Examinando por Autor "Miveh, Mohammad Reza"
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Ítem A four-stage framework for optimal scheduling strategy of smart prosumers with vehicle-to-home capability under real time pricing based on interval optimization(Wiley, 2023-09) Tostado-Véliz, Marcos; Ghadimi, Ali Asghar; Miveh, Mohammad Reza; Myyas, Ra'ed Nahar; Jurado-Melguizo, FranciscoWith the emergence of the Smart Grid concept, utility companies require more active participation of home users in the power sector. This changing paradigm is enabled by the wide deployment of multiple home assets such as small renewable-based generators or storage facilities. In this context, consumers are no longer conceived as pure loads but also active agents that can exchange energy with the grid. To promote this active participation, utility companies promote different price-based demand response programs to change the consumer patterns on pursuing a more efficient and economic system operation. In this regard, home energy management programs are becoming an essential tool for efficiently managing the different home users while addressing multiple demand response goals at minimum cost. In essence, a home energy management system is a computational optimization tool, which has to handle multiple uncertainties brought by weather forecast or energy pricing. This paper tackles this issue by developing a novel robust home energy management program based on interval optimization. In contrast to other related approaches, the proposal avoids the explicit use of interval arithmetic. Instead, the different uncertain parameters are sequentially incorporated into the scheduling task through different stages and interval-based formulation. The developed methodology incorporates weather, load, energy pricing and plug-in electric vehicle related uncertainties. A benchmark case study in a smart prosumer layout serves to prove the effectiveness of the new approach.Ítem A novel interval-based formulation for optimal scheduling of microgrids with pumped-hydro and battery energy storage under uncertainty(Wiley, 2022-05-06) Ahmadi, Saeid; Tostado-Véliz, Marcos; Ghadimi, Ali Asghar; Miveh, Mohammad Reza; Jurado, FranciscoNowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large-scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid-connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval-based formulation, for optimal scheduling of microgrids considering battery and pumped-hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped-hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval-based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval-based formulation for the optimal scheduling of microgrid with pumped-hydro and battery energy storage under uncertainty.Ítem A Novel Stochastic Mixed-Integer-Linear-Logical Programming Model for Optimal Coordination of Hybrid Storage Systems in Isolated Microgrids Considering Demand Response(MDPI, 2022-10-25) Tostado-Véliz, Marcos; Ghadimi, Ali Asghar; Miveh, Mohammad Reza; Sánchez-Lozano, Daniel; Escámez, Antonio; Jurado, FranciscoStorage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as ‘green’ storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives.Ítem Multi-energy microgrid optimal operation with integrated power to gas technology considering uncertainties(Elsevier, 2022-01) Mobasseri, Ali; Tostado-Véliz, Marcos; Ghadimi, Ali Asghar; Miveh, Mohammad Reza; Jurado-Melguizo, FranciscoIn recent years, multi-energy microgrids (MEMGs) have emerged as an invaluable framework for enabling the use of clean and efficient electro-thermal resources as well as the integration of multi-energy storage facilities. Uncertainties modelling in such systems is a challenge because of the heterogeneity of the resources and consumers involved. This paper tackles this issue by proposing a hybrid robust energy management tool for MEMGs encompassing electric, heat, hydrogen and gas sub-networks. The variety of uncertainties brought by unpredictable demand and renewable generation are managed using adequate techniques. This way, renewable generation is modelled using the Hong 2m + 1 approach, the electrical and heat demands are managed using the information gap decision theory and the fuel-cell electric vehicles refueling demand is modelled via scenarios. The novel methodology is validated on a benchmark case study, in which extensive simulations are performed. The obtained results demonstrate the accurateness of the novel proposal and its effectiveness to manage a wide variety of uncertainties. The evidence for accurateness is that the difference in the objective function with the Monte Carlo and Hong 2m + 1 uncertainty modelling approaches only differs by ∼0.2%. Moreover, the new proposal is computationally competitive with the Monte Carlo simulation, improving its computation time by 2–3 times.Ítem Uncertainty-aware energy management strategies for PV-assisted refuelling stations with onsite hydrogen generation(Elsevier, 2022-09-10) Tostado-Véliz, Marcos; Ghadimi, Ali Asghar; Miveh, Mohammad Reza; Bayat, Mohammad; Jurado-Melguizo, FranciscoOne of the main barriers for the wide penetration of fuel cell electric vehicles is the lack of proper infrastructures for hydrogen transportation that hinders the implantation of refuelling stations. This barrier could be overcome by deploying onsite hydrogen generators based on mature electrolysis and hydrogen storage technologies. This way, the necessity of hydrogen transportation is avoided. In addition, electrolysers can be onsite supplied by means of renewable generators like photovoltaic panels, while the produced hydrogen can also be destined to generate electricity through fuel cells thus obtaining a monetary revenue. Thereby, the economy of the system may be improved in order to make viable this kind of infrastructures. However, the optimal coordination of the different assets is challenging and requires the use of energy management tools to pursue the optimal performance of the installation. In this kind of infrastructures, the energy management problem is performed under substantial uncertainties; moreover, these unknown parameters have a very different character. Thus, while energy pricing and renewable generation can be forecasted using conventional techniques, refuelling demand is highly unpredictable. To this end, this paper proposes a novel stochastic-interval model for the optimal scheduling of photovoltaic-assisted refuelling stations. The new proposal uses interval notation to model the inherent uncertainty of renewable generation and energy pricing, while the vehicle demand is modelled using a more suitable approach based on scenarios. In this regard, a comprehensive stochastic model for fuel cell electric vehicles is developed, which is based on reported driving behaviour and common characteristics of commercial vehicles. To solve the problem subjected to uncertainties, an iterative solution methodology is developed which allows adopting risk-seeker and risk-averse operational strategies. A case study is analysed to validate the new proposal and discussing the importance of the different economic activities that can be exploited in refuelling stations. Results reveal the importance of selling energy to the grid in order to complement the revenues obtained from refuelling; however, this process is highly impacted by uncertainties and the operational strategy, observing variations up to 50% in the total profit depending on the strategy adopted.