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A novel culture evolution learning is presented to achieve an optimal carbon-energy combined-flow (OCECF). A shared responsibility of carbon emission between electricity producers and electricity consumers is introduced in OCECF, such that a double counting of carbon emission can be eliminated. The proposed algorithm is inspired from the culture construction, culture communication, and cultural inheritance in the human society. First, a culture matrix of each country is constructed by the Q-value matrix of Q-learning, which can be simultaneously updated by its people. Second, each country can learn the superior culture from other more-advanced countries, while this so-called culture communication can improve the quality of the obtained optimal solution. Finally, the culture inheritance can be achieved by transfer learning between different optimization tasks, thus the convergence can be dramatically accelerated. The performance of CEA has been evaluated for OCECF on two IEEE benchmark systems, and a practical urban power grid of southern China, respectively. Simulation results demonstrate that CEA has a high convergence stability and fast convergence, which is around 4.02 to 51.43 times faster than that of conventional heuristic algorithms.

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This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China. [ABSTRACT FROM AUTHOR]

This paper builds an optimal carbon-energy combined-flow (OCECF) model to optimize the carbon emission and energy losses of power grids simultaneously. A novel multi-agent cooperative reduced-dimension Q(λ) (MCR-Q(λ)) is proposed for solving the model. Firstly, on the basis of the traditional single-objective Q(λ) algorithm, the solution space is reduced effectively to shrink the size of Q-value matrices. Then, based on the concept of ant cooperative cooperation, multi-agents are used to update the Q-value matrices iteratively, which can significantly improve the updating rate. The simulation in the IEEE 118-bus system indicates that the proposed technique can decrease the convergence speed by hundreds of times as compared with conventional Q(λ), keeping high global stability, which is very suitable for dynamic OCECF in a large and complex power grid compared with other algorithms. [ABSTRACT FROM AUTHOR]