UK National Grid Research Articles

The Residual Load Duration Curve (rLDC) to model an energy system

This paper develops a framework to represent the action of different generation and balancing technologies based on the statistics of the demand and generation using the concept of the Load Duration Curve (LDC). The aim is to capture the statistics in terms of energy and power provision from a generation type given by its inherent resource, technical and economic constraints. This paper provides a brief overview of the fundamental types of generation and balancing, and how their action modifies the load duration curve to eventually arrive at a balanced system. Based on empirical data from the UK National Grid, the statistical properties of renewable energy sources and of the energy and power capacities of energy storage technologies, different basic functional relationships between the residual Load Duration Curves (rLDC) will be derived for these technologies.

Lifetime greenhouse gas emissions from offshore hydrogen production

With a limited global carbon budget, it is imperative that decarbonisation decisions are based on accurate, holistic accounts of all greenhouse gas (GHG) emissions produced to assess their validity. Here the upstream GHG emissions of potential UK offshore Green and Blue hydrogen production are compared to GHG emissions from hydrogen produced through electrolysis using UK national grid electricity and the ‘business-as-usual’ case of continuing to combust methane. Based on an operational life of 25 years and producing 0.5MtH2 per year for each hydrogen process, the results show that Blue hydrogen will emit between 200-262MtCO2e of GHG emissions depending on the carbon capture rates achieved (39%–90%), Green hydrogen produced, via electrolysis using 100% renewable electricity from offshore wind will emit 20MtCO2e, and hydrogen produced via electrolysis powered by the National Grid will emit between 103-168MtCO2e, depending of the success of its NetZero strategy. The ‘business-as-usual’ case of continuing to combust methane releases 250MtCO2e over the same lifetime. This study finds that Blue hydrogen at scale is not compatible with the Paris Agreement, reduces energy security and will require a substantial GHG emissions investment which excludes it from being a ‘low carbon technology’ and should not be considered for any decarbonisation strategies going forward.

Support vector regression based on improved Harris Hawk optimization algorithm for power load forecasting

Electricity load forecasting is an important part of power system operation, planning and management. Support vector regression (SVR) is a commonly used and efficient method for medium and long-term load forecasting. The hyperparameters of SVR have a very high influence on its performance and are not easily determined. In this paper, an improved Harris Hawk optimization algorithm (TAHHO) is proposed for optimizing the hyperparameters of SVR, and a TAHHO-SVR medium- and long-term prediction model is developed. TAHHHO enhances the convergence and stability of the original algorithm by introducing the survival of the fittest principle and the crossover operator of the artificial tree (AT) algorithm, which is validated in 13 benchmark test functions. The proposed TAHHO-SVR model is used for forecasting the UK National Grid dataset and demonstrates the feasibility and competitiveness of the model, which effectively improves the forecasting accuracy compared to HHO-SVR.

Design and Development of Superimposed Directional Comparison Protection Scheme

To achieve the UK Net Zero future by 2050, National Grid needs to integrate significantly more renewable generation into the power grid. This increases the level of harmonics, reduces system inertia and adversely affects the fault level and the performance of existing protection relays. One solution to the protection problem is the use of new types of protection that use the change in the voltage and current caused by the fault, often referred to as a superimposed or incremental based protection technique. This paper describes how a superimposed directional comparison protection scheme performed when applied to a reduced section of the full UK National Grid network and relates this to the operating performance of traditional protection. Tests are performed using the simulators DIgSILENT and RelaySimTest configured with different source levels, fault types, fault locations and fault resistances. Results show the superimposed based protection scheme achieves faster fault detection and tripping than conventional protection and is capable of detecting higher resistive faults on networks where the source capacities vary from strong to weak.

Optimized Voltage-Led Customer Load Active Service Using Genetic Algorithm in Distribution Networks

To mitigate the low frequency problem in a transmission system in an event of a power station failure or during low renewable generation production, UK National Grid (NG) Electricity System Operator has balancing mechanism in place with generators to provide temporary extra power, or with large energy users to reduce load demand or so call fast reserve services. This paper presents an alternative method to aggregately control the existing distribution network primary on load transformer tap changers as a voltage-led customer load active service. The main benefits of the proposed method are (i) to unlock the distribution network load demand flexibility without causing any negative impact on customers, and (ii) to provide the lowest cost of fast reserve service from a distribution network to transmission network. In this paper an optimal control strategy based on genetic algorithm is proposed and developed to achieve an optimized voltage-led customer load active service with the aim of finding the optimal dispatch of on load transformer tap changers by minimizing each transformer tap switching operation as well as network losses. Two practical 102 buses and 222 buses UK distribution networks have been modelled and used to demonstrate and compare the effectiveness of the proposed control methods under different operating conditions. The performances of the proposed method are also compared with both the rule-based and the branch-and-bound methods. The results show that the proposed optimal control strategy based on the genetic algorithm is more effective by achieving more accuracy and a faster solution for a large distribution network than other two methods. These are important findings as the fast reserve service by transmission network requires the accuracy of the load demand reduction delivery within 2 minutes.

A cooperative management strategy for battery energy storage system providing Enhanced Frequency Response

The increased integration of renewable energy sources in the power grid reduces system stability, which translates to more frequency fluctuation. The UK National Grid Electricity Transmission (NGET) has introduced different frequency regulation strategies—Enhanced​ Frequency Response (EFR), and more recently Dynamic Containment (DC) to mitigate this issue. Compared to DC, the EFR includes an adjustable envelope for state of charge (SOC) management which results in better flexibility of the battery energy storage system (BESS). This paper conducts technical analysis and comparison on the two EFR services designed for BESS, and further proposes a cooperative control strategy combining the advantages of the two services based on EFR in order to achieve a better solution for both power grid and BESS. Case studies were carried out using real NGET frequency data to illustrate the schemes and verify the proposed strategy. EFR service 2 shows better frequency performance for frequency regulation while EFR service 1 behaves better in SOC management. The proposed strategy manages to incorporate the strength of both EFR services, which can be beneficial to the grid and BESS.

Recurrence of Sub-Synchronous Oscillation Accident of Hornsea Wind Farm in UK and Its Suppression Strategy

A large-scale power system breakdown in the United Kingdom caused blackouts in several important cities, losing about 3.2 percent of the load and affecting nearly 1 million power users on 9 August 2019. On the basis of the accident investigation report provided by the UK National Grid, the specific reasons for the sub-synchronous oscillation of Hornsea wind farm were analyzed. The Hornsea wind power system model was established by MATLAB simulation software to reproduce the accident. To solve this problem, based on the positive and negative sequence decomposition, the control strategy of grid-side converter of doubly-fed induction generator is improved to control the positive sequence voltage of the generator terminal, which can quickly recover the voltage by compensating the reactive power at the grid side. Consequently, the influence of the fault is weakened on the Hornsea wind farm system, and the sub-synchronous oscillation of the system is suppressed. The simulation results verify the effectiveness of the proposed control strategy in suppressing the sub-synchronous oscillation of weak AC wind power system after being applied to doubly-fed induction generator, which serves as a reference for studying similar problems of offshore wind power.

Flexibility quantification of thermostatically controlled loads for demand response applications

The need for flexible networks is an emerging challenge for power system operators (SO). The use of additional support, such as demand response (DR), must be quantified in order to offer a reliable service, given that this information is vital for demand aggregators. Thermostatically controlled loads (TCLs) are one of the most promising options among DR solutions; due to TCLs’ thermal characteristics their power may be increased or reduced accounting as ancillary services. However, TCLs tend to synchronize their behavior, which may affect their capacity to provide flexibility.This paper proposes a method for quantifying TCLs’ power flexibility, taking into account different scenarios, types of controllers and loads. Two control methods are compared, and a modified control algorithm is applied to the controllers under analysis to avoid TCL synchronization. The analysis was validated by simultaneously using real demand data from the UK National Grid and temperature data for the same region and time frame.

Low emission vehicle integration: Will National Grid electricity generation mix meet UK net zero?

Assessing greenhouse gas (GHG) emissions produced from electric vehicles (EVs) and hydrogen vehicles (HVs) requires understanding of the carbon intensity of electricity generation. Without the decarbonisation of electricity generation, environmental benefits of low emission vehicles (LEVs) will be diminished. The UK aims to produce net zero emissions by phasing out and banning the sale of new conventionally fuelled vehicles (CFVs) by 2035 in favour of LEVs. A comparison of the UK’s planned and future electricity production systems between 2020 and 2050 was conducted to analyse different vehicle-type mix scenarios: (1) 100% CFVs, (2 A/B) 100% EVs/HVs, (3 A/B) EVs/HVs integrated from 2035 and (4 A/B) EVs/HVs integrated from 2025 onward. This was conducted using four energy scenarios from the UK National Grid: two degrees, steady progression, consumer evolution and community renewables. This study does not consider the embedded carbon costs of the construction and decommissioning of vehicles. Results demonstrated that while the four electricity generation scenarios reduce the projected emissions they fail to achieve low emission targets. The two degree scenario produced the lowest level of emissions under each vehicle-type mix scenario. Technological improvements of CFVs are not enough to meet targets. Therefore, phasing out and banning the sale of new CFVs from 2025 (rather than 2035) would provide a stronger impetus to reduce transport emissions. Although these targets are possible, encouraging a change in transport modes from individual travel to public transport whilst simultaneously replacing buses and trains with electric or hydrogen alternatives would see a greater emission decrease.

Static grid carbon factors – Can we do better?

The average carbon intensity (gCO2e/kWh) of electricity provided by the UK National Grid is decreasing and becoming more time variable. This paper reviews the impact on energy calculations of using various levels of data resolution (half hourly, daily, monthly and annual) and of moving to region specific data. This analysis is in two parts, one focused on the potential impact on Part L assessments and the other on reported carbon emissions for existing buildings. Analysis demonstrated that an increase in calculated emissions of up to 12% is possible when using an emissions calculation methodology employing higher resolution grid carbon intensity data. Regional analysis indicated an even larger calculation discrepancy, with some regions annual emissions increasing by a factor of ten as compared to other regions. This paper proposes a path forward for the industry to improve the accuracy of analysis by using better data sources. The proposed change in calculation methodology is analogous to moving from using an annual average external temperature to using a CIBSE weather profile for a specific city or using a future weather file. Practical application: This paper aims to quantify the inaccuracy of a calculation methodology in common use in the industry and key to building regulations (specifically Building Regulations Part L – Conservation of Fuel and Power) – translating electricity consumption into carbon emissions. It proposes an alternative methodology which improves the accuracy of the calculation based on improved data inputs.

Transformer winding type recognition based on FRA data and a support vector machine model

Frequency response analysis (FRA) is regarded as the most effective technique to detect mechanical faults of transformers. Over the years, FRA measurement data have been collected by utilities into transformer asset databases. The characteristic of FRA data is fundamentally determined by the transformer's equivalent electrical circuit, which consists of inductance and capacitance parameters that are windings' design and structure dependent. Different winding types tend to have different FRA characteristics, and a transformer's design information such as winding type, dimension etc. is often not known to the utility but critically important for asset management. This study reviews the state-of-the-art transformer FRA databases and application of machine learning techniques in this field, and proposes to apply a support vector machine (SVM) model onto the FRA data to identify the winding type. The SVM model is first trained by FRA traces of transformers with known winding types, and after testing, the SVM model is then applied to FRA traces with unknown winding information. A set of data from the UK's National Grid FRA database, was used to demonstrate and verify the SVM model. All transformers used in this study are 400/275/13 kV transmission transformers, which were designed using four different winding types, namely multiple layer, plain disc, interleaved disc and single helical windings. The proposed method can successfully identify the correct winding type.

Methodology for Tuning MTDC Supervisory and Frequency-Response Control Systems at Terminal Level under Over-Frequency Events

This paper proposes a methodology for tuning a supervisory and frequency-response outer loop control system of a multi-terminal direct current (MTDC) grid designed to transmit offshore wind energy to an onshore AC grid, and to provide frequency support during over-frequency events. The control structure is based on a master–slave scheme and ensures the achievement of frequency response, with specific implementation of the UK national grid code limited-frequency sensitive (LFSM) and frequency-sensitive (FSM) modes. The onshore AC grid is modelled with an equivalent frequency-response model to simulate the onshore AC grid dynamics under frequency deviations. The main innovation of this paper is the development of a methodology for tuning simultaneously two hierarchical levels of a MTDC coordinated control structure, i.e., the MTDC supervisor, given by the active power set point for slave terminal, and the slope of frequency-response functions at onshore terminals. Based on these two hierarchical levels, different strategies are evaluated in terms of frequency peak reductions and change of the frequency order type. Moreover, tuning guidance is given when a different MTDC control structure or different synchronous generator characteristics of the onshore AC grid are considered.

Coordination trial of novel distributed energy resources management system to provide reactive power services to address transmission constraints

This study presents the end-to-end design solution of a novel distributed energy resource management system (DERMS) to provide reactive power services to the transmission network from distributed energy resources. These studies are part of the pre-commissioning tests before commencing the wave 1 trial of the innovation project Power Potential which is a joint effort between UK Power Networks and National Grid Electricity System Operator. The system integration and all the interfaces are described, followed by a summary of the integration test scenarios to run the system under both normal and failure conditions. The simulation results of the lab-integration testing in a pre-production environment are also presented considering various scenarios which can trigger the reactive power dispatch instruction on DERMS.

Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems

Deep learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite a few single node deep learning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete processing structure that can accommodate large scale data processing, version control, and deployment, all while staying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new, higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allow processes to be fed unique and transformed data at the point of need. This facilitates near real-time application and makes models available for further training or use at any node that has access to the database simultaneously. Nemesyst is well suited as an application framework for internet of things aggregated control systems, deploying deep learning techniques to optimise individual machines in massive networks. To demonstrate this framework, we adopted a case study in a novel domain; deploying deep learning to optimise the high speed control of electrical power consumed by a massive internet of things network of retail refrigeration systems in proportion to load available on the UK National Grid (a demand side response). The case study demonstrated for the first time in such a setting how deep learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative Adversarial Networks paired with Nemesyst, achieve compelling performance, whilst still being malleable to future adjustments as both the data and requirements inevitably change over time.

EVALUATION OF DEGRADATION EFFECT ON INTERCOOLED GAS TURBINE PERFORMANCE OPERATED IN FLEXIBLE MODE

This paper investigates the effect of type and level of degradation in industrial gas turbine components on its performance under flexible operation due to working as a back-up to renewable energy sources (RES). This investigation was carried out for a 2-shaft 100MW aero-derivative gas turbine with intercooler. Due to the influence of unpredictable nature of power produced by RES, power plants are now operating in a flexible manner, which will require the operator to either stop operation during high feed-in from renewables or reducing the power output from the power plant to a certain percentage. This in turn has an impact on the gas turbine performance and thermal efficiency, which is also affected by the type and level of degradation of their components compared to the non-degraded gas turbines. In-House performance simulation software (TURBOMATCH), which was developed in Cranfield University, was used to carry out gas turbine performance modelling according to daily flexible operation scenarios for all seasons. These daily operating scenarios, which describe the power settings and ambient conditions for a period of 24 hours, were developed from data obtained from the UK national grid and the meteorology office data base. Different levels of degradation in mass flow and efficiency for low-pressure compressor and high-pressure turbine were applied in this study. Results illustrate an obvious impact of degradation type and level on fuel flow, turbine entry temperature, blade cooling temperature, shaft rotational speed and thermal efficiency for different seasons. This study has resulted in a tool which may be useful to power plant operators in understanding the various operating scenarios according to the criteria they wish to choose.

A Novel Method to Valorize Frequency Support Procurement by Wind Power Plants

The provision of frequency support by renewable power is a key challenge to allow higher penetration of renewables. While the research efforts focus on proposing new control methods and market structures to realize and facilitate the provision of frequency services by wind power, less attention is given to financial compensation of proper responses. This paper proposes a comprehensive method to evaluate the net payments to participating wind power plants, including a penalty factor for responses that do not comply with grid codes and market bids. This method integrates frequency data and dynamic response of wind power assets during the events, to evaluate the compliance level under a certain grid code, and calculates the corresponding net payment. The paper demonstrates the method by integrating the worst frequency event in each month from the real recorded data of UK National Grid frequency response in 2016. The results obtained show clear diversities between the three examined support methods with clear influence of the incident wind speed. Kinetic energy extraction support method shows a little merit at high wind speeds, while pitch deloading method performs better at the majority of the examined case studies.

The future scope of large-scale solar in the UK: Site suitability and target analysis

Abstract This paper uses site suitability analysis to identify locations for solar farms in the UK to help meet climate change targets. A set of maps, each representing a given suitability criterion, is created with geographical information systems (GIS) software. These are combined to give a Boolean map of areas which are appropriate for large-scale solar farm installation. Several scenarios are investigated by varying the criteria, which include geographical (land use) factors, solar energy resource and electrical distribution network constraints. Some are dictated by the physical and technical requirements of large-scale solar construction, and some by government or distribution network operator (DNO) policy. It is found that any suitability map which does not heed planning permission and grid constraints will overstate potential solar farm area by up to 97%. This research finds sufficient suitable land to meet Future Energy Scenarios (UK National Grid outlines for the coming energy landscape).

Impact of Demand Side Response on a Commercial Retail Refrigeration System

The UK National Grid has placed increased emphasis on the development of Demand Side Response (DSR) tariff mechanisms to manage load at peak times. Refrigeration systems, along with HVAC, are estimated to consume 14% of the UK’s electricity and could have a significant role for DSR application. However, characterized by relatively low individual electrical loads and massive asset numbers, multiple low power refrigerators need aggregation for inclusion in these tariffs. In this paper, the impact of the Demand Side Response (DSR) control mechanisms on food retailing refrigeration systems is investigated. The experiments are conducted in a test-rig built to resemble a typical small supermarket store. The paper demonstrates how the temperature and pressure profiles of the system, the active power and the drawn current of the compressors are affected following a rapid shut down and subsequent return to normal operation as a response to a DSR event. Moreover, risks and challenges associated with primary and secondary Firm Frequency Response (FFR) mechanisms, where the load is rapidly shed at high speed in response to changes in grid frequency, is considered. For instance, measurements are included that show a significant increase in peak inrush currents of approx. 30% when the system returns to normal operation at the end of a DSR event. Consideration of how high inrush currents after a DSR event can produce voltage fluctuations of the supply and we assess risks to the local power supply system.

An optimisation framework for the strategic design of synthetic natural gas (BioSNG) supply chains

A general optimisation framework based on a spatially-explicit multiperiod mixed integer linear programming (MILP) model is proposed to address the strategic design of BioSNG supply chains. The framework considers procurement of feedstocks, plantation of energy crops, and different modes for transportation of feedstocks and final products. The mathematical framework allows researches and policy makers to investigate scenarios that promote the development of BioSNG supply chains in a regional and/or national context. The capabilities of the proposed model are illustrated through the implementation of a set of case studies based on the UK. The results revealed that domestic resources in the UK can supply up to 21.4% of the total gas demand projected by the UK National Grid in the scenario “Slow progression” for a planning horizon of 20years. However, despite the considerable potential for production of BioSNG, the role of the government through subsidisation schemes such as feed-in tariff and Renewable Obligation Certificates (ROCs) is crucial in order to make the development of these resources economically attractive for private sectors.

Non‐invasive identification of turbo‐generator parameters from actual transient network data

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non-invasive way is very challenging because of the inherent non-linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo-generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid.