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.

With the increasing penetration of distributed generations (DGs) and flexible loads in the distribution network (DN), the existing DN planning methods need an improvement. This paper proposes a two-layer multistage expansion planning method for DN adaptability enhancement, where the optimal construction/upgrade strategy of substations and lines along with the optimal PV configuration strategy are considered. A two-layer adaptive planning model is established and solved by the non-dominated sorting genetic algorithm II (NSGA II). The obtained Pareto-optimal front represents a set of optimal planning schemes meeting the demand of DN economy and adaptability, which is of great significance in theory and practice.