A moving horizon state estimator in the control of thermostatically controlled loads for demand response

Kara, Emre; Kolter, Zico; Bergés, Mario; Krogh, Bruce H.; Hug, Gabriela; Yuksel, Tugce

doi:10.1109/smartgridcomm.2013.6687966

Cited by 19 publications

(20 citation statements)

References 12 publications

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“…Power TAC's research goals include using autonomous agents for the benefit of sustainable energy, and therefore, in addition to the areas shown in Figure 1, it also falls within the area of computational sustainability. The literature on computational energy sustainability has been growing rapidly and includes applications such as smart home heating, micro-storage management, wind forecasting, wind turbine optimization, energy disaggregation, solar tracking, electric vehicle charging and others [89,122,121,40,119,21,128,61,60,59,38,129,54,53,55,52,70,41,117].…”

Section: Related Workmentioning

confidence: 99%

Autonomous trading in modern electricity markets

Urieli

2016

AI Matters

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The smart grid is envisioned to be a main enabler of sustainable, clean, efficient, reliable, and secure energy supply (U.S. Department of Energy, 2003). One of the milestones in the smart grid vision will be programs for customer participation in electricity markets through demand-side management and distributed generation; electricity markets will incentivize customers to adapt their demand to supply conditions, which will help to utilize intermittent energy resources such as from solar and wind, and to reduce peak-demand.

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Section: Related Workmentioning

confidence: 99%

Autonomous trading in modern electricity markets

Urieli

2016

AI Matters

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“…The thermal parameters are assumed to be uniformly and/or normally distributed. Furthermore these parameters are assumed to stay constant during the course of load simulation (Callaway et al 2011a;Mathieu et al 2012;Kara et al 2012;Kara et al 2013b).…”

Section: Introductionmentioning

confidence: 99%

Modeling Thermostatically Controlled Loads to Engage Households in the Smart Grid: Lessons Learned from Residential Refrigeration Units

Kara¹,

Bergés²,

Hug³

2014

Computing in Civil and Building Engineering (2014)

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As renewable generation capacity in the power grid increases, keeping the balance between the supply and demand becomes difficult. This threatens the grid's stability and security. Existing power reserve assets and regulation methodologies fail to provide the short-term responses required to keep the load and generation balanced as the amount of renewable generation increases. Hence, researchers proposed to increase the information exchange within the power network and to introduce realtime demand control to ensure robustness while accommodating the intermittent nature of these generation resources. Constituting a significant portion of the electrical demand of buildings, thermostatically controlled loads (TCLs) are wellsuited to provide real-time demand control. In this paper, we shed light on challenges associated with engaging TCLs to the power grid using a centralized control strategy. We focus on the challenges associated with simulating a realistic TCL population using the models that are proposed in the literature. Specifically, we use data collected from residential refrigeration units operating in 214 different households to propose a strategy to select parameters when simulating a TCL population.

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“…The authors are with the EEH-Power Systems Laboratory, Swiss Federal Institute of Technology (ETH) Zurich, 8092 Zurich, Switzerland {vrettos|jmathieu|andersson}@eeh.ee.ethz.ch focused on methods to minimize the need for additional infrastructure by means of modeling, state estimation, and control strategies [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [6] proposed a state estimation approach based on a similar model and an MHSE method, while [5] proposed a four state aggregate system model, similar to that in [7], and a particle filter to estimate aggregate TCL states. However, with each of these approaches one is only able to estimate the fraction of TCLs in discrete bins in the state space, not the states of individual TCLs.…”

Section: Introductionmentioning

confidence: 99%

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Demand response with moving horizon estimation of individual thermostatic load states from aggregate power measurements

Vrettos

Mathieu

Andersson

2014

2014 American Control Conference

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We present an optimization-based state estimation method that allows us to estimate the states of individual thermostatically controlled loads (TCLs), such as air conditioners and space heaters, from measurements of the power consumption of small aggregations of TCLs. The state estimator can be used together with a controller to provide ancillary services to power systems such as frequency control. The main advantage of this method is that it is designed to work with existing communication infrastructure. We assume that aggregate power measurements are available from distribution substations every few seconds, while TCL state measurements are available from smart meters only every 20 minutes. We model TCLs as hybrid systems and propose a moving horizon state estimator (MHSE), which is formulated as a mixed-integer linear program. We demonstrate the performance of the MHSE in two case studies: (a) estimation of TCL states in the absence of external control actions, and (b) a power tracking problem with closed-loop control using broadcast control inputs. To demonstrate the robustness of the method, we conduct a parametric analysis with respect to aggregation size and diversity, process noise characteristics, and control trajectory characteristics. The results show that the method generally provides accurate estimates of TCL states, resulting in improved controller performance in most cases, and is implementable in real-time with reasonable computational power.

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A moving horizon state estimator in the control of thermostatically controlled loads for demand response

Cited by 19 publications

References 12 publications

Autonomous trading in modern electricity markets

Autonomous trading in modern electricity markets

Modeling Thermostatically Controlled Loads to Engage Households in the Smart Grid: Lessons Learned from Residential Refrigeration Units

Demand response with moving horizon estimation of individual thermostatic load states from aggregate power measurements

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