Data Driven Stochastic Energy Management for Isolated Microgrids Based on Generative Adversarial Networks Considering Reactive Power Capabilities of Distributed Energy Resources and Reactive Power Costs

Shady M Sadek,Hossam E A Talaat,M A Moustafa Hassan,Walid A Omran

doi:10.1109/access.2020.3048586

Abstract

This paper proposes a two-stage stochastic energy management (EM) in an isolated microgrid (MG) to decide for the day-ahead optimal dispatch. The dispatch aims to effectively manage the MG power sources, including intermittent renewable energy sources (RESs), battery energy storage systems (BESSs), and diesel generators such that the expected operation costs, reactive power costs, spinning reserve, and load shedding are minimized. The Generative Adversarial Networks (GANs) is utilized in this paper as a data driven scenario generation method to model the uncertainties in the output power of the RESs to be used in the stochastic programming formulation. Then, the fast forward scenario reduction algorithm is used to reduce the number of scenarios with the help of SCENRED/GAMS software. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations in the dispatches from the optimal solutions. Hence, this paper co-optimizes the costs related to both active and reactive powers of diesel generators. In addition, this study considers the reactive power capability of inverter-interfaced distributed energy resources (DERs). Moreover, the detailed models for the different resources are presented, especially for diesel generators where the actual capability curves are used instead of the widely used box constraints. The problem is formulated as a nonlinear programming problem in the General Algebraic Modeling System (GAMS) software and is solved by the CONOPT solver.

Highlights

Uncertainty in power systems has a significant impact on the optimal decisions in both the planning and operation stages
The optimization problem is solved with neglecting/considering the reactive power costs of the diesel generators while neglecting/considering the reactive power capabilities from inverter interfaced distributed energy resources (DERs) to investigate their impact on the MG operation while considering renewable energy sources (RESs) uncertainties
The operation costs of diesel generators usually include fuel costs related to active power only without considering those related to reactive power costs

Summary

INTRODUCTION

Uncertainty in power systems has a significant impact on the optimal decisions in both the planning and operation stages. The uncertainties from RESs were not considered which may affect the optimal dispatch and the calculated operation cost results. In the aforementioned studies, uncertainties in RESs were considered in the EM model but the contribution of the reactive power from inverter interfaced distributed energy resources (DERs) was not considered. A two-stage stochastic optimization is proposed for solving the network-constraint multi-period day-ahead energy management problem in an isolated MG to decide for the optimal dispatch and the expected redispatch in the operation stage. Generative Adversarial Networks (GANs) technique is utilized as a model-free scenario generation method to model RESs uncertainties This technique avoids the disadvantages of the model based techniques as it does not require specifying models or fitting probability distributions.

UNCERTAINTY MODELING OF RENEWABLE ENERGY RESOURCES

TEST SYSTEM DESCRIPTION

RESULTS AND DISCUSSIONS

CONCLUSION