Active Network Management and Uncertainty Analysis in Distribution Networks

In distribution networks, the traditional way to eliminate network stresses caused by increasing generation and demand is to reinforce the primary network assets. A cheaper alternative is active network management (ANM) which refers to real-time network control to resolve power flow, voltage, fault current and security issues. However, there are two limitations in ANM. First, previous ANM strategies investigated generation side and demand side management separately. The generation side management evaluates the value from ANM in terms of economic generation curtailment. It does not consider the potential benefits from integrating demand side response such as economically shifting flexible load over time. Second, enhancing generation side management with load shifting requires the prediction of network stress whose accuracy will decrease as the lead time increases. The uncertain prediction implies the potential failure of reaching expected operational benefits. However, there is very limited investigation into the trade-offs between operational benefit and its potential risk. In order to tackle the challenges, there are two aspects of research work in this thesis. 1) Enhanced ANM. It proposes the use of electric vehicles (EVs) as responsive demand to complement generation curtailment strategies in relieving network stress. This is achieved by shifting flexible EV charging demand over time to absorb excessive wind generation when they cannot be exported to the supply network. 2) Uncertainty management. It adopts Sharpe Ratio and Risk Adjust Return On Capital concepts from financial risk management to help the enhanced ANM make operational decisions when both operational benefit and its associated risk are considered. Copula theory is applied to further integrate correlations of forecasting errors between nodal power injections (caused by wind and load forecasting) into uncertainty management. The enhanced ANM can further improve network efficiency of the existing distribution networks to accommodate increasing renewable generation. The cost-benefit assessment informs distribution network operators of the trade-off between investment in ANM strategy and in the primary network assets, thus helping them to make cost-effective investment decisions. The uncertainty management allows the impact of risks that arise from network stress prediction on the expected operational benefits to be properly assessed, thus extending the traditional deterministic cost-benefit assessment to cost-benefit-risk assessment. Moreover, it is scalable to other systems in any size with low computational burden, which is the major contribution of this thesis.