Suche

Rayati Mohammad

Chargé de Ra&D HES

Hauptvertrag

Chargé de Ra&D HES

Büro: b01b

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

Es müssen keine Daten für diesen Abschnitt angezeigt werden.

2023

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

Wissenschaftlicher Artikel ArODES

Mohammad Rayati, Mokhtar Bozorg, Mauro Carpita, Rachid Cherkaoui

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

Link zur Publikation

Zusammenfassung:

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

Wissenschaftlicher Artikel ArODES

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

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

Link zur Publikation

Zusammenfassung:

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.

2022

Electricity market design and implementation in the presence of asymmetrically informed strategic producers and consumers :

Wissenschaftlicher Artikel ArODES

a surrogate optimization-based mechanism

Mohammad Rayati, Demosthenis Teneketzis

Energy Economics, 2022, vol. 109, article no. 105929

Link zur Publikation

Zusammenfassung:

We consider electricity networks where the agents (producers and consumers) are strategic and possess asymmetric information about the networks’ status. The network model accounts for power losses, line congestion, and financial transmission rights (FTRs). We propose a mechanism (a set of rules for energy production and consumption) that takes into account the network model, the agents’ strategic behavior and their informational asymmetries, and has the following properties at all Nash equilibria of the game induced by it: (i) it is budget balanced (the sum of taxes received by the agents and the sum of subsidies paid to the agents is equal to zero); (ii) it implements the optimal power flow (OPF) dispatch (equivalently it implements the social welfare maximizing dispatch); (iii) it is price efficient (the price received per unit of energy production is equal to the price paid per unit of energy consumption, and they are both equal to the price corresponding to the OPF dispatch); (iv) it is individually rational (the strategic agents voluntarily participate in the mechanism). We also propose a tâtonnement-process (an algorithm), based on a best-estimate method, and prove that it converges to a Nash equilibrium of the game induced by the proposed mechanism. The allocations (energy production and consumption) resulting at each step of the tâtonnement-process are a feasible solution of the OPF problem.

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

Wissenschaftlicher Artikel ArODES

Mohammad Rayati, Mokhtar Bozorg, Rachid Cherkaoui, Mauro Carpita

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

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

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

Wissenschaftlicher Artikel ArODES

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

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.

2020

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

Wissenschaftlicher Artikel ArODES

Mohammad Rayati, Mokhtar Bozorg, Rachid Cherkaoui

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.

2022

Pricing differentially private smart meter data in distribution networks

Konferenz ArODES

Mohammad Rayati, Mokhtar Bozorg

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

Link zur Konferenz

Zusammenfassung:

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

Konferenz ArODES

Lu Wang, Mokhtar Bozorg, Mohammad Rayati, Rachid Cherkaoui

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

Link zur Konferenz

Zusammenfassung:

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

Konferenz ArODES

Ataeem Shabnam, Mohammad Rayati, Carlos Andres Pena, Omid Alizadeh-Mousaveh, Mokhtar Bozorg

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

Link zur Konferenz

Zusammenfassung:

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 :

Konferenz 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

Link zur Konferenz

Zusammenfassung:

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.

2020

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

Konferenz 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

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.

Errungenschaften