Preprint Article Version 1 NOT YET PEER-REVIEWED

Active Power Dispatch Optimization for a Grid-Connected Microgrid with Uncertain Multi-Type Loads

Kai Lv 1 , Hao Tang 1,* , Yijin Li 1 , Xin Li 1
  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Version 1 : Received: 2 September 2016 / Approved: 2 September 2016 / Online: 2 September 2016 (11:23:55 CEST)

How to cite: Lv, K.; Tang, H.; Li, Y.; Li, X. Active Power Dispatch Optimization for a Grid-Connected Microgrid with Uncertain Multi-Type Loads. Preprints 2016, 2016090005 (doi: 10.20944/preprints201609.0005.v1). Lv, K.; Tang, H.; Li, Y.; Li, X. Active Power Dispatch Optimization for a Grid-Connected Microgrid with Uncertain Multi-Type Loads. Preprints 2016, 2016090005 (doi: 10.20944/preprints201609.0005.v1).

Abstract

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

Subject Areas

multi-type loads; active power dispatch optimization; simulated-annealing Q-learning

Readers' Comments and Ratings (0)

Discuss and rate this article
Views 109
Downloads 127
Comments 0
Metrics 0
Discuss and rate this article

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.