Metaheuristics for Transmission Network Expansion Planning

This chapter presents the characteristics of the metaheuristic algorithms used to solve the transmission network expansion planning (TNEP) problem. The algorithms used to handle single or multiple objectives are discussed on the basis of selected literature contributions. Besides the main objective given by the costs of the transmission system infrastructure, various other objectives are taken into account, representing generation, demand, reliability and environmental aspects. In the single-objective case, many metaheuristics have been proposed, in general without making strong comparisons with other solution methods and without providing superior results with respect to classical mathematical programming. In the multi-objective case, there is a better convenience of using metaheuristics able to handle conflicting objectives, in particular with a Pareto front-based approach. In all cases, improvements are still expected in the definition of benchmark functions, benchmark networks and robust comparison criteria

Optimal design methodologies under the carbon emission trading program using MIP, GA, SA, and TS

In this paper, an adaptation of MIP, GA, SA, and TS to network planning under the carbon emission trading program is described and computational results are given. As will be shown, the results are very encouraging. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses, the power generation costs and carbon emission costs. The optimization model has the ability to minimize the total costs and provides the best solutions, which are both cost-effective and environmentally friendly. This method of solution is demonstrated on the real problem. Finally, the performance of the proposed procedure is compared with that of the most well-known as mixed-integer programming.Optimization models Carbon emissions System planning

Social sustainability of treatment technologies for bioenergy generation from the municipal solid waste using best worst method

Despite the fundamental role of the social aspect in the implementation of sustainability in the bio-based industries, most of the sustainability assessments research have addressed the environmental and economic dimensions. However, the social dimension has been neglected and it can cause an irreparable outcome in the biotechnology industries. Following this issue, this study propounds a modified systemic approach for a social sustainability impact assessment of the treatment technologies for converting waste into bioenergy, based on a review on the common social assessment methods. As it is known, the guideline presented by the United Nations Environment Program (UNEP) and the Society of Environmental Toxicology and Chemistry (2009) due to considering social life cycle assessment has a comprehensive look at the stakeholders. Therefore, in this paper, UNEP method was selected. However, it needs to be modified based on the bio-energy supply chain derived from municipal solid waste. For this purpose, the bioenergy value chain derived from municipal solid waste was designed and combined with UNEP guideline, to complete the level of stakeholder subgroups and the levels of the indicators. The final method of the social assessment system was presented to the board of experts and finalized. In order to design the measurement part of the social assessment system, because of a multi criteria decision making nature of the social sustainability evaluation of the conversion technologies of municipal solid waste to bio-energies, a recent developed multi-criteria decision making method so-called Best Worst Method (BWM) was used in two stages. The criteria are ranked according to their average weight obtained through Best Worst method. One of the major novelties in this research is the way of application of the best worst technique in the second stage. The model was implemented in the case of Tehran as one of the pioneering Iranian municipalities with high potential to produce bioenergy. The results of this study help decision makers to decide where to concentrate their attention during the implementation stage, and to increase social sustainability in their bioenergy supply chains derived waste