Browsing by Author "Najafi, Arsalan"
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- A hybrid decentralized stochastic-robust model for optimal coordination of electric vehicle aggregator and energy hub entities
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-12-15) Najafi, Arsalan; Pourakbari-Kasmaei, Mahdi; Jasinski, Michal; Lehtonen, Matti; Leonowicz, ZbigniewElectric vehicle aggregator (EVAGG) is an independent entity that facilitates exchanging electricity between electric vehicles (EVs) and the grid. Energy hub (EH) is another independent entity playing a remarkable role in enhancing the efficiency, flexibility, and reliability of multi-energy systems. Although interacting between various agents is beneficial to enhance their capability, it is challenging to schedule such interconnected entities. In this paper, EVAGG and EH, as independent entities, are scheduled independently and only exchange the information of electrical energy. The EVAGG scheduling is a function of EV owners’ driving patterns, including EVs’ arrival and departure times and the initial state of charge. Besides, both the EVAGG and EH operations are affected by the uncertainty of the locational marginal prices. Hence, this paper proposes a hybrid decentralized robust optimization-stochastic programming (DRO-SP) model based on the alternating direction method of multipliers to coordinate the management of entities. Stochastic programming is used to model the uncertainties of the EVs patterns, while the uncertainties of the locational marginal prices are modeled via robust optimization to grasp the worst-case realization. Simulation results demonstrate the effectiveness of the proposed hybrid DRO-SP in terms of economic scheduling the entities while guaranteeing information privacy between entities. - A max–min–max robust optimization model for multi-carrier energy systems integrated with power to gas storage system
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-04) Najafi, Arsalan; Pourakbari-Kasmaei, Mahdi; Jasinski, Michal; Lehtonen, Matti; Leonowicz, ZbigniewThe volatile nature of the electricity market prices and renewable resources imposes remarkable challenges for multi-energy systems operators to make the appropriate decisions. Therefore, this paper offers a linear max–min–max robust optimization-based decision-making tool that incorporates both uncertainties of the electricity market price and the wind generation. Besides interaction with the electricity market, the EH purchases natural gas to feed the combined heat and power (CHP) and boiler units and supply gas demands. An electrical storage system is also used to smooth the unfavorable volatility nature of the electricity market price. Besides, an uncertainty budget model is proposed to consider both negative and positive deviation of electricity market prices, which gives the capability to increase the robustness of the system against the error of forecasting uncertainty sources. The nonlinearities arisen from the model are linearized using effective approaches and the resulted linear mathematical model is solved by GAMS. Moreover, the power to gas (P2G) storage system is integrated with the EH in order to create a link between the electrical and natural gas networks by converting the electricity to hydrogen and then to natural gas. Simulation results demonstrate that using P2G saves 6.9% in gas purchase cost and 2.13% in total cost. - A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-01-01) Najafi, Arsalan; Pourakbari-Kasmaei, Mahdi; Jasinski, Michal; Lehtonen, Matti; Leonowicz, ZbigniewIntroducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various energy types. On the other hand, the energy systems have usually been exposed to uncertain environments due to the presence of renewable energy sources (RESs) and interaction with the electricity markets. Hence, this paper develops a novel optimization framework based on a hybrid information gap decision theory (IGDT) and robust optimization (RO) to handle the optimal self-scheduling of the EH within a medium-term horizon for large consumers. The proposed mixed-integer linear programming (MILP) framework aims to capture the advantages of both the IGDT and RO techniques in dealing with the complicated binary variables and achieving the worst-case realization arisen from wind turbine generation and day-ahead (DA) electricity market uncertainties. The RO optimization approach is presented to model the DA electricity price uncertainty while the uncertainty related to the wind turbine generations is taken into account by the IGDT. Numerical results validate the capability of the model facing uncertainties. The amount of total operation cost of the EH increases by 8.6 % taking into account the worst-case realization of uncertainties through the proposed hybrid IGDT-RO compared to the case considering perfect information. Besides, the results reveal that optimal decisions can be taken by the operator using the proposed hybrid IGDT-RO model. - A Novel Control Strategy to Active Power Filter with Load Voltage Support Considering Current Harmonic Compensation
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2020) Torabi Jafrodi, Saeedeh; Ghanbari, Mojgan; Mahmoudian, Mehrdad; Najafi, Arsalan; Rodrigues, Eduardo M.G.; Pouresmaeil, EdrisThis paper outlines some modifications to conventional active power filters (APFs) to compensate for the non-linearity of the load current. Since the APFs inject the required non-linearity of the load harmonic current to make the current source sinusoidal, a combination of passive power filters (PPFs) and APFs in series connection are more effective rather than individual usages. The proposed control approach based on sliding mode control (SMC) with a suitable sliding surface selection being applied to the proposed hybrid APF to increase the flexibility and reduce the complexity of the controller. An outstanding tracking process based on the reference current in the rotating dq frame is tested and guarantees the rapid convergence exponentially. An extra control loop is provided for DC link voltage regulation to minimize the DC ripples and control the APF three-phase output voltage levels. The presented solution provides an effective and straightforward load voltage support, maintaining an excellent dynamic performance in load changing and current compensation. The experimental results represent the authenticity of the proposed hybrid APF performance through several different tests, implying a feasible control approach for active filtering systems. - Participation of hydrogen-rich energy hubs in day-ahead and regulation markets: A hybrid stochastic-robust model
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2023-06-01) Najafi, Arsalan; Homaee, Omid; Jasiński, Michał; Pourakbari-Kasmaei, Mahdi; Lehtonen, Matti; Leonowicz, ZbigniewHydrogen-based technologies are one of the pathways toward a carbon-neutral world. This paper proposes a hybrid stochastic-robust framework for the optimal operation of hydrogen-based energy hubs (EHs) for a short-term horizon. The EH operator, as a large consumer, should make decisions to procure the required energy to meet the demands dealing with uncertainties in real-time. To manage the EH efficiently, the EH operator participates in the regulation market (RM) deploying regulation up (RU) and regulation down (RD) actions to compensate for the errors stem from the forecasting procedure in intra-hours. In addition, the EH should support the required hydrogen for hydrogen vehicles (HVs) on a hydrogen refueling station. Power-to-gas unit alongside a gas storage system links the electricity and natural gas network, and it facilitates the heat demand errors in intra-hours using gas-fed boilers. The uncertainty of the demands and the initial state of charge of the HVs are addressed by stochastic programming, while the uncertainty of the RM prices is considered by robust optimization to reach the worst-case realization of the RM prices. The simulation results demonstrate the effectiveness of the proposed framework in managing the hydrogen-rich EH and energy storage systems with the day-ahead and real-time horizons. The amount of 14% error in electricity demands in the intra hours is resulted in 10% increases in purchasing from the RM at the same time. - A risk-based optimal self-scheduling of smart energy hub in the day-ahead and regulation markets
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-01-10) Najafi, Arsalan; Tavakoli, Ahmad; Pourakbari-Kasmaei, Mahdi; Lehtonen, MattiUtilizing multi-carrier energies such as wind energy, electric vehicle (EV) and battery banks are a significant step toward a cleaner production. Hence, This paper proposes a stochastic-based decision-making framework for the efficient short-term management of smart energy hub (EH) in restructured power systems with high penetration of renewable energy. The electrical and natural gas carriers are the input of smart EH, while the electricity and heat demands are considered as the outputs. The EH, including battery storage system (BSS) and EV fleet, is managed in the regulation market and day-ahead (DA) horizons. The energy hub operator makes optimal decisions regarding the natural gas network and energy supply for the thermal and electricity customers. An optimal self-scheduling model is developed to take into account the day ahead (DA) and regulation markets (RM) and decisions regarding generations of electrical and thermal devices as well as EV aggregator decisions. The primary goal of the proposed framework is minimizing the cost of procuring electricity and heat energy carriers in DA and regulation markets, including upward/downward regulations via a stochastic mixed-integer linear programming (MILP) approach. In order to get the uncertainty around the exact outcomes of RM prices, wind generation, and EV patterns, the model also takes into account the conditional value at risk (CVaR) term. The proposed formulation is examined by applying to a smart EH. Results show the effectiveness and usefulness of the proposed framework in managing the smart EHs efficiently. (c) 2020 Elsevier Ltd. All rights reserved. - The role of EV based peer-to-peer transactive energy hubs in distribution network optimization
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-08-01) Najafi, Arsalan; Pourakbari-Kasmaei, Mahdi; Jasinski, Michal; Contreras, Javier; Lehtonen, Matti; Leonowicz, ZbigniewThis paper proposes a novel bi-level strategic energy trading framework to minimize the operation cost of the distribution network (DN) interacting with peer-to-peer (P2P) transactive energy hubs with electric vehicles. A distribution system operator at the upper level minimizes its total cost from purchasing electricity in the wholesale market, generating with its own microturbines, and selling electricity to the energy hubs. Each transactive energy hub at the lower level reacts to the offer price received from the upper level, interacting with the other energy hubs. Each energy hub has a parking lot to harvest the benefit from asynchronous storage of electricity in other energy hubs stemming from the difference between the arrival or departure times of the electric vehicles. A single-leader multi-follower game approach is developed to model the DN-energy hubs game structure. Then, an iterative model is proposed to find the equilibrium point between the leader and the followers, while the distributed problem of the interaction between the followers at the LL is solved by the Alternating Direction Method of Multipliers (ADMM). Numerical results for the IEEE 33-bus test system with two energy hubs show the effectiveness of the proposed transactive model between the energy hubs and the DN. - Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2019-04-12) Najafi, Arsalan; Marzband, Mousa; Mohammadi-Ivatloo, Behnam; Contreras, Javier; Pourakbari-Kasmaei, Mahdi; Lehtonen, Matti; Godina, RaduEnergy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.