On the hierarchical control framework for distributed energy storage management in large-scale distribution networks

In recent years, the medium voltage (MV) distribution networks have undergone a continuity of modifications as a massive number of renewable energy generation resources in the form of small-scale DGs (e.g., wind turbines, Combined Heat and Power (CHP) and solar energy) and energy storage systems (ESSs) penetrate into the utility grid. This trend has mainly been driven by advances in Distributed Energy Resources (DER) technology and the pursuit of low-carbon energy provision in many nations. As the DGs and the energy storage units are often deployed locally to the demands and the surplus energy could also be flexibly stored or absorbed by the grid which brings the obvious benefits for managing the power loads in peak times. At present many power utilities are being faced with the reality of conventional centralized control systems limitations, as they can greatly degrade due to the complexity of dealing with the network events that would require enormous amount of data to be properly managed, or even not pragmatic in realistic deployment. To meet this emerging challenge, this paper presents a hierarchical control framework that enables the distribution network operators (DNOs) to implement an approximately optimal energy dispatch based on the distributed energy storage systems distributed across a large-scale distribution networks, and further promote t.he demand response performance in terms of reliable power supply to the critical power loads whilst improve the global energy utilization efficiency of the renewable DGs. In such a way, the proportion of power supply from the renewable sources can be significantly promoted and the cost-effective network management can be achieved.

[1]  JiKeng Lin,et al.  Island partition of the distribution system with distributed generation , 2010 .

[2]  Felix F. Wu,et al.  Network reconfiguration in distribution systems for loss reduction and load balancing , 1989 .

[3]  N. Hatziargyriou,et al.  Microgrids: an overview of ongoing research, development, anddemonstration projects , 2007 .

[4]  Gerard J. M. Smit,et al.  Management and Control of Domestic Smart Grid Technology , 2010, IEEE Transactions on Smart Grid.

[5]  Bruno Francois,et al.  Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications , 2011, IEEE Transactions on Industrial Electronics.

[6]  Qiang Yang,et al.  Power Factor Optimization of Distributed Generations in Distribution Networks Based on Improved Particle Swarm Optimization Method , 2012 .

[7]  Farshid Keynia,et al.  Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy , 2010, IEEE Transactions on Smart Grid.

[8]  A. Daneels,et al.  Современные SCADA-системы , 2017 .

[9]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[10]  Chresten Træholt,et al.  A Decentralized Storage Strategy for Residential Feeders With Photovoltaics , 2014, IEEE Transactions on Smart Grid.

[11]  Gexiang Zhang,et al.  A Comprehensive Learning Quantum-Inspired Evolutionary Algorithm , 2011 .

[12]  Wang Xudong,et al.  Reliability evaluation for the distribution system with distributed generation , 2011 .

[13]  Farshid Keynia,et al.  A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection , 2014 .