Scottish Power (United Kingdom)

Very long-term load forecasts (VLTLFs) spanning 15 to 25 years are indispensable for strategic electricity infrastructure planning. These forecasts are even more challenging when the historical load data is scarce in addition to uncertain future load growth. This paper presents a methodology to generate seasonal peak load forecasts for grid supply points (GSPs) until 2050, despite having limited years of available recorded data. This is demonstrated with two real UK GSPs with 5 years of available data. First, macroeconomic and demographic variables (regional GDP and household statistics) are mined and interpolated to match seasonal resolution. A transfer-learning backcasting step employs ridge regression, which is compared and selected for its superior performance in reconstructing historical GSP loads. The exogenous variables are first forecasted using ARIMA, and the extended dataset then feeds into seasonal ARIMAX models to project future load peaks. Finally, projected additional demand from electric vehicles and heat pumps is incorporated based on DSO’s Distribution Future Energy Scenarios (DFES). For the first GSP considered, the 2035 and 2050 peak forecasts differ from DFES’ Electric Engagement scenario by 15% and 17%, respectively, while for the second one, the analogous differences are 12% and 8%. The methodology delivers robust, explainable VLTLFs with quantified uncertainty, demonstrating its suitability where load data availability is limited.

The integration of renewable generation into the existing power grid heavily relies on converter-based technologies. The increasing penetration of Inverter-Based renewable sources (IBRs) in the network impacts system stability, necessitating the development of new grid codes and industrial practices. IBRs are typically classified into Grid-Forming (GFM) and Grid-Following (GFL) control approaches, with grid codes approximating these controllers’ behaviours as voltage and current sources, respectively. Furthermore, identifying the control type of Electromagnetic Transients (EMT) vendor models is challenging using frequency-domain analysis. This paper proposes a method to assess converter characteristics by comparing the ideal voltage and current source behaviours using the time-domain response of converters. The results provide a clear methodology for characterizing GFM and GFL converters using time-domain signals alone, employing a fixed 5ms window to analyse transient behaviour.

Local demand-side flexibility offers a cost-effective alternative to traditional grid reinforcement; however, participation remains in its infancy with its full potential yet to be unlocked. In particular, residential flexibility remains a vast and largely untapped resource. The innovative FUSION project investigated how to encourage flexibility from industrial, commercial and residential consumers with access to a broad spectrum of flexible assets connected to low voltage network. FUSION trialled commoditised local demand-side flexibility through a structured and competitive market, based on the Universal Smart Energy Framework (USEF) over an 18-month period. This paper discusses practical experience with flexibility markets, interaction with customers as well as the customer and aggregator experience with USEF innovative elements; a market coordination mechanism for standardising trading, a trading protocol for communication and data exchange between market participants, and the concept of 'free bids' enabling ad-hoc bidding outside of contractual arrangements. It also analyses the impact of baselining performance on the DSO and flexibility providers, as well the key DSO flexibility procurement cost drivers.

Abstract Dispatch of Flexibility Services is defined as the process through which the DSO informs a flexibility provider of the required level of service within operational timescales. This chapter provides more context on how processes for flexibility service dispatch have been developed and co-ordinated.

Abstract Distributed energy resource's inherent flexibility can be a significant enabler in facilitating a faster and lower cost connection to the GB system. This chapter introduces the concept of Flexible Connections and how these have helped facilitate the connection of significant volumes of zero carbon generation in GB. The relationships and interactions of Flexible Connections and Flexibility Services are also discussed.

Digital substations are enablers for the electricity network power flow to be controlled and directed safely and securely from generation to demand, these make them a highly attractive target for cyber-attackers aimed at disrupting operations. As a result, Protection, Automation and Control (PAC) devices will be required not only as business usual to maintain effective detection of network normal and fault conditions but also to detect anomaly cyber-attack conditions. This paper proposes an anomaly detection method based on time series data and a Back Propagation (BP) neural network algorithm. The BP neural network is used to classify the anomaly data via extracting anomaly system state behaviour characteristics and then comparing them with normal system states. By using the supervising training and learning process, an anomaly Intrusion Detection System (IDS) is developed and used to determine when and whether anomaly data would occur. A benchmark 4 bus power system used by ABB and a typical distribution network substation with 2 power source infeeders and 4 load feeders were selected and modelled using Digsilent Power Factory simulation tool to obtain different time series network condition data as inputs to the implemented IDS. Results show that the BP neural network can accurately detect all tested anomaly cyber-attack conditions and provide the correct system state. The comparison between the proposed method and ABB algorithm proposed Kirchhoff’s laws-based distributed agreement algorithm was made. By comparisons, the advantages and disadvantages of using the proposed method are discussed and highlighted.

This paper presents the consequences of load growth on distribution networks (DN) by considering three future scenarios. The three scenarios (all loads increase, loads increase by geographical location, and only certain load class increases) have been simulated and studied by running power flow (PF) in DIgSILENT PowerFactory and monitoring the result after each step size of load increment (10%). Furthermore, assessing an existing distribution network’s capability considered three aspects (bus voltages, line loadings, and line active power losses) in addition to load flow convergence. The findings from these case studies provide valuable insights into the potential challenges and areas for improvement in the tested distribution network as load demand increases in the future.

The continuous demand growth in the power systems will change the load composition and load models. This paper illustrates the impact of load growth and load models on the steady-state operation of the distribution network by conducting a range of case studies. The load growth was modelled by increasing the loads at particular geographical locations in the network and load types to 230% or until the load flow (LF) simulation stopped converging. The analysed load models included different static load models, such as polynomial and constant power load models. The LF simulations were conducted in DIgSILENT PowerFactory. The simulation results demonstrated how load models affect the network’s ability to withstand demand increase before the LF simulation collapses. The results also showed how the loads’ sizes and locations of synchronous generators impact the line loadings, line active power losses, and bus voltages.