Generation Expansion Planning Using the Decision Tree Technique

Farghal, S.A.; El-Dewieny, Reda M.K.; Aziz, M. Roshdy Abdel

doi:10.1016/0378-7796(87)90051-4

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…These are analytic hierarchy approach [53, 54], network flow programming [55], decision tree [56, 57], dynamic programming [58, 59], game theory [19], iterative algorithm [60], mixed integer programming [61–63], linear programming [64] and bender's decomposition [65].…”

Section: Solving Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive review of generation and transmission expansion planning

Hemmati¹,

Hooshmand²,

Khodabakhshian³

2013

IET Generation, Transmission & Distribution

250

162

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Section: Solving Methodsmentioning

confidence: 99%

“…An index is generally considered for constraint, such as LOLP [68, 71, 72, 74], EENS [68, 72] and LOLE [69, 75, 84, 85]. In some researches [56, 70] the reliability is considered as a part of objective function in order to maximise the reliability index or minimising the outage cost.…”

Section: Reliabilitymentioning

confidence: 99%

Comprehensive review of generation and transmission expansion planning

Hemmati¹,

Hooshmand²,

Khodabakhshian³

2013

IET Generation, Transmission & Distribution

250

162

View full text Add to dashboard Cite

“…This at times can be represented as an economic criterion, which is usually introduced in an effort to embed the generating system reliability considerations directly in the minimum cost model. Hence, reliability of the system can be formulated as part of the objective function(s) 104,117 or included as part of the constraints. 7,9,12,29,[118][119][120] A typical generation expansion plan is expected to satisfy the reliability criterion and ensure that the reliability requirements are met as specified by appropriate standards.…”

Section: Reliability Criteria In Gep Studiesmentioning

confidence: 99%

A comprehensive state‐of‐the‐art survey on power generation expansion planning with intermittent renewable energy source and energy storage

2019

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Summary Generation expansion planning (GEP) is a power plant mix problem that identifies what, where, when, and how new generating facilities should be installed and when old units be retired over a specific planning horizon. GEP ensures that the quantity of electricity generated matches the electricity demand throughout the planning horizon. This kind of planning is of importance because most production and service delivery is dependent on availability of electricity. Over the years, the traditional GEP approaches have evolved to produce more realistic models and new solution algorithms. For example, with the agitation for green environment, the inclusion of renewable energy plants and energy storage in the traditional GEP model is gradually gaining attention. In this regards, a handful of research has been conducted to identify the optimal expansion plans based on various energy‐related perspectives. The appraisal and classification of studies under these topics are necessary to provide insights for further works in GEP studies. This article therefore presents a comprehensive up‐to‐date review of GEP studies. Result from the survey shows that the integration of demand side management, energy storage systems (ESSs), and short‐term operational characteristics of power plants in GEP models can significantly improve flexibility of power system networks and cause a change in energy production and the optimal capacity mix. Furthermore, this article was able to identify that to effectively integrate ESS into the generation expansion plan, a high temporal resolution dimension is essential. It also provides a policy discussion with regard to the implementation of GEP. This survey provides a broad background to explore new research areas in order to improve the presently available GEP models.

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“…Because the MINLP models are often transformed to the MILP problems to facilitate model solving and to guarantee the local solution, some researchers have started to develop pure MILP model without the need for Benders decomposition application. First MILP models were based on the decision tree method [23]. Nowadays, decision tree is a basis for advanced MILP solvers: Another group of the MILP energy mix optimization models focus on the uncertainty of the input data and model parameters: A flexibility of power generating units is another, but very important feature that should be incorporated in the energy system models.…”

Section: Modelmentioning

confidence: 99%

“…∀ ∈ _ _ = _ _ • _ _ (22) Finally, the power balance is presented in (23). In each year t, the total available power in the system must cover the prospective peak load P_D t with required margin, which is expressed by percentile factor K_PRESERVE t .…”

Section: Long-term Generation Expansion Planningmentioning

confidence: 99%

MILP model for long-term energy mix planning with consideration of power system reserves

2016

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The purpose of this paper is to present the novel long-term energy mix optimization model eMix, which takes into account daily requirements of power system operation (primary, secondary and tertiary reserve) and how should they react to increasing RES penetration. Until now there has been no computational effective, long-term optimization model of energy mix with individual representation of power generating assets that allows for RES development incorporation and consider the daily requirement of power system operation. Existing unit commitment models, although comprehensive, are not appropriate for long-term, large-scale problems because of computational limitations. The eMix links long-term planning (investments) with shortterm requirements of power system operation reserves (primary, secondary, tertiary) provided by dispatchable units. Additionally, development of the RES introduces stronger requirements to the reserve's level in order to maintain the flexibility of the power system operation. This relation has significant impact on the final energy mix and was modeled in the eMix. The costs are calculated annually for each individual unit (given to the application of Mixed Integer Linear Programming), to reflect the real conditions of energy sector operation, where annual repayment of loans, amortization as well as operation and maintenance costs are applied. eMix is also fully scalable and allows for freely definable interval and horizon of the calculation. To prove the quality of presented model, the authors calibrated it with the data from Polish power system for different scenarios. Results indicate that the consideration of financial costs and required reserves increase the reliability of the optimization outcomes.

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Generation Expansion Planning Using the Decision Tree Technique

Cited by 10 publications

References 2 publications

Comprehensive review of generation and transmission expansion planning

Comprehensive review of generation and transmission expansion planning

A comprehensive state‐of‐the‐art survey on power generation expansion planning with intermittent renewable energy source and energy storage

MILP model for long-term energy mix planning with consideration of power system reserves

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