GB Power Research Articles

Vulnerability and Impact of Machine Learning-Based Inertia Forecasting Under Cost-Oriented Data Integrity Attack

With the increasing penetration of renewables, the power system is facing unprecedented challenges of low-inertia levels. The inherent ability of the system to defense disturbance and power imbalance through inertia response is degraded, and thus, system operators need to make faster and more efficient scheduling operations. As one of the most promising solutions, machine learning (ML) methods have been investigated and employed to realize effective inertia forecasting with considerable accuracy. Nevertheless, it is yet to understand its vulnerability with the growing threat of cyberattacks. To this end, this paper proposes a methodological framework to explore the vulnerability of ML-based inertia forecasting models, with a special focus on data integrity attacks. In particular, a cost-oriented false data injection attack is proposed, for the first time, with the primary objective to significantly increase the system operation cost while retaining the stealthiness of the attack via minimizing the differences between the pre-perturbed and after-perturbed inertia forecasts. Moreover, we propose four vulnerability assessment metrics for the ML-based inertia forecasting models. Case studies on the GB power system demonstrate the vulnerability and impact of the ML-based inertia forecasting models, as well as the stealthiness and transferability of the proposed cost-oriented data integrity attacks.

Simulating the GB power system frequency during underfrequency events 2018–19

AbstractLightning hit a transmission power line outside London, England on 9 August 2019. There followed a loss of power from a cascade of generator outages that exceeded contingency reserves, leading to an exceptional fall in grid frequency causing widespread transport disruptions and the disconnection of over 1 m households. Simulating such events typically involves a system of differential equations representing the overall generation and load present at the time. A standard model based on the swing equation assumes unlimited capacity in aggregated resources, and the availability of these services throughout the duration of the frequency deviation. In simulating the effect of outages on the GB Grid frequency on 2019/8/9, the effect of limiting these services to the capacity of resources engaged during the event is examined. It is shown that by taking these refinements into account the timing and extent of the frequency nadir can be successfully estimated. Insight is gained into the responses of various grid characteristics and how they interact with unplanned generation imbalances. Using this adapted model, further events on the GB grid are examined to validate the influence of these features. With the model’s effectiveness validated, novel mitigating measures to preserve the stability of a low-inertia grid can be evaluated.

Electricity market relationship between Great Britain and its neighbors: distributional effects of Brexit

BackgroundBeyond Great Britain, Brexit could also have ripple effects on the electricity systems of certain other EU member states. This paper investigates the possible effects of reduced growth in interconnectivity between Great Britain and mainland Europe by 2030 on the electricity system in GB and across other EU member states in addition to the effects of Pound depreciation. Effects are analyzed across a “Green Scenario” and “Blue Scenario” in 2030, based on the ENTSO-E (European Network of Transmission System Operators-Electricity) 10-year development plans. There is a greater expansion of nuclear and renewables in Green than in Blue and, in Blue, the British CO2 price is higher than in the EU. Within each scenario, there are four variants: full vs. reduced expansion of interconnection capacity, in combination with no devaluation and 10% depreciation of the British Pound. The EMME (Electricity Market Model for Europe) is used to model these impacts across the different scenario variants.ResultsInterconnector utilization is more volatile in the Green Scenario variants, leading to concerns about investor incentives, especially given the increased uncertainty under Brexit. In terms of electricity prices, GB consumers lose out across both Blue and Green scenario variants, whereas EU and GB producers both gain and lose in different variants. Across the Green Scenario variants, EU neighbors’ trade balances with GB deteriorate slightly, but the impact is far stronger in Blue due to a loss of opportunities to export power. GB sees significant increases in electricity costs across scenario variants. Green scenario variants offer potential for modest emission reductions in certain EU nations, whereas Blue Scenario variants lead to greater emission reductions in the EU neighbors which contrasts with a sharp rise in GB emissions.ConclusionsThere is a significant link between NTC expansion and wholesale prices. Delayed or cancelled NTC expansion could negatively affect the GB power system’s low-carbon transition. Pound depreciation and reduced expansion of NTCs lead to shifts in generation-related CO2 emissions. A higher cost burden for electricity is a risk for GB, whereas, for EU neighbors, their trade position with the UK risks deteriorating.

Consumer-centric decarbonization framework using Stackelberg game and Blockchain

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%.

Applying reinforcement learning and tree search to the unit commitment problem

Recent advances in artificial intelligence have demonstrated the capability of reinforcement learning (RL) methods to outperform the state of the art in decision-making problems under uncertainty. Day-ahead unit commitment (UC), scheduling power generation based on forecasts, is a complex power systems task that is becoming more challenging in light of increasing uncertainty. While RL is a promising framework for solving the UC problem, the space of possible actions from a given state is exponential in the number of generators and it is infeasible to apply existing RL methods in power systems larger than a few generators. Here we present a novel RL algorithm, guided tree search, which does not suffer from an exponential explosion in the action space with increasing number of generators. The method augments a tree search algorithm with a policy that intelligently reduces the branching factor. Using data from the GB power system, we demonstrate that guided tree search outperforms an unguided method in terms of computational complexity, while producing solutions that show no performance loss in terms of operating costs. We compare solutions against mixed-integer linear programming (MILP) and find that guided tree search outperforms a solution using reserve constraints, the current industry approach. The RL solutions exhibit complex behaviours that differ qualitatively from MILP, demonstrating its potential as a decision support tool for human operators.

Investing in generation and storage capacity in a liberalised electricity market: An agent based approach

The power sector is undergoing a period of significant change, in terms of the mix of generation technologies, as well as the structure of energy markets, regulation and assets ownership. The shift to a more liberalised electricity system has resulted in an increase in the number of decision makers. This paper demonstrates an agent-based approach for investigating the long-term investment in the GB power generation sector, whilst considering the operability of the system. A key focus of this study is to investigate the efficacy of a range of policies to reduce the emissions and facilitate investment in renewable generation and battery storage. In order to capture the value of battery storage, the hourly operation of the electricity system, considering short-term variation of demand, renewable generation and wholesale electricity prices (including negative prices during high renewable and low demand events), was incorporated in the long-term investment model. The modelling results show while the cost of battery storage is expected to decrease gradually in future, a substantial subsidy is still required to justify investment in battery storage. The deployment of battery storage provides a significant reduction in the overall power generation system cost, peak demand and carbon emissions.

Design of load data simulation source

In the remote sensing satellite system, the main function of the data transmission subsystem is to receive all the data transmitted by the payload subsystem and then transmit it accurately to the data receiving station on the ground. In the absence of satellite payloads, to meet the requirements of the joint remote sensing satellite test link, we studied and designed a load data simulation source that can simultaneously output four channels of continuous data at different rates, and there are two operating modes to choose from. This design adopts Kintex UltraScale series FPGA with configurable Microblaze embedded soft core as the core controller, and the peripheral circuit includes four pieces of 2GB DDR4 memory, power supply and LVDS interface output. The actual test results show that the load data simulation source has high stability and good universality, and can perfectly meet the ground debugging and testing requirements of remote sensing satellite data transmission subsystem.

What does the GB power outage on 9 August 2019 tell us about the current state of decarbonised power systems?

The GB power blackout, that happened on 9 August 2019, was a unique stress test exposing fault lines brought about by the rapid changes due to the decarbonisation drive and penetration of smart grids technologies. It has demonstrated that, as a significant amount of new equipment and controls were added to the system in a very short time, the probability of common, hidden modes of failures has significantly increased. In the face of declining reliability, maintaining the status quo is not an option. While currently increasing the (N-1) security margin could prove to be expensive, the balance of costs and benefits is likely to change in future. Especially wider application of innovative frequency controls, including “virtual inertia” and Remedial Action Schemes, could help reduce the costs. Distributed Generation (DG) reached such a high penetration level that it cannot be treated any longer as negative demand. Traditional under-frequency load shedding should be made more selective. Interactions between the power system and other infrastructures are still poorly understood and there is a significant risk that if the current compartmentalised approach to their governance and operation is not changed, we may see more unexpected consequences of disturbances across the whole system.

Estimating the impact of variable renewable energy on base-load cycling in the GB power system

Between 2009 and 2017 the share of wind and solar energy sources in the GB electricity generation mix increased from 2.5% to 17%. Due to the variable nature of these renewable sources, large thermal power stations designed for constant base-load operation have been required to operate more flexibly to compensate for fluctuations in renewable generation. This flexible operation results in increased thermal stress and reduced efficiency causing increased operation, maintenance and fuel costs for these assets. In this paper we present the results of what is, to the best of our knowledge, the first empirical study on the impact of renewables generation on startups, ramping and part-loading (collectively, ‘cycling’) of base-load generators. We develop regression models using half-hourly generation data from 2009 to 2017 that capture the impact of increased renewable penetration while taking into account confounding factors including seasonality and demand. We find that with 2009-levels of renewable generation, cycling in 2017 would have been less severe, with 20% fewer startups. We also present estimates for cycling under National Grid Future Energy Scenarios to 2030 with implications for investment in generation assets. Additionally, the dataset derived in this research is made available and comprises the first open-access dataset on cycling.

Rancang Bangun Kombinasi Trafo 1 Ampere CT dan 5 Ampere Engkel Untuk Efisiensi Power Amplifier Class GB (Groudbridge)

Along with the development of technology about audio soundsystems, innovations and new designs about audio are very many, music is an entertainment that is almost every day encountered by people today, Amplifier products that are commonly marketed (finished products) are still very dominated by the outside market country. GB Power Amplifier class (Ground Bridge) is one type of power amplifier that requires a high input voltage in the audio world, the use of a combination of suitable transformers and with the output voltage transformer that is able to meet the required power capacity for the amplifier without having to search for transformer with a high output specification. So a good component selection can make the power amplifier's performance to the maximum.

Low carbon electricity systems for Great Britain in 2050: An energy-land-water perspective

The decarbonisation of the power sector is key to achieving the Paris Agreement goal of limiting global mean surface temperature rise to well below 2 °C. This will require rapid, national level transitions to low carbon electricity generation, such as variable renewables (VRE), nuclear and fossil fuels with carbon capture and storage, across the world. At the same time it is essential that future power systems are sustainable in the wider sense and thus respect social, environmental and technical limitations. Here we develop an energy-land-water nexus modelling framework and use it to perform a scenario analysis with the aim of understanding the planning and operational implications of these constraints on Great Britain’s (GB) power system in 2050. We consider plausible scenarios for limits on installed nuclear capacity, siting restrictions that shape VRE deployment and water use for thermal power station cooling. We find that these factors combined can lead to up to a 25% increase in the system’s levelised cost of electricity (LCOE). VRE siting restrictions can result in an up to 13% increase in system LCOE as the deployment of onshore wind is limited while nuclear capacity restrictions can drive an up to 17% greater LCOE. We also show that such real-world limitations can cause substantial changes in system design both in terms of the spatial pattern of where generators are located and the capacity mix of the system. Thus we demonstrate the large impact simultaneously considering a set of nexus factors can have on future GB power systems. Finally, given our plausible assumptions about key energy-land-water restrictions and emission limits effecting the GB power system in 2050, the cost optimal penetration of VREs is found to be at least 50%.

Demand Side Contributions for System Inertia in the GB Power System

As the penetration of intermittent, low carbon generation increases, system inertia will decrease dramatically and pose a major threat to system stability. The majority of research in this area investigates the methods of providing inertia from the supply side, but this paper develops a novel method that will evaluate the demand side contributions to system inertia based on historical data of frequency outage events in the GB power system. This is achieved by isolating the generation contribution from the total system inertia and considering the power/frequency ratio as an indicator of the additional inertia contribution from spinning reserve. The results show that the demand side could contribute an average of 1.75-s inertia constant in the system load base, representing 20% of the total system inertia in the GB power system. Finally, the system inertia distribution, the rate of change of frequency and the minimum primary frequency response requirements are investigated at a variety of demand levels.

The changing sensitivity of power systems to meteorological drivers: a case study of Great Britain

The increasing use of intermittent renewable generation (such as wind) is increasing the exposure of national power systems to meteorological variability. This study identifies how the integration of wind power in one particular country (Great Britain, GB) is affecting the overall sensitivity of the power system to weather using three key metrics: total annual energy requirement, peak residual load (from sources other than wind) and wind power curtailment.The present-day level of wind power capacity (approximately 15 GW) is shown to have already changed the power system’s overall sensitivity to weather in terms of the total annual energy requirement, from a temperature- to a wind-dominated regime (which occurred with 6GW of installed wind power capacity). Peak residual load from sources other than wind also shows a similar shift. The associated changes in the synoptic- and large-scale meteorological drivers associated with each metric are identified and discussed. In a period where power systems are changing rapidly, it is therefore argued that past experience of the weather impacts on the GB power system may not be a good guide for the impact on the present or near-future power system.

Automation of the supplier role in the GB power system using blockchain-based smart contracts

An electricity supply smart contract was developed and demonstrated to perform pre-time-of-use price negotiation between demand and generation and posttime- of-use settlement and payment. The smart contract was demonstrated with 1000 loads/generators with usages simulated using lognormal probability distributions. It combines payment of deposit, negotiation of price based on estimates, settlement based on actual usage and enactment of payments using crypto-currency. The settlement procedure rewards customers that adjusted to balance the system. The smart contract was written in the Solidity programming language and implemented with a simulated Ethereum blockchain using testrpc and go-ethereum. In the example test case, a price was agreed, settled and payment enacted.

What does a distribution system operator need to know about the findings of the future power system architecture project?

The Future Power System Architecture (FPSA) project examines the changes to the GB power system expected over the next 15–20 years, and the challenges these will present. The project brief considered functions from a whole system perspective. This study summarises the key findings and conclusions from the distribution system operator (DSO) perspective.

Frequency Response in the GB Power System from Responsive CHPs

The increasing penetration of intermittent, low carbon generation poses a major threat to transmission system frequency stability. To mitigate this issue, future system frequency response can be provided by not only the generation side, such as from large, synchronous generators, but also by the customer side, such as from flexible technologies. This paper develops a novel model to evaluate the contribution from domestic Combined Heat and Power (CHP) plants to system frequency in the GB power system. This is achieved by controlling the electricity production from CHPs in response to significant fluctuations in system frequency. The results show that domestic CHP could increase system frequency stability with minimal adverse impact on the customers.

Short-run impact of electricity storage on CO2 emissions in power systems with high penetrations of wind power: A case-study of Ireland

This article studies the impact on CO2 emissions of electrical storage systems in power systems with high penetrations of wind generation. Using the Irish All-Island power system as a case-study, data on the observed dispatch of each large generator for the years 2008 to 2012 was used to estimate a marginal emissions factor of 0.547 kgCO2/kWh. Selected storage operation scenarios were used to estimate storage emissions factors – the carbon emissions impact associated with each unit of storage energy used. The results show that carbon emissions increase in the short-run for all storage technologies when consistently operated in ‘peak shaving and trough filling’ modes, and indicate that this should also be true for the GB and US power systems. Carbon emissions increase when storage is operated in ‘wind balancing’ mode, but reduce when storage is operated to reduce wind power curtailment, as in this case wind power operates on the margin. For power systems where wind is curtailed to maintain system stability, the results show that energy storage technologies that provide synthetic inertia achieve considerably greater carbon reductions. The results highlight a tension for policy makers and investors in storage, as scenarios based on the operation of storage for economic gains increase emissions, while those that decrease emissions are unlikely to be economically favourable. While some scenarios indicate storage increases emissions in the short-run, these should be considered alongside long-run assessments, which indicate that energy storage is essential to the secure operation of a fossil fuel-free grid.

Challenges on primary frequency control and potential solution from EVs in the future GB electricity system

System inertia reduction, driven by the integration of renewables, imposes significant challenges on the primary frequency control. Electrification of road transport not only reduces carbon emission by shifting from fossil fuel consumption to cleaner electricity consumption, but also potentially provide flexibility to facilitate the integration of renewables, such as supporting primary frequency control. In this context, this paper develops a techno-economic evaluation framework to quantify the challenges on primary frequency control and assess the benefits of EVs in providing primary frequency response. A simplified GB power system dynamic model is used to analyze the impact of declining system inertia on the primary frequency control and the technical potential of primary frequency response provision from EVs. Furthermore, an advanced stochastic system scheduling tool with explicitly modeling of inertia reduction effect is applied to assess the cost and emission driven by primary frequency control as well as the benefits of EVs in providing primary frequency response under two representative GB 2030 system scenarios. This paper also identifies the synergy between PFR provision from EVs and “smart charging” strategy as well as the impact of synthetic inertia from wind turbines.

Calibration and sensitivity analysis of long-term generation investment models using Bayesian emulation

Investments in generation are high risk, and the introduction of renewable technologies exacerbated concern over capacity adequacy in future power systems. Long-term generation investment (LTGI) models are often used by policymakers to provide future projections given different input configurations. To understand both uncertainty around these projections and the ways they relate to the real-world, LTGI models can be calibrated and then used to make predictions or perform a sensitivity analysis (SA). However, LTGI models are generally computationally intensive and so only a limited number of simulations can be carried out. This paper demonstrates that the techniques of Bayesian emulation can be applied to efficiently perform calibration, prediction and SA for such complex LTGI models. A case study relating to GB power system generation planning is presented. Calibration reduces the uncertainty over a subset of model inputs and estimates the discrepancy between the model and the real power system. A plausible range of future projections that is consistent with the available knowledge (both historical observations and expert knowledge) can be predicted. The most important uncertain inputs are identified through a comprehensive SA. The results show that the use of calibration and SA approaches enables better decision making for both investors and policymakers.

Benefits of Demand-Side Response in Providing Frequency Response Service in the Future GB Power System

The demand for ancillary service is expected to increase significantly in the future GB electricity system due to high penetration of wind. In particular, the need for frequency response, required to deal with sudden frequency drops following a loss of generator, will increase because of the limited inertia capability of wind plants. This paper quantifies the requirements for primary frequency response and analyses the benefits of frequency response provision from DSR. The results show dramatic changes in frequency response requirements driven by high penetration of wind. Case studies carried out by using an advanced stochastic generation scheduling model suggest that the provision of frequency response from DSR could greatly reduce the system operation cost, wind curtailment and carbon emissions in the future GB system characterised by high penetration of wind. Furthermore, the results demonstrate that the benefit of DSR shows significant diurnal and seasonal variation, whereas an even more rapid (instant) delivery of frequency response from DSR could provide significant additional value. Our studies also indicate that the competing technologies to DSR, namely battery storage and more flexible generation could potentially reduce its value by up to 35%, still leaving significant room to deploy DSR as frequency response provider.