A Five-Level MILP Model for Flexible Transmission Network Planning Under Uncertainty: A Min–Max Regret Approach

Moreira, Alexandre; Štrbac, Goran; Moreno, Rodrigo; Street, Alexandre; Konstantelos, Ioannis

doi:10.1109/tpwrs.2017.2710637

Cited by 48 publications

(31 citation statements)

References 32 publications

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“…Therefore, the representation of multi-scale uncertainty in TEP models has been playing a relevant role in the recent literature across the various approaches used to address the TEP problem (stochastic, robust, and distributionally robust). We refer the interest reader to [6], [4], [7], [8], and [9] for further details.…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

“…It is worth mentioning that while part of the recent literature on ARO have considered probability agnostic models (see [11], [5], and [4]), relevant efforts have been made to account for the information extracted from data to devise more realistic descriptions of the short-term uncertainty. We refer the interested reader to [12], [13], and [14] for applications in short-term operational models and to [9] for a hybridrobust-and-stochastic approach applied to the TEP problem.…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

“…Scenario analysis is largely used in several real power systems such as WECC, Chile, ERCOT, California Independent System Operator, UK National Grid (see[4] and[2]). …”

mentioning

confidence: 99%

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Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach

Velloso

Pozo

Street

2020

IEEE Trans. Power Syst.

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We present a distributionally robust optimization (DRO) approach for the transmission expansion planning problem, considering both long-and short-term uncertainties on the system demand and renewable generation. On the longterm level, as it is customary in industry applications, the deep uncertainties arising from social and economic transformations, political and environmental issues, and technology disruptions are addressed by long-term scenarios devised by experts. The system planner is then allowed to consider exogenous long-term scenarios containing partial information about the random parameters, namely, the average and the support set. For each constructed long-term scenario, a conditional ambiguity set is used to model the incomplete knowledge about the probability distribution of the uncertain parameters in the short-term. Consequently, the mathematical problem is formulated as a DRO model with multiple conditional ambiguity sets. The resulting infinite-dimensional problem is recast as an exact, although very large, finite-deterministic mixedinteger linear programming problem. To circumvent scalability issues, we propose a new enhanced-column-and-constraintgeneration (ECCG) decomposition approach with an additional Dantzig-Wolfe procedure. In comparison to existing methods, ECCG leads to a better representation of the recourse function and, consequently, tighter bounds. Numerical experiments based on the benchmark IEEE 118-bus system are reported to corroborate the effectiveness of the method.

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Section: A Motivation and Literature Reviewmentioning

confidence: 99%

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

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Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach

Velloso

Pozo

Street

2020

IEEE Trans. Power Syst.

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“…Along this line, a comprehensive survey on theoretical and practical advancements in transmission investment and planning methodologies, from both an economic and an engineering point of view, is reported in [3]. Some of the most relevant issues that are currently being tackled include: determining efficient methods for combined transmission/generation expansion [4], accounting for impact of large-scale wind integration under uncertainties [5,6], and developing new investment paradigms for transmission expansion in deregulated power markets [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Ex-ante dynamic network tariffs for transmission cost recovery

Savelli

Paola

2020

Applied Energy

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This paper proposes a novel tariff scheme and a new optimization framework in order to address the recovery of fixed investment costs in transmission network planning, particularly against rising demand elasticity. At the moment, ex-post network tariffs are utilized in addition to congestion revenues to fully recover network costs, which often leads to over/under fixed cost recovery, thus increasing the investment risk. Furthermore, in the case of agents with elastic market curves, ex-post tariffs can cause several inefficiencies, such as mistrustful bidding to exploit ex-post schemes, imperfect information in applied costs and cleared quantities, and negative surplus for marginal generators and consumers. These problems are exacerbated by the increasing price-elasticity of demand, caused for example by the diffusion of demand response technologies. To address these issues, we design a dynamic ex-ante tariff scheme that explicitly accounts for the effect of tariffs in the longterm network planning problem and in the underlying market clearing process. Using linearization techniques and a novel reformulation of the congestion rent, the long-term network planning problem is reformulated as a single mixed-integer linear problem which returns the combined optimal values of network expansion and associated tariffs, while accounting for price-elastic agents and lumpy investments. The advantages of the proposed approach in terms of cost recovery, market equilibrium and increased social welfare are discussed qualitatively and are validated in numerical case studies. y b τ d t,m,i,k it replaces the product b τ m,i d t,k y b τ g t,m,i,p it replaces the product b τ m,i g t,p

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“…For instance, transmission lines intentional attacks were considered when deciding transmission expansion strategy using stochastic programming approach [23]. A five-level MILP model was proposed for transmission network planning considering transmission lines outages [24].…”

Section: Introductionmentioning

confidence: 99%

Sizing battery storage for islanded microgrid systems to enhance robustness against attacks on energy sources

Lai

Wang

Shi

et al. 2019

J. Mod. Power Syst. Clean Energy

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Power system security against attacks is drawing increasing attention in recent years. Battery energy storage systems (BESSs) are effective in providing emergency support. Although the benefits of BESSs have been extensively studied earlier to improve the system economics, their role in enhancing the system robustness in overcoming attacks has not been adequately investigated. This paper addresses the gap by proposing a new battery storage sizing algorithm for microgrids to limit load shedding when the energy sources are attacked. Four participants are considered in a framework involving interactions between a robustness-oriented economic dispatch model and a bilevel attacker-defender model. The proposed method is tested with the data from a microgrid system in Kasabonika Lake of Canada. Comprehensive case studies are carried out to demonstrate the effectiveness and merits of the proposed approach.

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A Five-Level MILP Model for Flexible Transmission Network Planning Under Uncertainty: A Min–Max Regret Approach

Cited by 48 publications

References 32 publications

Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach

Distributionally Robust Transmission Expansion Planning: A Multi-Scale Uncertainty Approach

Ex-ante dynamic network tariffs for transmission cost recovery

Sizing battery storage for islanded microgrid systems to enhance robustness against attacks on energy sources

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