General Power Research Articles

State-of-art review of beyond design basis evaluation approach of a generic nuclear power plant in the UK

Abstract The Fukushima accident prompted rapid development in the appreciation and codification of BDB methodology into regulatory guidance and this is still continuing, especially at IAEA, which has recently and is due to publish several new standards documents directly relevant to BDB analysis. The objective of this paper is to provide a state-of-art review on how BDB evaluations are undertaken in the UK nuclear industry. Then, using the work undertaken for the Generic Design Assessment (GDA) in the UK for UK HPR1000, this paper proposed a more systematic approach for “cliff-edge” evaluation based on a generic nuclear power plant design currently being developed in the UK.

Techno-economic analysis of geothermal combined with direct and biomass-based carbon dioxide removal for high-temperature hydrothermal systems

Limiting global temperature rise to between 1.5 and 2 °C will likely require widespread deployment of carbon dioxide removal (CDR) to offset sectors with hard-to-abate emissions. As financial resources for decarbonization are finite, strategic deployment of CDR technologies is essential for maximizing atmospheric CO2 reductions. Carbon capture and sequestration (CCS), using either direct air capture (DACCS) or bioenergy (BECCS) technologies has a particular synergy with geothermal electricity generation. This is because expensive geothermal infrastructure can be leveraged to transport dissolved CO2 for storage in subsurface reservoirs.Here, we present a techno-economic comparison of renewable electricity generation coupled with either BECCS or DACCS at high-temperature, low-gas hydrothermal systems. We use a systems model that quantifies energy, carbon and financial flows through a generic hybrid power plant. At a CO2 market price of $100/tonne, the geothermal-BECCS system has a lower median cost of electricity generation ($88/MWh) than geothermal-DACCS ($181/MWh) and conventional geothermal ($89/MWh).Geothermal-BECCS also had the lowest costs of overall emissions abatement, $122/tCO2, accounting for carbon removal and assuming displacement of fossil-fuel generation. Abatement costs are even lower, $45/tCO2, for BECCS retrofit of existing geothermal plants, owing to discounted costs of pre-existing injection wells, steam fields, and plant equipment.For a case study based on a geothermal field in New Zealand's Taupō Volcanic Zone (TVZ), we determined that achieving CDR rates of 1 MtCO2/year via new geothermal-BECCS builds would require 62 standard geothermal wells and 790 kt/year of feedstock and result in 511 MWe in installed capacity. In contrast, geothermal-DACCS would need 49 wells and no external fuel source to achieve 1 MtCO2/year scale but result in only 190 MWe in installed capacity. Both pathways are calculated to require similar upfront investment costs at $2.2 billion and $2.3 billion for geothermal-BECCS and geothermal-DACCS respectively.Although geothermal-DACCS removes CO2 at high rates, its high parasitic load increases the overall decarbonization cost ($187/tCO2). In contrast, when biomass hybridization is considered, geothermal-BECCS has a lower cost of emissions abatement and produces 20 % more electricity than the benchmark geothermal plant. We conclude that this increase in electricity production makes geothermal-BECCS the more cost-effective geothermal-based CDR configuration. Finally, we argue that revenues from net-negative CO2 emissions and increased power production make geothermal-CDR a cost-competitive decarbonization technology.

Short-term voltage stability analysis in power systems: A voltage solvability indicator

This paper presents an analytical study for short-term voltage stability based on a novel indicator. First, it is shown that the reduced Jacobian matrix of network equations can be transformed to obtain an approximately symmetric matrix which is usually positive definite when the system under study is stable. The minimum eigenvalue of the matrix is suggested as a short-term Voltage Solvability Indicator (VSI). Then, properties of the reduced conductive matrix and susceptance matrix which have significant impacts on VSI are examined. The focus of the study is power systems with multiple constant power injections, while VSI can also be applied to analyze the impact of generic power system models. It is shown that as the penetration level of constant power injections increases, VSI decreases and the system becomes more vulnerable to voltage collapse. Finally, the relationship between the eigenvalues of the reduced Jacobian matrix and the structure-preserving Jacobian matrix is established. As a result, the suggested VSI can be efficiently computed by exploring the sparsity of structure-preserving Jacobian matrix of network equations. Case studies of several test systems including a simplified 575-machine 8117-bus East China power system are reported, demonstrating the effectiveness and practicability of the proposed method.

THE FINANCIAL IMPACT OF CARBON EMISSIONS ON POWER UTILITIES UNDER CLIMATE SCENARIOS

Power utilities, especially those that generate electricity by burning fossil fuels, produce significant amounts of carbon emissions. Mitigation of [Formula: see text]-equivalent ([Formula: see text]) emissions can be achieved by replacing power plants with renewable power installations and by adopting carbon-sequestration technologies. Physical upgrades are expensive, but carbon taxes, or the purchase of certificates and allowances on a voluntary carbon market, can be costly, too. Carbon costs may increasingly become a threatening liability for power utilities, eating into profits and undermining the financial viability of emission-intensive electricity generation. Thus, we consider an asset-and-liability, structural firm model to investigate the creditworthiness of a generic power utility. The utility’s assets dynamics are driven by the financial returns generated from the sold electricity for a set tariff which is modeled by a simple stochastic process. The liabilities not only depend on fuel, running, and depreciation costs, but also on the costs of [Formula: see text] emissions. As a case study, we consider Eskom, the South African power utility. We show the evolution of Eskom’s default probability under various fuel mix plans and technologies (as per SA’s Integrated Resources Plan (IRP) 2019), and under the Network for Greening the Financial System (NGFS) carbon price scenarios. The obtained results and insights present a trying path ahead, especially for carbon-intensive power utilities.

Radiation dose assessments for generic nuclear power plants part I: Routine operation

The main task of the site evaluation report for nuclear installation concerning nuclear safety is the protection of the public and environment from the radiological consequences of radioactive releases in both cases of normal operation and accident conditions. The atmosphere is the most important pathway to be considered in the assessment of the environmental impact of radioactive materials released from nuclear facilities. The present study is a comprehensive investigation of environmental assessments for the dose calculation model resulting from routine operation NPPs of 1000 Mw (e). The procedure consists of different parts, beginning with the manipulation of collected meteorological data such as wind speed and direction, temperature, incoming solar radiation and utilizing a set of empirical formulae for evaluating night solar radiation emitted from the ground, and then evaluating hourly stability classes and joint frequency distribution of winds by developing code. The dilution factor was evaluated using computer code (XOQDOQ code). Finally, the radiation dose assessments resulting from the routine operation of NPPs were calculated. The processes are adapted with IAEA recommendations documents, safety guides, and ICRP recommendations. The results reveal that there is no detectable value that affects the people surrounding the site nor the environmental area concerning low population zone or exclusion area.

Evaluation of simple wind power forecasting methods applied to a long-term wind record from Scotland

We present an analysis of the ability to predict the power output from a nominal wind turbine or wind farm a few hours ahead using only locally available data – either the current and recent wind speed or power output. A third method combines the current state with knowledge of the long-term climatology. The wind speed data were taken from a 46-year long record of hourly readings at a Scottish coastal site and converted to power output and electricity production using a generic wind turbine power curve. The wind speed data or the calculated power output at a given time were used to predict the output a few hours ahead, either using persistence, a linear model, or a model based on the mean daily cycle extracted from the long-term record. Since many wind farm operators base their forecast on current wind speed or output measures alone, this analysis will provide some quantification of the quality of this approach, either to help them plan their operation or be able to put these simple methods in quantitative context of more complex methods.

Discussions on the Instantaneous Reactive Power Theory Terms in the General Case Based on the Symmetric Components

This paper deals with the presentation of different terms of the powers presented in the Instantaneous Reactive Power Theory (IRPT) in the general case of a generic power system voltage. This presentation is based on the use of the symmetric components in the E. Clarke stationary frame, where the instantaneous real active power, the instantaneous imaginary reactive power and the instantaneous zero sequence power are divided into their DC components and AC components function of the symmetric components. The contributions of these terms in the transfer of the energy from the power supply to the load in four wire system are explained, hence, a good choice of the power to be compensated using shunt active power filter can be obtained. Finally, simulation results are presented under unbalanced and distorted generic power system voltages, where different terms of the instantaneous reactive power theory are clarified and presented based on symmetrical components.

Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation

This paper develops a data-driven framework to synthesize local Volt/Var control strategies for distributed energy resources (DERs) in power distribution grids (DGs). Aiming to improve DG operational efficiency, as quantified by a generic optimal reactive power flow (ORPF) problem, we propose a two-stage approach. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> stage involves learning the manifold of optimal operating points determined by an ORPF instance. To synthesize local Volt/Var controllers, the learning task is partitioned into learning local surrogates (one per DER) of the optimal manifold with voltage input and reactive power output. Since these surrogates characterize efficient DG operating points, in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">second</i> stage, we develop local control schemes that steer the DG to these operating points. We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DG operating point agreeing with the local surrogates. We use neural networks to model the surrogates and enforce the identified conditions in the training phase. AC power flow simulations on the IEEE 37-bus network empirically bolster the theoretical stability guarantees obtained under linearized power flow assumptions. The tests further highlight the optimality improvement compared to prevalent benchmark methods.

Generic optimal power flow for active distribution networks

Active distribution networks (ADN) may operate in different modes according to the generation demand balance and the capacity of the primary grid for imposing a constant frequency. Conventionally, a customized optimization model is used for each operating mode. Unlike that conventional approach, this article proposes a general optimization model capable of operating the system in three different modes: grid-connected, islanded with a surplus of generation, and islanded with a deficit of generation. Real-time operation is required in this framework with guarantees such as global optimum, uniqueness of the solution, and fast algorithm convergence; for this reason, a convex approach is employed for grid modeling. Numerical experiments demonstrate that the proposed optimization-based operation model can handle the three types of operation while ensuring the safety operation with frequency and voltage within expected limits.

Evaluation of uncertainties associated with best estimates of parameters in the deterministic analysis of a severe accident

This paper describes an approach to evaluate the uncertainties of the results of deterministic severe accident analysis. The approach was developed in order to follow best practices of best estimate methodology and to fulfill the Russian regulatory requirements NP-001–15 in the area of atomic energy use. In accordance with these requirements, deterministic safety analyses must be accompanied with the evaluation of errors and uncertainties of the obtained results. The application of the proposed approach is demonstrated through the example of a deterministic analysis of a severe LOCA initiated by a large break at a generic nuclear power plant with VVER-1000. Within the given example a best estimate value and associated uncertainty of hydrogen mass generated at in-vessel stage is analyzed.

A predictive tool for power system operators to ensure frequency stability for power grids with renewable energy integration

The increased penetration levels of renewable energy resources (RESs) transform the power generation schema into one that is more susceptible to changes due to weather conditions, complicated load profiles, and reduced inertia levels. One of the main challenges that power grids can encounter under varying and low system inertia (SI) is poor frequency response (FR). Therefore, this paper proposes a novel online tool for assessing, predicting, and enhancing the frequency stability (FS) of power systems with renewable power generation and energy storage systems (ESS). Firstly, the tool provides accurate tracking of the SI in real-time settings using recursive least square identification and Kalman filtering to provide accurate and computationally efficient results. Secondly, the tool estimates the FR of the whole system in a virtual environment for different contingencies and inertia levels. The accuracy of the FR is improved by classifying the frequency response models (FRMs) based on the SI levels. Lastly, the proposed tool computes the optimal additional estimated reserve power (ERP) required from PV and battery energy storage systems (BESS) to provide inertial and primary frequency support to the power grid at the onset of a contingency. In addition, a reserve power allocation and load-shedding strategies are proposed and implemented to dynamically adjust the reserve power of PV power plants. This dynamic control of the PV power plant reserve capacity guarantees the availability of adequate reserves for addressing unforeseen systems contingencies. Moreover, dedicated FR controllers for PV and BESS are employed to emulate the synchronous machine’s inertial response (IR) and primary frequency control (PFC). The performance of the proposed tool and the FR controllers are tested and validated on a generic power grid and the IEEE 39 bus system using MATLAB Simulink and the OPAL-RT real-time simulation software.

Study Explores Design, Potential of Electric Submersible Generators

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214361, “Electric Submersible Generators: Using ESPs in an Unfamiliar Way,” by Charles E. Collins, Kenneth J. Frazier, and Bryan Coates, SPE, Baker Hughes. The paper has not been peer reviewed. _ The electrical submersible generator (ESG) is a modern interpretation of an old idea. The ESG can generate electricity to meet surface power needs or feed directly into the grid to create a revenue stream under the proper injection conditions. In typical configurations, a standard centrifugal pump is used in combination with an induction motor operated in such a way for the mechanical work of the pump to be converted into electrical energy. The complete paper explores ESG design considerations, theoretical underpinnings, and potential applications. What Is an ESG? A generic geothermal power plant typically consists of production wells that can be either artesian or artificially lifted. The heated well fluid will transfer the heat to a working fluid that rotates turbines to generate electricity. The reservoir fluid eventually will be reinjected into the formation to be warmed again and reproduced. It is common to use conventional electrical submersible pump (ESP) equipment to produce the fluids from the formation. The ESG concept focuses on injection wells from these power plants. Instead of drawing power from a source and moving fluid to the surface, the ESG takes injected fluid from the surface and creates power that can be used to offset surface power requirements onsite or sold directly back to the grid to generate revenue. The components of the ESG are very similar to those of an ESP, with an intake, discharge, seal, and cable attached to the motor. However, many of the components act essentially in reverse. The motor becomes a generator as it is driven by the pump acting as a turbine. The conventional ESP discharge becomes the intake, and the intake becomes the discharge. The cable still transmits the energy from the motor to the surface and vice versa. The seal section isolates the generator from the well fluid, handles thrust from the turbine, and equalizes the external pressure of the well with the internal pressure of the system. The generator effectively acts as a brake for the turbine. While most ESP stages are not typically designed to operate as a turbine, many will still maintain a system efficiency expected of ESPs. Application Guidelines As with conventional ESP systems, proper application of this equipment ensures desired run life and optimal electrical generation. General steps of equipment selection include the following: - Collect basic wellbore information - Verify flow rate, temperature, and pressure of injected fluid - Size the turbine accordingly based on the expected flow rate and pressure - Determine the size of the generator based on the load from the turbine and the expected heat rise

Assessment of Linear Quadratic Regulator Based Optimal Stabilization of Weak-Grid-Following Inverter Interface

An optimal state- feedback based decentralized stabilizing control scheme for weak-grid connected inverter interfaces is proposed here. Linear Quadratic Regulator (LQR) based feedback control options viz. small-signal perturbations in the reference signals, and in the control gains are explored for the same. It is shown that, with full-state feedback of local control variables, the stability enhancement is not much effective with gain perturbations. Rather, the reference set-point change is shown to stabilize the inverter to weak-grid interconnection. Eliminating the need of communication assisted auxiliary inputs or the need for their estimation, the proposed approach is effective in achieving stability margin enhancement for wider range of X/R ratios and grid impedances. The cost of achieving stabilization using totally device-level information is the compromise made in set-point adjustment. However, this can be adopted for emergency weak-grid scenarios in a generic power system. The proposed control augmentation is analysed for different operating scenarios in which one or the other control loops are enabled, and corresponding modifications in the controller are discussed. The proposed claims are validated using small-signal models, simulation studies in MATLAB/Simulink as well as with a power-hardware-in-the-loop (PHIL) experimental setup.

Dynamic heterogeneity in polydisperse systems: A comparative study of the role of local structural order parameter and particle size.

In polydisperse systems, describing the structure and any structural order parameter (SOP) is not trivial as it varies with the number of species we use to describe the system, M. Depending on the degree of polydispersity, there is an optimum value of M = M0 where we show that the mutual information of the system increases. However, surprisingly, the correlation between a recently proposed SOP and the dynamics is highest for M = 1. This effect increases with polydispersity. We find that the SOP at M = 1 is coupled with the particle size, σ, and this coupling increases with polydispersity and decreases with an increase in M. Careful analysis shows that at lower polydispersities, the SOP is a good predictor of the dynamics. However, at higher polydispersity, the dynamics is strongly dependent on σ. Since the coupling between the SOP and σ is higher for M = 1, it appears to be a better predictor of the dynamics. We also study the Vibrality, an order parameter independent of structural information. Compared to SOP, at high polydispersity, we find Vibrality to be a marginally better predictor of the dynamics. However, this high predictive power of Vibrality, which is not there at lower polydispersity, appears to be due to its stronger coupling with σ. Therefore, our study suggests that for systems with high polydispersity, the correlation of any order parameter and σ will affect the correlation between the order parameter and dynamics and need not project a generic predictive power of the order parameter.

Investigation of hybrid power plant configurations for an offshore vessel with co-simulation approach

This work presents a full-system simulator consisting of a generic power system integrated with a vessel model and real-time capabilities. The whole system simulation facilitates evaluating the overall system performance by considering components’ interactions according to the maneuvering and environmental effects. Indeed, flexibility in the configuration and size of the power system enables the investigation of different concepts according to various maneuvering scenarios. Co-simulation approach is employed to integrate the models with various domains effectively. In addition, the bond graph modeling strategy as a power based method is used. The developed power system contains a diesel genset, a PEMFC, a battery with average electrical components, and a power management system. The configuration of the power system and size of power sources are modifiable. Various hybrid configurations and power capacities can be designed with validated power sources against marine vendors. In addition, the integrated offshore supply vessel with Dynamic Positioning (DP) and cruise controller and sea state forces induces the corresponding load demand of the operation to the power system. In summary, different operation scenarios with sea states, the thrusters’ states and allocation algorithm, DC link voltage, power electrical converters controller, and fuel consummations are captured in one model framework. To demonstrate the application of the model and emphasis the importance of total system simulation, three power system configurations are designed and simulated with two operational modes of DP and cruise with various sea states.

Design rules to establish a credible More‐Electric Engine baseline power architecture concept

AbstractThe More‐Electric Engine (MEE), with its electrified engine auxiliary systems and increased multi‐shaft power offtake, is likely to become an increasingly central aspect of future More‐Electric Aircraft. Consequently, lightweight but resilient electrical power architectures are needed for these future MEE applications. However, whilst a range of MEE architectures exist in the research literature, no effective baseline architecture or standardised feature identification has been proposed to specifically address their unique design requirements. Accordingly, any underpinning technology‐focused research for critical MEE subsystems may ultimately have a reduced effectiveness without this credible baseline. Based on comprehensive design analyses, preliminary design requirements and anticipated operational modes, this article proposes key design rules for the formation of the first generic baseline MEE electrical power system architecture concept. Guidance is provided on features such as the number of power generation systems, the number and topologies of distribution channels, type of power conversion, essential load redundancy, and the location of emergency power supply. This article also provides full transparency of the design process so that key decision points can be revisited to capture application‐specific requirements and updates to certification requirements.

Production of PBHs from inflaton structures

At times prior to Big Bang Nucleosynthesis, the universe could show a primordial structure formation period if dominated by a fast oscillating inflaton field during reheating. In this context, we have postulated a new mechanism of primordial black hole formation [L. E. Padilla, J. C. Hidalgo, and K. A. Malik, Phys. Rev. D, vol. 106, p. 023519, Jul 2022], that draws the analogy between an extended reheating era and the scalar field dark matter model, stipulating the gravitational collapse of inflaton halos and inflaton stars. In this paper we look at the requirements for the realization of this new mechanism. We show that a generic primordial power spectrum with a peak at small scales is most suitable for the production of a considerable number of PBHs. When such requirement is met, and if reheating lasts long enough, large populations of PBHs with $M_{\rm PBH}\sim 1~\mathrm{gram}$ may be produced. We find in particular, that the mass fraction of PBHs is orders of magnitude larger than that obtained when PBHs form via direct collapse in a universe dominated by radiation or pressure-less dust. Looking at observable implications of our findings, we explore the possibility that the PBHs component may dominate the energy density of the universe at some point after the end of reheating.

Portfolio optimization of generic energy storage-based virtual power plant under decision-dependent uncertainties

Distributed energy resources (DERs) can be integrated into a smart and aggregated entity, namely a virtual power plant (VPP). This integration is beneficial to facilitate their flexibility utilization and optimally allocate their capacity to participate in competitive electricity markets. While various types of uncertainties exist in the market operation of VPP, such as the stochastic power of renewable energy resources (RES), etc., the literature generally overlooked the decision-dependent uncertainties (DDUs) with the operation of generic energy storage (GES). To be risk-hedging, this manuscript proposes a novel approach for risk-averse portfolio optimization of VPP with special attention to DDUs. To achieve tractable optimization, we propose a two-stage chance-constrained stochastic optimization model and three reformulations of different types of uncertainties. Furthermore, a modified and iterative progressive hedging algorithm is further proposed to handle the conditional value at risk (CVaR) and DDU items and efficiently solve the problem with large-scale GES portfolios. Exhaustive case studies based on real-world data demonstrate that VPP has to face the consequence of a significant real-time penalty (6.36 % loss in average profit) and losses in GES power (about 6.5 MW) when overlooking the DDUs' consequences. Moreover, it is more beneficial for greedy decision-makers to incorporate the DDUs' impact into their less risk-aversion strategy. Additionally, comparative results prove the validity of the proposed algorithm and its superiority in solving problems with large-scale GES portfolios and high-performance computing systems (over 30 % CPU time saved).

SP-GNN: Learning structure and position information from graphs

Graph neural network (GNN) is a powerful model for learning from graph data. However, existing GNNs may have limited expressive power, especially in terms of capturing adequate structural and positional information of input graphs. Structure properties and node position information are unique to graph-structured data, but few GNNs are capable of capturing them. This paper proposes Structure- and Position-aware Graph Neural Networks (SP-GNN), a new class of GNNs offering generic and expressive power of graph data. SP-GNN enhances the expressive power of GNN architectures by incorporating a near-isometric proximity-aware position encoder and a scalable structure encoder. Further, given a GNN learning task, SP-GNN can be used to analyze positional and structural awareness of GNN tasks using the corresponding embeddings computed by the encoders. The awareness scores can guide fusion strategies of the extracted positional and structural information with raw features for better performance of GNNs on downstream tasks. We conduct extensive experiments using SP-GNN on various graph datasets and observe significant improvement in classification over existing GNN models.

Strategic Modelling of Industrial Noise with Limited Input Datav

Historically, strategic modelling of industrial noise exposure on a national scale has taken a wide range of approaches. Where sound emission data of industrial sites are not available, numerous assumptions are often made. These include simplified modelling of industrial sites and adoption of generic sound power levels for different industry types. In most cases such assumptions significantly increase the uncertainty in strategic noise mapping outcomes. This paper presents an alternative approach to strategic modelling of industrial sound which is based on the environmental permitting practice of large industrial sites in England. Drawing on this approach, a cost-effective and scalable modelling procedure has been developed to estimate sound emission levels and radiation patterns of industrial activities accounting for the local context of the site. This paper details the alternative modelling approach along with example prediction outputs. Potential future refinements to improve the modelling accuracy and opportunities to enhance site-specific input data in collaboration with national authorities are also discussed.