Energy Planning for Distributed Generation Energy System: The Optimization Work
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Bibliographic record
Abstract
Behind the public eye a quiet revolution is taking place, one that will permanently alter our relationship with energy. Most people today have heard about deregulation of the electric utility industry. Recently, privatization of most important energy sectors (electricity) in Iran has turned former monopolies into free market competitors. This has been specially the case with the unbundling of vertically integrated energy companies in the electricity sector where generation, transmission, and distribution activities have been split. Community consciousness of fossil fuel resource depletion and environmental impact caused by large scale power plants is growing. Because of large land area, losses in Iran power transmission network are significant. These reasons caused greater interest in distributed generation (DG) small scale, demand site technologies based on renewable energy sources. Energy planning has to be carried out by modeling all sectors of energy system from primary energy sources (fossil fuels, renewable) to end use technologies for determination of optimal configuration of energy systems. Energy planning is a powerful tool for showing the effects of certain energy policies, which helps decision makers choose the most appropriate strategies in order to expand DG technologies and taking into account environmental impacts and costs to the community. Energy planning is carried out in Iran's energy system. Therefore, we have defined a reference energy system for Iran. The aim of this paper is to evaluate the contribution of DG technologies when energy planning is carried out. For this purpose, the energy system optimization model MESSAGE has been utilized to take into account the presence of DG technologies. To provide a detailed description of DG production, a power grid scheme is considered. Planning procedure follows an optimization process based on the cost function minimization in the presence of technical and energy-policy and environmental constraints. In Section 2, a brief explanation of model MEESAGE is given. In this section you will know main parts and aim of the model. In section 3, a brief review of the spread of DG technologies is reported. In Section 4, the reference energy system of Iran relating to the proposed optimization procedure and structure of model MESSAGE is illustrated. In section 5, Model validation is studied. The test results of several scenarios applied to Iran's energy system are reported in Section 6.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it