A microgrid (MG) is an independent energy system catering to a specific area, such as a college campus, hospital complex, business center, or neighbourhood (Alsharif, 2017a, Venkatesan et
A low-carbon economic dispatch model of a multi-microgrid–integrated energy system is constructed based on the upper energy storage capacity, charge and discharge power, and user-side demand response with the lowest annual
This paper proposes a microgrid adaptive robust optimal dispatch model with different robust adjustment parameters. The robust equivalent characterization method is used to convert uncertain parameters
This paper presents an improved deep reinforcement learning (DRL) algorithm for solving the optimal dispatch of microgrids under uncertaintes. First, a multi-objective interval optimization
In order to minimize the operating cost and gaseous pollutant emission of the multi-microgrid system, which is composed of renewable energies and electric vehicles and so on, this paper builds a 24 hours day-ahead multi-objective
According to the optimization method, the optimization dispatch method of microgrid can be divided into deterministic method and uncertainty method. The deterministic method takes the predicted value of renewable distributed power as an accurate known quantity and then optimizes the dispatch of the microgrid.
First, a multi-objective interval optimization dispatch (MIOD) model for microgrids is constructed, in which the uncertain power output of wind and photovoltaic (PV) is represented by interval variables. The economic cost, network loss, and branch stability index for microgrids are also optimized.
By increasing the lower bound of the loop, the upper and lower bounds of the Benders algorithm can reach the same value faster, and the final optimization result can be obtained faster. This paper proposes a microgrid adaptive robust optimal dispatch model with different robust adjustment parameters.
As a core technology of microgrid, optimal dispatching of the microgrid is an important support to deal with the uncertainty of renewable energy and load and ensure the economic and reliable operation of the microgrid [5, 6]. Regarding the optimal dispatch of microgrids, a large number of references have been studied.
In order to cope with the microgrid optimization scheduling problem of combined cooling, heating, and power (CCHP), reference applies a coordinated adaptive robust optimization method with multiple time scales to optimize microgrids with multiple different energy types.
A two-stage robust optimization model is established to minimize the operating cost of the microgrid under the premise of ensuring the robustness of the microgrid. An improved Benders algorithm is proposed to solve the established optimization model.
