Elsevier

Applied Energy

Volume 161, 1 January 2016, Pages 279-289
Applied Energy

A peak-load reduction computing tool sensitive to commercial building environmental preferences

https://doi.org/10.1016/j.apenergy.2015.10.009Get rights and content

Highlights

  • Optimal control of each thermal zone’s cooling set points.

  • Relationship between peak load savings, internal/external loads and PMV index analyzed.

  • Extended DR and slow restoration of zones to normal operation reduces demand rebound.

Abstract

Demand Response (DR) as an option for electric utility peak load management has gained significant attention in the recent past as it helps to avoid stress conditions and possibly defer or avoid construction of new power generation, transmission and distribution infrastructures. DR in commercial buildings can play a major role in reducing peak load and mitigate network overloading conditions. Small and medium-sized commercial buildings have not historically played much role as a DR resource both due to lack of hardware and software tools and awareness. This paper presents a peak load reduction computing tool for commercial building DR applications. The proposed tool provides optimal control of building’s cooling set points with the aim to reduce building’s peak load, while maintaining occupant comfort measured by the Predicted Mean Vote (PMV) index. This is unlike other studies which use global cooling set point adjustment resulting in an uneven distribution of occupant satisfaction across the building. The approach is validated by experimentation conducted on a simulated medium-sized office building, which reflects an existing commercial building in Virginia, USA. Research findings indicate that the proposed methodology can effectively reduce the simulated building’s peak load and energy consumption during a DR event, while maintaining occupant comfort requirements. The paper also addresses the issue of rebound peaks following a DR event, and offers a means to help avoid this situation.

Introduction

Demand Response (DR), as an option for electric utility peak load management, has gained significant attention in the recent past which can decrease both energy and power consumptions [1], [2]. Most large commercial buildings (100,000 sq. ft. or more) are equipped with Energy Management Systems (EMS) which provide opportunities for peak load reduction [3], [4]. The use of EMS is not widespread in small and medium-sized commercial buildings (<100,000 sq. ft.) [3]. Due to limited availability of DR methods and tools, building owners typically miss building specific DR opportunities [5]. They do not approach DR strategy development systematically and are unable to correctly estimate DR effectiveness [6], [7]. The best DR strategies for any building should take into account potential for peak load reduction, electrical energy savings, customer comfort and economics [8]. Overly stringent Heating, Ventilation and Air Conditioning (HVAC)-based DR requirements not only affect occupant comfort and convenience but also create a new peak at off-peak times when DR events end [8]. This new peak also needs to be taken into account for any DR planning. However, building owners usually perform minimal analysis of their load data and adapt DR strategies as described in [8].

In this study, a particular attention is given to the HVAC load. Authors in [9] identify that commercial buildings in the U.S. are usually overcooled and it is a cultural practice to purposely set a low temperature. Authors in [8] discuss HVAC-based DR strategies including global temperature adjustment of zones and systemic adjustments to the air distribution and cooling systems. Findings in [10], [11], [12] indicate that among HVAC based DR strategies, global temperature adjustment of zones, where entire facility cooling set points are raised to some absolute values, best achieves DR goal. Authors in [13] summarize energy savings results from different case studies involving global summer set point temperature increase. However, authors in [14] show that applying global set point changes during peak hours with pre-cooling efforts to a large multi zone commercial building results in poor distribution of HVAC capacity across zones and an uneven distribution of occupant satisfaction across the building. Authors in [14] design a HVAC control strategy to adapt a building’s DR on a zone-by-zone basis for planning in advance HVAC operation to balance energy costs, greenhouse gas emissions and occupant thermal comfort.

A recent study indicates that building occupants rank thermal comfort to be of similar importance to visual and acoustic comfort and indoor air quality [15]. Authors in [16] present a review of human thermal comfort in the built environment and identify a gap in thermal comfort studies in relation to interdisciplinary research. Studies [17], [18], [19] propose thermostat strategies to understand the trade-off between energy consumption and thermal comfort. Authors in [20] investigate the relationship between building insulation and air conditioning unit supply air temperature to provide better comfort. Authors in [21] identify various factors affecting building thermal comfort and building material to improve it. Authors in [22] investigate energy saving potential of PMV based control in glass façade buildings and suggest careful design of components of glass facade can achieve thermal comfort and energy savings. Authors in [23] control the indoor air velocity to maintain the PMV index within thermal comfort range to achieve energy savings. Authors in [24] perform thermal comfort simulation by integrating building thermal behavior analysis with PMV thermal comfort model to identify appropriate low-energy cooling opportunities which achieve better thermal comfort. Authors in [25] assess summer comfort of a modeled building which depends upon thermal performance of the building envelope with external climate and internal gains and losses which intervene with comfort criteria. Authors in [26] develop a living space thermal-comfort regulator which maintains PMV index within specified limits.

Literature review reveals that an optimal control of each thermal zone’s cooling load is needed since all thermal zones do not behave the same, they may not be able to evenly share the DR shed burden. Higher increase in the cooling set points for zones with high solar gains drastically effects occupant thermal comfort. To address the above knowledge gaps the authors propose to design a peak load reduction computing tool for commercial building DR which provides optimal control of each thermal zone’s cooling load in a medium-sized office building and an insight into how thermal comfort is related to peak load and energy consumption. The tool maximizes building’s economic benefits while being sensitive to occupant needs. Following the DR event, HVAC systems use extra energy to remove the heat gained during reduced service levels of DR event to bring back the system to normal conditions and hence experience rebound. This rebound is investigated and a means to mitigate this impact is suggested.

Section snippets

Methodology of the study

The study presents a peak load reduction computing tool for commercial building DR – validated by simulation – and investigates the impact of raising cooling set point schedule for a simulated medium-sized office building during DR event on each thermal zone’s PMV index, building peak load and energy consumption. The study has been performed for the summer season when cooling load is high during afternoon hours in an office building. In this study, EnergyPlus, a building energy simulation tool,

Simulation results and discussions

Simulations are performed for a summer day. Note that the façade design for the three floors of the simulated medium-sized office building is same; the main climatic element, solar radiation, affects the building occupant thermal comfort as it includes the amount of heat transferred to the building. The simulated medium-sized office building’s north axis is specified to true North. The 4-perimeter zones on all floors are exposed to external environment as they have exterior walls and windows,

Conclusions

Thermal zones do not behave the same; hence a global temperature adjustment will result in poor distribution of HVAC capacity across zones and an uneven distribution of occupant satisfaction across the building. The peak load reduction computing tool for commercial building DR developed in this study optimally controls cooling set points of each thermal zone in a simulated medium-sized office building while maintaining occupant thermal comfort and achieves optimized peak load savings. The PMV

Acknowledgement

This work is supported in part by the U.S. National Science Foundation under Grant# ECCS-1232076.

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