479 results found
Blatiak A, Bellizio F, Badesa L, et al., 2022, Value of optimal trip and charging scheduling of commercial electric vehicle fleets with Vehicle-to-Grid in future low inertia systems, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 31, ISSN: 2352-4677
Pang Q, De Paola A, Trovato V, et al., 2022, Value of Interconnectors Operating in Simultaneous Energy-Frequency Response Markets, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 3381-3393, ISSN: 0885-8950
O'Malley C, Badesa L, Teng F, et al., 2022, Frequency response from aggregated V2G chargers with uncertain EV connections, IEEE Transactions on Power Systems, Pages: 1-14, ISSN: 0885-8950
Fast frequency response (FR) is highly effective at securing frequency dynamics after a generator outage in low inertia systems. Electric vehicles (EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant source of FR in future. However, the uncertainty associated with V2G aggregation, driven by the uncertain number of connected EVs at the time of an outage, has not been fully understood and prevents its participation in the existing service provision framework. To tackle this limitation, this paper, for the first time, incorporates such uncertainty into system frequency dynamics, from which probabilistic nadir and steady state frequency requirements are enforced via a derived moment-based distributionally-robust chance constraint. Field data from over 25,000 chargers is analysed to provide realistic parameters and connection forecasts to examine the value of FR from V2G chargers in annual operation of the GB 2030 system. The case study demonstrates that uncertainty of EV connections can be effectively managed through the proposed scheduling framework, which results in annual savings of Misplaced &6,300 or 37.4 tCO2 per charger. The sensitivity of this value to renewable capacity and FR delays is explored, with V2G capacity shown to be a third as valuable as the same grid battery capacity.
Karasavvidis M, Papadaskalopoulos D, Strbac G, 2022, Optimal Offering of a Power Producer in Electricity Markets With Profile and Linked Block Orders, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 2704-2719, ISSN: 0885-8950
Zhang X, Dong Z, Huang W, et al., 2022, A Novel Preheating Coordination Approach in Electrified Heat Systems, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 3092-3103, ISSN: 0885-8950
Huang W, Zhang X, Li K, et al., 2022, Resilience Oriented Planning of Urban Multi-Energy Systems With Generalized Energy Storage Sources, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 37, Pages: 2906-2918, ISSN: 0885-8950
Aunedi M, Yliruka M, Dehghan S, et al., 2022, Multi-model assessment of heat decarbonisation options in the UK using electricity and hydrogen, Renewable Energy, Vol: 194, Pages: 1261-1276, ISSN: 0960-1481
Delivering low-carbon heat will require the substitution of natural gas with low-carbon alternatives such as electricity and hydrogen. The objective of this paper is to develop a method to soft-link two advanced, investment-optimising energy system models, RTN (Resource-Technology Network) and WeSIM (Whole-electricity System Investment Model), in order to assess cost-efficient heat decarbonisation pathways for the UK while utilising the respective strengths of the two models. The linking procedure included passing on hourly electricity prices from WeSIM as input to RTN, and returning capacities and locations of hydrogen generation and shares of electricity and hydrogen in heat supply from RTN to WeSIM. The outputs demonstrate that soft-linking can improve the quality of the solution, while providing useful insights into the cost-efficient pathways for zero-carbon heating. Quantitative results point to the cost-effectiveness of using a mix of electricity and hydrogen technologies for delivering zero-carbon heat, also demonstrating a high level of interaction between electricity and hydrogen infrastructure in a zero-carbon system. Hydrogen from gas reforming with carbon capture and storage can play a significant role in the medium term, while remaining a cost-efficient option for supplying peak heat demand in the longer term, with the bulk of heat demand being supplied by electric heat pumps.
Olympios AV, Aunedi M, Mersch M, et al., 2022, Delivering net-zero carbon heat: technoeconomic and whole-system comparisons of domestic electricity- and hydrogen-driven technologies in the UK, Energy Conversion and Management, Vol: 262, ISSN: 0196-8904
Proposed sustainable transition pathways for moving away from natural gas in domestic heating focus on two main energy vectors: electricity and hydrogen. Electrification would be implemented by using vapour-compression heat pumps, which are currently experiencing market growth in many countries. On the other hand, hydrogen could substitute natural gas in boilers or be used in thermally–driven absorption heat pumps. In this paper, a consistent thermodynamic and economic methodology is developed to assess the competitiveness of these options. The three technologies, along with the option of district heating, are for the first time compared for different weather/ambient conditions and fuel-price scenarios, first from a homeowner’s and then from a whole-energy system perspective. For the former, two-dimensional decision maps are generated to identify the most cost-effective technologies for different combinations of fuel prices. It is shown that, in the UK, hydrogen technologies are economically favourable if hydrogen is supplied to domestic end-users at a price below half of the electricity price. Otherwise, electrification and the use of conventional electric heat pumps will be preferred. From a whole-energy system perspective, the total system cost per household (which accounts for upstream generation and storage, as well as technology investment, installation and maintenance) associated with electric heat pumps varies between 790 and 880 £/year for different scenarios, making it the least-cost decarbonisation pathway. If hydrogen is produced by electrolysis, the total system cost associated with hydrogen technologies is notably higher, varying between 1410 and 1880 £/year. However, this total system cost drops to 1150 £/year with hydrogen produced cost-effectively by methane reforming and carbon capture and storage, thus reducing the gap between electricity- and hydrogen-driven technologies.
Yuan Q, Ye Y, Tang Y, et al., 2022, A novel deep-learning based surrogate modeling of stochastic electric vehicle traffic user equilibrium in low-carbon electricity-transportation nexus, APPLIED ENERGY, Vol: 315, ISSN: 0306-2619
Zhang X, Ameli H, Dong Z, et al., 2022, Values of latent heat and thermochemical energy storage technologies in low-carbon energy systems: whole system approach, Journal of Energy Storage, Vol: 50, ISSN: 2352-152X
Thermal energy storage (TES) is widely expected to play an important role in facilitating the decarbonization of the future energy system. Although significant work has been done in assessing the values of traditional sensible TES, less is known about the role, impact and value of emerging advanced TES at the system level. This is particularly the case of latent heat thermal energy storage (LHTES) and thermochemical energy storage (TCS). In this context, this paper is dedicated to evaluating the techno-economic values for the whole UK energy system of LHTES and TCS technology using an integrated whole energy system model. First, the key concepts of the whole system modelling framework are introduced. Unique to this work is that the economic benefits delivered by LHTES and TCS to different levels of theUK energy system infrastructure and various energy sectors through the deployment of TES are explicitly analyzed, which comprehensively demonstrates the values of selected TES technologies from the whole system perspective. A series of sensitivity studies are implemented to analyze the advantages and disadvantages of LHTES and TCS underdifferent conditions. The simulation results indicate that TES can benefit different sectors of the whole energy system and drive significant cost savings, but the whole system values of TES is closely dependent on the decarbonization requirement. Although LHTES is characterized by relatively low capital costs, when TES penetration is limited and carbon target is tight, the advantage of TCS is outstanding due to its high energy density.
Bellizio F, Bugaje A-ABL, Cremer J, et al., 2022, Verifying Machine Learning conclusions for securing Low Inertia systems, SUSTAINABLE ENERGY GRIDS & NETWORKS, Vol: 30, ISSN: 2352-4677
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
Qiu D, Wang Y, Sun M, et al., 2022, Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach, APPLIED ENERGY, Vol: 313, ISSN: 0306-2619
Al Kindi A, Aunedi M, Pantaleo A, et al., 2022, Thermo-economic assessment of flexible nuclear power plants in future low-carbon electricity systems: Role of thermal energy storage, Energy Conversion and Management, Vol: 258, ISSN: 0196-8904
The increasing penetration of intermittent renewable power will require additional flexibility from conventional plants, 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 proposes options for upgrading a 1610-MWel nuclear power plant with the addition of a thermal energy storage system and secondary power generators. The total whole-system benefits of operating the proposed configuration are quantified for several scenarios in the context of the UK’s national electricity system using a whole-system model that minimises the total system costs. The proposed configuration allows the plant to generate up to 2130 MWel during peak load, representing an increase of 32% in nominal rated power. This 520 MWel of additional power is generated by secondary steam Rankine cycle systems (i.e., with optimised cycle thermal efficiencies of 24% and 30%) and by utilising thermal energy storage tanks with a total heat storage capacity of 1950 MWhth. Replacing conventional with flexible nuclear power plants is found to generate whole-system cost savings between £24.3m/yr and £88.9m/yr, with the highest benefit achieved when stored heat is fully discharged in 0.5 h. At an estimated cost of added flexibility of £42.7m/yr, the proposed flexibility upgrades to such nuclear power plants appears to be economically justified with net system benefits ranging from £4.0m/yr to £31.6m/yr for the examined low-carbon scenarios, provided that the number of flexible nuclear plants in the system is small. This suggests that the value of this technology is system dependent, and that system characteristics should be adequately considered when evaluating the benefits of diffe
Bellizio F, Xu W, Qiu D, et al., 2022, Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market, PROCEEDINGS OF THE IEEE, ISSN: 0018-9219
Davari MM, Ameli H, Ameli MT, et al., 2022, Impact of local emergency demand response programs on the operation of electricity and gas systems, Energies, Vol: 15, ISSN: 1996-1073
With increasing attention to climate change, the penetration level of renewable energy sources (RES) in the electricity network is increasing. Due to the intermittency of RES, gas-fired power plants could play a significant role in backing up the RES in order to maintain the supply–demand balance. As a result, the interaction between gas and power networks are significantly increasing. On the other hand, due to the increase in peak demand (e.g., electrification of heat), network operators are willing to execute demand response programs (DRPs) to improve congestion management and reduce costs. In this context, modeling and optimal implementation of DRPs in proportion to the demand is one of the main issues for gas and power network operators. In this paper, an emergency demand response program (EDRP) is implemented locally to reduce the congestion of transmission lines and gas pipelines more efficiently. Additionally, the effects of optimal implementation of local emergency demand response program (LEDRP) in gas and power networks using linear and non-linear economic models (power, exponential and logarithmic) for EDRP in terms of cost and line congestion and risk of unserved demand are investigated. The most reliable demand response model is the approach that has the least difference between the estimated demand and the actual demand. Furthermore, the role of the LEDRP in the case of hydrogen injection instead of natural gas in the gas infrastructure is investigated. The optimal incentives for each bus or node are determined based on the power transfer distribution factor, gas transfer distribution factor, available electricity or gas transmission capability, and combination of unit commitment with the LEDRP in the integrated operation of these networks. According to the results, implementing the LEDRP in gas and power networks reduces the total operation cost up to 11% and could facilitate hydrogen injection to the network. The proposed hybrid model is implem
Wang Y, Qiu D, Strbac G, 2022, Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems, APPLIED ENERGY, Vol: 310, ISSN: 0306-2619
Ademovic Tahirovic A, Angeli D, Strbac G, 2022, Heterogeneous network flow and Petri nets characterize multilayer complex networks, Scientific Reports, Vol: 12, ISSN: 2045-2322
Interacting subsystems are commonly described by networks, where multimodal behaviour found in most natural or engineered systems found recent extension in form of multilayer networks. Since multimodal interaction is often not dictated by network topology alone and may manifest in form of cross-layer information exchange, multilayer network flow becomes of relevant further interest. Rationale can be found in most interacting subsystems, where a form of multimodal flow across layers can be observed in e.g., chemical processes, energy networks, logistics, finance, or any other form of conversion process relying on the laws of conservation. To this end, the formal notion of heterogeneous network flow is proposed, as a multilayer flow function aligned with the theory of network flow. Furthermore, dynamic equivalence is established with the framework of Petri nets, as the baseline model of concurrent event systems. Application of the resulting multilayer Laplacian flow and flow centrality is presented, along with graph learning based inference of multilayer relationships over multimodal data. On synthetic data the proposed framework demonstrates benefits of multimodal flow derivation in critical component identification. It also displays applicability in relationship inference (learning based function approximation) on multimodal time series. On real-world data the proposed framework provides, among others, multimodal flow interpretation of U.S. economic activity, uncovering underlying empirical steady state probability distribution, as well as inherent network (economic) robustness.
Qiu D, Dong Z, Zhang X, et al., 2022, Safe reinforcement learning for real-time automatic control in a smart energy-hub, APPLIED ENERGY, Vol: 309, ISSN: 0306-2619
Hua W, Jiang J, Sun H, et al., 2022, Consumer-centric decarbonization framework using Stackelberg game and Blockchain, APPLIED ENERGY, Vol: 309, ISSN: 0306-2619
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
The electrification of transport seems inevitable as part of global decarbonization efforts, but power system integration of electric vehicles faces numerous challenges, including a disproportionately high demand peak necessitating expensive infrastructure investments. Moreover, long-term developments in the power sector are characterized by great uncertainty, which increases the risk of making incorrect investment decisions leading to stranded assets. A cost-effective system integration of electrified transport would therefore not be possible without the implementation of smart charging concepts in combination with strategic network expansion planning that considers the impact of uncertainties. This paper proposes investment and operation models of Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and Vehicle-to-Building (V2B) for the large-scale and long-term network expansion planning problem under multi-dimensional uncertainty. Additionally, it presents a multi-stage stochastic planning framework that can identify optimal investment strategies such that the expected system cost is minimized and the risk of stranded investments is reduced. The models are demonstrated on the IEEE 24-bus test system and applied in a case study of the power system of Great Britain. The results highlight G2V, V2G and V2B as effective non-network alternatives to conventional reinforcement that could generate substantial economic savings and act as hedging instruments against uncertainty. For the case of Great Britain, the Option Values of G2V, V2G, and V2B could amount to £1.2bn, £10.8bn, and £10.1bn, respectively, over a 40-year horizon. Although the quantified values are system-specific, the paper presents key observations on the role of smart charging concepts as investment options that can be generalized for any low-carbon power system.
Moreno R, Trakas DN, Jamieson M, et al., 2022, Microgrids Against Wildfires: Distributed Energy Resources Enhance System Resilience, IEEE POWER & ENERGY MAGAZINE, Vol: 20, Pages: 78-89, ISSN: 1540-7977
Pang Q, De Paola A, Strbac G, et al., 2022, The impact of variable wind and demand levels on the flexible operation of interconnectors in Great Britain
This paper analyses the potential benefits for flexible operation of interconnectors in cross-border power systems. The underlying modelling approach enables the simultaneous cross-border exchange of power and inertia-dependent frequency response services through the interconnector. Two main operational paradigms are adopted. The first considers the interconnectors as centrally-operated assets; the latter envisages them as profit-seeking private entities. Furthermore, the proposed work adopts a sampling-based method to assess the impact of wind and demand variability on the short-term operation of the interconnectors, evaluating the sensitivities of relevant operational and economical metrics with respect to wind availability and demand levels. A comprehensive set of case studies is developed considering the Great Britain (GB)-France interconnected systems. The studies show significant value for interconnectors under a wide range of system conditions.
Amiri MM, Ameli H, Ameli MT, et al., 2022, Investigating the effective methods in improving the resilience of electricity and gas systems, Whole Energy Systems: Bridging the Gap via Vector-Coupling Technologies, Publisher: Springer, Cham, ISBN: 978-3-030-87652-4
Consumption of natural gas and the share of renewable energy in meeting global energy demand have grown significantly. Consequently, gas and electrical grids are becoming more integrated with fast responding gas-fired power stations, providing the primary backup source for renewable electricity in maintaining supply-demand balance. For an engineering system (e.g., gas and electricity systems infrastructure), many definitions of similar essence have been proposed, focusing on the ability to deal with disruptions. Taking the importance of actions prior, during, and afterward of an adverse event in mind, resilience is defined as a system’s ability to anticipate, resist, absorb, respond to, adapt to, and recover from a disturbance. Hence, in this chapter the importance of resiliency in the electricity and gas network’s cooperation is demonstrated, and different strategies and methods to increase resiliency are investigated.
Falugi P, O’Dwyer E, Zagorowska MA, et al., 2022, MPC and optimal design of residential buildings with seasonal storage: a case study, Active Building Energy Systems, Editors: Doyle, Publisher: Springer International Publishing, Pages: 129-160, ISBN: 9783030797416
Residential buildings account for about a quarter of the global energy use. As such, residential buildings can play a vital role in achieving net-zero carbon emissions through efficient use of energy and balance of intermittent renewable generation. This chapter presents a co-design framework for simultaneous optimisation of the design and operation of residential buildings using Model Predictive Control (MPC). The adopted optimality criterion maximises cost savings under time-varying electricity prices. By formulating the co-design problem using model predictive control, we then show a way to exploit the use of seasonal storage elements operating on a yearly timescale. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating on multiple timescales. In particular, numerical results from a low-fidelity model report approximately doubled bill savings and carbon emission reduction compared to the a priori sizing approach.
Badesa L, Matamala C, Zhou Y, et al., 2022, Assigning Shadow Prices to Synthetic Inertia and Frequency Response Reserves From Renewable Energy Sources, IEEE Transactions on Sustainable Energy, Pages: 1-15, ISSN: 1949-3029
Wang J, Pinson P, Chatzivasileiadis S, et al., 2022, On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems, IEEE Transactions on Sustainable Energy, Pages: 1-15, ISSN: 1949-3029
Ademovic Tahirovic A, Angeli D, Strbac G, 2021, A complex network approach to power system vulnerability analysis based on rebalance based flow centrality, 2021 IEEE PES General Meeting, Publisher: IEEE
The study of networks is an extensively investigated field of research, with networks and network structure often encoding relationships describing certain systems or processes. Critical infrastructure is understood as being a structure whose failure or damage has considerable impact on safety, security and wellbeing of society, with power systems considered a classic example. The work presented in this paper builds on the long-lasting foundations of network and complex network theory, proposing an extension in form of rebalance based flow centrality for structural vulnerability assessment and critical component identification in adaptive network topologies. The proposed measure is applied to power system vulnerability analysis, with performance demonstrated on the IEEE 30-, 57-and 118-bus test system, out performing relevant methods from the state-of-the-art. The proposed framework is deterministic (guaranteed), analytically obtained (interpretable) and generalizes well with changing network parameters, providing a complementary tool to power system vulnerability analysis and planning.
Bugaje AAB, Cremer JL, Sun M, et al., 2021, Selecting decision trees for power system security assessment, Energy and AI, Vol: 6
Power systems transport an increasing amount of electricity, and in the future, involve more distributed renewables and dynamic interactions of the equipment. The system response to disturbances must be secure and predictable to avoid power blackouts. The system response can be simulated in the time domain. However, this dynamic security assessment (DSA) is not computationally tractable in real-time. Particularly promising is to train decision trees (DTs) from machine learning as interpretable classifiers to predict whether the system-wide responses to disturbances are secure. In most research, selecting the best DT model focuses on predictive accuracy. However, it is insufficient to focus solely on predictive accuracy. Missed alarms and false alarms have drastically different costs, and as security assessment is a critical task, interpretability is crucial for operators. In this work, the multiple objectives of interpretability, varying costs, and accuracies are considered for DT model selection. We propose a rigorous workflow to select the best classifier. In addition, we present two graphical approaches for visual inspection to illustrate the selection sensitivity to probability and impacts of disturbances. We propose cost curves to inspect selection combining all three objectives for the first time. Case studies on the IEEE 68 bus system and the French system show that the proposed approach allows for better DT-selections, with an 80% increase in interpretability, 5% reduction in expected operating cost, while making almost zero accuracy compromises. The proposed approach scales well with larger systems and can be used for models beyond DTs. Hence, this work provides insights into criteria for model selection in a promising application for methods from artificial intelligence (AI).
Dong Z, Angeli D, De Paola A, et al., 2021, An iterative algorithm for regret minimization in flexible demand scheduling problems, Advanced Control for Applications: Engineering and Industrial Systems, Vol: 3
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.
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