For a class of Multi-machine excitation system with transmission delays, an output feedback adaptive quantized control protocol based on k-filter observer has been proposed. Not all the states in Multi-machine excitation power system need to be measured, a class of k-filter observers is constructed to estimate the unmeasured state in the system, and to compensate the estimated error of the neural network approximator, the nonlinearity introduced by the quantizer and external disturbances. The traditional assumption of the upper bound of time-delay function is no longer needed. Instead, time-delay function approximator is used to deal with the influence of transmission delay between excitation systems. This kind of time-delay function approximator is composed of neural network. And according to the finite coverage lemma, it can approximate the unknown function with time delay only by entering a limited number of previous states. Moreover, by the initialization technique, arbitrary small L ∞ tracking error is achieved. Finally, the control signal is quantized by the quantizer and then transmitted to the communication cable, so that the Multi-machine excitation system is realized by computer. A group of experimental results show the effectiveness of the proposed control protocol.

Compared to the step tariff, the real-time pricing (RTP) could be more stimulated for household consumers to change their electricity consumption behaviors. It can reduce the reserve capacity, peak load, and of course the electricity bill, which could achieve the purpose of saving energy. This paper proposes a coordinated optimization algorithm and data-driven RTP strategy in electricity market. First, the electricity price is divided into two parts, basic electricity price and fluctuating price. When the electricity consumption is equal to the average daily electricity consumption, the price is defined as the basic electricity price, which is the clearing electricity price. The consumer electricity data are analyzed. A random forest algorithm is adopted to predict the load data. Optimal adjustment parameters are obtained and the load fluctuation and the fluctuation of the electricity price are further quantified. Secondly, the appliances are modeled. The operation priority is established based on the preferences of customers and the Monte Carlo method is used to form the power load curve. Then, the smart energy planning unit is proposed to optimize the appliances on/off time and running time of residential electrical appliances. An incentive mechanism is used to further standardize the temporary electricity consumption. An improved multiobjective particle swarm optimization (IMOPSO) algorithm is adopted, which adopts the linear weighted evaluation function method to maximize the consumer’s social welfare while minimizing the electricity bill. The simulation proves that the stability of the power grid is improved while obtaining the best power strategy.

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In view of the voltage fluctuation and flicker of distribution network caused by rolling mill type typical load in some distribution networks and the reduction of power quality of power grid, it is necessary to analyze the voltage characteristics of distribution network, and adopt an effective governance measure to suppress harmonics in distribution network and improve power quality of power grid. The mathematical models of several typical loads corrected based on the measured data are established, and the influence radiation range of the corrected rolling mill load model on the distribution network voltage is analyzed, and a SVC (static var compensator) governance measure with SOGI phase-locked strategy is adopted for governance. Finally, combined with the actual operation parameters of a substation of State Grid, the power system simulation model is built by using simulation software, and the effectiveness of the governance measures adopted in this paper is verified by simulation analysis.

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