WO2017019000A1 - Organes de commande de charge agrégée et procédés associés - Google Patents

Organes de commande de charge agrégée et procédés associés Download PDF

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Publication number
WO2017019000A1
WO2017019000A1 PCT/US2015/042117 US2015042117W WO2017019000A1 WO 2017019000 A1 WO2017019000 A1 WO 2017019000A1 US 2015042117 W US2015042117 W US 2015042117W WO 2017019000 A1 WO2017019000 A1 WO 2017019000A1
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Prior art keywords
thermostatic
control
electrical energy
loads
controller
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PCT/US2015/042117
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English (en)
Inventor
David P. Chassin
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Battelle Memorial Institute
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Priority to CA2993611A priority Critical patent/CA2993611C/fr
Priority to PCT/US2015/042117 priority patent/WO2017019000A1/fr
Publication of WO2017019000A1 publication Critical patent/WO2017019000A1/fr

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D23/00—Control of temperature
    • G05D23/19—Control of temperature characterised by the use of electric means
    • G05D23/1919—Control of temperature characterised by the use of electric means characterised by the type of controller
    • G05D23/1924—Control of temperature characterised by the use of electric means characterised by the type of controller using thermal energy, the availability of which is aleatory
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/52—Indication arrangements, e.g. displays
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/10—The network having a local or delimited stationary reach
    • H02J2310/12—The local stationary network supplying a household or a building
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/10—The network having a local or delimited stationary reach
    • H02J2310/12—The local stationary network supplying a household or a building
    • H02J2310/14—The load or loads being home appliances
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Definitions

  • This disclosure relates to aggregate load controllers and associated methods. BACKGROUND OF THE DISCLOSURE
  • the traditional utility approach to renewable intermittency is to allocate additional firm reliability resources to replace all potentially non-firm renewables resources. These firm resources are generally fast-responding thermal fossil resources and where possible hydro- electrical resources as well.
  • the impact of this approach is quantified as an intermittency factor, which discounts the contribution of wind in addition to its capacity factor and limits the degree to which they can contribute to meeting peak demand.
  • the intermittency factor does not account for the ramping requirements created by potentially fast-changing renewable resources.
  • the need for fast-ramping resources discourages the dispatch of high-efficiency fossil and nuclear generation assets while promoting low-efficiency fossil and hydro where available for regulation and reserve services.
  • Demand response is widely regarded as a low-cost alternative to fast-response generation reserves that reduces the dispatch of inefficient generation resources.
  • load control strategies for demand response applications can be challenging to deploy. This is in part because the competing objectives of local and global control. It is also in part because of the complexity of the models and the simplifications required to make their analysis and design analytically tractable, numerically feasible in simulations for large-scale adequacy, and realizable in renewable integration studies.
  • One conventional practice for direct load control employs so- called“one-shot” load shedding strategies for emergency peak load relief only.
  • This approach uses a controllable subset of thermostatic loads in a particular class, e.g., water heaters or air-conditioners, which are transitioned to a curtailed regime that reduces the population average power demand. After a time, these responsive load are released and return to their normal operating regimes. This strategy exhibits fluctuations in load during the initial response as well as demand recovery rebounds after the loads are released.
  • “one-shot" direct load control strategies are sometimes enhanced using multiple subgroups of the responsive loads dispatched in a sequence that smooths the overall response of the load control system.
  • Aggregate building thermal load models present additional challenges when thermostatic loads are being considered.
  • a switched-mode representation of the individual building thermal response is used to account for hysteresis of thermostats, which in turn gives rise to high-order non-linear aggregate load models.
  • Models also include so-called“refractory states” associated with state transition delays rather than thermal parameters due to deadband of the thermostats.
  • Tractable state space models of aggregate loads rely on model-order reduction strategies that linearize the system model and limit the number of state variables used to represent responsive loads, as illustrated in Fig. 1. These state space models represent thermostats with non-zero deadband.
  • At least some embodiments are directed towards apparatus, systems, and associated methods for controlling thermostatic loads which overcome shortcomings of the conventional control strategies.
  • Fig. 1 is a state-space model of aggregate conventional thermostatic loads with refractory states in a heating mode.
  • Fig. 2 is a state-space model of discrete-time zero-deadband aggregate thermostatic loads according to one embodiment.
  • Fig. 3 is a graphical representation of discrete-time thermostat transition probabilities for on and off states according to one embodiment.
  • Fig. 4 is a block diagram of a direct load controller according to one embodiment.
  • Fig. 5 is a discrete-time root-locus of aggregate zero deadband thermostatic loads according to one embodiment.
  • Fig. 6 is a graphical representation of 100 MW proportional load control with maximum attenuating proportional control gains according to one embodiment.
  • Fig. 7 is a graphical representation of a proportional/derivative controller response to step input at various conditions according to one embodiment.
  • Fig. 8 is a block diagram of a unity damped system according to one embodiment.
  • Fig. 9a is a graphical representation of a unity damped response of aggregate load controllers according to one embodiment.
  • Fig. 9b is a graphical representation of a deadbeat response of aggregate load controllers according to one embodiment.
  • Fig. 10a is a graphical representation of 100 MW impulse load control at -15 o C according to one embodiment.
  • Fig. 10b is a graphical representation of 100 MW proportional load control at -15 o C according to one embodiment.
  • Fig. 11a is a graphical representation of 100 MW unity damping load control at -15 o C according to one embodiment.
  • Fig. 11b is a graphical representation of 100 MW deadbeat load control at -15 o C according to one embodiment.
  • Fig. 12a is a graphical representation of 100 MW tuned load control at -15 o C according to one embodiment.
  • Fig. 12b is a graphical representation of 100 MW integral feedback control at -15 o C according to one embodiment.
  • Fig. 13 is a graphical representation of zero and pole locations for a random population of 1 million homes at various outdoor air temperatures according to one embodiment.
  • Fig. 14 is a functional block diagram of an aggregate load controller according to one embodiment. DETAILED DESCRIPTION OF THE DISCLOSURE
  • At least some embodiments of the disclosure described below are directed to aggregate load controllers and associated methods.
  • the discussion initially proceeds with respect to development of a model of aggregate load which receives electrical energy from an electrical utility.
  • the aggregate load which is modeled, and controlled corresponds to thermostatic loads which are coupled with a feeder of an electrical utility.
  • the aggregate load controller controls thermostatic controllers (e.g., thermostats) which operate to control changes in operational modes or states of thermostatic loads being controlled (e.g., control on, off, heating and cooling modes of operation of thermostatic loads such as heat pumps, air conditioners, etc.).
  • the aggregate load controller is configured to decrease or increase load upon the electrical utility to implement demand response control strategies in example embodiments.
  • the model of aggregate load is developed with respect to control of thermostatic controllers which operate with zero deadband in some embodiments and which may also be referred to as zero deadband (T ⁇ 0) thermostatic controllers or thermostats. Additional details of zero deadband thermostatic controllers which may be controlled by the aggregate load controller are disclosed in a US Patent Application titled Thermostats and Operational Methods, filed the same day as the present application, and naming David P. Chassin, Alyona Ivanova, Emily Swan, Martin Slama, Gezachin Asmelash Gherberioragis, Abhishek Parmar, Dr. Panajotis Agathoklis, and Nedjib Djilali as inventors, and the teachings of which are incorporated herein by reference. Thereafter, the discussion provides example embodiments of aggregate load controllers which may be used to control a plurality of zero deadband thermostatic controllers in residences and businesses.
  • conventional thermostats including analog and digital thermostats operate with a deadband to avoid quick cycling of associated apparatus being controlled. More specifically, these thermostats control a change of state of an associated apparatus being controlled when the temperature of a conditioned area being controlled falls below or rises above a temperature setpoint, as we all as an associated deadband (e.g., 2°C).
  • an associated deadband e.g. 2°C
  • Example embodiments of the disclosure are described below with respect to control of zero deadband thermostats when a temperature of a conditioned area being controlled falls below or rises above a desired temperature setpoint (for example, as set by an occupant of the conditioned area being controlled), and at discrete moments in time which are separated from one another by a common interval.
  • a sampling time or interval t s of the zero deadband discrete-time thermostats when changes to the operational modes or states of the apparatus being controlled may be made is set to be greater than a minimum runtime of the apparatus being controlled (t s > t min ), also referred to as the minimum heating/cooling system refractory state time.
  • the standard discrete-time control problem can be considered, as shown in Fig. 2, where the states x 1 and x 2 represent the number of responsive thermostatic loads in the on and off states, respectively.
  • the time t min is generally regarded to be in the range of 1 to 2 minutes, so designs where t s ⁇ 1 minute were not considered to avoid reintroducing the refractory states in the model.
  • the use of zero deadband thermostatic controllers and a sampling/control interval greater than the minimum runtime in accordance with some of the described embodiments simplifies the control system and removes the refractory states providing a linear control system which may be implemented using simplified control theory.
  • the rate parameters ⁇ on and ⁇ off represent the fraction of those devices that cross a desired temperature setpoint T D in any given interval t s (e.g., desired temperature setpoint T D of a conditioned interior area of a residence as selected by the occupant of the house).
  • the rate parameters of the discrete-time model are determined from how the thermostatic controller setpoint threshold T D divides the population occupying each state in one embodiment.
  • Equation (2) The demand response system state space representation is developed from Equation (1) for the net change in load y(k) ⁇ 0 based on a load control signal u(k) > 0 as shown in Equation (2):
  • G represents the aggregate load response
  • h represents the aggregate load control input matrix
  • c represents the aggregate load output matrix.
  • the input matrix h will be determined by the utility's choice of which signal is sent to thermostatic controllers to turn the thermostatic controllers on and off.
  • the particulars of the output matrix c are determined by the nature of the response that is of interest, e.g., total load reduced or increased, or net change in load.
  • the rates of change of temperature error are determined from the second-order thermal response C A C M as set forth in Equation (3):
  • TD is the desired temperature setpoint relative to outdoor air temperature
  • T A is indoor air temperature relative to outdoor air temperature
  • U A is the thermal conductance of the indoor air to the outdoor air
  • C A is the heat capacity of the indoor air
  • U M is thermal conductance of the indoor air to the building's solid mass
  • CM is the heat capacity of the building's solid mass.
  • the heat function q(t) includes both the internal, solar and ventilation heat gains and losses q I (t), as well as the heat gain or loss q H (t) resulting from operation of the heating/cooling system.
  • the values of the rates for an individual home may be derived according to the following:
  • the heating/cooling systems are assumed to be sized appropriately so that r off (t) ⁇ 0 ⁇ r on (t) when heating and r on (t) ⁇ 0 ⁇ r off (t) when cooling for all t.
  • Example embodiments are described below with respect to the heating case, although it is understood that the cooling case is similar in every respect with sign changes where appropriate.
  • the mean rates of devices during the interval k to k + 1 is denoted r ⁇ (k).
  • a thermostatic controller operating at the lowest rate is denoted r ⁇ (k) - 3 ⁇ (k) where ⁇ (k) is the standard deviation of rates r(k).
  • This thermostatic controller has a lower probability of crossing the setpoint threshold TD than one with at the highest rate r ⁇ (k) + 3 ⁇ (k).
  • the distribution of devices within the rectangle is assumed to have virtually zero skew and very nearly all of the device rates in the population are assumed to fall within the range r ⁇ ⁇ 3 ⁇ for both the on and off states.
  • 3 ⁇ ⁇ r ⁇ is assumed for both the on and off states since the thermostatic loads are assumed to be appropriately sized for their applications.
  • the pulse transfer function for "one-shot" load curtailment is provided in Equation (4) when discrete-time zero- deadband thermostats are employed:
  • the system is Type 1 and is therefore closed-loop proportional control not expected to exhibit any steady state error in response to a step input.
  • the system is marginally stable.
  • the root locus is always real, with one branch between b and 1, and a second branch that extends from a to– ⁇ .
  • the example aggregate load controller includes a plurality of subsystems including a state estimator in the form of a reduced-order observer 14, a direct load controller 16 and integral error feedback 18 in one embodiment. Other embodiments of the aggregate load controller are possible.
  • the aggregate load controller 10 controls increases and decreases in the consumption of electrical energy by thermostatic loads of the aggregate load 12 in one embodiment.
  • An example embodiment of a computing device including control circuitry which may be configured to implement the illustrated controller 10 is described below with respect to Fig. 14.
  • the aggregate load controller 10 operates as a discrete controller which samples inputs and updates outputted control signals at discrete moments in time in the described embodiment.
  • the controller accesses an error signal, an estimation signal and an input signal discussed below at a plurality of discrete moments in time, and adjusts or updates the control signal at the discrete moments in time.
  • control signals which are applied to the thermostatic controllers are a common control signal in one embodiment (e.g., all thermostatic loads coupled with the feeder receive the same control signal in one implementation).
  • An input or reference signal r(kt s ) is provided to the controller 10 which indicates a request for an increase or decrease in consumption of electrical energy of the thermostatic loads of aggregate load 12 which receive electrical energy, for example, from a feeder of an electrical utility.
  • the input signal may be used in one illustrative example where electrical energy upon the feeder from wind generation decreases, and in response, the electrical utility may desire that the input signal control a decrease in electrical load upon the feeder, for example, by 100 MW to account for the decrease of electrical energy from wind generation.
  • Operations of the aggregate load controller attempt to adjust the consumption of electrical energy utilized by the thermostatic loads in accordance with the requested amount in one embodiment.
  • Fig. 4 the aggregate load 12, reduced-order observer 14, direct load controller 16 and integral error feedback 18 are represented by respective blocks A, B, C and D with additional details of example embodiments thereof provided below the depicted general controller design of Fig. 4.
  • the aggregate load 12 may be represented by the transfer function 20 for modelling purposes which corresponds to Equation 4 in the illustrated example.
  • the aggregate load is the thermostatic loads which are coupled with the feeder of the utility to be controlled.
  • the control signal u(kt s ) is received by the thermostatic controllers of the aggregate load 12 which control increases or decreases in the consumption of electrical energy by the thermostatic loads coupled with the utility’s feeder.
  • the response y(kt s ) of the aggregate load 12 may be determined by measuring the load upon the feeder of the electrical utility in one embodiment.
  • Reduced-order observer 14 receives the control signal u(kt s ) and input or reference signal r(kt s ) and generates an estimation signal x ⁇ (kt s ) which is indicative of the internal state of the aggregate load 12 including estimates of the operational states of a plurality of thermostatic loads which receive electrical energy from the electrical utility (e.g., the estimation signal indicating the estimate of the number of devices which are off and on as n off and n on ).
  • Direct load controller 16 receives a signal which is indicative of cumulative error q(kt s ), the input or reference signal r(kt s ), and the state estimation signal x(kt s ) and generates the control signal u(kt s ) which is applied to the aggregate load 12 to control the increase or decrease in amount of electric energy which is consumed by the thermostatic loads.
  • the control signal may be generated based upon fluctuations in the price of electrical energy and/or rise or fall of supply or demand in illustrative examples.
  • the integral error feedback 18 receives the response y(kt s ) of the aggregate load which is indicative of the amount of electrical energy used by the thermostatic loads, and the input or reference signal r(kt s ) and generates the error signal q(kt s ).
  • the error signal is indicative of cumulative error of differences between a desired amount of electrical energy being utilized from the electrical utility and the actual amount of the electrical energy being utilized from the electrical utility during the operation of the aggregate load controller 10.
  • the cumulative error signal q(kt s ) may be used to adjust the controlling of the electrical energy consumption by direct load controller 16 in one embodiment.
  • Example controller design parameters for the illustrated example general aggregate load controller are as follows:
  • • h is the system input matrix for the response to the signal u(k). This is generally a curtailment signal and indicates how many devices are turned off.
  • the following example designs of the aggregate load controller have an objective that the controller be well suited to direct dispatch of demand response resources.
  • Other designs of the aggregate load controller may be used in other embodiments.
  • the proposed aggregate load controller can be reconfigured to study the various control strategies and/or responses shown in a plurality of examples in Table 1.
  • the flexible design of the controller allows for use of many of the basic control strategies that are typically employed for discrete-time linear time-invariant systems. This is done with the understanding that some of the parameters may change over time intervals much longer than the time horizon over which most demand response control objectives are stipulated.
  • the state transition rates ⁇ on and ⁇ off may change as a function of outdoor air temperature, but the relationship is straightforward to obtain for the aggregate population of thermostatic loads and will be sufficiently consistent between seasons to allow simple system identification approaches to provide accurate long term model parameters.
  • a simple method of identifying parameters of the model is discussed below starting at paragraph 0083 based on the impulse response, which is discussed next.
  • the steady state response is: which observed as the population average duty cycle R and is independent of the u(k) for k > 0 provided that It is also
  • any signal u(k) > 0 adds more thermostatic loads to the controlled population while u(k) ⁇ 0 will remove thermostatic loads from the controlled population.
  • u(k) returns
  • Table 1 Example aggregate load controller design configurations The behavior of proportional control is considered by examining the root locus of the system. With–1 ⁇ a ⁇ b ⁇ 1, the system has two real poles and a zero that is between the poles, as shown in Fig. 5.
  • proportional-integral-derivative (PID) control yields a stable aggregate load controller but the performance characteristics may be unsatisfactory for conditions expected to be encountered in a realistic utility operational setting.
  • the response can be slow and oscillatory under higher load conditions when reliable aggregate load control is most needed, as shown in Fig. 6.
  • the constraints on K limit the possibility of improving performance to such an extent that proportional control may be impractical for some direct load control applications.
  • the stability margin of the system is not suitable for operation in a noisy conditions because the phase margin is always 180°.
  • model parameters ⁇ and d ⁇ creates a source of constant disturbances in the system that can result in a steady state error.
  • integral error feedback control is used in some embodiments.
  • An alternative approach to mitigate model error is to include information obtained directly from controllable devices. This would be the case if bidding mechanisms are used, such as when retail markets are implemented.
  • a deadbeat controller that uses only two load control impulses to achieve steady state may be used.
  • This example controller has the advantage that it does not continually draw on the uncontrolled population of thermostatic loads to achieve the control objective. However, it has the disadvantage that it may overshoot on the second time-step, as shown in Fig. 9B.
  • the state x ⁇ (k) and output y(k) is determined using the matrices:
  • steady state error may be expected when the values of â and b ⁇ are not accurately determined.
  • an integral error feedback using an augmented state may be implemented
  • the integral feedback error is included in the state-space representation using the augmented controllability matrix
  • the augmented Toeplitz matrix is
  • This control design eliminates the steady-state error induced by model errors in ⁇ on and ⁇ off with a settling time determined by the pole zq .
  • the utility may decide the placement of the poles z1, z2, and z q and which may all be at zero in one embodiment.
  • the controller designs were tested on an agent-based simulation of 100,000 residential thermostats using a second-order building thermal model, including internal and solar gains and ventilation losses.
  • the second order models are linearized for the given outdoor temperature resulting in first-order models for each house such the individual homes have distinct air temperature change rates as a function of the state of the heating system.
  • the thermal model of the simulation is not the aggregate model itself, and therefore the controller is tested against a different plant model than the underlying plant model used for the controller design.
  • the simulation models therefore include disturbances from model error and measurement noise arising from the design model order reduction itself.
  • thermostat setpoint changes are applied to a subset of uncontrolled homes.
  • the magnitude of the setpoint change is generally a function of the fastest rate of change, which at peak load is approximately r off .
  • the magnitude of this value was chosen to ensure that the impulse response resulted in a 100% response at the first time step.
  • controller design parameters discussed in Section 3 are generated for peak load conditions using the thermal parameters shown in Table 4.
  • Table 4 House thermal parameters A summary of the controller design parameters studied are shown in Table 5.
  • Fig. 11A The response of the unity damping controller is shown in Fig. 11A.
  • the effect of model error can be seen in the initial response, during which it fails to achieve the desired level of curtailment.
  • the response of deadbeat control has the expected significant overshoot, but also exhibits large steady state error, as shown in Fig. 11B.
  • the response of the tuned controller shows a compromise between the unity damping and deadbeat controller designs, but still exhibits a large steady state error, as shown in Fig. 12A.
  • the integral error feedback control response shown in Figure 12B addresses the problems identified in the previous controller designs.
  • the aggregate load controller using integral error feedback control exhibits an acceptable level of overshoot and maintains the desired curtailment level for more than 90 minutes.
  • Each control impulse transfers thermostatic loads between the unresponsive population and the responsive population, altering the responsive population's state x(k + 1) by simply adding the new population's x k (k + 1) response to the input u(k).
  • the aggregate response can be shaped to track an arbitrary reference signal r(k) (which corresponds to a desired amount of a load upon a feeder of the utility, such as an adjustment to the current load), provided sufficient thermostatic loads are available in the responsive or background unresponsive population to supply the net change for each impulse u(k).
  • the following describes two useful extensions which may be applied to the above-described aggregate load controllers.
  • the first is symmetric control which uses all thermostatic loads in the unresponsive population instead of only devices that are on. In some cases, this approach may be more practical for utilities to deploy, and offers the added benefit of addressing possible privacy concerns resulting from any strategy that requires the utility to know whether one particular device is actually on before choosing which thermostatic loads to signal.
  • a second possible extension addresses model sensitivity concerns and reduces the variability in the control response by using impulse responses to calibrate the internal model used by the reduced-order observer.
  • the load is observed based on the number of thermostatic devices that remain on rather than the number of thermostatic devices that are turned off.
  • the steady state error of the aggregate load controller depends on the observer parameters ⁇ off and ⁇ on , particularly in the first few time steps before the integral error feedback can compensate for any accumulated output error.
  • a stimulus such as a single impulse response
  • a stimulus can be used to provide a relatively quick and simple method of model parameter identification using a response of the thermostatic loads as a result of the applied stimulus.
  • Equation (1) the values of model parameters ⁇ on and ⁇ off corresponding to the rates of the thermostatic loads changing operational mode or state (e.g., changing between on and off) may be estimated according to the following
  • the values of the model parameters are used to configure the thermostatic controller to control amounts of electrical energy which are utilized by the thermostatic loads.
  • the values of the model parameters ⁇ on and ⁇ off are used in Equations 12 and 13 to define the controller design parameters, such as controller gains K c and K q and the reference input gain h, of the controller shown in Fig. 4.
  • the above example embodiments enable utility-scale direct load control when the controlled loads employ discrete-time zero deadband (T ⁇ 0 ) thermostatic controllers. Dispatchers may use small adjustments to consumers’ setpoints to modulate the total load with greater precision than possible using current setback control of thermostats with non-zero deadbands.
  • a linear aggregate load model is constructed as described above in one embodiment and its fundamental characteristics are used to develop a number of alternative aggregate load control designs from first-principles according to additional example embodiments of the disclose.
  • the aggregate load model may be used to design a closed-loop direct load controller for a discrete-time utility- scale demand response dispatch system.
  • the aggregate controlled load is stable, controllable and observable and has both the transient and steady-state response characteristics necessary to serve equally well for utilities that seek to control load using either direct load control or price-based indirect load control strategies in example implementations.
  • circuitry 100 of a computing device which is configured to implement the aggregate load controller is shown.
  • the illustrated circuitry includes a user interface 120, processing circuitry 122, storage circuitry 124, and communications circuitry 126.
  • Other embodiments are possible including more, less and/or alternative components.
  • User interface 120 is configured to interact with a user including conveying data to a user as well as receiving inputs from the user, for example, inputs indicating desired amounts of electrical energy upon a feeder of an electrical utility which is to be curtailed.
  • processing circuitry 122 is arranged to process data, control data access and storage, issue commands, and control other desired operations.
  • processing circuitry 122 may be referred to as control circuitry and configured to control the operations of the aggregate load controller illustrated in Fig. 4.
  • Processing circuitry 122 comprises circuitry configured to implement desired programming provided by appropriate computer- readable storage media in at least one embodiment.
  • the processing circuitry 122 may be implemented as one or more processor(s) and/or other structure configured to execute executable instructions including, for example, software and/or firmware instructions.
  • Other example embodiments of processing circuitry 122 include hardware logic, PGA, FPGA, ASIC, state machines, and/or other structures alone or in combination with one or more processor(s). These examples of processing circuitry 122 are for illustration and other configurations are possible.
  • Storage circuitry 124 is configured to store programming such as executable code or instructions (e.g., software and/or firmware), electronic data, databases, image data, or other digital information and may include computer-readable storage media. At least some embodiments or aspects described herein may be implemented using programming stored within one or more computer-readable storage medium of storage circuitry 124 and configured to control appropriate processing circuitry 122.
  • the computer-readable storage medium may be embodied in one or more articles of manufacture which can contain, store, or maintain programming, data and/or digital information for use by or in connection with an instruction execution system including processing circuitry 122 in one embodiment.
  • computer-readable storage media may be non-transitory and include any one of physical media such as electronic, magnetic, optical, electromagnetic, infrared or semiconductor media.
  • Some more specific examples of computer- readable storage media include, but are not limited to, a portable magnetic computer diskette, such as a floppy diskette, a zip disk, a hard drive, random access memory, read only memory, flash memory, cache memory, and/or other configurations capable of storing programming, data, or other digital information.
  • Communications circuitry 126 is arranged to implement communications of the aggregate load controller with respect to external devices and/or networks (not shown), for example, outputting the control signal for application to the thermostatic loads being controlled.
  • communications interface 126 may be arranged to communicate information bi-directionally with respect to the aggregate load controller.
  • Communications interface 126 may be implemented as a network interface card (NIC), serial or parallel connection, USB port, Firewire interface, Ethernet port, flash memory interface, or any other suitable arrangement for implementing communications of the controller.
  • NIC network interface card
  • communications circuitry 126 outputs control signals to the thermostatic controllers of the thermostatic loads.

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Abstract

L'invention concerne des organes de commande de charge agrégée et des procédés associés. Selon un aspect, un procédé de fonctionnement d'un organe de commande de charge agrégée comprend l'utilisation d'un organe de commande de charge agrégée possédant un état initial, l'application d'un stimulus à une pluralité d'organes de commande thermostatiques qui sont configurés pour commander une pluralité de charges thermostatiques respectives qui reçoivent de l'énergie électrique à partir d'un réseau électrique public pour fonctionner dans une pluralité de modes de fonctionnement différents, l'accès à des données concernant une réponse des charges thermostatiques en tant que résultat du stimulus appliqué, l'utilisation des données concernant la réponse, la détermination d'une valeur d'au moins un paramètre de conception de l'organe de commande de charge agrégée et l'utilisation de la valeur déterminée de l'au moins un paramètre de conception et la configuration de l'organe de commande de charge agrégée pour commander des quantités de l'énergie électrique qui sont utilisées par les charges thermostatiques.
PCT/US2015/042117 2015-07-24 2015-07-24 Organes de commande de charge agrégée et procédés associés WO2017019000A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100217651A1 (en) * 2009-02-26 2010-08-26 Jason Crabtree System and method for managing energy resources based on a scoring system
US20100314942A1 (en) * 2009-06-15 2010-12-16 Google Inc. Supplying grid ancillary services using controllable loads
EP2416465A2 (fr) * 2010-08-02 2012-02-08 General Electric Company Système de délestage pour réponse de demande sans système AMI/AMR
WO2014089463A2 (fr) * 2012-12-07 2014-06-12 Battelle Memorial Institute Procédé et système pour utiliser des ressources côté demande pour réaliser une régulation de fréquence à l'aide d'une attribution dynamique de ressources en énergie
US20140277761A1 (en) * 2013-03-15 2014-09-18 Nest Labs, Inc. Controlling an hvac system in association with a demand-response event
WO2015109207A1 (fr) * 2014-01-17 2015-07-23 Stc. Unm Systèmes et procédés pour intégrer des ressources énergétiques distribuées

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100217651A1 (en) * 2009-02-26 2010-08-26 Jason Crabtree System and method for managing energy resources based on a scoring system
US20100314942A1 (en) * 2009-06-15 2010-12-16 Google Inc. Supplying grid ancillary services using controllable loads
EP2416465A2 (fr) * 2010-08-02 2012-02-08 General Electric Company Système de délestage pour réponse de demande sans système AMI/AMR
WO2014089463A2 (fr) * 2012-12-07 2014-06-12 Battelle Memorial Institute Procédé et système pour utiliser des ressources côté demande pour réaliser une régulation de fréquence à l'aide d'une attribution dynamique de ressources en énergie
US20140277761A1 (en) * 2013-03-15 2014-09-18 Nest Labs, Inc. Controlling an hvac system in association with a demand-response event
WO2015109207A1 (fr) * 2014-01-17 2015-07-23 Stc. Unm Systèmes et procédés pour intégrer des ressources énergétiques distribuées

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CA2993611A1 (fr) 2017-02-02

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