scholarly journals Flexibility quantification of thermostatically controlled loads for demand response applications

2022 ◽  
Vol 202 ◽  
pp. 107592
Author(s):  
María Victoria Gasca ◽  
Federico Ibáñez ◽  
David Pozo
Keyword(s):  
Demand Response ◽  
Thermostatically Controlled Loads

Emergent Behavior in a Population of Thermostatically Controlled Loads With Peer-to-Peer Communication

2019 ◽  
Author(s):  
Ryan Schwartz ◽  
John F. Gardner
Keyword(s):  
Demand Response ◽  
Air Conditioning ◽  
Large Scale ◽  
Large Population ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Peer Communication ◽  
Agent Based ◽  
Thermostatically Controlled Loads ◽  
Residential Air Conditioning

Abstract Thermostatically controlled loads (TCLs) are often considered as a possible resource for demand response (DR) events. However, it is well understood that coordinated control of a large population of previously un-coordinated TCLs may result in load synchronization that results in higher peaks and large uncontrolled swings in aggregate load. In this paper we use agent based modeling to simulate a number of residential air conditioning loads and allow each to communicate a limited amount of information with their nearest neighbors. As a result, we document emergent behavior of this large scale, distributed and nonlinear system. Using the techniques described here, the population of TCLs experienced up to a 30% reduction in peak demand following the DR event. This behavior is shown to be beneficial to the goals of balancing the grid and integrating increasing penetration of variable generators.

Stability Analysis of Demand Response Systems Utilizing Locally Communicating Thermostatically Controlled Loads

2019 ◽  
Author(s):  
Jason Kuwada ◽  
John F. Gardner
Keyword(s):  
Stability Analysis ◽  
Demand Response ◽  
Aggregate Behavior ◽  
Communication Topology ◽  
Response Systems ◽  
Thermostatically Controlled Loads ◽  
Negative Impacts ◽  
Insight Into ◽  
Varying Population ◽  
Aggregate System

Abstract Thermostatically Controlled Loads (TCLs) have shown great potential for Demand Response (DR) events. However, it has been commonly seen that DR events using TCLs may cause load synchronization and unwanted oscillatory effects, especially in homogeneous populations. In an attempt to mitigate the negative impacts of DR events, a decentralized method is proposed that modifies each thermostat behavior based on the activity of nearby TCLs. This feedback introduces the possibility of instability in the aggregate behavior. A stability analysis is performed on a model of the aggregate system and the results of that analysis compared to simulation results. Several populations were considered, varying population size, communication topology, thermostat deadband and heterogeneity of the population. While the linearized analysis failed to accurately predict instabilities in the aggregate system, it did provide insight into global behavior.

scholarly journals Chance-Constrained Optimization Dispatch and Demand Response Control of Thermostatically Controlled Loads and Plug-in Electric Vehicles

2021 ◽  
Author(s):  
Jianqiang Hu ◽  
Jinde Cao
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Demand Response ◽  
Wind Farm ◽  
Sample Average Approximation ◽  
Chance Constraint ◽  
Response Control ◽  
Renewable Power ◽  
Look Ahead ◽  
Thermostatically Controlled Loads

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

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