2021
Authors
Ortiz, JMH; Melgar-Dominguez, OD; Javadi, MS; Santos, SF; Mantovani, JRS; Catalao, JPS;
Publication
2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE)
Abstract
This paper presents a strategy based on mixedinteger linear programing (MILP) model to improve the resilience in electric distribution systems (EDSs). The restoration process considers operational resources such as the optimal coordination of dynamic switching operations, islanding operation of distributed generation (DG) units, and displacement of mobile emergency generation (MEG) units. In addition, the benefits of considering a demand response (DR) program to improve the recoverability of the system are also studied. The switching operations aim to separate the in-service from the out-of-service part of the system keeping the radiality of the grid. The proposed MILP model is formulated as a stochastic scenario-based problem where the uncertainties are associated with PV-based power generation and demand consumption. The objective function minimizes the amount of energy load shedding after fault, and the generation curtailment of the PV-based DG. To validate the proposed strategy, a 33-bus EDS is analyzed under different test cases. Results show the benefits of coordinating the dynamic switching operations, the optimal scheduling of MEG units, and a demand response program during the restoration process.
2021
Authors
Santos, SF; Gough, M; Ferreira, JPD; Javadi, MS; Osorio, GJ; Vafamand, N; Arefi, MM; Catalao, JPS;
Publication
2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE)
Abstract
There is an urgent need to reduce the combustion of fossil fuels and replace these sources with renewable energy sources. The two major renewable energy resources, solar PV and wind generation, are variable. This variability makes balancing the electrical system more difficult. One way to manage this volatile system is to use markets for ancillary services to ensure that the electrical grid can operate in a safe, efficient and reliable manner. This paper proposes a methodology for a group of smaller consumers to be aggregated together so that they can effectively bid into markets for ancillary services. The methodology is tested on the Portuguese reserve regulation market and the financial viability of such aggregation is explored. Results show that aggregating consumer bids for downward regulation services can be financially viable in the Portuguese market. Reducing the minimum bid size increased the participation of the consumers thus increasing revenues.
2021
Authors
Neisarian, S; Arefi, MM; Vafamand, N; Javadi, MS; Catalao, JPS;
Publication
2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE)
Abstract
Due to the salient features of direct current (DC) microgrids (MGs) in integrating renewable energy sources, this paper offers a robust finite-time nonlinear observer (FTNO) for DC MGs comprising linear resistive and nonlinear constant power loads (CPLs) and a buck converter. It is assumed that the capacitor voltage is only accessible and the power system is subject to unknown time-varying uncertainties. A novel nonlinear observer is designed to estimate the inductance curren2t to prevent the ripples produced by current sensors and to eliminate the price of utilizing expensive sensors. The global finite-time stability analysis of the observer error dynamic is investigated via a Lyapunov function and an explicit finite convergence time (FCT) is derived. The convergence rate of the estimated current is tunable by adjusting the parameters in FCT. Eventually, simulations are carried out to confirm the superiority of the proposed observer performance in estimating unknown inductance current in a particular finite time.
2021
Authors
Mahdavi, M; Javadi, MS; Wang, F; Catalao, JPS;
Publication
2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE)
Abstract
Distribution networks have a prominent role in electricity delivery to individual consumers. Nevertheless, their energy losses are higher than transmission systems, which this issue affects the distribution operational costs. Hence, the minimization of power losses in distribution networks has particular importance for the system operators. Distribution network reconfiguration (DNR) is an effective way to reduce energy losses. However, some research works regarding DNR have not considered load variations in power loss calculations. Load level has an essential role in network losses determination and significantly influences the energy losses amount. On the other hand, considering load variations in DNR increases the computational burden and processing time of the relevant algorithms. Therefore, this paper presents an effective reconfiguration framework for minimization of distribution losses, while the energy demand is changing. The simulation results show the effectiveness of the proposed strategy for optimal reconfiguration of distribution systems in presence of load variations.
2021
Authors
Pazhoohesh, M; Javadi, MS; Gheisari, M; Aziz, S; Villa, R;
Publication
IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
Abstract
Data quality plays a crucial role in the context of smart buildings. Meanwhile, missing data is relatively common in acquired datasets from sensors within the smart buildings. Poor data could result in a big bias in forecasting, control and operational services. Despite the common techniques to handle missing data, it is essential to systematically select the most appropriate approach for such missing values. This paper aims to focus on the lift systems as one of the essential parts in the smart buildings by exploring the most appropriate data imputation methods to handle missing data and to provide its service and allow a better understanding of patterns to issue the correct control actions based on forecasted models. The imputed data is not only investigated statistically but also modelled through machine learning algorithm to explore the impact of selecting inappropriate imputation techniques. Seven imputation techniques deployed on datasets with three level of missing values including 10%, 20% and 30% and the performance of methods examined through the normalized root mean square error (NRMSE) approach. In addition, the interaction between imputation techniques and a machine learning algorithm, namely random forest were examined. Findings from this paper can be employed in identifying an appropriate imputation technique not only within the lift datasets, but smart building context.
2021
Authors
Lucas, A; Geneiatakis, D; Soupionis, Y; Nai-Fovino, I; Kotsakis, E;
Publication
Energies
Abstract
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