Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications

Abstract

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference. Finally, this paper thoroughly discusses the diversified application scenarios of virtual collection, hoping to provide helpful information for the development of the DPVS industry.

Presenting a Stochastic Model of Simultaneous Planning Problem of Distribution and Subtransmission Network Development considering the Reliability and Security Indicators

“…Te limitations of the network indicators such as the transmission power of the lines and the voltage of the buses are also shown in constraints (17) and (18), respectively [24,25]. It is worth mentioning that the limit of posts, DGs [26][27][28], and capacitors are also present in relations ( 19)-( 21), respectively [20].…”

Optimal Operation of Distributed Generations in Four-Wire Unbalanced Distribution Systems considering Different Models of Loads

“…Terefore, this scheme includes an optimization formulation. Te optimization work contains objective functions [19][20][21][22][23][24][25][26][27] and constraints [28][29][30][31][32][33][34][35][36]. Also, to apply optimization to the distribution network, smart technologies [37][38][39][40][41] and telecommunications equipment [42][43][44][45] should be used in the network.…”

Risk‐constrained two‐stage scheduling of a virtual energy hub integrated with intelligent parking lots in the presence of electrical and thermal energy markets and responsive loads

“…The power of PV and ST units can be calculated using solar irradiation, system efficiency and surface area, as determined by (12) and (13), respectively [30, 31]. $$$$\begin{equation}{{\mathrm{P}}}_{{\mathrm{PV}}}\left( {{\mathrm{t,s}}} \right) = {{\mathrm{A}}}_{{\mathrm{PV}}} \times {{{\eta}}}_{{\mathrm{PV}}} \times {\mathrm{I}}\left( {{\mathrm{t,s}}} \right),\end{equation}$$$$ $$$$\begin{equation}{{\mathrm{P}}}_{{\mathrm{ST}}}\left( {{\mathrm{t,s}}} \right) = {{\mathrm{A}}}_{{\mathrm{ST}}} \times {{{\eta}}}_{{\mathrm{ST}}} \times {\mathrm{I}}\left( {{\mathrm{t,s}}} \right).\end{equation}$$$$…”

“…The power of PV and ST units can be calculated using solar irradiation, system efficiency and surface area, as determined by ( 12) and ( 13), respectively [30,31].…”