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Combined heat and power system intelligent economic dispatch: A deep reinforcement learning approach
توزیع اقتصادی سیستم گرمایشی و هوشمند: یک رویکرد یادگیری تقویتی عمیق-2020 This paper proposed a Deep Reinforcement learning (DRL) approach for Combined Heat and Power (CHP)
system economic dispatch which obtain adaptability for different operating scenarios and significantly decrease
the computational complexity without affecting accuracy. In the respect of problem description, a vast of
Combined Heat and Power (CHP) economic dispatch problems are modeled as a high-dimensional and nonsmooth
objective function with a large number of non-linear constraints for which powerful optimization algorithms
and considerable time are required to solve it. In order to reduce the solution time, most engineering
applications choose to linearize the optimization target and devices model. To avoid complicated linearization
process, this paper models CHP economic dispatch problems as Markov Decision Process (MDP) that making the
model highly encapsulated to preserve the input and output characteristics of various devices. Furthermore, we
improve an advanced deep reinforcement learning algorithm: distributed proximal policy optimization (DPPO),
to make it applicable to CHP economic dispatch problem. Based on this algorithm, the agent will be trained to
explore optimal dispatch strategies for different operation scenarios and respond to system emergencies efficiently.
In the utility phase, the trained agent will generate optimal control strategy in real time based on current
system state. Compared with existing optimization methods, advantages of DRL methods are mainly reflected in
the following three aspects: 1) Adaptability: under the premise of the same network topology, the trained agent
can handle the economic scheduling problem in various operating scenarios without recalculation. 2) High
encapsulation: The user only needs to input the operating state to get the control strategy, while the optimization
algorithm needs to re-write the constraints and other formulas for different situations. 3) Time scale flexibility: It
can be applied to both the day-ahead optimized scheduling and the real-time control. The proposed method is
applied to two test system with different characteristics. The results demonstrate that the DRL method could
handle with varieties of operating situations while get better optimization performance than most of other
algorithms. Keywords: Combined heat and power economic dispatch | Deep reinforcement learning | Proximal policy optimization |
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