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Bozorg Mokhtar

Professeur HES associé

Hauptvertrag

Professeur HES associé

Telefon-Nummer: +41 24 557 62 50

Büro: B53

Haute école d'Ingénierie et de Gestion du Canton de Vaud
Route de Cheseaux 1, 1400 Yverdon-les-Bains, CH

Institut
IESE - Institut d'Energie et Systèmes Electriques

BSc HES-SO en Génie électrique - Haute école d'Ingénierie et de Gestion du Canton de Vaud

Réseaux électriques
Régulation automatique
Projets interdisciplinaires

Laufend

Real-Time Distribution Grid Control and Flexibility Provision under Uncertainties

Rolle: Hauptgesuchsteller/in

Financement: Socle HEIG-VD Ra&D; Auto-fin IESE; OFEN; Partenaires tiers

Description du projet : In this project we propose and validate effective forecasting and optimal control algorithms to ensure efficient and secure operation of low voltage distribution grids, as well as flexibility provision from distribution grids toward upstream grids, under uncertainties. A two-levels rolling optimization framework to ensure optimal and secure operation of distribution grids under uncertainties will be developed and experimentally validated. The first level deals with prescheduling of controllable resources in a time ahead basis, whereas the second level deals with real time online scheduling of all the controllable resources. Moreover, an appropriate forecasting system will be developed to provide day-ahead and near real-time forecast of uncertain parameters, in accordance with the optimization framework. Finally, we validate the proposed methodology and demonstrate its effectiveness under realistic uncertainty sources (e.g., PV power generation), for a low voltage distribution grid.

Forschungsteam innerhalb von HES-SO: Favre-Perrod Patrick, Gavin Serge, Bicca Christian, Bernasconi Cédric, Bozorg Mokhtar, Fesefeldt Marten, Rayati Mohammad, Carpita Mauro

Partenaires académiques: FR - EIA - Institut ENERGY; DEPsys sàrl; EPFL; Bozorg Mokhtar, IESE

Durée du projet: 19.09.2019 - 30.09.2021

Montant global du projet: 125'600 CHF

Statut: Laufend

Grid Data Digger

Rolle: Hauptgesuchsteller/in

Financement: CTI; DEPsys sàrl

Description du projet : Over the last decade, the electric grid changed dramatically with the introduction of distributed and volatile power production means, such as private solar panels. Thus,Distribution System Operators (DSOs) are facing daily challenges to handle frequent grid operational limit violations (GOLV). Additionally, installed grid measurement devices, though they provide huge amount of data, give only a partial monitoring of Low Voltage (LV) grids. Consequently, interpretation of these data is hard, and decision even harder. Innovative part of this project is to valorize available data to DSOs through a data-driven, and automated process. It aims at providing necessary information and analysis for secure and optimal operation of distribution grids using descriptive, diagnostic, predictive, and preventive data analytics applications. Key needed researches: Development of data-driven algorithms based on machine learning techniques aiming at 1) identification of main trends, correlations and irregularities on voltage/current/power profiles of LV grids, 2) complex event identification regarding several correlated GOLVs/events, 3) prediction of GOLVs few hours ahead of real-time, and 4) proposition of control actions for flexible resources to prevent GOLVs. Study and modeling of data orchestration for harmonizing different input data from various sources (e.g., measurement devices, smart meters, etc.) to be used in the abovementioned algorithms and to provide a fast and efficient environment for running the algorithms. To develop a big-data platform with above data-driven applications, called 'Grid Data Digger', various competencies from different engineering fields, including computer science, machine learning, embedded software, and power systems are brought together. DEPsys, as the main implementation partner, will commercialize the 'Grid Data Digger' platform and sell it to its new and existing DSO customers. The income increase is estimated as 8 MCHF by 2022.

Forschungsteam innerhalb von HES-SO: Dassatti Alberto, Fatemi Nastaran, Hochet Guillaume, Gavin Serge, Pena Carlos Andrés, Burella Pérez Julián Mariano, Kissling Simon, Meier Christopher, Bürer Mary Jean, Bozorg Mokhtar, Fesefeldt Marten, Rodriguez Zaloña Oscar, Paruta Paola, Ataee Shabnam, Carpita Mauro

Partenaires académiques: IICT; IESE; Rodriguez Zalona Oscar, ReDS

Durée du projet: 08.05.2019 - 31.05.2021

Montant global du projet: 225'379 CHF

Statut: Laufend

Abgeschlossen

SCCER 2 Phase 2 - WP3 - Multi-terminal AC-DC grids and power electronics

Rolle: Mitarbeiter

Requérant(e)s: IESE

Financement: HES-SO; CTI (Via HE-FR, EPFL)

Description du projet : SCCER FURIES Phase 2 WP3 aims to provide power electronic systems manufacturers as well as network operators with validated approaches for hybrid AC-DC systems in the following areas: fault management (detection, protection, clearing) coordinated planning and operation (e.g. for power quality), power electronic converter concept and devices with proven reliability and high performance, coordinated operation and distributed provision of ancillary services in grid planning and operation. (see 7.2. Innovation potential) In order to achieve this, WP3 will establish the 'SCCER-FURIES lab network' which aims to link the current and new infrastructures of participants of the competence center working on AC/DC technologies and facilitate the testing of innovative solutions. It is envisaged that the establishment of these new capabilities will contribute to shortening the time to market of new power electronic and DC solutions. Five technological prototypes (a hybrid DC circuit breaker, an optimized solid state transformer, a fault location device for MV networks, a soft open point, a prototype galvanically insulated modular converter and ancillary services framework) will be demonstrated in this laboratory network. Furthermore, the fault location device and the soft open point will be deployed in Romande Energie's network and several of these prototypes will be developed in cooperation with SCCER cooperation partners.

Forschungsteam innerhalb von HES-SO: Houmard Douglas, Gavin Serge, Pidancier Thomas, Bozorg Mokhtar, Carpita Mauro

Partenaires professionnels: IESE

Durée du projet: 01.01.2017 - 24.11.2017

Montant global du projet: 171'925 CHF

Statut: Abgeschlossen

2022

Distributionally robust chance constrained optimization for providing flexibility in an active distribution network

Scientifique ArODES

Rayati Mohammad, Bozorg Mokhtar, Cherkaoui Rachid, Carpita Mauro

IEEE Transactions on Smart Grid, To be published

Link zur Publikation

Zusammenfassung:

In this paper, we propose a distributionally robust chance constrained (DRCC) optimization problem for the operation of an active distribution network (ADN). The ADN’s operator uses the proposed problem to centrally optimize the dispatch plan of his resources, namely photovoltaic (PV) and battery energy storage (BES) systems, and to participate in wholesale real/reactive power and flexibility markets. We model the uncertainties in the problem by knowing a set of probability distributions, i.e., an ambiguity set. The uncertainties include production capability of PV systems, end-users’ consumption, requested flexibility by the external network’s operator, and voltage magnitude at the point of common coupling (PCC). The resulting formulation is a DRCC optimization problem for which a solution methodology based on freely available solvers is presented. We evaluate the performance of proposed solution in the numerical results section by comparing it with two benchmark models based on stochastic and chance constrained (CC) optimization.

2021

Bayesian bootstrapping in real-time probabilistic photovoltaic power forecasting

Scientifique ArODES

Bozorg Mokhtar, Bracale Antonio, Carpita Mauro, De Falco Pasquale, Mottola Fabio, Proto Daniela

Solar Energy, 2021, vol. 225, pp. 577-590

Link zur Publikation

Zusammenfassung:

Modern distribution systems are characterized by increasing penetration of photovoltaic generation systems. Due to the uncertain nature of the solar primary source, photovoltaic power forecasting models must be developed in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to any energy management system, and they are recommended to enable a wider range of decision making and optimization strategies. Real-time probabilistic photovoltaic power forecasting is performed in this paper by using an approach based on Bayesian bootstrap. Particularly, the Bayesian bootstrap is applied to three probabilistic forecasting models (i.e., linear quantile regression, gradient boosting regression tree and quantile regression neural network) to provide sample bootstrap distributions of the predictive quantiles of photovoltaic power. The heterogeneous nature of the selected models allows evaluating the performance of the Bayesian bootstrap within different forecasting frameworks. Several benchmarks and error indices and scores are used to assess the performance of Bayesian bootstrap in probabilistic photovoltaic power forecasting. Tests carried out on two actual photovoltaic power datasets for probabilistic forecasting demonstrates the effectiveness of the proposed approach.

Generation data of synthetic high frequency solar irradiance for data-driven decision-making in electrical distribution grids

Scientifique ArODES

Rayati Mohammad, De Falco Pasquale, Proto Daniela, Bozorg Mokhtar, Carpita Mauro

Energies, 2021, vol. 14, no. 16, article no. 4734

Link zur Publikation

Zusammenfassung:

In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth’s orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The CSI data of each group are divided into bins of magnitudes and the transition probabilities among the bins are identified to deliver a Markov Chain (MC) model to track the intraday weather condition variations. The proposed model is tested on the measurements of two PV systems located at two different climatic regions: (a) Yverdon-les-Bains, Switzerland; and (b) Oahu, Hawaii, USA. The model is compared with a previously published N-state MC model and the performance of the proposed model is elaborated.

Evaluation of future scenarios for gas distribution networks under hypothesis of decreasing heat demand in urban zones

Scientifique ArODES

Fesefeldt Marten, Capezzali Massimiliano, Bozorg Mokhtar, de Lapparent Matthieu

Energy, 2021, vol. 231, article no. 120909

Link zur Publikation

Zusammenfassung:

The consumption of buildings for the production of heat is expected to decrease in Switzerland in the coming years, in particular following policies encouraging the refurbishment of buildings. This will notably have an impact on the natural gas network, in parallel with the penetration of electric-driven heat pumps. Through a detailed optimization scheme, the evolution of the natural gas (NG) distribution network is studied over a future period of forty years, i.e. up to 2050, on the territory of a large Swiss canton. By way of installing large shares of co-generation units, it is shown that the NG network does not lose its meshed structure, while continuing to play a central role in the production of heat and the generation of part of the additional electricity demand associated with the concomitant penetration of heat pumps. As a novel result, the developed optimization framework allows a detailed, geographically precise view of both the evolution of the NG network, as well as of the optimal location of selected technologies. The adoption of energy networks convergence in urban zones therefore can lead to relevant synergies, avoiding over-investments, increasing system resilience and fostering the use of efficient technologies.

A state-space approach to the modelling and control of the neutral leg of a four legs, three-phase inverter

Scientifique ArODES

Passalacqua Massimiliano, Grosjean Gislain, Kissling Simon, Bozorg Mokhtar, Marchesoni Mario, Carpita Mauro

IEEE Transactions on Industrial Electronics, 2022, vol. 69, no. 4, pp. 4056 - 4064

Link zur Publikation

Zusammenfassung:

In this paper, a novel approach to determine the model of an ICNL (Independently Controlled Neutral Leg) is proposed. Based on this model, a cascaded control method is developed to provide a steady neutral point connection for a three-phase four-wire inverter and to balance the two DC capacitors voltages of a split DC-bus. Validation of the model is realised by simulations and the control has been tested by experiments. Results have shown good performance even with a high level of neutral current, which demonstrate the ability of the system to work within an unbalanced load or grid.

Greedy placement of measurement devices on distribution grids based on enhanced distflow state estimation

Scientifique ArODES

Paruta Paola, Pidancier Thomas, Bozorg Mokhtar, Carpita Mauro

Sustainable Energy, Grids and Networks, 2021, vol. 26

Link zur Publikation

Zusammenfassung:

The needs for improving observability of medium and low voltage distribution networks has been significantly increased, in recent year. In this paper, we focus on practical approaches for placement of affordable Measurement Devices (MDs), which are providing three phases voltage, current, and power measurements with certain level of precision. The placement procedure is composed of a state-estimation algorithm and of a greedy placement scheme. The proposed state-estimation algorithm is based on the Distflow model, enhanced to consider the shunt elements (e.g., cable capacitances) of the network, which are not negligible in low voltage networks with underground cables. The greedy placement scheme is formulated such that it finds the location of minimum required number of MDs while certain grid observability limits are satisfied. These limits are defined as the accuracy of state-estimation results in terms of voltage magnitudes and line currents over all nodes and lines, respectively. The effectiveness of the proposed placement procedure has been validated on a realistic test grid of 10 medium voltage nodes and 75 low voltage nodes, whose topology and parameters were made available from the Distribution System Operator (DSO) of the city of Geneva, Switzerland.

2020

Coordinating strategic aggregators in an active distribution network for providing operational flexibility

Scientifique ArODES

Rayati Mohammad, Bozorg Mokhtar, Cherkaoui Rachid

Electric Power Systems Research, 2020, vol. 189, article no. 106737

Link zur Publikation

Zusammenfassung:

The support of distributed energy resources (DERs) for providing operational flexibility can be beneficial from the transmission system operators’ (TSOs’) viewpoint. Despite all opportunities, using DERs within an active distribution network (ADN) for providing flexibility brings operational challenges such as lines congestions and excessive voltage deviations management. Moreover, there are information asymmetries challenges for coordinating the DERs specifically when the strategic aggregators are introduced to manage them. In this paper, a local coordinating mechanism is proposed for the strategic aggregators to provide the operational flexibility. This paper furthers efforts toward proposing an iterative distributed algorithm converging to the Nash equilibrium (NE) of the non-cooperative game incorporated in the proposed mechanism. To validate the performance of the proposed mechanism, the IEEE 33 nodes radial distribution network is considered for a case study. The efficiency of the obtained NE and the proposed iterative distributed algorithm are thoroughly analyzed in the case study.

Bayesian bootstrap quantile regression for probabilistic photovoltaic power forecasting

Scientifique ArODES

Bozorg Mokhtar, Bracale Antonio, Caramia Pierluigi, Carpinelli Guido, Carpita Mauro, De Falco Pasquale

Protection and Control of Modern Power Systems, 2020, vol. 5, article no. 21

Link zur Publikation

Zusammenfassung:

Photovoltaic (PV) systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing. Because of the uncertain nature of the solar energy resource, PV power forecasting models are crucial in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies. This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model, in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model. A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients, raising the predictive ability of the final forecasts. Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2% when compared to relevant benchmarks.

L'intelligence artificielle au service des réseaux :

Professionnel ArODES

le projet Grid Data Digger

Bozorg Mokhtar, Fatemi Nastaran, Pena Carlos Andres, Mousavi Omid, Carpita Mauro

bulletin.ch = Fachzeitschrift und Verbandsinformationen von Electrosuisse und VSE = Bulletin SEV/AES : revue spécialisée et informations des associations Electrosuisse et AES, 2020, vol. 8, pp. 19-23

Link zur Publikation

Zusammenfassung:

Comment garantir un fonctionnement optimal et sécurisé des réseaux de distribution d’électricité dans un contexte d’évolution constante? La plateforme «Grid Data Digger» utilise des méthodes d’analyse de données massives (big data) et d’apprentissage automatique (machine learning) pour aider les gestionnaires de réseaux à relever ce défi.

15 kVA three-phase low-voltage SOP prototype laboratory tests results

Scientifique ArODES

Pidancier Thomas, Bozorg Mokhtar, Roggo Dominique, Favre-Perrod Patrick, Carpita Mauro

Energies, 2020, vol. 13, article no. 3895

Link zur Publikation

Zusammenfassung:

In this paper, a 15 kVA soft open point converter is presented. The converter design and manufacturing have been presented in previous papers. The aim of this paper was to show the results of the devices in a full-scale laboratory environment, Gridlab, made of four distribution LV feeders, each rated 40 A. The tests demonstrated the good dynamics of the SOP control and its usefulness in performing a suitable PQ control. They also showed an improvement in the voltage profile and in the load capacity of the overall network. In the final part of the paper, the feedback earned during the development and the test of this first prototype are presented. This feedback will benefit the team for the design of a new improved 50 kVA version.

Balancing management of strategic aggregators using non-cooperative game theory

Scientifique ArODES

Ryati Mohammad, Bozorg Mokhtar, Ranjbar Ali Mohammad, Cherkaoui Rachid

Electric Power Systems Research, 2020, vol. 184, article no. 106297

Link zur Publikation

Zusammenfassung:

The aggregators are intermediary players at the distribution system level. They manage the financial transactions of resources such as micro-turbines (MTs), photo-voltaic (PV) production systems, flexible demands (FDs), and must-run demands (MDs) in the energy market. Based on the regulation of many transmission system operators (TSOs) in European countries, an aggregator is obliged to be assigned to a balancing group (BG) represented by a balancing group manager (BGM). The TSO measures the energy imbalances at the BG level. Meanwhile, the BGM is responsible for the management of financial transactions between the aggregators and the TSO. The strategic aggregators can establish a common BG instead of individual BGs. Then, they gain more payoffs since their energy imbalances are compensated more efficiently. However, the BGM should have the complete knowledge of the aggregators’ confidential information, which includes the evaluation of aggregators concerning the cost/utility functions of associated resources. The aggregators have no incentive to disclose this valuable information. To solve this challenge, an iterative distributed algorithm based on non-cooperative game theory is proposed. In the numerical results, the performance of proposed common BG has been evaluated thanks to the comparison with a benchmark case where the aggregators establish their own individual BGs.

L’intelligence artificielle au service des réseaux de distribution éléctrique

Scientifique

Mokhtar Bozorg, Mauro Carpita, Nastaran Fatemi, Carlos Andrés Pena, Omid Mousavi

Bulletin SEV/VSE, Electrosuisse, 2020

Link zur Publikation

2018

SMILE-FA :

Rapport ArODES

the theoretical and application Study on a Metering and Intelligent tool for Low Voltage grid control Enhancement - Flexibility Assessment

Carpita Mauro, Houmard Douglas, Siemaszko Daniel, Wasterlain Sébastien, Bozorg Mokhtar, Karlov Alexandre, Schuler Jean-Roland, Jaton Joël, Alizadeh-Mousavi Omid, Reynaud Simon, Pinto Antony, Bischof Jonathan, Bifrare Arnoud, Paolone Mario

2018, Bern : Swiss Federal Office of Energy SFOE, 54 p.

Link zur Publikation

Zusammenfassung:

In diesem Projekt untersuchten wir sowohl theoretische als auch praktische Aspekte im Zusammenhang mit der Anwendung von �berwachungssystemen von Niederspannungsnetze auf die Netzregelung und Flexibilit�tsbewertungen. In diesem Zusammenhang haben wir zuerst existierende Methoden und Algorithmen verbessert, die im Rahmen des vorherigem SMILE Projektes (finanziert von der BFE) zur modelllosen Methodik f�r die Berechnung von Sensitivit�tskoeffizienten von Knotenspannungen in Bezug auf die Einspeisung von Wirk- und Blindleistung entwickelt wurden. In einem zweiten Schritt haben wir das Potential von verteilten PV-Anlagen zur Bereitstellung der Prim�rspannungsregelung von Niederspannungsnetzen untersucht. Das optimale Regelungsproblem ist so formuliert, dass es eine anteilige faire Planung der steuerbaren PV-Anlagen gew�hrleistet. Ausgehend von der zentralisierten Formulierung des optimalen Regelungsproblems wurde auch ein dezentraler Algorithmus zum Umgang mit �berspannungen entwickelt. Die dezentrale Version des Algorithmus basiert auf einer dualen Dekompositionsmethode und konvergiert nachweislich gegen die gleiche L�sung wie der zentralisierte Algorithmus. Drittens haben wir die Aspekte des Cybersicherheitsschutzes der vorgeschlagenen Niederspannungs�berwachungsl�sung untersucht. W�hrend dieser Aktivit�t wurde eine vollst�ndige Bedrohungsmodellierung des Systems erstellt, eine Risikobewertung durchgef�hrt und eine Sicherheitsarchitektur entwickelt. Die Sicherheitsarchitektur basiert auf einem Virtual Private Network (VPN), das die Vertraulichkeit und Integrit�t des Informationsaustauschs zwischen Smart Grid-Ger�ten und der Backend-Infrastruktur (Backend-Server) gew�hrleistet. Diese Sicherheitsarchitektur wurde erfolgreich getestet und in der Produktion eingesetzt. Ein Penetrationstest wurde an der BackendInfrastruktur (inklusive webbasiertes Frontend), den Infrastrukturverbindungsmodulen sowie an den Modulen selbst durchgef�hrt. Im vierten Schritt wurden die Methoden zur Berechnung von Spannungssensitivit�tskoeffizienten in Bezug auf die Einspeisung von Wirk- und Blindleistung unter bekannten Betriebsbedingungen im Labor "Smart Grid - Reseaux Intelligent- ReIne" des HEIG-VD getestet und validiert. Die Ergebnisse des Validierungstests haben gezeigt, dass die relativen Unterschiede zwischen den Sensitivit�tskoeffizienten gut sind und belegen die Leistungen der modelllosen Methodik. Abschliessend haben wir einen modelllosen und dezentralen Ansatz zur optimalen Regelung auf Basis der gesch�tzten Sensitivit�tskoeffizienten in einem Verteilungsnetz in der Schweiz implementiert und erfolgreich getestet.

Influencing the bulk power system reserve by dispatching power distribution networks using local energy storage

Scientifique ArODES

Bozorg Mokhtar, Sossan Fabrizio, Le Boudec Jean-Yves, Paolone Mario

Electric Power Systems Research, 2018, vol. 163, part A, pp. 270-279

Link zur Publikation

Zusammenfassung:

Multiple research works and power systems operational practices have qualitatively associated the progressive connection of stochastic renewable energy resources with the increase of power systems reserve requirements. At the same time, the price and technology of MW-class Battery Energy Storage Systems (BESSs) have considerably improved, which opens up the possibility to make electric distribution networks dispatchable. In this paper, we investigate the impact on the bulk power system of dispatchable electric distribution networks that host a large share of stochastic resources. The essential questions inspiring this research are: (1) Assuming that BESSs are deployed to achieve dispatchability of distribution grids embedding stochastic resources, what is the impact on the bulk power system reserve requirement? (2) Is this large-scale integration of BESSs economically viable compared to centralized reserve procurement from traditional power plants? To address these questions, we consider the case of the Danish transmission grid and the associated fleet of conventional power plants and compare it against locally dispatched distribution grids. We perform stochastic simulations to quantify and validate the amount of reserve necessary to operate these power systems with a desired reliability level. We establish a numerical equivalence between saved conventional reserve capacity and amount of BESS storage deployed in distribution networks. Then, we quantify the economic pay-back times of BESSs capital expenditure (CAPEX). The results show that: (1) large scale deployment of BESSs with dispatchable distribution networks is a viable technical solution to address flexibility requirements for the bulk power system and (2) this solution is economically viable with a pay-back time in the range of 11–14 years compared to providing flexibilities from conventional power plants.

2016

Developing offer curves for an electric railway company in reserve markets based on robust energy and reserve scheduling

Scientifique ArODES

Bozorg Mokhtar, Ahmadi-Khatir Ali, Cherkaoui Rachid

IEEE Transactions on Power Systems, 2016, vol. 31, no. 4, pp. 2609 - 2620

Link zur Publikation

Zusammenfassung:

This paper proposes an appropriate offering strategy method for an electric railway company (ERC) to participate in reserve markets. In this respect, first the problem of energy and reserve scheduling for the ERC is modeled in a deterministic way. Next, a robust optimization technique is used to solve the problem taking into account the uncertain energy and reserve prices as well as the uncertain hourly energy demand of the electric railway substations. Afterward, a reserve offering curve construction algorithm based on the solution of robust energy and reserve scheduling is proposed. This algorithm takes into account the correlation between upward and downward reserve prices. Finally, to show the effectiveness of the proposed method, a realistic case study based on the characteristic of an ERC in Switzerland is presented.

2015

Stochastic capacity expansion planning of remote microgrids with wind farms and energy storage

Scientifique ArODES

Hajipour Ehsan, Bozorg Mokhtar, Fotuhi-Firuzabad Mahmud

IEEE Transactions on Sustainable Energy, 2015, vol. 6, no. 2, pp. 491 - 498

Link zur Publikation

Zusammenfassung:

A majority of remote power systems are going to be supplied by diesel-renewable resources such as wind and photovoltaic energy in the future. However, the unpredictable nature of wind generation increases the concern about the reliable operation of these isolated microgrids. Using energy storage systems (ESSs) is recently accepted as an efficient solution to the volatility and intermittency of renewable energy sources. In this paper, a stochastic programming based on the Monte Carlo approach is introduced for optimal planning of remote systems. So far, most literatures have focused exclusively on the energy storage initial sizing. However, capacity expansion of ESS through the time span can result in significant cost saving and will be illustrated in this paper. Factors such as reliability criteria together with the investment and the operation costs are taken into account in the proposed methodology. This method utilizes practical operational constraints of ESS including efficiency and life cycle. Considering life cycle constraint reinforces the proposed method to completely investigate the difference between ESS technologies. The results of case study demonstrate that the proposed capacity expansion algorithm could lead to about 10% more profit over the traditional energy storage sizing.

2013

Probabilistic spinning reserve provision model in multi-control zone power system

Scientifique ArODES

Ahmadi-Khatir Ali, Bozorg Mokhtar, Cherkaoui Rachid

IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 2819 - 2829

Link zur Publikation

Zusammenfassung:

Inter-zonal trading in multi-area power system (MAPS) improves the market efficiency and the system reliability by sharing the resources (energy and reserve services) across zonal boundaries. Actually, each area can operate with less reserve resources than would normally be required for isolated operation. The aim of this work is to propose a model that includes the problem of optimal spinning reserve (SR) provision into the security constraint unit commitment (SCUC) formulation based on the reliability criteria for a MAPS. The loss of load probability (LOLP) and the expected load not served (ELNS) are evaluated as probabilistic metrics in the case of a multi-control zone power system. Moreover, we demonstrate how these criteria can be explicitly incorporated into the market-clearing formulation. The non-coincidental nature of spinning reserve requirement across the zonal boundary is effectively modeled. Two system cases including a small-scale (six-bus) test system and the IEEE reliability test system (IEEE-RTS) are used to demonstrate the effectiveness of the presented model.

2020

A derivative-persistence method for real time photovoltaic power forecasting

Conference ArODES

Mokhtar Bozorg, Mauro Carpita

Proceedings of 2020 International Conference on Smart Grids and Energy Systems (SGES), 23-26 November 2020, Perth, Australia

Link zur Konferenz

Zusammenfassung:

This paper focuses on very-short-term photovoltaic power forecasting based solely on power measurement. The approach proposed in the paper is tailored for real time operation of smart grids and microgrids, that deals with energy management and control of the various resources of the grid and, particularly, on those based on renewable energy sources. These resources are characterized by variable power production, thus constituting a challenge for the effectiveness of strategies and tools of management and control. This paper refers to photovoltaic power production and presents a new real time forecasting method which improves the classical persistence model. The proposed method is based on the idea of conveniently weighting information on past data by imposing continuity of the function, of its first derivative and of the second derivative so obtaining three estimates of the function in the forecast interval which are then opportunely weighted to provide the forecast. The effectiveness of the proposed approach is shown in the numerical application based on actual measured data. The accuracy of the proposed approach is also tested through comparisons with two models based on the persistence and auto regressive moving average models.

State estimation for medium and low voltage distribution grids based on near real-time grid measurements and delayed smart meters data

Conference ArODES

Mokhtar Bozorg, Mohammad Rayati, Mauro Carpita

Proceedings of 2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe), 7-11 September 2020, Lyon, France

Link zur Konferenz

Zusammenfassung:

In this paper, first, a distribution system state estimation (DSSE) algorithm based on Distflow model is presented. The model is designed for radial low voltage (LV) distribution grids considering traversal components such as the cable capacitances. Next, to obtain the pseudo-measurements required for the DSSE algorithm, we developed and compared three intraday nodal load forecast (INLF) methods that take; i) near real-time stream of data from the grid measurement devices (MDs), i.e., voltage and current magnitudes at limited number of lines/nodes, and ii) the batch data set from smart meters (SMs) available for previous days. The accuracy associated with the INLF methods is quantified in terms of statistical characteristics such as average of symmetric mean absolute percentage error (sMAPE) over all nodes of the distribution grid. Afterwards, the impact of this accuracy on the DSSE results is investigated using real data available for a distribution grid in city of Geneva, Switzerland.

Incidence of Input Forecast Error in Microgrid Operation

Conference ArODES

Mokhtar Bozorg

Proceedings of 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 24-26 June 2020, Sorrento, Italy

Link zur Konferenz

Zusammenfassung:

This paper deals with the optimal operation of microgrids in presence of uncertainties. Loads and distributed energy resources such as renewable-based generation units and electrical energy storage systems are supposed to be clustered and managed by a microgrid operator. The proposed approach allows the operator to optimally schedule the microgrid's control variables by properly managing the uncertainties affecting the input parameters. Different forecasting methods are adopted within an optimal day ahead scheduling procedure which is devoted to the total cost minimization while preserving a secure operation of the network. The proposed approach accounts for uncertainties affecting the active power of loads and renewable generators, the reactive power of loads, and the electricity prices. Comparison of different forecasting methods allows evidencing the impact of uncertainties on the operation of the microgrid.

2019

ReIne, a flexible laboratory for emulating and testing the distribution grid

Conference ArODES

Douglas Houmard, Jean-François Affolter, Mokhtar Bozorg, Mauro Carpita

Proceedings of EPE'19 ECCE Europe, 2-6 September 2019, Genoa, Italy

Link zur Konferenz

Zusammenfassung:

The energy transition will generate a strong transformation of the electricity network, in Switzerland and all over the world. Distribution networks are expected to play a vital role, even though hystorically they have not been designed for this purpose. In order to better study and plan the changes to be made, the ReIne (REseaux INtElligents, French acronym for “Smart Grids”) laboratory has been conceived and built to test various solutions. The actual novelty of this laboratory resides in its intrinsic flexibility, allowing reproducing several grid topologies and different situations of loading and generation. It will allow the on field study of a wide range of smart and micro grid problematics, from short-term and midterm scheduling to very fast dynamics issues of connected devices.

A robust optimization framework for the day-ahead scheduling of active distribution networks including energy storage systems

Conference ArODES

Mokhtar Bozorg

Proceedings of the 13th IEEE PowerTech (POWERTECH) 2019

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Zusammenfassung:

In this paper we present an adaptive robust optimization framework for the day-ahead scheduling of Active Distribution Networks (ADNs) where the controlled devices are distributed Energy Storage Systems (ESSs). First, the targeted problem is formulated using a two-stage optimization approach. The first-stage decisions determine the amount of import/export energy from the external grid at each hour, as well as energy exchanges for each ESS. The second stage deals with the intra-day control, given the first stage decisions. In order to effectively consider the impacts of uncertainties, the problem is transformed into a ‘min-max-min’ formulation. Here, we minimize the first (day-ahead scheduling) and second stage (intra-day operation) costs while uncertainties are maximally affecting the cost function of the second stage. The Benders dual cut algorithm is employed for the solution of the optimization problem. IEEE 34 bus standard network is the benchmark grid for assessing the performances and effectiveness of the developed robust optimization process.

Allocation of active power reserve from active distribution networks using a cost-benefit approach :

Conference ArODES

application to swissgrid network

Mokhtar Bozorg

Proceedings of the 13th IEEE PowerTech (POWERTECH) 2019

Link zur Konferenz

Zusammenfassung:

The flexibility of distributed energy resources (DERs) accommodated in active distribution networks (ADNs) can be aggregated and then used to provide ancillary services to the transmission system. In this context, this paper presents a linear optimization method for the transmission system operator (TSO) to allocate its required active power reserve from aggregated resources installed in active distribution systems (ARADSs) as well as dispatchable bulk power plants (DBPPs). It consists in a linear optimization problem that minimizes the sum of the expected cost of active power reserve allocated from all possible providers (including ARADSs and DBPPs) and the expected cost of load not served over a desired time horizon. The value of lost load (VOLL) index is used as a criterion to realize an economical balance between the expected cost of allocated reserve and expected cost of load not served. The method leverages scenarios of power system contingencies and forecast errors of loads and renewable generation to represent typical operational uncertainties. A simulation proofof-concept using real-data from the transmission system operator of Switzerland, Swissgrid, is provided to illustrate the performance of the method.

2018

Dispatch and primary frequency control with electrochemical storage :

Conference ArODES

a system-wise verification

Mokhtar Bozorg

Proceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

Link zur Konferenz

Zusammenfassung:

Uncertainty levels in forecasting of renewable generation and demand are known to affect the amount of reserve required to operate the power grid with a given level of reliability. In this paper, we quantify the effects on the system reserve and reliability, due to the local dispatch of stochastic demand and renewable generation. The analysis is performed considering the model of the IEEE 39-bus system, with detailed dynamic models of conventional generation, wind generation, demand and an under-frequency load shedding mechanism. The analysis compares to cases: the base case, where renewable generation and demand power are stochastic and the power reserve is provided by conventional generation, against the case where the operation of traditionally stochastic resources is dispatched according to pre-established dispatch plans thanks to controlling local flexibility. Simulations reproduce the post-contingency dynamic behavior of the grid due to outages of generators. The contingencies are selected to trigger under frequency load shedding mechanisms, hence to demonstrate the different levels of system operation reliability for the two case studies. Simulation results show that dispatching traditionally stochastic generation scores better regarding to expected energy not served, producing an increase of the system reliability.

Allocation of frequency control reserve from aggregated resources of active distribution systems

Conference ArODES

Mokhtar Bozorg

Proceedings of the Power Systems Computation Conference (PSCC) 2018

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Zusammenfassung:

This paper addresses the allocation of frequency control services (FCSs) from aggregated resources of active distribution systems (ARADSs) for balancing the transmission system while considering credible contingencies as well as forecast error of loads and generations through scenarios. First the paper introduces a generic framework for modeling ARADS including distributed energy resources (DERs) as seen from the transmission system operator (TSO) perspective. Afterwards, based on the proposed modeling framework and relying on a DC power flow model, the problem is formulated as a linear optimization problem consisting in minimizing the cost of FCSs provision and deployment from all possible providers including ARADSs and GENCOs. Nodal and total Expected Load Not Served (ELNS) indices are used to measure the security of the transmission system for the different scenarios. Finally, a proofof- concept of the proposed planning strategy is proposed by considering the IEEE 24-bus system.

2015

Energy and reserve scheduling for Electric Railway power system using stochastic optimization

Conference ArODES

Mokhtar Bozorg

Proceedings of the IEEE Eindhoven PowerTech 2015

Link zur Konferenz

Zusammenfassung:

This paper proposes an optimal joint energy and reserve scheduling model for electric railway power system which is operated by an Electric Railway Company (ERC). The model relies on a two stage stochastic optimization method that accounts for the uncertainties of the ERC energy demand and the availability of its generators and interconnecting converters. A case study based on the characteristics of Swiss railway power system in the western part of Switzerland is presented to demonstrate the effectiveness of the proposed model.

2014

Statistical analysis of optimal energy and security controls under different sources of uncertainties

Conference ArODES

Mokhtar Bozorg

Proceedings of the International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) 2014

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Zusammenfassung:

The management of uncertainties is a challenging task for reliable and secure operation of power systems. The uncertainties come from multiple sources, including the forecast errors of wind power and load, the forced outage of generating units, loss of transmission equipments, etc. This paper classifies different uncertainties based on their binary and continuous attributes. The main idea is to investigate the effect of each source of uncertainties on the cost of energy and security controls. For this purpose, a specific optimization method is developed which takes into account a forecasted scenario and a stochastic scenario. This optimization problem is solved for a fixed forecasted scenario and a varying stochastic scenario. The stochastic scenarios are constructed using a Monte Carlo Simulation that considers various sources of uncertainties. The main advantage of the proposed optimization is that the number of incorporated stochastic scenarios does not increase the size of the optimization problem. The models of different uncertainties, particularly wind power forecast errors, are discussed in depth. This optimization allows obtaining the statistical moments and constructing the probability distributions. The proposed optimization approach is then applied to the IEEE RTS 24-bus system. The probability distributions and statistical moments of objective functions and control variables are assessed for three cases, namely: (i) with only binary uncertainties, (ii) with only continuous uncertainties and (iii) with both of them.

2013

Converter placement in the interconnection of railway power grid and public power grid based on cost-reliability approach

Conference ArODES

Mokhtar Bozorg

Proceedings of the 10th IEEE PowerTech Conference 2013

Link zur Konferenz

Zusammenfassung:

The railway load in Switzerland is expected to increase in the next years. Increasing the capacity of the interconnection between the railway power system and the public grid is a reasonable solution to deal with this problem. This paper proposes a way to find the locations and capacities for new interconnections and the capacity expansion for the existing one. To show the effectiveness of the proposed model, the networks in the Suisse Romande region (French part of Switzerland) is considered as a case study.

2012

Investigation of the blackouts complexity regarding spinning reserve and frequency control in interconnected power systems

Conference ArODES

Mokhtar Bozorg

Proceedings of the 12th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

Link zur Konferenz

Zusammenfassung:

The dynamics of power system blackouts and the associated Self-Organized Criticality (SOC) behavior is a subject that receives continuous attention in view of its inherent complexity and relevant consequences. Within this context, the paper focuses on the short-term dynamic and, more specifically, aims at studying the role of the spinning reserve and frequency control on the blackout mechanism. The main contribution is to study the influence of the power system interconnections on the pre and post blackout system behavior. For this investigation, a statistical procedure, based on the Monte Carlo Simulation (MCS) method, is proposed to perform a blackout risk analysis considering cascading outages and generators frequency responses. The proposed procedure is then applied to the IEEE 118 bus system as an interconnected network characterized by three areas. The distribution of the blackout size and the number of component outages are assessed for the whole system as well as for each area.

2010

Interruptible load contracts implementation in stochastic security constrained unit commitment

Conference ArODES

Mokhtar Bozorg

Proceedings of the IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems 2010

Link zur Konferenz

Zusammenfassung:

Providing more choices to customers for determining the cost of their required reliability levels is one of the most important targets of restructured power system. Centralized management of reliability in Vertically Integrated Utility (VIU) applying the same policies to different customers is replaced with decentralized management allowing customers to participate in reliability management based on their required reliability levels. Hence, the importance of implementing interruptible load contracts that specifies each customer’s reliability level has been increased in power system studies. In this paper, stochastic programming based on Monte Carlo approach has been used to implement interruptible load contracts in security constrained unit commitment. The performance of the proposed method is demonstrated by applying it to a 6-bus system.

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