Publications
51 results found
Giannelos S, Borozan S, Aunedi M, et al., 2023, Modelling smart grid technologies in optimisation problems for electricity grids, Energies, Vol: 16, Pages: 1-15, ISSN: 1996-1073
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.
Giannelos S, Moreira A, Papadaskalopoulos D, et al., 2023, A machine learning approach for generating and evaluating forecasts on the environmental impact of the buildings sector, Energies, Vol: 16, Pages: 1-37, ISSN: 1996-1073
The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO2) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO2 emissions. Given the uncertainty around the future level of the corresponding CO2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance.
Sioshansi R, Denholm P, Arteaga J, et al., 2022, Energy-Storage Modeling: State-of-the-Art and Future Research Directions, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 860-875, ISSN: 0885-8950
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- Citations: 19
Konstantelos I, Wiebe J, Moss R, et al., 2022, Fab-Wide Scheduling of Semiconductor Plants: A Large-Scale Industrial Deployment Case Study, Pages: 3297-3308, ISSN: 0891-7736
This paper presents a novel fab-wide scheduling approach for large semiconductor manufacturing plants. The challenge lies in the scale and complexity of the problem at hand; thousands of wafers must be scheduled on hundreds of machines across several steps while respecting a wide array of operational constraints such as the use of photolithography reticles, timelinks and flow control limits. We begin by surveying the state-of-the art and presenting some key opportunities that arise in the context of global scheduling. The proposed approach is presented, highlighting its hierarchical structure and how it can interface with local toolset schedulers. We present some illustrative examples and aggregate statistics obtained during ongoing trials at Seagate Springtown. We demonstrate that the proposed approach can result in substantial cycle time improvements when compared to myopic dispatch methods and a marked reduction in the need of manual intervention for controlling flows.
Heylen E, Papadaskalopoulos D, Konstantelos I, et 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.
Cremer JL, Konstantelos I, Strbac G, 2020, Optimized operation rules for imbalanced classes, 2019 IEEE Power & Energy Society General Meeting (PESGM), Publisher: IEEE, Pages: 1-5
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.
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.
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.
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-
De Girardier R, Rousis AO, Konstantelos I, et al., 2019, Operational optimization of a microgrid with differential algebraic constraints
This paper is concerned with the optimal operation of microgrids, which progressively operate closer to their stability limits due to high penetration of renewable generation and the ever-increasing interconnection with the main grid. To tackle this problem, the paper proposes an appropriate transient stability constrained OPF (TSC-OPF) that integrates system dynamic constraints governed by relevant differential equations. A direct transcription method and specifically a simultaneous collocation method is utilized, as it has been found to exhibit greater performance in comparison with other methods reported in the literature. The developed model is applied in the operational assessment of a generic, islanded microgrid comprising of a conventional and a renewable generator to demonstrate the benefits for its transient performance. It has been shown that the added transient stability constraints enable the determination of a dispatch that avoids the violation of any stability limits.
Oulis Rousis A, Konstantelos I, Fatouros P, et al., 2019, An AC OPF with voltage – frequency coupling constraints for addressing operational challenges of AC/DC microgrids, REM2018: Renewable Energy Integration with Mini/Microgrid, Publisher: Elsevier, Pages: 261-266, ISSN: 1876-6102
This paper focuses on developing an appropriate optimization technique for solving the optimal operation problem of hybrid AC/DC microgrids. A non-linear mathematical formulation is deployed to solve the problem subject to the exhaustive set of constraints and equations pertaining to the operation of AC and DC networks. The proposed approach, being able to capture technical characteristics, such as voltage and frequency, through the detailed power flow algorithm, provides accurate solutions and therefore can address operational challenges of MGs. The approach is applied to a highly-generalizable microgrid comprising of AC and DC generators and loads, as well as storage technologies in order to demonstrate the benefits. The simulation results demonstrate how voltage and frequency are effectively captured across the whole network via the utilised formulation and the power flow through the interlinking converter is associated with frequency (i.e. 49-51 Hz) and voltage variation (i.e. 0.95-1.05 p.u.).
Hussain A, Rousis AO, Konstantelos I, et al., 2019, Impact of uncertainties on resilient operation of microgrids: A data-driven approach, IEEE Access, Vol: 7, Pages: 14924-14937, ISSN: 2169-3536
In this paper, the impact of uncertainties in loads, renewable generation, market price signals, and event occurrence time on the feasible islanding and survivability of microgrids is analyzed. A data-driven approach is proposed for estimating the maximum deviation level of uncertain parameters dynamically based on historical data. Similarly, fragility curves are utilized for determining the preparation time for the potential events based on the estimated event occurrence time and physical constraints of the microgrid components. In addition, a resilience-oriented demand response program is proposed for enhancing the utilization of renewables and other available resources for reducing the load shedding during the emergency period. Finally, a resilience index is proposed for quantifying the benefits of the proposed method for the resilience-oriented operation of microgrids. In normal mode, the impact of event occurrence time and uncertainty level is analyzed via an adaptive robust optimization method. In emergency mode, 10 000 Monte Carlo scenarios of all the uncertain parameters are generated, and their impact on the operation cost, amount of load shed, and the range of the proposed resilience index are analyzed for each case.
Cremer J, Konstantelos I, Tindemans S, et 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.
Konstantelos I, Sun M, Tindemans S, et al., 2019, Using vine copulas to generate representative system states for machine learning, IEEE Transactions on Power Systems, Vol: 34, Pages: 225-235, ISSN: 0885-8950
The increasing uncertainty that surrounds electricity system operation renders security assessment a highly challenging task; the range of possible operating states expands, rendering traditional approaches based on heuristic practices and ad hoc analysis obsolete. In turn, machine learning can be used to construct surrogate models approximating the system's security boundary in the region of operation. To this end, past system history can be useful for generating anticipated system states suitable for training. However, inferring the underlying data model, to allow high-density sampling, is problematic due to the large number of variables, their complex marginal probability distributions and the non-linear dependence structure they exhibit. In this paper we adopt the C-Vine pair-copula decomposition scheme; clustering and principal component transformation stages are introduced, followed by a truncation to the pairwise dependency modelling, enabling efficient fitting and sampling of large datasets. Using measurements from the French grid, we show that a machine learning training database sampled from the proposed method can produce binary security classifiers with superior predictive capability compared to other approaches.
Fatouros P, Konstantelos I, Papadaskalopoulos D, et al., 2019, Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty, IEEE Transactions on Sustainable Energy, Vol: 10, Pages: 459-469, ISSN: 1949-3029
The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise, can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming (SDDP) approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a p-order vector autoregressive (VAR) model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.
Giannelos S, Konstantelos I, Strbac G, 2019, Investment Model for Cost-effective Integration of Solar PV Capacity under Uncertainty using a Portfolio of Energy Storage and Soft Open Points, IEEE Milan PowerTech Conference, Publisher: IEEE
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- Citations: 7
Konstantelos I, Jamgotchian G, Tindemans S, et al., 2018, Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids, IEEE-Power-and-Energy-Society General Meeting (PESGM), Publisher: IEEE, ISSN: 1944-9925
Giannelos S, Konstantelos I, Strbac G, 2018, Option value of demand-side response schemes under decision-dependent uncertainty, IEEE Transactions on Power Systems, Vol: 33, Pages: 5103-5113, ISSN: 0885-8950
Uncertainty in power system planning problems can be categorized into two types: exogenous and endogenous (or decision-dependent) uncertainty. In the latter case, uncertainty resolution depends on a choice (the value of some decision variables), as opposed to the former case in which the uncertainty resolves automatically with the passage of time. In this paper, a novel stochastic multistage planning model is proposed that considers endogenous uncertainty around consumer participation in demand-side response (DSR) schemes. This uncertainty can resolve following DSR deployment in two possible ways: locally (at a single bus) and globally (across the entire system). The original formulation is decomposed with the use of Benders decomposition to improve computational performance. Two versions of Benders decomposition are applied: the classic version involving sequential implementation of all operational subproblems and a novel version, specific to problems with endogenous uncertainty, which allows for the parallel execution of only those operational subproblems that are guaranteed to have a unique contribution to the solution. Case studies on 11-bus and 123-bus systems illustrate the process of endogenous uncertainty resolution and underline the strategic importance of deploying DSR ahead of time.
Cremer JL, Konstantelos I, Strbac G, et 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.
Oulis Rousis A, Tzelepis D, Konstantelos I, et al., 2018, Design of a Hybrid AC/DC Microgrid Using HOMER Pro: Case Study on an Islanded Residential Application, Inventions, Vol: 3, ISSN: 2411-5134
This paper is concerned with the design of an autonomous hybrid alternating current/direct current (AC/DC) microgrid for a community system, located on an island without the possibility of grid connection. It is comprised of photovoltaic (PV) arrays and a diesel generator, AC loads, and battery energy storage devices for ensuring uninterruptible power supply during prolonged periods of low sunshine. A multi-objective, non-derivative optimisation is considered in this residential application; the primary objective is the system cost minimisation, while it is also required that no load shedding is allowed. Additionally, the CO2 emissions are calculated to demonstrate the environmental benefit the proposed system offers. The commercial software, HOMER Pro, is utilised to identify the least-cost design among hundreds of options and simultaneously satisfy the secondary objective. A sensitivity analysis is also performed to evaluate design robustness against the uncertainty pertaining to fuel prices and PV generation. Finally, an assessment of the capabilities of the utilised optimisation platform is conducted, and a theoretical discussion sheds some light on the proposal for an enhanced design tool addressing the identified issues.
Konstantelos I, Strbac G, 2018, Capacity value of energy storage in distribution networks, Journal of Energy Storage, Vol: 18, Pages: 389-401, ISSN: 2352-152X
Security of supply in electricity distribution networks has been traditionally delivered by conventional assets such as transformers and circuits to supply energy to consumers. Although non-network solutions, such as energy storage (ES), can also be used to provide security of supply by carrying out peak shaving and maintaining supply for the duration of a network outage, present network design standards do not provide a framework for quantifying their security contribution and corresponding capacity value. Given the fundamentally different operating principles of ES, it is imperative to develop novel methodologies for assessing its contribution to security of supply and enable a level playing field to be established for future network planning. To this end, a novel probabilistic methodology based on chronological Monte Carlo simulations is developed for computing the Effective Load Carrying Capability (ELCC) of an energy storage plant. Substantial computational speed-up is achieved through event-based modelling and decomposing between energy and power constraints. The paper undertakes, for the first time, the in-depth analysis of key factors that can affect ES security contribution; plant and network outage frequency and duration, network redundancy level, demand shape, islanding operation capability and ES availability. ES capacity value is shown to decrease in networks with an unreliable connection to the grid; time to restore supply is shown to be more important that frequency of faults. Capacity value increases in cases of peaky demand profiles, while the ability to operate in islanded conditions is shown to be a critical factor. These findings highlight the need for sophisticated network design standards. The proposed methodology enables planners to consider ES solutions and allows network and non-network assets to comp
Falugi P, Konstantelos I, Strbac G, 2018, Planning with multiple transmission and storage investment options under uncertainty: a nested decomposition approach, IEEE Transactions on Power Systems, Vol: 33, Pages: 3559-3572, ISSN: 0885-8950
Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies such as energy storage can improve operational flexibility and assist with the cost-effective integration of renewables. Given the long lifetime of these network assets and their substantial capital cost, it is imperative to decide on their deployment on a long-term cost-benefit basis. However, such an analysis can result in large-scale Mixed Integer Linear Programming (MILP) problems which contain many thousands of continuous and binary variables. Complexity is severely exacerbated by the need to accommodate multiple candidate assets and consider a wide range of exogenous system development scenarios that may occur. In this manuscript we propose a novel, efficient and highly-generalizable framework for solving large-scale planning problems under uncertainty by using a temporal decomposition scheme based on the principles of Nested Benders. The challenges that arise due to the presence of non-sequential investment state equations and sub-problem non-convexity are highlighted and tackled. The substantial computational gains of the proposed method are demonstrated via a case study on the IEEE 118 bus test system that involve planning of multiple transmission and storage assets under long-term uncertainty.
Giannelos S, Konstantelos I, Strbac G, 2018, Option Value of Dynamic Line Rating and Storage, IEEE International Energy Conference (ENERGYCON), Publisher: IEEE, ISSN: 2164-4322
Giannelos S, Konstantelos I, Strbac G, 2018, Endogenously Stochastic Demand Side Response Participation on Transmission System Level, IEEE International Energy Conference (ENERGYCON), Publisher: IEEE, ISSN: 2164-4322
Sun M, Teng F, Konstantelos I, et al., 2018, An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources, Energy, Vol: 145, Pages: 871-885, ISSN: 0360-5442
Transmission Network Expansion Planning (TNEP) in modern electricity systems is carried out on a cost-benefit analysis basis; the planner identifies investments that maximize the social welfare. As the integration of Renewable Energy Sources (RES) increases, there is a real challenge to accurately capture the vast variability that characterizes system operation within a planning problem. Conventional approaches that rely on a large number of scenarios for representing the variability of operating points can quickly lead to computational issues. An alternative approach that is becoming increasingly necessary is to select representative scenarios from the original population via clustering techniques. However, direct clustering of operating points in the input domain may not capture characteristics which are important for investment decision-making. This paper presents a novel objective-based scenario selection framework for TNEP to obtain optimal investment decisions with a significantly reduced number of operating states. Different clustering frameworks, clustering variable s and clustering techniques are compared to determine the most appropriate approach. The superior performance of the proposed framework is demonstrated through a case study on a modified IEEE 118-bus system.
Konstantelos I, Strbac G, 2018, The role of storage in transmission investment deferral and management of future planning uncertainty, ENERGY STORAGE AT DIFFERENT VOLTAGE LEVELS: TECHNOLOGY, INTEGRATION, AND MARKET ASPECTS, Editors: Zobaa, Ribeiro, Aleem, Afifi, Publisher: INST ENGINEERING TECH-IET, Pages: 113-145, ISBN: 978-1-78561-349-4
Konstantelos I, Djapic P, Strbac G, et al., 2017, Contribution of energy storage and demand-side response to security of distribution networks, CIRED 2017, Publisher: IEEE, Pages: 1650-1654, ISSN: 2515-0855
The smart grid paradigm envisages the wide penetration of distributed energy resources, such as demand-side response (DSR) schemes and energy storage (ES). Despite their potential to improve security of supply at the distribution level, existing design standards in most jurisdictions consider solely conventional assets; conceptual and methodological gaps prevent DSR and ES from being embedded into formal network design practices. As such, the crucial question that arises is how to assess the security contribution of these technologies so as to level the playing field and encourage the transition to a smart grid. Here, the authors introduce two capacity metrics: equivalent firm capacity and equivalent load-carrying capability. The authors describe their application to DSR and ES, showcase results from the UK Power Networks' Smarter Network Storage and Low Carbon London projects, and provide suggestions on the incorporation of smart assets in future design standards.
Strbac G, Aunedi M, Konstantelos I, et al., 2017, Opportunities for Energy Storage: Assessing Whole-System Economic Benefits of Energy Storage in Future Electricity Systems, IEEE Power and Energy Magazine, ISSN: 1540-7977
Fatouros P, Konstantelos I, Papadaskalopoulos D, et al., 2017, A stochastic dual dynamic programming approach for optimal operation of DER aggregators, IEEE PowerTech 2017, Publisher: IEEE
The operation of aggregators of distributed energy resources (DER) is a highly complex task that is affected by numerous factors of uncertainty such as renewables injections, load levels and market conditions. However, traditional stochastic programming approaches neglect information around temporal dependency of the uncertain variables due to computational tractability limitations. This paper proposes a novel stochastic dual dynamic programming (SDDP) approach for the optimal operation of a DER aggregator. The traditional SDDP framework is extended to capture temporal dependency of the uncertain wind power output, through the integration of an n-order autoregressive (AR) model. This method is demonstrated to achieve a better trade-off between solution efficiency and computational time requirements compared to traditional stochastic programming approaches based on the use of scenario trees.
Giannelos S, Konstantelos I, Strbac G, 2017, A new class of planning models for option valuation of storage technologies under decision-dependent innovation uncertainty
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