Adjustable robust optimization in enabling optimal day-ahead economic dispatch of CCHP-MG considering uncertainties of wind-solar power and electric vehicle

Abstract

At present, electric vehicles (EVs), small-scale wind power, and solar power have been increasingly integrated into modern power system via the combined cooling heating and power based microgrid (CCHP-MG). However, inside the microgrid the uncertainties of EVs charging, wind power, and solar power significantly impact the economy of CCHP-MG operation. Therefore to improve the economy deteriorated by the uncertainties, this paper presents a two-stage adjustable robust optimization to achieve the minimal operational cost for CCHP-MG. Before the realizations of the uncertainties, the day-ahead stage as the first stage decides an operational strategy that can withstand the worst-case uncertainties. As long as the uncertainties are observed, the realtime stage as the second stage adjusts the operational units to compensate the errors caused by the day-ahead operational strategy. Due to the difficulties of the model solution, this paper further adopts the duality theory, Big-M method, and column-and-constraint generation (C&CG) decomposition to convert the model into two tractable mixed integer linear programming (MILP) problems. Further, C&CG iteration algorithm is also employed to solve the MILPs, which can ultimately provide an optimal economic day-ahead dispatch strategy capable of handling uncertainties. The experimental results demonstrate the effectiveness of the presented approach.

A two‐stage adaptive‐robust optimization model for active distribution network with high penetration wind power generation

“…In [23][24][25], a box uncertainty set considering the temporal smoothing effects of multiple wind generations was used to form a robust energy dispatching model of the combined wind-photovoltaic-pumped storage system and improve the accommodation of renewables. In [26], an uncertain set budget is used to restrict the number of worst-case realizations for the uncertainties of wind-solar power generation and EV charging load demand, and an adjustable robust optimization model was established to achieve the minimal operational cost of combined cooling, heating, and power based microgrids. For [27], the temporal coupled uncertainty set was also used to regulate the conservativeness of uncertain electric, heating, cooling and hydrogen demands, and a robust and stochastic hybrid optimization model was presented for the planning of electricity-hydrogen integrated energy systems.…”

“…In summary, there are either spatially coupled [20][21][22] or temporally coupled [23][24][25][26][27] uncertain sets for the RO model. However, simultaneously considering the spatially and temporally coupled correlations of uncertain renewables in RO of distribution networks are not common.…”

Modeling and Optimization of Pure Electric Vehicle Path Planning-Siting Based on Enterprise Self-built Microgrid and Energy Storage System

“…Duarte et al (2020) [3] studied a multi-process production scheduling based on on-site renewable energy supply with energy storage system and grid as backup, meanwhile the impact of time and level of use policy on power purchase cost of the national grid supply price was considered. Chen et al (2017) [1] artificially reduced the impact of uncertainty within the microgrid due to electric vehicle charging, wind power and solar power generation on the CCHP-MG operational economics, a two-stage adjustable robust optimization method was proposed to achieve the minimum operating cost of CCHP-MG.…”