Imperial College London

Dr Spyros Giannelos

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate







Electrical EngineeringSouth Kensington Campus





Publication Type

10 results found

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

Beulertz D, Charousset S, Most D, Giannelos S, Yueksel-Erguen Iet al., 2019, Development of a Modular Framework for Future Energy System Analysis

This paper gives an overview of the modeling framework that is being developed within the Horizon2020 project plan4res. In the context of energy transition, integration of high shares of renewable energies will play a vital role for achieving the proposed climate targets. This brings new challenges to modeling tools, including data construction, implementation and solution techniques. In order to address these challenges, plan4res aims to create a well-structured and highly modular framework that will provide insights into the needs of future energy systems. An overview of the central modeling aspects is given in this paper. Finally, three case studies are presented that show the adequacy and relevance of the proposed optimization framework.

Conference paper

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

Conference paper

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.

Journal article

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

Conference paper

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

Conference paper

Giannelos S, Konstantelos I, Strbac G, 2017, A new class of planning models for option valuation of storage technologies under decision-dependent innovation uncertainty

Working paper

Konstantelos I, Giannelos S, Strbac G, 2016, Strategic valuation of smart grid technology options in distribution networks, IEEE Transactions on Power Systems, Vol: 32, Pages: 1293-1303, ISSN: 0885-8950

The increasing penetration of renewabledistributed generation (DG) sources in distribution networks canlead to violations of network constraints. Thus, significantnetwork reinforcements may be required to ensure that DGoutput is not constrained. However, the uncertainty around themagnitude, location and timing of future DG capacity rendersplanners unable to take fully-informed decisions and integrateDG at a minimum cost. In this paper we propose a novelstochastic planning model that considers investment inconventional assets as well as smart grid assets such as demandsideresponse, coordinated voltage control and soft open points(SOPs). The model also considers the possibility of active powergeneration curtailment of the DG units. A node-variableformulation has been adopted to relieve the substantialcomputational burden of the resulting mixed integer non-linearprogramming (MINLP) problem. A case study shows that smarttechnologies can possess significant strategic value due to theirinherent flexibility in dealing with different system evolutiontrajectories. This latent benefit remains undetected undertraditional deterministic planning approaches which may hinderthe transition to the smart grid.

Journal article

Giannelos S, Konstantelos I, Strbac G, 2016, Stochastic optimisation-based valuation of smart grid options under firm DG contracts, 2016 IEEE International Energy Conference (ENERGYCON), Publisher: IEEE

Under the current EU legislation, Distribution NetworkOperators (DNOs) are expected to provide firm connections to newDG, whose penetration is set to increase worldwide creating theneed for significant investments to enhance network capacity.However, the uncertainty around the magnitude, location andtiming of future DG capacity renders planners unable to accuratelydetermine in advance where network violations may occur. Hence,conventional network reinforcements run the risk of assetstranding, leading to increased integration costs. A novel stochasticplanning model is proposed that includes generalized formulationsfor investment in conventional and smart grid assets such asDemand-Side Response (DSR), Coordinated Voltage Control (CVC)and Soft Open Point (SOP) allowing the quantification of theiroption value. We also show that deterministic planning approachesmay underestimate or completely ignore smart technologies.

Conference paper

Giannelos S, Konstantelos I, Strbac G, 2015, Option value of Soft Open Points in distribution networks, IEEE Powertech, Publisher: IEEE

We propose a novel stochastic planning model thatconsiders investment in conventional assets as well as in SoftOpen Points, as a means of treating voltage and thermalconstraints caused by the increased penetration of renewabledistributed generation (DG) sources. Soft Open Points areshown to hold significant option value under uncertainty;however, their multiple value streams remain undetected undertraditional deterministic planning approaches, potentiallyundervaluing this technology and leading to a higher risk ofstranded assets.

Conference paper

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