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This research presents a completely decarbonized system structured with renewable energies for the fragile ecosystem of the Galapagos Islands of Ecuador, with a horizon to the year 2050. The transition process begins with clear legal actions and allocating economic resources that clearly promote a change in the energy matrix and the transition would be achieved until the year 2050. The Galapagos Islands are the second largest marine reserve on the planet, declared a World Heritage Site in 1978 for UNESCO. The archipelago, a beautiful ecological attraction, has tourism as its main economic source. The biggest problem is in the supply of energy that mainly comes from fossil fuels, in addition to that, the smell of fuel in several places is out of tune with the harmonious nature and its species. The efforts began to be noticed when in the Archipelago the new implementations of renewable energy began to become a reality with the purpose of stopping using fossil fuels. In this sense, there is the potential to take advantage of the resources available in specific sites, especially the photovoltaic and wind solar considered in the National Plan All a Life. The change in the productive matrix from the national level also implies a change in demand, including at the transportation level with the use of electric vehicles and the use of electric stoves in homes. The purpose is that energy services in the Galapagos Islands progressively increase, on the one hand, replacing fossil fuels and on the other, supplying the growing demand with a view to 2050, taking advantage of renewable energies until reaching 100%. • The scenario outlined is 100% renewable energy by 2050 in the Galapagos Islands. •The Galapagos archipelago has great renewable energy potential. •The decarbonization in Galapagos will have a positive impact on the socioeconomic and environmental aspects. •Decarbonization is feasible by 2050 depending on the economic situation. •The electricity system of Galapagos will allow it to maintain its status as a Cultural Heritage of Humanity. [ABSTRACT FROM AUTHOR]

Abstract: As a result of increasing wind farms penetration in power systems, the wind farms begin to influence power system, and thus the modelling of wind farms has become an interesting research topic. Nowadays, doubly fed induction generator based on wind turbine is the most widely used technology for wind farms due to its main advantages such as high-energy efficiency and controllability, and improved power quality. When the impact of a wind farm on power systems is studied, the behavior of the wind farm at the point common coupling to grid can be represented by an equivalent model derived from the aggregation of wind turbines into an equivalent wind turbine, instead of the complete model including the modelling of all the wind turbines. In this paper, a new equivalent model of wind farms with doubly fed induction generator wind turbines is proposed to represent the collective response of the wind farm by one single equivalent wind turbine, even although the aggregated wind turbines operate receiving different incoming winds. The effectiveness of the equivalent model to represent the collective response of the wind farm is demonstrated by comparing the simulation results of equivalent and complete models both during normal operation and grid disturbances. [Copyright &y& Elsevier]

The olive tree in Spain can generate large quantities of by-product biomass suitable for gasification. Gasification technologies under development would enable these fuels to be used in gas turbines. Biomass conversion to a clean essentially ash-free form, usually by gasification and purification, is necessary to obtain high efficiency. This paper reports results of detailed full-load performance modelling of cogeneration systems based on gasifier/gas turbine technologies. [Copyright &y& Elsevier]

Control of autonomous diesel–wind systems is difficult because of time-varying dynamical properties, most of them as a result of plant non-stationary, non-linearity and random disturbances. In this paper, the system is composed of a pitch controlled wind turbine with an induction generator connected to an ac bus bar in parallel with a diesel generator set having a synchronous generator. A power plant can generate electric power using biomass from the olive tree in Spain. To have optimal response under disturbances, adaptive schemes appear to be a particularly attractive choice. The recursive least squares method and minimum-variance control algorithm is implemented to design the controller. This paper considers the application of an adaptive control methodology that allows the controller to automatically adjust to changing process variables and thereby provide uniform response over a wide range of operating conditions. [Copyright &y& Elsevier]

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of converting tons of wood chips per day into a gaseous fuel that is fed into a diesel engine. The controller inputs are the engine speed error and its derivative for the governor part of the controller, and the voltage error and its derivative for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented. It is shown that by tuning the fuzzy logic controllers, optimal time domain performance of the autonomous wind–diesel system can be achieved in a wide range of operating conditions compared to fixed-parameter fuzzy logic controllers and PID controllers. [Copyright &y& Elsevier]

The power output obtained from the hydrokinetic renewable system fluctuates with changes in weather conditions, which could cause adverse effects on the voltage, frequency and stability of the electrical grid. In this article, an evaluation of power smoothing is performed for a renewable hydrokinetic on grid system using a supercapacitor. The power fluctuations are mitigated and smoothed by the proposed energy control and supplied to the utility grid and household load. Moreover, this paper studies the impact of the temporal resolution of data sampling with respect to the self-consumption of prosumers under technical, economic and environmental parameters using Matlab/Simulink software. The simulation results show that the fluctuations of the hydrokinetic turbine output power can be significantly mitigated, using the supercapacitor based storage system for different temporal resolutions reducing up to 90% of power peaks and power fluctuations produced by the hydrokinetic turbine and the load. Besides, the use of supercapacitors allows increasing the self-consumption of prosumers up to 17.27% for time resolutions of 1 min. Finally, the proposed control reduces up to 0.54 kgCO 2 /day at cost of energy 0.15 USD/kWh. [Display omitted] [ABSTRACT FROM AUTHOR]

–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 methodology for optimal planning photovoltaic-battery systems. • Grid outages are characterized via scenarios generated from historical data. • The response to demand incorporates different price-based incentives. • Optimal programming of typical deferred devices. • Several case study on a smart prosumer environment are analysed. [ABSTRACT FROM AUTHOR]

In recent years, photovoltaic energy production has experienced significant progress, being integrated into the grid through large-scale distributed systems. The intermittent nature of solar irradiance coupled with the presence of photovoltaic failures causes fluctuations that could compromise the quality and stability of electrical grid. This paper presents a novel photovoltaic power smoothing method in a combination with moving averages and ramp rate to reduce fluctuations with hybrid storage systems (supercapacitors/batteries), the main novelty involves optimizing the number of charging/discharging cycles under PV failures. To achieve this goal, a photovoltaic failure detection method is proposed that uses machine learning to process big data by monitoring the behavior of photovoltaic. The experiments have been done under controlled conditions in the microgrid laboratory of the University of Cuenca. The results show the reduction of the supercapacitor operation with respect to other power smoothing methods. Moreover, the monitoring system is capable of detecting a failure in photovoltaic systems with a root mean squared error of 0.66 and the computational effort is reduced using the new smoothing technique. In this sense, the initial execution time is 4 times lower compared to the moving average method. [Display omitted] [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The development of hybrid systems that combine different energy sources has become increasingly important to meet the growing demand for clean and sustainable energy. These systems can offer a reliable and efficient solution to reduce dependence on conventional energy sources and address the issues of climate change and environmental degradation. This study focuses on two types of hybrid systems, namely PV/biomass and PV/diesel, which are commonly used in off-grid areas or places with limited access to electricity. The proposed optimization framework aims to determine the optimal sizing of each component in the system, including PV panels, batteries, and backup sources, to maximize the performance and efficiency of the hybrid system. The Gradient Pelican Optimization Algorithm (GPOA), which is an advanced optimization technique, is proposed to solve the optimization problem. The algorithm is modified to include a local escaping operator, which allows the algorithm to escape local optima and achieve better solutions. The outcomes of the simulation show that the GPOA algorithm outperforms other well-known algorithms in terms of accuracy and computational efficiency. The simulation results also demonstrate that the PV/biomass hybrid system outperforms the PV/diesel hybrid system in terms of cost and environmental impact. The proposed framework and optimization methodology can be useful for designing and optimizing other hybrid systems and can be applied to various applications, including off-grid and remote areas, distributed power systems, and microgrids. The findings of this study can also contribute to the development and promotion of renewable energy and sustainable development. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response, photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations and improve installation performance. However, during this procedure other functionalities that energy storage could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that interacts with parameters of the electrical grid at the common connection point and also allows the compensation of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand forecasting model that improves the supervision, automation and analysis of daily energy production. A series of results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds. The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model. [Copyright &y& Elsevier]

Decarbonization of the energy sector calls for new energy sources and power-system operation paradigms. In this context, consumers can partake in system operation as loads and generators by leveraging own resources, such as rooftop photovoltaics (PVs). These prosumers can be gathered into energy communities (ECs) and operated in a centralized manner to exploit eventual synergies optimally. The ECs are usually connected to a distribution network and thus interact with the distribution grid operator (DSO), complicating the scheduling strategies under agent interactions. Moreover, the DSO can thus manage his own resources such as distributed generators or medium-scale storage systems. The operation becomes more complex due to multi-source uncertainties in ECs and DSO. This paper addresses this issue by developing a novel bi-level energy management framework for the robust optimal coordination of ECs and DSO against uncertainties. In this framework, the upper-level agents provide the information by solving the designed optimization problems for the EC operation, sending it to the lower-level agents to facilitate the optimal scheduling of distributed assets. In particular, a novel robust reformulation of the lower level problem is proposed, to handle with uncertainties in renewable generation, demands, and energy prices fixed by the local retailer. The resulting formulation is mixed-integer-linear programming that is efficiently manageable by common solvers. Simulation results on the standard IEEE 33-bus distribution system with six connected ECs and various distributed assets (e.g., renewable generators and storage systems) demonstrate the effectiveness and robustness of the proposed framework. The results also evidence the vital role of storage systems in maximizing the DSO's profits while reducing the ECs' operating costs. [ABSTRACT FROM AUTHOR]

Biomass gasification plants for electricity generation are an environmentally friendly technology. One of their main problems is their poor responsiveness to load variations when the plant operates off-grid. For this reason, they are usually grid-connected systems. Therefore, as a solution, an energy management system that allows the plant to operate off-grid is proposed. The system has been obtained through the optimal design of unidirectional and bidirectional Z-source converters and their coupling with their series outputs. This approach has been applied to a biomass gasification power plant. To validate the system, the results of an experimental 10 kW gasification plant are presented. With the plant working off-grid, there is a sudden load increase of 2.5 kW, and due to the proposed system performance, there is only an output voltage oscillation of less than 1%, preserving a stable power supply. This proposed system allows the plant to work isolated or coupled to a weak micro-grid of renewable energies. Obtaining a permanent supply of quality and also improving its efficiency. Thus, the gasification technology can be used for electricity generation in isolated areas of many developing countries, where the electrical grid is not totally developed, but there are available biomass sources. [ABSTRACT FROM AUTHOR]

This paper presents a novel energy dispatching based on Model Predictive Control (MPC) for off-grid photovoltaic (PV)/wind turbine/hydrogen/battery hybrid systems. The renewable energy sources supply energy to the hybrid system and the battery and hydrogen system are used as energy storage devices. The denominated “hydrogen system” is composed of fuel cell, electrolyzer and hydrogen storage tank. The MPC generates the reference powers of the fuel cell and electrolyzer to satisfy different objectives: to track the load power demand and to keep the charge levels of the energy storage devices between their target margins. The modeling of the hybrid system was developed in MATLAB-Simulink, taking into account datasheets of commercially available components. To show the proper operation of the proposed energy dispatching, a simpler strategy based on state control was presented in order to compare and validate the results for long-term simulations of 25 years (expected lifetime of the system) with a sample time of one hour. [ABSTRACT FROM AUTHOR]