Based on decreasing the flexibility of the power grid through the integration of large-scale renewable energy, a multi-energy storage system architectural model and its coordination operational strategy with the same flexibility as in the pumped storage power station and battery energy storage system (BESS) are studied. According to the new energy fluctuation characteristics and the different peak valley parameters in the power grid, this paper proposes a electricity heat hydrogen multienergy storage system (EHH-MESS) and its coordination and optimization operational model to reduce the curtailment of wind power and photovoltaic (PV) to the power grid and improve the flexibility of the power grid. Finally, this paper studied the simulation model of an energy storage optimization control strategy after the multi-energy storage system is connected to the distribution networks, and analyzed three operational modes of the multi-energy storage system. The simulation results show that the EHH-MESS proposed in this paper has a better power grid regulation flexibility and economy, and can be used to replace the battery energy storage system based on MATLAB.
In order to improve the efficiency of photovoltaic array, which need maximum tracing power. Perturbation and observation method has become one of the algorithm of the wide range research because of its simple and easy. But the traditional P&O will be oscillation in near the maximum power point due to its fixed step disturbance and leads to some loss of power and in certain circumstances and incorrect judgment will be in steady state. In order to overcome these shortcomings, a variety of improved methods are proposed, and the main improvements of those are summarized. Some simulation experiments have been done for these methods, and these methods are proved to be feasible.
At present, the power flow analysis algorithm of power system has not been paid enough attention. Aiming at the problem of low accuracy and slow speed of conventional power flow algorithm, this paper proposes a new method of Improved Bisection Searching Technique(IBST) for power flow analysis, which is suitable for the active power distribution network. This method is used to find the upper and nasal point of PV curves, and compared with the classical continuous flow method. The search method can improve the calculation accuracy and speed. The IBST can be used to determine the corresponding load growth rate for the first time when the system load flow is not convergent. And the algorithm can quickly identify the range of previous iteration convergence, but the next iteration does not converge. The effectiveness of the proposed method is verified by the analysis of the IEEE30 node system which is added to the photovoltaic energy storage unit.
As the ubiquitous electric power internet of things (UEPIoT) evolves and IoT data increases, traditional scheduling modes for load dispatch centers have yielded a variety of challenges such as calculation of real-time optimization, extraction of time-varying characteristics and formulation of coordinated scheduling strategy for capacity optimization of electric heating and cooling loads. In this paper, a deep neural network coordination model for electric heating and cooling loads based on the situation awareness (SA) of thermostatically controlled loads (TCLs) is proposed. First, a sliding window is used to adaptively preprocess the IoT node data with uncertainty. According to personal thermal comfort (PTC) and peak shaving contribution (PSC), a dynamic model for loads is proposed; meanwhile, personalized behavior and consumer psychology are integrated into a flexible regulation model of TCLs. Then, a deep Q-network (DQN)-based approach, using the thermal comfort and electricity cost as the comprehensive reward function, is proposed to solve the sequential decision problem. Finally, the simulation model is designed to support the validity of the deep neural network coordination model for electric heating and cooling loads, by using UEPIoT intelligent dispatching system data. The case study demonstrates that the proposed method can efficiently manage coordination with large-scale electric heating and cooling loads.
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