The integration of renewable power generation introduces randomness and uncertainties in power systems, and the reactive power optimization with interval uncertainty (RPOIU) problem has been constructed to acquire the voltage control strategy. However, the large amount of uncertain data and the coexistence of discrete and continuous control variables increase the difficulty of solving the RPOIU problem. This paper proposes a data-driven hybrid interval reactive power optimization based on the security limits method (SLM) and the improved particle swarm optimization (IPSO) to solve the RPOIU problem. In this method, the large amount of historical uncertain data is processed by data-driven to obtain the boundary of optimal uncertainty set. The control variable optimization is decomposed into continuous variable optimization and discrete variable optimization. The continuous variables are optimized by applying the SLM with the discrete variables fixed, and the discrete variables are optimized by the IPSO with the continuous variables fixed. The two processes are applied alternately, and the values of the control variables obtained by each method are used as the fixed variables of the other method. Based on simulations carried out for the IEEE 30-bus system with three optimization methods, we verified that the voltage control strategy obtained by the data-driven hybrid optimization could ensure that the state variable intervals satisfied the constraints. Meanwhile, the values of the real power losses obtained by the proposed method were smaller than those obtained by the SLM and IPSO. The simulation results demonstrated the effectiveness and value of the proposed method.

The negotiation behaviours of different traders in the process of direct power purchase by large consumers are simulated by using Multi-agent technology. With the Q-learning algorithm based on previous quotation data, he agent can strengthen its own learning capacity and timely adjust its bid price against its opponent's action. Meanwhile, in order to make sure of the justice of market competition, a two-stage negotiation mechanism of 'negotiations + auction' is proposed, which gives one more opportunity to the generator agent who has a lower reserve price but fails to reach an agreement, due to the underestimation of the situation of the negotiations. It also makes power contract price reflect the real diversity of different generating costs, and can inspire the generators to get the negotiating initiative by lowering their costs. Keywords-power market; Q-learning; bilateral contract; 1-N negotiation; direct power purchase by large consumerI.

Zinc oxide nanoparticles, with the hexagonal flake structure, are of significant interest across a range of applications including photocatalysis and biomedicine. Simonkolleite (Zn5(OH)8Cl2·H2O), a layered double hydroxide, is a precursor...

Many challenging and new issues arise in China's power market process. In this paper the general and specific characteristics of power market research are analyzed and an integrated simulation system (IPMSS) based on experimental economics (EE) and agent-based computational economics (ACE) is designed. Further works include proposing a hybrid experimental method of experimenter and agent, and carrying out with different power market models configured under three experimental approaches. The IPMSS applies a hybrid mode of B/S and C/S with three layers: Client program/Web browser, application server and database server, which has a robust architecture of data communication and simulation interaction. There are three roles -administrator, trainer and trainee, and all experimental process are strictly controlled. The real power market operation scenario is vividly reproduced by the flexible setting of experimental parameters and the visual experimental results. At last a regional power system is taken as an example to verify the feasibility and validity of the proposed simulation system.