306 research outputs found

    Improved Battery Models of an Aggregation of Thermostatically Controlled Loads for Frequency Regulation

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    Recently it has been shown that an aggregation of Thermostatically Controlled Loads (TCLs) can be utilized to provide fast regulating reserve service for power grids and the behavior of the aggregation can be captured by a stochastic battery with dissipation. In this paper, we address two practical issues associated with the proposed battery model. First, we address clustering of a heterogeneous collection and show that by finding the optimal dissipation parameter for a given collection, one can divide these units into few clusters and improve the overall battery model. Second, we analytically characterize the impact of imposing a no-short-cycling requirement on TCLs as constraints on the ramping rate of the regulation signal. We support our theorems by providing simulation results.Comment: to appear in the 2014 American Control Conference - AC

    Wind Farms and Flexible Loads Contribution in Automatic Generation Control: An Extensive Review and Simulation

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    With the increasing integration of wind energy sources into conventional power systems, the demand for reserve power has risen due to associated forecasting errors. Consequently, developing innovative operating strategies for automatic generation control (AGC) has become crucial. These strategies ensure a real-time balance between load and generation while minimizing the reliance on operating reserves from conventional power plant units. Wind farms exhibit a strong interest in participating in AGC operations, especially when AGC is organized into different regulation areas encompassing various generation units. Further, the integration of flexible loads, such as electric vehicles and thermostatically controlled loads, is considered indispensable in modern power systems, which can have the capability to offer ancillary services to the grid through the AGC systems. This study initially presents the fundamental concepts of wind power plants and flexible load units, highlighting their significant contribution to load frequency control (LFC) as an important aspect of AGC. Subsequently, a real-time dynamic dispatch strategy for the AGC model is proposed, integrating reserve power from wind farms and flexible load units. For simulations, a future Pakistan power system model is developed using Dig SILENT Power Factory software (2020 SP3), and the obtained results are presented. The results demonstrate that wind farms and flexible loads can effectively contribute to power-balancing operations. However, given its cost-effectiveness, wind power should be operated at maximum capacity and only be utilized when there is a need to reduce power generation. Additionally, integrating reserves from these sources ensures power system security, reduces dependence on conventional sources, and enhances economic efficiency

    Optimal dispatch of uncertain energy resources

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    The future of the electric grid requires advanced control technologies to reliably integrate high level of renewable generation and residential and small commercial distributed energy resources (DERs). Flexible loads are known as a vital component of future power systems with the potential to boost the overall system efficiency. Recent work has expanded the role of flexible and controllable energy resources, such as energy storage and dispatchable demand, to regulate power imbalances and stabilize grid frequency. This leads to the DER aggregators to develop concepts such as the virtual energy storage system (VESS). VESSs aggregate the flexible loads and energy resources and dispatch them akin to a grid-scale battery to provide flexibility to the system operator. Since the level of flexibility from aggregated DERs is uncertain and time varying, the VESSs’ dispatch can be challenging. To optimally dispatch uncertain, energy-constrained reserves, model predictive control offers a viable tool to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. To improve the system operation, flexible VESSs can be formulated probabilistically and can be realized with chance-constrained model predictive control. The large-scale deployment of flexible loads needs to carefully consider the existing regulation schemes in power systems, i.e., generator droop control. In this work first, we investigate the complex nature of system-wide frequency stability from time-delays in actuation of dispatchable loads. Then, we studied the robustness and performance trade-offs in receding horizon control with uncertain energy resources. The uncertainty studied herein is associated with estimating the capacity of and the estimated state of charge from an aggregation of DERs. The concept of uncertain flexible resources in markets leads to maximizing capacity bids or control authority which leads to dynamic capacity saturation (DCS) of flexible resources. We show there exists a sensitive trade-off between robustness of the optimized dispatch and closed-loop system performance and sacrificing some robustness in the dispatch of the uncertain energy capacity can significantly improve system performance. We proposed and formulated a risk-based chance constrained MPC (RB-CC-MPC) to co-optimize the operational risk of prematurely saturating the virtual energy storage system against deviating generators from their scheduled set-point. On a fast minutely timescale, the RB-CC-MPC coordinates energy-constrained virtual resources to minimize unscheduled participation of ramp-rate limited generators for balancing variability from renewable generation, while taking into account grid conditions. We show under the proposed method it is possible to improve the performance of the controller over conventional distributionally robust methods by more than 20%. Moreover, a hardware-in-the-loop (HIL) simulation of a cyber-physical system consisting of packetized energy management (PEM) enabled DERs, flexible VESSs and transmission grid is developed in this work. A predictive, energy-constrained dispatch of aggregated PEM-enabled DERs is formulated, implemented, and validated on the HIL cyber-physical platform. The experimental results demonstrate that the existing control schemes, such as AGC, dispatch VESSs without regard to their energy state, which leads to unexpected capacity saturation. By accounting for the energy states of VESSs, model-predictive control (MPC) can optimally dispatch conventional generators and VESSs to overcome disturbances while avoiding undesired capacity saturation. The results show the improvement in dynamics by using MPC over conventional AGC and droop for a system with energy-constrained resources

    An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †

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    The design and implementation of management policies for plug-in electric vehicles (PEVs) need to be supported by a holistic understanding of the functional processes, their complex interactions, and their response to various changes. Models developed to represent different functional processes and systems are seen as useful tools to support the related studies for different stakeholders in a tangible way. This paper presents an overview of modeling approaches applied to support aggregation-based management and integration of PEVs from the perspective of fleet operators and grid operators, respectively. We start by explaining a structured modeling approach, i.e., a flexible combination of process models and system models, applied to different management and integration studies. A state-of-the-art overview of modeling approaches applied to represent several key processes, such as charging management, and key systems, such as the PEV fleet, is then presented, along with a detailed description of different approaches. Finally, we discuss several considerations that need to be well understood during the modeling process in order to assist modelers and model users in the appropriate decisions of using existing, or developing their own, solutions for further applications

    Demand Response on domestic thermostatically controlled loads

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