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This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

The randomness and volatility of wind power generation are the main reasons restricting the capacity of power grid to absorb wind power. Thermal power units are the main force of power grid frequency modulation. The speed of load adjustment rate determines their response ability to power grid load. Based on the analysis of the characteristics of wind power generation, the nonlinear multiscale decomposition of the automatic power generation control (AGC) load command is carried out. Combined with the different load regulation rates of different types of units, the rational allocation of unit combinations can effectively ensure the power grid load regulation capacity and compensate the random disturbance of wind power.

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In order to enhance the ability of thermal unit to participate in fault recovery under the large-scale wind power consumption and grid’s power loss, the power grid emergency optimization control method based on wind power and thermal power adjustable capacity is proposed. Based on the analysis of the influencing factors of wind power, considering the matching operating conditions of wind power output, the strategy of unit lockout control under grid over-frequency is proposed. According to the regional control error(ACE) of the power grid, one power emergency control method based on one-key climbing is constructed. The scheme can make full use of the thermal power unit load regulation space and short-term rapid adjustment rate to improve the grid’s ability to cope with large power loss and ensure grid frequency stability.

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A novel identification method for critical oscillation mode is presented, which makes use of inter-area oscillation and relevant generator contribution factor. Firstly, the relevance between oscillation mode and electric parameters is analyzed. A state equation method is introduced to describe strongly relevant generator sets in multi-machine power system. Secondly, the oscillation assessing index is designed to calculate the risk of the rotor angle instability. And then, oscillation contribution factor, which concerns risk of inter-area oscillation relevant generators, is raised by means of synthesizing parameters like amplitude, damping ratio and attenuation obtained by Prony algorithm. The specified procedures of identifying critical oscillation mode are proposed based on the risk factor. Finally, simulation results of IEEE 10-machine 39-bus system and Sichuan power grid show that: the proposed method is able to identify critical oscillation mode and assess oscillation risk of the strongly relevant generators under different disturbance. Comparing the obtained result with that from energy class of oscillation modes verifies the feasibility and effectiveness of the proposed method.