Globally, the pace of railway system electrification and decarbonization is accelerating to support the worldwide sustainability goals for energy and transportation. Increased electricity demand from transport decarbonization, notably within the railway sector, places additional stress on already constrained power grids. Utilizing local renewable generation and energy storage systems to meet this increased demand has emerged as a crucial strategy to enhance the flexibility and resilience of railway power supply systems. This paper proposes to deploy railway microgrids at weak nodes of the existing co-phase traction power supply lines, maximizing the use of current infrastructure (including traction networks and local power grids) to address energy gaps while mitigating exacerbated three-phase voltage imbalance issues caused by increasing electricity demands. A computational cost-aware heuristic reinforcement learning method is developed first for optimal energy scheduling. Then, two agents operating with the proposed algorithm are employed to coordinate energy management between railway microgrids and co-phased feeder stations. Case studies, including a test scenario based on real operating conditions, show that the proposed solution not only accommodates rising electricity demand but also reduces daily operating costs by 15.1% in representative test scenarios, and 19.8% on average under real-world operation conditions. The proposed reinforcement learning algorithm is shown to save up to 81% computation time compared to other conventional approaches. It further substantially strengthens the ability of co-phase traction power supply systems to manage three-phase voltage imbalance, offering a cost-effective mitigation strategy.
The energy landscape is changing, and the way electricity network customers generate, use, and interact with energy is evolving. This means that, as SP Energy Networks, our role – how we plan, design, and operate the network for our customers – must evolve with it. With increasing dependencies across energy sectors and vectors, there is a growing need for Whole System coordination. This paper outlines how we believe Whole System thinking should be implemented within electricity networks in Great Britain (GB) Whole System implementation is a critical enabler to ensure that Net Zero can be realised at the lowest overall cost for consumers, which is why we’re delivering our Whole System business transformation journey, embedding Whole System approaches across our ways of working as an electricity networks business.
This paper introduces a scalable frequency-dependent impedance measurement method suitable for Transmission Owners to assess the stability of the future renewable-energy-based electrical power network. The method is applicable to both offline electromagnetic transient studies as well as to the real-world installation. The proposed method has been tested in an electromagnetic transient simulation environment with a generic HVDC bipole scheme and an equivalent AC system. Results show that the method can accurately measure the converter’s frequency dependent impedance and stability margins in the presence of switching noise. The stability analyses are conducted by employing advanced control system theory techniques such as the multiple-input-multiple-output generalised Nyquist criterion and generalised Bode plots. Moreover, the results have been obtained with a sophisticated software tool developed for such purpose and tailored for power transmission systems. This method will help enable the smooth transition from conventional generation to renewable energy sources while ensuring a safe supply of electricity as well as helping attain the UK’s ambition to reach a net-zero economy by 2050.
This paper addresses the mitigation of impedance based out-of-step protection challenges using synchrophasor measurements, particularly in the context of high renewable generation integration. A case study from Great Britain’s transmission network is analysed, focusing on increased renewable generation and high load flow conditions across the B6 boundary from north to south. Severe generation outages in the south pose a significant threat to the overall stability of the network, necessitating the strategic isolation of the system through controlled islanding between the north and south. Traditional impedance-based out-of-step relays have proven unreliable from the analysis because it can fail to operate sometimes or it can maloperate. The analysis demonstrates that integrating angle measurements from Phasor Measurement Units (PMUs) into the Transmission System Operator's (TSO) out-of-step relays significantly enhances their reliability, thereby improving system stability under high renewable generati on scenarios.
Improving observability in low-voltage networks is critical for enabling the integration of low-carbon technologies and ensuring efficient network operation. This paper presents a data-driven methodology for improving LV network (LVN) observability, assuming only partial adoption of smart meters (SMs) by end users. The methodology uses a machine learning (ML) technique to estimate the load consumption of nodes without SMs, using SM data at nodes with known SM measurements. Subsequently, a probabilistic model is employed to refine these estimations, accounting for uncertainties and variability in consumption patterns. The effectiveness of the method is validated through state estimation (SE), which utilizes realistic LV distribution network with known topology, appropriate network model, and available SM measurements, including active power (P) and voltage magnitude (V). Results demonstrate that the proposed framework significantly enhances the accuracy and reliability of estimation of load consumption, providing a practical solution for improving LVN observability under limited measurement conditions.
