State Space Modeling and Control of Aggregated TCLs for Regulation Services in Power Grids

Hierarchical control of thermostatically controlled loads oriented smart buildings

While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can plan an active role in power systems via Demand Response (DR), defined by the Department of Energy (DOE) as “a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized” [29]. DR can provide a variety of benefits including reducing peak electric loads when the power system is stressed and fast timescale energy balancing. Therefore, DR can improve grid reliability and reduce wholesale energy prices and their volatility. This dissertation focuses on analyzing both recent and emerging DR paradigms. Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present methods for using 15-minute-interval electric load data, commonly available from C&I facilities, to help building managers understand building energy consumption and ‘ask the right questions’ to discover opportunities for DR. Additionally, we present a regression-based model of whole building electric load, i.e., a baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of 38 C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult to precisely quantify changes in electricity consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We find that, in general, baseline model error is large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation. These results have implications for DR program design and deployment. Emerging DR paradigms focus on faster timescale DR. Here, we investigate methods to coordinate aggregations of residential thermostatically controlled loads (TCLs), including air conditioners and refrigerators, to manage frequency and energy imbalances in power systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. Specifically, we compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We develop Markov Chain models to describe the temperature state evolution of heterogeneous populations of TCLs, and use Kalman filtering for both state and joint parameter/state estimation. We present a look-ahead proportional controller to broadcast control signals to all TCLs, which always remain in their temperature dead-band. Simulations indicate that it is possible to achieve power tracking RMS errors in the range of 0.26–9.3% of steady state aggregated power consumption. Results depend upon the information available for system identification, state estimation, and control. We find that, depending upon the performance required, TCLs may not need to provide state information to the central controller in real time or at all. We also estimate the size of the TCL potential resource; potential revenue from participation in markets; and break-even costs associated with deploying DR-enabling technologies. We find that current TCL energy storage capacity in California is 8–11 GWh, with refrigerators contributing the most. Annual revenues from participation in regulation vary from $10 to $220 per TCL per year depending upon the type of TCL and climate zone, while load following and arbitrage revenues are more modest at $2 to $35 per TCL per year. These results lead to a number of policy recommendations that will make it easier to engage residential loads in fast timescale DR.

A phase model approach for thermostatically controlled load demand response

This paper presents a new control structure for participation of aggregated thermostatically controlled loads (TCLs) in the frequency regulation services. The aggregation is based on the state space model (SSM) of TCLs. The proposed structure is characterized by a low communication requirement and an accurate regulation capacity estimation. The TCL control center translates the estimated regulation capacity to an identical control signal with a simple structure which is communicated to all individual TCLs. Simulation results show that the proposed control structure can accurately describe the aggregated behaviour of a large population of TCLs. The frequency regulation is realized in coordination with conventional generators.

The massive integration of renewable energy sources poses increased power generation fluctuations, necessitating enhanced regulation capacities to maintain a power balance in supply and demand. Beyond conventional generators, advanced information and communication technologies offer an alternative approach, utilizing flexible loads, mainly thermostatically controlled loads (TCLs) of buildings for grid regulation services. TCLs garner significant attention due to their unique thermal inertia capability in demand response (DR) programs. However, to manage the distributed TCLs effectively, it is vital to study the performance of the aggregator, which acts as a bridge between the grid operator and end‐users and thus assumes a pivotal role in the management of TCLs. This paper extensively reviews and compares fundamental technologies inherent to TCLs within the context of DR applications, covering basic models, response modes, control techniques, dispatch models, and strategies. Further, the review explores these critical aspects and addresses current challenges and potential prospects. Moreover, as an illustration, the study has developed and analyzed a comprehensive model of the automatic generation control (AGC) system to integrate TCLs for power balancing services in large‐scale wind energy‐integrated power systems. A power dispatch strategy has been designed for the AGC model to integrate the loading capacities of TCLs in providing grid ancillary services. The designed dispatch strategy prioritizes utilizing TCL capacity over conventional generating units, reducing the overall operation cost, and decreasing the grid’s dependency on conventional power plans. DigSilent PowerFactory software was used to obtain the simulation results, demonstrating the significant efficacy of aggregated TCL response in actively contributing to power support during grid balancing services.

It has been demonstrated that thermostatically controlled loads (TCLs) can offer regulatory service in demand response. The automatic generation control (AGC) signal was tracked in this chapter using a TCL online optimization model. The TCLs are adjusted in the optimization model using various control commands, and a clustering-based TCLs control structure is suggested. A mapping relationship between the change in temperature setpoint and the on/off state of TCLs was created in order to solve the optimization problem. Therefore, the problem is converted into a 0-1 nonlinear programming problem that can be solved using a binary dynamic multi-swarm particle swarm optimization method (DMS-PSO-CLS). The binary DMS-PSO-CLS algorithm is an effective way to tackle the optimization problem, according to simulation results. It is expected that TCLs will be controlled separately for frequency regulation in the grid.

A significant portion of electricity consumed worldwide is used to power thermostatically controlled loads (TCLs) such as air conditioners, refrigerators, and water heaters. Because the short-term timing of operation of such systems is inconsequential as long as their long-run average power consumption is maintained, they are increasingly used in demand response (DR) programs to balance supply and demand on the power grid. Here, we use the phase model representation of TCLs to design and evaluate control policies for modulating the power consumption of aggregated loads with parameter heterogeneity and stochastic drift. In particular, we design a phase model based minimum energy control law that modulates the duty cycle of a TCL in order to reduce its energy consumption. We further demonstrate that the designed control policy can be used to effectively modulate the aggregate power of a heterogeneous TCL population while maintaining load diversity and minimizing power overshoots. More importantly, an acceptable quality of service for the utility customers is maintained. The developed control policy can be used to compensate for the intermittent generation by renewable energy sources (RESs) such as wind and solar by regulating the aggregated load of a TCL ensemble, and hence will facilitate the broader integration of RESs.

Thermostatically controlled loads (TCLs) have demonstrated their potentials in demand response. One of the key challenges for TCLs to be integrated into the system-level operation is building a compact aggregated model, in which the TCL primary behaviors are accurately captured. In this paper, TCLs are aggregated as a virtual generator and two batteries according to their different compressor types and control methods for smoothing out multi-time-scale variability of wind power generation. This will bring system operator great convenience to manage TCLs and conventional components when the system-level decisions are made. Accordingly, accurate parameters of virtual generator and batteries are critical to effectively coordinate TCLs with other resources in the system operation. However, it tends to be difficult to obtain such aggregated parameters as a result of insufficient data for each TCL. To address this problem, high-dimensional model representation (HDMR) is introduced to estimate the aggregated parameters of virtual generator and batteries using the probability distribution of TCL parameters. A numerical simulation study demonstrates that aggregated parameters of virtual generator and batteries can be accurately estimated by HDMR. And virtual generator and batteries are able to follow actual behaviors of TCL populations in power system operations.

Thermostatically controlled loads (TCLs) represent a valuable source of flexibility for the system. Depending on network needs, these devices could alter their nominal energy consumption and provide multiple ancillary services, facilitating the cost-effective transition to a low-carbon power system. Previous work mainly focused on investigating single service provision from TCLs, while intersections among different services have not been considered. Furthermore, the intrinsic energy payback effect was not fully included within optimisation tools for TCL scheduling. This paper presents a novel demand side response model (DSRM), which enables the optimal scheduling of energy/power consumption of a heterogeneous population of TCLs and the simultaneous allocation of multiple ancillary services. The model explicitly considers the effect of the energy recovery after delivering the services so that the deliverability of scheduled services from TCLs is always guaranteed. The proposed DSRM is integrated into an advanced stochastic unit commitment model to investigate the system benefits of the flexibility from TCLs. Case studies demonstrate that: 1) time-varying provision of multiple services from TCLs significantly increases their benefits; 2) TCL operation which aims to minimise the amplitude of the energy recovery causes sub-optimal utilisation of the devices; and 3) ignoring the energy payback leads to overestimate the TCL value.

Thermostatically controlled loads (TCLs) are playing a promising role in demand response (DR) programs as a result of their huge capability. It tends to be difficult to accurately obtain aggregated performances of TCLs when they are controlled to provide services for the grid as a result of the insufficient and inaccurate physical parameters. In order to address this problem, high dimensional model representative (HDMR) is employed to build the relationship between aggregated performance index (i.e. the baseline power, capacity and cold load pickup) of TCLs and the uncertain physical parameters. Then aggregated TCL performances can be estimated by fewer important parameters quickly and accurately, which will save lots of efforts for constructing TCL models with DR functionality by paying less attention to unimportant parameters. Simulations demonstrate that the aggregated performance index of TCLs can be accurately calculated by HDMR.

Grouping control strategy for aggregated thermostatically controlled loads

The development of smart metering technologies makes it possible to apply the thermostatically controlled loads (TCLs) into the demand response (DR) programs such as direct load control (DLC). To characterize the dynamics of a large number of TCLs, the computationally efficient sate-queuing (SQ) model is designed in some recent research works. However, very few attentions have been given to its accuracy. This paper reviews the existing SQ modeling approach, and reveals its limitations in the accuracy by comprehensive comparison and analysis. The results show that the aggregate TCLs do not exactly have the Markov property, and the existing SQ modeling approach cannot accurately characterize the aggregate dynamics of the TCLs. The findings of this paper are very important for the management and control of large amount of TCLs.

This paper proposes a strategy to control a group of thermostatically controlled loads (TCLs) such that the variability in their aggregate load is reduced. This strategy could be deployed in areas of a distribution network that experience voltage excursions due to net load fluctuations, such as areas with high penetrations of photovoltaic (PV) generation and/or electric vehicles (EVs), We limit variation in the power consumption of a group of TCLs using a control strategy previously developed for large aggregations of switched systems. Using this strategy, we constrain the number of TCLs that are on (i.e., actively consuming power) between upper and lower bounds. In simulations, the control strategy successfully decreases the range over which TCL power consumption varies. Percent reductions in range are greatest for medium group sizes: we find a median reduction of 82% for groups of 50 TCLs, 74% for groups of 1000 TCLs, and 59% for groups of 5 TCLs. Reducing the variability of a distribution network's power injections helps to reduce voltage variability. In a simulation of a distribution line supplying 25 households, half with PV systems, the control strategy reduces the total range of voltage by 0.02 p.u. and prevents a violation of the 0.95 p.u. limit. Lastly, we propose a new control strategy for a more realistic TCL model that includes compressor lockout. The new strategy performs comparably to the original strategy and is demonstrated through simulation.

This paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user's information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model.

More flexibility is desirable with the proliferation of variable renewable resources for balancing supply and demand in power systems. Thermostatically controlled loads (TCLs) attract tremendous attentions because of their specific thermal inertia capability in demand response (DR) programs. To effectively manage numerous and distributed TCLs, intermediate coordinators, e.g., aggregators, as a bridge between end users and dispatch operators are required to model and control TCLs for serving the grid. Specifically, intermediate coordinators get the access to fundamental models and response modes of TCLs, make control strategies, and distribute control signals to TCLs according the requirements of dispatch operators. On the other hand, intermediate coordinators also provide dispatch models that characterize the external characteristics of TCLs to dispatch operators for scheduling different resources. In this paper, the bottom-up key technologies of TCLs in DR programs based on the current research have been reviewed and compared, including fundamental models, response modes, control strategies, dispatch models and dispatch strategies of TCLs, as well as challenges and opportunities in future work.