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P2P power trading is necessary for efficiently using consumer electricity not subject to FIT. However, the execution rules for P2P power trading do not include restrictions on voltage, and there is a trade-off between the activation of the P2P power trading market through the mass introduction of PV and the optimization of the voltage of the power distribution system. In addition, there is a tendency for output curtailment to be biased toward consumers connected to the end of the grid. Since consumers cannot choose the interconnection location, there are concerns about unfairness. In this study, we investigate a new P2P model that includes voltage constraints for the execution rules of P2P power trading to avoid voltage deviation while ensuring benefits and fairness for the participants. In the proposed model, to increase the incentive to participate in the P2P power trading market, we consider compensating consumers who receive output curtailment signals due to voltage constraints. In addition, the profit is secured by differentiating the compensation cost unit price depending on the contract's availability. A case study was conducted on this model using the IEEE 33 bus system. The results show that the proposed model is superior. [ABSTRACT FROM AUTHOR]

This paper proposes a model‐free decision algorithm for battery energy storage system (BESS) charging/discharging using deep reinforcement learning (DRL) to regulate off‐grid frequency fluctuation. This method is novel since the frequency regulation problem is cast in an off‐grid system to a deep Q‐network framework, which avoids directly solving a specific optimization model or model‐based control. The advantage of the proposed method is that the agents learn to comprehensively make short‐term forecasts of demand and PV output, analyse trends in forecast errors, and create control signals within the framework of DRL. A reward function with two criteria, frequency target value and frequency constraint value, guides the agent to suppress the frequency fluctuation by adjusting the weight coefficient. The effectiveness of the proposed method is verified using computational simulations with the off‐grid system model and actual power consumption data. The proposed method enables controlling the BESS to satisfy the balance of supply and demand on the off‐grid, thus realizing a control method that does not require predictive information. Moreover, the frequency control can be achieved more accurately in a short computation time than the prediction‐based method using the seasonal auto‐regressive integrated moving average. [ABSTRACT FROM AUTHOR]

This paper describes ramp rate control of a photovoltaic (PV) generator with an electric double-layer capacitor. The capacitor absorbs rapid fluctuations of PV generation, and allows the generator to change its output at a limited ramp rate. The output is mainly determined by a moving average of the PV generation, but the capacitor voltage is also counted to keep the voltage at a specified value, and accordingly, to make the capacitor small. An expression for the capacitor size is derived. The feedback gain of the capacitor voltage is also examined to maintain enough energy storage in the capacitor. Some experiments are executed to demonstrate and validate the proposed method. Index Terms--Capacitor, control, energy storage, photovoltaic (PV) power system, power system dynamics.

A photoconductive sensor made ofhydrogenated amorphous silicon material is proposed as the elemental detector of a matrix-driven linear image sensor array. The sensor contains a gate electrode and has a scanning time one-third of its conventional counterparts. Aderived model is used to evaluate its photoresponse properties after which a good fit is found between computed values and experimental measurements.