426 results found
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
The increasing penetration of small-scale distributed energy resources (DER) has the potential to support cost-efficient energy balancing in emerging electricity systems, but is also fundamentally affecting the conventional operation paradigm of the latter. In this context, innovative market mechanisms need to be devised to better coordinate and provide incentives for DER to utilize their flexibility. Peer-to-Peer (P2P) energy trading has emerged as an alternative approach to facilitate direct trading between consumers and prosumers interacting in an energy collective and fosters more efficient local demand–supply balancing. While previous research has primarily focused on the technical and economic benefits of P2P trading, little effort has been made towards the incorporation of prosumers’ heterogeneous characteristics in the P2P trading problem. Here, we address this research gap by classifying the participating prosumers into multiple clusters with regard to their portfolio of DER, and analyzing their trading decisions in a simulated P2P trading platform. The latter employs the mid-market rate (MMR) local pricing mechanism to enable energy trading among prosumers and penalizes the contribution to the system demand peak of each prosumer. We formulate the P2P trading problem as a multi-agent coordination problem and propose a novel multi-agent deep reinforcement learning (MADRL) method to address it. The proposed method is founded on the combination of the multi-agent deep deterministic policy gradient (MADDPG) algorithm and the technique of parameter sharing (PS), which not only enables accelerating the training speed by sharing experiences and learned policies between all agents in each cluster, but also sustains the policies’ diversity between multiple clusters. To address the non-stationarity and computational complexity of MADRL as well as persevering the privacy of prosumers, the P2P trading platform acts as a trusted third party which augme
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 and Networks, Vol: 26
High-impact and low-probability (HILP) events can cause severe damage to power systems. Networked microgrids (MGs) with distributed generation resources provide a viable solution for the resilience enhancement of power systems. However, most literature utilizes centralized control methods based on energy management systems to model networked MGs and employs static storage units to enhance resilience, which are both unrealistic. In this paper, a hierarchical control approach based on detailed AC OPF algorithm is developed to capture technical constraints relating to voltage, angle and power loss as well as obtaining accurate solutions of power exchange between MGs, while the routing of electric vehicles (EVs) is incorporated into the model to reduce load shedding during extreme events. Uncertainties relating to load profiles and renewable energy sources are captured via stochastic programming. The impacts of limited generation resources and different levels of contingencies (including multiple line faults) are captured in the model to mimic a realistic scenario and verify the effectiveness of the proposed resilience enhancement strategy. Appropriate sensitivity analyses are suggested to investigate the influence of uncertain event occurrence time, tie-line capacity and EV scheduling horizon.
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.
Rousis AO, 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%.
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
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, ISSN: 0885-8950
Multi-energy systems (MESs) make it possible to satisfy consumer's energy demand using multiple coupled energy infrastructures, thus increasing the reliability of the energy supply compared to separate energy systems (SESs). To accurately and efficiently assess and improve the reliability of MESs, this paper proposes a MES reliability and vulnerability assessment method using energy hub (EH) model. The energy conversion, transmission and storage in MESs are compactly and linearly described by EH model, making reliability and vulnerability assessment of MESs tractable. Indices for MES vulnerability assessment are proposed to find the key components for improving MES reliability. Multi-parametric linear programming (MPLP) with a self-adaptive critical region set is proposed to reduce the computational burden caused by iteratively solving LP problems for a large number of samples during the assessment process. The results of a case study show that the proposed reliability and vulnerability assessment method is able to effectively evaluate the energy supply reliability of different energy sectors in MES as well as find the critical component of an MES from reliability perspective to support its planning. The proposed algorithm, i.e., MPLP with a self-adaptive critical region set, can improve the computational efficiency by an order of magnitude compared to the traditional LP method.
Zhang T, Sun M, Cremer JL, et al., 2021, A Confidence-Aware Machine Learning Framework for Dynamic Security Assessment, IEEE Transactions on Power Systems, ISSN: 0885-8950
Dynamic Security Assessment for the future power system is expected to be increasingly complicated with the higher level penetration of renewable energy sources and the widespread deployment of power electronic devices, which drive new dynamic phenomena. As a result, the increasing complexity and the severe computational bottleneck in real time operation encourages researchers to exploit machine learning to extract offline security rules for the online assessment. However, traditional machine learning methods lack in providing information on the confidence of their corresponding predictions. Understanding better the confidence of the prediction made is of key importance for Transmission System Operators (TSOs) to use and rely on these machine learning methods. Specifically, from the perspective of topological changes, it is often unclear whether the machine learning model can still be used. Hence, being aware of the confidence of the prediction supports the transition to use machine learning in real time operation. In this paper, we propose a novel Conditional Bayesian Deep Auto-encoder (CBDAC) based security assessment framework to compute a confidence metric of the prediction. This informs not only the operator to judge whether the prediction can be trusted, but it also allows for judging whether the model needs updating.
Strbac G, Papadaskalopoulos D, Chrysanthopoulos N, et al., 2021, Decarbonization of Electricity Systems in Europe Market Design Challenges, IEEE POWER & ENERGY MAGAZINE, Vol: 19, Pages: 53-63, ISSN: 1540-7977
Badesa L, Teng F, Strbac G, 2021, Conditions for Regional Frequency Stability in Power System SchedulingPart I: Theory, IEEE Transactions on Power Systems, 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 SchedulingPart II: Application to Unit Commitment, IEEE Transactions on Power Systems, 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.
Tindemans SH, Strbac G, 2021, Low-Complexity Decentralized Algorithm for Aggregate Load Control of Thermostatic Loads, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, Vol: 57, Pages: 987-998, ISSN: 0093-9994
Fu P, Pudjianto D, Strbac G, 2020, Integration of power-to-gas and low-carbon road transport in Great Britain's future energy system, IET RENEWABLE POWER GENERATION, Vol: 14, Pages: 3393-3400, ISSN: 1752-1416
Wang Y, Rousis AO, Strbac G, 2020, On microgrids and resilience: A comprehensive review on modeling and operational strategies, RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 134, ISSN: 1364-0321
Tindemans S, Strbac G, 2020, Accelerating system adequacy assessment using the multilevel Monte Carlo approach, ELECTRIC POWER SYSTEMS RESEARCH, Vol: 189, ISSN: 0378-7796
Trovato V, De Paola A, Strbac G, 2020, Distributed Control of Clustered Populations of Thermostatic Loads in Multi-Area Systems: A Mean Field Game Approach, ENERGIES, Vol: 13
Few S, Djapic P, Strbac G, et al., 2020, Assessing local costs and impacts of distributed solar PV using high resolution data from across Great Britain, Renewable Energy, Vol: 162, Pages: 1140-1150, ISSN: 0960-1481
Highly spatially resolved data from across Great Britain (GB) are combined with a distribution network modelling tool to assess impacts of distributed photovoltaic (PV) deployment up to 2050 on local networks, the costs of avoiding these impacts, and how these depend upon context. Present-day deployment of distributed PV, meter density, and network infrastructure across GB are found to be highly dependent on rurality, and data on these are used to build up three representative contexts: cities, towns, and villages. For each context, distribution networks are simulated, and impacts on these networks associated with PV deployment and growth in peak load up to 2050 calculated. Present-day higher levels of PV deployment in rural areas are maintained in future scenarios, necessitating upgrades in ambitious PV scenarios in towns and villages from around 2040, but not before 2050 in cities. Impacts of load growth are more severe than those of PV deployment, potentially necessitating upgrades in cities, towns, and villages from 2030. These are most extensive in cities and towns, where long feeders connect more customers, making networks particularly susceptible to impacts. Storage and demand side response are effective in reducing upgrade costs, particularly in cities and towns.
Azizipanah-Abarghooee R, Terzija V, Malekpour M, et al., 2020, Guest Editorial: Challenges and New Solutions for Enhancing Ancillary Services and Grid Resiliency in Low Inertia Power Systems, IET GENERATION TRANSMISSION & DISTRIBUTION, Vol: 14, Pages: 4975-4977, ISSN: 1751-8687
Shahbazbegian V, Ameli H, Ameli M, et al., 2020, Stochastic optimization model for coordinated operation of natural gas and electricity networks, Computers and Chemical Engineering, Vol: 142, Pages: 1-18, ISSN: 0098-1354
Renewable energy sources will anticipate significantly in the future energy system paradigm due to their low cost of operation and low pollution. Considering the renewable generation (e.g., wind) intermittency, flexible gas-fired power plants will continue to play their essential role as the main linkage of natural gas and electricity networks, and hence coordinated operation of these networks is beneficial. Furthermore, uncertainty is always found in gas demand prediction, electricity demand prediction, and output power of wind generation. Therefore, in this paper, a two-stage stochastic model for operation of natural gas and electricity networks is implemented. In order to model uncertainty in these networks, Monte Carlo simulation is applied to generate scenarios representing the uncertain parameters. Afterwards, a scenario reduction algorithm based on distances between the scenarios is applied. Stochastic and deterministic models for natural gas and electricity networks are optimized and compared considering integrated and iterative operation strategies. Furthermore, the value of flexibility options (i.e., electricity storage systems) in dealing with uncertainty is quantified. A case study is presented based on a high pressure 15-node gas system and the IEEE 24-bus reliability test system to validate the applicability of the proposed approach. The results demonstrate that applying the stochastic model of gas and electricity networks as well as considering integrated operation strategy in the presence of flexibility provides different benefits (e.g., 14% cost savings) and enhances the system reliability in the case of contingency.
Badesa L, Teng F, Strbac G, 2020, Optimal Portfolio of Distinct Frequency Response Services in Low-Inertia Systems, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol: 35, Pages: 4459-4469, ISSN: 0885-8950
Aunedi M, Pantaleo AM, Kuriyan K, et al., 2020, Modelling of national and local interactions between heat and electricity networks in low-carbon energy systems, Applied Energy, Vol: 276, Pages: 1-18, ISSN: 0306-2619
Decarbonisation of the heating and cooling sector is critical for achieving long-term energy and climate change objectives. Closer integration between heating/cooling and electricity systems can provide additional flexibility required to support the integration of variable renewables and other low-carbon energy sources. This paper proposes a framework for identifying cost-efficient solutions for supplying district heating systems within both operation and investment timescales, while considering local and national-level interactions between heat and electricity infrastructures. The proposed optimisation model minimises the levelised cost of a portfolio of heating technologies, and in particular Combined Heat and Power (CHP) and polygeneration systems, centralised heat pumps (HPs), centralised boilers and thermal energy storage (TES). A number of illustrative case studies are presented, quantifying the impact of renewable penetration, electricity price volatility, local grid constraints and local emission targets on optimal planning and operation of heat production assets. The sensitivity analysis demonstrates that the cost-optimal TES capacity could increase by 41–134% in order to manage a constraint in the local electricity grid, while in systems with higher RES penetration reflected in higher electricity price volatility it may be optimal to increase the TES capacity by 50–66% compared to constant prices, allowing centralised electric HP technologies to divert excess electricity produced by intermittent renewable generators to the heating sector. This confirms the importance of reflecting the whole-system value of heating technologies in the underlying cost-benefit analysis of heat networks.
Narbondo L, Falugi P, Strbac G, 2020, Application of energy storage in systems with high penetration of intermittent renewables
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.
Rostami AM, Ameli H, Ameli MT, et al., 2020, Secure operation of integrated natural gas and electricity transmission networks, Energies, Vol: 13, ISSN: 1996-1073
The interaction between natural gas and electricity networks is becoming more significant due to the projected large penetration of renewables into the energy system to meet the emission targets. This is due to the role of gas-fired plants in providing backup to renewables as the linkage between these networks. Therefore, this paper proposes a deterministic coordinated model for the secure and optimal operation of integrated natural gas and electricity transmission networks by taking into account the N-1 contingency analysis on both networks. In order to reduce the computational burden and time, an iterative algorithm is proposed to select the critical cases and neglect other contingencies, which do not have a significant impact on the energy system. The proposed integrated mixed-integer nonlinear programming operational model is evaluated and compared to another enhanced separated model on the IEEE 24-bus and 15-node gas test systems. The results emphasize the importance and effectiveness of the proposed framework (up to 6.7% operational costs savings are achieved).
Aunedi M, Strbac G, 2020, Whole-system Benefits of Vehicle-To-Grid Services from Electric Vehicle Fleets
This paper proposes a whole-system optimisation framework to assess the economic and environmental implications of supplying electricity to electric vehicle (EV) fleets across different charging scenarios, including unmanaged, smart and Vehicle-To-Grid (V2G) charging. Case studies carried out for the 2025 and 2030 UK power system scenarios suggest that the incremental cost of supplying fleet EVs can be reduced several times if vehicles follow a smart rather than an unmanaged charging regime. Implementing V2G solutions can deliver both net cost savings to the system as well as a reduction in system carbon emissions due to reduced requirements for electricity infrastructure capacity, improved integration of renewable energy and more efficient provision of frequency regulation services. Cost savings and carbon emission reduction per vehicle in the V2G case can reach up to £885 per EV per year and 243 gCO2 per km, respectively, with the greatest benefits observed in scenarios with high renewable penetration and low uptake of other flexible options.
O'Malley C, Aunedi M, Teng F, et al., 2020, Value of Fleet Vehicle to Grid in Providing Transmission System Operator Services
In this paper a new aggregated model for electric vehicle (EV) fleets is presented that considers their daily and weekly usage patterns. A frequency-constrained stochastic unit commitment model is employed to optimally schedule EV charging and discharging as well as the provision of frequency response (FR) in an electricity system, while respecting the vehicles' energy requirements and driving schedules. Through case studies we demonstrate that an EV with vehicle to grid (V2G) capability can reduce system costs in a future GB electricity grid by up to £12, 000 per year, and reduce CO2 emissions by 60 tonnes per year, mainly due to reduced curtailment of wind power. The paper also quantifies the changes in the benefits of fleet V2G resulting from variations in FR delivery time, the penetration of wind or the uptake of alternative flexibility providers. Finally, a battery degradation model dependent on an EV's state of charge is proposed and implemented in the stochastic scheduling problem. It enables significant degradation cost reductions of 16% with only a 0.4% reduction of an EV's system value.
Meenakumar P, Aunedi M, Strbac G, 2020, Optimal Business Case for Provision of Grid Services through EVs with V2G Capabilities
Vehicle-To-grid (V2G) technology facilitates bidirectional energy transfer to and from electric vehicles (EV), utilizing the EV batteries as distributed mobile storage assets and offering additional flexibility at the grid level. Nevertheless, to maximize the potential for providing flexibility through V2G services, it is likely that some form of EV asset aggregation will be required given the highly distributed nature of EV as small-scale flexible resources. In this context, the research presented in this paper aims to develop a linear optimization model to maximize the revenues obtained by a V2G EV aggregator through the provision of grid services in the UK electricity market. The paper also focuses on identifying the commercial value of V2G, based on the developed revenue maximization model, as function of various use cases such as commercial fleet vehicles or workplace charging, while also exploring the sensitivity of potential revenues with respect to various parameters associated with the driving profiles, including simulated usage patterns and modelling of various price parameters in the day-Ahead and real-Time electricity markets.
Lee W-J, Strbac G, Hu Z, et al., 2020, Special Issue on Advanced Approaches and Applications for Electric Vehicle Charging Demand Management, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, Vol: 56, Pages: 5682-5683, ISSN: 0093-9994
Li J, Ye Y, Strbac G, 2020, Stabilizing peer-to-peer energy trading in prosumer coalition through computational efficient pricing, Electric Power Systems Research, Vol: 189
Load balancing issues in distribution networks have emerged alongside the large-scale deployment of distributed renewable generation sources. In light of this challenge, peer-to-peer (P2P) energy trading constitutes a promising approach for delivering secure and economic supply-demand balance when faced with variable load and intermittent renewable generation through matching energy demand and supply locally. However, state-of-the-art mechanisms for governing P2P energy trading either fail to suitably incentivize prosumers to participate in P2P trading or suffer severely from the curse of dimensionality with their computational complexity increase exponentially with the number of prosumers. In this paper, a P2P energy trading mechanism based on cooperative game theory is proposed to establish a grand energy coalition of prosumers and a computationally efficient pricing algorithm is developed to suitably incentivize prosumers for their sustainable participation in the grand coalition. The performance of the proposed algorithm is demonstrated by comparing it to state-of-the-art mechanisms through numerous case studies in a real-world scenario. The superior computational performance of the proposed algorithm is also validated.
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