Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems

Can, Li; Conejo, Antonio J.; Liu, Peng; Omell, Benjamin; Siirola, John Daniel; Grossmann, Ignacio E.

doi:10.1016/j.ejor.2021.06.024

Cited by 72 publications

(29 citation statements)

References 35 publications

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“…Mixed-integer linear programming (MILP) model is exerted for finding the optimal answer for this issue. In [21] a mixed-integer linear programming formulation is provided for the generation and transmission expansion planning problem simultaneously, which is resolved by a nested benders decomposition and a tailored benders decomposition algorithm in the case study of Texas.…”

Section: Introductionmentioning

confidence: 99%

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

Jalalzad¹,

Yektamoghadam

Haghighi

et al. 2022

Energies

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In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching–learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

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Section: Introductionmentioning

confidence: 99%

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

Jalalzad¹,

Yektamoghadam

Haghighi

et al. 2022

Energies

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“…The performance of day-ahead planning and real-time operation supported by the energy storage system is analyzed in [ 17 ]. The robust two-stage energy management and sharing model is presented in [ 18 ]. The first stage solved the problem of energy sharing schedule and price uncertainties by converting bi-level optimization into mixed-integer linear programming.…”

Section: Introductionmentioning

confidence: 99%

“…The first stage solved the problem of energy sharing schedule and price uncertainties by converting bi-level optimization into mixed-integer linear programming. The second stage is applying an online mechanism for scheduling and imposing penalties on deviated prosumers, who try to adjust their preceding schedule [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Stochastic adaptive-service level agreement-based energy management model for smart grid and prosumers

Ahmed

Khan²,

Ullah³

et al. 2022

PLoS ONE

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The growing issue of demand-supply management between the prosumers and the local energy market requires an efficient and reliable energy management model. The microlayers, such as prosumers, energy districts, and macro players, namely retail dealers and wholesale dealers play a pivotal role in achieving mutual benefits. The stochastic nature of renewable energy generation in energy districts requires an effective model that can contemplate all stochastic complexities. Therefore, this paper proposes a mutual trade model between energy districts and smart grid to authorize the prosumers for mutual energy transactions under the stochastic adaptive-service level agreement. Moreover, multiple smart contacts are developed between the stakeholders to design adaptability and stochastic behavior of wind speed and solar irradiance. The real-time adaptations of the stochastic adaptive-service level agreement are based on technical beneficial feasibility and achieved through stochastic and adaptive functions. The optimized solution based on a genetic algorithm is proposed for the energy cost and energy surplus of prosumers and output parameters of the mutual trade model (grid revenue). In the context of mutual benefits associated with balanced demand and supply, the economic load dispatch and simplex method maximization are used for optimized demand-supply energy management. Moreover, the effectiveness of the proposed adaptive and stochastic mutual trade model is validated through simulation and statistical analysis.

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“…These two incentives have also aroused the attention of many scholars in the research field. In the expansion planning studies, Wang et al [18], Li et al [9], Lin et al [12] and Li et al [17] introduced the carbon tax (CTX) on the source side, while Tao et al [23] imposed the carbon tax on the demand side. Wang et al [24] considered both source-side and demand-side carbon responsibility and introduced the carbon tax.…”

Section: Introductionmentioning

confidence: 99%

“…To achieve a better planning effect, scholars usually consider coordinated planning research on power generation expansion planning (GEP), transmission expansion planning (TEP), and storage expansion planning (SEP), for example, GEP-TEP [8,9], GEP-SEP [10][11][12][13], and TEP-SEP [14]. Among them, most scholars pay attention to the GEP-SEP coordinated optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A bi‐layer stochastic coordinated planning framework for wind‐battery power systems considering bilateral carbon trading

Nan

Feng

Deng

et al. 2022

IET Generation Trans & Dist

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Renewable energy and energy storage are essential technologies for decarbonizing energy systems. Expansion planning of the two technologies considering source-side carbon responsibility has been well studied. However, expansion planning considering both source-side and load-side carbon responsibility, which may simultaneously stimulate the carbon reduction potential of source and load, has been rarely studied. To fill this research gap, this paper proposes a bi-layer wind turbine generator (WTG) and demand-side battery (DSB) coordinated planning framework considering wind power and load uncertainties. Moreover, a novel source-load bilateral carbon trading (BCT) mechanism based on the Aumann-Shapley method is proposed to stimulate the WTG and DSB configuration. In the bi-layer planning framework with BCT mechanism, WTG and DSB capacity planning with the two-stage stochastic optimization are conducted in the upper and lower layer, respectively. Finally, test results on a modified IEEE 24-bus system demonstrate that the proposed framework with BCT mechanism can effectively motivate the planning configuration of WTG and DSB and carbon mitigation. Compared with the models with unilateral carbon trading mechanisms, the proposed model has advantages in stimulating configuration of WTG and DSB, reducing carbon emissions, and balancing source-load economic burden from carbon trading.

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Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems

Cited by 72 publications

References 35 publications

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

Stochastic adaptive-service level agreement-based energy management model for smart grid and prosumers

A bi‐layer stochastic coordinated planning framework for wind‐battery power systems considering bilateral carbon trading

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