Sampling-Based Model Predictive Control of PV-Integrated Energy Storage System Considering Power Generation Forecast and Real-Time Price

Juan Ospina,Gwen Lofman,Mario Harper,M Omar Faruque,Rick Meeker,Nikhil Gupta,Emmanuel G Collins,Alvi Newaz

doi:10.1109/jpets.2019.2935703

Abstract

This paper proposes a novel control solution designed to solve the local and grid-connected distributed energy resources (DERs) management problem by developing a generalizable framework capable of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while minimizing the overall cost. The strategy developed aims to find the ideal combination of solar, grid, and energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system. Both offline and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP), and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when compared to the other baseline control algorithms.

Highlights

A N unprecedented growth of distributed energy resources (DERs), especially PV and wind, has driven the decentralization of power systems and the increase in deployment of distributed generation (DG) and distributed storage (DS) systems by both utility companies and consumers
Contrasting from the papers examined, the solution proposed in this paper provides a different approach to solve the online energy management (EM) scheduling problem using a graph-search approach that takes into account the current and future status of the system to find the optimal planned path solutions that minimize the cost of using the available DER
EXPERIMENTAL RESULTS For a thorough comparison, the performance of the Sampling-Based Model Predictive Control (SBMPC) controller is evaluated against other baseline cases such as two manual control cases and four optimization algorithms (genetic algorithm (GA), particle swarm optimization (PSO), QP interior-point method (QP-IP), and sequential quadratic programming (SQP))

Summary

Introduction

A N unprecedented growth of DERs, especially PV and wind, has driven the decentralization of power systems and the increase in deployment of distributed generation (DG) and distributed storage (DS) systems by both utility companies and consumers. Energy storage systems are becoming more competitive, and companies are starting to heavily invest in the development of lithium-ion batteries, thermal storage, and other types of DS systems to decrease energy costs and stabilize the distribution system. DG and DS systems have the potential of becoming the cornerstone of the future smart grid. These systems are still not ready for a harmonious integration to the grid due to their lack of proper control and intermittent nature [1]. Translations and content mining are permitted for academic research only

Methods

Results

Conclusion