This paper proposes a series of new metrics for the reliability and economic assessment of microgrids in distribution system. These metrics include reliability parameters for a microgrid in the islanded mode, indices indicating distributed generation (DG) and load characteristics in the microgrid, microgrid economic indices, and customer based microgrid reliability indices. A two-step Monte Carlo simulation (MCS) method is proposed to assess the reliability and economics of a microgrid with intermittent DGs as well as the reliability of distribution system with microgrids. An application in IEEE-RBTS shows the effectiveness of the reliability and economic assessment technique with the proposed metrics, which can provide scientific and comparative information for the design and operation of microgrids.

The widely adoption of distributed renewable energy sources (DREs) effectively reduces carbon emission and beat atmospheric haze in developing countries. However, random disturbance issues emerge in power grids with DREs when applying traditional centralized automatic generation control (AGC) strategies. Therefore, a multi-agent distributed control strategy is proposed for AGC in this work, which is mainly based on the concept of deep reinforcement learning, and developed by the strategy of action discovery. Moreover, area control error and the amount of carbon emission are employed in reward functions to obtain optimal solutions in the implementing process of the proposed strategy. Simulations are provided in the work to show the effectiveness of the strategy, while comparisons are also offered, where the simulating results obtained by two other intelligent AGC algorithms are used as references, according to which, the superiority of the proposed strategy is confirmed.

The increasing penetration of photovoltaic (PV) systems presents significant impacts on distribution network (DN) operations. To this end, an accurate evaluation of PV hosting capacity (PVHC) can help utilities to carry out PV capacity planning and configuration more effectively, ensuring the efficiency, economy, and reliability of DNs. First, the definition of interval overvoltage probability (IOP) is introduced and the IOP based PVHC evaluation method is presented. Next, this method is improved considering PV and load uncertainties, where the interval arithmetic (IA) and affine arithmetic (AA) are both applied to deal with uncertainties. In addition, the overvoltage severity is taken into account on the basis of IOP, and the concept of interval overvoltage risk (IOR) is proposed. A practical 55-bus rural feeder in China is used to illustrate the advantages of the proposed method compared with the conventional method, and also to verify the value in decision-making of PV planning for utilities. Moreover, the sensitivity of interval valued PVHC against the number of Monte Carlo simulation (MCS) and width of PV installation capacity interval (PICI) is analyzed. And the impacts of on-load tap changer (OLTC) tap positions and static var compensator (SVC) configurations are also explored. Finally, the proposed method is tested on the NYSEG 292-bus system to validate its adaptability on a larger and unbalanced system.

The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.