Publications
541 results found
Dong Z, Zhang X, Strbac G, 2021, Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems, ENERGY, Vol: 237, ISSN: 0360-5442
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- Citations: 4
Bugaje A-AB, Cremer JL, Sun M, et al., 2021, Selecting decision trees for power system security assessment, ENERGY AND AI, Vol: 6, ISSN: 2666-5468
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- Citations: 6
Bellizio F, Cremer JL, Sun M, et al., 2021, A causality based feature selection approach for data-driven dynamic security assessment, ELECTRIC POWER SYSTEMS RESEARCH, Vol: 201, ISSN: 0378-7796
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- Citations: 7
Dong Z, Angeli D, Paola A, et al., 2021, An iterative algorithm for regret minimization in flexible demand scheduling problems, Advanced Control for Applications, Vol: 3, Pages: 1-33, ISSN: 2578-0727
A major challenge to develop optimal strategies for allocation of flexible demand toward the smart grid paradigm is the uncertainty associated with the real-time price and electricity demand. This article presents a regret-based model and a novel iterative algorithm which solves the minimax regret optimization problem. This algorithms exhibits low computational burden compared with traditional linear programming methods and affords iterative convergence through updates of feasible power schedules, thus enabling a scalable parallel implementation for large device populations. Specifically, our approach seeks to minimize the induced worst-case regret over all price scenarios and solves the optimal charging strategy for the electrical devices. The convergence of the method and optimality of the computed solution is justified and some numerical simulations are discussed for the case of a single device operating under different types of price realizations and uncertainty bounds.
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.
O'Malley C, Badesa L, Teng F, et al., 2021, Probabilistic scheduling of UFLS to secure credible contingencies in low inertia systems, IEEE Transactions on Power Systems, ISSN: 0885-8950
The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency's nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel constraint from the swing equation to contain the frequency nadir using all of these services. The expected cost of UFLS is found probabilistically to facilitate its comparison to the other frequency services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone (SOC), resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to 559m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored.
Badesa L, Teng F, Strbac G, 2021, Conditions for regional frequency stability in power system scheduling—Part I: theory, IEEE Transactions on Power Systems, Vol: 36, Pages: 5558-5566, ISSN: 0885-8950
This paper considers the phenomenon of distinct regional frequencies recently observed in some power systems. First, a reduced-order mathematical model describing this behaviour is developed. Then, techniques to solve the model are discussed, demonstrating that the post-fault frequency evolution in any given region is equal to the frequency evolution of the Centre Of Inertia plus certain inter-area oscillations. This finding leads to the deduction of conditions for guaranteeing frequency stability in all regions of a power system, a deduction performed using a mixed analytical-numerical approach that combines mathematical analysis with regression methods on simulation samples. The proposed stability conditions are linear inequalities that can be implemented in any optimisation routine allowing the co-optimisation of all existing ancillary services for frequency support: inertia, multi-speed frequency response, load damping and an optimised largest power infeed. This is the first reported mathematical framework with explicit conditions to maintain frequency stability in a power system exhibiting inter-area oscillations in frequency.
Badesa L, Teng F, Strbac G, 2021, Conditions for regional frequency stability in power system scheduling—Part II: application to unit commitment, IEEE Transactions on Power Systems, Vol: 36, Pages: 5567-5577, ISSN: 0885-8950
In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region.
Wang Y, Rousis AO, Strbac G, 2021, A Three-Level Planning Model for Optimal Sizing of Networked Microgrids Considering a Trade-Off Between Resilience and Cost, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 36, Pages: 5657-5669, ISSN: 0885-8950
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- Citations: 21
Al Kindi A, Aunedi M, Pantaleo A, et al., 2021, Thermo-economic assessment of flexible nuclear power plants in the UK’s future low-carbon electricity system: role of thermal energy storage, 16th Conference on Sustainable Development of Energy, Water and Environment Systems, Publisher: SDEWES
Nuclear power plants are commonly operated as baseload units due to their low variable costs, high investment costs and limited ability to modulate their output. The increasing penetration of intermittent renewable power will require additional flexibility from conventional generation units, in order to follow the fluctuating renewable output while guaranteeing security of energy supply. In this context, coupling nuclear reactors with thermal energy storage could ensure a more continuous and efficient operation of nuclear power plants, while at other times allowing their operation to become more flexible and cost-effective. This study considers options for upgrading a 1610-MWel nuclear power plant with the addition of a thermal energy storage system and secondary power generators. The analysed configuration allows the plant to generate up to 2130 MWel during peak load, representing an increase of 32% in nominal rated power. The gross whole-system benefits of operating the proposed configuration are quantified over several scenarios for the UK’s low-carbon electricity system. Replacing conventional with flexible nuclear plant configuration is found to generate system cost savings that are between £24.3m/yr and £88.9m/yr, with the highest benefit achieved when stored heat is fully discharged in 0.5 hours (the default case is 1 hour). At an estimated cost of added flexibility of £42.7m/yr, the proposed flexibility upgrade to a nuclear power plant appears to be economically justified for a wide range of low-carbon scenarios, provided that the number of flexible nuclear units in the system is small.
Trask A, Wills K, Green T, et al., 2021, Impacts of COVID-19 on the Energy System, Impacts of COVID-19 on the Energy System
This Briefing Paper explores the impactthe COVID-19 pandemic had on the UK’senergy sector over the course of thefirst government-mandated nationallockdown that began on 23 March 2020.Research from several aspects of theIntegrated Development of Low-carbonEnergy Systems (IDLES) programme atImperial College London is presented inone overarching paper. The main aim isto determine what lessons can be learntfrom that lockdown period, given theunique set of challenges it presented inour daily lives and the changes it broughtabout in energy demand, supply, anduse. Valuable insights are gained intohow working-from-home policies,electric vehicles, and low-carbon gridscan be implemented, incentivised, andmanaged effectively.
Falugi P, O'Dwyer E, Kerrigan EC, et al., 2021, Predictive control co-design for enhancing flexibility in residential housing with battery degradation, 7th IFAC Conference on Nonlinear Model Predictive Control, Publisher: Elsevier, Pages: 8-13, ISSN: 2405-8963
Buildings are responsible for about a quarter of global energy-related CO2 emissions. Consequently, the decarbonisation of the housing stock is essential in achieving net-zero carbon emissions. Global decarbonisation targets can be achieved through increased efficiency in using energy generated by intermittent resources. The paper presents a co-design framework for simultaneous optimal design and operation of residential buildings using Model Predictive Control (MPC). The framework is capable of explicitly taking into account operational constraints and pushing the system to its efficiency and performance limits in an integrated fashion. The optimality criterion minimises system cost considering time-varying electricity prices and battery degradation. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results from a low-fidelity model show substantial carbon emission reduction and bill savings compared to an a-priori sizing approach.
Pan G, Gu W, Hu Q, et al., 2021, Cost and low-carbon competitiveness of electrolytic hydrogen in China, ENERGY & ENVIRONMENTAL SCIENCE, Vol: 14, Pages: 4868-4881, ISSN: 1754-5692
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- Citations: 22
Huang W, Du E, Capuder T, et al., 2021, Reliability and Vulnerability Assessment of Multi-Energy Systems: An Energy Hub Based Method, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 36, Pages: 3948-3959, ISSN: 0885-8950
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- Citations: 41
Heylen E, Teng F, Strbac G, 2021, Challenges and opportunities of inertia estimation and forecasting in low-inertia power systems, Renewable and Sustainable Energy Reviews, Vol: 147, Pages: 1-12, ISSN: 1364-0321
Accurate inertia estimates and forecasts are crucial to support the system operation in future low-inertia power systems. A large literature on inertia estimation methods is available. This paper aims to provide an overview and classification of inertia estimation methods. The classification considers the time horizon the methods are applicable to, i.e., offline post mortem, online real time and forecasting methods, and the scope of the inertia estimation, e.g., system-wide, regional, generation, demand, individual resource. The framework presented in this paper facilitates objective comparisons of the performance of newly developed or improved inertia estimation methods with the state-of-the-art methods in their respective time horizon and with their respective scope. Moreover, shortcomings of the existing inertia estimation methods have been identified and suggestions for future work have been made.
Zhang T, Sun M, Cremer JL, et al., 2021, A Confidence-Aware Machine Learning Framework for Dynamic Security Assessment, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 36, Pages: 3907-3920, ISSN: 0885-8950
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- Citations: 11
Ye Y, Tang Y, Wang H, et al., 2021, A Scalable Privacy-Preserving Multi-agent Deep Reinforcement Learning Approach for Large-Scale Peer-to-Peer Transactive Energy Trading, IEEE Transactions on Smart Grid, Pages: 1-1, ISSN: 1949-3053
Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm towards maximizing the flexibility value of prosumers’ distributed energy resources (DERs). Despite reinforcement learning constitutes a well-suited model-free and data-driven methodological framework to optimize prosumers’ energy management decisions, its application to the large-scale coordinated management and P2P trading among multiple prosumers within an energy community is still challenging, due to the scalability, non-stationarity and privacy limitations of state-of-the-art multi-agent deep reinforcement learning (MADRL) approaches. This paper proposes a novel P2P transactive trading scheme based on the multi-actor-attention-critic (MAAC) algorithm, which addresses the above challenges individually. This method is complemented by a P2P trading platform that incentivizes prosumers to engage in local energy trading while also penalizes each prosumer’s addition to rebound peaks. %The proposed method is applied to the coordination of prosumers operating multiple and diverse DERs, including photovoltaic (PV) generators, energy storage (ES) units and two types of shiftable loads. Case studies involving a real-world, large-scale scenario with 300 residential prosumers demonstrate that the proposed method significantly outperforms the state-of-the-art MADRL methods in reducing the community’s cost and peak demand.
Li J, Ye Y, Papadaskalopoulos D, et al., 2021, Distributed consensus-based coordination of flexible demand and energy storage resources, IEEE Transactions on Power Systems, Vol: 36, Pages: 3053-3069, ISSN: 0885-8950
Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability.
Qiu D, Ye Y, Papadaskalopoulos D, et al., 2021, Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach, APPLIED ENERGY, Vol: 292, ISSN: 0306-2619
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- Citations: 47
Aunedi M, Wills K, Green T, et al., 2021, Net-zero GB electricity: cost-optimal generation and storage mix, Great Britain's electricity generation capacity mix for net-zero carbon emissions, Publisher: Energy Futures Lab
Cremer JL, Strbac G, 2021, A machine-learning based probabilistic perspective on dynamic security assessment, International Journal of Electrical Power and Energy Systems, Vol: 128, 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.
Wang Y, Rousis AO, Strbac G, 2021, A resilience enhancement strategy for networked microgrids incorporating electricity and transport and utilizing a stochastic hierarchical control approach, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 26, ISSN: 2352-4677
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- Citations: 21
Oulis Rousis A, Tzelepis D, Pipelzadeh Y, et al., 2021, Provision of Voltage Ancillary Services Through Enhanced TSO-DSO Interaction and Aggregated Distributed Energy Resources, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol: 12, Pages: 897-908, ISSN: 1949-3029
Baig AM, Badesa L, Strbac G, 2021, Importance of linking inertia and frequency response procurement: the Great Britain case, Publisher: arXiv
In order to decarbonise the electricity sector, the future Great Britain (GB)power system will be largely dominated by non-synchronous renewables. This willcause low levels of inertia, a key parameter that could lead to frequencydeterioration. Therefore, the requirement for ancillary services that containfrequency deviations will increase significantly, particularly given theincrease in size of the largest possible loss with the commissioning of largenuclear plants in the near future. In this paper, an inertia-dependentStochastic Unit Commitment (SUC) model is used to illustrate the benefits oflinking inertia and frequency response provision in low-inertia systems. Wedemonstrate that the cost of procuring ancillary services in GB could increaseby 165% if the level of inertia is not explicitly considered when procuringfrequency response. These results highlight the need to re-think the structureof ancillary-services markets, which in GB are nowadays held one month ahead ofdelivery.
Badesa L, Strbac G, Magill M, et al., 2021, Ancillary services in Great Britain during the COVID-19 lockdown: A glimpse of the carbon-free future, Applied Energy, Vol: 285, Pages: 1-10, ISSN: 0306-2619
The COVID-19 pandemic led to partial or total lockdowns in several countries during the first half of 2020, which in turn caused a depressed electricity demand. In Great Britain (GB), this low demand combined with large renewable output at times, created conditions that were not expected until renewable capacity increases to meet emissions targets in coming years. The GB system experienced periods of very high instantaneous penetration of non-synchronous renewables, compromising system stability due to the lack of inertia in the grid. In this paper, a detailed analysis of the consequences of the lockdown on the GB electricity system is provided, focusing on the ancillary services procured to guarantee stability. Ancillary-services costs increased by £200m in the months of May to July 2020 compared to the same period in 2019 (a threefold increase), highlighting the importance of ancillary services in low-carbon systems. Furthermore, a frequency-secured scheduling model is used in the present paper to showcase the future trends that GB is expected to experience, as penetration of renewables increases on the road to net-zero emissions by 2050. Several sensitivities are considered, demonstrating that the share of total operating costs represented by ancillary services could reach 35%.
Feng C, Wang Y, Chen Q, et al., 2021, Smart grid encounters edge computing: opportunities and applications, ADVANCES IN APPLIED ENERGY, Vol: 1, ISSN: 2666-7924
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- Citations: 43
Shabazbegian V, Ameli H, Ameli MT, et al., 2021, Co-optimization of resilient gas and electricity networks; a novel possibilistic chance-constrained programming approach, Applied Energy, Vol: 284, ISSN: 0306-2619
Gas-fired power plants are commonly employed to deal with the intermittency of renewable energy resources due to their flexible characteristics. Therefore, the intermittency in the power system transmits to the gas system through the gas-fired power plants, which makes the operation of these systems even more interdependent. This study proposes a novel possibilistic model for the integrated operation of gas and power systems in the presence of electric vehicles and demand response. The model takes into account uncertainty in demand prediction and output power of wind farms, which is based on possibility and necessity theories in fuzzy logic through modeling uncertain parameters by Gaussian membership function. Moreover, a contingency analysis algorithm based on maximin optimization is developed to enhance the resiliency in the integrated operation of these systems by finding the worst-case scenario for the outage of components. The proposed model is implemented on a Belgium gas network and IEEE 24-bus electricity network. It is demonstrated that the possibilistic model allows the gas network to respond to the demand variations by providing a sufficient level of linepack within the pipelines. As a result, gas-fired power plants are supposed to commit more efficiently to cope with the intermittency of wind farms, which reduce the wind curtailment by 26%. Furthermore, it is quantified that through applying the contingency analysis algorithm in presence of demand response and electrical vehicles, the costs of operation and load shedding is reduced up to 17% and 83%, respectively.
Ye Y, Qiu D, Wang H, et al., 2021, Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning, ENERGIES, Vol: 14
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- Citations: 19
Narbondo L, Falugi P, Strbac G, 2021, Application of energy storage in systems with high penetration of intermittent renewables, IEEE PES Transmission and Distribution Conference and Exhibition - Latin America (T and D LA), Publisher: IEEE, Pages: 1-6, ISSN: 2381-3571
Nowadays, in Uruguay, a considerable amount of energy produced by renewable resources is curtailed inducing frequent substantial reductions in the spot market prices. This paper analyses the incorporation of energy storage into the Uruguayan network, taking the different perspectives of a private investor and a central planner. From the investor point of view, we investigate the option of doing energy arbitrage in the wholesale market, taking advantage of the spot price fluctuations. From the national perspective, we develop an optimal power flow planning model to perform a cost-benefit analysis of batteries’ integration in reducing thermal generation. We conclude that, from a private investor perspective, fluctuations in the spot prices are not enough to make investments in batteries profitable with current prices. On the other hand, from a national perspective, results are more promising, obtaining very high revenues in some case studies.
Li J, Ye Y, Papadaskalopoulos D, et al., 2021, Computationally Efficient Pricing and Benefit Distribution Mechanisms for Incentivizing Stable Peer-to-Peer Energy Trading, IEEE Internet of Things Journal, Vol: 8, Pages: 734-749
Peer-to-peer (P2P) energy trading has emerged as a promising market paradigm towards maximizing the value of distributed energy resources (DER) for electricity prosumers, by enabling direct energy trading among them. However, state-of-the-art P2P mechanisms either fail to adequately incentivize prosumers to participate, prevent prosumers from accessing the highest achievable monetary benefits, or suffer severely from the curse of dimensionality. This paper proposes two computationally efficient mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core tâtonnement process while the second involves a novel pricing mechanism based on the solution of a single linear program. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition.
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