Optimal Techno-Economic Planning of a Smart Parking Lot—Combined Heat, Hydrogen, and Power (SPL-CHHP)-Based Microgrid in the Active Distribution Network

By installing distributed generation (DG) sources in a distribution system, there is a change from the inactive state, accompanied by one-way power flow, to the active state, with the possibility of bilateral power flow. Authorities involved in the electricity industry manage the consumption side by bringing in particular programs called demand response programs. To implement these programs, it is crucial to create infrastructure, including the installation of smart measuring units in the consumption sector. In this paper, we investigate the optimal design of smart meters and combined hydrogen, heat, and power in the active distribution system to provide two functions aimed at reducing voltage drop and minimizing the total planning costs by taking different scenarios into account. In the combined hydrogen, heat, and power (CHHP)-based DGs, due to the low efficiency of the electrolyzer, its power is supplied by a smart parking lot (including wind turbines, photovoltaic systems, and batteries). To model the unit’s uncertainties, a long short-time memory (LSTM) model is employed. Utilizing the technique for order preference by similarity to ideal solution (TOPSIS), a state that enhances both functions is acquired from different scenarios. All of the simulations are carried out in two 33-bus systems.

Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations

In this study, the forecasting-aided state estimation (FASE) problem for the active distribution system (ADS) with distributed generations (DGs) is investigated, considering the constraint of data transmission. First of all, the system model of the ADS with DGs is established, which expands the scope of the ADS state estimation from the power network to the DGs. Moreover, in order to improve the efficiency of data transmission under the limited communication bandwidth, a component-based event-triggered mechanism is employed to schedule the data transmission from the measurement terminals to the estimator. It can efficiently reduce the amount of data transmission while guaranteeing the performance of system state estimation. Second, an event-triggered unscented Kalman filter (ET-UKF) algorithm is proposed to conduct the state estimation of the ADS with mixed measurements. To this end, the unscented transform (UT) technique is employed to approximate the probability distribution of the state variable after nonlinear transformation, which can reach more than second order, and then, an upper bound of the filtering error covariance is derived and subsequently minimized at each iteration. The gain of the desired filter is obtained recursively by following a certain set of recursions. Finally, the effectiveness of the proposed method is demonstrated by using the IEEE-34 distribution test system.