Ignacio J. Ramírez-Rosado

New multiobjective tabu search algorithm for fuzzy optimal planning of power distribution systems

This paper presents a new multiobjective Tabu search (NMTS) algorithm to solve a multiobjective fuzzy model for optimal planning of distribution systems. This algorithm obtains multiobjective nondominated solutions to three objective functions: fuzzy economic cost, level of fuzzy reliability, and exposure (maximization of robustness), also including optimal size and location of reserve feeders to be built for maximizing the level of reliability at the lowest economic cost (for a given level of robustness). The main characteristics of the NMTS algorithm are: search of planning solutions using several objective functions simultaneously; partition of the space of solutions to diversify the search; intensification of the search by ranking lists of the best network nodes of the distribution system; and an elaborated Tabu list that stores visited network nodes, avoiding unwanted movements. The NMTS algorithm has been intensively tested in real distribution systems, proving its practical application in large power distribution systems.

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.

Possibilistic model based on fuzzy sets for the multiobjective optimal planning of electric power distribution networks

This paper presents a new possibilistic (fuzzy) model for the multiobjective optimal planning of power distribution networks that finds out the nondominated multiobjective solutions corresponding to the simultaneous optimization of the fuzzy economic cost, level of fuzzy reliability, and exposure (optimization of robustness) of such networks, using an original and powerful meta-heuristic algorithm based on Tabu Search. This model determines the optimal location and size of the future feeders and substations in distribution networks with dimensions significantly larger than the ones usually presented in papers on the matter. The model also allows to determine the optimal reserve feeders (location and size) that provide the best distribution network reliability at the lowest cost for a given level of robustness (exposure). The model and the algorithm have been intensively tested in real distribution networks, which proves their practical application to large power distribution systems.

GIS spatial analysis applied to electric line routing optimization

This paper presents a new methodology for automated route selection for the construction of new power lines, based on geographic information systems (GIS). It uses a dynamic programming model for route optimization. Environmental restrictions are taken into account together with all of the operating, maintenance, and equipment installation costs, including a new approach to the costs associated with the slope of the terrain crossed by the power lines. The computing and visual representation capacities of GIS are exploited for the selection of economic corridors, keeping the total costs under a threshold imposed by the user. Intensive simulation examples illustrate the power and flexibility of the proposed methodology.

Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques

We present and compare two short-term statistical forecasting models for hourly average electric power production forecasts of photovoltaic (PV) plants: the analytical PV power forecasting model (APVF) and the multiplayer perceptron PV forecasting model (MPVF). Both models use forecasts from numerical weather prediction (NWP) tools at the location of the PV plant as well as the past recorded values of PV hourly electric power production. The APVF model consists of an original modeling for adjusting irradiation data of clear sky by an irradiation attenuation index, combined with a PV power production attenuation index. The MPVF model consists of an artificial neural network based model (selected among a large set of ANN optimized with genetic algorithms, GAs). The two models use forecasts from the same NWP tool as inputs. The APVF and MPVF models have been applied to a real-life case study of a grid-connected PV plant using the same data. Despite the fact that both models are quite different, they achieve very similar results, with forecast horizons covering all the daylight hours of the following day, which give a good perspective of their applicability for PV electric production sale bids to electricity markets.

Compromise seeking for power line path selection based on economic and environmental corridors

This paper presents a new multicriteria decision aid system (DAS) to obtain acceptable power line paths integrating the diverse socioeconomic interests of the different groups involved in the planning process, such as utilities, environmental agents, or local and regional authorities. The DAS is based on the intensive use of geographic information systems, as well as multicriteria weighting techniques reflecting all group interests. This new DAS can be used to overcome the problems raised by initially opposing positions among different groups stemming from diverse technological, economic, environmental, and/or social interests. The technique is illustrated by an intensive simulation example from a case study reproducing some of the phases of a negotiation process.

Powerful planning tools

Distributed power generation offers a solution to the limitations in the capacity of distributed systems and, at the same time, improves the reliability of the overall power system by increasing its generation capacity reserves. The planning process to integrate dispersed generation in power networks must take into account multiple factors such as the existing resources, the technology used in the generator, economic costs, and the environmental impact. Geographic information systems (GIS), software technologies developed for spatial data analysis, are suitable tools for solving these problems, and they allow the simultaneous evaluation of key technical, economic and environmental factors. The development of new techniques under the GIS platforms has increased the capabilities of GIS, allowing the systems to adapt to optimal DG planning studies. Using adequate software under the GIS platform, users can obtain useful information on the economic or technical viability of any distributed power generation facility. Governments, environmental agencies, utilities, private investors, financial corporations, and local authorities can become users of these tools and active players in the field of distributed power generation planning.

An advanced model for short-term forecasting of mean wind speed and wind electric power

The integration of wind energy conversion systems into electric power networks has become an important problem in the integral forecast of short-term demand. Due to the intermittent nature of wind, it is difficult to ensure the power generated by wind farms a few minutes ahead. In this article a new model based on Fourier decomposition and fuzzy inference systems is used for the short-term forecast of the mean wind speed and the electric power generated by wind turbines. Only past values of wind speeds are used. The results obtained with this model are better than those obtained with a relevant set of forecasting models using the same data.

Negotiation aid system for promotion of distributed generation and renewables

This paper describes a new concept of a negotiation aid system, developed over a GIS (geographic information system) and designed to facilitate reaching compromises among agents such as investors, environmental groups and governmental agencies, when deciding the location and sizing of new renewable energy sources in a region. The core model of an actor is similar to a fuzzy inference system of the Takagi-Sugeno type, built from a definition of preferences and levels of acceptability. An outranking method is employed to define geographical places of less conflict among the several actors negotiating. An application to the region of La Rioja, in Spain, is described.

Spatial decision support system for site permitting of distributed generation facilities

Distributed generation (DG) facilities require, like other energy projects, a sitting review process to acquire the permits and approval needs for construction and operation. In this process, different groups and individuals with different roles, interests and priorities are involved. This paper presents a spatial decision support system (SDSS) that helps to identify permissible areas to install DG facilities. Wind energy facilities are used in this paper to exemplify the use of the SDSS.