Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate







1106Electrical EngineeringSouth Kensington Campus





Publication Type

10 results found

Cremer JL, Strbac G, 2020, A Machine-learning based Probabilistic Perspective on Dynamic Security Assessment, International Journal of Electrical Power and Energy Systems, ISSN: 0142-0615

Probabilistic security assessment and real-time dynamic security assessments(DSA) are promising to better handle the risks of system operations. Thecurrent methodologies of security assessments may require many time-domainsimulations for some stability phenomena that are unpractical in real-time.Supervised machine learning is promising to predict DSA as their predictionsare immediately available. Classifiers are offline trained on operatingconditions and then used in real-time to identify operating conditions that areinsecure. However, the predictions of classifiers can be sometimes wrong andhazardous if an alarm is missed for instance. A probabilistic output of the classifier is explored in more detail andproposed for probabilistic security assessment. An ensemble classifier istrained and calibrated offline by using Platt scaling to provide accurateprobability estimates of the output. Imbalances in the training database and acost-skewness addressing strategy are proposed for considering that missedalarms are significantly worse than false alarms. Subsequently, risk-minimisedpredictions can be made in real-time operation by applying cost-sensitivelearning. Through case studies on a real data-set of the French transmissiongrid and on the IEEE 6 bus system using static security metrics, it isshowcased how the proposed approach reduces inaccurate predictions and risks.The sensitivity on the likelihood of contingency is studied as well as onexpected outage costs. Finally, the scalability to several contingencies andoperating conditions are showcased.

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

Cremer JL, Konstantelos I, Strbac G, 2019, Optimized Operation Rules for Imbalanced Classes, ISSN: 1944-9925

© 2019 IEEE. Supervised machine learning methods were applied to assess the reliability of the power system. Typically, the reliability boundary that defines the operation rules is learned using a training database consisting of a large number of potential operation states. Many of these operation states are historical observations and these are typically all reliable operation states. However, to learn a classifier that can predict unseen operation states requires unreliable operation states as well. Thus, a statistical model is typically fitted to the historical observations, and then, unreliable operation states are sampled from this model. Still, the share of reliable states may be much larger than the portion of unreliable states. This imbalance in the data results in biasing the learning methods toward predicting reliable states with higher accuracy than unreliable states. However, an unreliable operating state involves (per-definition) a risk of failing system operation. Therefore, a higher accuracy is required in predicting unreliable states rather than in reliable states. This paper focuses on accounting for this bias when learning from imbalanced data. To optimally learn operation rules for an imbalanced training database a novel Optimal Classification Tree (OCT) is applied. We modify the OCT approach to address the corresponding bias that is introduced in an imbalanced training database. Our fully Controllable and Optimal Classification Tree (COCT) approach controls directly in the objective function the class weights of each operation state that is used for training. By using a database from the French transmission grid it is showcased how the proposed COCT method results in fewer missed alarms than the standard approach that is used to learn operation rules.

Conference paper

Cremer J, Konstantelos I, Tindemans S, Strbac Get al., 2019, Data-driven power system operation: Exploring the balance between cost and risk, IEEE Transactions on Power Systems, Vol: 34, Pages: 791-801, ISSN: 0885-8950

Supervised machine learning has been successfully used in the past to infer a system's security boundary by training classifiers (also referred to as security rules) on a large number of simulated operating conditions. Although significant research has been carried out on using classifiers for the detection of critical operating points, using classifiers for the subsequent identification of suitable preventive/corrective control actions remains underdeveloped. This paper focuses on addressing the challenges that arise when utilizing security rules for control purposes. The inherent trade-off between operating cost and security risk is explored in detail. To optimally navigate this trade-off, a novel approach is proposed that uses an ensemble learning method (AdaBoost) to infer a probabilistic description of a system's security boundary and Platt Calibration to correct the introduced bias. Subsequently, a general-purpose framework for building probabilistic and disjunctive security rules of a system's secure operating domain is developed that can be embedded within classic operation formulations. Through case studies on the IEEE 39-bus system, it is showcased how security rules can be efficiently utilized to optimally operate the system under multiple uncertainties while respecting a user-defined cost-risk balance. This is a fundamental step towards embedding data-driven models within classic optimisation approaches.

Journal article

Pau M, Cremer JL, Ponci F, Monti Aet al., 2019, Day-Ahead Scheduling of Electric Heat Pumps for Peak Shaving in Distribution Grids, Pages: 27-51, ISSN: 1865-0929

© 2019, Springer Nature Switzerland AG. In future electric distribution networks, demand flexibility offered by controllable loads will play a key role for the effective transition towards the smart grids. Electric heat pumps are flexible loads whose operation can be controlled, to some extent, to foster the efficient operation of the distribution grids. This paper presents an optimization algorithm that defines a smart day-ahead scheduling of electric heat pumps aimed at achieving power peak shaving in the distribution grid, while providing customers with the desired thermal comfort over the day. The proposed optimization relies upon a Mixed Integer Linear Programming approach and allows defining both the time schedule and the operating points of the heat pump, guaranteeing an energy efficient solution for the customers. Performed tests show the benefits achievable by means of the proposed optimal scheduling both at the distribution grid level and at the customer side, proving the goodness of the conceived solution.

Conference paper

Sun M, Cremer J, Strbac G, 2018, A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration, Applied Energy, Vol: 228, Pages: 546-555, ISSN: 0306-2619

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.

Journal article

Cremer JL, Konstantelos I, Strbac G, Tindemans SHet al., 2018, Sample-derived disjunctive rules for secure power system operation, International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Publisher: IEEE

Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the task of prediction and propose a comprehensive approach to use predictors, such as Decision Trees (DT), within a standard optimization framework for pre- and post-fault control purposes. In particular, we present a generalizable method for embedding rules derived from DTs in an operation decision-making model. We begin by pointing out the specific challenges entailed when moving from a prediction to a control framework. We proceed with introducing the solution strategy based on generalized disjunctive programming (GDP) as well as a two-step search method for identifying optimal hyper-parameters for balancing cost and control accuracy. We showcase how the proposed approach constructs security proxies that cover multiple contingencies while facing high-dimensional uncertainty with respect to operating conditions with the use of a case study on the IEEE 39-bus system. The method is shown to achieve efficient system control at a marginal increase in system price compared to an oracle model.

Conference paper

Pau M, Cremer JL, Ponci F, Monti Aet al., 2017, Impact of customers flexibility in heat pumps scheduling for demand side management

© 2017 IEEE. In the smart grid scenario, Demand Response (DR) and Demand Side Management (DSM) programs are considered as strategic to obtain a more efficient operation of the grid. The flexibility given by the final customers plays a key role to unlock the potential benefits offered by the application of these schemes. A classical example of flexible load that can be exploited for DR and DSM purposes is the electric heat pump. This paper aims at evaluating the main factors affecting the flexibility available in the management of electric heat pumps for space heating. The performed analysis allows identifying some indexes to quantify the available flexibility and highlights how the thermal comfort requirements of the customers affect the provided level of flexibility. Sample simulations show the impact of these flexibility terms on the results of a DSM program designed for power peak shaving at grid level. The possible use of the defined indexes for sorting the customers flexibility and for estimating the potential benefits offered by the adopted DSM scheme is also investigated and discussed.

Conference paper

Cremer JL, Pau M, Ponci F, Monti Aet al., 2017, Optimal Scheduling of Heat Pumps for Power Peak Shaving and Customers Thermal Comfort, 6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Publisher: SCITEPRESS, Pages: 23-34

Conference paper

Zhang Q, Cremer JL, Grossmann IE, Sundaramoorthy A, Pinto JMet al., 2016, Risk-based integrated production scheduling and electricity procurement for continuous power-intensive processes, COMPUTERS & CHEMICAL ENGINEERING, Vol: 86, Pages: 90-105, ISSN: 0098-1354

Journal article

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