Search results

Obviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach.

To the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

As seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor.

To the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

In hot and dry climates, air conditioning accounts for a large portion of total energy consumption; therefore, this paper aims to investigate the impact of sol-air temperature and ground temperature on the loss of cooling energy in hot and dry regions of Iran.

In line with this objective, the values of sol-air temperature along different directions and ground temperature at different depths were assessed with respect to climatic data of Yazd City. The impact of sol-air temperature and ground temperature on the rate of heat loss was investigated. So, energy loss of the walls aligned to four primary directions was calculated. This process was repeated for a 36 m² building with three different shape factors. All analyses were conducted for the period from May to September, during which buildings need to be cooled by air conditioners.

Numerical analyses conducted for hot and dry climate show that sol-air temperature leads to a 41-17 per cent increase in the wall’s energy loss compared with ambient temperature. Meanwhile, building the wall below the surface leads to a significant reduction in energy loss. For example, building the wall 400 cm below the surface leads to about 74.8-79.2 per cent energy saving compared with above ground design. The results also show that increasing the direct contact between soil and building envelope decreases the energy loss, so energy loss of a building that is built 400 cm below the surface is 53.7-55.3 per cent lower than that of a building built above the surface.

The impact of sol-air temperature and ground temperature on the cooling energy loss of a building in hot and dry climate was investigated. Numerical analysis shows that solar radiation increases heat loss from building envelope. Soil temperature fluctuations decrease with depth. Heat loss from building envelope in an underground building is lower than that from building envelope in a building built above the ground. Three different shape factors showed that sol-air temperature has the maximum impact on square-shaped plan and minimal impact on buildings with east-west orientation.