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Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real-time pricing (RTP) schemes. In this paper, we present a new method to optimise the size of and manage utility-scale wind–PV systems using PHS with energy arbitrage under RTP. PHS is used to supply load consumption and/or energy arbitrage. Further, both load-supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. PHS is not worth considering with the present cost of components. In load-supply systems in Zaragoza (Spain), we found that PHS would be worth considering if its cost was lower than 850 EUR/kW (considering all PHS components except reservoirs) +20 EUR/m3 for reservoirs (equivalent to 105 EUR/kWh with a 70 m head), whereas in Gran Canaria Island (with a considerably higher irradiation and wind speed), the required PHS cost is considerably lower (~350 EUR/kW + 10 EUR/m3). For power-generating systems, PHS required costs ranging from 400–700 EUR/kW + 15–20 EUR/m3 for obtaining the optimal PV–wind–PHS system with economic results similar to those of the optimal power-generating system without PHS. Thus, the renewable–PHS system with energy arbitrage under RTP could be profitable for many locations globally given the wide range of the PHS cost; however, each case is different and must be evaluated individually. The presented model can be used for optimising the renewable–PHS system in any location with any costs and RTP schemes.

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.

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.

Estimating the degradation costs of lithium-ion batteries is essential to the designs of many systems because batteries are increasingly used in diverse applications. In this study, cyclic and calendar degradation models of lithium batteries were considered in optimization problems with randomized non-cyclic batteries use. Such models offer realistic results. Electrical, thermal, and degradation models were applied for lithium nickel cobalt manganese oxide (NMC) and lithium iron phosphate (LFP) technologies. Three possible strategies were identified to estimate degradation costs based on cell models. All three strategies were evaluated via simulations and validated by comparing the results with those obtained by other authors. One strategy was discarded because it overestimates costs, while the other two strategies give good results, and are suitable for estimating battery degradation costs in optimization problems that require deterministic models.

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

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

This paper presents a study that analyses the effect of financing costs on grid parity in photovoltaic (PV) installations by applying a probabilistic methodology. Three different case studies, located in Spain, have been considered, with 500 kW, 50 kW and 5 kW grid-connected PV generators. The technical and economic calculations were performed, considering the interest rate, yield across the Spanish geography, and PV module cost as parameters. The Monte Carlo method was applied to consider the full probabilistic range of values given to the different variables. The goal of this study was to determine, for the studied cases, the levelised cost of energy (LCOE) and the internal rate of return by considering realistic values of the variables. A success rate parameter was calculated, which determined the likelihood of the number of times that the LCOE was below the retail cost of electricity. All the cases were evaluated by applying 10,000 iterations, considering the standard deviations and means defined.