Examinando por Autor "Jurado, Francisco"

Mostrando 1 - 20 de 21

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, Francisco
Nowadays, 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.
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, Francisco
Storage 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.
A risk-aware P2P platform involving distributed generators, energy communities and storage assets
(Elsevier, 2024-10-15) Tostado-Véliz, Marcos; Mansouri, Seyed Amir; Jordehi, Ahmad Rezaee; Habeeb, Salwan Ali; Jurado, Francisco
The decentralization of power systems and networks calls up for a more active participation of end users. In this context, new market and power trading models are arisen. Catalyzed by the evolution of communication infrastructures under the Smart Grid concept, new paradigms such as peer-to-peer (P2P) trading are becoming more common nowadays. This paper develops a P2P platform model, involving the participation of distributed generators (dispatchable and renewable), storage facilities and energy communities. Economic-oriented models are presented for each peer, considering arbitrage capability from storage, generation and flexibility provision. An original market structure is proposed seeking for equilibrium among agents. Moreover, risk-aware operating strategies are developed, which consider adaptive interval formulation of uncertainties. The new approach allows adopting risk-averse or risk-seeker strategies, thus allowing to consider the impact of uncertainties in a flexible fashion. The new platform is tested on a 5-peers case. The impact of demand and renewable penetration on local prices is assessed, concluding that cheap generation contributes to reducing prices and thus improving the economy of users, which can trade energy locally under low prices. Moreover, the impact of uncertainties is also analyzed, observing that the uncertainty level and the risk strategy adopted impact notably on the expected realization of uncertainties. It is also shown that the developed tool effectively seeks for improving the economy of users, even when pessimistic conditions of uncertainties are assumed. Results demonstrate that energy communities are more severely impacted for uncertainties, due to their reduced regulation capability. Finally, the developed tool is further validated in fifty P2P instances from an economic and computational point of view.
A three-stage Stochastic-IGDT model for photovoltaic-battery domestic systems considering outages and real-time pricing
(Elsevier, 2022-10-10) Tostado-Véliz, Marcos; Jha, Bablesh Kumar; Kamel, Salah; Pindoriya, Naran M.; Jurado, Francisco
Energy consumption from residential sector is a growing concern nowadays. It has been widely demonstrated that domestic consumption worldwide could be notably reduced implementing home energy management tools. This kind of programs allows to schedule the different controllable home assets to optimize the electricity bill and increment the penetration of onsite renewable technologies, which contributes to a further reduction in residential consumption. On the other hand, grid outages are still frequent nowadays because natural disasters or weak infrastructures. Therefore, home energy management tools do not ignore these events. To tackle this issue, this paper develops a novel three-stage solution procedure for home energy management problems considering grid outages. To this end, a novel stochastic-IGDT formulation of the home energy management problem is proposed, by which the uncertain parameters are incorporated via scenarios and grid outages are modelled using IGDT. The presented formulation is Mixed-Integer Linear programming and allows to obtain a schedule result for the home appliances immune against outages without ignoring the economy of system. A benchmark prosumer environment with Real-Time pricing is considered as a case study. The results prove the effectiveness of the developed methodology. In particular, the effectiveness of the developed formulation in modelling grid outages as well as the scheduling performance at different conflictive objectives are highlighted. In addition, the effect of primarily considering robustness or economy is discussed together the importance of onsite photovoltaic generation in incrementing the reliability of the home system. Finally, the capability of the developed procedure to jointly consider cost and outages on a whole is foregrounded. In fact, the developed methodology is able to assume up to 6.5 outage hours without increasing the electricity bill, thus evidencing its capability to obtain a result immune against outages, whereas economic concerns are also considered on a whole.
An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters
(Elsevier, 2023-06-15) Tostado-Véliz, Marcos; Hasanien, Hany M.; Jordehi, Ahmad Rezaee; Turky, Rania A.; Gómez-González, Manuel; Jurado, Francisco
With the advance of communication infrastructures and the necessity of increasing the efficiency of energy systems, electricity networks are evolved towards more decentralized architectures and operational schemes. In this context, microgrid clusters are emerging as a valuable framework for optimal integrating renewable sources, demand response initiatives, and storage systems. When the cluster is competitive, the different agents partaking in the cluster compete for trading energy with others, for which an upstream agent (coordinator) sets nodal prices in a similar way to conventional energy markets. This paper is focused on this aspect by developing a novel price setting mechanism for islanded microgrid clusters based on an original equilibrium problem with an equilibrium constraints structure. The new proposal concerns about privacy issues, for which an original diagonalization algorithm is proposed by which only boundary information is transferred from microgrids to the coordinator. Moreover, uncertainties from renewable generation and demand are accommodated using interval notation and equivalent scenarios. The overall problem is raised as a bi-level model, which is further linearized and transformed into a single-level framework tractable by off-the-shelf solvers. A 4-microgrid cluster integrated into a 9-bus network serves as an illustrative case study. The impact of uncertainties is profusely studied, showing that total energy exchanged among microgrids can decrease by 61 % when the influence of uncertain parameters is notable. Other relevant aspects are discussed, and the practicability of the new proposal is demonstrated by analysing its computational performance. Moreover, the developed tool is further validated on a medium-scale network involving a large number of microgrids.
Best-case-aware planning of photovoltaic-battery systems for multi-mode charging stations
(Elsevier, 2024-05) Tostado-Véliz, Marcos; Jordehi, Ahmad Rezaee; Zhou, Yuekuan; Mansouri, Seyed Amir; Jurado, Francisco
The proliferation of charging stations entails multiple challenges for power systems. In this regard, the installation of photovoltaic-battery systems may help to mitigate the negative effects of charging points. However, such assets should be carefully planned, paying attention to economic aspects, principally. Most of existing works optimize the photovoltaic-battery system in charging infrastructures taking a representative-space of the involved variables (e.g. photovoltaic potential, charging demand or energy prices). However, this approach tends to ignore low-probable scenarios. Thus, the best-case scenario for charging demand (i.e. that for which the highest charging profit is accessible) may not be included in the analysis and therefore such demand could be not attended properly, thus losing this monetary opportunity. This paper focuses on this issue and questions if considering the best-case scenario into planning photovoltaic-battery systems for charging stations is worthwhile or not. To this end, a novel best-case-aware planning tool is developed, including the best-case scenario through a novel chance-constrained formulation. The overall problem is then decomposed into a master-slave structure by which the economy of the system is optimized together with the number of scenarios for which the best-case profile can be attended. A case study serves to validate the developed tool and shed light on the questions arisen in this work. In particular, it is checked that considering the best-case scenario into planning tools is questionable from a monetary point of view. Nevertheless, its inclusion unlocks some collateral advantages such as incrementing the users’ satisfaction or reducing the grid-dependency.
Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation
(IEEE, 2022) Hussien, Ahmed Moreab; Turky, Rania A.; Alkuhayli, Abdulaziz; Hasanien, Hany M.; Tostado-Véliz, Marcos; Jurado, Francisco
This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system.
Cornerstones for greater participation of smart renewable energy on clustered islands: The case of Guayas in Ecuador towards 2050
(Elsevier, 2025-06) Icaza-Alvarez, Daniel; Jurado, Francisco; Østergaard, Poul Alberg; Tostado-Véliz, Marcos; Flores, Carlos
This article presents a new approach to long-term energy planning based on the concept of smart energy systems. Unfortunately, fossil fuels have had a negative impact on fragile ecosystems, including the coastal islands in the Guayas province of Ecuador considered in this case study. The objective is to structure an energy system that responds to the growing demand by taking advantage of the renewable energy potential of the islands. The cornerstones that support the generation system are based mainly on wind and photovoltaic energy that follows a multisectoral approach, including the heating and cooling, transport, desalination, gas and electricity sectors. The analyses were applied with the support of the EnergyPLAN tool in an intelligent energy structure according to the land use plans, elements that put together the puzzle to design the path towards the 100 % renewable transition that is much friendlier to the environment. This is a novel study that seeks to take advantage of the renewable energy potential available on the islands themselves. Finally, after analyzing the results, it is concluded that the energy mix for 2050 can consider 49.12 % wind power and 38.59 % solar photovoltaic, as well as in smaller proportions 7.02 % biomass and 5.26 % biofuels, thereby achieving 100 % of renewable energy in the grouped islands thanks to their energy potential. Decision makers may consider this study a reference before committing resources and carrying out modern energy planning.
Environmental impact of the most representative Spanish olive oil farming systems: A life cycle assessment study
(Elsevier, 2024-02-25) Fernández-Lobato, Lázuli; Ruiz-Carrasco, Beatriz; Tostado-Véliz, Marcos; Jurado, Francisco; Vera, David
Agricultural production is an essential activity in the global economy that must advance towards the design of sustainability projects hand-in-hand with consumers, companies, and policymakers. An exhaustive study, in line with the guidelines set by government entities, is required to quantify this impact in an ample spectrum of environmental categories. Life Cycle Assessment (LCA) is a powerful tool that enables obtaining such information, as other authors have already demonstrated in different sectors. This study employs LCA to determine the environmental impact of virgin olive oil production, considering different agricultural and industrial production systems in Spain. For this purpose, a wide range of cultivated olive tree crops and different types of olive oil mill facilities in Andalusia have been studied-the territory of Spain with the highest dedication to olive oil production. This area has a strong emphasis on developing projects within this economic sector. The study focuses on olives, virgin olive oil and hectares of cultivation land, adopting a “cradle-to-gate” approach, and including economic allocation, considering the main processes related to its production in the agricultural and industrial phases. The study time covers the five most recent harvests (2017/18 to 2021/22) to obtain appropriate and updated environmental impact values. The results from the study time indicate that higher densification, irrigation, and slope crops lead to a higher environmental impact. Specifically, the climate change category of the functional unit ranges between 1.90 (low yield crops) and 6.09 kg of CO2 equivalent (super-intensive irrigated), while in the most representative cases, extensive crops, it results in 2.90 (rainfed) and 3.49 (irrigated) kg of CO2 equivalent. It should be noted that this study breaks new ground by thoroughly assessing the environmental impact of different olive oil production methods in Spain. It offers unique insights into sustainability within this vital sector, addressing a significant gap in current research.
Experimental assessment of a pilot-scale gasification plant fueled with olive pomace pellets for combined power, heat and biochar production
(ELSEVIER, 2023-07-15) Aguado-Molina, Roque; Escámez, Antonio; Jurado, Francisco; Vera, David
This research work examines the performance of an experimental gasification plant fueled with exhausted olive pomace pellets for the concurrent production of electricity, heat and biochar in the olive oil industry. The gasification plant consists of an air-blown downdraft fixed-bed gasifier that generates a lean fuel gas, termed producer gas, in a self-sustaining autothermal process. After conditioning of the producer gas in a cooling and cleaning unit, a four-stroke spark-ignition engine coupled to an electric generator is eventually used as power generation unit. An extensive experimental assessment of this facility was performed under partial and nominal load operation and was supplemented by a physicochemical analysis of the carbonaceous solid material discharged from the gasifier. The mass and energy balances of the gasification plant were calculated, including the carbon conversion efficiency and diverse energy conversion efficiencies. The results revealed an overall stable operation of the gasification plant in terms of composition and heating value of the producer gas and cogenerative production of electricity and heat in the engine–generator set. Under nominal operating conditions, the net electrical efficiency of the gasification plant was 12%–13%, with an average carbon conversion efficiency of the biomass feedstock into producer gas just above 80% and an average cold gas efficiency close to 70%.
Information Gap Decision Theory-based day-ahead scheduling of energy communities with collective hydrogen chain
(Elsevier, 2023-03-05) Tostado-Véliz, Marcos; Mansouri, Seyed Amir; Rezaee-Jordehi, Ahmad; Icaza-Alvarez, Daniel; Jurado, Francisco
Hydrogen is called to play a vital role in the future decarbonization of the electricity industry. Among its multiple applications, this energy carrier may improve the energy storage, replacing or complementing the traditional battery banks thanks to its higher energy density. However, the low efficiency and cost of associated devices as well as the difficulty in transport make unfeasible the implantation of hydrogen storage systems at the residential level. However, emerging paradigms like energy communities may change this concept making viable the installation of hydrogen chains in the domestic sector. This paper focuses on day-ahead scheduling of energy communities with integrated collective hydrogen storage system. To this end, a three-stage methodology is developed in which the first level is focused on individual home energy management, the second level handles with peer-to-peer energy trading among prosumers and the last level determines the energy exchanging profile with the utility grid accounting with the hydrogen chain. To handle with uncertainties from renewable sources, demand and energy price, the Information Gap Decision Theory (IGDT) is employed, by which an uncertainty-aware scheduling program can be obtained minimizing the negative effects of uncertain parameters. A case study is performed on a six-prosumer energy community with electrolysis, hydrogen vessel and fuel-cell, allowing both purchasing and selling energy with the grid. The results serve to prove the effectiveness of the developed methodology as well as demonstrate the possible impact of unknowns in energy community operation, and how the hydrogen chain can help to improve the economy and self-sufficiency of the system.
Long-term planning for the integration of electric mobility with 100% renewable energy generation under various degrees of decentralization: Case study Cuenca, Ecuador
(Elsevier, 2023-12) Icaza-Alvarez, Daniel; Jurado, Francisco; Tostado-Véliz, Marcos
Urban borders are expanding in cities, solar photovoltaic and wind energy are being used and decentralized more and more, while the electrification of transport systems is in permanent progress. Users trust more in the modernization of electrical systems giving rise to various applications. The efforts made by both the public and private sectors are isolated and are not framed within comprehensive planning. For this reason, cities must be fully planned and contemplated in their land use plans. This article presents a long-term roadmap for the comprehensive electrification of mobility. To achieve a proper approach, it is based on the EnergyPLAN tool that uses the concept of smart energy and determines the long-term scenarios, the case of study is for the City of Cuenca in Ecuador. It seeks to take advantage of the potential of renewable energies available in the territory, which are evaluated and provide the necessary energy to feed future decentralized transport systems with a view to 2050. The long-term results show that the energy mix would be composed of wind with 37.3%, followed by solar photovoltaic with 33.9% and hydroelectric with 25.4%. There are others technologies such as biomass that do not exceed 3.4%.
Multiobjective home energy management systems in nearly-zero energy buildings under uncertainties considering vehicle-to-home: A novel lexicographic-based stochastic-information gap decision theory approach
(Elsevier, 2023-01-15) Tostado-Véliz, Marcos; Hasanien, Hany M.; Kamel, Salah; Turky, Rania A.; Jurado, Francisco; Elkadeem, M.R.
Residential sector is being promoted to evolve towards nearly-Zero Energy Buildings (nZEBs), which draw a yearly net energy consumption near zero. This target can be attained through on-site renewable generation, achieving high efficiency in consumption. In this context, Home Energy Management (HEM) systems become an indispensable tool for obtaining optimally coordinating smart appliances, renewable generation, and on-site storage facilities. Due to the high unpredictability of renewable generation and some emerging appliances like electric vehicles, these tools must be able to deal with different uncertainties properly. At the same time, a variety of objectives are jointly considered. The existing approaches commonly fail to deal jointly with these two premises. This paper aims to fill this gap by developing a novel solution for HEM systems in nZEBs. The proposed procedure uses lexicographic optimization to find a compromise solution among objectives. At the same time, the variety of uncertainties caused by unpredictable weather, demand, energy pricing, and electric vehicle behavior is adequately modeled using a hybrid stochastic-Information Gap Decision Theory (IGDT) approach. The mathematical modeling is sufficiently comprehensive (comprising various energy sources and vehicle-to-home capability) and tractable due to its integer-linear structure. A case study on a benchmark nearly zero energy home is considered to validate the developed approach, which its results reveal its effectiveness in terms of minimizing various objective functions while the degree of robustness is preserved and the whole procedure is efficient yet.
Operation of energy hubs with storage systems, solar, wind and biomass units connected to demand response aggregators
(Elsevier, 2022-08) Nasir, Mohammad; Jordehi, Ahmad Rezaee; Tostado-Véliz, Marcos; Tabar, Vahid Sohrabi; Mansouri, Seyed Amir; Jurado, Francisco
Energy Hubs (EHs) play an important role in sustainable cities; they are multi-carrier energy systems that can satisfy different energy needs of consumers by relying on the conversion and storage of energy sources as well as renewable energy sources. With efficient and reliable energy supply, EHs may significantly contribute in developments of sustainable cities. In this paper, day-ahead scheduling of EHs is done, while they are connected to demand response aggregators. The studied EH includes photovoltaic and wind renewable sources, biomass, hydrogen electrolyzer, combined heat and power unit, solar heater, boiler, electric, thermal and hydrogen storage systems. Besides electric grid and gas network as input sources, EH may purchase electricity from demand response aggregators. Information gap decision theory (IGDT) is employed as a risk-aware method to handle uncertainties of electric, thermal and hydrogen demands, photovoltaic and wind power, solar heat and electricity prices. The scheduling is carried out from the perspective of the uncertainty free, risk-averse and risk- seeking decision-makers. The problem is formulated as a mixed-integer model and is solved using CPLEX solver in General algebraic modeling system (GAMS). The impact of risk awareness and deviation factors of critical and target costs on day-ahead scheduling and EH operation costs is investigated. The results show that the transaction with demand response aggregator decreases EH operation cost by 20.1%. The results also show that electric, thermal and hydron storage systems respectively decrease the operation cost by 3, 1.7 and 2.1%.
Optimal Design of Photovoltaic Domestic Installations Considering Second-Purpose Batteries
(IEEE, 2023) Gómez-González, Manuel; Tostado-Véliz, Marcos; Valverde, Manuel; Jurado, Francisco
About 11 million tons of retired batteries are expected to be produced globally by 2030, of which a huge percentage will proceed from individual owners who charge their vehicles at home. To overcome this issue, multiple studies focused on the viability of reusing mobility batteries for different applications. However, this kind of analysis in individual dwellings are still scarce. This paper aims at filling this gap by developing a novel optimization methodology for design of photovoltaic arrays in domestic installations considering second-purpose batteries from mobility. In particular, we aim at analyzing the effect of using batteries from vehicles when they are no longer valid for mobility. Thus, we consider the problem in which these batteries are used for stationary applications at home, observing their effects on the photovoltaic design as well as other aspects like total project cost and self-consumption rates. To this end, a planning strategy is developed including suitable models of photovoltaic units, flexible loads and electrical vehicle. The developed methodology is tested on a benchmark prosumer environment and different scenarios are profusely studied. The results obtained demonstrate that the use of second-purpose batteries seem profitable in smart homes, reducing the total cost by 15% and increasing the self-consumption rate by 20%. Moreover, other aspects like its effect on the final scheduling and the behavior under limited photovoltaic capacity are commented. Lastly, the importance of the developed methodology for future similar studies is highlighted.
Optimal participation of prosumers in energy communities through a novel stochastic-robust day-ahead scheduling model
(Elsevier, 2023-05) Tostado-Véliz, Marcos; Jordehi, Ahmad Rezaee; Icaza, Daniel; MAnsouri, Seyed Amir; Jurado, Francisco
With the advent of smart grids, novel businesses like energy communities are becoming more frequent, thus enabling alternative energy transactions for smart prosumers like peer-to-peer mechanisms, that may increment the efficiency of residential installations while reducing the electricity bill. However, the optimal participation in such frameworks is a formidable challenge because the multiple uncertainties involved and energy paths enabled, which increments the number of decision variables and pricing mechanisms. This paper addresses this issue by developing a novel day-ahead scheduling model for prosumers integrated in energy communities based on a stochastic-robust approach. The developed formulation contemplates energy transactions with the utility grid, the community and other peers, besides the intrinsic uncertainties that arise from these processes. The heterogeneity of the unknown parameters is effectively addressed by using different uncertainty models, thus, while the predictable parameters are modelled using robust formulation, the highly volatile uncertainties are treated via scenarios. A case study is presented with the aim of validating the new tool as well as analyse the different energy transactions and their monetary implications. The obtained results evidence the important role of storage assets in reducing the electricity bill by 86 %, which is achieved by incrementing the exportable capacity of the dwelling by 84 %. The impact of uncertainties is also studied, expecting more pessimistic profiles at expenses of incrementing the monetary cost in 0.37-$.
Optimal sitting and sizing of hydrogen refilling stations in distribution networks under locational marginal prices
(Elsevier, 2024-11-15) Tostado-Véliz, Marcos; Horrillo-Quintero, Pablo; García-Triviño, Pablo; Fernández-Ramírez, Luis M.; Jurado, Francisco
The decarbonization of the mobility sector motivates to increase the penetration of battery and fuel-cell electric vehicles. The proliferation of these mobility modes will be accompanied by the massive installation of charging and refilling infrastructures into existing networks. This work focuses on fuel-cell vehicles, for which refilling points are needed. Difficulties in hydrogen transportation can be circumvented by deploying onsite hydrogen generation assets (electrolysers) and storage, which can be partially or fully supplied through local renewable generators. Nevertheless, such assets require a considerable initial investment, being necessary the use of planning tools in order to maximize revenues for private investors. This paper focuses on this issue. In particular, an optimal sitting and sizing tool for hydrogen refilling stations with onsite storage and electrolysers is developed. The developed methodology considers the influence of locational marginal prices, which are cleared by the distribution system operator in order to translate the real electricity cost per node. This pricing strategy helps to best allocate assets through the network and thus resulting valuable for planners in order to site refilling infrastructures properly. An original multi-year iterative algorithm based on the multi-cut Benders' decomposition is proposed in order to alleviate the intrinsic high computational cost of the planning tool while accommodate long-term inflation and degradation rates of parameters. A number of simulations are performed on the well-known IEEE 33-bus system. Results verify that locational marginal pricing effectively translates the nodal electricity cost to end-users. Remark, the total electrolysis capacity turns out to be the most significant parameter, reducing further the cost of the project, while storage capacity has a limited influence. Results highlight the importance of the infrastructure capacity when determining the placement and sizing of electrolysers, thus supporting decisions when upgrading existing infrastructure. The impact of the number of stations to be installed and the budget cap is also analysed, showing that both parameters have similar influence and may reduce the total project cost by 70% approximately. The typical scheduling behaviour of the electrolysis-storage facilities is discussed, showing how storage is capable to provide energy arbitrage exploiting locational marginal prices. Finally, the computational performance of the developed algorithm is assessed, verifying that the new tool is efficient and portable.
Optimal sizing of hybrid PV–diesel–biomass gasification plants for electrification of off-grid communities: An efficient approach based on Benders’ decomposition
(Elsevier, 2024-06-15) Tostado-Véliz, Marcos; Escámez, Antonio; Aguado-Molina, Roque; Sánchez-Lozano, Daniel; Jurado, Francisco; Vera, David
Nowadays, millions of people in remote areas do not enjoy an uninterrupted power supply due to the lack of connectivity to the main power grid. Under such circumstances, the only feasible way to access electricity is typically local power generation, often relying on diesel engine–generator sets or photovoltaic arrays. However, on many occasions, this configuration does not fully exploit all available local resources, including biomass. Indeed, most isolated areas have access to local biomass production from agricultural activities, which can be used for local electricity generation through gasification. This paper addresses this challenge by developing an innovative optimal sizing tool for hybrid power plants integrating biomass gasifiers, specifically designed for isolated areas with access to local biomass production. The novel approach models the particular features of biomass gasification technologies, including long on/off times or restrictive ramping limits. To this end, an efficient methodology based on representative weeks is proposed, which is combined with a solution strategy based on the multi-cut Benders’ decomposition, thus resulting in a tractable framework that can deal with a huge amount of data efficiently. One of the most salient features of the new proposal is the consideration of local biomass production, which is included in the methodology through an original algorithm. Accordingly, a certain amount of biomass is sourced locally, leading to more accurate and reliable results. The new methodology is applied to a benchmark off-grid community in Ghana. The results demonstrate that the use of gasifiers reduces the project cost notably (by 90%) driven by the reduced biomass cost, which can be supplemented by locally generated biomass from agricultural activities. In addition, this technology constitutes a clean source of energy, reducing the total CO emissions by 83% compared to a baseline case in which only diesel generators are used. Moreover, it is demonstrated that biomass gasification can effectively act as base load power generation technology to reliably cover most of the local demand, thereby enabling a clean and inexpensive dispatchable local power generation. Finally, a sensitivity analysis reveals that the economic feasibility of the plant is more sensitive to the biomass cost than the selling price of biochar, resulting in a 33% increment in the total project cost when the price of biomass increases from 0 to 0.4 $/kg. Nevertheless, gasification remains as the predominant power generation technology even under unfavorable prices.
Smart energy transition with the inclusion of floating wind energy in existing hydroelectric reservoirs with a view to 2050. Ecuadorian case study
(Elsevier, 2023-11) Icaza-Alvarez, Daniel; Jurado, Francisco; Tostado-Véliz, Marcos
Ecuador promotes an energy matrix with zero net emissions by 2050, knowing that hydroelectric power from a reservoir has been fundamental in the electrical system. The reservoirs comprise large unused areas, in many of these sites there are interesting wind speeds thanks to the wind tunnels that are formed between hill and hill and can be used by installing floating wind turbines. This research presents an alternative to increase the driving actions of Ecuador to structure its 100% renewable energy system in a diversified way. For this reason, the resulting impact is analyzed by including Floating Wind Power (FWP) systems and four points of interest are analyzed in this study: Mazar, Coca Codo Sinclair, Manduriacu and Delsitanisagua. The energy mix is evaluated using EnergyPLAN software, a specialized tool to evaluate diversified smart systems of completely renewable electricity in the long term. This study is novel, breaks the traditional schemes in Ecuador and provides a different vision for decision makers, such as investors, legislators and researchers to discuss before committing economic resources. The results show that in 2050 floating wind energy would be contributing 11.13% of the total electricity in Ecuador and 16.27% of the wind component. Interpreting these values, the floating wind component may be significant and would further diversify energy production in this South American country.
Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm
(MDPI, 2022-11-07) Shaheen, Mohamed A.M.; Ullah, Zia; Qais, Mohamed H.; Hasanien, Hany M.; Chua, Kian J.; Tostado-Véliz, Marcos; Turky, Rania A.; Jurado, Francisco; Elkadeem, Mohamed R.
Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

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Examinando por Autor "Jurado, Francisco"