Examinando por Autor "Jurado-Melguizo, Francisco"

Mostrando 1 - 20 de 109

A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
(MDPI, 2021-07) Tostado-Véliz, Marcos; Kamel, Salah; Escámez-Álvarez, Antonio; Vera-Candeas, David; Jurado-Melguizo, Francisco
This paper presents a novel Power-Flow solution paradigm based on the structure of the members of the Runge–Kutta family. Solution approaches based on the introduced solution paradigm are intrinsically robust and can achieve high-order convergences rates. It is demonstrated that some well-known Power-Flow solution methods are in fact special cases of the developed framework. Explicit and embedded formulations are discussed, and two novel solution methodologies based on the Explicit Heun and Embedded Heun–Euler’s methods are developed. The introduced solution techniques are validated in the EU PEGASE systems, considering different starting points and loading levels. Results show that the developed methods are quite reliable and efficient, outperforming other robust and standard methodologies. On the basis of the results obtained, we can affirm that the introduced solution paradigm constitutes a promising framework for developing novel Power-Flow solution techniques.
A comprehensive electrical-gas-hydrogen Microgrid model for energy management applications
(Elsevier, 2021-01) Tostado-Véliz, Marcos; Arévalo, Paul; Jurado-Melguizo, Francisco
Recently, a growing interest on multienergy Microgrids has been observed. This kind of grids involves different energy vectors and treat them on a whole. The most typical cases contemplate electrical, natural gas and hydrogen subsystems. Multiple efforts have been conducted on modelling this kind of grids for energy management problems. However, it is observed that most of references studied do not faithfully modelling this kind of grids or directly omit some of the mentioned subsystems, which difficults the accurately representation of these grids. This paper aims at developing a comprehensive but tractable yet multienergy MG model, which allows to accurately represent the interaction between electrical, natural gas and hydrogen subsystems. To that end, the developed framework includes detailed models of the different elements which are typically encountered in this kind of grids such as Gas-to-Power or Power-to-Gas facilities. Also, charging stations for electrical, natural gas and hydrogen vehicles are considered. Different tariffs and vehicle charging modes can be easily incorporated within the developed framework. The proposed model is validated with a case study in typical winter and summer scenarios based on real data. Results show that the developed model is able to accurately represent the operational behavior of multienergy Microgrids, which may be valuable for multiple research and educational tools.
A data-driven methodology to design user-friendly tariffs in energy communities
(Elsevier, 2024-03) Tlenshiyeva, Akmaral; Tostado-Véliz, Marcos; Hasanien, Hany M.; Khosravi, Nima; Jurado-Melguizo, Francisco
In recent years, energy communities have emerged as a feasible solution to empower domestic end-users to engage in local power trading with their neighbours, in an attempt to improve the efficiency and economy of residential consumers. From a mercantilist point of view, launching local markets with eventual local electricity prices might be beneficial for community users as they are inhibited from external volatile prices and possible market imperfections. However, local pricing strategies should take into account users’ preferences and avoid undesirable effects of response fatigue (i.e. excessive number of response signals within a short-time period). This way, local electricity tariffs should be stable and send coherent response signals easily interpretable by users. In this sense, the necessity of developing proper designing tools for local electricity tariffs is clear. This paper focuses on this issue. In particular, the main novelties of this paper are twofold: on the one hand, the developed tool designs community tariffs over a year basis instead of daily spot prices, as made in existing approaches. Thereby, the resulting tariff keeps stable yearly similar to conventional tariffs offered by retailers worldwide. Secondly, the designed tariff takes into account the negative effects of response fatigue, so that the considered pricing mechanism limits the number of pricing signals sent to consumers, taking this feature as an external parameter. This way, the designer is able to tune up the total number of pricing signals that users received within a time period, thus ensuring that they are not discouraged to partake in the community. The proposed design approach is raised as a data-driven framework, taking advantage of real databases collecting demand, renewable generation and retailer prices. Such profiles serve as inputs for a designed bi-level Stackelberg-based problem, in which the reaction of prosumers is implicitly assumed. A case study is conducted on a benchmark energy community. Different tariff mechanisms are analysed such as flat, time-of-use and happy hours tariffs. The results obtained serve to validate the new proposal as well as analyse the effect of local market mechanisms in energy communities.
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, Francisco
With 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.
A local electricity market mechanism for flexibility provision in industrial parks involving Heterogenous flexible loads
(Elsevier, 2024-04) Turdybek, Balgynbek; Tostado-Véliz, Marcos; Mansouri, Seyed Amir; Jordehi, Ahmad Rezaee; Jurado-Melguizo, Francisco
Industrial parks allow industries to share infrastructure and thus saving money, finally redounding in improving the economy of many countries worldwide. Given the objectives of carbon neutrality imposed by different entities, it results mandatory promoting energy efficiency in industrial parks. Aligning with such objective, encouraging industries to provide energy flexibility becomes essential. In the electricity sector, such flexibility can be provided through optimally managing local assets such as energy storage and flexible loads. However, flexibility provision should be promoted by implanting proper pricing mechanisms. This paper focuses on this issue by developing a local market clearing mechanism for industrial parks, whose main novelty redounds in the inclusion of a fair pricing mechanism through which industries are paid by flexibility provision. Different types of flexible loads are considered and modelled (i.e. curtailable, interruptible and deferrable), so that the new proposal is suitable for leveraging fully capabilities of industrial flexible loads. The whole pricing mechanism is raised as a bi-level game-based model, by which local energy and flexibility prices are revealed in a coordinated way. Challenges brought by the inclusion of binary variables (needed for modelling some types of flexible loads) are solved by proposing a solution algorithm based on the well-known Column & Constraint Generation Algorithm. The resulting optimization framework is Mixed Integer Linear Programming, being therefore solvable by off-the-shelf solvers. A case study is presented to validate the new proposal as well as highlight some important aspects related to local markets in industrial parks and its practical implantation.
A MILP framework for electricity tariff-choosing decision process in smart homes considering ‘Happy Hours’ tariffs
(Elsevier, 2021-10) Tostado-Véliz, Marcos; Mouassa, Souhil; Jurado-Melguizo, Francisco
Nowadays, electricity end users can choose among a huge variety of different electricity plans on a deregulated energy market. The wide variety of tariffs besides the advent of novel agents like smart consumers and prosumers, are becoming the tariff-choosing process more complex. This paper proposes a MILP optimization framework which aims at facilitating this task. More precisely, the main endings of the developed framework are: (i) determine the most suitable tariff for smart consumers and prosumers based on historical consumption data, (ii) determine the optimal hours to be hired for a so-called ‘Happy hours’ tariff plan. In addition, other useful results can be directly obtained from the developed tool. The developed approach carries out a MILP optimization framework for optimal scheduling a series of flexible appliances through various characteristic days obtained from clustering historical collected data. This process is repeatedly executed for the different tariff options and, finally, the most attractive one is selected. A case study on the Spanish retail market for a benchmark prosumer environment is used for showing the capabilities of the developed framework.
A mixed-integer-linear-logical programming interval-based model for optimal scheduling of isolated microgrids with green hydrogen-based storage considering demand response
(Elsevier, 2022-04) Tostado-Véliz, Marcos; Kamel, Salah; Hasanien, Hany; Turky, Rania; Jurado-Melguizo, Francisco
Hydrogen produced from renewable sources (green hydrogen) will be recognized as one of the main trends in future decarbonized energy systems. Green hydrogen can be effectively stored from surplus renewable energy to thus reducing dependency of fossil fuels. As it is entirely produced from renewable sources, green hydrogen generation is strongly affected by intermittent behaviour of renewable generators. In this context, proper uncertain modelling becomes essential for adequately management of this energy carrier. This paper deals with this issue, more precisely, a novel optimal scheduling model for robust optimal scheduling of isolated microgrids is developed. The proposal encompasses a green hydrogen-based storage system and various demand-response programs. Logical rules are incorporated into the conventional optimal scheduling tool for modelling green hydrogen production, while uncertain character of weather and demand parameters is added via interval-based formulation and iterative solution procedure. The developed tool allows to perform the scheduling plan under pessimistic or optimistic point of views, depending on the influence assumed by uncertainties in the objective function. A case study serves to validate the model and highlight the paper of green hydrogen-based storage facilities in reducing fossil fuel consumptions and further exploit renewable sources.
A New Methodology for Smoothing Power Peaks Produced by Electricity Demand and a Hydrokinetic Turbine for a Household Load on Grid Using Supercapacitors
(MDPI, 2021-11) Arévalo, Paul; Tostado-Véliz, Marcos; Jurado-Melguizo, Francisco
The power fluctuations produced by electric vehicles represent a drawback in large-scale residential applications. In addition to that, short power peaks could pose a risk to the stability of the electrical grid. For this reason, this study presents a feasibility analysis for a residential system composed of electric vehicle chargers. The objective is focused on smoothing the power fluctuations produced by the charge by a supercapacitor through adequate energy control; in addition, self-consumption is analyzed. Data sampling intervals are also analyzed; the modeling was performed in Matlab software. The results show that there are errors of up to 9% if the data are measured at different sampling intervals. On the other hand, if the supercapacitor is considered, the system saves 59.87% of the energy purchased from the utility grid per day, and the self-consumption of electricity by prosumers can increase up to 73%. Finally, the hydrokinetic/supercapacitor/grid system would save up to 489.1 USD/year in the cost of purchasing electricity from the grid and would increase by 492.75 USD/year for the sale electricity.
A Novel Family of Efficient Power-Flow Methods With High Convergence Rate Suitable for Large Realistic Power Systems
(IEEE, 2021-03) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
High-order power-flow (PF) solution methods are techniques with higher convergence rate than the standard Newton-Raphson (NR). This feature normally provokes that less iterations are necessary for achieving the required convergence tolerance. This advantage enables important computational savings in realistic large-scale power systems, since some expensive computations may be avoided. This article develops and analyzes a novel family of multistep PF solution techniques. The introduced solution paradigm just involves an LU decomposition along with other cheap computations each iteration. In addition, it is proved that its convergence order is equal to the number of steps considered. A comparison with other high-order PF techniques available in the literature reveals that the introduced paradigm is more efficient when more than three steps are taken. Hence, two PF methods with fourth and fifth convergence rate are developed and validated in 12 systems under different demand conditions. Results prove that the developed PF solution methods are able to notably outperform NR and the other high-order PF solvers proposed in the literature.
A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems
(Elsevier, 2022-08-15) Tostado-Véliz, Marcos; Kamel, Salah; Aymen, Flah; Jurado-Melguizo, Francisco
With the emergence of smart appliances and communication infrastructures, Home Energy Management Systems have gained importance to help home users to reduce their electricity bills. A Home Energy Management System is a tool able to optimally coordinate the different home assets such as controllable appliances, onsite generators and storage facilities, among others. Such kind of tools has become more complex with the appearance of dynamic pricing tariffs and novel appliances such as electric vehicles. In this context, scheduling tools must attain a high level of robustness against uncertainties as well as being able to consider the particular behaviour of unpredictable energy pricing and vehicle routines, while some complementary objectives like thermal comfort are not ignored. This paper addresses this issue by developing a novel hybrid robust-multi objective home energy management approach. The novel proposal is based on information gap decision theory and lexicographic optimization, which are combined in an original way to attain a scheduling plan immune against the negative effect of uncertainties, while the economy and comfort of the building are jointly considered. The developed mathematical formulation is Mixed Integer Linear Programming, employing advanced linearization techniques to overcome the problems arisen from nonlinear models. Its particular integer-linear structure makes the developed optimization problem easily tractable by conventional solvers and average machines. Also, further capabilities are explored such as the possibility of selling energy to the grid and the vehicle-to-home ability of electric vehicles. Extensive simulations are performed on a benchmark prosumer environment considering real time pricing and time-of-use tariffs. The result serves to prove that the developed methodology is able to deal with uncertainties whereas different objective functions are jointly accounted.
A novel methodology for comprehensive planning of battery storage systems
(Elsevier, 2021-05) Arévalo, Paul; Tostado-Véliz, Marcos; Jurado-Melguizo, Francisco
Battery storage system design has become a crucial task for nanogrids and microgrids planning, as it strongly determines the techno-economic viability of the project. Despite that, most of developed methodologies for optimally planning this kind of systems still present some important issues like high computational burden or insufficient results. This paper develops a novel methodology for battery storage system planning in nanogrids and microgrdis, which aims at overcoming the main issues presented by other methodologies. To achieve this goal, our proposal originally combines different software, clustering techniques and optimization tools. As salient features of the developed approach, it is worth remarking its efficiency, versatility, ability to manage with different time horizons and comprehensiveness. A prospective nanogrid in the region of Cuenca, Ecuador, serves as illustrative case study to show the capabilities, efficiency and effectiveness of the proposed approach as providing sufficient guidelines for its universal applicability. Among other relevant results, our proposal is able to determine that, for the studied grid, the daily operating cost can be reduced up to 17% by using Nickel-Cadmium batteries, however, the usage of Lead-Acid and Sodium-Sulfur technologies resulted more attractive through the project lifetime due to their longer lifetimes and relatively low capital costs.
A novel methodology for optimal sizing photovoltaic-battery systems in smart homes considering grid outages and demand response
(Elsevier, 2021-06) Tostado-Véliz, Marcos; Icaza-Alvarez, Daniel; Jurado-Melguizo, Francisco
This paper deals with the optimal sizing of a hybrid photovoltaic-battery storage system for home energy management considering reliability against grid outages and demand response. To that end, a novel optimization framework is developed which aims at minimizing the electricity bill while the reliability of the system is ensured for certain common outages. In order to ensure the accuracy of the results, a large amount of characteristic outages along with demand, solar irradiance and temperature profiles are generated from real data. Clustering techniques are used for reducing this data to those most characteristics profiles and manage with the unpredictable behaviour of the outage events. Demand response is incorporated via different incentives like tariffs based on time of use and real time pricing, along with the optimal scheduling of different typical deferrable appliances. A case study on a smart-prosumer environment serves to illustrate the capabilities of the developed approach as providing sufficient guidelines for its universal applicability. Different cases studies are simulated considering different battery technologies and electricity tariffs for comparison. Various aspects related with the reliability against grid outages are also analysed like its impact on the project cost or the influence of demand response strategies.
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-Melguizo, Francisco
Power-Flow (PF) solution in isolated microgrids has attracted notable attention recently, because these systems present various particularities compared with the traditional PF solution in large meshed transmission networks. In this sense, this paper develops a novel Newton-like PF solver for isolated microgrids. The new proposal is based on the Modified Midpoint method and shows high convergence order with relatively low computational burden. These characteristics bring superior theoretical performance compared with the standard Newton–Raphson (NR) method and other high order techniques, which has been conventionally used. Extensive simulations are performed on various small-, and large-scale benchmark Microgrids under different loading conditions and R/X ratios. Results provided serve to confirm the theoretical features of the developed solver, outperforming the NR technique as well as other recently developed solvers in all the studied systems with acceptable reliability even under high stressed conditions.
A Novel Power Flow Solution Paradigm for Well and Ill-Conditioned Cases
(IEEE, 2021-08) Tostado-Véliz, Marcos; Alharbi, Talal; Alrumayh, Omar; Kamel, Salah; Jurado-Melguizo, Francisco
This paper develops a novel four-stage power flow solver for ill-conditioned systems. Although the developed solver could be considered efficient, it is not competitive with the Newton-Raphson method in well-conditioned cases. With the aim of being fully competitive in a wide range of cases and scenarios, the developed algorithm is integrated within a novel efficient solution paradigm. As a result, a robust and efficient solution framework, competitive in both well and ill-conditioned cases, is obtained. The new proposals are tested in various well and ill-conditioned cases from 30-, to 13,659-buses. Results obtained with the developed solvers are promising.
A novel solar panel cleaning mechanism to improve performance and harvesting rainwater
(Elsevier, 2022-05) Myyas, Ra'ed Nahar; Al-Dabbassa, Mohammad; Tostado-Véliz, Marcos; Jurado-Melguizo, Francisco
First generation Photovoltaic (PV) systems need regular washing to avoid efficiency degradation. Dust deposition on the surface limits solar penetration into photovoltaics and consequently the PV output. Efficiency may fall by 50% after a month without cleaning the modules. This effect strongly depends on the area, being desert climates more problematic because of the proliferation of dust particles and eventual high wind speeds. This research aims to illustrate the idea of an innovative intelligent device with wide applications and advantages, which improves the efficiency of solar cells by a self-cleaning mechanism, keeping the temperature of solar cells from rising, recycling the cleaning water, and harvesting rainwater falling. In this research, an experiment was performed in the city of Salt (Jordan) to investigate the purification of solar cells at the energy production plant above the Najashi Mosque. To clean the dust periodically, an automated cleaner was installed that detects the dust on the solar panel and automatically cleans the module. Various cleaning methods were compared: manual cleaning, automatic cleaning, manual injection water, compressed air. Some outstanding features of the new proposal are identified, making it the ideal device for resolving cleaning difficulties, high temperatures, and increasing solar cell performance. It can be also utilized to gather rainwater by employing the vast areas of solar cells scattered over the world. The findings of this study may help in preserving the environment by harvesting sun and rainwater, enhancing PV efficiency, and achieving decarbonization in the energy industry.
A powerful power-flow method based on Composite Newton-Cotes formula for ill-conditioned power systems
(Elsevier, 2020-03) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
This paper proposes a novel Power-Flow method based on Composite Newton-Cotes formula. The proposed method aims to be robust and efficient enough to properly manage ill-conditioned power systems. Self-adapted mechanisms for tuning the parameters of proposed PF method are provided. The proposed method is tested on several ill-conditioned systems ranging from 3012 to 27,318 buses at loadbase and heavy loading conditions. Moreover, its ability to handle the generators’ reactive limits is also tested. The obtained results prove that the proposed method is remarkably robust and more efficient than other well-known Power-Flow techniques.
A Robust Power Flow Algorithm Based on Bulirsch–Stoer Method
(IEEE, 2019-07) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
In this paper, we address the load-flow (LF) problem of very large scale systems. These types of systems show a very narrow region of attraction, and most of LF solvers tend to fail when a flat initial guess point is used. On the other hand, the solution of these systems frequently involves very large matrices and vectors. Consequently, a robust LF method must be used to find the correct solution of these systems. This paper proposes a robust and efficient LF solver based on the Bulirsch-Stoer algorithm. Moreover, a simple modification is proposed in order to improve its computational performance. The proposed methods are tested using various very large scale systems (i.e., more than 3000 buses) and compared with several standard and robust LF techniques. The obtained results show that the proposed methods are more suitable for solving the LF problem of very large scale systems.
A Robust-Based Home Energy Management Model for Optimal Participation of Prosumers in Competitive P2P Platforms
(MDPI, 2024-11-16) Al Zetawi, Alaa; Tostado-Véliz, Marcos; Hasanien, Hany M.; Jurado-Melguizo, Francisco
Nowadays, advanced metering and communication infrastructures make it possible to enable decentralized control and market schemes. In this context, prosumers can interact with their neighbors in an active manner, thus sharing resources. This practice, known as peer-to-peer (P2P), can be put into practice under cooperative or competitive premises. This paper focuses on the second case, where the peers partaking in the P2P platform compete among themselves to improve their monetary balances. In such contexts, the domestic assets, such as on-site generators and storage systems, should be optimally scheduled to maximize participation in the P2P platform and thus enable the possibility of obtaining monetary incomes or exploiting surplus renewable energy from adjacent prosumers. This paper addresses this issue by developing a home energy management model for optimal participation of prosumers in competitive P2P platforms. The new proposal is cast in a three-stage procedure, in which the first and last stages are focused on domestic asset scheduling, while the second step decides the optimal offering/bidding strategy for the concerned prosumer. Moreover, uncertainties are introduced using interval notation and equivalent scenarios, resulting in an amicable computational framework that can be efficiently solved by average machines and off-the-shelf solvers. The new methodology is tested on a benchmark four-prosumer community. Results prove that the proposed procedure effectively maximizes the participation of prosumers in the P2P platform, thus increasing their monetary benefits. The role of storage systems is also discussed, in particular their capability of increasing exportable energy. Finally, the influence of uncertainties on the final results is illustrated.
A Stochastic-IGDT model for energy management in isolated microgrids considering failures and demand response
(Elsevier, 2022-07-01) Tostado-Véliz, Marcos; Kamel, Salah; Aymen, Flah; Jordehi, Ahmad Rezaee; Jurado-Melguizo, Francisco
In power systems, contingencies and outages are more frequent nowadays due to climate changing effects. This circumstance along equipment aging may lead to unexpected failures and outages in power and energy systems. This issue is especially critical in isolated microgrids, which must be supplied by means of own resources and onsite assets. In these systems, unexpected failures may notably provoke a detriment of the economy and users’ satisfaction. In order to minimize the impact of these incidents, this paper proposes a novel energy management tool for isolated microgrids that are robust against failures. To this end, a novel stochastic-IGDT formulation is developed, by which typical uncertainties are comfortably modelled via scenarios while components’ failures are treated in a robust fashion using IGDT. A solution procedure is proposed in which the operation cost is also considered in order to reproduce useful results and limit the cost of reliability. A variety of simulations are conducted in order to validate the developed model and discuss the particularities derived from considering failures in the energy management task. Moreover, the role of demand response programs is also foregrounded. In particular, the demand response programs allow reducing the operation costs by 3 % while the scheduling result admits up to 13 h more of accumulated failures, thus confirming a positive effect of such initiatives in both economy and robustness against failures.
A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations
(Elsevier, 2022-08-15) Tostado-Véliz, Marcos; Kamel, Salah; Hasanien, Hany M.; Arévalo, Paul; Turky, Rania A.; Jurado-Melguizo, Francisco
Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand.

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Examinando por Autor "Jurado-Melguizo, Francisco"