Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity

Halder, Abhishek; Geng, Xinbo; Kumar, P. R.; Xie, Le

doi:10.1109/tpwrs.2016.2628055

Cited by 33 publications

(29 citation statements)

References 17 publications

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“…With the initial conditions and parameters of the heterogeneous TCL population as in section V‐A in the work of Halder et al,

\overset{τ}{true} = \frac{1}{3}

(which was verified to be feasible using ), comfort tolerances

{false{Δ_{i} false}}_{i = 1}^{N}

sampled randomly from a uniform distribution over [0.1°C,1.1°C], and for

\overset{π}{true} false(t false)

and

{\overset{θ}{true}}_{a} false(t false)

as in Figure , the LSE solves the optimal control problem ‐ by first convexifying the controls u i ( t ) ∈ {0,1}↦ v i ( t ) ∈ [0,1] for i = 1,…, N , and then recovering the optimal controls

{false{u_{i}^{*} false}}_{i = 1}^{N}

using Theorem . For this computation, we used 1‐minute time step for Euler discretization of dynamics , and solved the resulting LP with 1 million 440 thousand decision variables (see Section 3.3) using MATLAB linprog.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…The brick colored curve in Figure corresponds to the real‐time aggregate consumption for the TCL population with same T m , and real‐time ambient temperature θ a ( t ) as in the solid blue curve in Figure , for a PID velocity control gain tuple ( k p , k i , k d ) used to control the setpoint boundaries as part of a mixed centralized‐decentralized control. We refer the readers to section III.1 in the work of Halder et al for details on the real‐time setpoint control. The purpose of Figure is to highlight how the solution of the open‐loop optimal control problem ‐ can be used by the LSE as a reference aggregate consumption to be tracked in real‐time, to elicit demand response.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…The black curve shown above is the optimal power consumption trajectory

P_{total}^{ref} false(t false) = P_{e} \sum_{i = 1}^{N} u_{i}^{*} false(t false)

computed by solving the optimal control problem ‐, via Algorithm 1 with T m = 1.5 minutes. The brick colored curve is the real‐time controlled aggregate consumption P total ( t ) corresponding to control gain tuple ( k p , k i , k d ) = (10 −4 ,10 −6 ,10 −4 ) used to move the thermostatic boundaries (see section 3.1 in the work of Halder et al for details). The load serving entity can invoke demand response by controlling the setpoints of the thermostatically controlled load (TCL) population in such a way that their real‐time aggregate consumption P total ( t ) track the reference aggregate consumption

P_{total}^{ref} false(t false)

.…”

Section: Numerical Simulationmentioning

confidence: 99%

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Optimal power consumption for demand response of thermostatically controlled loads

Halder

Geng

Fontes

et al. 2018

Optim Control Appl Methods

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Summary We consider the problem of determining the optimal aggregate power consumption of a population of thermostatically controlled loads such as air conditioners. This is motivated by the need to synthesize the demand response for a load serving entity (LSE) catering a population of such customers. We show how the LSE can opportunistically design the aggregate reference consumption to minimize its energy procurement cost, given day‐ahead price, load forecast, and ambient temperature forecast, while respecting each individual load's comfort range constraints. The resulting synthesis problem is intractable when posed as a direct optimization problem after Euler discretization of the dynamics, since it results in a mixed‐integer linear programming problem with number of variables typically of the order of millions. In contrast, in this paper, we show that the problem is amenable to continuous‐time optimal control techniques. Numerical simulations elucidate how the LSE can use the optimal aggregate power consumption trajectory thus computed, for the purpose of demand response.

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“…With the initial conditions and parameters of the heterogeneous TCL population as in section V‐A in the work of Halder et al,

\overset{τ}{true} = \frac{1}{3}

(which was verified to be feasible using ), comfort tolerances

{false{Δ_{i} false}}_{i = 1}^{N}

sampled randomly from a uniform distribution over [0.1°C,1.1°C], and for

\overset{π}{true} false(t false)

and

{\overset{θ}{true}}_{a} false(t false)

as in Figure , the LSE solves the optimal control problem ‐ by first convexifying the controls u i ( t ) ∈ {0,1}↦ v i ( t ) ∈ [0,1] for i = 1,…, N , and then recovering the optimal controls

{false{u_{i}^{*} false}}_{i = 1}^{N}

Section: Numerical Simulationmentioning

confidence: 99%

Section: Numerical Simulationmentioning

confidence: 99%

“…The black curve shown above is the optimal power consumption trajectory

P_{total}^{ref} false(t false) = P_{e} \sum_{i = 1}^{N} u_{i}^{*} false(t false)

P_{total}^{ref} false(t false)

.…”

Section: Numerical Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal power consumption for demand response of thermostatically controlled loads

Halder

Geng

Fontes

et al. 2018

Optim Control Appl Methods

Self Cite

View full text Add to dashboard Cite

show abstract

“…The popularization of advanced metering infrastructures has made possible the implementation of control strategies for inverter air conditioner loads and the large-scale application of trunked dispatching. In [8], an architecture and supporting algorithms were proposed for privacy-preserving thermal inertial load management as a service provided by the load serving entity (LSE). It focused on an LSE managing a population of its customers' air conditioners, and proposed a contractual model where the LSE guarantees quality of service to each customer, in terms of keeping their indoor temperature trajectories within respective bands around the desired individual comfort temperatures.…”

Section: Introductionmentioning

confidence: 99%

Control Strategy for Inverter Air Conditioners under Demand Response

et al. 2019

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Air conditioning loads are important resources for demand response. With the help of thermal energy storage capacity, they can reduce peak load, improve the reliability of power grid operations, and enhance the emergency capacity of a power grid, without affecting the comfort of the users. In this paper, a virtual energy storage model for inverter air conditioning loads, which reflects their operating characteristics and is more conducive to practical application, is established. Two parts are involved in the virtual energy storage model: An electrical parameter part, based on the operating characteristics, and a thermal parameter part, based on the equivalent thermal parameter model. The control function and restrictive conditions of the virtual energy storage are analyzed and a control strategy, based on virtual state-of-charge ranking, is proposed. The strategy controls the inverter air conditioners through re-assigning indoor temperature set-points within the pre-agreed protocol interval and gives priority those with a higher virtual state of charge. As a result, electric power consumption is reduced while the temperature remains unchanged, so that a shortage in the power system can be compensated for as much as possible, while the comfort of users is guaranteed. Simulation and example analyses show that the strategy is effective in controlling air conditioning loads. Additionally, the influences of load reduction target magnitude and communication time-step on the performance of the control strategy are analyzed.

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“…Renewable energy balancing by power tracking [3,4], frequency regulation [7][8][9], and peak load shedding [10,11] are the most commonly-adopted control objectives. Direct on-off scheduling using a predicted electricity price signal [12,13], energy management based on packet scheduling algorithms [14,15], and privacy preserving-based management strategies [16] have also been studied in the recent literature.…”

Section: Introductionmentioning

confidence: 99%

Automatic Coordination of Internet-Connected Thermostats for Power Balancing and Frequency Control in Smart Microgrids

Bashash

Lee

2019

Energies

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This paper proposes a novel feedback control strategy, so-called clock-like controller (CLC), to balance power supply and demand in smart microgrids by adjusting the setpoint temperatures of air conditioning (AC) loads. In the CLC algorithm, the grid operator communicates with the individual thermostats via the Internet and adjusts their setpoints by discrete temperature intervals (e.g., ±0.5 °C). Numerical simulations indicate that the proposed algorithm is able to deliver a smooth controllability of the aggregate AC power despite discrete temperature offsets. It can also be used for peak load shedding to mitigate the power generation cost. The CLC algorithm is then integrated into the grid frequency control problem, in which both power generators and loads in the network attempt to regulate the frequency of the system despite disturbances from demand, renewable sources, and local weather conditions. An autonomous microgrid model including a steam and a hydro generator, a solar energy source, and a large number of thermostatic loads is developed to evaluate and demonstrate the proposed method. Simulation results indicate that the AC loads with CLC algorithm can help maintain the power system frequency during extreme events when demand exceeds the maximum generation capacity available to the network. Under normal conditions, the contribution of demand-side control is marginalized by the fast responding generators, because of time delays in the frequency measurement and internet communication network.

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Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity

Cited by 33 publications

References 17 publications

Optimal power consumption for demand response of thermostatically controlled loads

Optimal power consumption for demand response of thermostatically controlled loads

Control Strategy for Inverter Air Conditioners under Demand Response

Automatic Coordination of Internet-Connected Thermostats for Power Balancing and Frequency Control in Smart Microgrids

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