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

Nnachi, Gideon Ude; Hamam, Yskandar; Graham, Richards Coneth

doi:10.1109/access.2019.2936682

Cited by 43 publications

(13 citation statements)

References 162 publications

(160 reference statements)

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“…Its purpose is to provide a periodic power system expansion plan for the regional power load growth. On the basis of ensuring the transmission network reliability, it is still a major challenge to improve the reliability and quality of power supply by constructing new power equipment with minimize cost [12]. In addition, the TNEP task often regards about 10 years later as the target year, but most power operation data can only be obtained through prediction, which would bring huge uncertainty.…”

Section: A Summary Of Transmission Expansion Network Planningmentioning

confidence: 99%

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Wang

Zhou

et al. 2021

IEEE Access

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Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes a novel method for transmission network expansion planning (TNEP). Since TNEP is a large scale and mixedinteger linear programming (MILP) problem, as the transmission network scale and the optimal constraints increase, the numerical calculation and heuristic learning-based methods suffer from heavy computational complexities in calculation and training. Besides, due to the black box characteristic, the solution processes of the heuristic learning-based methods are inexplicable and usually require repeated training. By using DDQN-ResNet, this paper constructs a high-performance and flexible method to solve large-scale and complex-constrained TNEP problem. Firstly, we form a two-objective TNEP model, in which one objective is to minimize the comprehensive cost, and another objective is to maximize the transmission network reliability. The comprehensive cost takes into account the expansion cost, the network loss cost, and the maintenance cost. The transmission network reliability is evaluated by the expected energy not served (EENS) and the electrical betweenness. Secondly, from the TNEP model, the TNEP task is constructed based on the Markov decision process. By abstracting the task, the TNEP environment is obtained for DDQN-ResNet. In addition, to identify the construction value of lines, an agent is establish based on DDQN-ResNet. Finally, we perform the static planning and visualize the reinforcement learning process. The dynamic planning is realized by reusing the training experience. The validity and flexibility of DDQN-ResNet are verified on RTS 24-bus test system.

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Section: A Summary Of Transmission Expansion Network Planningmentioning

confidence: 99%

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Wang

Zhou

et al. 2021

IEEE Access

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“…The indexes EENS and EWES are calculated in (25) and (26), respectively. These indexes are given in MWh/year and are obtained by the respective expected power in MW times the duration of 1 year (h/year), i.e., 8760 h.…”

Section: Performance Assessment (Step 7)mentioning

confidence: 99%

“…Reviews over TNEP challenges and optimization algorithms can be found in [3,24,25]. In [3], some gaps identified by the authors are presented and a methodology capable of dealing with TNEP under several levels of load and wind power is introduced.…”

Section: Introductionmentioning

confidence: 99%

Reliability-constrained dynamic transmission expansion planning considering wind power generation

Paula

Oliveira

et al. 2020

Electr Eng

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This paper proposes a two-stage framework to solve the long-term transmission network expansion planning (TNEP) problem ensuring user-defined reliability levels and wind curtailment over the planning horizon. In the first stage, the static TNEP (S-TNEP) problem is solved to define where a set of new lines must be installed at the end of planning horizon. In the second stage, a multistage procedure is performed to solve the dynamic TNEP (D-TNEP) in order to determine the moment that each transmission line should be built. Both S-TNEP and D-TNEP are decomposed into investment decision and performance assessment through a bi-level scheme based on Benders decomposition; thus, the reliability is considered and evaluated over the planning process. The reliability and performance indexes are obtained through the non-chronological Monte Carlo simulation considering the random behavior of transmission lines failures, load fluctuations and uncertainties over wind availability. The loss of wind probability performance index is introduced to measure the probability of wind curtailment in each connection point of the system, and it is used to prevent wind farms from being underused. The proposed methodology is tested in modified versions of the 24-bus IEEE reliability test system and the IEEE 118-bus test system. Keywords Transmission expansion planning • Wind power generation • Reliability assessment • Monte Carlo simulation • Benders decomposition • Short-term uncertainties Sets and subscripts B Set of system buses E Set of existing lines C Set of candidate lines Ω E i Set of existing lines connected to bus i Ω C i Set of candidate lines connected to bus i i System bus i j Line between buses i and j k Existing or candidate transmission lines s Operative state B A. N. de Paula

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“…The primary objective of strategic power system planning is to meet future demand and integrate renewable energy sources (RES), with the restriction of being as reliable, economical, and environmentally friendly as possible [1]. These requirements result in many different optimization targets and planning problem formulations, as the exhaustive literature overviews show [2][3][4]. Standard industry practice in power system planning of distribution and sub-transmission grids is to manually design the power system based on a few hypothetical worst-casesituations [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Year High-Voltage Power System Planning Considering Active Power Curtailment

Schäfer

Braun

2020

Energies

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Integrating active power curtailment (APC) of renewable energy sources (RES) in power system planning reduces necessary investments in the power system infrastructure. In current target grid planning methods, APC is considered by fixed curtailment factors without considering the provided flexibility to its full extent. Time-series-based planning methods allow the integration of the time dependency of RES and loads in power system planning, leading to substantial cost savings compared to the worst-case method. In this paper, we present a multi-year planning strategy for high-voltage power system planning, considering APC as an alternative investment option to conventional planning measures. A decomposed approach is chosen to consider APC and conventional measures in a long-term planning horizon of several years. The optimal investment path is obtained with the discounted cash flow method. A case study is conducted for the SimBench high-voltage urban benchmark system. Results show that the time-series-based method allows for reducing investments by up to 84% in comparison to the worst-case method. Furthermore, a sensitivity analysis shows the variation in total expenditures with changing cost assumptions.

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

Cited by 43 publications

References 162 publications

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet

Reliability-constrained dynamic transmission expansion planning considering wind power generation

Multi-Year High-Voltage Power System Planning Considering Active Power Curtailment

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