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The development of electric vehicles (EVs) depends on several factors: the EV’s acquisition price, autonomy, the charging process and the charging infrastructure. This paper is focused on the last factor: the design of an EV fast-charging station. In order to improve the profitability of the fast-charging stations and to decrease the high energy demanded from the grid, the station includes renewable generation (wind and photovoltaic) and a storage system. Unlike other papers, this one uses a detailed model of the charging process that considers the arrival time and state of charge of electric vehicles. First, the Monte Carlo method is used to model the EV demand and the renewable generation. Later, a genetic algorithm (GA) optimizes the installation and operation of the EV fast-charging station. It finds the optimal solution that maximizes the profit measured by its net present value (NPV). Several cases are studied to analyse the influence of renewable energies and storage systems. The obtained results show that a mix of renewable energies and storage systems attains the best cost efficient solution.

The effects of optimal dimensioning and integration of distributed generation (DG) on an electricity distribution system (DS) from a probabilistic viewpoint is presented in this paper, as a new contribution to earlier studies. The proposed methodology pays special attention to preventing reverse power flow at substation as a consequence of excessive integration of renewable energy based DG. As the analysis of large amounts of data typically measured on an annual basis could be exhausting from a computational perspective, a methodology based on estimating the potential of wind and solar resources is applied; from this procedure, those months of highest renewable potential are selected so that indirectly those situations with probability of reverse power flow at substation are considered. After this, time series of load demand per node and phase are generated using typical profiles and the corresponding peak-load expected. Finally, all this information is introduced on an optimization algorithm based on a genetic algorithm in order to minimize the net present cost over the project lifetime, obtaining the type and number of photovoltaic (PV) panels and wind turbines (WTs) to be installed. This approach allows integrating detailed mathematical models of DG related to PV and wind generation, including specific factors frequently reported by the manufacturers such as temperature coefficients, nominal operating cell temperature, particular WT power curves, and variable efficiency of power converter, among other characteristics. The proposed method is illustrated by studying a DS supposed to be located in Zaragoza, Spain, with 35 nodes under unbalanced conditions, with residential as well as small, medium, and large commercial electricity demands. Focusing our attention on the month of February, due to its high renewable potential, the proposed technique was applied resulting in a system mainly based on wind energy of at least 40% of the substation capacity. This model could be used to perform the renewable energy integration analysis on DS, starting from typical load profiles, hourly estimations of solar and wind resources, and data frequently provided by PV panels and WT manufacturers.

In developing countries, electrification in remote areas, where access to energy is limited or null, has been one of the biggest challenges in recent years. Isolated microgrids with renewable generation are an efficient alternative for the energy supply in these areas. The objective of this work was to analyse the techno-economic viability of 6 isolated microgrids in different locations in the non-interconnected zone of Colombia, considering different climatic conditions, the availability of renewable resources, the current consumption profile, and a modified profile applying demand-side management. Modelling and simulation were performed considering storage systems based on lithium and lead-acid batteries. The resulting simulations provide the optimal system cost, emissions levels, electricity cost and battery lifetime. This study demonstrates that isolated hybrid microgrids with renewable energy are a feasible alternative to solve access to energy problems, reducing the need for diesel generators and optimizing the use of renewable energies and battery-based storage systems.

Isolated microgrids with renewable energy are an alternative to provide energy service in remote areas or islands. To obtain a viable solution from the economic and environmental point of view it is important to consider the costs and technology of the batteries, since these are one of the main component in an isolated microgrid because they act as a backup to manage the intermittence of solar radiation. In this article the optimization of a hybrid microgrid for the Mucura island in Colombia is analyzed. The optimization model includes battery types and technologies, component cost and fuel cost. The results show the viability of the optimization under different scenarios where a reduction of system costs is obtained.

Standalone (off-grid) renewable energy systems supply electricity in places where there is no access to a standard electrical grid [...]

This paper presents a mathematical formulation of lithium-ion batteries, including aging and temperature effects. The model is developed by integrating the simplified single particle model (SSPM) and reduced-order model (ROM) to predict solid electrolyte interphase growth (SEI). Results show agreement with the experimental data at 25 °C operating temperature and moderate cycling currents. A maximum error of 3.6% in finding the battery discharged Ah is observed in harsh operating conditions, including 60 °C and approaching the end of life of the battery. Due to the typical operating conditions of stand-alone renewable energy systems, more accurate estimations are expected. Finally, this methodology is utilized to predict the lifetime of lithium-ion batteries that are combined with PV generators to supply electricity to an isolated house situated near Zaragoza, Spain, under two control strategies. The results indicate realistic lifetime predictions when using the model in real operating conditions for this kind of system. Besides, by maintaining the batteries in states of charge of lower than 70%, around 55% increase in the battery lifetime can be achieved at the cost of 0.23% reduction in the percentage of the electrical load that is able to be covered by the stand-alone system.

The current state of the electrical sector in Praia (Cape Verde capital city) is characterized by the high cost of electricity and high levels of technical and commercial losses which is caused by the lack of resources of fossil origin. It is also aggravated by an inadequate investment policy forcing a deep restructuring of the entire sector. In order to take advantage of the existing local natural resources and to have a more efficient, robust, and a fair electric system, it seems inevitable to bet on innovative, intelligent, and secure technology which allows tight integration of renewable energy –mainly wind and photo-voltaic energy. In this regard, the present article discusses the economic, social, and environmental impacts of a smart grid for Praia city. Based on a proposed SG architecture that integrates the existing endogenous resources and technologies, it was possible to identify the main advantages and challenges that the implementation of SG technologies would have for the city.

This paper proposes a new methodology to find the most economic system configuration and energy management strategy for Li-ion battery based off-grid renewable energy systems. A system level macroscopic model and a microscopic battery lifetime prediction model are incorporated into the optimization framework to simulate hourly performance of the system. Due to the computational efficiency of the model, optimization is carried out using enumerative method (evaluating all the possible combinations of components and control strategies) to ensure finding the global optimum solution of the problem. To investigate the effectiveness of the proposed methodology, the optimization results are compared with a baseline scenario which is an installed PV-battery system to provide electricity for an isolated house situated near Zaragoza, Spain. Results indicate that the optimized scenario leads to 9.7% reduction in the levelized cost of energy and 48.6% improvement in the battery service period in comparison with the baseline scenario. Moreover, by considering a 0.5% unmet load, the economic feasibility of the system and the battery longevity are enhanced to 14.6% and 78.4%, respectively. Finally, to evaluate the effect of battery unit price and future trends on the optimization results, sensitivity analysis is performed.

The large-scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi-static time-series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic-cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.

Lithium-ion batteries play an important role in the life quality of modern society as the dominant technology for use in portable electronic devices such as mobile phones, tablets and laptops. Beyond this application lithium-ion batteries are the preferred option for the emerging electric vehicle sector, while still underexploited in power supply systems, especially in combination with photovoltaics and wind power. As a technological component, lithium-ion batteries present huge global potential towards energy sustainability and substantial reductions in carbon emissions. A detailed review is presented herein on the state of the art and future perspectives of Li-ion batteries with emphasis on this potential.

In this paper we introduce a Simplified Single Particle Model (SSPM), which is obtained from the general mathematical formulation of Li-ion batteries. The model is validated by using different commercial graphite/LiFePO4 cells, and results show agreement with more complicated models and experimental data for low operating currents of less than 1C. A maximum relative error of less than 2% can be observed to estimate cell voltage in the plateau region of the charge/discharge curves. Therefore, the proposed model is suitable in the case of stand-alone renewable energy systems, where battery current is typically lower than C/10. By increasing the current, the SSPM deviates from more accurate models and experimental data. However, it is sufficiently precise and computationally efficient to be used in the simulation and optimization framework of off-grid renewable energy systems. Finally, the proposed model will be applied to two different case studies; one for an installed PV-battery and the other for a Wind-battery system which are located near Zaragoza, Spain, in order to predict battery state over time.

This paper presents a multi-objective problem, whose resolution is carried out using a task management methodology and an evolutionary algorithm. The multi-objective problem includes demand response to real-time prices (RTP). Two objectives have been considered: the daily cost of electricity and consumer dissatisfaction, minimizing both. Hourly prices corresponding to a tariff currently existing in Spain have been used to evaluate the daily cost of the consumed electricity. The degree of compliance with the daily task programming required by the user has been used to evaluate the dissatisfaction of the consumer. Using a multi-objective evolutionary algorithm (NSGA-II), it has been possible to use the concept of Pareto optimality in order to determine the best solutions for the problem. The results show that the applied methodology can lead to cost savings between 6% and 12% without substantially changing the consumption habits of the consumer. If the consumer is willing to change his/her consumption habits, then the cost savings can reach 50%. In addition to cost savings, this methodology can reduce energy losses in the electric grid because the shift of consumption from peak to off-peak hours.

Energy supply in remote areas (mainly in developing countries such as Colombia) has become a challenge. Hybrid microgrids are local and reliable sources of energy for these areas where access to the power grid is generally limited or unavailable. These systems generally include a diesel generator, solar modules, wind turbines, and storage devices such as batteries. Battery life estimation is an essential factor in the optimization of a hybrid microgrid since it determines the system&rsquo, s final costs, including future battery replacements. This article presents a comparison of different technologies and battery models in a hybrid microgrid. The optimization is achieved using the iHOGA software, based on data from a real microgrid in Colombia. The simulation results allowed the comparison of prediction models for lifespan calculation for both lead&ndash, acid and lithium batteries in a hybrid microgrid, showing that the most accurate models are more realistic in predicting battery life by closely estimating real lifespans that are shorter, unlike other simplified methods that obtain much longer and unrealistic lifetimes.

This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation. Quasi-static time series is embedded in an optimization method based on a genetic algorithm to adequately represent the uncertainty introduced by solar photovoltaic generation and electricity demand and its effect on DS operation. From the analysis of a typical DS, the reactive power compensation rating power results in an increment of 24.9% when compared to the classical genetic algorithm model. However, the incorporation of quasi-static time series analysis entails an increase of 26.8% on the computational time required.

Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.

In central European countries, district heating is a common and standard way to cover the residential heat demand. While the consumers are connected to the heat network and electricity grid, they are being increasingly encouraged to become prosumers by having own distributed renewable energy generation. At the same time, the energy performance requirements for new and renovated buildings are setting progressively higher energy efficiency standards. This tendency is effectively reducing the carbon footprint of the residential sector in Central Europe and will eventually lead to low and zero carbon buildings becoming the standard. One of the emerging technologies within this approach are Photovoltaic thermal hybrid solar collectors, which combine heat and power generation and are easy to integrate in buildings. In this study, the possibility to use such a system, supported by a heat pump, in a Central European multi-family house is addressed. Three configurations are considered; the building, the hybrid and district heating configuration. In the building configuration the heat from the solar collectors is used directly for domestic hot water and space heating and no use is made of excess heat. In the hybrid configuration, the direct heat consumption in the building is prioritized, while excess heat is exported to the district heating network. Finally, in the district heating configuration, the solar system operates as a micro-plant that supplies all produced heat to the district heating network. The study shows that the use of Photovoltaic thermal hybrid solar collectors in combination with district heating provides important benefits in terms of sustainability, energy security, carbon abatement and costs. Thereby, configurations that can export heat to the network have better energy efficiency and result more profitable.

In this paper a new stochastic-heuristic methodology for the optimisation of the electrical supply of stand-alone (off-grid) hybrid systems (photovoltaic-wind-diesel with battery storage) is shown. The objective is to minimise the net present cost of the system. The stochastic optimisation is developed by means of Monte Carlo simulation, which takes into account the uncertainties of irradiation, temperature, wind speed and load (correlated Gaussian random variables), using their probability density functions and the variance-covariance matrix. Also the uncertainty of diesel fuel price inflation rate was considered. The heuristic approach uses genetic algorithms to obtain the optimal system (or a solution near the optimal) in a reasonable computation time. This methodology includes an accurate weighted Ah-throughput battery model with several control variables, which can be set in the modern battery controllers or inverter/chargers with State of Charge control. A case study is analysed as an example of the application of this methodology, obtaining the stochastic optimisation an optimal system similar to the one obtained by the deterministic optimisation. It is recommended to perform first the deterministic optimisation (with low computation time), then the search space should be reduced and finally the stochastic optimisation can be obtained in a reasonable computation time.

Around 18% of the world’s population still don’t have access to electricity, most of them living in rural areas in South Asia, Southeast Asia, and Sub-Saharan Africa. Kerosene lamps are widely used for lighting in these regions, but imply a big number of disadvantages including low light quality, reduced indoor air quality and safety concerns. Furthermore, the consumption of kerosene for lighting is very energy inefficient and implies a relatively high cost for the added value it provides, while its price volatility is a major concern for dependant developing regions. Global carbon dioxide emissions from kerosene lamps exceed 200 mega tons annually. A solar home system using light emitting diode lamps provides an effective solution for this problem. This paper elaborates such a solar home system while focusing on overcoming implementation barriers including lack of technical support and affordability. An evolutionary techno-economic assessment, considering the time period 2015–2030, is provided for the proposed system. This emphasizes not only the existing but also the increasing advantage of solar home systems over kerosene lamps.

While in the developed countries electrification is paving the way for progress and prosperity, nowadays electricity is still not accessible for about 18% of the world’s population. Lack of power grids is the main reason that prevents millions in remote areas in developing countries from using electricity for the daily basic needs. PV systems provide an effective solution for these regions, but affordability remains an issue. This barrier can be widely overcome on the short term by limiting PV power supply to very high added value applications and by properly exploiting innovations, especially in energy efficiency and cost reductions. Additional to that, the long-term perspectives of off-grid PV are very favourable based on its ongoing technological improvements and cost reductions. This paper studies four off-grid PV cases of which each could cover a combination of basic energy needs regarding light, cooking, food conservation and electronic appliances. Case I considers a system that supplies power for LED lamps and electronic devices. Accordingly, Case II adds a fridge and Case III an electric rice cooker to Case I, while Case IV adds both. The paper elaborates on available technologies and future developments regarding all components in order to assess the long term evolution and potential of these applications, most specifically how their affordability would evolve over time. The modelling and optimization of the four cases are performed using the software iHOGA, which is an efficient tool to provide the lowest cost solution for off-grid PV systems. The use of iHOGA for the four cases and the installation years 2020, 2030 and 2040, taking thereby into account different developing regions, provide an evolutionary techno-economic assessment of these applications and a clear picture about the developments to be expected from off-grid PV in general.

This article shows a new method for the optimisation of stand-alone (off-grid) hybrid systems (photovoltaic–diesel–battery) to supply the electricity of mobile systems such as non-governmental organization hospitals, temporary camps or other mobile facilities to be placed temporally in remote or conflictive areas. If there is difficult or dangerous access, the most important objective to be minimised is the total weight of the system. Also, the cost is an important variable to minimise. Nowadays, the majority of these systems are diesel-only or diesel-battery systems. However, depending on the duration of the temporary system, a photovoltaic–diesel–battery system can have a lower weight and/or cost. Three types of optimisation are considered: (i) minimisation of the weight of the system; (ii) minimisation of the cost; and (iii) minimisation of both weight and cost. The two first are conducted by genetic algorithms, and the last one is performed using multi-objective evolutionary algorithms. An example of application of this method to a temporary hospital in Central African Republic is shown, concluding that in the cases of more than 90 days photovoltaic (flexible crystalline silicon panels) + diesel + battery is the solution which minimises weight. When minimising cost, all the cases include photovoltaic with high penetration.

Stand-alone (off-grid) renewable systems are usually composed by photovoltaic panels and/or wind turbines, with batteries (usually lead-acid) and in some cases including diesel generator. In many cases the total cost of the batteries (including replacement during the lifetime of the system) is the highest cost. The model of batteries used in simulation and optimisation of stand-alone renewable systems has a great importance as it allows the estimation of the lifetime of the batteries, which is one of the most important variables to calculate the Net Present Cost of the system and also the Levelised Cost of Energy. The lifetime estimation of lead-acid batteries is a complex task because it depends on the operating conditions of the batteries. In many engineering works and research studies, the estimation of battery lifetime is error-prone, obtaining values much higher than the real ones. In this paper we compare different models of lead-acid batteries, used in the simulation and optimisation of different stand-alone systems. We conclude that in many cases we obtain good results by using a complex weighted Ah-throughput model for the batteries, however using the classical models the estimation of the batteries lifetime is too optimistic.