Special issue on standalone renewable energy system: Modeling and controlling

1. Introduction Standalone (off-grid) renewable energy systems supply electricity in places where there is no access to a standard electrical grid. These systems may include photovoltaic generators, wind turbines, hydro turbines or any other renewable electrical generator. Usually this kind of system includes electricity storage (commonly, lead-acid batteries, but also other types of storage can be used, such as lithium batteries, other battery technologies, supercapacitors and hydrogen). In some cases, a backup generator (usually powered by fossil fuel, diesel or gasoline) is part of the hybrid system. Low-power standalone systems are usually called off-grid systems and typically power single households by diesel generators or by solar photovoltaic (PV) systems (solar home systems) [1]. Systems of higher power are called micro- or mini-grids, which can supply several households or even a whole village. Mini- or micro-grids, powered by renewable sources, can be classified as smart grids, allowing information exchange between the consumers and the distributed generation [2]. The modelling of the components, the control of the system and the simulation of the performance of the whole system are necessary to evaluate the system technically and economically. The optimization of the sizing and/or the control is also an important task in this kind of systems..

Analysis of photovoltaic self-consumption systems

Components and installation prices could make the self-consumption of solar photovoltaic (PV) systems competitive. In this paper, we explore different self-consumption options, off-grid PV systems (with back-up generator and/or batteries), and grid-connected PV systems under net-metering policies. The calculation of the net present cost (NPC) reveals that the grid-connected PV-only case (for the net-metering scheme) is the most attractive from the technical and financial points of view, with a levelised cost of energy less than 0.1 €/kWh. Off-grid PV + Diesel + Batteries has a higher cost, around two or three times the grid-connected PV-only under net metering. Additionally, the off-grid PV + Diesel is less attractive from a financial point of view, which has a cost of around 10 times the PV-only under net metering. In addition, the values of life cycle CO2 emissions in each of the cases studied have been compared, and we have concluded that although the off-grid PV + Diesel + Batteries system presents lower CO2 emissions than the PV-only system, the existence of batteries does not allow one to affirm that the PV + Diesel + Batteries system is the best from an environmental point of view

Comparison of economic performance of lead-acid and li-ion batteries in standalone photovoltaic energy systems

Standalone renewable energy systems usually incorporate batteries to get a steady energy supply. Currently, Li-ion batteries are gradually displacing lead-acid ones. In practice, the choice is made without previous comparison of its profitability in each case. This work compares the economic performance of both types of battery, in five real case studies with different demand profiles. For each case, two sets of simulations are carried out. In one of the sets, the energy demand is supplied by a standalone photovoltaic system and, in the other one, by a standalone hybrid photovoltaic-diesel system. Through optimization processes, the economic optimum solutions are obtained. In addition, sensitivity analyses on various parameters have been carried out, seeking the influence in favor of one or another type of battery. The results show that if the type of battery is changed, to achieve the economic optimum the entire system must be resized. In some cases, the economic optimum is reached with Li-ion and in others with lead-acid batteries, depending on the demand profiles. Thus, both types of batteries can be profitable options in standalone energy systems, with a greater tendency to lead-acid in fully photovoltaic systems and to Li-ion in hybrids. The price reductions that would make Li-ion the only choice is quantified. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Optimization of isolated hybrid microgrids with renewable energy based on different battery models and technologies

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’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–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

Comparison of lead-acid and li-ion batteries lifetime prediction models in stand-alone photovoltaic systems

Several models for estimating the lifetimes of lead-acid and Li-ion (LiFePO4 ) batteries are analyzed and applied to a photovoltaic (PV)-battery standalone system. This kind of system usually includes a battery bank sized for 2.5 autonomy days or more. The results obtained by each model in different locations with very different average temperatures are compared. Two different locations have been considered: the Pyrenees mountains in Spain and Tindouf in Argelia. Classical battery aging models (equivalent full cycles model and rainflow cycle count model) generally used by researchers and software tools are not adequate as they overestimate the battery life in all cases. For OPzS lead-acid batteries, an advanced weighted Ah-throughput model is necessary to correctly estimate its lifetime, obtaining a battery life of roughly 12 years for the Pyrenees and around 5 years for the case Tindouf. For Li-ion batteries, both the cycle and calendar aging must be considered, obtaining more than 20 years of battery life estimation for the Pyrenees and 13 years for Tindouf. In the cases studied, the lifetime of LiFePO4 batteries is around two times the OPzS lifetime. As nowadays the cost of LiFePO4 batteries is around two times the OPzS ones, Li-ion batteries can be competitive with OPzS batteries in PV-battery standalone systems

Optimisation of energy supply at off-grid healthcare facilities using Monte Carlo simulation

In this paper, we present a methodology for the optimisation of off-grid hybrid systems (photovoltaic-diesel-battery systems). A stochastic approach is developed by means of Monte Carlo simulation to consider the uncertainties of irradiation and load. The optimisation is economic; that is, we look for a system with a lower net present cost including installation, replacement of the components, operation and maintenance, etc. The most important variable that must be estimated is the batteries lifespan, which depends on the operating conditions (charge/discharge cycles, corrosion, state of charge, etc.). Previous works used classical methods for the estimation of batteries lifespan, which can be too optimistic in many cases, obtaining a net present cost of the system much lower than in reality. In this work, we include an advanced weighted Ah-throughput model for the lead-acid batteries, which is much more realistic. The optimisation methodology presented in this paper is applied in the optimisation of the electrical supply for an off-grid hospital located in Kalonge (Democratic Republic of the Congo). At the moment, the power supply relies on a diesel generator; batteries are used in order to ensure the basic supply of energy when the generator is unavailable (night hours). The optimisation includes the possibility of adding solar photovoltaic (PV) panels to improve the supply of electrical energy. The results show that optimal design could achieve a 28% reduction in the levelised cost of energy and a 54% reduction in the diesel fuel used in the generator, thereby reducing pollution. Furthermore, we discuss possible improvements to the telecommunications of the hospital

Novel probabilistic optimization model for lead-acid and vanadium redox flow batteries under real-time pricing programs

The integration of storage systems into smart grids is being widely analysed in order to increase the flexibility of the power system and its ability to accommodate a higher share of wind and solar power. The success of this process requires a comprehensive techno-economic study of the storage technology in contrast with electricity market behaviour. The focus of this work is on lead-acid and vanadium redox flow batteries. This paper presents a novel probabilistic optimization model for managing energy storage systems. The model is able to incorporate the forecasting error of electricity prices, offering with this a near-optimal control option. Using real data from the Spanish electricity market from the year 2016, the probability distribution of forecasting error is determined. The model determines electricity price uncertainty by means of Monte Carlo Simulation and includes it in the energy arbitrage problem, which is eventually solved by using an integer-coded genetic algorithm. In this way, the probability distribution of the revenue is determined with consideration of the complex behaviours of lead-acid and vanadium redox flow batteries as well as their associated operating devices such as power converters

Modeling of a stand-alone solar photovoltaic water pumping system for irrigation

Solar photovoltaic water pumping systems have been research topics in recent decades. The purpose was to develop much more profitable and efficient systems to meet the needs of pumping water for livestock and irrigation. This paper describes the design of a stand-alone photovoltaic water pumping system. A Boost converter is used to apply the Maximum Power Point Tracking (MPPT) algorithm. Similarly, a three-phase voltage source converter (VSC) is used to supply the asynchronous motor. The installation must provide a continuous water flow during the irrigation interval. It has been verified that in adverse weather conditions (cloud transits or partly cloudy) it is necessary to incorporate a decentralized-hybrid energy storage system (based on batteries or ultracapacitors), or excessively oversize the standalone photovoltaic system, to supply the water pump. The model has been simulated in Matlab-Simulink. In this way, different simulations have been developed to verify the basic characteristics of the proposed system. The results of the simulated model and the conclusions obtained are also presented in this paper

Contract design of direct-load control programs and their optimal management by genetic algorithm

A computational model for designing direct-load control (DLC) demand response (DR) contracts is presented in this paper. The critical and controllable loads are identified in each node of the distribution system (DS). Critical loads have to be supplied as demanded by users, while the controllable loads can be connected during a determined time interval. The time interval at which each controllable load can be supplied is determined by means of a contract or compromise established between the utility operator and the corresponding consumers of each node of the DS. This approach allows us to reduce the negative impact of the DLC program on consumers’ lifestyles. Using daily forecasting of wind speed and power, solar radiation and temperature, the optimal allocation of DR resources is determined by solving an optimization problem through a genetic algorithm where the energy content of conventional power generation and battery discharging energy are minimized. The proposed approach was illustrated by analyzing a system located in the Virgin Islands. Capabilities and characteristics of the proposed method in daily and annual terms are fully discussed, as well as the influence of forecasting errors

Operating conditions of lead-acid batteries in the optimization of hybrid energy systems and microgrids

The promotion and deployment of storage technologies in autonomous and grid-connected systems plays a relevant part in the massive integration of renewable power sources required for the worldwide development of a sustainable society. In this regard, analyzing the behavior of electrochemical storage devices such as lead-acid batteries installed on hybrid energy systems and microgrids in terms of their lifetime and economic profitability is an important research topic. Since renewable generation is characterized by its random nature, lead-acid batteries typically work under stress conditions, which directly influence their lifetime in a negative way by increasing the net present cost. Due to the fast growing of renewable sources as a consequence of governmental policies and incentives, the number of manufacturers to be considered worldwide is becoming really high, so that optimization techniques such as genetic algorithms (GAs) are frequently used in order to consider the performance of a high number of manufacturers of wind turbines, photovoltaic panels and lead-acid batteries subject to the environmental conditions of the location under analysis to determine a cost-effective design. In this paper, GA method combined with weighted Ah ageing model is improved by including expert experiences by means of stress factors and the categorization of operating conditions, as a new contribution to earlier studies. The effectiveness of the proposed method is illustrated by analyzing a hybrid energy system to be installed in Zaragoza, Spain, resulting in a near-optimal design in a reduced computational time compared to the enumerative optimization method