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

Abstract

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.

Cloud Energy Storage Investment by Collaboration of Microgrids for Profit and Reliability Enhancement Considering a TSO-DSO Yearly Reward

“…It is worth mentioning that other evolutionary algorithms, such as GA and PSO, can be applied instead of TLBO. TLBO is based on a teacher's influence on students' results in a class [48]. In the proposed optimization model (equations ( 1)-( 22)), because of considering power flow-related constraints, the problem is nonlinear and non-convex.…”

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

“…The TLBA has been previously applied in an efficient way for several engineering optimization problems [28]. Some examples of these successful implementations are reactive power control in electrical systems [29], service restoration in distribution feeders [30], Tsallis-entropy-based feature selection classification [31], generation expansion-planning problem [32], design of passive filters [33], dissimilar resistance spot-welding process [34], water supply pipe condition prediction [35], robot manipulator calibration [36], harmonic elimination in multi-level inverters [37], operation analysis of a grid-connected photovoltaic (PV) with battery system [38] and parameter extraction of PV modules [39,40]. The abovementioned advantages of the TLBA and its successful applications in a wide array of engineering problems are the main reasons for the selection of the TLBA in this article.…”

Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms

“…The GA is used in many applications, such as game theory [ 29 ], polymer design [ 30 ], multi-objective problems [ 31 ], lung cancer prognosis [ 32 ], wind power prediction [ 33 ], social networks [ 34 ], combustion engine [ 35 ], photovoltaic systems [ 36 ], task scheduling [ 37 ], traffic flow model [ 38 ], automotives [ 39 ], heat transfer [ 40 ], Complex networks [ 41 ], traffic management [ 42 ], Plasticity Echo State Network [ 43 ], prostate cancer [ 44 ], pruning for neural network [ 45 ], wireless sensor networks [ 46 ], Virtual machine [ 47 ], electric vehicles [ 48 ], IOT network topologies [ 49 ], stock market forecasting model [ 50 ], and task assignments to agents [ 51 ].…”