Integrating Low-Carbon Technologies in Electricity Markets: Network-Constrained Bidding Strategies for Distribution System Aggregators

The integration of Low Carbon Technologies (LCTs), including renewables, and Electric Heat Pumps (EHPs), is becoming increasingly important due to global decarbonisation efforts aimed at reducing greenhouse gas emissions. This shift towards LCTs presents significant opportunities and challenges for power systems. In this context, Distribution System Aggregators (DSAs) play a vital role in facilitating the effective participation and integration of these technologies into the electricity markets. To understand the impact of residential EHPs on Low Voltage (LV) distribution networks, this thesis begins by assessing the volume restrictions that LV feeders are likely to require on the widespread installation of EHPs in different domestic settings. The installation of EHPs increases the domestic customers’ demand while reducing the natural diversification of loads in LV networks, leading to potential implications for network assets’ loading levels and voltage drops. To address the challenges DSAs face in maximising profit and effectively integrating LCTs, it is crucial to develop bidding strategies that consider resources’ constraints and account for network operational limits. Building on this understanding, a network-constrained bidding strategy model for DSAs is developed. The proposed model incorporates network constraints by utilising estimations of network variables and their sensitivities to generation/demand variation. The results demonstrate that the DSA can effectively manage its resources for profit maximisation while considering local network constraints. Managing market price volatility and interdependence across multiple markets is another challenge faced by DSAs. First, a model based on Gaussian Process Regression (GPR) is developed, which predicts different market prices and quantifies their uncertainty, incorporating the interdependence of markets. The proposed model enables DSA to formulate the optimal strategies for involvement in multiple markets as a stochastic multi-step two-stage problem. Additionally, this thesis introduces a second approach based on data reconciliation to capture the uncertainty and interrelation between different energy markets. Both of these approaches leverage market learing results to enhance the price prediction accuracy for subsequent markets. Extensive simulations using large price datasets demonstrate the superior performance of the proposed methodologies, resulting in a considerable increase in DSA profit compared to state-of-the-art approaches. To balance risk management and profit maximisation, this thesis turns to developing a Chance Constrained (CC) bidding strategy model for DSAs. A bidding strategy model allowing for the concomitant uncertainties of multiple market prices, renewable generation variability, and forecast errors of network states is developed. The CC formulation makes it viable for the DSA to balance the risk of being too conservative in its approach, which may limit its profit potential, against the risk of over-exploiting resources and potentially violating network limits. To model the uncertain parameters, first, the normal distribution is employed. However, in practice, uncertain parameters cannot always be represented using the normal distribution. To overcome this challenge, a Distributionally Robust Chance-Constrained (DRCC) framework is developed. Simulations conducted on test systems validate the effectiveness of the proposed approach. In conclusion, this research contributes to developing network-constrained stochastic bidding strategies for DSAs, enabling the optimal integration of LCTs into electricity markets. The proposed models and methodologies address the challenges of network constraints, market price volatility, and interdependence and strike a balance between risk management and profit maximisation. Considering these aspects, DSAs can play a crucial role in facilitating the transition towards a low-carbon energy system.