Energy models: Methods and characteristics

Neshat, Najmeh; Amin-Naseri, Mohammad Reza; Danesh, Farzaneh

doi:10.17159/2413-3051/2014/v25i4a2243

Cited by 24 publications

(10 citation statements)

References 50 publications

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“…In summary, optimization problem deals with finding the optimal choice out of a set of alternatives by either maximizing or minimizing set of functions in real time [33]. Energy aspect of optimization models are used in optimizing energy investment decisions endogenously and the results are normally the optimal solution for some input variables to satisfy certain constraints.…”

Section: Review Of Optimization Techniques For Tnep Problemsmentioning

confidence: 99%

A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization Algorithms

2019

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Long term planning in power transmission network expansion provides a well ordered and profitable extension of power equipment and facilities to meet the expected electric energy demand with an allowable degree of reliability. However, high quality and improved reliability in energy supply have to be balanced with the available funds. The need to expand transmission network can never be over emphasized. Transmission Network Expansion Planning (TNEP) is a periodical measure that must be carried out due to dynamic societies that attract extra energy demands. It is highly important to minimize the network reinforcement and operational costs while satisfying the increase in demand imposed by technical and economic conditions over the planning horizon. Several optimization algorithms for TNEP problems have been developed and applied over the past decades. This paper presents a comprehensive state-of-the-art survey on the TNEP optimization algorithms. The approach of this paper is in the area of highlights of the various available TNEP algorithms, their applications, viability, computational complexities and drawbacks, which can aid in the identifications of the proper methods that can yield an optimal solution to TNEP problem. INDEX TERMS Algorithm, hybrid, meta-heuristics, optimization techniques, power network expansion planning, power system, transmission network expansion.

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Section: Review Of Optimization Techniques For Tnep Problemsmentioning

confidence: 99%

A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization Algorithms

2019

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“…Hence, residual load forecasting is the center of attention in this area of research from the both academic and practical point of view. Different forecasting approaches, from statistical to computational intelligent have been applied in the past [3,4]. Based on the number of techniques, the related literature is typically divided into two main categories: stand-alone and hybrid methods consisting of one and more than one technique respectively and based on the type of the underlying technique, stand-alone methods are divided into three methods namely Statistical, Casual and Computational Intelligence methods.…”

Section: Introductionmentioning

confidence: 99%

ParDeeB: A Graph Framework for Load Forecasting Based on Parallel DeepNet Branches

2021

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Recently, energy demand forecasting has emerged as a significant area of research because of its prominent impact on greenhouse gases (GHGs) emission and global warming. The problems of load forecasting are characterized by complex and nonlinear nature and also long-term historical dependency.Up to now, several approaches from statistical to computational intelligent have been applied in this research filed. The literature agrees with the fact that deep learning approach is more capable in dealing with these characteristics among existing approaches. However, the recent state-of-the-art deep network models are not robust against different historical dependency. In this

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“…GEP addresses the problem of identifying the most adequate technologies, expansion size and timing for power generation to satisfy future demands in terms of social, environmental, and economic criteria. Existing GEP models in the literature have been reviewed broadly from both a conceptual (Kagiannas et al , 2004) and a detailed modeling perspective (Bakirtzis et al , 2012; Neshat et al , 2014; Krishnan et al , 2016; Wierzbowski et al , 2016). The inflexibility, variability and relative unpredictability of intermittent nature of renewable power sources cause the investors and regulators to encounter risk in their investment and expansion decisions.…”

Section: Introductionmentioning

confidence: 99%

Technological learning modelling towards sustainable energy planning

Neshat

Hadian²,

Alangi

2019

JEDT

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Purpose 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. Design/methodology/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. Findings 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. Originality/value 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.

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Energy models: Methods and characteristics

Cited by 24 publications

References 50 publications

A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization Algorithms

A Comprehensive State-of-the-Art Survey on the Transmission Network Expansion Planning Optimization Algorithms

ParDeeB: A Graph Framework for Load Forecasting Based on Parallel DeepNet Branches

Technological learning modelling towards sustainable energy planning

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