scholarly journals An Extreme Scenario Method for Robust Transmission Expansion Planning with Wind Power Uncertainty

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2116 ◽  
Author(s):  
Zipeng Liang ◽  
Haoyong Chen ◽  
Xiaojuan Wang ◽  
Idris Ibn Idris ◽  
Bifei Tan ◽  
...  
Keyword(s):  
Wind Power ◽  
Transmission Lines ◽  
Benders Decomposition ◽  
Electrical Power ◽  
Mixed Integer ◽  
Two Stage ◽  
Operating Variables ◽  
Expansion Planning ◽  
Power Resources ◽  
Bus System

The rapid incorporation of wind power resources in electrical power networks has significantly increased the volatility of transmission systems due to the inherent uncertainty associated with wind power. This paper addresses this issue by proposing a transmission network expansion planning (TEP) model that integrates wind power resources, and that seeks to minimize the sum of investment costs and operation costs while accounting for the costs associated with the pollution emissions of generator infrastructure. Auxiliary relaxation variables are introduced to transform the established model into a mixed integer linear programming problem. Furthermore, the novel concept of extreme wind power scenarios is defined, theoretically justified, and then employed to establish a two-stage robust TEP method. The decision-making variables of prospective transmission lines are determined in the first stage, so as to ensure that the operating variables in the second stage can adapt to wind power fluctuations. A Benders’ decomposition algorithm is developed to solve the proposed two-stage model. Finally, extensive numerical studies are conducted with Garver’s 6-bus system, a modified IEEE RTS79 system and IEEE 118-bus system, and the computational results demonstrate the effectiveness and practicability of the proposed method.

scholarly journals Co-Optimized Analysis and Design of Electric and Natural Gas Infrastructures

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 2012 ◽  
Author(s):  
Santiago Lemos-Cano ◽  
James McCalley
Keyword(s):  
Natural Gas ◽  
Transmission Lines ◽  
Power Flow ◽  
Planning Process ◽  
The United States ◽  
Integrated System ◽  
Mixed Integer ◽  
Eastern Region ◽  
Analysis And Design ◽  
Expansion Planning

This paper proposes and implements a long-term deterministic capacity expansion model for the co-optimization of electric and natural gas infrastructures. It determines the required investments in generation units, transmission lines and pipelines for meeting future demands, while representing electricity and natural gas flows using DC Power Flow and Weymouth equations, respectively. A Mixed Integer Nonlinear Programming (MINLP) problem is developed, from which a linearized version is derived. A 26 node integrated gas-electric system for the Eastern Region of the United States is used to demonstrate the model’s capabilities. Results show that the model provides an accurate operational representation of the integrated system, and, therefore, enhances the expansion planning process.

scholarly journals Two-Stage Stochastic Programming Scheduling Model for Hybrid AC/DC Distribution Network Considering Converters and Energy Storage System

2019 ◽  
Vol 10 (1) ◽  
pp. 181
Author(s):  
Peng Kang ◽  
Wei Guo ◽  
Weigang Huang ◽  
Zejing Qiu ◽  
Meng Yu ◽  
...  
Keyword(s):  
Wind Power ◽  
Reactive Power ◽  
Distribution Network ◽  
Stochastic Scheduling ◽  
Second Order ◽  
Mixed Integer ◽  
Two Stage ◽  
Dc Distribution ◽  
Scheduling Model ◽  
Second Order Cone

The development of DC distribution network technology has provided a more efficient way for renewable energy accommodation and flexible power supply. A two-stage stochastic scheduling model for the hybrid AC/DC distribution network is proposed to study the active-reactive power coordinated optimal dispatch. In this framework, the wind power scenario set is utilized to deal with its uncertainty in real time, which is integrated into the decision-making process at the first stage. The charging/discharging power of ESSs and the transferred active/reactive power by VSCs can be adjusted when wind power uncertainty is observed at the second stage. Moreover, the proposed model is transformed into a mixed integer second-order cone programming optimization problem by linearization and second-order cone relaxation techniques to solve. Finally, case studies are implemented on the modified IEEE 33-node AC/DC distribution system and the simulation results demonstrate the effectiveness of the proposed stochastic scheduling model and solving method.

scholarly journals Transmission Network Expansion Planning Considering Phase-Shifter Transformers

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Celso T. Miasaki ◽  
Edgar M. C. Franco ◽  
Ruben A. Romero
Keyword(s):  
Mathematical Model ◽  
Transmission Lines ◽  
Phase Shifter ◽  
Planning Process ◽  
Main Idea ◽  
Mixed Integer ◽  
Planning Problem ◽  
Transmission Network ◽  
Network Expansion ◽  
Expansion Planning

This paper presents a novel mathematical model for the transmission network expansion planning problem. Main idea is to consider phase-shifter (PS) transformers as a new element of the transmission system expansion together with other traditional components such as transmission lines and conventional transformers. In this way, PS are added in order to redistribute active power flows in the system and, consequently, to diminish the total investment costs due to new transmission lines. Proposed mathematical model presents the structure of a mixed-integer nonlinear programming (MINLP) problem and is based on the standard DC model. In this paper, there is also applied a specialized genetic algorithm aimed at optimizing the allocation of candidate components in the network. Results obtained from computational simulations carried out with IEEE-24 bus system show an outstanding performance of the proposed methodology and model, indicating the technical viability of using these nonconventional devices during the planning process.

scholarly journals Integrating Wind Electrical Energy for the Marine Electrical Power System

2019 ◽  
Vol 9 (1) ◽  
pp. 4413-4418
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Wind Energy ◽  
Wind Power ◽  
Electrical Power ◽  
Electrical Energy ◽  
Energy Output ◽  
Future Research ◽  
Electrical Power System ◽  
Bus System

Utilization of renewable energy for the reduction of fuel consumption and green house gas (GHG) emissions in the shipping industry has been increased rapidly in the recent years. Wind energy is a clean renewable energy with no pollution which is abundantly available at sea. This paper proposes two different possible configurations of connecting wind power energy into the ship’s main grid bus system . Wind electrical energy output has been connected to ship’s main ac bus system in one configuration and it is connected to ship’s main dc bus system. Even though Wind assisted ship propulsion (WASP) had been started already in the last decades in the form of wing sails, kites, Flettener rotor etc which could assist auxiliary propulsion of the ships, the application of wind power generator on the ship is not often applied. Therefore this paper has a relevant significance in applying wind electrical energy for the marine electrical power system needs. This paper also reveals the benefits and challenges in the area of onboard wind generation and opens future research possibilities in integrating wind energy into marine industry.

Generation Expansion Planning with High Penetration of Wind Power

2016 ◽  
Vol 17 (4) ◽  
pp. 401-423 ◽  
Author(s):  
Ishan Sharan ◽  
R. Balasubramanian
Keyword(s):  
Wind Power ◽  
Mixed Integer ◽  
Planning Model ◽  
Generation Expansion Planning ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Generation Expansion ◽  
Transmission Expansion ◽  
High Penetration ◽  
Operational Issues

Abstract Worldwide thrust is being provided in generation of electricity from wind. Planning for the developmental needs of wind based power has to be consistent with the objective and basic framework of overall resource planning. The operational issues associated with the integration of wind power must be addressed at the planning stage. Lack of co-ordinated planning of wind turbine generators, conventional generating units and expansion of the transmission system may lead to curtailment of wind power due to transmission inadequacy or operational constraints. This paper presents a generation expansion planning model taking into account fuel transportation and power transmission constraints, while addressing the operational issues associated with the high penetration of wind power. For analyzing the operational issues, security constrained unit commitment algorithm is embedded in the integrated generation and transmission expansion planning model. The integrated generation and transmission expansion planning problem has been formulated as a mixed integer linear problem involving both binary and continuous variables in GAMS. The model has been applied to the expansion planning of a real system to illustrate the proposed approach.

A two-stage skilled manpower planning model with demand uncertainty

2018 ◽  
Vol 11 (4) ◽  
pp. 526-551 ◽  
Author(s):  
Mohsen Sadeghi-Dastaki ◽  
Abbas Afrazeh
Keyword(s):  
Large Scale ◽  
Demand Uncertainty ◽  
Benders Decomposition ◽  
Latin Hypercube Sampling ◽  
Sample Average Approximation ◽  
Manpower Planning ◽  
Mixed Integer ◽  
Planning Model ◽  
Two Stage ◽  
Content Type

Purpose Human resources are one of the most important and effective elements for companies. In other words, employees are a competitive advantage. This issue is more vital in the supply chains and production systems, because of high need for manpower in the different specification. Therefore, manpower planning is an important, essential and complex task. The purpose of this paper is to present a manpower planning model for production departments. The authors consider workforce with individual and hierarchical skills with skill substitution in the planning. Assuming workforce demand as a factor of uncertainty, a two-stage stochastic model is proposed. Design/methodology/approach To solve the proposed mixed-integer model in the real-world cases and large-scale problems, a Benders’ decomposition algorithm is introduced. Some test instances are solved, with scenarios generated by Monte Carlo method. For some test instances, to find the number of suitable scenarios, the authors use the sample average approximation method and to generate scenarios, the authors use Latin hypercube sampling method. Findings The results show a reasonable performance in terms of both quality and solution time. Finally, the paper concludes with some analysis of the results and suggestions for further research. Originality/value Researchers have attracted to other uncertainty factors such as costs and products demand in the literature, and have little attention to workforce demand as an uncertainty factor. Furthermore, most of the time, researchers assume that there is no difference between the education level and skill, while they are not necessarily equivalent. Hence, this paper enters these elements into decision making.

scholarly journals Distributionally Robust Model of Energy and Reserve Dispatch Based on Kullback–Leibler Divergence

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1454 ◽  
Author(s):  
Ce Yang ◽  
Dong Han ◽  
Weiqing Sun ◽  
Kunpeng Tian
Keyword(s):  
Robust Optimization ◽  
Benders Decomposition ◽  
Mixed Integer ◽  
Worst Case ◽  
Total Cost ◽  
Kl Divergence ◽  
Leibler Divergence ◽  
Expected Total Cost ◽  
Distributionally Robust ◽  
Bus System

This paper proposes a distance-based distributionally robust energy and reserve (DB-DRER) dispatch model via Kullback–Leibler (KL) divergence, considering the volatile of renewable energy generation. Firstly, a two-stage optimization model is formulated to minimize the expected total cost of energy and reserve (ER) dispatch. Then, KL divergence is adopted to establish the ambiguity set. Distinguished from conventional robust optimization methodology, the volatile output of renewable power generation is assumed to follow the unknown probability distribution that is restricted in the ambiguity set. DB-DRER aims at minimizing the expected total cost in the worst-case probability distributions of renewables. Combining with the designed empirical distribution function, the proposed DB-DRER model can be reformulated into a mixed integer nonlinear programming (MINLP) problem. Furthermore, using the generalized Benders decomposition, a decomposition method is proposed and sample average approximation (SAA) method is applied to solve this problem. Finally, simulation result of the proposed method is compared with those of stochastic optimization and conventional robust optimization methods on the 6-bus system and IEEE 118-bus system, which demonstrates the effectiveness and advantages of the method proposed.

scholarly journals A loose Benders decomposition algorithm for approximating two-stage mixed-integer recourse models

2020 ◽  
Author(s):  
Niels van der Laan ◽  
Ward Romeijnders
Keyword(s):  
Error Bounds ◽  
Benders Decomposition ◽  
Decomposition Algorithm ◽  
Mixed Integer ◽  
Large Problem ◽  
Two Stage ◽  
Convex Approximations ◽  
Integer Recourse ◽  
Benders Decomposition Algorithm ◽  
Problem Instances

Abstract We propose a new class of convex approximations for two-stage mixed-integer recourse models, the so-called generalized alpha-approximations. The advantage of these convex approximations over existing ones is that they are more suitable for efficient computations. Indeed, we construct a loose Benders decomposition algorithm that solves large problem instances in reasonable time. To guarantee the performance of the resulting solution, we derive corresponding error bounds that depend on the total variations of the probability density functions of the random variables in the model. The error bounds converge to zero if these total variations converge to zero. We empirically assess our solution method on several test instances, including the SIZES and SSLP instances from SIPLIB. We show that our method finds near-optimal solutions if the variability of the random parameters in the model is large. Moreover, our method outperforms existing methods in terms of computation time, especially for large problem instances.

scholarly journals A Long-Term Evaluation on Transmission Line Expansion Planning with Multistage Stochastic Programming

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1899 ◽  
Author(s):  
Sini Han ◽  
Hyeon-Jin Kim ◽  
Duehee Lee
Keyword(s):  
Stochastic Programming ◽  
Transmission Line ◽  
Transmission Lines ◽  
Mixed Integer ◽  
Planning Problem ◽  
Multistage Stochastic Programming ◽  
Dual Decomposition ◽  
Expansion Planning ◽  
Integer Variables ◽  
Progressive Hedging

The purpose of this paper is to apply multistage stochastic programming to the transmission line expansion planning problem, especially when uncertain demand scenarios exist. Since the problem of transmission line expansion planning requires an intensive computational load, dual decomposition is used to decompose the problem into smaller problems. Following this, progressive hedging and proximal bundle methods are used to restore the decomposed solutions to the original problems. Mixed-integer linear programming is involved in the problem to decide where new transmission lines should be constructed or reinforced. However, integer variables in multistage stochastic programming (MSSP) are intractable since integer variables are not restored. Therefore, the branch-and-bound algorithm is applied to multistage stochastic programming methods to force convergence of integer variables.In addition, this paper suggests combining progressive hedging and dual decomposition in stochastic integer programming by sharing penalty parameters. The simulation results tested on the IEEE 30-bus system verify that our combined model sped up the computation and achieved higher accuracy by achieving the minimised cost.

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