliable, economic, and secure under different operating conditions. The MG must also guarantee a high quality power supply to both local loads and electrical grid. Index TermsMany works have focused on hybrid microgrids and have proposed a number of control schemes for different mode of operations [2]- [8]. A multiagent-based energy management system to optimizes the economic operation of a MG is presented in [2]. A reactive power sharing algorithm in hierarchical droop control is developed in [3]. A novel coordinated voltage control scheme with islanding capability for a MG is proposed in [4]. adapt to uncertainties and they can be used also when the model of the system to be controlled is not available. Recently, the NNs with the learning capability are widely applied for the control of complex power systems. In [9], a back-propagation NN is applied for the real-time estimation of the wind speed. A novel discrete-time NN controller for the control of DC distribution system is designed in [10]. In [11], a...

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“…In [26], an incremental welfare consensus protocol is developed for the optimal scheduling of DG and loads. In [27], energy storage is optimally controlled through ICC. In [28], a primal-dual perturbed sub-gradient method is applied locally while averaging consensus is applied to estimate the global cost functions and constaints.…”

Section: Introductionmentioning

confidence: 99%

“…Some research has presented asynchronous averaging algorithms such as [30] and [31], however such approaches require either pairing between agents and blocking of communication during updates, or some form of local synchronization between agents, which may lead to communication inefficiencies. The second problem is line loss, which is generally neglected entirely, or sometimes approximated as a percentage of total demand [22,27]. These approximations will lead to inaccuracies in any arrived at solution.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch

Millar

Jiang

2018

J. Mod. Power Syst. Clean Energy

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The heterogeneous nature of smart grid components and the desire for smart grids to be scalable, stable and respect customer privacy have led to the need for more distributed control paradigms. In this paper we provide a distributed optimal power flow solution for a smart distribution network with separable global costs, separable non-convex constraints, and inseparable linear constraints, while considering important aspects of network operation such as distributed generation and load mismatch, and nodal voltage constraints. An asynchronous averaging consensus protocol is developed to estimate the values of inseparable global information. The consensus protocol is then combined with a fully distributed primal dual optimization utilizing an augmented Lagrange function to ensure convergence to a feasible solution with respect to power flow and power mismatch constraints. The presented algorithm uses only local and neighbourhood communication to simultaneously find the mismatch between power generation, line loss and loads, to calculate nodal voltages, and to minimize distributed costs, leading to a completely distributed solution of the global problem. An IEEE test feeder system with a reasonable number of nodes is used to illustrate the proposed method and efficiency.

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Cooperative Control of Distributed Energy Storage Systems in a Microgrid

Cited by 222 publications

References 27 publications

Optimal Cooperative Management of Energy Storage Systems to Deal with Over- and Under-Voltages

Optimal Cooperative Management of Energy Storage Systems to Deal with Over- and Under-Voltages

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch

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