Publications
18 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.
Giannelos S, Borozan S, Moreira A, et al., 2023, Techno-economic analysis of smart EV charging for expansion planning under uncertainty, IEEE Belgrade PowerTech Conference, Publisher: IEEE
Giannelos S, Borozan S, Strbac G, 2022, A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty, ENERGIES, Vol: 15
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- Citations: 8
Borozan S, Giannelos S, Strbac G, 2022, Strategic network expansion planning with electric vehicle smart charging concepts as investment options, ADVANCES IN APPLIED ENERGY, Vol: 5, ISSN: 2666-7924
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- Citations: 22
Borozan S, Giannelos S, Aunedi M, et al., 2022, Option Value of EV Smart Charging Concepts in Transmission Expansion Planning under Uncertainty, 21st IEEE Mediterranean Electrotechnical Conference (IEEE MELECON), Publisher: IEEE, Pages: 63-68, ISSN: 2158-8481
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- Citations: 3
Giannelos S, Jain A, Borozan S, et al., 2021, Long-term expansion planning of the transmission network in India under multi-dimensional uncertainty, Energies, Vol: 14, Pages: 7813-7813, ISSN: 1996-1073
Considerable investment in India’s electricity system may be required in the coming decades in order to help accommodate the expected increase of renewables capacity as part of the country’s commitment to decarbonize its energy sector. In addition, electricity demand is geared to significantly increase due to the ongoing electrification of the transport sector, the growing population, and the improving economy. However, the multi-dimensional uncertainty surrounding these aspects gives rise to the prospect of stranded investments and underutilized network assets, rendering investment decision making challenging for network planners. In this work, a stochastic optimization model is applied to the transmission network in India to identify the optimal expansion strategy in the period from 2020 until 2060, considering conventional network reinforcements as well as energy storage investments. An advanced Nested Benders decomposition algorithm was used to overcome the complexity of the multistage stochastic optimization problem. The model additionally considers the uncertainty around the future investment cost of energy storage. The case study shows that deployment of energy storage is expected on a wide scale across India as it provides a range of benefits, including strategic investment flexibility and increased output from renewables, thereby reducing total expected system costs; this economic benefit of planning with energy storage under uncertainty is quantified as Option Value and is found to be in excess of GBP 12.9 bn. The key message of this work is that under potential high integration of wind and solar in India, there is significant economic benefit associated with the wide-scale deployment of storage in the system.
Giannelos S, Djapic P, Pudjianto D, et 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.
Greenwood DM, Djapic P, Sarantakos I, et al., 2020, Pragmatic method for assessing the security of supply in future smart distribution networks, Pages: 221-224
Future distribution networks will be able to provide security of supply through a combination of conventional and smart solutions. This has the potential to require complex and time-consuming assessments using cost-benefit analysis and probabilistic, risk-based methods. The key goal of this project is to create a method for evaluating the security of supply from a combination of conventional and smart solutions which is rigorous enough to provide robust answers, but simple enough that it can be used routinely by planning engineers without in-depth knowledge of risk, statistics, probability, or reliability theory. This will be accomplished through an iterative, data-driven approach and validated via established risk analysis methods. This study presents underpinning analysis for the development of that method, including in-depth risk studies and sensitivity analysis of real distribution networks.
Beulertz D, Charousset S, Most D, et 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.
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
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.
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
Giannelos S, Konstantelos I, Strbac G, 2017, A new class of planning models for option valuation of storage technologies under decision-dependent innovation uncertainty
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.
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.
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.
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