Mads Almassalkhi

Model-Predictive Cascade Mitigation in Electric Power Systems With Storage and Renewables—Part I: Theory and Implementation

A novel model predictive control (MPC) scheme is developed for mitigating the effects of severe line-overload disturbances in electrical power systems. A piece-wise linear convex approximation of line losses is employed to model the effect of transmission line power flow on conductor temperatures. Control is achieved through a receding-horizon model predictive control (MPC) strategy which alleviates line temperature overloads and thereby prevents the propagation of outages. The MPC strategy adjusts line flows by rescheduling generation, energy storage and controllable load, while taking into account ramp-rate limits and network limitations. In Part II of this paper, the MPC strategy is illustrated through simulation of the IEEE RTS-96 network, augmented to incorporate energy storage and renewable generation.

Incentive-based coordinated charging control of plug-in electric vehicles at the distribution-transformer level

Distribution utilities are becoming increasingly aware that their networks may struggle to accommodate large numbers of plug-in electric vehicles (PEVs). In particular, uncoordinated overnight charging is expected to be problematic, as the corresponding aggregated power demand exceeds the capacity of most distribution substation transformers. In this paper, a dynamical model of PEVs served by a single temperature-constrained substation transformer is presented and a centralized scheduling scheme is formulated to coordinate charging of a heterogeneous PEV fleet. We employ the dual-ascent method to derive an iterative, incentive-based and non-centralized implementation of the PEV charging algorithm, which is optimal upon convergence. Then, the distributed open-loop problem is embedded in a predictive control scheme to introduce robustness against disturbances. Simulations of an overnight charging scenario illustrate the effectiveness of the so-obtained incentive-based coordinated PEV control scheme in terms of performance and enforcing the transformers thermal constraint.

Model-Predictive Cascade Mitigation in Electric Power Systems With Storage and Renewables-Part II: Case-Study

The novel cascade-mitigation scheme developed in Part I of this paper is implemented within a receding-horizon model predictive control (MPC) scheme with a linear controller model. This present paper illustrates the MPC strategy with a case-study that is based on the IEEE RTS-96 network, though with energy storage and renewable generation added. It is shown that the MPC strategy alleviates temperature overloads on transmission lines by rescheduling generation, energy storage, and other network elements, while taking into account ramp-rate limits and network limitations. Resilient performance is achieved despite the use of a simplified linear controller model. The MPC scheme is compared against a base-case that seeks to emulate human operator behavior.

Cascade mitigation in energy hub networks

The paper establishes a formulation for energy hub networks that is consistent with mixed-integer quadratic programming problems. Line outages and cascading failures can be considered within this framework. Power flows across transmission lines and pipelines are compared with flow bounds, and tripped when violations occur. The outaging of lines is achieved using a mixed-integer disjunctive model. A model predictive control (MPC) strategy is developed to mitigate cascading failures, and prevent propagation of outages from one energy-carrier network to another. The MPC strategy seeks to alleviate overloads by adjusting generation and storage schedules, subject to ramp-rate limits and governor action. If overloads cannot be eliminated by rescheduling alone, MPC determines the minimum amount of load that must be shed to restore system integrity. The MPC strategy is illustrated using a small 12 hub network and a much larger network that includes 132 energy hubs.

Impact of energy storage on cascade mitigation in multi-energy systems

In this paper, we establish energy-hub networks as multi-energy systems and present a relevant model-predictive cascade mitigation control (MPC) scheme within the framework of energy hubs. The performance of both open- and closed-loop mitigation schemes is investigated for various energy storage scenarios. The results are illustrated using a small 11-hub network and a larger 69-hub network and show that sizing and performance ratings of energy storage devices have significant effect on cascade mitigation control in multi-energy systems. Specifically, we conclude that increasing energy storage capacity and limiting the rate of energy delivery improves long-term performance of our closed-loop MPC scheme.

Enabling city-scale multi-energy optimal dispatch with energy hubs

This paper further extends the class of energy hubs that can be modeled with a concise system description and in a computationally efficient optimization framework to permit rapid analysis of multi-energy systems. The new hub models are then embedded in the multi-energy system analysis tool Hubert and solves the multi-period optimal dispatch (MPOD) problem for a broad class of energy hub systems. Specifically, this paper presents recent improvements developed for Hubert, including the use of piece-wise linear modeling to capture nonlinear converter efficiencies, limits on hub component outputs to reflect physical limits of converters, and hub emission limits. These developments enable appropriate modeling of multi-energy micro-grids and cities and are illustrated with a multi-energy model of The University of Vermonts campus under different capital planning scenarios and modeling assumptions. Interestingly, the shortcomings of using a traditional constant-efficiency hub converter model are illustrated with an energy storage sizing application for multi-energy systems. It is shown that the traditional hub models can significantly undersize energy storage as compared to the more accurate piece-wise linear energy hub formulation.

Incorporating storage as a flexible transmission asset in power system operation procedure

Managing uncertainty caused by the large-scale integration of wind power is a challenge in both the day-ahead planning and real-time operation of a power system. Increasing system flexibility is the key factor in preserving operational reliability. While distributed energy storage is a promising way to increase system flexibility, its benefits have to be optimally exploited to justify its high installation cost. Optimally operating distributed energy storage in an uncertain environment requires decisions on multiple time scales. Additionally, storage operation needs to be coordinated with the scheduling and dispatching of conventional generators. This paper proposes and demonstrates a three-level framework for coordinating day-ahead, near real-time and minute-by-minute control actions of conventional generating units and distributed energy storage. A case study illustrates the interactions between the three levels and the effectiveness of this approach both in terms of economics and operational reliability.

Towards a macromodel for Packetized Energy Management of resistive water heaters

This paper presents a state bin transition (macro)model for a large homogeneous population of thermostatically controlled loads (TCLs). The energy use of these TCLs is coordinated with a novel bottom-up asynchronous, anonymous, and randomizing control paradigm called Packetized Energy Management (PEM). A macro-model for a population of TCLs is developed and then augmented with a timer to capture the duration and consumption of energy packets and with exit-ON/OFF dynamics to ensure consumer quality of service. PEM permits a virtual power plant (VPP) operator to interact with TCLs through a packet request mechanism. The VPP regulates the proportion of accepted packet requests to allow tight tracking of balancing signals. The developed macro-model compares well with (agent-based) micro-simulations of TCLs under PEM and can be represented by a controlled Markov chain.

Packetized energy management: Asynchronous and anonymous coordination of thermostatically controlled loads

Because of their internal energy storage, electrically powered, distributed thermostatically controlled loads (TCLs) have the potential to be dynamically managed to match their aggregate load to the available supply. However, in order to facilitate consumer acceptance of this type of load management, TCLs need to be managed in a way that avoids degrading perceived quality of service (QoS), autonomy, and privacy. This paper presents a real-time, adaptable approach to managing TCLs that both meets the requirements of the grid and does not require explicit knowledge of a specific TCLs state. The method leverages a packetized, probabilistic approach to energy delivery that draws inspiration from digital communications. We demonstrate the packetized approach using a case-study of 1000 simulated water heaters and show that the method can closely track a time-varying reference signal without noticeably degrading the QoS. In addition, we illustrate how placing a simple ramp-rate limit on the aggregate response overcomes synchronization effects that arise under prolonged peak curtailment scenarios.

Model-predictive cascade mitigation in electric power systems with storage and renewables, Part I: Theory and implementation

A novel model predictive control (MPC) scheme is developed for mitigating the effects of severe line-overload disturbances in electrical power systems. A piece-wise linear convex approximation of line losses is employed to model the effect of transmission line power flow on conductor temperatures. Control is achieved through a receding-horizon model predictive control (MPC) strategy which alleviates line temperature overloads and thereby prevents the propagation of outages. The MPC strategy adjusts line flows by rescheduling generation, energy storage and controllable load, while taking into account ramp-rate limits and network limitations. In [1], the MPC strategy is illustrated through simulation of the IEEE RTS-96 network, augmented to incorporate energy storage and renewable generation.