Distributed optimisation of energy management in smart grids
Creators: name: Li, Chaojie
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400803
subject: Electrical energy generation (incl. renewables, excl. photovoltaics)
classificationCode: 400805
subject: Electrical energy transmission, networks and systems

description: As the electrical infrastructure is rapidly reaching its maximum capacity, the smart grid<br>becomes a promising solution. The essential elements of smart grid, such as advanced metering<br>infrastructure (AMI) and renewable energy resources, challenge existing energy managerial<br>approaches. The operation of electric grid is no longer unidirectional and top-down<br>oriented. Although traditional power systems have been well-developed to address the variable<br>nature of load demands, the additional variability and uncertainty by high penetration<br>renewable integration could pose a significant threat to the stability of power grid.<br>The work in this thesis focuses on the feasibility of improving the management strategy<br>of smart grids which could be harnessed by a power system operator. The work can be<br>broadly categorised into four parts by the problems from the demand side to the supply<br>side.<br><br>First, the problem of residential demand-side management (DSM) is formulated as a<br>game theoretical model with sparse constraints that are largely associated with relieving<br>customers’ discomfort from load shifting or interrupting. A sparse approximation is used<br>to convexify the objective function, which generates a nonsmooth optimisation problem. A<br>bidirectional framework for solving the demand-side management game model is developed<br>in a distributed manner, where efficient computational algorithms are designed to seek a<br>Nash equilibrium of DSM game.<br><br>Second, a plug-in electric vehicle (PEV) charging strategy is studied by considering the<br>risk of transformer aging. A data analysis is utilized to characterise the PEV charging pattern,<br> including driving behaviour, which has a significant impact on the charging demand.<br><br>A stochastic game is proposed for PEV charging, where the risk of the transformer aging<br>is measured by Conditional value at risk (CVaR). The existence of generalised Nash<br>equilibrium (GNE) is studied while the corresponding seeking algorithm is developed. The<br>sample average approximation (SAA) is applied to capture a SAA Nash equilibrium of the<br>stochastic game which results in an optimal risk-averse charging strategy.<br><br>Third, a novel energy trading model is investigated for microgrid operations, where<br>a risk-averse optimal trading strategy is optimised by the consideration of the risk from<br>uncertainty. To maximise each microgrids’ profit from energy trading while minising the<br>risk of overbidding, a two-stage stochastic game with Cournot Nash pricing mechanism is<br>introduced tomodel the payoff function of each microgrid, where the risk of overcommitting<br>energy supply is characterised by CVaR measurement. Combined with SAA technique,<br>an alternating direction method of multipliers (ADMM) based algorithm for solving the<br>stochastic game model is developed.<br><br>Fourth, after developing a fully distributed optimisation for solving economic dispatch<br>problem, an event-trigger scheme is proposed which could significantly reduce the communication<br>cost. The q -logarithmic barrier is employed to handle the capacity constraint of the<br>economic dispatch problem which is referred to as a distributed interior point method. The<br>minimum connected dominating set (CDS) based resource allocation algorithm is presented<br>for initialising the supply-demand balance constraint which can be guaranteed by using a<br>Laplacian graph based updating during the whole procedure of the algorithm.