« Retour

Bozorg Mokhtar

Professeur HES associé

Contrat principal

Professeur HES associé

Téléphone: +41 24 557 62 50

Bureau: B53

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

Institut
IE - Institut des énergies

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

Réseaux électriques
Régulation automatique
Projets interdisciplinaires

MA BFH/HES-SO en Architecture - HES-SO Master

Réseaux électriques avancés
Power Grids : System and Devices

Terminés

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

AGP

Rôle: Collaborateur/trice

Requérant(e)s: IESE

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

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.

Equipe de recherche au sein de la HES-SO: Houmard Douglas , Gavin Serge , Pidancier Thomas , Bozorg Mokhtar , Carpita Mauro

Partenaires professionnels: IESE

Durée du projet: 01.01.2017 - 31.12.2017

Montant global du projet: 171'925 CHF

Statut: Terminé

2024

Sizing and experimental validation of a selfsync droop-based grid forming inverter with direct voltage control

Article scientifique ArODES

Philippe Morey, Marc Pellerin, Douglas Houmard, Mokhtar Bozorg, Mauro Carpita

Sustainable Energy, Grids and Networks, 38, 2024, 101332

Lien vers la publication

Résumé:

Currently, the transient stability of the grid is mainly ensured by the inertial behavior of its synchronous generators. The increase of renewable energy sources connected to the grid through grid following (GFL) inverters is severely affecting the inertia of the grid and its stability is beginning to be compromised. Moreover, in case of a large disturbance, these renewable energy sources disconnect from the grid by design, thus exacerbating the problem. The idea of mimicking the behavior of synchronous generators in an inverter and participating in the grid stability gave birth to the so-called grid forming (GFM) inverters. This implies a change in paradigm from current controlled sources (GFL) to voltage-controlled sources (GFM). Despite known stability issues in current controlled converters, a typical approach to GFM converters with LCL output filters is based on cascaded current and voltage control loops. An internal current loop controls the inverter’s output currents and an external voltage loop controls the filter capacitors’ voltages. The problem is that this solution is complex and is also marginally stable. Alternatively, in this paper we explore a direct voltage control, which is based on droop control. The proposed control’s output is used for the direct control of the inverter’s output voltage in amplitude and phase. A complete analysis is performed, and a method is proposed to determine the appropriate control parameters as well as the sizing of the inverter components. Finally, an experimental setup is presented and the effectiveness of the proposed method is shown in simulations as well as in real-life experiments.

Jumeau numérique de réseaux de distribution

Article professionnel ArODES

Mokhtar Bozorg, Nastaran Fatemi, Christopher Meier, Mohammad Rayati, Luca Tomasini

bulletin.ch = Fachzeitschrift und Verbandsinformationen von Electrosuisse und VSE = Bulletin SEV/AES : revue spécialisée et informations des associations Electrosuisse et AES, 2024, 1, 21-24

Lien vers la publication

Résumé:

Les jumeaux numériques ont le potentiel de révolutionner la gestion des réseaux de distribution. Ces représentations virtuelles d’un système physique en temps réel intègrent et enrichissent les données de diverses sources : la plateforme TwinDiGrid permet ainsi de visualiser l’état du réseau, d’identifier les points critiques et de simuler les scénarios du futur.

Sizing and Experimental Validation of a Selfsync Droop-Based Grid Forming Inverter with Direct Voltage Control

Article scientifique

Morey Philippe, Pellerin Marc, Houmard Douglas, Bozorg Mokhtar, Carpita Mauro

Sustainable Energy, Grids and Networks, 2024 , vol. 38

Lien vers la publication

Résumé:

2023

Distributed risk-constrained strategy management of cloud energy storage for residential and electricity market services provision

Article scientifique ArODES

Amir Hamidi, Mokhtar Bozorg, Mohsen Hamzeh, Rachid Cherkaoui

Sustainable Energy, Grids and Networks, 36 (2023), 101183

Lien vers la publication

Résumé:

Energy storage systems can improve the performance of home energy management systems and also provide ancillary services in electricity markets. However, the economic and technical insufficiency of individual home storage units, makes employing them inefficient for domestic storage and market applications, respectively. Therefore, central storage units with capacity sharing capability to small consumers as Cloud Energy Storages (CESs) are becoming a promising solution, and are also potentially able to contribute to ancillary service provision in the market. In this context, this paper aims to develop a novel stochastic day-ahead distributed management framework for CES to simultaneously adopt the optimal strategy of storage allocation for photovoltaic-integrated homes and participation in energy and ancillary service markets. The developed distributed framework is formulated within the Benders decomposition approach in which CES strategy and home energy management systems are optimized through master and subproblems based on linear mathematical models, respectively. Furthermore, the uncertainty of market prices is handled by the Conditional Value at Risk (CVaR) method. The developed model is assessed through real-world case studies of ERCOT. The results highlight the effectiveness of the collaborative service framework for both CES and households. Energy arbitrage through local storage services compensates for CES's challenges such as degradation and low power rating in the provision of energy services. These improvements benefit CES owners with higher net revenues and households with reduced cost of electricity supply.

Operation-aware system design for synergistic integration of regenerative braking energy of railway systems into EV fast charging stations

Article scientifique ArODES

Sepehr Najafi Larijani, Seyed Saeed Fazel, Mokhtar Bozorg, Hamed Jafari Kaleybar

IEEE Access, 2023, 11, pp. 121811 - 121827

Lien vers la publication

Résumé:

Fast charging stations (FCSs) for electric vehicles are associated with large power demands, often coinciding with peak power demands from other consumers. This places significant strain on distribution networks (DNs). To address this issue, this paper proposes a promising method that harnesses the untapped potential of railway systems and wasted regenerative braking energy (RBE) of trains in combination with a battery energy storage system (BESS) to supply EV charging station and participate in the FCS energy supply chain (FCS-ESCH). The proposed integrated approach, as the intermodal sustainable electric transportation system (SETS), aims to reduce the peak power demand of FCSs especially at strategic locations, such as parking areas close to electric railway stations (ERSs) and park and ride areas. To achieve this goal, an operation-aware optimization model is developed that determines the optimal sizes of the interfacing converter and the BESS, while also facilitating optimal energy exchange planning. A generic model for trains’ RBE production is proposed and adapted to the operation-aware optimization model to verify the effectiveness of the proposed approach by conducting simulations of a real case study that accurately replicates system behavior with the desired level of precision. Furthermore, sensitivity analyses are performed to explore the impact of varying the power rating of the converters responsible for transmitting RBE to the FCS-ESCH, as well as the per-unit price of BESS energy capacity. The results showcase the potential of employing SETS as an efficient means to mitigate the challenges associated with FCSs, especially peak shaving and cost reductions, paving the way for a more sustainable electric transportation system.

Causality between residential electricity consumption and explanatory factors

Article scientifique ArODES

Serge Guefano, Mokhtar Bozorg, Mauro Carpita, Pauline Darline Etet-Baha, Francis Lénine Djanna Koffi, Jean Gaston Tamba, Jacques Etame

Energy Strategy Reviews, 2023, vol. 49, article no. 101155

Lien vers la publication

Résumé:

This research adds to the assessment of the causal relationships between residential electricity consumption and its explanatory factors. Johansen cointegration, autoregressive distributed lag model, the vector error correction model, and Wald causality tests are used. In both the short and long run, a unidirectional causality towards electricity consumption is identified. The sustainable development of the electricity sector in Cameroon, as defined by the national development strategy for 2030 and the long-term electricity sector development plan for 2030, requires an increase in GDP per capita, the adoption of a compact less energy-intensive urban model, the strengthening of control over the evolution of the number of subscribers on the distribution networks, and the reduction of CO2 emissions from thermal electricity generation.

Stochastic optimization and Markov chain-based scenario generation for exploiting the underlying flexibilities of an active distribution network

Article scientifique ArODES

Mohammad Rayati, Mokhtar Bozorg, Mauro Carpita, Rachid Cherkaoui

Sustainable Energy, Grids and Networks, 2023, vol. 34, article no. 100999

Lien vers la publication

Résumé:

This paper proposes a scalable stochastic optimization model and a Markov chain-based scenario generation method to benefit from an active distribution network’s (ADN’s) flexibility. The optimization variables are the dispatch plan, such as the active and reactive power of battery energy storage (BES) and photovoltaic (PV) systems, as well as the active and reactive power and flexibilities given to the transmission network at the point of common coupling (PCC). The uncertainty vector, on the other hand, is made up of the PV system’s production capability, electricity demands, the flexibility request of the transmission system operator (TSO), and the voltage at the PCC. The resulting stochastic optimization problem is a second-order cone programming (SOCP) problem that is solved using freely available convex solvers. To validate the performance of the proposed stochastic optimization, the tests were carried out in a laboratory, where a flexible structure mimics different distribution network topologies, such as a real low-voltage radial one in Switzerland.

Hierarchical electricity-carbon trading for regional virtual federated prosumers

Article scientifique ArODES

Lu Wang, Mohammad Rayati, Mokhtar Bozorg, Rachid Cherkaoui, Zhi Wu, Wei Gu

Electric Power Systems Research, 2023, vol. 217, article no. 109160

Lien vers la publication

Résumé:

Facing the dilemma of mitigating carbon emissions and growing energy demand, this paper proposes a hierarchical electricity-carbon trading mechanism for virtual federated prosumers (VFPs) in regional markets. At the inter-regional level, a trading platform coordinates the energy-sharing and wholesale markets, intending to minimize the cost of VFPs. The clearing of the energy-sharing market is formulated as a non-cooperative game, in which a flow-based distributed consensus algorithm is developed to obtain equilibrium with limited shared information. On the other hand, at the intra-regional level, each VFP as selfless auctioneer leverages discriminatory weights and benchmark prices to allocate the electricity-carbon budget intra-VFP (i.e., among the prosumer nodes within the VFP) and to maximize social welfare, for which a distributed feedback allocation algorithm is proposed. The hierarchical electricity-carbon trading is solved by a progressive alternate iterative algorithm. A case study based on a practical regional grid verifies the effectiveness of the proposed algorithms and shows that the mechanism improves energy efficiency and reduces carbon emissions.

Impact of heat pump and cogeneration integration on power distribution grids based on transition scenarios for heating in urban areas

Article scientifique ArODES

Marten Fesefeldt, Massimiliano Capezzali, Mokhtar Bozorg, Riina Karjalainen

Sustainability, 2023, vol. 15, no. 6, article no. 4985

Lien vers la publication

Résumé:

Electrification of final use sectors such as heating and mobility is often proposed as an effective pathway towards decarbonization of urban areas. In this context, power-driven heat pumps (HP) are usually strongly fostered as alternatives to fossil-burning boilers in municipal planning processes. In continental climates, this leads to substantially increased electricity demand in winter months that, in turn may lead to stress situations on local power distribution grids. Hence, in parallel to the massive implementation of electric HP, strategies must be put in place to ensure the grid stability and operational security, notably in terms of voltage levels, as well as transformer and line’s capacity limits. In this paper, three such strategies are highlighted within the specific situation of a mid-sized Swiss city, potentially representative of many continental, central Europe urban zones as a test-case. The hourly-based power flow simulations of the medium- and low-voltage distribution grids show the impact of various future scenarios, inspired from typical territorial energy planning processes, implying various degrees of heat pumps penetration. The first strategy relies on the implementation of decentralized combined heat and power (CHP) units, fed by the existing natural gas network and is shown to provide an effective pathway to accommodate heat pump electricity demand on urban power distribution grids. Two alternative solutions based on grid reinforcements and controlled usage of reactive power from photovoltaic (PV) inverters are additionally considered to ensure security constraints of grid operation and compared with the scenario relying on CHP deployment.

2022

An adaptative emergency approach for hybrid networked microgrids resilience

Article scientifique ArODES

Ayda Shaker, Mokhtar Bozorg, Amin Safari, Sajad Najafi-Ravadanegh

IEEE Access, 10, 103164-103175

Lien vers la publication

Résumé:

The low inertia of renewable-based distributed energy resources (DERs) renders hybrid networked microgrids (NμGs) dynamically susceptible to transients. Such fragility makes it very difficult for NμGs operators to maintain a reasonable margin for the resilient operation during extreme condition contingencies. This paper presents a three-stage emergency approach to improve resilience of NμGs through maintaining dynamic security. The proposed approach targets preserving the resilient operation of NμGs by preventing unnecessary tripping of the DERs after unintentional islanding incident. To do so, a resilient operation zone (ROZ) is introduced which determines the secure operating zone for NμGs and the limits for implementing the corrective countermeasures for resilience augmentation. The proposed approach is outlined in three stages: First, offline analysis is carried out to model and calculate the ROZ. Second, hybrid NμGs operating point is monitored at the pre-event stage and the calculated ROZ at offline stage is adapted to the operating conditions. The third stage is responsible for real-time evaluation of hybrid NμGs security using the ROZ and implementation of the countermeasures. Comprehensive simulation studies presented in this paper demonstrate effectiveness of the proposed scheme for enhancing resilience of hybrid NμGs.

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

Article scientifique ArODES

Mohammad Rayati, Mokhtar Bozorg, Rachid Cherkaoui, Mauro Carpita

IEEE Transactions on Smart Grid, 2022, vol. 13, no 4, pp. 2920 - 2934

Lien vers la publication

Résumé:

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

Paola Paruta, Thomas Pidancier, Mokhtar Bozorg, Mauro Carpita

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

Mohammad Rayati, Mokhtar Bozorg, Rachid Cherkaoui

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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 :

Article professionnel ArODES

le projet Grid Data Digger

Mokhtar Bozorg, Nastaran Fatemi, Carlos Andrés Peña-Reyes, Omid Mousavi, Mauro Carpita

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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

Article scientifique

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

Bulletin SEV/VSE, Electrosuisse, 2020

Lien vers la publication

2018

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

Article scientifique ArODES

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

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

Mokhtar Bozorg, Ali Ahmadi-Khatir, Rachid Cherkaoui

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

Ehsan Hajipour, Mokhtar Bozorg, Mahmud Fotuhi-Firuzabad

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

Lien vers la publication

Résumé:

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

Article scientifique ArODES

Ali Ahmadi-Khatir, Mokhtar Bozorg, Rachid Cherkaoui

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

Lien vers la publication

Résumé:

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.

2024

Admittance matrix estimation of radial distribution grids using power and voltage magnitude measurements

Conférence ArODES

Mohammad Rayati, Mokhtar Bozorg, Omid Alizadeh-Mousavi

Electric Power Systems Research ; Proceedings of the 23rd Power Systems Computation Conference (PSCC 2024), 4-7 July 2024, Paris-Saclay, France

Lien vers la conférence

Résumé:

Accurate knowledge of the admittance matrix of distribution grids is essential for grid operation and control as more renewable energy sources and electric vehicles are integrated into distribution grids. Due to limited observability, measurement noise, topology assumptions, and partial data availability, the exact calculation of the admittance matrix of distribution grids is challenging. In this paper, we present a method for estimating the admittance matrix of radial distribution grids. The proposed method uses low-cost measurement devices that record ten-minute-based measurements of voltage magnitudes, active power, and reactive power at a single end of the lines or transformers instead of second-based phasor measurements. The impedance of lines or transformers is then estimated using a statistical optimization subject to the distribution flow (DistFlow) model. The effectiveness of proposed method is demonstrated by simulation, laboratory testing, and field measurements taken from a distribution grid in Switzerland.

2023

Distributed bidding strategy for an aggregator valuing uncertain prosumers in energy and ancillary service markets

Conférence ArODES

Amir Hamidi, Mokhtar Bozorg, Mohsen Hamzeh, Rachid Cherkaoui

Proceedings of the 2023 19th International Conference on the European Energy Market (EEM)

Lien vers la conférence

Résumé:

Aggregators have an influential intermediary role to exploit the flexibility of prosumers like smart homes for procuring the required capacity of ancillary services to support power systems. Local generation and consumption uncertainties of prosumers affect their cost reduction objectives which in turn have a significant impact on the flexibility capacity provided by them. So, towards boosting the operational flexibility capacity in the market, the aggregator’s bidding and remuneration frameworks should devise a valuing scheme of prosumers considering this impact to induce positive incentives relieving capacity uncertainties. This scheme, simultaneously, should observe data privacy concerns. To this end, this paper proposes a day-ahead distributed bidding strategy framework for an aggregator to participate in regulation and energy markets using a new rewarding scheme derived from a flexibility certainty valuing approach. This framework is developed within the Benders decomposition method, in which the aggregator performs valuing of prosumers in the master problem through their implicit information extracted from home energy management systems modeled in the subproblems. The performance of the framework to appropriately reward prosumers is presented through numerical results for an illustrative case study.

Dynamic analysis of future scenarios of power systems dominated by inverter-based renewable resources

Conférence ArODES

Guido Maione, Philippe Morey, Mauro Carpita, Mokhtar Bozorg, Giuseppe Marco Tina

Proceedings of 2023 International Conference on Clean Electrical Power (ICCEP)

Lien vers la conférence

Résumé:

Conventional power plants based on synchronous generators are being replaced with renewable energy sources, consequently increasing the proportion of inverter-based resources in the system. In this context, the total grid inertia is decreasing and system services that were traditionally delivered by synchronous generator need to be provided by inverter-based resources. To this end, it is necessary to develop and implement advanced inverter controls, such as grid-forming capability, that allow the provision of newly defined services. In this paper, dynamic analyses are performed, using DigSilent PowerFactory, on a power system with a significant presence of inverter-based resources. The study is based on the Swiss future scenarios. The effectiveness of grid forming inverters, based on droop control as well as virtual synchronous machine algorithms, on frequency support is studied. The results show that grid forming inverters have the potential to counteract the negative effects of traditional grid following inverter-based resources, as currently used by renewable power plants.

2022

Pricing differentially private smart meter data in distribution networks

Conférence ArODES

Mohammad Rayati, Mokhtar Bozorg

Proceedings of 2022 18th International Conference on the European Energy Market (EEM)

Lien vers la conférence

Résumé:

More electrification in the mobility and building sectors, as well as increased electricity generation from distributed renewable energy sources such as household photovoltaic systems, are two of the most promising paths toward a more environmentally sustainable energy system. Electric power distribution system operators (DSOs) must improve the network’s observability in this context to address a variety of technical and market-related concerns, including local network congestion, flexibility exchanges, and resource allocation. To achieve this goal, DSOs are installing smart meters at end-users’ locations, as well as measuring devices and monitoring systems on low and medium-voltage networks. Despite the fact that smart meters are an important part of this transformation, privacy laws prevent data from being used for anything other than normal operation and billing without the consent of end-users. We present a model for dealing with differentially private data from smart meters. After that, we present an optimization problem for pricing such differentially private smart meters data, taking into account the value generated for the DSO through state estimation. Using real-anonymous smart meter data from a DSO in Switzerland, we evaluate the effectiveness of our suggested mechanism for buying such differentially private data.

A flow-based distributed trading mechanism in regional electricity market with energy hub

Conférence ArODES

Lu Wang, Mokhtar Bozorg, Mohammad Rayati, Rachid Cherkaoui

Proceedings of the 2022 18th International Conference on the European Energy Market (EEM)

Lien vers la conférence

Résumé:

The concept of the energy hub (EH) has been emerged to accommodate renewable energy sources in a multi-energy system to deploy the synergies between electricity and other energy sources. However, the market mechanisms for integration of the EHs into the energy markets are not sufficiently elaborated. This paper proposes a flow-based distributed trading mechanism in the regional electricity market (REM) with EH. The regional system operator (RSO) coordinates the net transactions of the regional transmission system in two markets, i.e., a REM market integrated with an EH and the wholesale electricity market of the upstream grid. As an independent stakeholder, each nodal agent uses price disparity to achieve cross-arbitrage from both markets. The EH is a third player intending to maximize his profit from trading in the REM and the wholesale natural gas market. We develop a distributed algorithm based on the alternating direction method of multipliers (ADMM) to obtain the equilibrium solution. The DC power flow is decomposed into optimization problems for the RSO and the agents at different nodes, which can be solved in a distributed manner to achieve the global optimality without violating the privacy of agents. A case study based on a realistic regional transmission system verifies the effectiveness of the proposed mechanism and shows that the mechanism is effective in the decomposition of power flow and the increment of energy efficiency.

A data-driven algorithm for short-term prediction of over-voltage and under-voltage events in distribution grids

Conférence ArODES

Ataeem Shabnam, Mohammad Rayati, Carlos Andrés Peña-Reyes, Omid Alizadeh-Mousaveh, Mokhtar Bozorg

Proceedings of the 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

Lien vers la conférence

Résumé:

This paper proposes algorithms for short-term over- and under-voltage prediction in distribution grids. The proposed algorithms are developed using time-series of voltage and current measurements, which does not require the knowledge of distribution grid model (topology and parameters of the components). Various algorithms based on random forest classifier (RFC) and random forest regressor (RFR) methods, two prominent machine learning methods, are developed regarding different feature selection possibilities. The developed algorithms are tested and validated on two real datasets (grid measurement data from GridEye devices in two low voltage grids in Switzerland). An algorithm based on RFR method, with recent information including the measurement data of the last week at the same time of prediction, outperforms other algorithms. The proposed algorithm can predict over- and under-voltage events with 85% accuracy four hours ahead of the real time.

Real-time distribution grid control and flexibility under uncertainties :

Conférence ArODES

laboratory demonstration

Mohammad Rayati, Mokhtar Bozorg, Mauro Carpita, Pasquale De Falco, Pierluigi Caramia, Antonio Bracale, Daniela Proto, Fabio Mottola

Proceedings of the IEEE Mediterranean Electrotechnical Conference (MELECON) 2022

Lien vers la conférence

Résumé:

In this paper, we assess the effectiveness of forecasting and optimization algorithms on a laboratory demonstration platform that mimics a domestic distribution grid with a high penetration of photovoltaic (PV) systems. Despite the uncertainties, the considered algorithms ensure efficient and secure real-time (RT) operation of the distribution grid, as well as the provision of flexibility services from the low-voltage (LV) distribution grid to the upstream medium-voltage (MV) grid. Uncertainties arise from the variations in PV systems power production and end-users’ power consumption, as well as RT deployment of flexibility services. As a result of the considered algorithms, the distribution grid becomes active in the provision of flexibility services. The forecasting and optimization algorithms are based on Bayesian bootstrap quantile regression (BBQR) and distributionally robust chance-constrained (DRCC) programming, respectively. This paper also evaluates the framework of the laboratory demonstration platform for the deployment of the considered algorithms.

STATCOM optimal placement method applied to a Swiss urban distribution network considering future photovoltaic integration scenarios

Conférence ArODES

Guillaume Courteau, Enea Auberson, Patrick Favre-Perrod, Olivier de Marignac, Mokhtar Bozorg, Mauro Carpita

Proceedings of Speedam 2022

Lien vers la conférence

Résumé:

This paper proposes a pragmatic oriented exhaustive method to select a location that maximises the steadystate support of a static synchronous compensator (STATCOM) in a MV distribution network. This method is intended to be used during preliminary study, when STATCOM size and control strategy are not yet defined. To choose the most effective location, performance indices based on the network's required behaviour need to be defined. The presented method was designed in the frame of a research project named COSTAM, dealing with STATCOM behaviours in a real MV distribution network with massive photovoltaic (PV) production. The proposed method was applied to a real case MV network considering future PV integration scenarios. Network modelling, load/generation scenarios based on 2050 PV integration forecast, performance indices and STATCOM control strategies were defined for this case study, leading to the optimal STATCOM location selection. Method and results applied to this case study are detailed in this article to illustrate the general method. The main advantage of this method is its pragmatic approach. It is easily applicable by Distributed System Operators (DSO) in their network.

2020

A derivative-persistence method for real time photovoltaic power forecasting

Conférence ArODES

Mokhtar Bozorg, Mauro Carpita, Pasquale De Falco, Davide Lauria, Fabio Mottola, Daniela Proto

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

Lien vers la conférence

Résumé:

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

Conférence ArODES

Mohammad Rayati, Thomas Pidancier, Mauro Carpita, Mokhtar Bozorg

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

Lien vers la conférence

Résumé:

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

Conférence ArODES

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

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

Lien vers la conférence

Résumé:

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

Conférence ArODES

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

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

Lien vers la conférence

Résumé:

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

Conférence ArODES

Mostafa Nick, Mokhtar Bozorg, Rachid Cherkaoui, Mario Paolone

Proceedings of the 13th IEEE PowerTech (POWERTECH) 2019

Lien vers la conférence

Résumé:

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 :

Conférence ArODES

application to swissgrid network

Mohsen Kalantar-Neyestanaki, Mokhtar Bozorg, Fabrizio Sossan, Rachid Cherkaoui

Proceedings of the 13th IEEE PowerTech (POWERTECH) 2019

Lien vers la conférence

Résumé:

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 :

Conférence ArODES

a system-wise verification

Yihui Zuo, Fabrizio Sossan, Mokhtar Bozorg, Mario Paolone

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

Lien vers la conférence

Résumé:

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

Conférence ArODES

Mohsen Kalantar-Neyestanaki, Mokhtar Bozorg, Fabrizio Sossan, Rachid Cherkaoui

Proceedings of the Power Systems Computation Conference (PSCC) 2018

Lien vers la conférence

Résumé:

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

Conférence ArODES

Mokhtar Bozorg, Rachid Cherkaoui

Proceedings of the IEEE Eindhoven PowerTech 2015

Lien vers la conférence

Résumé:

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

Conférence ArODES

Omid Alizadeh Mousavi, Rachid Cherkaoui, Mokhtar Bozorg

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

Lien vers la conférence

Résumé:

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

Conférence ArODES

Mokhtar Bozorg, Daniel Lopez, Rachid Cherkaoui

Proceedings of the 10th IEEE PowerTech Conference 2013

Lien vers la conférence

Résumé:

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

Conférence ArODES

Omid Alizadeh Mousavi, Mokhtar Bozorg, Rachid Cherkaoui, Mario Paolone

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

Lien vers la conférence

Résumé:

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

Conférence ArODES

Mokhtar Bozorg, Ehsan Hajipour, Seyed Hamid Hosseini

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

Lien vers la conférence

Résumé:

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.

Réalisations

PEOPLE@HES-SO Annuaire et Répertoire des compétences

Bozorg Mokhtar

Professeur HES associé

Professeur HES associé

PEOPLE@HES-SO
Annuaire et Répertoire des compétences