Distributed Control Method for Large-Scale HVAC Systems in University Campus Buildings

Abstract

The invention discloses a distributed control method for large-scale heating and ventilation air-conditioning systems in university campus buildings. Coordinated operation of the heating and ventilation air-conditioning systems in all regions in a plurality of buildings is achieved by adopting a model predictive control and alternating direction multiplier method, and the sum of the energy cost and the heat energy non-comfort cost is minimized under the constraint that the total input power and the acceptable temperature range of all the regions are not violated. The distributed control methodparticularly comprises the following working procedures that (1) a local controller of the heating and ventilation air-conditioning system in each region is used for predicting the electricity pricein a prediction interval, the external temperature and the user comfort degree preference temperature; (2) optimal input power decisions for a plurality of time slots in the future are given by all the local controllers according to a control algorithm; (3) the input power decision of the first time slot is made to act on actual operation of the heating and ventilation air-conditioning system; and(4) the prediction interval rolls back to one time slot, and the above three steps are repeatedly conducted. The method has the advantages of being high in expandability, high in flexibility, capableof protecting the privacy of a user and the like.

Description

technical field [0001] The invention relates to a distributed control method for a large-scale HVAC system in a university campus building, and belongs to the technical field of energy optimization and control of the HVAC system. Background technique [0002] The energy consumption of buildings in a country accounts for about 40% of the total energy consumption and 70% of the total electricity consumption. Among all building types, educational buildings account for around 2% of total energy consumption. For example, the annual energy costs of a university building system can be as high as $2 million. Therefore, optimizing the energy consumption cost of university campus buildings has very important practical significance. Since HVAC systems account for more than 40 percent of energy consumption in educational buildings, effectively controlling the operation of HVAC systems can reduce energy costs for university campus buildings. The most intuitive way to achieve this goal...

AI Technical Summary

Problems solved by technology

However, considering electricity price, outdoor temperature and uncertainty of optimal temperature preference in each region, regional occupancy pattern, energy cost and thermal discomfort of users, time-coupling constraints related to indoor temperature change in each region, and all HVAC Space coupling constraints related to the total energy consumption of the air conditioning system, there is still a lack of a distributed coordinated control method for HVAC systems in multiple buildings and multiple areas on the university campus

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