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Hydrogen is an emerging technology changing the context of heating with cleaner combustion than traditional fossil fuels. Studies indicate the potential to repurpose the existing natural gas infrastructure, offering consumers a sustainable, economically viable option in the future. The integration of hydrogen in combined heat and power systems could provide residential energy demand and reduce environmental emissions. However, the widespread adoption of hydrogen will face several challenges, such as carbon dioxide emissions from the current production methods and the need for infrastructure modification for transport and safety. Researchers indicated the viability of hydrogen in decarbonizing heat, while some studies also challenged its long-term role in the future of heating. In this paper, a comprehensive literature review is carried out by identifying the following key aspects, which could impact the conclusion on the overall role of hydrogen in heat decarbonization: (i) a holistic view of the energy system, considering factors such as renewable integration and system balancing; (ii) consumer-oriented approaches often overlook the broader benefits of hydrogen in emission reduction and grid stability; (iii) carbon capture and storage scalability is a key factor for large-scale production of low-emission blue hydrogen; (iv) technological improvements could increase the cost-effectiveness of hydrogen; (v) the role of hydrogen in enhancing resilience, especially during extreme weather conditions, raises the potential of hydrogen as a flexible asset in the energy infrastructure for future energy supply; and finally, when considering the UK as a basis case, (vi) incorporating factors such as the extensive gas network and unique climate conditions, necessitates specific strategies. [ABSTRACT FROM AUTHOR]

The reversible solid oxide cell (rSOC) is a promising technology for advancing energy decarbonization by enabling bidirectional conversion between electricity and hydrogen in a single device. However, previous studies have not fully explored the operational flexibility of rSOCs due to inadequate consideration of heat recovery potentials and dynamics of operating mode transitions. To address this research gap, this paper presents a model-based optimal operation method for managing multi-energy transactions in rSOC-based microgrids, aiming to minimize operation costs. The method incorporates detailed operational models of the rSOC, including a lumped thermal model to account for heat recovery capability and modeling of various operating modes and their transitions. Additionally, a linearization process is introduced to address nonlinear and implicit operational constraints, resulting in a computationally efficient mixed-integer linear programming (MILP) formulation for the operation model. Comparative case studies are conducted using modified energy portfolios of a Danish energy island. The results demonstrate that the proposed method effectively captures operating mode transitions within the rSOC and enhances its profitability via waste heat recovery. Notably, the rSOC model contributes to enhanced operational flexibility through heat recovery behaviors and a wider temperature range, resulting in substantial economic savings for the microgrid. • Dynamic transition modeling of operating modes in rSOCs. • Waste heat recovery analysis from rSOCs for district heating systems. • Computationally efficient economic operation model for rSOC-integrated microgrids. • Unleashing rSOC flexibility through heat recovery and extended temperature range. • Enhanced cost-effectiveness of microgrids through heat recovery benefits of rSOCs. [ABSTRACT FROM AUTHOR]

The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This paper presents a comprehensive review of the state-of-the-art research and developments concerning the flexibility in the operation of low-carbon integrated gas and electricity networks (IGENs) as part of the whole system approach. Methods and solutions to provide and improve flexibility in the mentioned systems are studied and categorized. Flexibility is the system's ability to deal with changes and uncertainties in the network while maintaining an acceptable level of reliability. The presented review underscores the significance of this convergence in facilitating demand-side management, renewable energy integration, and overall system resilience. By highlighting the technical, economic, and regulatory aspects of such integration, this paper aims to guide researchers, policymakers, and industry stakeholders toward effective decision-making and the formulation of comprehensive strategies that align with the decarbonization of energy systems. [ABSTRACT FROM AUTHOR]

Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This article derives a decentralized load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achieve low computational complexity and an ability to handle discrete time steps of variable length—desirable features for embedding in appliances and high-throughput simulations. Simulation results of large populations of heterogeneous appliances illustrate the accurate aggregate control of power consumption and high computational efficiency. Tracking accuracy is quantified as a function of population size and time step size, and correlations in the tracking error are investigated. The controller is shown to be robust to errors in model specification and to sudden perturbations in the form of random refrigerator door openings. [ABSTRACT FROM AUTHOR]

This study evaluates application of three different nanopowder mixtures for decomposition of diclofenac (DCF), one of frequently detected pharmaceutical in wastewater. Analyzed three photocatalytic mixtures ZnO/SnO2, ZnO/TiO2 and ZnO/In2O3 are for the first time used for diclofenac degradation. A set of experiments were performed in order to investigate influence of catalyst concentration (0.10–0.60 mg mL−1), initial concentration of diclofenac (0.002–0.010 mg mL−1) and pH value (5–9). The increase in the catalyst concentration leads to a decrease in the degradation rate constant, which is the most pronounced in the ZnO/TiO2 and ranges from 0.47 (6) min−1 to 0.25 (3) min−1. The influence of pH on efficacy shows completely different effects: ZnO/In2O3 is most effective in alkaline environments, ZnO/TiO2 in neutral environments, while ZnO/SnO2 efficiency is good in both alkaline and acidic environments. Initial concentrations of diclofenac showed a complex effect on the degradation rate. The four dominant intermediates were detected by LC MS/MS technique. In case of all three nanomaterials, intensive degradation was achieved in first 30 minutes. The economical analysis of photocatalytic treatment was provided where the preparation of nanomaterials does not demand high costs and with the highest diclofenac concentration, total operation costs are the lowest (77.14 US$/kWh). [ABSTRACT FROM AUTHOR]

Electric heat pumps (HPs), hybrid heating technology and district heating networks (DHN) are the main low-carbon heating technologies that can deliver the ambitious carbon reduction target in the UK. The optimal design of the heating system on a national scale to maximize the economic benefits regarding both investment cost and operation cost while satisfying the carbon target remains an open question. This paper aims to compare the economic advantages as well as the associated impacts on the electricity system under the full deployments of ASHP (air source HP), hybrid HP-Bs (ASHP and gas boilers) and DHNs. To do so, a novel whole-system approach is applied with the explicit consideration of the coordinated operation between heat and electricity system. The optimized strategy for heat sector decarbonisation is also demonstrated, providing an outline of the optimal deployment for different heating technologies in terms of their penetrations and deployed areas. The UK case study suggests the significant economic advantage of the hybrid HP-B over the other two heating technologies, while DHN may play an important role in urban areas under the optimized heat decarbonisation strategy. The results also clearly demonstrate the changes on the electricity side driven by the different decarbonisation strategies in the heating system. [ABSTRACT FROM AUTHOR]

The benefits of new transmission investment significantly depend on deployment patterns of renewable electricity generation that are characterized by severe uncertainty. In this context, this paper presents a novel methodology to solve the transmission expansion planning problem under generation expansion uncertainty in a min–max regret fashion, when considering flexible network options and $n-1$ security criterion. To do so, we propose a five-level mixed integer linear programming (MILP) based model that comprises: (i) the optimal network investment plan (including phase shifters), (ii) the realization of generation expansion, (iii) the co-optimization of energy and reserves given transmission and generation expansions, (iv) the realization of system outages, and (v) the decision on optimal post-contingency corrective control. In order to solve the five-level model, we present a cutting plane algorithm that ultimately identifies the optimal min–max regret flexible transmission plan in a finite number of steps. The numerical studies carried out demonstrate: (a) the significant benefits associated with flexible network investment options to hedge transmission expansion plans against generation expansion uncertainty and system outages, (b) strategic planning-under-uncertainty uncovers the full benefit of flexible options which may remain undetected under deterministic, perfect information methods, and (c) the computational scalability of the proposed approach. [ABSTRACT FROM PUBLISHER]

The electricity system balancing is becoming increasingly challenging due to the integration of Renewable Energy Sources (RES). At the same time, the dependency of electricity network on gas supply system is expected to increase, as a result of employing flexible gas generators to support the electricity system balancing. Therefore the capability of the gas supply system to deliver gas to generators under a range of supply and demand scenarios is of a great importance. As potential solutions to improve security of gas and electricity supply, this paper investigates benefits of employing flexible multi-directional compressor stations as well as adopting a fully integrated approach to operate gas and electricity networks. A set of case studies for a GB gas and electricity networks in 2030 have been defined to quantify the value of an integrated operation paradigm versus sequential operation of gas and electricity networks. The results indicate there are significant overall system benefits (up to 65% in extreme cases) to be gained from integrated optimization of gas and electricity systems, emphasizing the important role of gas network infrastructure flexibility in efficiently accommodating the expected expansion of intermittent RES in future power systems. [ABSTRACT FROM AUTHOR]

Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9. figshare.c.3790294. Robust estimation of risks from small samples Simon H. Tindemans and Goran Strbac Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK SHT, 0000-0001-8369-7568 Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact of estimation errors may be very large. This paper presents a robust non-parametric Bayesian method to infer possible underlying distributions. Themethod obtains rigorous error bounds even for small samples taken from ill-behaved distributions. The approach taken has a natural interpretation in terms of the intervals between ordered observations, where allocation of probability mass across intervals is well specified, but the location of that mass within each interval is unconstrained. This formulation gives rise to a straightforward computational resampling method: Bayesian interval sampling. In a comparison with common alternative approaches, it is shown to satisfy strict error bounds even for ill-behaved distributions. [ABSTRACT FROM AUTHOR]

High penetration of renewable generation will increase the requirement for both operating reserve (OR) and frequency response (FR), due to its variability, uncertainty, and limited inertia capability. Although the importance of optimal scheduling of OR has been widely studied, the scheduling of FR has not yet been fully investigated. In this context, this paper proposes a computationally efficient mixed integer linear programming formulation for a full stochastic scheduling model that simultaneously optimizes energy production, OR, FR, and underfrequency load shedding. By using value of lost load (VOLL) as the single security measure, the model optimally balances the cost associated with the provision of various ancillary services against the benefit of reduced cost of load curtailment. The proposed model is applied in a 2030-GB system to demonstrate its effectiveness. The impact of installed capacity of wind generation and setting of VOLL is also analyzed. [ABSTRACT FROM AUTHOR]

Significant uncertainty surrounds the future development of electricity systems, primarily in terms of size, location and type of new renewable generation to be connected. In this paper we assess the potential for flexible network technologies, such as phase-shifting transformers, and non-network solutions, such as energy storage and demand-side management, to constitute valuable interim measures within a long-term planning strategy. The benefit of such flexible assets lies not only in the transmission services provided but also in the way they can facilitate and de-risk subsequent decisions by deferring commitment to capital-intensive projects until more information on generation development becomes available. A novel stochastic formulation for transmission expansion planning is presented that includes consideration of investment in these flexible solutions. The proposed framework is demonstrated with a case study on the IEEE-RTS where flexible technologies are shown to constitute valuable investment options when facing uncertainties in future renewable generation development. [ABSTRACT FROM PUBLISHER]

The classical formulation of the transmission switching problem as a mixed‐integer problem is intractable for large systems in real‐time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real‐time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118‐bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state‐of‐the‐art machine learning methods proposed for transmission switching. [ABSTRACT FROM AUTHOR]

Realizing the significant demand flexibility potential in deregulated power systems requires its suitable integration in electricity markets. Part I of this work has presented the theoretical, algorithmic and implementation aspects of a novel pool market mechanism achieving this goal by combining the advantages of centralized mechanisms and dynamic pricing schemes, based on Lagrangian relaxation (LR) principles. Part II demonstrates the applicability of the mechanism, considering two reschedulable demand technologies with significant potential, namely electric vehicles with flexible charging capability and electric heat pump systems accompanied by heat storage for space heating. The price response sub-problems of these technologies are formulated, including detailed models of their operational properties. Suitable case studies on a model of the U.K. system are examined in order to validate the properties of the proposed mechanism and illustrate and analyze the benefits associated with the market participation of the considered technologies. [ABSTRACT FROM PUBLISHER]

In the deregulated power systems setting, the realization of the significant demand flexibility potential should be coupled with its integration in electricity markets. Centralized market mechanisms raise communication, computational and privacy issues while existing dynamic pricing schemes fail to realize the actual value of demand flexibility. In this two-part paper, a novel day-ahead pool market mechanism is proposed, combining the solution optimality of centralized mechanisms with the decentralized demand participation structure of dynamic pricing schemes and based on Lagrangian relaxation (LR) principles. Part I presents the theoretical background, algorithmic approaches and suitable examples to address challenges associated with the application of the mechanism and provides an implementation framework. Non-convexities in reschedulable demand participants' price response and their impacts on the ability of the basic LR structure to reach feasible market clearing solutions are identified and a simple yet effective LR heuristic method is developed to produce both feasible and high quality solutions by limiting the concentrated shift of reschedulable demand to the same low-priced time periods. [ABSTRACT FROM PUBLISHER]

The article offers information on Advanced Approaches and Applications for Electric Vehicle Charging Demand Management. It mentions type of rapidly increasing power demands, electric vehicle (EV) charging loads have unique characteristics, as they are typically equipped with battery storage and moving capability.

Time-domain scheduling simulation is the most effective tool for predicting the operational costs in wind-integrated power systems, because it can represent the inter-temporal constraints that limit the balancing actions of the thermal plant, storage, and demand-side measures. High wind penetrations demand just-in-time commitment decisions that reflect the uncertainties in the wind infeed, so that it is desirable to generate the scheduling decisions using stochastic unit commitment (SUC) with rolling planning. However, the computational burden can make such methods impractical in long simulations. We present an efficient formulation of the SUC problem that is designed for use in scheduling simulations of single-bus power systems. Unlike traditional SUC techniques, the proposed formulation uses a quantile-based scenario tree structure that avoids the need for exogenous operating reserves. We compare the performance of various tree topologies in year-long simulations of a large system. Simple quantile-based trees give statistically significant cost improvements over fixed-quantile deterministic methods and compare favorably with trees based on Monte Carlo-generated scenarios. [ABSTRACT FROM PUBLISHER]

Abstract: Although penetration of wind generation may displace a significant amount of energy produced by large conventional plant, there are issues associated with the extent to which wind generation will be able to replace the capacity and flexibility of conventional generating plant. This is important since wind power is variable, so it will be necessary to retain a significant proportion of conventional plant to ensure security of supply especially under conditions of high demand and low wind. Hence, the capacity value of wind generation will be limited as it will not be possible to displace conventional generation capacity on a “megawatt for megawatt” basis. Wind power is variable and not easy to predict, hence various forms of additional reserves will be needed to maintain the balance between supply and demand at all times. Additionally, if the majority of wind generation plant is located in Scotland and the North of England, reinforcement of the transmission network will be needed to accommodate the increases in the north-south flow of electricity. In this paper an assessment of the costs and benefits of wind generation on the UK electricity system is carried out, assuming different levels of wind power capacity. Overall, it is concluded that the system will be able to accommodate significant increases in wind power generation with relatively small increases in overall costs of supply, about 5% of the current domestic electricity price in case of 20% energy produced by wind power. [Copyright &y& Elsevier]

This paper considers the impact of uncertain wind forecasts on the value of stored energy (such as pumped hydro) in a future U.K. system, where wind supplies over 20% of the energy. Providing more of the increased requirement for reserves from standing reserve sources could increase system operation efficiency, enhance wind power absorption, achieve fuel cost savings, and reduce CO2 emissions. Generally, storage-based standing reserve's value is driven by the amount of installed wind and by generation system flexibility. Benefits are more significant in systems with low generation flexibility and with large installed wind capacity. Storage is uniquely able to stock up generated excesses during high-wind/low-demand periods, and subsequently discharge this energy as needed. When storage is combined with standing reserve provided from conventional generation (e.g., open-cycle gas turbines), it is valuable in servicing the highly frequent smaller imbalances. [ABSTRACT FROM AUTHOR]

Abstract: In an electricity grid with large wind penetration additional system balancing costs are incurred due to the intermittent nature of wind. In this work, we evaluated the benefits of using storage for providing standing reserve as part of the overall reserve needs, in terms of savings in fuel cost and CO2 emissions associated with system balancing, compared to other solutions. We found that providing a greater part of the increased reserves needed from standing reserve in the form of pumped hydro storage increases efficiency of system operation and reduces the amount of wind power that cannot be absorbed. [Copyright &y& Elsevier]

The idea that increasing the capacity of the transmission network should improve the security of the system and reduce the probability of blackouts is intuitively appealing. However, this intuition does not withstand scrutiny. [Copyright &y& Elsevier]

Suggests the reduction of the cost of providing generation reserve without reducing the reliability of the electricity supply. Who benefits from reserve; Determination of the optimal amount of reserve needed; Overview of a method for allocating the cost of reserve.

Presents a study which proposed a method for the simulation and analysis of alternative reactive power market arrangements based on combined reactive power capacity and energy payments. Cost components of providing reactive power support; Formulation of the problem; Overview of the case studies presented.

Presents information on a study which described a linear programming approach for computing the maximum savings in operating costs that could be secured from installing flexible alternating current transmission systems (FACTS). Definition of FACTS; Problem formulation and modeling; Conclusion.

Presents a study which analyzed the effect that the market structure can have on the elasticity of the demand for electricity. Methods used for setting prices in a electricity market; Integration with a scheduling program; Conclusions.

• A risk-metric for using machine learning in probabilistic security assessment. • A calibrated training process for accurate probability outputs of machine learning. • A probabilistic balance of machine learning with conventional security assessment. • Robustness against frequent changes in likelihood of contingencies. • Study on French system shows superiority in accuracy, robustness, and 90 % speed-up. Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of security assessments may require many time-domain simulations for some stability phenomena that are unpractical in real-time. Supervised machine learning is promising to predict DSA as their predictions are immediately available. Classifiers are offline trained on operating conditions and then used in real-time to identify operating conditions that are insecure. However, the predictions of classifiers can be sometimes wrong and hazardous if an alarm is missed for instance. A probabilistic output of the classifier is explored in more detail and proposed for probabilistic security assessment. An ensemble classifier is trained and calibrated offline by using Platt' scaling to provide accurate probability estimates of the output. Imbalances in the training database and a cost-skewness addressing strategy are proposed for considering that missed alarms are significantly worse than false alarms. Subsequently, risk-minimised predictions can be made in real-time operation by applying cost-sensitive learning. Through case studies on a real data-set of the French transmission grid and on the IEEE 6 bus system using static security metrics, it is showcased how the proposed approach reduces inaccurate predictions and risks. The sensitivity on the likelihood of contingency is studied as well as on expected outage costs. Finally, the scalability to several contingencies and operating conditions are showcased. [ABSTRACT FROM AUTHOR]

Block orders are increasingly adopted in many electricity markets as they enable power producers to implicitly express their unit commitment characteristics and address price volatility risks. However, previous work on the optimal offering problem of a price-taking producer participating in such markets has only considered the simplest type of block orders (regular block orders-RBO) and has neglected relevant realistic market regulations. This paper addresses these simplifications by proposing a new optimal offering model which also considers the more complex profile block orders (PBO) and linked block orders (LBO). This model is generic in that it can treat the block orders’ time spans either as fixed parameters (following the modelling approach of previous work) or as endogenous decision variables, which constitutes a completely novel modeling approach. The presented case studies demonstrate that this novel approach yields a tremendously lower number of binary variables and more efficient computational performance, when PBO and LBO are considered. Furthermore, the case studies analyze the physical conditions under which the introduction of different types of block orders increases the examined producer’s profit. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to partial or total lockdowns in several countries during the first half of 2020, which in turn caused a depressed electricity demand. In Great Britain (GB), this low demand combined with large renewable output at times, created conditions that were not expected until renewable capacity increases to meet emissions targets in coming years. The GB system experienced periods of very high instantaneous penetration of non-synchronous renewables, compromising system stability due to the lack of inertia in the grid. In this paper, a detailed analysis of the consequences of the lockdown on the GB electricity system is provided, focusing on the ancillary services procured to guarantee stability. Ancillary-services costs increased by £200m in the months of May to July 2020 compared to the same period in 2019 (a threefold increase), highlighting the importance of ancillary services in low-carbon systems. Furthermore, a frequency-secured scheduling model is used in the present paper to showcase the future trends that GB is expected to experience, as penetration of renewables increases on the road to net-zero emissions by 2050. Several sensitivities are considered, demonstrating that the share of total operating costs represented by ancillary services could reach 35%. • The GB electricity grid experienced an operational challenge due to low inertia. • Cost of ancillary services during lockdown was 3 times higher than in 2019. • This cost could reach up to 35% of total operating costs by 2030. • Fast frequency response shows to be very beneficial in the future system. [ABSTRACT FROM AUTHOR]

Proposes a method for determining how much of the active and reactive power output of each generator is contributed by each load. Procedures in tracing active power from generators to loads; Application of the contributions method to reactive problems; Necessity for generator to produce significant amount of reactive power to supply the reactive losses caused by the active flows.

Demonstrates that, in a competitive electricity pool, demand-side bidding (DSB) and other forms of flexible generation that can cause sharp and unwarranted price increase if the production schedule is established on the basis of a minimization of the total production cost. Overview of the value of flexibility; Effect of load recovery on production; Numerical example.

Describes a method for computing the optimal balance between preventive and corrective security actions for electric power systems. Importance of preventive measures in power system security; Formulation of the problem; Solution method.

Proposes a method based on the traceable contributions of each generator and of each load to branch flows, instead of relying on marginal contributions to allocate the usage of the transmission system. How the contributions become traceable; Conditions for maximum flow; Adoption of net convention in assigning branch flows to generators and loads.

The anticipated electrification of the transport sector may lead to significant increase in the future peak electricity demand, resulting in potential violations of network constraints. As a result, a considerable amount of network reinforcement may be required in order to ensure that the expected additional demand from electric vehicles that are to be connected will be safely accommodated. In this paper we present the Backwards Induction Framework (BIF), which we use for identifying the optimal investment decisions, for calculating the option value of smart charging of EV and the cost of stranded assets; these concepts are crystallized through illustrative case studies. Sensitivity analyses depict how the option value of smart charging and the optimal solution are affected by key factors such as the social cost associated with not accommodating the full EV capacity, the flexibility of smart charging, and the scenario probabilities. Moreover, the BIF is compared with the Stochastic Optimization Framework and key insights are drawn. [ABSTRACT FROM AUTHOR]

• Accurate system adequacy assessment of large power systems requires complex models. • Convergence of risk metrics using conventional Monte Carlo methods is often too slow. • The multilevel Monte Carlo approach can make efficient use of approximate models. • Large speedups are demonstrated for common system adequacy problems. • Source code and data have been made available. Accurately and efficiently estimating system performance under uncertainty is paramount in power system planning and operation. Monte Carlo simulation is often used for this purpose, but convergence may be slow, especially when detailed models are used. Previously published methods to speed up computations may severely constrain model complexity, limiting their real-world effectiveness. This paper uses the recently proposed Multilevel Monte Carlo (MLMC) framework, which combines outputs from a hierarchy of simulators to boost computational efficiency without sacrificing accuracy. It explains which requirements the MLMC framework imposes on the model hierarchy, and how these naturally occur in power system adequacy assessment problems. Two adequacy assessment examples are studied in detail: a composite system and a system with heterogeneous storage units. An intuitive speed metric is introduced for easy comparison of simulation setups. Depending on the problem and metric of interest, large speedups can be obtained. [ABSTRACT FROM AUTHOR]

Interacting subsystems are commonly described by networks, where multimodal behaviour found in most natural or engineered systems found recent extension in form of multilayer networks. Since multimodal interaction is often not dictated by network topology alone and may manifest in form of cross-layer information exchange, multilayer network flow becomes of relevant further interest. Rationale can be found in most interacting subsystems, where a form of multimodal flow across layers can be observed in e.g., chemical processes, energy networks, logistics, finance, or any other form of conversion process relying on the laws of conservation. To this end, the formal notion of heterogeneous network flow is proposed, as a multilayer flow function aligned with the theory of network flow. Furthermore, dynamic equivalence is established with the framework of Petri nets, as the baseline model of concurrent event systems. Application of the resulting multilayer Laplacian flow and flow centrality is presented, along with graph learning based inference of multilayer relationships over multimodal data. On synthetic data the proposed framework demonstrates benefits of multimodal flow derivation in critical component identification. It also displays applicability in relationship inference (learning based function approximation) on multimodal time series. On real-world data the proposed framework provides, among others, multimodal flow interpretation of U.S. economic activity, uncovering underlying empirical steady state probability distribution, as well as inherent network (economic) robustness. [ABSTRACT FROM AUTHOR]

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

This paper considers the phenomenon of distinct regional frequencies recently observed in some power systems. First, a reduced-order mathematical model describing this behaviour is developed. Then, techniques to solve the model are discussed, demonstrating that the post-fault frequency evolution in any given region is equal to the frequency evolution of the Centre Of Inertia plus certain inter-area oscillations. This finding leads to the deduction of conditions for guaranteeing frequency stability in all regions of a power system, a deduction performed using a mixed analytical-numerical approach that combines mathematical analysis with regression methods on simulation samples. The proposed stability conditions are linear inequalities that can be implemented in any optimisation routine allowing the co-optimisation of all existing ancillary services for frequency support: inertia, multi-speed frequency response, load damping and an optimised largest power infeed. This is the first reported mathematical framework with explicit conditions to maintain frequency stability in a power system exhibiting inter-area oscillations in frequency. [ABSTRACT FROM AUTHOR]

In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region. [ABSTRACT FROM AUTHOR]

This paper presents a novel, unified approach for generating high-quality datasets for training machine-learned models for real-time security assessment in power systems. Synthetic data generation methods that extrapolate beyond historical data can be inefficient in generating feasible and rare operating conditions (OCs). The proposed approach balances the trade-off between historically relevant OCs and rare but feasible OCs. Unlike conventional methods that rely on historical records or generic sampling, our approach results in datasets that generalise well beyond similar distributions. The proposed approach is validated through experiments on the IEEE 118-bus system, where a decision tree model trained on data generated using our approach achieved 97 % accuracy in predicting the security label of rare OCs, outperforming baseline approaches by 41 % and 20 %. This work is crucial for deploying reliable machine-learned models for real-time security assessment in power systems undergoing decarbonisation and integrating renewable energy sources. • Machine-learned models as surrogates to predict real-time security of power systems. • Quality datasets are crucial to train high performing Machine Learning (ML) models. • Method trades-off historically relevant operating conditions (OCs) and 'rare' feasible OCs. • Method generates datasets that generalise well beyond similar distributions. • Studies carried out on the IEEE 118-bus validate the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a resilience-oriented energy management approach (R-OEMA) designed to bolster the resilience of networked microgrids (NMGs) in the face of extreme events. The R-OEMA method strategically incorporates preventive scheduling techniques for hydrogen (H2) systems, renewable units, controllable distributed generators (DGs), and demand response programs (DRPs). It seeks to optimize the delicate balance between maximizing operating revenues and minimizing costs, catering to both normal and critical operational modes. The evaluation of the R-OEMA framework is conducted through numerical simulations on a test system comprising three microgrids (MGs). The simulations consider various disaster scenarios entailing the diverse durations of power outages. The results underscore the efficacy of the R-OEMA approach in augmenting NMG resilience and refining operational efficiency during extreme events. Specifically, the approach integrates hydrogen systems, demand response, and controllable DGs, orchestrating their collaborative operation with predictive insights. This ensures their preparedness for emergency operations in the event of disruptions, enabling the supply of critical loads to reach 82% in extreme disaster scenarios and 100% in milder scenarios. The proposed model is formulated as a mixed-integer linear programming (MILP) framework, seamlessly integrating predictive insights and pre-scheduling strategies. This novel approach contributes to advancing NMG resilience, as revealed by the outcomes of these simulations. [ABSTRACT FROM AUTHOR]

Highly spatially resolved data from across Great Britain (GB) are combined with a distribution network modelling tool to assess impacts of distributed photovoltaic (PV) deployment up to 2050 on local networks, the costs of avoiding these impacts, and how these depend upon context. Present-day deployment of distributed PV, meter density, and network infrastructure across GB are found to be highly dependent on rurality, and data on these are used to build up three representative contexts: cities, towns, and villages. For each context, distribution networks are simulated, and impacts on these networks associated with PV deployment and growth in peak load up to 2050 calculated. Present-day higher levels of PV deployment in rural areas are maintained in future scenarios, necessitating upgrades in ambitious PV scenarios in towns and villages from around 2040, but not before 2050 in cities. Impacts of load growth are more severe than those of PV deployment, potentially necessitating upgrades in cities, towns, and villages from 2030. These are most extensive in cities and towns, where long feeders connect more customers, making networks particularly susceptible to impacts. Storage and demand side response are effective in reducing upgrade costs, particularly in cities and towns. • Real data is used to simulate representative local networks across GB rurality contexts. • PV and load growth impacts and integration costs are calculated in scenarios to 2050. • PV deployment necessitates network upgrades in towns and villages from 2040. • Load growth necessitates network upgrades in cities, towns, and villages from 2030. • Storage and DSM bring greatest network upgrade savings in city and town contexts. [ABSTRACT FROM AUTHOR]

A reduced level of system inertia due to renewable integration increases the need for cost-effective provision of ancillary services, such as Frequency Response (FR). In this paper a closed-form solution to the differential equation describing frequency dynamics is proposed, which allows to obtain frequency-security algebraic constraints to be implemented in optimization routines. This is done while considering any finite number of FR services with distinguished characteristics, such as different delivery times and activation delays. The problem defined by these frequency-security constraints can be formulated as a Mixed-Integer Second-Order Cone Program (MISOCP), which can be efficiently handled by off-the-shelf conic optimization solvers. This paper also takes into account the uncertainty in inertia contribution from the demand side by formulating the frequency-security conditions as chance constraints, for which an exact convex reformulation is provided. Finally, case studies highlighting the effectiveness of this frequency-secured formulation are presented. [ABSTRACT FROM AUTHOR]

This paper focuses on developing an appropriate combinatorial optimization technique for solving the optimal sizing problem of hybrid ac–dc microgrids. A novel two-stage iterative approach is proposed. In the first stage, a meta-heuristic technique based on a tailor-made genetic algorithm is used to tackle the optimal sizing problem, while, in the second, a non-linear solver is deployed to solve the operational problem subject to the obtained design/investment decisions. The proposed approach, being able to capture technical characteristics such as voltage and frequency through a detailed power flow algorithm, provides accurate solutions and, therefore, can address operational challenges of microgrids. Its capability to additionally capture contingencies ensures that the proposed sizing solutions are suitable both during normal operation and transient states. Finally, the genetic algorithm provides convergence of the model with relative computational simplicity. The proposed model is applied to a generalizable microgrid comprising of ac and dc generators and loads, as well as various types of storage technologies in order to demonstrate the benefits. The load and natural resources data correspond to real data. [ABSTRACT FROM AUTHOR]

Various supervised machine learning approaches have been used in the past to assess the power system security (also known as reliability). This is typically done by training a classifier on a large number of operating points whose postfault status (stable or unstable) has been determined via time-domain simulations. The output of this training process can be expressed as a security rule that is used online to classify an operating point. A critical, and little-studied aspect of these approaches is the interpretability of the rules produced. The lack of interpretability is a well-known issue of some machine learning approaches, especially when dealing with difficult classification problems. In the case of the security assessment of the power system, which is a complex mission-critical task, interpretability is a key requirement for the adoption and deployment by operators of these approaches. In this paper, for the first time, we explore the tradeoff between predictive accuracy and interpretability in the context of power system security assessment. We begin by demonstrating how decision trees (DTs) can be used to learn data-driven security rules and use the tree depth as a measure for interpretability. We leverage disjunctive programming to formulate novel training methods, capable of learning high-quality DTs while still maintaining interpretability. In particular, we propose two new approaches: 1) optimal classification trees is proposed for training DTs of low-depth and 2) greedy optimization-based tree is proposed for training DTs of intermediate depth, where the increased computational burden is managed by exploiting the nested tree structure. We also demonstrate that the ability to generate high-quality interpretable rules can actually translate to impressive benefits in terms of training requirements. Through case studies on the IEEE 68-bus system, we demonstrate that the proposed methods can produce DTs of higher quality compared to the state-of-the-art approach classification and regression tree approach, also if the DT was trained on a significant smaller database, resulting in computational savings of 80%. Given that generating a large training database is a practical bottleneck in these data-driven approaches, this is a significant breakthrough for real-world application. [ABSTRACT FROM AUTHOR]

The reduced level of system inertia in low-carbon power grids increases the need for alternative frequency services. However, simultaneously optimizing the provision of these services in the scheduling process, subject to significant uncertainty, is a complex task given the challenge of linking the steady-state optimization with frequency dynamics. This paper proposes a novel frequency-constrained stochastic unit commitment model which, for the first time, co-optimizes energy production along with the provision of synchronized and synthetic inertia, enhanced frequency response, primary frequency response and a dynamically-reduced largest power infeed. The contribution of load damping is modeled through a linear inner approximation. The effectiveness of the proposed model is demonstrated through several case studies for Great Britain's 2030 power system, which highlight the synergies and conflicts among alternative frequency services, as well as the significant economic savings and carbon reduction achieved by simultaneously optimizing all these services. [ABSTRACT FROM AUTHOR]

The use of wind power has grown strongly in recent years and is expected to continue to increase in the coming decades. Solar power is also expected to increase significantly. In a power system, a continuous balance is maintained between total production and demand. This balancing is currently mainly managed with conventional power plants, but with larger amounts of wind and solar power, other sources will also be needed. Interesting possibilities include continuous control of wind and solar power, battery storage, electric vehicles, hydrogen production, and other demand resources with flexibility potential. The aim of this article is to describe and compare the different challenges and future possibilities in six systems concerning how to keep a continuous balance in the future with significantly larger amounts of variable renewable power production. A realistic understanding of how these systems plan to handle continuous balancing is central to effectively develop a carbon-dioxide-free electricity system of the future. The systems included in the overview are the Nordic synchronous area, the island of Ireland, the Iberian Peninsula, Texas (ERCOT), the central European system, and Great Britain. [ABSTRACT FROM AUTHOR]

The decarbonisation of the electricity grid is expected to create new electricity flows. As a result, it may require that network planners make a significant amount of investments in the electricity grids over the coming decades so as to allow the accommodation of these new flows in a way that both the thermal and voltage network constraints are respected. These investments may include a portfolio of infrastructure assets consisting of traditional technologies and smart grid technologies. One associated key challenge is the presence of uncertainty around the location, the timing, and the amount of new demand or generation connections. This uncertainty unavoidably introduces risk into the investment decision-making process as it may lead to inefficient investments and inevitably give rise to excessive investment costs. Smart grid technologies have properties that enable them to be regarded as investment options, which can allow network planners to hedge against the aforementioned uncertainty. This paper focuses on key smart technologies by providing a critical literature review and presenting the latest mathematical modelling that describes their operation. [ABSTRACT FROM AUTHOR]

Adequate capacity planning of substations and feeders primarily depends on an accurate estimation of the future peak electricity demand. Traditional coincident peak demand estimation is carried out based on the empirical metric, after diversity maximum demand, indicating individual peak consumption levels and demand diversification across multiple residents. With the privilege of smart meters in smart cities, this paper proposes a data-driven probabilistic peak demand estimation framework using fine-grained smart meter data and sociodemographic data of the consumers, which drive fundamental electricity consumptions across different categories. In particular, four main stages are integrated in the proposed approach: load modeling and sampling via the proposed variable truncated R-vine copulas method, correlation-based customer grouping, probabilistic normalized maximum diversified demand estimation, and probabilistic peak demand estimation for new customers. Numerical experiments have been conducted on real demand measurements across 2639 households in London, collected from Low Carbon London project's smart-metering trial. The mean absolute percentage error and the pinball loss function are used to quantitatively demonstrate the superiority of the proposed approach in terms of the point estimate value and the probabilistic result, respectively. [ABSTRACT FROM AUTHOR]

This paper studies key aspects of preventive and corrective security strategies to coordinate flexible transmission network infrastructure. To do so, we propose a two-stage stochastic optimization model that can efficiently coordinate available preventive and corrective control actions (pre- and post-contingency) from flexible network technologies, generation, and demand, while explicitly considering the likelihood of postcontingency events and wind/solar uncertainty. This stochastic/probabilistic model constitutes the counterfactual against which current deterministic, preventive operational practices are compared. Flexible network equipment such as HVDC and FACTS devices are efficiently modeled through a tight MILP representation and network losses are also included through a linear representation. Through several case studies, we demonstrate the advantages of probabilistic, corrective security to improve coordination of HVDC and FACTS setpoints and thus reduce network congestion and reserve holding levels, improving the overall efficiency of the system operation. We also quantify the value loss associated with current deterministic, preventive control operational practices when coordinating setpoints of flexible network equipment, demonstrating that adding flexible network technology could even increase system costs under a preventive security approach. [ABSTRACT FROM AUTHOR]

Highlights • A novel data-driven load and wind power scenario generation framework is proposed. • A sufficient number of unseen but important scenarios are generated for TEP. • Interspatial dependencies between loads and wind power output are accurately captured. • The method can identify near optimal investment decisions with higher net benefits. Abstract Transmission expansion planning (TEP) is facing unprecedented challenges with the rise of integrated renewable energy resources (RES), flexible load elements, and the potential electrification of transport and heat sectors. Under this reality, the inadequate information of the stochastic parameters’ behavior may lead to inefficient expansion decisions, especially in the context of very high renewable penetration. This paper proposes a novel data-driven scenario generation framework for the TEP problem to generate unseen but important load and wind power scenarios while capturing inter-spatial dependencies between loads and wind generation units’ output in various locations, using a vine-copula based high-dimensional stochastic variable modeling approach. The superior performance of the proposed model is demonstrated through a case study on a modified IEEE 118-bus system. The expected result of using the expected value problem solution (EEV) and the net benefits of transmission expansion (NBTE) are used as the evaluation metrics to quantitatively illustrate the advantages of the proposed approach. In addition, the case of very high wind penetration is carried out to further highlight the importance of the multivariate stochastic dependence of load and wind power generation. The results demonstrate that the proposed scenario generation method can result in near-optimal investment decisions for the TEP problem that make more net benefits than using limited number of historical data. [ABSTRACT FROM AUTHOR]