Publications
541 results found
Ameli MT, Ameli H, Strbac G, et al., 2021, Reliability and resiliency assessment in integrated gas and electricity systems in the presence of energy storage systems, Publisher: Academic Press
Strbac G, Papadaskalopoulos D, Chrysanthopoulos N, et al., 2021, <i>Decarbonization of Electricity Systems in Europe</i> Market Design Challenges, IEEE POWER & ENERGY MAGAZINE, Vol: 19, Pages: 53-63, ISSN: 1540-7977
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- Citations: 27
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
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- Citations: 6
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
Integrating decarbonisation strategies for road transport and electricity is vital to minimise the overall cost of meeting the carbon target. This integration maximises the synergy across different energy sectors to improve the value and utilisation of investment, especially in low-carbon technologies across all sectors. This study presents an integrated multi-energy optimisation model to evaluate the economic performance and system implications of different road-transport decarbonisation strategies and analyse the synergy with the power sector decarbonisation. The large-scale optimisation model is formulated to consider the interactions across electricity, hydrogen, and transport sectors and used to determine the optimal solutions for investment and sector-coupling operation in the system. The proposed model is tested using a range of transport decarbonisation scenarios considering the deployment of electric or hydrogen vehicles or their combination and the integration with the power system. The studies analyse the economic performance and optimal energy system portfolios across different scenarios. The results demonstrate the importance of road-transport and power-to-gas integration in Great Britain's future energy system.
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
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- Citations: 86
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
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- Citations: 2
Tindemans S, Strbac G, 2020, Accelerating system adequacy assessment using the multilevel Monte Carlo approach, ELECTRIC POWER SYSTEMS RESEARCH, Vol: 189, ISSN: 0378-7796
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- Citations: 5
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
A reduced level of system inertia due to renewable integration increases the need for cost-effective provision of ancillary services, such as Frequency Response (FR). In this paper a closed-form solution to the differential equation describing frequency dynamics is proposed, which allows to obtain frequency-security algebraic constraints to be implemented in optimization routines. This is done while considering any finite number of FR services with distinguished characteristics, such as different delivery times and activation delays. The problem defined by these frequency-security constraints can be formulated as a Mixed-Integer Second-Order Cone Program (MISOCP), which can be efficiently handled by off-the-shelf conic optimization solvers. This paper also takes into account the uncertainty in inertia contribution from the demand side by formulating the frequency-security conditions as chance constraints, for which an exact convex reformulation is provided. Finally, case studies highlighting the effectiveness of this frequency-secured formulation are presented.
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.
Strbac G, Pudjianto D, Aunedi M, et al., 2020, Role and value of flexibility in facilitating cost-effective energy system decarbonisation, PROGRESS IN ENERGY, Vol: 2
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- Citations: 15
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).
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
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- Citations: 1
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.
Rostami AM, Ameli H, Ameli MT, et al., 2020, Information-Gap Decision Theory for Robust Operation of Integrated Electricity and Natural Gas Transmission Networks, 2020 International Conference on Smart Energy Systems and Technologies (SEST)
Natural gas consumption and the share ofrenewable energy in meeting global energy demand has growndramatically in the recent years. On the other hand, the rapidgrowth of gas-fired generating units (GFU) (i.e., producing lowercarbon dioxide emissions compared to coal-fired generating units),could play a key role in more integration of renewable energysources (RESs) into the system due to their high flexibility.Therefore, the interaction between the electricity and natural gasnetworks (ENGN) becomes more challenging. This paper proposesa robust multi objective integrated mixed integer nonlinearoptimization model, utilizing information-gap decision theory(IGDT), for secure and optimal operation of ENGN consideringsecurity constraints as well as gas and electricity load demanduncertainties. This bi-objective optimization problem is modifiedusing normalization in the weighted sum method in order toensuring the consistency of the optimal solutions. The proposedframework is validated on the modified IEEE 24-bus powersystem with a 15-node natural gas system.
Ameli H, qadrdan M, Strbac G, 2020, Coordinated operation of gas and electricity systems for flexibility study, Frontiers in Energy Research, Vol: 8, ISSN: 2296-598X
The increase interdependencies between electricity and gas systems, driven by gas-fired power plants and gas electricity-driven compressors, necessitates detailed investigation of such interdependencies, especially in the context of increased share of renewable energy sources.6 In this paper, the value of an integrated approach for operating gas and electricity systems is assessed. An outer approximation with equality relaxation (OA/ER) method is used to deal with the optimization class of mixed integer non-linear problem of integrated operation of gas and electricity systems. This method significantly improved the efficiency of the solution algorithm and achieved nearly 40% reduction in computation time compared to successive linear programming. The value of flexibility technologies including flexible gas compressors, demand side response, battery storage, and power-to-gas is quantified in the operation of integrated gas and electricity systems in GB 2030 energy scenarios for different renewable generation penetration levels. The modeling demonstrates that the flexibility options will enable significant cost savings in the annual operational costs of gas and electricity systems (up to 21%). On the other hand, the analysis carried out indicates that deployment of flexibility technologies support appropriately the interaction between gas and electricity systems.
Oderinwale T, Papadaskalopoulos D, Ye Y, et al., 2020, Investigating the impact of flexible demand on market-based generation investment planning, International Journal of Electrical Power & Energy Systems, Vol: 119, Pages: 105881-105881, ISSN: 0142-0615
Demand flexibility has attracted significant interest given its potential to address techno-economic challenges associated with the decarbonisation of electricity systems. However, previous work has investigated its long-term impacts through centralized generation planning models which do not reflect the current deregulated environment. At the same time, existing market-based generation planning models are inherently unable to capture the demand flexibility potential since they neglect time-coupling effects and system reserve requirements in their representation of the electricity market. This paper investigates the long-term impacts of demand flexibility in the deregulated environment, by proposing a time-coupling, bi-level optimization model of a self-interested generation company’s investment planning problem, which captures for the first time the energy shifting flexibility of the demand side and the operation of reserve markets with demand side participation. Case studies investigate different cases regarding the flexibility of the demand side and different market design options regarding the allocation of reserve payments. The obtained results demonstrate that, in contrast with previous centralised planning models, the proposed model can capture the dependency of generation investment decisions and the related impacts of demand flexibility on the electricity market design and the subsequent strategic response of the self-interested generation company.
Guo J, Badesa Bernardo L, Teng F, et al., 2020, Value of point-of-load voltage control for enhanced frequency response in future GB power system, IEEE Transactions on Smart Grid, Vol: 11, Pages: 4938-4948, ISSN: 1949-3053
The need for Enhanced Frequency Response (EFR)is expected to increase significantly in future low-carbon GreatBritain (GB) power system. One way to provide EFR is touse power electronic compensators (PECs) for point-of-loadvoltage control (PVC) to exploit the voltage dependence of loads.This paper investigates the techno-economic feasibility of suchtechnology in future GB power system by quantifying the totalEFR obtainable through deploying PVC in the urban domesticsector, the investment cost of the installment and the economicand environmental benefits of using PVC. The quantificationis based on a stochastic domestic demand model and genericmedium and low-voltage distribution networks for the urbanareas of GB and a stochastic unit commitment (SUC) modelwith constraints for secure post-fault frequency evolution is usedfor the value assessment. Two future energy scenarios in thebackdrop of 2030 with ‘smart’ and ‘non-smart’ control of electricvehicles and heat pumps, under different levels of penetration ofbattery energy storage system (BESS) are considered to assessthe value of PEC, as well as the associated payback period. Itis demonstrated that PVC could effectively complement BESStowards EFR provision in future GB power system.
Shen F, Wu Q, Xu Y, et al., 2020, Hierarchical service restoration scheme for active distribution networks based on ADMM, International Journal of Electrical Power & Energy Systems, Vol: 118, Pages: 1-10, ISSN: 0142-0615
Effective self-healing schemes enhance the resilience of active distribution networks (ADNs). As a critical part of self-healing, service restoration aims to restore outage areas with minimal un-supplied demands. With the increasing complexity and size of ADNs, distribution system operators (DSOs) face a more complicated service restoration problem. Thus, it is important to obtain optimal service restoration plans and reduce computational complexity. To achieve this goal, a hierarchical service restoration scheme is proposed to obtain service restoration plans based on the alternating direction method of multipliers (ADMM). The optimal service restoration problem is formulated as a mixed-integer linear programming (MILP) model considering the switching sequence, distributed generation (DG) units and controllable loads, and is solved using the ADMM-based algorithm in a hierarchical manner. In the proposed scheme, each zone of the ADN has a local service restoration controller solving its sub-problem with information from a central service restoration controller. The central controller solves a global coordination problem with information from all the zones. Three case studies were conducted with the 44-node test system, modified IEEE 123-node system and Brazil 948-node system. The results show that the proposed hierarchical service restoration can obtain optimal service restoration plans and reduce computational complexity. Moreover, computation time can be reduced substantially by using the proposed hierarchical scheme for large-scale ADNs.
Ameli H, Qadrdan M, Strbac G, et al., 2020, Investing in flexibility in an integrated planning of natural gas and power systems, IET Energy Systems Integration, Vol: 2, Pages: 101-111, ISSN: 2516-8401
The growing interdependencies between natural gas and power systems, driven by gas-fired generators and gas compressors supplied by electricity, necessitates detailed investigation of the interactions between these vectors, particularly in the context of growing penetration of renewable energy sources. In this research, an expansion planning model for integrated natural gas and power systems is proposed. The model investigates optimal investment in flexibility options such as battery storage, demand side response, and gas-fired generators. The value of these flexibility options is quantified for gas and electricity systems in GB in 2030. The results indicate that the flexibility options could play an important role in meeting the emission targets in the future. However, the investment costs of these options highly impact the future generation mix as well as the type of reinforcements in the natural gas system infrastructure. Through deployment of the flexibility options up to £24.2b annual cost savings in planning and operation of natural gas and power systems could be achieved, compared to the case that no flexibility option is considered.
Li J, Ye Y, Strbac G, 2020, Stabilizing Peer-to-Peer Energy Trading in Prosumer Coalition Through Computational Efficient Pricing, 21st Power Systems Computation Conference
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.
Moreno R, Bezerra B, Rudnick H, et al., 2020, Distribution Network Rate Making in Latin America, IEEE POWER & ENERGY MAGAZINE, Vol: 18, Pages: 33-48, ISSN: 1540-7977
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- Citations: 4
Fu P, Pudjianto D, Zhang X, et al., 2020, Integration of hydrogen into multi-energy systems optimisation, Energies, Vol: 13, Pages: 1606-1606, ISSN: 1996-1073
Hydrogen presents an attractive option to decarbonise the present energy system. Hydrogen can extend the usage of the existing gas infrastructure with low-cost energy storability and flexibility. Excess electricity generated by renewables can be converted into hydrogen. In this paper, a novel multi-energy systems optimisation model was proposed to maximise investment and operating synergy in the electricity, heating, and transport sectors, considering the integration of a hydrogen system to minimise the overall costs. The model considers two hydrogen production processes: (i) gas-to-gas (G2G) with carbon capture and storage (CCS), and (ii) power-to-gas (P2G). The proposed model was applied in a future Great Britain (GB) system. Through a comparison with the system without hydrogen, the results showed that the G2G process could reduce £3.9 bn/year, and that the P2G process could bring £2.1 bn/year in cost-savings under a 30 Mt carbon target. The results also demonstrate the system implications of the two hydrogen production processes on the investment and operation of other energy sectors. The G2G process can reduce the total power generation capacity from 71 GW to 53 GW, and the P2G process can promote the integration of wind power from 83 GW to 130 GW under a 30 Mt carbon target. The results also demonstrate the changes in the heating strategies driven by the different hydrogen production processes.
Jamieson MR, Strbac G, Bell KRW, 2020, Quantification and visualisation of extreme wind effects on transmission network outage probability and wind generation output, IET SMART GRID, Vol: 3, Pages: 112-122
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- Citations: 7
Qiu D, Ye Y, Papadaskalopoulos D, et al., 2020, A Deep Reinforcement Learning Method for Pricing Electric Vehicles with Discrete Charging Levels, IEEE Transactions on Industry Applications, ISSN: 0093-9994
The effective pricing of electric vehicles (EV) charging by aggregators constitutes a key problem towards the realization of the significant EV flexibility potential in deregulated electricity systems, and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging / discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and / or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This paper proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy, and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements, and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners.
Ye Y, Qiu D, Sun M, et al., 2020, Deep reinforcement learning for strategic bidding in electricity markets, IEEE Transactions on Smart Grid, Vol: 11, Pages: 1343-1355, ISSN: 1949-3053
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants' physical non-convex operating characteristics, while conventional RL methods require discretization of state and / or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy.
Huyghues-Beaufond N, Tindemans S, Falugi P, et al., 2020, Robust and automatic data cleansing method for short-term load forecasting of distribution feeders, Applied Energy, Vol: 261, Pages: 1-17, ISSN: 0306-2619
Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.
Heylen E, Papadaskalopoulos D, Konstantelos I, et al., 2020, Dynamic modelling of consumers’ inconvenience associated with demand flexibility potentials, Sustainable Energy, Grids and Networks, Vol: 21, Pages: 1-13, ISSN: 2352-4677
Demand flexibility, involving the potential to reduce or temporally defer electricity demand, is regarded as a key enabler for transitioning to a secure, cost-efficient and low-carbon energy future. However, previous work has not comprehensively modelled the inconvenience experienced by end-consumers due to demand modifications, since it has focused on static modelling approaches. This paper presents a novel model of inconvenience cost that simultaneously accounts for differentiated preferences of consumer groups, time and duration of interruptions, differentiated valuation of different units of power and temporal redistribution of shiftable loads. This model is dynamic and future-agnostic, implying that it captures the time-coupling characteristics of consumers’ flexibility and the temporal evolution of interruptions, without resorting to the unrealistic assumption that time and duration of interruptions are foreknown. The model is quantitatively informed by publicly available surveys combined with realistic assumptions and suitable sensitivity analyses regarding aspects excluded from existing surveys. In the examined case studies, the developed model is applied to manage an aggregator’s portfolio in a scenario involving emergence of an adequacy issue in the Belgian system. The results illustrate how considering each of the above factors affects demand management decisions and the inconvenience cost, revealing the value of the developed model.
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