Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

Zhao, Tianqiao; Ding, Zhengtao

doi:10.1109/tpwrs.2017.2741672

Cited by 84 publications

(54 citation statements)

References 30 publications

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“…The control of the energy fluxes should consider the expected level of energy production (even with possible short and middle term forecasts), the expected demand coming from the user side and the costs, both positive and negative, associated with the power exchange toward the grid. The open research problem is in finding the optimal algorithms with which the energy fluxes are managed and considering the constraints given by the limits in maximum power and maximum energy, which characterize the ESS used in the micro-grid [38][39][40][41][42][43][44][45].…”

Section: The Role Of Energy Storage In Micro-gridsmentioning

confidence: 99%

Hybrid Micro-Grids Exploiting Renewables Sources, Battery Energy Storages, and Bi-Directional Converters

2019

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This paper analyzes trends in renewable-energy-sources (RES), power converters, and control strategies, as well as battery energy storage and the relevant issues in battery charging and monitoring, with reference to a new and improved energy grid. An alternative micro-grid architecture that overcomes the lack of flexibility of the classic energy grid is then described. By mixing DC and AC sources, the hybrid micro-grid proposes an alternative architecture where the use of bi-directional electric vehicle chargers creates a micro-grid that directly interconnects all the partner nodes with bi-directional energy flows. The micro-grid nodes are the main grid, the RES and the energy storage systems, both, on-board the vehicle and inside the micro-grid structure. This model is further sustained by the new products emerging in the market, since new solar inverters are appearing, where a local energy storage for the RES is available. Therefore, the power flow from/towards the RES becomes bi-directional with improved flexibility and efficiency.

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Section: The Role Of Energy Storage In Micro-gridsmentioning

confidence: 99%

Hybrid Micro-Grids Exploiting Renewables Sources, Battery Energy Storages, and Bi-Directional Converters

2019

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“…The lower control level concentrates on power system modelling and aims to control BESS to track the power reference. An optimal distributed control scheme should be flexible, robust and address the problem with a costeffective way (Kumar Nunna & Srinivasan, 2016;Morstyn, Hredzak, & Agelidis, 2014;Morstyn, Hredzak, Member, & Vassilios, 2016;Xu, Zhang, Hug, Kar, & Li, 2015;Zhang, Rahbari-asr, Duan, & Chow, 2016;Zhao & Ding, 2018).…”

Section: Control Strategy For Distributed Mas-based Microgridmentioning

confidence: 99%

“…However, under the general multi-agent framework, the local information of the agent is updated by the interaction with its neighbourhood agent, which indicates the participants are forced to share their vital and invisible information such as cost function or gradient information. In order to protect the privacy of each participant, Zhao & Ding (2018) presented another cooperative optimal solution for multiple battery energy storage system in a microgrid to maintain the supply-demand mismatch under the wind power generation uncertainties. Similarly, Zhang et al (2016) presented an offline cooperative distributed energy scheduling algorithm, which allowed limited information for agent communication and facilitated reconfiguration of the distributed system.…”

Section: Control Strategy For Distributed Mas-based Microgridmentioning

confidence: 99%

A review of forecasting algorithms and energy management strategies for microgrids

2018

Systems Science & Control Engineering

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As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed. ARTICLE HISTORY

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“…In fact, those methods can be summarised into a few categories. Firstly, BESS is vastly used in microgrids [72]- [77] where balancing generated and consumed power is a special concern. Secondly, BESS is used in regular distribution grids such as [44]- [80] with power smoothing control of PV, wind generation fluctuations or diesel generators.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 2.11 Power grids services to be provided by BESS solutions [9] Firstly, BESS devices are studied in microgrids where they can dynamically balance generated and consumed power in isolated systems. For example, in [72] the cooperation between many energy storage units has been applied during wind power fluctuations.…”

Section: Battery Energy Storage System For Grid Voltage Controlmentioning

confidence: 99%

Voltage control in power distribution grids equipped with large-scale renewable energy sources

Krata¹

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High integration of renewable energy sources (RESs) and battery energy storage systems (BESS) into power distribution networks brings new challenges to grid control schemes. The old assumption of unidirectional power flow (from transmission system to loads) is no longer valid when almost every load bus can become temporarily or permanently a generation bus due to increasing power from renewable sources such as photovoltaic (PV) generation. In fact, a power flow direction can alter at any grid location many times a day, impacting existing voltage regulation schemes. In particular, due to the lack of coordination between different voltage control apparatus, a dynamic change in PV generation can cause the grid voltage magnitude to go beyond acceptable limits. Hence, daily power fluctuations, e.g. caused by moving clouds, can lead to over-or under-voltage situations with a regulation time that takes minutes before the voltage is recovered and the grid is ready for a next, unexpected disturbance. This prolonged and inaccurate traditional regulation process affects electrical devices connected to grids with medium-and large-PV generation. Even more importantly, insufficient voltage regulation can prevent new developments of RESs plants because power utility companies might not allow for more RESs generation if a grid voltage is prone to fluctuate.This research is focused on the behaviour of power distribution systems exposed to large-scale PV generation and BESS facilities. Specifically, the study aims to address questions of how those modern power technologies influence voltages and their control in a distribution network as well as how to improve cooperation between traditional and modern grid regulation devices by means of advanced control strategies. To answer the research questions, the study investigates the real-time performance of a distribution grid voltage control in the presence of dynamically changing large-scale RESs and BESS generation and provides a novel real-time coordinated control to address voltage fluctuations caused by uncertain PV generation. Furthermore, the developed voltage control method is based on a model-driven approach with the real-time controller predicting hundreds of possible regulation trajectories and choosing the one that fulfils optimisation criteria the most. In addition, the multidimensional, nonlinear, mixed-integer optimisation is executed based on parallel-computed time-series grid simulations, which reproduce grid behaviour, incorporate mutual interactions of voltage controllers and ii account for PV variability. Uniquely, the algorithm also considers autonomous responses from upstream voltage regulation devices and includes them into the solution.Next, the investigation was conducted on real-time models of a semi-rural grid located in South-East Queensland, Australia, with a large-scale PV plant (3.15MWp of power) and BESS facility as large as 0.6MW/0.76MWh. As a result of implementing the proposed control coordination to the studied grid, voltage fluctuations caused by dyn...

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Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

Cited by 84 publications

References 30 publications

Hybrid Micro-Grids Exploiting Renewables Sources, Battery Energy Storages, and Bi-Directional Converters

Hybrid Micro-Grids Exploiting Renewables Sources, Battery Energy Storages, and Bi-Directional Converters

A review of forecasting algorithms and energy management strategies for microgrids

Voltage control in power distribution grids equipped with large-scale renewable energy sources

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