Photovoltaic Uncertainty Research Articles

Kullback–Leibler divergence‐based distributionally robust optimisation model for heat pump day‐ahead operational schedule to improve PV integration

For its high coefficient of performance and zero local emissions, the heat pump (HP) has recently become popular in North Europe and China, which shows potential in absorbing local photovoltaic (PV) generation. In this study, the authors describe a distributionally robust optimisation (DRO)-based HP day-ahead operational schedule model (HP-DOSM) to match the PV power generation, which can well capture the uncertainties of weather, PV, and load prediction errors. Moreover, this DRO-based HP-DOSM can be transformed into a tractable deterministic model. The DRO method they proposed is suitable for linear expectation constrained optimisation whose ambiguity set is constructed using Kullback–Leibler divergence, which could be further transformed into deterministic conic/linear constraints. Compared with robust optimisation (RO) models, the authors’ model is less conservative since more statistical information on the uncertainties is utilised. Numerical tests were conducted to demonstrate its performance, compared with the RO model via Monte Carlo simulations.

Security Assessment of Distribution System with Distributed Photovoltaic

In order to evaluate the safe and stable operation of distribution network with the distributed photovoltaic (PV), the security of distribution network is researched. On the basis of electricity supply security, voltage quality and network losses, the index system of static security is established. The paper simulates the uncertainty and random characteristics of PV by OpenDSS. The typical scenes that PV accessed to the distribution network are designed. The paper summarizes the results of voltage fluctuation and network losses and uses indices to quantify it under different scenes. Based on the index system, the paper proposes some recommendations on PV permeability, interconnected locations, dispersion degrees and power factors when the distributed PV accessed to the distribution network.

Photovoltaic Investment Risk and Uncertainty for Residential Customers

The revenues generated by rooftop photovoltaic (PV) systems have several sources of uncertainty. We use a Monte Carlo framework to explore the sensitivity of PV investment returns to three categories of PV investment uncertainty: 1) interannual solar variability, 2) PV technical performance and maintenance costs, and 3) market risks including future electricity rates and the possibility that retail electricity rates will be restructured for PV customers. We find that PV investment risk and uncertainty is driven by market factors in some U.S. regions (California and Massachusetts) and by the PV technical performance in other U.S. regions (Missouri and Florida). We explore the relative impacts of three methods for reducing PV investment uncertainty: research-and-development-driven performance improvements, system performance guarantees that are common for third-party owned systems, and long-term power purchase contracts. We find that the effectiveness of each risk reduction option varies by region, depending on which factors drive regional PV investment uncertainty.

Dark Shadows

The U.S. Department of Energy's National Renewable Energy Laboratory, Sandia National Laboratories, the Solar Electric Power Association, the Utility Wind Integration Group, and the U.S. Department of Energy hosted a day-long public workshop on the variability of photovoltaic (PV) plants. The workshop brought together utilities, PV system developers, power system operators, and several experts to discuss the potential impacts of PV variability and uncertainty on power system operations.