Abstract

Abstract
This paper discusses static transmission expansion planning (STEP) in terms of minimizing the costs of investment and operations. We propose a transmission expansion model that divides into investment and operations problems. We use a binary particle swarm optimization algorithm (BPSO) to solve the investment problem and a DC optimal power flow (DCOPF) to solve the operations problem. We model uncertainty as stochastic demand at each node. A simulated case study numerically evaluates the efficiency of the proposed method. © 2013 IEEE.

Abstract
This study presents a multi-objective framework to evaluate the integration of distant wind farms with associated transmission network upgrades on optimal power system planning. The presented approach also extends the technique to include the consideration of energy limitations associated with the installed hydro generation facilities. This study attempts to emphasise on the reliability implications rather than the production cost evaluation aspects. The decision making is based on hierarchal level II (HL-II) Expected Energy Not Served as an entire power system reliability assurance, and capital cost plus annual operational cost as an economical index. Non-dominated Sorting Genetic Algorithm is adopted to achieve the Pareto front of the aforementioned multi-objective problem. A fuzzy satisfying method, designated as the distance metric, is used to represents a trade-off between different objectives. To numerically evaluate the efficiency of the proposed method, simulation results on three case studies are provided. In spite of huge computation burden at HL-II reliability assessment, the results indicate high efficiency of the proposed method. © The Institution of Engineering and Technology 2013.

Abstract

Abstract
Many transportation environmental life cycle analyses neglect the contribution of the energy supply infrastructures. In alternative light duty vehicle technologies, it has been shown through case studies that this can be a relevant factor. However, no model that can generalise the evaluation of energy and emissions from construction, maintenance and decommissioning of such infrastructure to analyse different scenarios currently exists. A model is proposed, focussing on electricity and on hydrogen supply through centralised steam methane reforming (H-2(a)) and on-site electrolysis (H-2(b)). The model outputs are in gCO(2eq)/MJ and MJ(eq)/MJ of the final energy. Model main inputs are the region's electricity mix, the annual distance driven, supply chain losses and the number of vehicles per station or chargers. The evaluation of the number of vehicles served per each charger/station as a function of annual distance driven is presented. The uncertainty is estimated by using the pedigree matrix, impact uncertainty and literature estimates. The model shows consistency in the results and uncertainty range. Charging policies that minimise the electricity infrastructure burden should incentivise approximately 37% of normal charging. H-2(a) pipeline lifetime should be extended. Efforts in the electrolyser should be undertaken to approximate the ratio of vehicles per station with a conventional one.

Abstract
The large-scale integration of microgeneration (MG) can bring several technical benefits, such as: improving the voltage profile, reducing power losses and allowing for network capacity investment deferral. Furthermore, it is now widely accepted that introducing new renewable MG, such as wind turbines, photovoltaic panels or biomass can help control carbon emissions, reduce our dependence on oil and contribute to a sustainable energy growth. This paper presents an empirical analysis of the benefits of MG on avoided losses, voltage profiles and branch congestion. The main goal is to clarify whether the current regulatory framework allows for obtaining all the MG potential gains. The main conclusion is that some legal constraints should be removed, or at least relaxed, in order to promote the growth of distributed power generation, particularly, for domestic MG.