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Hybrid Renewable Energy Systems have been considered as a suitable way to supply electricity. The hybrid energy (Solar-Wind-Storage-hydrogen-diesel) is increasingly used in various applications, especially at isolated sites. This study proposes the optimization and analysis of a stand-alone photovoltaic/battery/fuel cell/electrolyzer/hydrogen hybrid system (HS) to find out the optimal sizes and costs. In the first step, SAM software was used to estimate the global solar irradiance (GHI) for the studied area and to select the best PV, battery and fuel cell components. MATLAB software was handled to predict the power output of the renewable source (PV system) using an Artificial Neural Network (ANN) model. Then, simulation, optimization and techno-economic analysis were carried out in the second step with HomerPro software. Four proposed configurations are optimized in this work and different comparisons have been made regarding the found results. The results of the cost optimization were found to be 50861.21 $ and 0.95657 $ for NCP and COE, respectively. Moreover, the same HS configurations are simulated and optimized under Moroccan climate. • Hybrid Renewable Energy Systems is a suitable way to supply electricity. • A stand-alone photovoltaic/battery/fuel cell/electrolyzer/hydrogen hybrid system. • SAM software was used to estimate the global solar irradiance. • MATLAB was handled using an Artificial Neural Network model. • Optimization and techno-economic analysis with HomerPro software. [ABSTRACT FROM AUTHOR]

Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5 % and 2.3 % with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32 %, this result represents an annual cost savings of 42 % on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method. [Display omitted] • Hybrid supercapacitor-integrated dispatch model with analysis of pattern search method. • Dispatch-coupled sizing renewable sources and energy storage with power smoothing, improving energy quality. • Numerical analysis on different size optimization methods comparatively considering experimental data. • An improved pattern search optimization that exceeds the standard one. • Validation of the effectiveness of the proposed framework in a laboratory microgrid test system. [ABSTRACT FROM AUTHOR]

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]

• A novel methodology for optimal planning collective PV in ECs is developed. • Long-term variation of parameters are easily incorporated into the model. • A solution strategy based on multi-cut Benders' decomposition is described. • The proposed methodology is scalable and computationally tractable. • Collective PV helps to improve the economy and efficiency of the community. With the deregulation and the urgent necessity of decarbonizing the electricity sector, end users have been empowered to actively partake in the operation of the system. In this context, energy communities arise as a regulatory framework that allows aggregating a group of domestic consumers (or prosumers) in order to share own resources such as storage or photovoltaics. However, such collective assets should be properly designed in order to exploit them in an optimal way as well as avoid oversizing components, which may result in higher expenditures economically harmful. This new paradigm calls for developing proper computational tools for optimally planning and operating energy communities. In this paper, a novel tool for optimally planning collective photovoltaic arrays in cooperative energy communities is developed. This planning problem encompasses continuous and binary variables, which are evaluated over long time horizons and characterized through representative sets. These features make the problem computationally costly or even unaffordable for average machines. To overcome this issue, a novel solution strategy based on the multi-cut Benders' decomposition is developed. The new proposal exploits the particular structure of the multi-year problem to produce dense cuts strategies that rely on multiple computational benefits, while the optimization framework allows including long-time parameters (such as panel degradation) in an intuitive way. The new approach is tested on a number of scenarios, comparing it with the traditional solution method without applying decomposition. Results reveals that the original intractable problem becomes manageable by average machines when applying the proposed decomposition technique, thus demonstrating its practicability and importance. In addition, the results obtained serve to highlight the importance of increasing local generation in energy communities, in order to reduce the dependency of the grid as well as reducing the total energy cost for users. In particular, the total project cost reduces by 19 % in our particular case study. Other aspects have been also highlighted, as the importance of photovoltaics to increase the exportable capacity of the community observing an increment of 96 % in the exportable energy in our case study. In this sense, the developed tool results valuable for community planners, allowing them to take decisions on pursuing to improve the economy and efficiency of the entire community. [ABSTRACT FROM AUTHOR]

Load growth in electrical distribution networks became naturally occurring due to industrial development and human population demand growth. Consequently, system losses are continually raised while the voltage profile is reduced. This paper presents a novel hybrid method to determine the best locations and sizes of single and multiple of different renewable distributed energy resources (DER). The presented hybrid method is based on Salp Swarm Algorithm (SSA) and combined power loss sensitivity (CPLS). Integration of photovoltaics (PV) and wind turbines (WT) in distribution network is used to enhance the system voltage, minimize system losses and increase the system capacity. The effect of annual load growth in system load and system operating constraints are taken in consideration. IEEE 33-bus and 69-bus radial distribution systems (RDS) are used to validate the presented algorithm for integrating the DERS in distribution system. In addition, the presented algorithm is compared with different recent optimization algorithms in order to prove its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Sustainable bioenergy production relies on thoroughly understanding each country's biomass resources. Among North African countries, Tunisia possesses significant biomass potential. However, the country has not fully harnessed this potential for sustainable bioenergy production due to a limited awareness and recognition of its biomass resources. This article examines the biomass potential of three of Tunisia's most important agri-food value chains: olive oil, date palm, and almonds. First, the amount of cultivated area, average annual production, high-producing governorates, harvest time, irrigation method, and their production methods are evaluated. Then, the biomass potential for bioenergy of each waste and byproduct produced is assessed and reported according to their production rate, production value, and production season. Following, the components of these wastes are introduced and analyzed via proximate and ultimate analyses. Finally, the bioenergy potential of produced biomass is estimated. The results showed that among the three studied agri-food value chains, olive oil wastes and byproducts have the greatest potential for bioenergy production at 82 %, and only 10–12 % of the total olive oil production waste in the milling stage is used as biomass fuel for bioenergy. Furthermore, olive tree pruning (1.43–2 t/ha/year), date palm fruit (7.2–29.5 t/ha/year), and almond hulls (∼0.76 t/ha/year) have the greatest biomass potential for bioenergy, but their potential is largely ignored by traditional methods. [ABSTRACT FROM AUTHOR]

• Developing a three-level mechanism for FOEM of multi- RBMGS. • Computing the required flexibility of the main grid considering RBMGs programming. • Decentralized programming of IESHs and PEVs, taking into account the residents' SI. • Reducing the daily costs by extracting the flexibility from RBMG resources. • Increasing day-ahead programming safety by implementing a risk-averse strategy. A multi-stage mechanism for flexibility-oriented energy management (FOEM) of the distribution system is developed in this article, which main novelty is providing the flexibility requirements of the main grid by resources within renewable-based microgrids (RBMGs), including distributed generations (DGs), storage systems (SDs), internet-of-things enabled smart homes (IESHs), and plug-in electric vehicles (PEVs). In the first stage, IESHs are programmed in a decentralized space subject to satisfying the residents' satisfaction index (SI). In the second stage, RBMGs are programmed according to the announced plan of IESHs. Ultimately, in the third stage, the main grid operator calculates the required flexibility capacities of the system according to the announced plans of RBMGs, where a risk-averse strategy is adopted to manage the risk of programming. Next, the main grid operator announces the required flexibility capacities to RBMGs and IESHs so that they can re-plan with the possibility of participating in flexibility markets. The numerical results clarify that the participation of RBMGs, IESHs, and plug-in electric vehicles (PEVs) in providing flexibility capacities not only reduces the dependence of the distribution system on the upstream grid (UG), but also reduces the total daily costs by 30.7%. [ABSTRACT FROM AUTHOR]

The integration of several renewable energy sources (RESs), such as photovoltaic generation (PVG) and wind turbine generation (WTG), introduces a significant amount of uncertainty for the optimal planning of the electrical power systems. The optimal power flow (OPF) problem became more difficult to solve when RESs uncertainties were taken into account. In this research, the stochastic OPF problem is solved using an Adaptive Lightning Attachment Procedure Optimizer (ALAPO) with the integration of RESs. The ALAPO is proposed in this study to improve both the exploration and exploitation process of the conventional Lightning Attachment Procedure Optimizer (LAPO). In order to create a number of scenarios that account for the uncertainties associated with load demand, wind speed, and solar irradiation, the scenario based reduction method is utilized. Further, to determine the optimal setting of active power of generators, tap setting of the transformers, reactive power of the capacitor banks, and the voltage profile at the generator buses for each scenario of RESs the objective function considered in this study is to minimize the power loss, voltage deviation, and voltage stability index of the power system. The effectiveness of the proposed algorithm is tested on IEEE 57-bus system. The results in this study show that the proposed ALAPO is superior for solving the OPF with the integration of RESs compared to the conventional methods like LAPO, Particle Swarm Optimization (PSO), Sand Cat Swarm Optimizer (SCSO), Gray Wolf Optimizer (GOW), Whale Optimization Algorithm (WOA), Black Widow Optimization Algorithm (BWO), and Capuchin Search Algorithm (CapSA). [Display omitted] • Integration of several renewable energy. • Optimal power flow (OPF) problem. • Adaptive Lightning Attachment Procedure Optimizer (ALAPO). • Voltage profile at the generator buses is the objective function considered. [ABSTRACT FROM AUTHOR]

• A new home energy management tool considering negawatt trading is developed. • A novel algorithm is developed to convert the problem into a negawatt-aware tool. • Conventional uncertainties are modelled via scenarios. • Electricity bill is reduced by 45% if negawatt opportunities are optimally exploited. • Negawatts exported is incremented by 35% if storage capacity increases by 2 kWh. The decarbonization and sustainability of smart cities is calling for new schemes and businesses capable of increasing the efficiency of energy systems. In this context, energy communities are emerged as a valuable framework to optimally integrate prosumers into electricity networks and take the maximum advantage of domestic resources such as controllable appliances or rooftop photovoltaic arrays. Within communities, prosumers can exchange energy through peer-to-peer platforms, but they can also exchange negawatts. This trading mechanism focuses on acquiring or selling consumption rights rather than actual energy, thus further incrementing the flexibility of the system. In this regard, this paper develops a novel stochastic-based home energy management model considering negawatt trading. An original iterative algorithm is presented by which the developed negawatt-focus problem is converted into a more useful and practical negawatt-aware mechanism, by which the scheduling plan is ready to exploit trading opportunities without notably incrementing the electricity bill, which is considered the primary objective of home energy management applications. Several results are presented to validate the new tool, illustrate the concept of negawatt trading and how it affects to power allocation in prosumer installations. Results show that negawatt trading supposes a monetary opportunity for prosumers if it is optimally exploited, enabling a reduction of the electricity bill by 45 % compared with the conventional case. Moreover, the role of batteries in negawatt trading is highlighted, providing energy backup when importable power must be reduced. Actually, total negawatts exported increase by 34 % when storage capacity increases from 3 to 5 kWh. [ABSTRACT FROM AUTHOR]

In a developing world where the demand for energy increases every day, the use of hybrid systems is a viable and important solution. The rivers present a significant potential for the electrical generation from the hydrokinetic use of the river currents, as well as the use of the solar radiation by means of the generation of photovoltaic solar energy for the electrical supply in rural areas. This paper presents a combination of both forms of electricity generation, photovoltaic generators and river turbine, to configure a hybrid system together with a subsystem of support based on hydrogen and batteries to guarantee the electrical supply of a set of isolated loads. The complete design of a hybrid system satisfies the energy requirements of a load. The methodology includes the optimal location of the turbine in a cross section of the river to maximize the performance of the hydroelectric generator, and the techno-economic study of all components of the hybrid system for an efficient use of renewable energy resources. The results show that the hybrid system provides the power demanded by the loads, hydroelectric and solar photovoltaic generation are optimized, and the battery storage subsystem loading status is kept avoiding deep discharges. [ABSTRACT FROM AUTHOR]

• Real case studies of Tazouta and Fez rural area in Morocco. • Hybrid biomass, wind, and solar electricity generation in areas those are rich in renewable energies. • Potential of bioenergy for producing power in rural areas of Fez-Meknes region in Morocco. High dependence of Morocco's energy sector on imported fossil fuels and subsequent associated expensive import bills, as well as global agreements with greenhouse gas emission reduction, has motivated Morocco to utilize renewable energy sources such as hydro, wind, and solar for energy generation. However, in recent years, the use of wind and solar energies in Morocco has accelerated more than hydropower usage because of the reduction in water levels as a result of rainfall drop, global warming, and natural droughts. Morocco has a great potential for electricity generation from solar and wind energies because of many sunny days, vast desert areas, long coastlines, and appropriate wind blow. However, accommodations of wind and solar generating units in electric power systems are difficult because of wind speed variations, solar irradiation uncertainty, and costly energy storage systems. Regarding the huge amounts of biowaste in Morocco, power generation from biomass can resolve this problem and compensate for the decrease in wind and solar energy generation during cloudy, rainy, and/or non-windy moments. Therefore, in the present paper, the capability of hybrid electric energy generation, using biomass, wind, and solar in a Moroccan village of Tazouta and the rural area of Fez city is evaluated. The proposed mixed-integer linear programming model was implemented in A Mathematical Programming Language (AMPL) using a linear solver of CPLEX. The biomass, solar, and wind power plants are biogas units fed by cow manure, 1-kW photovoltaic (PV) modules, and 5.1-kW wind turbines, respectively. The evaluation of the results indicates that hybrid utilization of wind, PV, and biomass energy resources is a more appropriate way for electricity generation in Tazouta and Fez rural areas compared to the individual operation of wind turbines and other combinations of mentioned renewable energy sources. [ABSTRACT FROM AUTHOR]

This research presents the modelling and simulation of an innovative small-scale power system composed of a downdraft gasifier, an externally fired gas turbine (EFGT) and an Organic Rankine Cycle (ORC) as a bottoming unit for increasing the electrical energy generation. Olive tree pruning (217 kg/h) was the biomass used, producing more than 200 kWe and an electrical efficiency of 20.7%. The producer gas achieved suitable mole composition (H 2 : 14.8%, CO: 19.5%, CH 4 : 2.2%, CO 2 : 9.3%, H 2 O: 12.0% and N 2 : 41.5%), lower calorific value (4.53 MJ/kg) and gasification efficiency (89%). The hot producer gas was burnt directly at atmospheric pressure in an EFGT, producing 150 kW e and exhaust gases at 290 °C. Five ORC working fluids were studied to maximize the electricity generation: isopentane, benzene, cyclohexane, R113, R245fa. Isopentane was the most suitable fluid, reaching 57.1 kW e and 20.45% of ORC thermal efficiency. On the other hand, despite R113 gave minor electricity production (49.1 kW e ), this allowed to generate thermal power for CHP applications. [ABSTRACT FROM AUTHOR]

Summary Domain-Specific Languages (DSLs) allow for the building of applications that ease the labour of both software engineers and domain experts thanks to the level of abstraction they provide. In cases where the domain is restricted to Client-Side Web Applications (CSWA), XML-based languages, frameworks and widgets are commonly combined in order to provide fast, robust and flexible solutions. This article presents an approach designed to create XML-based DSL solutions for CSWA that includes an evaluation engine, a programming model and a lightweight development environment. The approach is able to evaluate multiple XML-based DSL programs simultaneously to provide solutions to those Domain Specific Problems for CSWAs. To better demonstrate the capabilities and potential of this novel approach, we will employ a couple of case studies, namely Anisha and FeedPsi . [ABSTRACT FROM AUTHOR]

• Developing a new stochastic-interval model for energy management in communities. • The developed model considers controllable demand, vehicle-to-grid and energy storage. • The developed model allows to adopt pessimistic or optimistic strategies. • Obtained results reveal the benefits on gathering prosumers on communities. • Community operation reduces the energy that must be acquired from the grid. With the advent of smart cities, residential consumers have evolved towards the concept of prosumers. This paradigm calls for new businesses like energy communities. Energy management of such frameworks is essential to maximize the collective welfare. This paper addresses this issue by developing an energy management framework that accounts for flexible demand, storage devices and electric vehicles. Its two-level structure allows to address the energy exchanging among prosumers in the 1st stage, while the 2nd stage focuses on optimally schedule the different collective assets. A novel stochastic-interval model is proposed to handle with uncertainties from demand, renewable generation, vehicles' behaviour and energy pricing. A case study is performed on a six-prosumer community. Results serve to validate the new tool and analyse the importance of smart devices. In addition, the new proposal allows to adopt different operating strategies. Actually, the total operating cost may increase by 90% if a pessimistic point of view is adopted while the importance of the vehicle-to-grid capability is marginal otherwise. Furthermore, the energy purchased could be reduced by 7% when adopting a community arrangement, supposing an improvement in the economy and environmental indicators of the network. Other relevant aspects are identified and discussed in depth. [ABSTRACT FROM AUTHOR]

• Day-ahead scheduling has been carried out for energy hub. • Uncertainties of demands, PV, solar heat, wind and electricity prices are considered. • Demand response aggregator has been used as an input source of electricity. • Demand response effect on EH operation cost has been investigated. • Effect of storage systems on hub operation costs have been evaluated. 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%. [ABSTRACT FROM AUTHOR]

Spain is the first olive oil maker worldwide. Yearly, the olive oil industry generates large amounts of by-products: olive pomace, tree pruning, pits, leaves and branches. This work presents the experimental and feasibility study of a pilot plant for the conversion of olive tree pruning and olive pits into electrical and thermal power. The pilot plant is composed of a downdraft gasifier, gas cooling-cleaning stage and spark ignition engine with a modified carburetor. The experimental results showed satisfactory cold gas efficiency (in the range of 70.7–75.5%) and good lower calorific value of the producer gas for both raw materials (4.8 and 5.4 MJ kg − 1 ). Moreover, the plant achieved acceptable values for the electric and CHP efficiency: 15% and almost 50%, respectively. Finally, the investment achieved reasonable profitability index with a payback period of 5–6 years. As a result, the energy recovery potential from the olive industry wastes may represent a good opportunity to promote distributed generation systems. [ABSTRACT FROM AUTHOR]