A Novel Distributed Economic Model Predictive Control Approach for Building Air-Conditioning Systems in Microgrids

Abstract

Abstract:With the penetration of grid-connected renewable energy generation, microgrids are facing stability and power quality problems caused by renewable intermittency. To alleviate such problems, demand side management (DSM) of responsive loads, such as building air-conditioning system (BACS), has been proposed and studied. In recent years, numerous control approaches have been published for proper management of single BACS. The majority of these approaches focus on either the control of BACS for attenuating power fluctuations in the grid or the operating cost minimization on behalf of the residents. These two control objectives are paramount for BACS control in microgrids and can be conflicting. As such, they should be considered together in control design. As individual buildings may have different owners/residents, it is natural to control different BACSs in an autonomous and self-interested manner to minimize the operational costs for the owners/residents. Unfortunately, such "selfish" operation can result in abrupt and large power fluctuations at the point of common coupling (PCC) of the microgrid due to lack of coordination. Consequently, the original objective of mitigating power fluctuations generated by renewable intermittency cannot be achieved. To minimize the operating costs of individual BACSs and simultaneously ensure desirable overall power flow at PCC, this paper proposes a novel distributed control framework based on the dissipativity theory. The proposed method achieves the objective of renewable intermittency mitigation through proper coordination of distributed BACS controllers and is scalable and computationally efficient. Simulation studies are carried out to illustrate the efficacy of the proposed control framework.

Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis

“…The second AI tool is used for the predictive control functions of ANN: fuzzy or model-based predictive control (MPC) [ 32 , 42 , 47 , 48 , 50 , 65 , 72 , 75 , 87 , 92 , 94 , 101 , 103 , 104 ]. Predictive control provides feedback of the results of the prediction to the system to allow for the adjustment of a system’s control parameters.…”

A Robust Electric Spring Model and Modified Backward Forward Solution Method for Microgrids with Distributed Generation

“…To achieve these objectives, the transition from the actual electrical system to a smart grid (SG) requires the improvement of the reliability, security, and efficiency of the electrical system [2]. Furthermore, the migration to a SG involves the flexible handling of the power intermittences associated with the stochastic nature of renewable energy sources (RESs) [3], implementation of monitoring systems, and control schemes that guarantee bidirectional power flow and optimal operation of the SG [4]. An alternative that the electrical industry has been using to modernize the electrical distribution network is the implementation of electrical microgrids (µGs) [5,6].…”

Comparison and Interpretation Methods for Predictive Control of Mechanics

“…Subsequent to the establishment of optimal benchmarks, adaptive [13][14][15][16][17][18][19][20] and learning controls [21][22][23][24][25][26][27][28][29][30][31][32] arose with a subsequent effort to combine the two, which makes adaptive controllers optimal, where optimization occurs after first establishing the control equation, such as by using 'approximate' dynamic programming [33]. Assuming distributed models, Reference [34] and Reference [35] used neighbor-based optimization to develop predictive controls. Other researchers [36] sought extensions into nonlinear systems with actuator failures, while Reference [37] tried to use neural networks to find the model and response in a predictive topology.…”

“…Zhang et al sought to augment dissipative theory to propose a novel distributed control framework to simultaneously optimize conflicting cost statements in demand-side management schemes (e.g., building air conditioning), while seeking to mitigate power fluctuations. Their proposed method achieved the objective of renewable intermittency mitigation through proper coordination of distributed controllers, and the solution proved to be scalable and computationally efficient [35]. Bonfitto et al presented a linear offset-free model predictive controller applied to active magnetic bearings, which exploits the performance and stability advantages of a classical model-based control while overcoming the effects of the plant-model mismatch on reference tracking by incorporating a disturbance observer to augment the plant model equations.…”