In rural areas or in isolated communities in developing countries it is increasingly common to install micro-renewable sources, such as photovoltaic (PV) systems, by residential consumers without access to the utility distribution network. The reliability of the supply provided by these stand-alone generators is a key issue when designing the PV system. The proper system sizing for a minimum level of reliability avoids unacceptable continuity of supply (undersized system) and unnecessary costs (oversized system). This paper presents a method for the accurate sizing of stand-alone photovoltaic (SAPV) residential generation systems for a pre-established reliability level. The proposed method is based on the application of a sequential random Monte Carlo simulation to the system model. Uncertainties of solar radiation, energy demand, and component failures are simultaneously considered. The results of the case study facilitate the sizing of the main energy elements (solar panels and battery) depending on the required level of reliability, taking into account the uncertainties that affect this type of facility. The analysis carried out demonstrates that deterministic designs of SAPV systems based on average demand and radiation values or the average number of consecutive cloudy days can lead to inadequate levels of continuity of supply.

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The development of microgrids is of great interest to facilitate the integration of distributed generation in electricity networks, improving the sustainability of energy production. Microgrids in DC (DC-MG) provide advantages for the use of some types of renewable generation and energy storage systems, such as batteries. In this article, a possible practical implementation of an isolated DC-MG for residential use with a cooperative operation of the different nodes is proposed. The main criterion is to achieve a very simple design with only primary control in a residential area. This application achieves a simple system, with low implementation costs, in which each user has autonomy but benefits from the support of the other users connected to the microgrid, which improves its reliability. The description of the elements necessary to create this cooperative system is one of the contributions of the work. Another important contribution is the analysis of the operation of the microgrid as a whole, where each node can be, arbitrarily, a consumer or an energy generator. The proposed structures could promote the use of small distributed generation and energy storage systems as the basis for a new paradigm of a more sustainable electricity grid of the future.

The World Health Organization (WHO) warns that the presence of magnetic fields due to the circulation of industrial frequency electrical currents may have repercussions on the health of living beings. Hence, it is crucially important that we are able to quantify these fields under the normal operating conditions of the facilities, both in their premises and in their surroundings, in order to take the appropriate corrective measures and assure the safety conditions imposed, in force, by regulations. For this purpose, CRMag® software has been developed. Using the simplified Maxwell equations for low frequencies, CRMag® calculates and represents the magnetic flux density (MFD) that electrical currents produce in the environment. Users can easily model electrical facilities through a friendly and simple data entry. MFDs calculated by CRMag® have been validated in real facilities and laboratory tests. With this software, exposure levels can be studied in any hypothetical scenario, even in inaccessible zones. This allows designers to guarantee that legal limits (occupational, general population, or precautionary levels related to epidemiological studies) are fulfilled. A real case study has been described to show how the reconfiguration of conductors in a distribution transformer substation (DTS) allows significant reductions in MFD in some points outside the facility.

The integration of renewable generation in electricity networks is one of the most widespread strategies to improve sustainability and to deal with the energy supply problem. Typically, the reinforcement of the generation fleet of an existing network requires the assessment and minimization of the installation and operating costs of all the energy resources in the network. Such analyses are usually conducted using peak demand and generation data. This paper proposes a method to optimize the location and size of different types of generation resources in a network, taking into account the typical evolution of demand and generation. The importance of considering this evolution is analyzed and the methodology is applied to two standard networks, namely the Institute of Electrical and Electronics Engineers (IEEE) 30-bus and the IEEE 118-bus. The proposed algorithm is based on the use of particle swarm optimization (PSO). In addition, the use of an initialization process based on the cross entropy (CE) method to accelerate convergence in problems of high computational cost is explored. The results of the case studies highlight the importance of considering dynamic demand and generation profiles to reach an effective integration of renewable resources (RRs) towards a sustainable development of electric systems.Sustainability 2019, 11, 7111 2 of 26 design stage, then the investment plans obtained could be inadequate. The sustainable development of electrical power systems by installing and integrating renewable generators also improves the diversity of energy supply and enables the objectives for addressing climate change approved by most countries to be met [5]. However, despite the aforementioned variable and unpredictable availability of these resources, the methods used to optimize the design of the generation of a network are generally not prepared to be effective and accurate under these conditions [6].The optimal design of a power grid is a problem of great complexity that involves combining the appropriate selection of topology with the optimal location, size, and operating strategy of generators. Each of these objectives constitutes a complex problem. The optimal design of the generation system in a power grid (location and size of generators) is a non-linear optimization problem that has received considerable attention in recent years. It can be considered a problem with an economic objective (to minimize the cost of generation), like in [7], or as a multi-objective problem, if other criteria are added, such as optimizing voltages in buses or reducing CO 2 emissions, for example [8]. The combinatorial nature of the problem and the mathematical difficulty involved in its solution has often led to evolutionary optimization algorithms, such as particle swarm optimization (PSO), like in [9], or heuristics (genetic algorithms, for example), like in [10][11][12].The literature contains many methods for the optimal sizing and placement of generation units in electrical networks, taking into account peak demand and ge...