An Optimization-Based Supervisory Control and Coordination Approach for Solar-Load Balancing in Building Energy Management

James Allen,Ari Halberstadt,John Powers,Nael H El-Farra

doi:10.3390/math8081215

James Allen, Ari Halberstadt + Show 2 more

Open Access

https://doi.org/10.3390/math8081215

Copy DOI

Abstract

This work considers the problem of reducing the cost of electricity to a grid-connected commercial building that integrates on-site solar energy generation, while at the same time reducing the impact of the building loads on the grid. This is achieved through local management of the building’s energy generation-load balance in an effort to increase the feasibility of wide-scale deployment and integration of solar power generation into commercial buildings. To realize this goal, a simulated building model that accounts for on-site solar energy generation, battery storage, electrical vehicle (EV) charging, controllable lighting, and air conditioning is considered, and a supervisory model predictive control (MPC) system is developed to coordinate the building’s generation, loads and storage systems. The main aim of this optimization-based approach is to find a reasonable solution that minimizes the economic cost to the electricity user, while at the same time reducing the impact of the building loads on the grid. To assess this goal, three objective functions are selected, including the peak building load, the net building energy use, and a weighted sum of both the peak load and net energy use. Based on these objective functions, three MPC systems are implemented on the simulated building under scenarios with varying degrees of weather forecasting accuracy. The peak demand, energy cost, and electricity cost are compared for various forecast scenarios for each MPC system formulation, and evaluated in relation to a rules-based control scheme. The MPC systems tested the rules-based scheme based on simulations of a month-long electricity consumption. The performance differences between the individual MPC system formulations are discussed in the context of weather forecasting accuracy, operational costs, and how these impact the potential of on-site solar generation and potential wide-spread solar penetration.

Highlights

With the concerted efforts in recent times aimed at lowering the usage and consumption of conventional energy resources, the use of renewable energy generation and its integration with the grid have been growing
The performance differences between the individual model predictive control (MPC) system formulations are discussed in the context of weather forecasting accuracy, operational costs, and how these impact the potential of on-site solar generation and potential wide-spread solar penetration
In the remainder of this section, we briefly describe the component subsystems considered in the building, and provide an overview of the corresponding models used in the MPC system implementation

Summary

Introduction

With the concerted efforts in recent times aimed at lowering the usage and consumption of conventional energy resources, the use of renewable energy generation and its integration with the grid have been growing This push towards an aggressive reduction of the carbon footprint of modern living and infrastructure has naturally had a focus on curtailing conventional energy consumption in the larger consuming sectors, i.e., industrial energy use. With the large-scale potential applications of small local distributed generation systems in both the residential and commercial markets, a significant untapped bed of potential renewable energy generation exists and could significantly reduce the need for conventional energy generation in these sectors This topic has received significant attention in recent years (e.g., [2,3,4,5,6,7]). This new marked penetration potential, is dependent on the ability of the existing grid infrastructure to handle this large-scale deployment and integration of these small local renewable generation systems (e.g., [8])

Objectives

Results

Conclusion