A mathematical model for long range expansion planning of generation and transmission in electric utility systems

Sawey, R.M.; Zinn, Christopher

doi:10.1109/t-pas.1977.32377

Cited by 35 publications

(8 citation statements)

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“…3. Long-term temporal granularity: Though many TEP models are based on a single investment decision stage ("one-shot" or "static" planning) [29], dynamic TEP models [4] have become increasingly popular because of improved computational abilities and the need for plans to include timing of investments.…”

Section: Literature Reviewmentioning

confidence: 99%

Value of model enhancements: quantifying the benefit of improved transmission planning models

Hobbs

2019

IET gener. transm. distrib.

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A framework to quantify the value of model enhancements (VOME) in transmission planning models is proposed and applied to a case study of the large-scale, long-term planning of the Western Electricity Coordinating Council (WECC) system. The VOME, which is closely related to the concept of the value of information from decision analysis, quantifies the probability-weighted improvement in the system performance resulting from changes in decisions that result from model enhancements. The WECC case study shows that it is practical to quantify VOME and illustrates the type of insights that can be obtained. The values of four types of model enhancements are compared. The results show major benefits from considering long-run uncertainty using multiple scenarios of technology, policy, and economics; these benefits are as much as 14% of total benefits of new transmission built in the first ten years. But less benefit is obtained from more temporal granularity, more complex network representations, and inclusion of unit commitment constraints and costs. This framework can be applied to quantify the value of model enhancements in any planning context, such as integrated resource planning. Nomenclature () Expected present worth of system cost of making decision , based on the model with all enhancements () Binary parameter: if () = 1, then enhancement i is included in the model with setting ; if zero, then the enhancement is excluded. For instance, if there are three candidate enhancements, then 1 (*) = 1, 2 (*) = 0, 3 (*) = 1 indicates a model with only Enhancements 1 and 3 implemented. Set of enhancements, indexed by i, j = 1…n Optimal first stage transmission investments ("decision") from a model with enhancements setting specified by. (E.g., ω * , where 1 (*) = 1, 2 (*) = 0, 3 (*) = 1 , indicates investments from a model with only Enhancements 1 and 3 implemented.) Decision of no transmission investments in stage 1 Optimal decision from the model with all enhancements (i.e., () = 1 for all i). Model enhancement setting, describing what enhancements are included in the model formulation. Ω The set of all possible permutations of enhancements other than i

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Section: Literature Reviewmentioning

confidence: 99%

Value of model enhancements: quantifying the benefit of improved transmission planning models

Hobbs

2019

IET gener. transm. distrib.

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“…As it is reported ": : : what matters most is not what the ministry writes in its decrees, but what the minister says in his telephone calls" (Ammons and McGinnis, 1985). Moreover, the energy analyses are based considerably on methodologies that will attribute scientifically what decision makers want to hear and not what they really need to know (Saway and Zinn, 1977).…”

Section: From Energy Planning To Energy Policy Makingmentioning

confidence: 99%

“…To the best of our knowledge, some of the main research efforts in energy planning include a generation expansion planning model for electric utilities (Ammons and McGinnis, 1985), a mathematical model for long range expansion planning in electric utility systems (Saway and Zinn, 1977), a modular state-of-the-art capacity expansion software package (Caramanis et al, 1982), a model for planning least-cost investments in generating capacity (Bloom, 1982), a hybrid genetic algorithm/dynamic programming approach (Park et al, 1985), an interactive software developed for integrating engineering experience and judgment (David and Zhao, 1989), a new multicriteria decision (MCD) procedure which combines dynamic programming with a production simulation method (Yang and Chen, 1989), an analytical approach for the production costing model and reliability measure (Park et al, 1998), and a generation planning model that incorporates the outage costs of customers as well as the utility (Wang and Min, 2000).…”

Section: From Energy Planning To Energy Policy Makingmentioning

confidence: 99%

Energy Policy Making: An Old Concept or a Modern Challenge?

Doukas

Patlitzianas

Kagiannas

et al. 2008

Energy Sources, Part B: Economics, Planning, and Policy

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The energy policy is directed by the three main objectives, namely the security of supply, the competitiveness of the energy market, and the environmental protection. In addition to this, now the energy policy is developed through a number of social and economical challenges. As a result, a comprehensive and modern energy policy making, which can be characterized by clarity and transparency, is necessary. Even though energy policy making has been the subject of many researchers, comprehensive and modern studies examining the multiple and often conflicting parameters that have to be incorporated is not present in the international literature. In the above context, the purpose of this article is to review and analyze the current concept and the challenges toward the modern energy policy making, based on the three energy policy objectives as well as the impact of the crucial parameters.

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“…Composite system expansion planning addresses the problem of broadening and strengthening an existing generation and transmission network to optimally serve a growing electricity market while satisfying a set of economic and technical constraints (Read et al, 1999(Read et al, , 2000. In conventional power systems, the composite generation and transmission expansion planning problem has been defined as minimising the cost of capacity additions subject to a reliability level constraint (Kandil, El-Debeiky, & Hasanien, 2000;Sawey & Zinn, 1977;Wang & McDonald, 1994), whereas in the restructured power system, the main objective of power system is to introduce competition in the power industry and to allow customers to select their suppliers based on price and reliability. Restructuring and deregulation result in the functional segregation of the vertically integrated utility consisting of generation, transmission and distribution into distinct utilities in which each performs a single function.…”

Section: Introductionmentioning

confidence: 99%

An augmented NSGA-II technique with virtual database to solve the composite generation and transmission expansion planning problem

Javadi

Saniei

Mashhadi

2013

Journal of Experimental & Theoretical Artificial Intelligen

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This paper presents a new computational technique in composite generation and transmission expansion planning considering reliability and cost assessment. The proposed procedure incorporates a virtual database in order to hedging the repetitive calculation by optimisation solver. Since generation and transmission expansion planning is a large scale, mixed-integer, nonlinear and non-convex optimisation task, the proposed technique accelerates the convergence time and reduces computational burden. The composite generation and transmission expansion planning problem is represented as a multi-objective optimisation problem. The virtual database-supported non-dominated sorting genetic algorithm (VDS-NSGA-II) is applied due to comparative assessment potential and good handling of the non-convex problems and noncommensurable objective functions. The virtual database eliminates the repetitive computational efforts in both reliability and hourly operational assessments. In this study, the expected energy not served at hierarchical level II is taken into account as a reliability index, whereas the entire system cost, including annually operational and investment costs, is considered as another objective function. The incidence matrixbased DC optimal power flow is adopted to reflect transmission flow constraint in a framework in which the disconnected bus problem would be handled in both objective functions. To numerically evaluate the efficiency of the proposed method, simulation results on a simple three-bus test system and the modified IEEE 24-bus reliability test system are provided. In spite of huge computation burden at HL-II reliability assessment, the results indicate high efficiency of the proposed VDS-NSGA-II.Keywords: composite generation and transmission system; composite expansion planning; virtual database supported non-dominated sorting genetic algorithm (VDS-NSGA-II); incidence matrix-based DC optimal power flow (IM-DCOPF) i Index for bus j Index for line D Discount rate Y Index of year M Index of planning horizon N Index of life of new assets PG Total power generation PD Total demand VPG Index of curtailed load (virtual generator) A Incidence matrix (node and branch)

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A mathematical model for long range expansion planning of generation and transmission in electric utility systems

Cited by 35 publications

References 1 publication

Value of model enhancements: quantifying the benefit of improved transmission planning models

Value of model enhancements: quantifying the benefit of improved transmission planning models

Energy Policy Making: An Old Concept or a Modern Challenge?

An augmented NSGA-II technique with virtual database to solve the composite generation and transmission expansion planning problem

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