Flexibility Assessment of An Active Distribution System with High Penetration of Distributed Energy Resources under Uncertainty

Abstract

The continued reliance on fossil fuels presents numerous challenges to modern energy systems, including environmental degradation, greenhouse gas emissions, supply insecurity, and high price of electricity. In response, there has been a clamour for the large-scale integration of renewable energy into the electricity grid, particularly at the distribution level. However, integrating variable renewable energy sources such as solar and wind creates opportunities for deploying Flexibility as a Service (FaaS) in power system operation. The uncertain and intermittent nature of these resources creates difficulty in maintaining real-time supply-demand balance, increasing operational costs and the risk of curtailment. This thesis investigates how to harness the flexibility as a service in Active Distribution Systems (ADS) with large-scale penetration of Distributed Energy Resources (DERs) by proposing and evaluating three core strategies which would collectively provide a holistic framework for future Active Network Management (ANM) schemes for such networks. First, a deterministic optimization, suitable for establishing a baseline for further analyses, is developed to analyse the impact of pricing mechanisms and the role of various EV aggregator types on grid performance. Secondly, a scenario-based stochastic optimisation is introduced to capture the uncertainty of renewable generation and load demand. To that end, a linearised formulation of the Optimal Power Flow (OPF) model incorporating the Big-M method is applied. Lastly, the study introduces a new techno-economic flexibility planning framework, formulated mathematically as a multi-objective optimisation problem, to assess the operational and economic impact of deploying new flexible resources for maintaining operational security in future ADS. All three optimisation models are implemented using the Advanced Integrated Multidimensional Modeling Software (AIMMS) modelling platform, with simulations conducted on the IEEE-33 bus distribution test system. Results from the study reveal significant improvements across key performance indicators: reduction in operational costs, minimisation of renewable energy curtailment, and improved voltage profiles. These findings demonstrate that a well-coordinated flexibility strategy is critical to achieving a cost-effective and low-carbon electricity distribution network under conditions of uncertainty.