Near-zero carbon stochastic dispatch optimization model for power-to-gas-based virtual power plant considering information gap status theory

Abstract

Purpose This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading. Design/methodology/approach In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system. Findings GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP. Research limitations/implications This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT. Originality/value This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

Research on key privacy protection technologies supporting flexible distributed resource layered aggregation transactions

“…VPP or LAT participate in power transactions, involving a wide variety of privacy data types of distributed resources, including user information, basic load data, electricity consumption data, declared electricity prices, etc. [14][15][16]. Users are unwilling to disclose their privacy data because the location and identity information in the data may leak privacy [17][18], and may even be used by criminals.…”