State-queuing Model Research Articles

An analogue on/off state-switching control method suitable for inverter-based air conditioner load cluster participating in demand response

With the significant trend towards variable frequency in heating ventilating and air-conditioner (HVAC) loads, the inverter-based air conditioner load (IACL) clusters are poised to become the mainstay in participating in demand response (DR). During the process of DR, once the indoor temperature exceeds the comfort constraint, HVAC load must be forced to exit DR, which brings correlation to the behaviour of the preceding and following periods, making cluster optimization modelling and solution difficult. In order to solve the problems, this paper proposes an analogue ON/OFF state-switching (AOSS) control method suitable for IACL cluster participating in DR. Firstly, the critical working point corresponding to the comfort constraint is analysed for eliminating the temporal coupling for IACL. Then, an AOSS control method is proposed based on the critical point analysis. The feasibility of the proposed AOSS control is validated by the experimental results, with the Hisense KFR-75W/T08SBp-A2 inverter-based air conditioning DR experimental platform. Furthermore, by using the proposed AOSS control, a day-ahead scheduling model is constructed, and, the traditional state-queuing (SQ) model is transplanted to the IACL cluster for short-term power control. And the case studies verified the effectiveness and applicability of the day-ahead scheduling model and the transplanted SQ model.

Research on Packet Control Strategy of Constant-Frequency Air-Conditioning Demand Response Based on Improved Particle Swarm Optimization Algorithm

To better utilize air-conditioning load in terms of demand side response potential and improve precision and speed, a control strategy of traditional temperature-control air conditioning, determining frequency load as the research object, and air conditioning determined with a frequency theory model and the Monte Carlo method, were used to construct a power aggregation model. This was combined with user feedback to study thermal comfort as a lateral load demand response resource to determine the potential demand response of power systems. Based on the state-queuing model, an air-conditioning load grouping control strategy using an improved particle swarm optimization algorithm is proposed which can accurately control the air-conditioning load following the reference load.

A regional power grid operation and planning method considering renewable energy generation and load control

The active development and application of renewable energy, such as wind power and solar power, has become an inevitable tendency, which will promote the increase of the proportion of renewable energy generation accordingly in regional power grids. The intermission and randomness of renewable energy generation lead to enormous challenges in the operation and planning of power systems. One of the current methods to accommodate and reduce renewable energy fluctuation is conventional storage devices, e.g. battery energy storage devices, but this approach has the limitation of a high operating cost, which hinders the technology from popularizing. We present a novel load control algorithm employing the technology of residential thermostatically controlled appliances to maximize renewable energy usage in regional power grids. The thermal dynamics of the thermostatically controlled loads are modeled as a simplified first-order equivalent thermal parameter model. The new load control algorithm, based on the state-queueing model with modified colored power algorithm, is developed to manage activities of residential thermostatically controlled appliances, which allows customers to set different colors as the response levels of their appliances. Such an algorithm can ensure both the comfort level and the fairness of customers. Both a practical case and a real case are applied to demonstrate the effectiveness and practicability of the proposed algorithm.

The extended 2-dimensional state-queuing model for the thermostatically controlled loads

The thermostatically controlled loads (TCLs) are one of the best candidates to participate in the demand response (DR) programs such as direct load control (DLC). The dynamics of large amounts of TCLs are characterized by the state-queuing (SQ) in many recent research works. However, the traditional SQ model is not accurate enough due to the limited information conveyed in the state vector. By analyzing the limitations of the traditional SQ modelling approach, this paper proposes a 2D SQ modelling approach for the TCLs that extends the state vector to a 2-dimensional state matrix. The first dimension represents the on/off states and the TCLs’ temperature intervals, whereas the second dimension reflects the temperature-varying-rate (TVR) information. And the transition of the states is established by the operation of the sub-matrixes. By introducing the additional TVR information, the accuracy of the modelling is improved. Numerous testing results verify the effectiveness of the proposed method.

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

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.

Performance evaluation of controlling thermostatically controlled appliances as virtual generators using comfort‐constrained state‐queueing models

This study presents a detailed performance evaluation of comfort-constrained state-queueing model for integration of thermostatically controlled appliances (TCAs) as virtual generators (VGs). The configuration of VG models is presented. The heating mode of heating, ventilating and air-conditioning (HVAC) unit – heat pump is used as an example TCA load to demonstrate VG operation. First, an estimated baseline of the aggregated HVAC loads is obtained based on outdoor temperature forecasts using thermal dynamic models. Then, the control method of HVAC units for intra-hour load balancing is proposed. The effects of different comfort-constraints, outdoor temperature profiles and thermal deadbands are modelled to evaluate the performance of TCAs as VGs. The technical functionality of the TCA-VG is described and demonstrated by the modelling results.

Modeling Uncertainties in Aggregated Thermostatically Controlled Loads Using a State Queueing Model

To study the impacts of price responsive demand on the electric power system requires better load models. This paper discusses the modeling of uncertainties in aggregated thermostatically controlled loads using a state queueing (SQ) model. The cycling times of thermostatically controlled appliances (TCAs) vary with the TCA types and sizes, as well as the ambient temperatures. The random consumption of consumers, which shortens or prolongs a specific TCA cycling period, introduces another degree of uncertainty. By modifying the state transition matrix, these random factors can be taken into account in a discrete SQ model. The impacts of considering load diversity in the SQ model while simulating TCA setpoint response are also studied.

A State-Queueing Model of Thermostatically Controlled Appliances

This paper develops a state-queueing model to analyze the price response of aggregated loads consisting of thermostatically controlled appliances (TCAs). Assuming a perfectly diversified load before the price response, we show that TCA setpoint changes in response to the market price will result in a redistribution of TCAs in on/off states and therefore change the probabilities for a unit to reside in each state. A randomly distributed load can be partially synchronized and the aggregated diversity lost. The loss of the load diversity can then create unexpected dynamics in the aggregated load profile. Raising issues such as restoring load diversity and damping the peak loads are also addressed in this paper.