Imperial College London

Professor Goran Strbac

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Chair in Electrical Energy Systems
 
 
 
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Contact

 

+44 (0)20 7594 6169g.strbac

 
 
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Assistant

 

Miss Guler Eroglu +44 (0)20 7594 6170

 
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Location

 

1101Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

378 results found

Oderinwale T, Papadaskalopoulos D, Ye Y, Strbac Get al., 2020, Investigating the impact of flexible demand on market-based generation investment planning, International Journal of Electrical Power & Energy Systems, Vol: 119, Pages: 105881-105881, ISSN: 0142-0615

Demand flexibility has attracted significant interest given its potential to address techno-economic challenges associated with the decarbonisation of electricity systems. However, previous work has investigated its long-term impacts through centralized generation planning models which do not reflect the current deregulated environment. At the same time, existing market-based generation planning models are inherently unable to capture the demand flexibility potential since they neglect time-coupling effects and system reserve requirements in their representation of the electricity market. This paper investigates the long-term impacts of demand flexibility in the deregulated environment, by proposing a time-coupling, bi-level optimization model of a self-interested generation company’s investment planning problem, which captures for the first time the energy shifting flexibility of the demand side and the operation of reserve markets with demand side participation. Case studies investigate different cases regarding the flexibility of the demand side and different market design options regarding the allocation of reserve payments. The obtained results demonstrate that, in contrast with previous centralised planning models, the proposed model can capture the dependency of generation investment decisions and the related impacts of demand flexibility on the electricity market design and the subsequent strategic response of the self-interested generation company.

Journal article

Shen F, Wu Q, Xu Y, Li F, Teng F, Strbac Get al., 2020, Hierarchical service restoration scheme for active distribution networks based on ADMM, International Journal of Electrical Power & Energy Systems, Vol: 118, Pages: 1-10, ISSN: 0142-0615

Effective self-healing schemes enhance the resilience of active distribution networks (ADNs). As a critical part of self-healing, service restoration aims to restore outage areas with minimal un-supplied demands. With the increasing complexity and size of ADNs, distribution system operators (DSOs) face a more complicated service restoration problem. Thus, it is important to obtain optimal service restoration plans and reduce computational complexity. To achieve this goal, a hierarchical service restoration scheme is proposed to obtain service restoration plans based on the alternating direction method of multipliers (ADMM). The optimal service restoration problem is formulated as a mixed-integer linear programming (MILP) model considering the switching sequence, distributed generation (DG) units and controllable loads, and is solved using the ADMM-based algorithm in a hierarchical manner. In the proposed scheme, each zone of the ADN has a local service restoration controller solving its sub-problem with information from a central service restoration controller. The central controller solves a global coordination problem with information from all the zones. Three case studies were conducted with the 44-node test system, modified IEEE 123-node system and Brazil 948-node system. The results show that the proposed hierarchical service restoration can obtain optimal service restoration plans and reduce computational complexity. Moreover, computation time can be reduced substantially by using the proposed hierarchical scheme for large-scale ADNs.

Journal article

Fu P, Pudjianto D, Zhang X, Strbac Get al., 2020, Integration of hydrogen into multi-energy systems optimisation, Energies, Vol: 13, Pages: 1606-1606, ISSN: 1996-1073

Hydrogen presents an attractive option to decarbonise the present energy system. Hydrogen can extend the usage of the existing gas infrastructure with low-cost energy storability and flexibility. Excess electricity generated by renewables can be converted into hydrogen. In this paper, a novel multi-energy systems optimisation model was proposed to maximise investment and operating synergy in the electricity, heating, and transport sectors, considering the integration of a hydrogen system to minimise the overall costs. The model considers two hydrogen production processes: (i) gas-to-gas (G2G) with carbon capture and storage (CCS), and (ii) power-to-gas (P2G). The proposed model was applied in a future Great Britain (GB) system. Through a comparison with the system without hydrogen, the results showed that the G2G process could reduce £3.9 bn/year, and that the P2G process could bring £2.1 bn/year in cost-savings under a 30 Mt carbon target. The results also demonstrate the system implications of the two hydrogen production processes on the investment and operation of other energy sectors. The G2G process can reduce the total power generation capacity from 71 GW to 53 GW, and the P2G process can promote the integration of wind power from 83 GW to 130 GW under a 30 Mt carbon target. The results also demonstrate the changes in the heating strategies driven by the different hydrogen production processes.

Journal article

Huyghues-Beaufond N, Tindemans S, Falugi P, Sun M, Strbac Get al., 2020, Robust and automatic data cleansing method for short-term load forecasting of distribution feeders, Applied Energy, Vol: 261, Pages: 1-17, ISSN: 0306-2619

Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.

Journal article

Malekpour M, Azizipanah-Abarghooee R, Teng F, Strbac G, Terzija Vet al., 2020, Fast Frequency Response From Smart Induction Motor Variable Speed Drives, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 35, Pages: 997-1008, ISSN: 0885-8950

Journal article

Soder L, Tomasson E, Estanqueiro A, Flynn D, Hodge B-M, Kiviluoma J, Korpas M, Neau E, Couto A, Pudjianto D, Strbac G, Burke D, Gomez T, Das K, Cutululis NA, Van Hertem D, Hoschle H, Matevosyan J, von Roon S, Carlini EM, Caprabianca M, de Vries Let al., 2020, Review of wind generation within adequacy calculations and capacity markets for different power systems, RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 119, ISSN: 1364-0321

Journal article

Heylen E, Papadaskalopoulos D, Konstantelos I, Strbac Get al., 2020, Dynamic modelling of consumers’ inconvenience associated with demand flexibility potentials, Sustainable Energy, Grids and Networks, Vol: 21, Pages: 1-13, ISSN: 2352-4677

Demand flexibility, involving the potential to reduce or temporally defer electricity demand, is regarded as a key enabler for transitioning to a secure, cost-efficient and low-carbon energy future. However, previous work has not comprehensively modelled the inconvenience experienced by end-consumers due to demand modifications, since it has focused on static modelling approaches. This paper presents a novel model of inconvenience cost that simultaneously accounts for differentiated preferences of consumer groups, time and duration of interruptions, differentiated valuation of different units of power and temporal redistribution of shiftable loads. This model is dynamic and future-agnostic, implying that it captures the time-coupling characteristics of consumers’ flexibility and the temporal evolution of interruptions, without resorting to the unrealistic assumption that time and duration of interruptions are foreknown. The model is quantitatively informed by publicly available surveys combined with realistic assumptions and suitable sensitivity analyses regarding aspects excluded from existing surveys. In the examined case studies, the developed model is applied to manage an aggregator’s portfolio in a scenario involving emergence of an adequacy issue in the Belgian system. The results illustrate how considering each of the above factors affects demand management decisions and the inconvenience cost, revealing the value of the developed model.

Journal article

Ye Y, Qiu D, Wu X, Strbac G, Ward Jet al., 2020, Model-Free Real-Time Autonomous Control for A Residential Multi-Energy System Using Deep Reinforcement Learning, IEEE Transactions on Smart Grid, Vol: 11, Pages: 3068-3068, ISSN: 1949-3053

Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.

Journal article

Ye Y, Qiu D, Wu X, Strbac G, Ward Jet al., 2020, Model-Free Real-Time Autonomous Control for A Residential Multi-Energy System Using Deep Reinforcement Learning, IEEE Transactions on Smart Grid, Pages: 1-1, ISSN: 1949-3053

Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.

Journal article

Li J, Ye Y, Strbac G, 2020, Incentivizing Peer-to-Peer Energy Sharing Using a Core Tâtonnement Algorithm, 2020 IEEE IEEE Power & Energy Society General Meeting

Conference paper

Georgiou S, Aunedi M, Strbac G, Markides CNet al., 2020, On the value of liquid-air and Pumped-Thermal Electricity Storage systems in low-carbon electricity systems, Energy, Vol: 193, ISSN: 0360-5442

We consider two medium-to-large scale thermomechanical electricity storage technologies currently under development, namely ‘Liquid-Air Energy Storage’ (LAES) and ‘Pumped-Thermal Electricity Storage’ (PTES). Consistent thermodynamic models and costing methods based on a unified methodology for the two systems from previous work are presented and used with the objective of integrating the characteristics of the technologies into a whole-electricity system assessment model and assessing their system-level value in various scenarios for system decarbonization. It is found that the value of storage depends on the cumulative installed capacity of storage in the system, with storage technologies providing greater marginal benefits at low penetrations. The system value of PTES was found to be slightly higher than that of LAES, driven by a higher storage duration and efficiency, although these results must be seen in light of the uncertainty in the (as yet, not demonstrated) performance of key PTES components, namely the reciprocating-piston compressors and expanders. At the same time, PTES was also found to have a higher power capital cost. The results indicate that the complexity of the decarbonization challenge makes it difficult to identify clearly a ‘best’ technology and suggest that the uptake of either technology can provide significant system-level benefits.

Journal article

Badesa L, Teng F, Strbac G, 2020, Pricing inertia and Frequency Response with diverse dynamics in a Mixed-Integer Second-Order Cone Programming formulation, Applied Energy, Vol: 260, Pages: 1-11, ISSN: 0306-2619

Low levels of system inertia in power grids with significant penetration of non-synchronous Renewable Energy Sources (RES) have increased the risk of frequency instability. The provision of a certain type of ancillary services such as inertia and Frequency Response (FR) is needed at all times, to maintain system frequency within secure limits if the loss of a large power infeed were to occur. In this paper we propose a frequency-secured optimisation framework for the procurement of inertia and FR with diverse dynamics, which enables to apply a marginal-pricing scheme for these services. This pricing scheme, deduced from a Mixed-Integer Second-Order Cone Program (MISOCP) formulation that represents frequency-security constraints, allows for the first time to appropriately value multi-speed FR.

Journal article

Giannelos S, Djapic P, Pudjianto D, Strbac Get al., 2020, Quantification of the energy storage contribution to security of supply through the F-factor methodology, Energies, Vol: 13, Pages: 826-826, ISSN: 1996-1073

The ongoing electrification of the heat and transport sectors is expected to lead to a substantial increase in peak electricity demand over the coming decades, which may drive significant investment in network reinforcement in order to maintain a secure supply of electricity to consumers. The traditional way of security provision has been based on conventional investments such as the upgrade of the capacity of electricity transmission or distribution lines. However, energy storage can also provide security of supply. In this context, the current paper presents a methodology for the quantification of the security contribution of energy storage, based on the use of mathematical optimization for the calculation of the F-factor metric, which reflects the optimal amount of peak demand reduction that can be achieved as compared to the power capability of the corresponding energy storage asset. In this context, case studies underline that the F-factors decrease with greater storage power capability and increase with greater storage efficiency and energy capacity as well as peakiness of the load profile. Furthermore, it is shown that increased investment in energy storage per system bus does not increase the overall contribution to security of supply.

Journal article

Song Y, Ding Y, Siano P, Meinrenken C, Zheng M, Strbac Get al., 2020, Optimization methods and advanced applications for smart energy systems considering grid-interactive demand response, APPLIED ENERGY, Vol: 259, ISSN: 0306-2619

Journal article

Qiu D, Papadaskalopoulos D, Ye Y, Strbac Get al., 2020, Investigating the Effects of Demand Flexibility on Electricity Retailers’ Business through a Tri-Level Optimization Model, IET Generation, Transmission & Distribution, Vol: 14, Pages: 1739-1750, ISSN: 1751-8687

The investigation of the effects of demand flexibility on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state-of-the-art, bi-level optimization modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. This paper proposes a tri-level optimization model which drops this assumption and represents endogenously the wholesale market clearing process, thus capturing the realistic implications of a retailer’s pricing strategies and the resulting demand response on the wholesale market prices. The scope of the examined case studies is threefold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers’ time-shifting flexibility on the retailer’s revenue from the consumers, its cost in the wholesale market, and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer’s relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed tri-level model by comparing its outcomes against the state-of-the-art bi-level modelling approach.

Journal article

Sun M, Zhang T, Wang Y, Strbac G, Kang Cet al., 2020, Using Bayesian deep learning to capture uncertainty for residential net load forecasting, IEEE Transactions on Power Systems, Vol: 35, Pages: 188-201, ISSN: 0885-8950

Decarbonization of electricity systems drives significant and continued investments in distributed energy sources to support the cost-effective transition to low-carbon energy systems. However, the rapid integration of distributed photovoltaic (PV) generation presents great challenges in obtaining reliable and secure grid operations because of its limited visibility and intermittent nature. Under this reality, net load forecasting is facing unprecedented difficulty in answering the following question: how can we accurately predict the net load while capturing the massive uncertainties arising from distributed PV generation and load, especially in the context of high PV penetration? This paper proposes a novel probabilistic day-ahead net load forecasting method to capture both epistemic uncertainty and aleatoric uncertainty using Bayesian deep learning, which is a new field that combines Bayesian probability theory and deep learning. The proposed methodological framework employs clustering in subprofiles and considers residential rooftop PV outputs as input features to enhance the performance of aggregated net load forecasting. Numerical experiments have been carried out based on fine-grained smart meter data from the Australian grid with separately recorded measurements of rooftop PV generation and loads. The results demonstrate the superior performance of the proposed scheme compared with a series of state-of-theart methods and indicate the importance and effectiveness of subprofile clustering and high PV visibility.

Journal article

Oulis Rousis A, Konstantelos I, Strbac G, 2020, A Planning Model for a Hybrid AC–DC Microgrid Using a Novel GA/AC OPF Algorithm, IEEE Transactions on Power Systems, Vol: 35, Pages: 227-237, ISSN: 0885-8950

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 metaheuristic 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.

Journal article

Badesa L, Teng F, Strbac G, 2020, Optimal Portfolio of Distinct Frequency Response Services in Low-Inertia Systems, Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Working paper

Wu Z, Guo F, Polak J, Strbac Get al., 2019, Evaluating grid-interactive electric bus operation and demand response with load management tariff, Applied Energy, Vol: 255, Pages: 1-12, ISSN: 0306-2619

Electric Vehicles are expected to play a vital role in the transition of smart energy systems. Lots of recent research has explored numerous underlying mechanisms to achieve the synergetic interactions in the electricity balancing process. In this paper, the grid-interactive operation of electric buses is first time integrated within a dynamic market frame using the Distribution Locational Marginal Price algorithm for load congestion management. Since the defined problem correlates the opportunity charging flexibility with the bus mobility over a network, the tempo-spatial distribution of energy needs can be reflected in the dynamic of service planning. The interactions between bus operators and suppliers are quantitatively modelled by a bi-level optimisation process to represent the electric bus service planning and electricity market clearing separately. The effectiveness of the proposed load management has been demonstrated using data collected from an integrated real-world bus network. Experiments show that engagement of electric bus charging load in demand response is helpful to alleviate the network congestion and to reduce the power loss by 7.2% in the distribution network. However, alleviated charging loads have exhibited counter-intuitive ability for load shifting. The restricted electric bus operational requirements leads to a 8.17% loss of charging demand, while the reliance on large batteries has increased by 10.57%. However, the sensitivity analysis also shows that as the battery cost declines, the such discourage implications on grid-interactive electric bus operation will decrease once the battery cost below 190/kWh. The optimal grid-ebus integration have to consider the trade-off between range add-up, reduced battery cost and additional benefits.

Journal article

Gong X, De Paola A, Angeli D, Strbac Get al., 2019, A game-theoretic approach for price-based coordination of flexible devices operating in integrated energy-reserve markets, Energy, Vol: 189, Pages: 1-12, ISSN: 0360-5442

This paper presents a novel distributed control strategy for large scale deployment of demand response. In the considered framework, large populations of storage devices and electric vehicles (EVs) participate to an integrated energy-reserve market. They react to prices and autonomously schedule their operation in order to optimize their own objective functions. The price signals are obtained through the resolution of an optimal power flow problem that explicitly takes into account the impact of demand response on the optimal power dispatch and reserve procurement of committed generation. Differently from previous approaches, the adopted game-theoretic framework provides rigorous theoretical guarantees of convergence and optimality of the proposed control scheme in a multi-price setup that includes ancillary services. The performance of the coordination scheme is also evaluated in simulation on the PJM 5-bus system, demonstrating its capability to flatten demand profiles and reduce the costs of generators and flexible devices.

Journal article

Sun M, Wang Y, Teng F, Ye Y, Strbac G, Kang Cet al., 2019, Clustering-based residential baseline estimation: a probabilistic perspective, IEEE Transactions on Smart Grid, Vol: 10, Pages: 6014-6028, ISSN: 1949-3061

Demand Response (DR) is one of the most cost-effective solutions for providing flexibility to power systems. The extensive deployment of DR trials and the roll-out of smart meters enable the quantification of consumer responsiveness to price signals via baseline estimation. The traditional deterministic baseline estimation approach can provide only a single value without consideration of uncertainty. This paper proposes a novel probabilistic baseline estimation framework that consists of a daily load profile pool construction stage, a deep learning-based clustering stage, an optimal cluster selection stage, and a quantile regression forests model construction stage. In particular, the concept of a daily load profile pool is introduced, and a deep-learning-based clustering approach is employed to handle a large number of daily patterns to further improve the baseline estimation performance. Case studies have been conducted on fine-grained smart meter data collected from a real dynamic time-of-use (dTOU) tariffs trial of the Low Carbon London (LCL) project. The superior performance of the proposed method is demonstrated based on a series of evaluation metrics regarding both deterministic and probabilistic estimation results.

Journal article

Alvarado D, Moreira A, Moreno R, Strbac Get al., 2019, Transmission Network Investment With Distributed Energy Resources and Distributionally Robust Security, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 34, Pages: 5157-5168, ISSN: 0885-8950

Journal article

Orths A, Anderson CL, Brown T, Mulhern J, Pudjianto D, Ernst B, OMalley M, McCalley J, Strbac Get al., 2019, Flexibility from energy systems integration: supporting synergies mmong sectors, IEEE Power and Energy Magazine, Vol: 17, Pages: 67-78, ISSN: 1540-7977

Energy systems integration, or sector coupling, has several drivers that span climate impact mitigation and economics to social and regulatory considerations. A key question is what is sector coupling, and how does it impact the flexibility of the energy system? Here, the energy system includes several sectors - electricity, gas, heat, and transportation - that have been independent for decades in most countries except for their coupling via combined heat and power (CHP) units. In energy systems integration, some sectors may provide flexibility to other sectors, while other sectors will require flexibility when interlinking. To support these synergies among sectors, it is important to explore and quantify mutual interactions as well as seek examples of how these integrations can provide flexibility and other benefits. From the perspective of the electricity sector, it is important to ensure that there is enough flexibility in the interconnected systems to support decarbonization goals, such as those set in the Paris Agreement, while ensuring operational reliability.

Journal article

De Paola A, Trovato V, Angeli D, Strbac Get al., 2019, A mean field game approach for distributed control of thermostatic loads acting in simultaneous energy-frequency response markets, IEEE Transactions on Smart Grid, Vol: 10, Pages: 5987-5999, ISSN: 1949-3053

This paper proposes a novel distributed solution for the operation of large populations of thermostatically controlled loads (TCLs) providing frequency support. A game-theory framework is adopted, modeling the TCLs as price-responsive rational agents that schedule their energy consumption and allocate frequency response provision in order to minimize their operational costs. The novelty of this work lies in the use of mean field games to abstract the complex interactions of large numbers of TCLs with the grid and in the introduction of an innovative market structure, envisioning distinct price signals for electricity and response. Differently from previous approaches, such prices are not designed ad hoc but are derived instead from an underlying system scheduling model.

Journal article

Sun M, Konstantelos I, Strbac G, 2019, A deep learning-based feature extraction framework for system security assessment, IEEE Transactions on Smart Grid, Vol: 10, Pages: 5007-5020, ISSN: 1949-3061

The ongoing decarbonisation of modern electricity systems has led to a substantial increase of operational uncertainty, particularly due to the large-scale integration of renewable energy generation. However, the expanding space of possible operating points renders necessary the development of novel security assessment approaches. In this paper we focus on the use of security rules, where classifiers are trained offline to characterize previously unseen points as safe or unsafe. This paper proposes a novel deep learning-based feature extraction framework for building security rules. We show how deep autoencoders can be used to transform the space of conventional state variables (e.g. power flows) to a small number of dimensions where we can optimally distinguish between safe and unsafe operation. The proposed framework is data-driven and can be useful in multiple applications within the context of security assessment. To achieve high accuracy, a novel objective-based loss function is proposed to address the issue of imbalanced safe/unsafe classes that characterizes electricity system operation. Furthermore, an R-vine copula-based model is proposed to sample historical data and generate large populations of anticipated system states for training. The superior performance of the proposed framework is demonstrated through a series of case studies and comparisons using the load and wind generation data from the French transmission system, which have been mapped to the IEEE 118-bus system.

Journal article

Badesa L, Teng F, Strbac G, 2019, Simultaneous scheduling of multiple frequency services in stochastic unit commitment, IEEE Transactions on Power Systems, Vol: 34, Pages: 3858-3868, ISSN: 0885-8950

The reduced level of system inertia in low-carbon power grids increases the need for alternative frequency services. However, simultaneously optimising 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 optimisation with frequency dynamics. This paper proposes a novel frequency-constrained Stochastic Unit Commitment (SUC) model which, for the first time, co-optimises energy production along with the provision of synchronised and synthetic inertia, Enhanced Frequency Response (EFR), Primary Frequency Response (PFR) and a dynamically-reduced largest power infeed. The contribution of load damping is modelled 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 optimising all these services.

Journal article

Gong X, De Paola A, Angeli D, Strbac Get al., 2019, Distributed coordination of flexible loads using locational marginal prices, IEEE Transactions on Control of Network Systems, Vol: 6, Pages: 1097-1110, ISSN: 2325-5870

This paper presents a novel distributed control strategy for large-scale deployment of flexible demand in power systems. A game theoretical setting is adopted, modeling the loads as rational players that aim to complete an assigned task at minimum cost and compete for power consumption at the cheapest hours of the day. The main novelty is the analysis of power systems with congestion: the proposed modeling framework envisages heterogeneous groups of loads that operate at different buses, connected by transmission lines of limited capacity. The locational marginal prices of electricity, different in general for each bus, are calculated through an optimal power flow problem, accounting for the impact of the flexible devices on power demand and generation. A new iterative scheme for flexible demand coordination is analytically characterized as a multivalued mapping. Its convergence to a stable market configuration (i.e., variational Wardrop equilibrium) and global optimality are analytically demonstrated, for any penetration level of flexible demand and any grid topology. Distributed implementations of the proposed control strategy are discussed, evaluating their performance with simulations on the IEEE 24-bus system.

Journal article

Cremer J, Konstantelos I, Strbac G, 2019, From optimization-based machine learning to interpretable security rules for operation, IEEE Transactions on Power Systems, Vol: 34, Pages: 3826-3836, ISSN: 0885-8950

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 post-fault 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 ofthese approaches.In this paper, for the first time, we explore the trade-offbetween predictive accuracy and interpretability in the contextof power system security assessment. We begin by demonstratinghow 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 stillmaintaining interpretability. In particular, we propose two newapproaches: (i) Optimal Classification Trees (OCT∗) is proposedfor training DTs of low-depth and (ii) Greedy Optimizationbased Tree (GOT) 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-

Journal article

Ye Y, Qiu D, Sun M, Papadaskalopoulos D, Strbac Get al., 2019, Deep Reinforcement Learning for Strategic Bidding in Electricity Markets, IEEE TRANSACTIONS ON SMART GRID, Vol: 11, Pages: 1343-1355, ISSN: 1949-3061

Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.

Journal article

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