This page has only limited features, please log in for full access.
This paper presents a short-term electric load forecasting model based on deep autoencoder with localized stochastic sensitivity (D-LiSSA). D-LiSSA can learn informative hidden representations from unseen samples by minimizing the perturbed error (including the training error and stochastic sensitivity) from historical load data. Specifically, this general deep autoencoder network as a deep learning model improves prediction accuracy and reliability. Moreover, a nonlinear fully connected feedforward neural network as a regression layer is applied to forecast the short-term load, with the generalization capability of the proposed model using hidden representations learned by D-LiSSA. The performance of D-LiSSA is evaluated using real-world public electric load markets of France (FR), Germany (GR), Romania (RO), and Spain (ES) from ENTSO-E. Extensive experimental results and comparisons with the classical and state-of-the-art models show that D-LiSSA yields accurate load forecasting results and achieves desired reliable capability. For instance, with the French case, D-LiSSA yields the lowest mean absolute error, mean absolute percentage error, root mean squared error; providing up to 61.89%, 63.20%, and 56.40% forecasting accuracy improvements as compared to the benchmark model for forecasting hourly horizon, respectively.
Ting Wang; Chun Sing Lai; Wing W.Y. Ng; Keda Pan; Mingyang Zhang; Alfredo Vaccaro; Loi Lei Lai. Deep autoencoder with localized stochastic sensitivity for short-term load forecasting. International Journal of Electrical Power & Energy Systems 2021, 130, 106954 .
AMA StyleTing Wang, Chun Sing Lai, Wing W.Y. Ng, Keda Pan, Mingyang Zhang, Alfredo Vaccaro, Loi Lei Lai. Deep autoencoder with localized stochastic sensitivity for short-term load forecasting. International Journal of Electrical Power & Energy Systems. 2021; 130 ():106954.
Chicago/Turabian StyleTing Wang; Chun Sing Lai; Wing W.Y. Ng; Keda Pan; Mingyang Zhang; Alfredo Vaccaro; Loi Lei Lai. 2021. "Deep autoencoder with localized stochastic sensitivity for short-term load forecasting." International Journal of Electrical Power & Energy Systems 130, no. : 106954.
With an increasing penetration of solar photovoltaic (PV) resources in distribution networks, voltage regulation becomes an important issue. In addition, due to the growth in air conditioning load in summer days, overloading management draws researchers’ attentions as well. This paper proposes a two-level consensus-driven distributed control strategy to coordinate virtual energy storage systems (VESSs), i.e. residential households with air conditioners, to avoid the violation of voltage and loading which are regarded as part of the main power quality issues in future distribution network. In the lower level, the precise modelling of VESSs is firstly built, then VESSs are aggregated via aggregators for better participating in the control scheme. Once the violation occurs, a consensus-driven control scheme in the upper level will be initiated to eliminate the error. Required active power adjustment is shared among VESSs aggregators via sparse communication networks, without compromising end users’ thermal comfort. Changes in the dynamic communication network topology are investigated to demonstrate their impacts on system performance. Simulation results based on a practical system in New South Wales, Australia is used to demonstrate the proposed control scheme, which can effectively manage voltage and loading in a distribution network with scalability and robustness.
Dongxiao Wang; Chun Sing Lai; Xuecong Li; Runji Wu; Xiaodan Gao; Loi Lei Lai; Xueqing Wu; Yi Xu; Yonggang Wen; Alfredo Vaccaro. Smart coordination of virtual energy storage systems for distribution network management. International Journal of Electrical Power & Energy Systems 2021, 129, 106816 .
AMA StyleDongxiao Wang, Chun Sing Lai, Xuecong Li, Runji Wu, Xiaodan Gao, Loi Lei Lai, Xueqing Wu, Yi Xu, Yonggang Wen, Alfredo Vaccaro. Smart coordination of virtual energy storage systems for distribution network management. International Journal of Electrical Power & Energy Systems. 2021; 129 ():106816.
Chicago/Turabian StyleDongxiao Wang; Chun Sing Lai; Xuecong Li; Runji Wu; Xiaodan Gao; Loi Lei Lai; Xueqing Wu; Yi Xu; Yonggang Wen; Alfredo Vaccaro. 2021. "Smart coordination of virtual energy storage systems for distribution network management." International Journal of Electrical Power & Energy Systems 129, no. : 106816.
Prevention and mitigation of low probability, high impact events is becoming a priority for power system operators, as natural disasters are hitting critical infrastructures with increased frequency all over the world. Protecting power networks against these events means improving their resilience in planning, operation and restoration phases. This paper introduces a framework based on time-varying interval Markov Chains to assess system’s resilience to catastrophic events. After recognizing the difficulties in accurately defining transition probabilities, due to the presence of data uncertainty, this paper proposes a novel approach based on interval mathematics, which allows representing the elements of the transition matrices by intervals, and computing reliable enclosures of the transient state probabilities. The proposed framework is validated on a case study, which is based on the resilience analysis of a power system in the presence of multiple contemporary faults. The results show how the proposed framework can successfully enclose all the possible outcomes obtained through Monte Carlo simulation. The main advantages are the low computational burden and high scalability achieved.
Antonio Pepiciello; Alfredo Vaccaro; Loi Lei Lai. An Interval Mathematic-Based Methodology for Reliable Resilience Analysis of Power Systems in the Presence of Data Uncertainties. Energies 2020, 13, 6632 .
AMA StyleAntonio Pepiciello, Alfredo Vaccaro, Loi Lei Lai. An Interval Mathematic-Based Methodology for Reliable Resilience Analysis of Power Systems in the Presence of Data Uncertainties. Energies. 2020; 13 (24):6632.
Chicago/Turabian StyleAntonio Pepiciello; Alfredo Vaccaro; Loi Lei Lai. 2020. "An Interval Mathematic-Based Methodology for Reliable Resilience Analysis of Power Systems in the Presence of Data Uncertainties." Energies 13, no. 24: 6632.
The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances.
Fabrizio De Caro; Amedeo Andreotti; Rodolfo Araneo; Massimo Panella; Antonello Rosato; Alfredo Vaccaro; Domenico Villacci. A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data. Energies 2020, 13, 6579 .
AMA StyleFabrizio De Caro, Amedeo Andreotti, Rodolfo Araneo, Massimo Panella, Antonello Rosato, Alfredo Vaccaro, Domenico Villacci. A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data. Energies. 2020; 13 (24):6579.
Chicago/Turabian StyleFabrizio De Caro; Amedeo Andreotti; Rodolfo Araneo; Massimo Panella; Antonello Rosato; Alfredo Vaccaro; Domenico Villacci. 2020. "A Review of the Enabling Methodologies for Knowledge Discovery from Smart Grids Data." Energies 13, no. 24: 6579.
The Multiple Microgrids (MMGs) concept has been identified as a promising solution for the management of large-scale power grids in order to maximize the use of widespread renewable energies sources. However, its deployment in realistic operation scenarios is still an open issue due to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, multiple time-varying communication delays consistently appear during data acquisition and the transmission process and their effects must be considered in the control design phase. To this aim, this paper addresses the voltage regulation control problem for MMGs systems in the presence of time-varying communication delays. To solve this problem, we propose a novel hierarchical two-layer distributed control architecture that accounts for the presence of communication latencies in the information exchange. More specifically, the upper control layer aims at guaranteeing a proper and economical reactive power dispatch among MMGs, while the lower control layer aims at ensuring voltage regulation of all electrical buses within each MG to the desired voltage set-point. By leveraging a proper Driver Generator Nodes Selection Algorithm, we first provide the best choice of generator nodes which, considering the upper layer control goal, speeds up the voltage synchronization process of all the buses within each MG to the voltage set-point computed by the upper-control layer. Then, the lower control layer, on the basis of this desired voltage value, drives the reactive power capability of each smart device within each MG and compensates for possible voltage deviations. Simulation analysis is carried out on the realistic case study of an MMGs system consisting of two identical IEEE 14-bus test systems and the numerical results disclose the effectiveness of the proposed control strategy, as well as its robustness with respect to load fluctuations.
Amedeo Andreotti; Bianca Caiazzo; Alberto Petrillo; Stefania Santini; Alfredo Vaccaro. Hierarchical Two-Layer Distributed Control Architecture for Voltage Regulation in Multiple Microgrids in the Presence of Time-Varying Delays. Energies 2020, 13, 6507 .
AMA StyleAmedeo Andreotti, Bianca Caiazzo, Alberto Petrillo, Stefania Santini, Alfredo Vaccaro. Hierarchical Two-Layer Distributed Control Architecture for Voltage Regulation in Multiple Microgrids in the Presence of Time-Varying Delays. Energies. 2020; 13 (24):6507.
Chicago/Turabian StyleAmedeo Andreotti; Bianca Caiazzo; Alberto Petrillo; Stefania Santini; Alfredo Vaccaro. 2020. "Hierarchical Two-Layer Distributed Control Architecture for Voltage Regulation in Multiple Microgrids in the Presence of Time-Varying Delays." Energies 13, no. 24: 6507.
The massive penetration of renewable power generation in modern power grids is an effective way to reduce the impact of energy production on global warming. Unfortunately, the wind power generation may affect the regular operation of electrical systems, due to the stochastic and intermittent nature of the wind. For this reason, reducing the uncertainty about the wind evolution, e.g. by using short-term wind power forecasting methodologies, is a priority for system operators and wind producers to implement low-carbon power grids. Unfortunately, though the complexity of this task implies the comparison of several alternative forecasting methodologies and dimensionality reduction techniques, a general and robust procedure of model assessment still lacks in literature. In this paper the authors propose a robust methodology, based on extensive statistical analysis and resampling routines, to supply the most effective wind power forecasting method by testing a vast ensemble of methodologies over multiple time-scales and on a real case study. Experimental results on real data collected in an Italian wind farm show the potential of ensemble approaches integrating both statistical and machine learning methods.
Fabrizio De Caro; Jacopo De Stefani; Gianluca Bontempi; Alfredo Vaccaro; Domenico Villacci. Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons. Technology and Economics of Smart Grids and Sustainable Energy 2020, 5, 1 -15.
AMA StyleFabrizio De Caro, Jacopo De Stefani, Gianluca Bontempi, Alfredo Vaccaro, Domenico Villacci. Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons. Technology and Economics of Smart Grids and Sustainable Energy. 2020; 5 (1):1-15.
Chicago/Turabian StyleFabrizio De Caro; Jacopo De Stefani; Gianluca Bontempi; Alfredo Vaccaro; Domenico Villacci. 2020. "Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons." Technology and Economics of Smart Grids and Sustainable Energy 5, no. 1: 1-15.
Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the impacts induced by extreme weather events has been considered as one of the most critical issues to address, since they can induce multiple, and large-scale system contingencies. In this context, the development of new computing paradigms for resilience analysis has been recognized as a very promising research direction. To address this issue, this paper proposes two methodologies, which are based on Time Varying Markov Chain and Dynamic Bayesian Network, for assessing the system resilience against extreme wind gusts. The main difference between the proposed methodologies and the traditional solution techniques is the improved capability in modelling the occurrence of multiple component faults and repairing, which cannot be neglected in the presence of extreme events, as experienced worldwide by several Transmission System Operators. Several cases studies and benchmark comparisons are presented and discussed in order to demonstrate the effectiveness of the proposed methods in the task of assessing the power system resilience in realistic operation scenarios.
Ennio Brugnetti; Guido Coletta; Fabrizio De De Caro; Alfredo Vaccaro; Domenico Villacci. Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts. Energies 2020, 13, 3501 .
AMA StyleEnnio Brugnetti, Guido Coletta, Fabrizio De De Caro, Alfredo Vaccaro, Domenico Villacci. Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts. Energies. 2020; 13 (13):3501.
Chicago/Turabian StyleEnnio Brugnetti; Guido Coletta; Fabrizio De De Caro; Alfredo Vaccaro; Domenico Villacci. 2020. "Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts." Energies 13, no. 13: 3501.
Optimal Power Flow (OPF) analysis represents the mathematical foundation of many power engineering applications. For the most common formalization of the OPF problem, all input data are specified using deterministic variables, and the corresponding solutions are deemed representative of the limited set of system conditions. Hence, reliable algorithms aimed at representing the effect of data uncertainties in OPF analyses are required in order to allow analysts to estimate both the data and solution tolerance, providing, therefore, insight into the level of confidence of OPF solutions. To address this issue, this Chapter outline the role of novel solution methodologies based on the use of Affine Arithmetic.
Alfredo Vaccaro. The Role of Affine Arithmetic in Robust Optimal Power Flow Analysis. Developments in Advanced Control and Intelligent Automation for Complex Systems 2020, 189 -196.
AMA StyleAlfredo Vaccaro. The Role of Affine Arithmetic in Robust Optimal Power Flow Analysis. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2020; ():189-196.
Chicago/Turabian StyleAlfredo Vaccaro. 2020. "The Role of Affine Arithmetic in Robust Optimal Power Flow Analysis." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 189-196.
The increasing penetration of variable distributed generation causes the transmission lines to operate closer to their design thermal limits. In this context, Dynamic Thermal Rating is a very promising technique, since it permits a better exploitation of the real capability margins of the infrastructures and eliminate network congestions. In this vein, the paper proposes a novel control strategy that allows maintaining the conductor temperature of a given line within its thermal limit through the real-time curtailment of distributed energy resources in the network. The impact of weather volatility and measurement uncertainty on the dynamic response of the controller is evaluated. A comprehensive case study, based on a real-world Italian sub-transmission system and measurement data serve to illustrate the dynamic behavior of the proposed controller. The effect of measurement noise and delays is also discussed. Finally, the performance of the proposed control strategy is compared with a conventional robust optimal power flow approach.
Guido Coletta; Alberto Laso; Guorun Margret Jonsdottir; Mario Manana; Domenico Villacci; Alfredo Vaccaro; Federico Milano. On-Line Control of DERs to Enhance the Dynamic Thermal Rating of Transmission Lines. IEEE Transactions on Sustainable Energy 2020, 11, 2836 -2844.
AMA StyleGuido Coletta, Alberto Laso, Guorun Margret Jonsdottir, Mario Manana, Domenico Villacci, Alfredo Vaccaro, Federico Milano. On-Line Control of DERs to Enhance the Dynamic Thermal Rating of Transmission Lines. IEEE Transactions on Sustainable Energy. 2020; 11 (4):2836-2844.
Chicago/Turabian StyleGuido Coletta; Alberto Laso; Guorun Margret Jonsdottir; Mario Manana; Domenico Villacci; Alfredo Vaccaro; Federico Milano. 2020. "On-Line Control of DERs to Enhance the Dynamic Thermal Rating of Transmission Lines." IEEE Transactions on Sustainable Energy 11, no. 4: 2836-2844.
A. Pepiciello; Guido Coletta; Alfredo Vaccaro; D. Villacci. The role of learning techniques in synchrophasor-based Dynamic Thermal Rating. International Journal of Electrical Power & Energy Systems 2020, 115, 1 .
AMA StyleA. Pepiciello, Guido Coletta, Alfredo Vaccaro, D. Villacci. The role of learning techniques in synchrophasor-based Dynamic Thermal Rating. International Journal of Electrical Power & Energy Systems. 2020; 115 ():1.
Chicago/Turabian StyleA. Pepiciello; Guido Coletta; Alfredo Vaccaro; D. Villacci. 2020. "The role of learning techniques in synchrophasor-based Dynamic Thermal Rating." International Journal of Electrical Power & Energy Systems 115, no. : 1.
Open access peer-reviewed chapter
Alfredo Vaccaro; Antonio Pepiciello; Ahmed Faheem Zobaa. Introductory Chapter: Open Problems and Enabling Methodologies for Smart Grids. Research Trends and Challenges in Smart Grids 2020, 1 .
AMA StyleAlfredo Vaccaro, Antonio Pepiciello, Ahmed Faheem Zobaa. Introductory Chapter: Open Problems and Enabling Methodologies for Smart Grids. Research Trends and Challenges in Smart Grids. 2020; ():1.
Chicago/Turabian StyleAlfredo Vaccaro; Antonio Pepiciello; Ahmed Faheem Zobaa. 2020. "Introductory Chapter: Open Problems and Enabling Methodologies for Smart Grids." Research Trends and Challenges in Smart Grids , no. : 1.
Modern power distribution systems require reliable, self-organizing and highly scalable voltage control systems, which should be able to promptly compensate the voltage fluctuations induced by intermittent and non-programmable generators. However, their deployment in realistic operation scenarios is still an open issue due, for example, to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, time-delays in data acquisition and transmission are unavoidable and their effects have to be taken into account in the control design phase. To this aim, in this paper, we propose a fully distributed cooperative control protocol allowing the voltage control to be achieved despite the presence of heterogeneous time-varying latencies. The idea is to exploit the distributed intelligence along the network, so that it is possible to bring out an optimal global behavior via cooperative distributed control action that leverages both local and the outdated information shared among the devices within the power network. Detailed simulation results obtained on the realistic case study of the IEEE 30-bus test system are presented and discussed in order to prove the effectiveness of the proposed approach in the task of solving complex voltage control problems. Finally, a robustness analysis with respect to both loads variations and hard communication delays was also carried to disclose the efficiency of the approach.
Amedeo Andreotti; Bianca Caiazzo; Alberto Petrillo; Stefania Santini; Alfredo Vaccaro. Decentralized Smart Grid Voltage Control by Synchronization of Linear Multiagent Systems in the Presence of Time-Varying Latencies. Electronics 2019, 8, 1470 .
AMA StyleAmedeo Andreotti, Bianca Caiazzo, Alberto Petrillo, Stefania Santini, Alfredo Vaccaro. Decentralized Smart Grid Voltage Control by Synchronization of Linear Multiagent Systems in the Presence of Time-Varying Latencies. Electronics. 2019; 8 (12):1470.
Chicago/Turabian StyleAmedeo Andreotti; Bianca Caiazzo; Alberto Petrillo; Stefania Santini; Alfredo Vaccaro. 2019. "Decentralized Smart Grid Voltage Control by Synchronization of Linear Multiagent Systems in the Presence of Time-Varying Latencies." Electronics 8, no. 12: 1470.
Phasor Measurement Units (PMUs) are leading the way towards advanced monitoring, control and protection applications in power systems. In order to work properly, they rely on a precise time synchronization architecture. Currently, the synchronization accuracy achieved is in the order of one microsecond.The utilization of PMUs in medium and low voltage systems requires nano-second scale time synchronization accuracy, due to the lower active power flows and thus to the lower phase angle differences between two buses of a power system.To face this issue, this paper advocates the role of supersynchronized PMUs that are based on a novel time and frequency transfer algorithm developed by Thales Alenia Space (Synchronet). As demonstrated by experimental activities developed on a real case study, the adoption of this new paradigm allows PMUs to reach nano-second scale synchronization accuracy, hence representing an effective solution for time-synchronization in modern smart grids.
Enrico Varriale; Quirino Morante; Alfredo Vaccaro; Antonio Pepiciello. Development and Experimental Validation of a Super-Synchronized Phasor Measurement Unit. 2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT) 2019, 1 -5.
AMA StyleEnrico Varriale, Quirino Morante, Alfredo Vaccaro, Antonio Pepiciello. Development and Experimental Validation of a Super-Synchronized Phasor Measurement Unit. 2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT). 2019; ():1-5.
Chicago/Turabian StyleEnrico Varriale; Quirino Morante; Alfredo Vaccaro; Antonio Pepiciello. 2019. "Development and Experimental Validation of a Super-Synchronized Phasor Measurement Unit." 2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT) , no. : 1-5.
The great diffusion of renewable energy sources is substituting traditional power plants, based on synchronous generators. This is leading to lower levels of inertia in modern power systems, thus reducing their frequency stability. In the context of future low-inertia systems, this paper introduces the novel concept of critical inertia, i.e. the minimum inertia needed at each network bus, in order to ensure the transient stability of the power system, and describes a methodology based on parametric identification for its assessment. The method is tested on a nine-bus power system and results related to different case studies are presented.
Antonio Pepiciello; Alfredo Vaccaro. An Optimization-based Method for Estimating Critical Inertia in Smart Grids. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) 2019, 2237 -2241.
AMA StyleAntonio Pepiciello, Alfredo Vaccaro. An Optimization-based Method for Estimating Critical Inertia in Smart Grids. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). 2019; ():2237-2241.
Chicago/Turabian StyleAntonio Pepiciello; Alfredo Vaccaro. 2019. "An Optimization-based Method for Estimating Critical Inertia in Smart Grids." 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) , no. : 2237-2241.
In this paper, we formally discuss a computational scheme, which combines a local weighted regression model with fuzzy transform (or F-transform for short). The latter acts as a reduction technique on the cardinality of the learning problem, resulting in a more efficient algorithm. We tested the proposed approach first through two typical benchmark problems, that is the Hénon and the Mackey–Glass chaotic time series, then we applied it to short-term forecasting problems. Short-term forecasting is important in the energy field for the management of power systems and for energy trading. Hence, we considered two typical application examples in this field, that is wind power forecasting and load forecasting. Numerical results show the effectiveness of the proposed approach through a comparison against alternative techniques.
Vincenzo Loia; Stefania Tomasiello; Alfredo Vaccaro; Jinwu Gao. Using local learning with fuzzy transform: application to short term forecasting problems. Fuzzy Optimization and Decision Making 2019, 19, 13 -32.
AMA StyleVincenzo Loia, Stefania Tomasiello, Alfredo Vaccaro, Jinwu Gao. Using local learning with fuzzy transform: application to short term forecasting problems. Fuzzy Optimization and Decision Making. 2019; 19 (1):13-32.
Chicago/Turabian StyleVincenzo Loia; Stefania Tomasiello; Alfredo Vaccaro; Jinwu Gao. 2019. "Using local learning with fuzzy transform: application to short term forecasting problems." Fuzzy Optimization and Decision Making 19, no. 1: 13-32.
Antonio Pepiciello; Domenico Villacci; Alfredo Vaccaro. Wide Area Monitoring Protection and Control Systems: the enablers for enhancing renewable energy hosting capacity. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) 2019, 1 .
AMA StyleAntonio Pepiciello, Domenico Villacci, Alfredo Vaccaro. Wide Area Monitoring Protection and Control Systems: the enablers for enhancing renewable energy hosting capacity. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). 2019; ():1.
Chicago/Turabian StyleAntonio Pepiciello; Domenico Villacci; Alfredo Vaccaro. 2019. "Wide Area Monitoring Protection and Control Systems: the enablers for enhancing renewable energy hosting capacity." 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) , no. : 1.
The paradigm of Virtual Power Plants, together with Smart Grids, can unleash the full potential of distributed energy resources, by introducing flexibility to power systems and by managing the increasing uncertainty and unreliability associated to them. The aggregation obtained through a Virtual Power Plant would not only benefit energy producers and users, maximizing their revenues, but it would also reduce the complexity of modern power systems, simplifying the balancing process carried out by Transmission System Operators.
Antonio Pepiciello; Giovanna Bernardo; Emanuele D'Argenzio; Alfredo Vaccaro. A Decision Support System for the Strategic Operation of Virtual Power Plants in Electricity Markets. 2019 International Conference on Clean Electrical Power (ICCEP) 2019, 370 -374.
AMA StyleAntonio Pepiciello, Giovanna Bernardo, Emanuele D'Argenzio, Alfredo Vaccaro. A Decision Support System for the Strategic Operation of Virtual Power Plants in Electricity Markets. 2019 International Conference on Clean Electrical Power (ICCEP). 2019; ():370-374.
Chicago/Turabian StyleAntonio Pepiciello; Giovanna Bernardo; Emanuele D'Argenzio; Alfredo Vaccaro. 2019. "A Decision Support System for the Strategic Operation of Virtual Power Plants in Electricity Markets." 2019 International Conference on Clean Electrical Power (ICCEP) , no. : 370-374.
The massive penetration of renewable power generators in modern power systems is pushing the research in developing reliable computing techniques aimed at addressing the issues caused by the increasing uncertainty sources induced by their intermittent power profiles and the corresponding power transactions. In this domain affine arithmetic-based power flow analysis has been recognized as one of the most promising research directions, since it allows to address many important power systems operation functions by directly keeping track of uncertainties propagation as part of the computation process. Anyway, AA-based computing requires the deployment of specific software modules implementing the main AA-based mathematical operators, which could increase the overall complexity of the solution algorithms. To reduce the complexity of AA-based power flow analysis, this paper proposes a new formulation of the uncertain power flow equations, which allows to explicitly compute the Jacobian matrix and apply a traditional Newton–Raphson based algorithm to solve the overall problem. Detailed results obtained on realistic power systems will be presented and discussed in order to evaluate the performances of the proposed method compared to other traditional AA-based methodologies.
Guido Coletta; Alfredo Vaccaro; Domenico Villacci. Fast and reliable uncertain power flow analysis by affine arithmetic. Electric Power Systems Research 2019, 175, 105860 .
AMA StyleGuido Coletta, Alfredo Vaccaro, Domenico Villacci. Fast and reliable uncertain power flow analysis by affine arithmetic. Electric Power Systems Research. 2019; 175 ():105860.
Chicago/Turabian StyleGuido Coletta; Alfredo Vaccaro; Domenico Villacci. 2019. "Fast and reliable uncertain power flow analysis by affine arithmetic." Electric Power Systems Research 175, no. : 105860.
Traditional energy systems were planned and operated independently, but the diffusion of distributed and renewable energy systems led to the development of new modeling concepts, such as the energy hub. The energy hub is an integrated paradigm, based on the challenging idea of multi-carrier energy systems, in which multiple inputs are conditioned, converted and stored in order to satisfy different types of energy demand. To solve the energy hub optimal scheduling problem, uncertainty sources, such as renewable energy production, price volatility and load demand, must be properly considered. This paper proposes a novel methodology, based on extended Affine Arithmetic, which enables the solving of the optimal scheduling problem in the presence of multiple and heterogeneous uncertainty sources. Realistic case studies are presented and discussed in order to show the effectiveness of the proposed methodology.
Antonio Pepiciello; Alfredo Vaccaro; Mario Mañana. Robust Optimization of Energy Hubs Operation Based on Extended Affine Arithmetic. Energies 2019, 12, 2420 .
AMA StyleAntonio Pepiciello, Alfredo Vaccaro, Mario Mañana. Robust Optimization of Energy Hubs Operation Based on Extended Affine Arithmetic. Energies. 2019; 12 (12):2420.
Chicago/Turabian StyleAntonio Pepiciello; Alfredo Vaccaro; Mario Mañana. 2019. "Robust Optimization of Energy Hubs Operation Based on Extended Affine Arithmetic." Energies 12, no. 12: 2420.
Optimization analyses are commonly used in microgrids to identify the most efficient and reliable operation of the available energy resources. Unfortunately, most of the times these programming problems rely on input parameters which are not accurately known. In this context, advanced computing paradigms for solving uncertainty optimization problems represent the most promising enabling methodology. These techniques may show their effectiveness during both the dispatch and the pre-dispatch phase, when operators need to solve the unit-commitment and the economic dispatch problems. To this aim, this paper discusses and compares experimentally some promising existing alternatives to deterministic methods to deal with the solution of optimization problems in the presence of data uncertainty.
Alfredo Vaccaro; Marina Petrelli; Alberto Berizzi. Robust Optimization and Affine Arithmetic for Microgrid Scheduling under Uncertainty. 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) 2019, 1 -6.
AMA StyleAlfredo Vaccaro, Marina Petrelli, Alberto Berizzi. Robust Optimization and Affine Arithmetic for Microgrid Scheduling under Uncertainty. 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). 2019; ():1-6.
Chicago/Turabian StyleAlfredo Vaccaro; Marina Petrelli; Alberto Berizzi. 2019. "Robust Optimization and Affine Arithmetic for Microgrid Scheduling under Uncertainty." 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) , no. : 1-6.