Abstract: 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.
Abstract: 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.
Abstract: 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.
Abstract: This paper proposes a novel model-predictive control scheme which combines both economic and security objectives to mitigate the effects of severe disturbances in electrical power systems. A linear convex relaxation of the AC power flow is employed to model transmission line losses and conductor temperatures. Then, a receding-horizon model predictive control (MPC) strategy is developed to alleviate line temperature overloads and prevent the propagation of outages. The MPC strategy seeks to alleviate temperature overloads by rescheduling generation, energy storage and other network elements, subject to ramp-rate limits and network limitations. The MPC strategy is illustrated with simulations of the IEEE RTS-96 network augmented with energy storage and renewable generation.
Abstract: 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.
Abstract: 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 transformer's thermal constraint.
Abstract: 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.
Abstract: Through a reformulation of energy hubs, this paper presents a novel format for describing general energy hub networks. This format underpins the development of tools for analyzing large-scale interconnected energy hub networks. The tools are developed in MATLAB and seamlessly interface with CPLEX optimization libraries to allow users to quickly implement and solve optimal scheduling problems. Our application takes a concise network description file as input, uses MATLAB to build the matrices for the entire system, and outputs the requested results from CPLEX. The work presented herein supports electrical and natural gas networks, wind generating capacity, district heat loads, and the main elements of energy hubs (converters and energy storage). Addition of other energy types and hub elements is straightforward.