Abstract

Air conditioning loads are important resources for demand response. With the help of thermal energy storage capacity, they can reduce peak load, improve the reliability of power grid operations, and enhance the emergency capacity of a power grid, without affecting the comfort of the users. In this paper, a virtual energy storage model for inverter air conditioning loads, which reflects their operating characteristics and is more conducive to practical application, is established. Two parts are involved in the virtual energy storage model: An electrical parameter part, based on the operating characteristics, and a thermal parameter part, based on the equivalent thermal parameter model. The control function and restrictive conditions of the virtual energy storage are analyzed and a control strategy, based on virtual state-of-charge ranking, is proposed. The strategy controls the inverter air conditioners through re-assigning indoor temperature set-points within the pre-agreed protocol interval and gives priority those with a higher virtual state of charge. As a result, electric power consumption is reduced while the temperature remains unchanged, so that a shortage in the power system can be compensated for as much as possible, while the comfort of users is guaranteed. Simulation and example analyses show that the strategy is effective in controlling air conditioning loads. Additionally, the influences of load reduction target magnitude and communication time-step on the performance of the control strategy are analyzed.

Highlights

  • Renewable energy sources, such as wind and solar power, have a high degree of unpredictability and time-variability [1]

  • Demand response can solve the problem of mismatching between supply and demand with relatively low cost, which is of great significance in facilitating the integration of new energy sources and reducing power shortages [6,7]

  • (VSOC) is defined to reflect the energy storage level of a Virtual energy storage (VES), based on which a virtual state-of-change (VSOC)-priority strategy is proposed to control inverter air conditioners to provide demand response services; (2) the electric power of inverter air conditioners is controlled at a level where the corresponding heating output exactly compensates for the heat loss, so that the indoor temperature will not go beyond a set limit during control; and (3) the impact of the shape and magnitude of load reduction targets and the length of communication time-step on the control performance is investigated

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Summary

Introduction

Renewable energy sources, such as wind and solar power, have a high degree of unpredictability and time-variability [1]. Compared to these studies [20,21,22,23], the present study has the following novel contributions: (1) Virtual state-of-charge (VSOC) is defined to reflect the energy storage level of a VES, based on which a VSOC-priority strategy is proposed to control inverter air conditioners to provide demand response services; (2) the electric power of inverter air conditioners is controlled at a level where the corresponding heating output exactly compensates for the heat loss, so that the indoor temperature will not go beyond a set limit during control; and (3) the impact of the shape and magnitude of load reduction targets and the length of communication time-step on the control performance is investigated. The performance of the proposed control strategy is verified, with the impact of the shape and magnitude of load reduction targets and the length of communication time-step assessed

Load Modeling of Inverter Air Conditioners
Electrical Quantity Relation
Thermal Parameter
Electrical
VSOC-Priority Inverter Air Conditioner Control Strategy
A VSOC-priority
VES Control Model for Inverter Air Conditioners
Control Strategy and Algorithm Cases
Tracking Performance with Different Shape of Load Reduction Targets
The Influence of the Magnitude of Loadtarget
Control performance of proposed the proposed
Control performance of proposed the proposed strategy with different
The Influence of Communication Time-step Length on the Control Performance
Findings
Conclusions
Full Text
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