Model Predictive Control of Distributed Air-Conditioning Loads to Compensate Fluctuations in Solar Power

Mahdavi, Nariman; Braslavsky, Julio H.; Serón, María M.; West, Samuel R.

doi:10.1109/tsg.2017.2717447

Cited by 126 publications

(65 citation statements)

References 27 publications

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“…We set the temperature range required by users to be [T min , T max ]. Combined with the temperature range, Equations (3) and (4) can be derived from Equations (1) and (2) if an air conditioning is in the heating mode. P is the average power of an air conditioning.…”

Section: A State-queuing Model Of Aggregate Air Conditioningsmentioning

confidence: 99%

“…Therefore, the problem of renewable energy consumption can be solved if the demand-side loads consume renewable energy actively. On the one hand, air conditioning loads, electric water heaters, and other thermostatically-controlled loads have great potential for scheduling [1][2][3][4], and they can temporarily store electrical energy in the form of thermal energy [5][6][7][8][9][10]. On the other hand, for the users, fine-tuning the working time and temperature, the thermal control loads have little impact on user comfort; on the power supply side, they can quickly respond to power dispatching [11].…”

Section: Introductionmentioning

confidence: 99%

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Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model

Liang

Han

2018

Applied Sciences

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With poor peak load regulating capacity, renewable energy generation is intermittent and fluctuating, which results in the insufficient acceptance capacity of the power grid. Based on the state-queuing model of aggregate air conditioning loads, this paper develops a control algorithm to achieve renewable energy consumption and output tracking. The load curves of the aggregate air conditioning loads can be controlled by changing the initial temperature distribution. Under different temperature distributions, the load curves represent a fixed fluctuation, which is the basis of output tracking. A virtual load curve set is established based on the state-queuing model. Regarding the load curves as basic signals, the expected renewable energy output can be tracked via an optimal combination of the basic load curves. The validity of the algorithm is testified by numerical emulation data.

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Section: A State-queuing Model Of Aggregate Air Conditioningsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model

Liang

Han

2018

Applied Sciences

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“…A centralized and distributed algorithm based on the state space model of heating ventilation and air-conditioning (HVAC) was proposed to reduce power fluctuations and improve satisfaction of demand side [9]. A model predictive control was described and applied to demand-side response services in [10]. A distributed model predictive control (DMPC) method based on an optimized aggregation model was applied to provide frequency regulation services [11].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

et al. 2019

Energies

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In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

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“…The presence of abundant load resources in power demand would enable it to provide ancillary services for the power supply side [2][3][4][5][6][7], for instance, the load following application in clean energy by controlling residential air-conditionings (ACs). As thermostatically controlled loads, ACs have the potential to store energy and account for a large proportion in power demand [1,[8][9][10][11][12]. AC resources can be utilized to consume clean energy, especially to follow the detail of the oscillatory clean energy output because they have large capacities and can be controlled flexibly.…”

Section: Introductionmentioning

confidence: 99%

Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption

Liang

Jin-ying

2019

Processes

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Air-conditionings have energy storage functions. Through reasonable aggregation control, the output tracking can be implemented for wind power with stronger fluctuation to enhance its utilization rate. Cloud technology and intelligent appliances enable the appliance vendor to implement information interaction with the air-conditioning through cloud platforms to realize flexible regulation. In this paper, a management and control method of air-conditioning based on cloud platform is established. Based on this structure, the air-conditionings are divided into several aggregation groups according to the similarity of parameters, and each group completes the consumption task collaboratively. The consumption evaluation model of the air-conditioning group is established. On this basis, the allocation problem on consumption task for the aggregated group is constructed to implement the optimal solution under the condition of guaranteeing the degree of completion and user comfort. Each group implements the control for air-conditioning inside the group through the sliding mode control model. The simulation experiment verifies that the algorithm can effectively follow the output of clean energy, while intervening less in the air-conditioning operation.

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Model Predictive Control of Distributed Air-Conditioning Loads to Compensate Fluctuations in Solar Power

Cited by 126 publications

References 27 publications

Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model

Renewable Energy Output Tracking Control Algorithm Based on the Temperature Control Load State-Queuing Model

Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

Air-Conditioning Resource Management and Control Method based on Cloud Platform for Wind Power Consumption

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