DIE-Artículos

URI permanente para esta colecciónhttps://hdl.handle.net/10953/230

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A hybrid intelligent model to predict the hydrogen concentration in the producer gas from a downdraft gasifier
(ELSEVIER, 2022-06-05) Aguado, Roque; Casteleiro-Roca, José-Luis; Vera, David; Calvo-Rolle, José Luis
This research work presents an artificial intelligence approach to predicting the hydrogen concentration in the producer gas from biomass gasification. An experimental gasification plant consisting of an air-blown downdraft fixed-bed gasifier fueled with exhausted olive pomace pellets and a producer gas conditioning unit was used to collect the whole dataset. During an extensive experimental campaign, the producer gas volumetric composition was measured and recorded with a portable syngas analyzer at a constant time step of 10 seconds. The resulting dataset comprises nearly 75 hours of plant operation in total. A hybrid intelligent model was developed with the aim of performing fault detection in measuring the hydrogen concentration in the producer gas and still provide reliable values in the event of malfunction. The best performing hybrid model comprises six local internal submodels that combine artificial neural networks and support vector machines for regression. The results are remarkably satisfactory, with a mean absolute prediction error of only 0.134% by volume. Accordingly, the developed model could be used as a virtual sensor to support or even avoid the need for a real sensor that is specific for measuring the hydrogen concentration in the producer gas.
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, 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%.
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.
Integrating the strategic response of parking lots in active distribution networks: An equilibrium approach
(Wiley, 2025-01) Tostado-Véliz, Marcos; Bhakar, Rohit; Javadi, Mohammad Sadegh; Nezhad, Ali Esmaeel; Jurado, Francisco
The increasing penetration of electric vehicles will be accompanied for a wide deployment of charging infrastructures. Large charging demand brings formidable challenges to existing power networks, driving them near to their operational limits. In this regard, it becomes pivotal developing novel energy management strategies for active distribution networks that take into account the strategic behaviour of parking lots. This paper focuses on this issue, developing a novel energy management tool for distribution networks encompassing distributed generators and parking lots. The new proposal casts as a tri-level game equilibrium framework where the profit maximization of lots is implicitly considered, thus ensuring that network-level decisions do not detract the profit of parking owners. The original tri-level model is reduced into a tractable single-level mixed-integer-linear programming by combining equivalent primal-dual and first-order optimality conditions of the distribution network and parking operational models. This way, the model can be solved using off-the-shelf solvers, with superiority against other approaches like metaheuristics. The developed model is validated in well-known 33-, and 85-bus radial distribution systems. Results show that, even under unfavourable conditions with limited distributed generation, charging demand is maximized, thus preserving the interests of parking owners. Moreover, the model is further validated through a number of simulations, showing its effectiveness. Finally, it is demonstrated that the developed tool scales well with the size of the system, easing its implementation in real-life applications.
Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer
(MDPI, 2023-08-17) Kareem Meteab, Kareem Meteab; Ali Habeeb Alsultani, Salwan; Jurado, Francisco
A microgrid (MG) can be defined as interconnected loads with distrusted generators (DGs) and energy storage systems that are connected to the grid (on-grid MG) or work autonomously (isolated MG) [1]. The MGs can provide the required energy for different customers, including commercial, residential and industrial loads [2]. Several benefits are obtained from MG implementation, including improvements in system reliability, reduced operating costs, resolution of system losses and overload problems, energy supply decentralization and emission reductions [3]. Use of electric vehicles (EVs) and renewable energy-based DGs (RDGs) like solar PV, wind turbines (WTs), hydropower and biomass has greatly increased for many reasons, including reduced dependence on conventional fuel-based DGs, reduced greenhouse gas emissions and the sustainability of electrical systems. The main feature of RDGs and EVs is their stochastic nature and uncertain behavior. Thus, solving the energy management (EM) of the MGs with EVs and RDGs is a challenging task. A scenario-based method was applied for solving the EM of MGs considering the uncertain parameters of RDGs and the loading [4]. The EM was solved using the equilibrium optimizer (EO) for multi-objective functions in the presence of WTs and PVs and with uncertainty of these resources and the load demand [5]. In [6], the EM problem was solved by considering the demand response and the uncertainties of the RDGs. A microgrid central controller was utilized for the EM of the MG and consists of PV-, WT- and biomass-based generators [7]. In [8], the EM of the MG was solved using mixed-integer programing with demand-side management in the presence of portable RDGs.
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.
An ADMM-enabled robust optimization framework for self-healing scheduling of smart grids integrated with smart prosumers
(Elsevier, 2024-06-01) Zhang, Pan; Mansouri, Seyed Amir; Jordehi, Ahmad Rezaee; Tostado-Véliz, Marcos; Alharthi, Yahya Z.; Safaraliev, Murodbek
Enhancing the reliability of energy networks and minimizing downtime is crucial, making self-healing smart grids indispensable for ensuring a continuous power supply and fortifying resilience. As smart grids increasingly incorporate decentralized prosumers, innovative coordination strategies are essential to fully exploit their potential and improve system self-healing capabilities. To address this need, this paper presents a novel bi-level strategy for managing the self-healing process within a smart grid influenced by Hydrogen Refueling Stations (HRSs), Electric Vehicle Charging Stations (EVCSs), and energy hubs. This approach taps into the combined potential of these prosumers to boost system self-healing speed and reliability. In the initial stage, the Smart Grid Operator (SGO) conducts self-healing planning during emergencies, communicating required nodal capacities to prevent forced load shedding and outlining incentives for smart prosumers. Subsequently, prosumers schedule their activities and contribute flexible capacities to the SGO. Bridging the first and second stages, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm ensures convergence between the SGO and prosumer schedules within a decentralized framework. This strategy underwent implementation on a 118-node distribution system using GAMS. Results demonstrate that the proposed concept reduces Forced Load Shedding (FLS) by 32.04% and self-healing costs by 17.48% through effective utilization of smart prosumers' flexible capacities. Furthermore, outcomes indicate that the SGO reduces FLS by 6.69% by deploying Mobile Electrical Energy Storages (MEESs) and Mobile Fuel Cell Trucks (MFCTs) to critical nodes.
An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster
(Elsevier, 2023-03-01) Mansouri, Seyed Amir; Jordehi, Ahmad Rezaee; Marzband, Mousa; Tostado-Véliz, Marcos; Jurado, Francisco; Aguado, José A.
The integrated exploitation of different energy infrastructures in the form of multi-energy systems (MESs) and the transformation of traditional prosumers into smart prosumers are two effective pathways to achieve net-zero emission energy systems in the near future. Managing different energy markets is one of the biggest challenges for the operators of MESs, since different carriers are traded in them simultaneously. Hence, this paper presents a hierarchical decentralized framework for the simultaneous management of electricity, heat and hydrogen markets among multi-energy microgrids (MEMGs) integrated with smart prosumers. The market strategy of MEMGs is deployed using a hierarchical framework and considering the programs requested by smart prosumers. A deep learning-based forecaster is utilized to predict uncertain parameters while a risk-averse information gap decision theory (IGDT)-based strategy controls the scheduling risk. A new prediction-based mechanism for designing dynamic demand response (DR) schemes compatible with smart prosumers’ behavior is introduced, and the results illustrate that this mechanism reduces the electricity and heat clearing prices in peak hours by 17.5% and 8.78%, respectively. Moreover, the results reveal that the introduced structure for hydrogen exchange through the transportation system has the ability to be implemented in competitive markets. Overall, the simulation results confirm that the proposed hierarchical model is able to optimally manage the competitive markets of electricity, heat and hydrogen by taking advantage of the potential of smart prosumers.
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, 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.
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.
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.
Low-voltage ride-through capability in a DFIG using FO-PID and RCO techniques under symmetrical and asymmetrical faults
(Nature, 2023-10-16) Sabzevari, Kiomars; Khosravi, Nima; Abdelghany, Muhammad Bakr; Belkhier, Youcef; Tostado-Véliz, Marcos; Kotb, Hossam; Govender, Scott
The power grid faults study is crucial for maintaining grid reliability and stability. Understanding these faults enables rapid detection, prevention, and mitigation, ensuring uninterrupted electricity supply, safeguarding equipment, and preventing potential cascading failures, ultimately supporting the efficient functioning of modern society. This paper delves into the intricate challenge of ensuring the robust operation of wind turbines (WTs) in the face of fault conditions, a matter of substantial concern for power system experts. To navigate this challenge effectively, the implementation of symmetrical fault ride-through (SFRT) and asymmetrical fault ride-through (AFRT) control techniques becomes imperative, as these techniques play a pivotal role in upholding the stability and dependability of the power system during adverse scenarios. This study addresses this formidable challenge by introducing an innovative SFRT–AFRT control methodology based on rotor components optimization called RCO tailored for the rotor side converter (RSC) within a doubly-fed induction generator (DFIG) utilized in wind turbine systems. The proposed control strategy encompasses a two-fold approach: firstly, the attenuation of both positive and negative components is achieved through the strategic application of boundary constraints and the establishment of reference values. Subsequently, the optimization of the control characteristic ‘ ’ is accomplished through the utilization of a particle swarm optimization (PSO) algorithm integrated within an optimization loop. This intricate interplay of mechanisms aims to optimize the performance of the RSC under fault conditions. To measure the efficacy of the proposed control technique, a comparative analysis is conducted. Fractional-order (FO) proportional–integral–derivative (PID) controllers are employed as an additional method to complement the novel approach. By systematically juxtaposing the performance of the proposed SFRT–AFRT control technique with the FO-PID controllers, a comprehensive evaluation of the proposed approach's effectiveness is attained. This comparative assessment lends valuable insights into the potential advantages and limitations of the novel control technique, thereby contributing to the advancement of fault mitigation strategies in WT systems. Finally, the paper highlights the economic viability of the proposed control method, suggesting its suitability for addressing broader power network issues, such as power quality, in future wind farm research.
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%.
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.
A novel Newton-like method with high convergence rate for efficient power-flow solution in isolated microgrids
(Wiley, 2023-03) Tostado-Véliz, Marcos; Bayat, Mohammad; Ghadimi, Ali Asghar; Jurado, Francisco
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.
Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions
(Elsevier, 2023-03) Ramadan, Ashraf; Ebeed, Mohamed; Kamel, Salah; Ahmed, Emad M.; Tostado-Véliz, Marcos
Renewable distributed generators (RDGs) have been widely used in distribution networks for technological, economic, and environmental reasons. The main concern with renewable-based distributed generators, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distribution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo simulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with considering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems.
Smart strategies for the penetration of 100% renewable energy for the Ecuadorian Amazon region by 2050
(Elsevier, 2023-01-01) Icaza-Alvarez, Daniel; Arias Reyes, Pablo; Jurado, Francisco; Tostado-Véliz, Marcos
This research presents a 100% renewable system configured based on its real potential and use of renewable energies for the Ecuadorian Amazon, considered one of the places with the greatest diversity of native plant species on the planet. The transition processes that are born from the central state seek to take a radical turn in the electricity sector but there is still no defined roadmap, this is where this research will allow it to be an important reference for its long-term transformation. The current system is based on fossil fuels and in this study an energy transition process is planned with the support of the EnergyPLAN tool that puts into practice the concept of intelligent energy, progressively and systematically achieving 100% renewable energy for 2050. Finally, after analyzing the results, it is concluded that the energy mix for 2050 can consider 36.47% hydro, 33.04% solar photovoltaic, 29.73% wind energy and other technologies to a lesser extent with 0.74%. Surpluses can be injected into the interconnection sites with Colombia and Peru, also achieving economic income to preserve and expand the infrastructure of the electricity sector. Decision makers can consider this study as a reference before committing economic resources and carrying out modern energy planning.
Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm
(MDPI, 2022-05-10) Qais, Mohammed H.; Hasanien, Hany M.; Turky, Rania A.; Alghuwainem, Saad; Tostado-Véliz, Marcos; Jurado, Francisco
This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA’s robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems.
Stochastic multi-stage multi-objective expansion of renewable resources and electrical energy storage units in distribution systems considering crypto-currency miners and responsive loads
(Elsevier, 2022-10) Tabar, Vahid Sohrabi; Banazadeh, Hamidreza; Tostado-Véliz, Marcos; Jordehi, Ahmad Rezaee; Nasir, Mohammad; Jurado, Francisco
In order to mitigate the influence of global warming and greenhouse gasses emission, different proceedings are suggested such as utilizing renewable energies and demand response programs. This paper investigates the expansion of renewable resources and electrical energy storage units in distribution systems towards reducing investment costs and environmental pollution. Since the development of components is not possible in a single-stage due to the limitation of staff and funds, a multi-stage programming is applied to consider various restrictions. According to the penetration of crypto-currency miners in recent years, their impact is evaluated on the problem as the high-rate energy consumers. Moreover, the demand side management strategy and risk-averse scenario-based approach are implemented to analyze the role of responsive loads and model the renewable resources uncertainty, respectively. The results approve that simultaneous expansion of wind turbines, photovoltaics and electrical storage systems decreases the total pollution and cost by 100% and 99.98% after the third year, respectively. The simulations also validate that crypto-currency miners reduce the total revenue by 4.16%, whereas the responsive loads increase it by 9.89%. As well, the fluctuations of wind and solar power decrease the total revenue by 13.27%, in return, the robustness notably improves.

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