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.
Building upon these foundations, this study develops a bi-level robust optimization model for MMG economic dispatch to optimize the energy management system of microgrids under the worst operating conditions, while taking into account the renewable energy uncertainty and load power fluctuation.
The economic cost, network loss, and branch stability index for microgrids are also optimized. The interval optimization is modeled as a Markov decision process (MDP). Then, an improved DRL algorithm called triplet-critics comprehensive experience replay soft actor-critic (TCSAC) is proposed to solve it.
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 dispatch (MIOD) model for microgrids is constructed, in which the uncertain power output of wind and photovoltaic (PV) is represented by interval variables.
This was accomplished by proposing a novel two-stage robust optimization dispatch model that consists of an upper-level robust dispatching model for the multi-microgrid system and a lower
Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the
In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst
Abstract—Traditional prediction-dependent dispatch methods can face challenges when renewables and prices predictions are unreliable in microgrid. Instead, this paper proposes a novel
With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from
The economy and the environmental benefits of microgrid operation are the key to solving the EOD problem. So far, many researchers have developed solutions and strategies to handle the
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
The widespread integration of renewable energy resources (RES) brings significant challenges to microgrid on establishing a dispatch decision due to intrinsic randomness and
Abstract—The integration of renewable energy sources (RES) into microgrids poses challenges to reliable and economic op-eration due to the inherent uncertainty and volatility of RES.
Based on the assumption that the microgrid adopts the grid-connected mode, this study proposes a bi-level robust optimization framework for interconnected system coordination to address
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