José L. Bernal-Agustín

Genetic algorithms applied to the design of large power distribution systems

This paper presents the application of a new genetic algorithm for the optimal design of large distribution systems, solving the optimal sizing and locating problems of feeders and substations using the corresponding fixed costs as well as the true nonlinear variable costs. It can be also applied to single stage or multistage distribution designs. The genetic algorithm has been tested with real size distribution systems achieving optimal designs in reasonable CPU times compared with respect to the dimensions of such distribution systems. On the other hand, these distribution systems present significantly larger sizes than the ones frequently found in the technical literature about the optimal distribution planning. Furthermore, original operators of the genetic algorithm have been developed in order to obtain global optimal solutions, or very close ones to them. An integer codification of the genetic algorithm has also been used to include several relevant design aspects in the distribution network optimization.

Reliability and Costs Optimization for Distribution Networks Expansion Using an Evolutionary Algorithm

This paper presents a multiobjective optimization methodology, using an evolutionary algorithm, for finding out the best distribution network reliability while simultaneously minimizing the system expansion costs. A nonlinear mixed integer optimization model, achieving the optimal sizing and location of future feeders (reserve feeders and operation feeders) and substations, has been used. The proposed methodology has been tested intensively for distribution systems with dimensions that are significantly larger than the ones frequently found in the papers about this issue. Furthermore, this methodology is general since it is suitable for the multiobjective optimization of n objectives simultaneously. The algorithm can determine the set of optimal nondominated solutions, allowing the planner to obtain the optimal locations and sizes of the reserve feeders that achieve the best system reliability with the lowest expansion costs. The model and the algorithm have been applied intensively to real-life power systems showing its potential of applicability to large distribution networks in practice.

NSGA and SPEA Applied to Multiobjective Design of Power Distribution Systems

This paper presents, for the first time, an application of two well-know multiobjective optimization techniques, namely, nondominated sorting genetic algorithm (NSGA) and strength Pareto evolutionary algorithm (SPEA), to the multiobjective design of power distribution systems. These algorithms have been applied to a multiobjective optimization problem with some technical constraints, minimizing the total costs while maximizing the reliability of the power distribution system. The NSGA uses a fitness sharing scheme to achieve diversity among the obtained solutions. In SPEA, it is necessary to apply a reduction procedure because of the number of solutions. For this purpose, a fuzzy c-means (FCM) clustering algorithm has been applied, with this being the first time that an FCM algorithm in the SPEA has been used. The obtained results from both techniques have been compared, concluding that both offer similar efficiency in order to solve the stated multiobjective optimization problem. The developed methodology is applicable to practical cases of design, allowing for additional requirements that the designer imposes

Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes

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.

Grid-Connected Renewable Electricity Storage: Batteries vs. Hydrogen

The random nature, abrupt variations of electricity generation, and high differences between the peaks and valleys of the demand curve and the generation curves of wind farms and photovoltaic generators can be avoided by means of electricity storage in batteries or hydrogen. The storage would bring many benefits to the electrical grid. However, present acquisition costs of batteries and hydrogen components are too high so that these systems cannot be profitable economically. In terms of energy, the storage in batteries is much better than in hydrogen because the roundtrip efficiency is much higher. In a matter of years, some forms of battery technology could be competitive for these applications.

Photovoltaic Grid Parity in Spain

Photovoltaic (PV) grid parity can be considered as the point at which PV electricity is generated at the same price as the electricity of the electrical grid is generated (which is a mix of different generation sources) or as the point at which PV electricity becomes cost-competitive with conventional grid-supplied electricity. Depending on which definition of grid parity is used, the year when it is reached may vary. A study of grid parity, considering both definitions, has been carried out with regard to Spain.

Optimal Design of PV/Wind/Battery Systems by Genetic Algorithms Considering the Effect of Charge Regulation

Hybrid power systems (HPS) play an important role in the social development of areas located far from the electric grid. The optimal sizing of these systems is difficult to determine due to the variable nature of renewable energy resources and the complex behavior of their components. An important device of HPS is the charge controller as it protects the battery bank against extreme operational conditions, directly affecting the acceptance of charge from the battery bank and consequently the ability of the system to store energy. This paper presents a study about optimization of stand-alone PV/wind/battery hybrid power systems based in a genetic algorithm that considers the effect of charge regulation in the energy capture of the battery bank.

New Methodology for the Optimization of the Management of Wind Farms, Including Energy Storage

Storing energy on wind farms could improve the power generation curve, avoiding the problems associated with abrupt variations and the random nature of wind power. New batteries such as flow batteries or NaS batteries are suitable to be used in storing energy on wind farms in intervals of some hours. A new methodology for the optimization of the management of wind farms, including energy storage, is shown. The objective is to maximize the benefits of selling electricity to the grid within 24 hours. The genetic algorithm technique was used for the optimization.

A Qualitative Evaluation of Operational Conditions in PV/Wind/Battery Systems

Hybrid power systems (HPS) play an important role in places located far from electric grids. The sizing of these systems is difficult to determine due to the variable nature of renewable energy resources and the complex behaviour of their components. The battery bank has a significant impact on the performance of hybrid systems due to their complex behaviour and high investment costs. This situation has motivated the development of different approaches to improve the mathematical model of the lead acid battery. However, the accuracy of some of these approaches is still unclear. In this paper, using qualitative information about determined operating conditions, a small capacity hybrid power system installed in Zaragoza is analyzed, concluding that this information could be useful to HPS designers.

Introducing Off-Grid Renewable Energy Systems for Irrigation in Mediterranean Crops

For Mediterranean crops, such as vineyards and olive trees, using electric pumps for pressurized irrigation systems is typical. In both developing and developed countries, the crop location is frequently far from the electric grid and electricity is often supplied using a diesel genset. The use of renewable energy could achieve significant reductions in emissions of greenhouse gases and provide other advantages, such as cleaning, reducing operating and maintenance costs, and increasing sustainability. Although the environment has abundant renewable resources such as solar irradiation and wind, obtaining technical and economic feasibility of renewable energy systems is difficult. Problems include relatively high amounts of required power, the concentration of energy demand within a fraction of the year, and the need for power stability during the period of operation. Simple methods to design and size renewable generation are not suitable. This paper studies power demand of drip irrigation systems from six study cases of Mediterranean crops (vineyards and olive trees), available local renewable resources, and ways to obtain technical and economic feasibility using renewable energy in these cases. Finally, a basis for the design in each case using a process of simulation and optimization is determined.