An Optimised Reactive Power Ancillary Service in Wind Power Integrated Systems

Abstract

Due to legally binding decarbonisation targets and emerging technologies, the power system is undergoing unprecedented changes. Traditional generators are being replaced by renewable generators. Among them, wind power is considered to be one of the most important renewable energy resources. However, the reactive power capabilities of wind turbines are usually limited. Then, this replacement of traditional generators leads to insufficient reactive power compensation, which may cause voltage instability in the power systems. In this case, any contingency or outage will pose a more serious threat to the stable operation of the power systems. In addition, the reliability of existing power system optimisation schemes may be affected by wind power integration. It is deemed necessary to formulate a new scheme for the reactive power optimisation strategy in the wind power integrated systems. This thesis presents a coordinated reactive power ancillary service strategy for the power systems with integrated wind farms. The strategy considers a day-ahead reactive power procurement (RPP) strategy and a day-ahead optimised reactive power dispatch (ORPD) strategy with hourly modifications. The purpose of the proposed RPP strategy is to reduce total reactive power cost. Based on a chance-constrained stochastic optimisation, the strategy employs day-ahead predicted wind energy and load demand data considering uncertainties and contingencies to set reserved capacities for all reactive power providers. Using this strategy, both the unit commitment and economic dispatch of reactive power are achieved. In addition, the stochastic optimisation of the RPP strategy is achieved by a decision tree framework with an improved genetic algorithm. As for the ORPD strategy, the objectives are to minimise voltage deviations, active power losses, wind turbine harmonics emission, and the number of switching operations of on-load tap changers (OLTCs). In this ORPD strategy, the deterministic optimisation has been solved using an improved genetic algorithm based on the elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and a roulette wheel selection. The proposed optimal reactive power ancillary service strategy can be applied to both transmission and distribution systems and has been rigorously tested by using IEEE 33-bus test system, PG&E 69-bus test system and modified real GB network. Results obtained confirmed the efficacy and applicability of the proposed strategy.

Date of Award	1 Aug 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Haiyu Li (Supervisor)