Marcelo A. Elizondo

Aggregate model for heterogeneous thermostatically controlled loads with demand response

Due to the potentially large number of Distributed Energy Resources (DERs) - demand response, distributed generation, distributed storage - that are expected to be deployed, it is impractical to use detailed models of these resources when integrated with the transmission system. Being able to accurately estimate the transients caused by demand response is especially important to analyze the stability of the system under different demand response strategies, where dynamics on time scales of seconds to minutes are important. On the other hand, a less complex model is more amenable to study stability of a large power system, and to design feedback control strategies for the population of devices to provide ancillary services. The main contribution of this paper is to develop an aggregated model for a heterogeneous population of Thermostatic Controlled Loads (TCLs) to accurately capture their collective behavior under demand response. The aggregated model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. The developed aggregated model is validated against simulations of thousands of detailed building models using GridLAB-D (an open source distribution simulation software) under both steady state and severe dynamic conditions.

Centralized and decentralized control for demand response

Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generators are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their response performances in terms of delay time and predictability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the controllability and predictability of centralized control to achieve the best performance of the smart grid.

Energy Storage for Power Systems Applications: A Regional Assessment for the Northwest Power Pool (NWPP)

Wind production, which has expanded rapidly in recent years, could be an important element in the future efficient management of the electric power system; however, wind energy generation is uncontrollable and intermittent in nature. Thus, while wind power represents a significant opportunity to the Bonneville Power Administration (BPA), integrating high levels of wind resources into the power system will bring great challenges to generation scheduling and in the provision of ancillary services. This report addresses several key questions in the broader discussion on the integration of renewable energy resources in the Pacific Northwest power grid. More specifically, it addresses the following questions: a) how much total reserve or balancing requirements are necessary to accommodate the simulated expansion of intermittent renewable energy resources during the 2019 time horizon, and b) what are the most cost effective technological solutions for meeting load balancing requirements in the Northwest Power Pool (NWPP).

National Assessment of Energy Storage for Grid Balancing and Arbitrage: Phase 1, WECC

To examine the role that energy storage could play in mitigating the impacts of the stochastic variability of wind generation on regional grid operation, the Pacific Northwest National Laboratory (PNNL) examined a hypothetical 2020 grid scenario in which additional wind generation capacity is built to meet renewable portfolio standard targets in the Western Interconnection. PNNL developed a stochastic model for estimating the balancing requirements using historical wind statistics and forecasting error, a detailed engineering model to analyze the dispatch of energy storage and fast-ramping generation devices for estimating size requirements of energy storage and generation systems for meeting new balancing requirements, and financial models for estimating the life-cycle cost of storage and generation systems in addressing the future balancing requirements for sub-regions in the Western Interconnection. Evaluated technologies include combustion turbines, sodium sulfur (Na-S) batteries, lithium ion batteries, pumped-hydro energy storage, compressed air energy storage, flywheels, redox flow batteries, and demand response. Distinct power and energy capacity requirements were estimated for each technology option, and battery size was optimized to minimize costs. Modeling results indicate that in a future power grid with high-penetration of renewables, the most cost competitive technologies for meeting balancing requirements include Na-S batteries and flywheels.

Development and Validation of Aggregated Models for Thermostatic Controlled Loads with Demand Response

One of the salient features of the smart grid is the wide spread use of distributed energy resources (DERs) like small wind turbines, photovoltaic (PV) panels, energy storage (batteries, flywheels, etc), Plug-in Hybrid Electric Vehicles (PHEVs) and controllable end-use loads. The affect of these distributed resources on the distribution feeder and on transmission system operations needs to be understood. Due to the potentially large number of DERs that are expected to be deployed, it is impractical to use detailed models of these resources when integrated with the transmission system. This paper focuses on developing aggregated models for a population of Thermostatic Controlled Loads (TCLs) which are a class of controllable end-use loads. The developed reduced-order models are validated against simulations of thousands of detailed building models using an open source distribution simulation software (Grid LAB-D) under both steady state and dynamic conditions (thermostat setback program as a simple form of demand response).

A review of dynamic generator reduction methods for transient stability studies

Due to the complex interconnected nature of power system, aggregate or reduced system is commonly used by operators and engineers for various power system studies. Transient stability studies need dynamically reduced models to include the dynamic effects of the generating units. Dynamic generator reduction is therefore an important component of power system dynamic reduction. The many different methods for generator reduction in power systems taken from the earliest available literature are discussed. This paper aims to provide an easy reference to researchers interested in exploring dynamic reduction of power systems.

Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal “basis” of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.

Optimal control of distributed energy resources using model predictive control

In an isolated power system (rural microgrid), distributed energy resources (DERs), such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizing costs associated with low battery life of energy storage, and maximizing the ability to maintain real-time power balance during operations. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem under high penetration of wind. Simulation studies using different prediction horizons further demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in the system caused by wind and demand.

Distributed Hierarchical Control Architecture for Transient Dynamics Improvement in Power Systems

In this paper, a novel distributed hierarchical control architecture is proposed for large-scale power systems. The newly proposed architecture facilitates faster and more accurate frequency restoration during primary frequency control, by providing decentralized robust control to several selected pilot generators in each area. At the local level, these decentralized robust controllers are designed to quickly damp oscillations and restore frequency after large faults and disturbances in the system. Incorporating this supplementary governor control helps the system reach the nominal frequency without necessarily requiring secondary frequency control. Thus, at the area level, automatic generation control (AGC) actions are alleviated in terms of conducting frequency restoration. Moreover, at the area level, AGC coordinates with the decentralized robust controllers to successfully perform tie-line power balancing, while efficiently damping low-frequency inter-area oscillations. The interaction of local and area controllers is validated through detailed simulations.

Evaluating the Feasibility to Use Microgrids as a Resiliency Resource

Regulated electricity utilities are required to provide safe and reliable service to their customers at a reasonable cost. To balance the objectives of reliable service and reasonable cost, utilities build and operate their systems to operate under typical historic conditions. As a result, when abnormal events such as major storms or disasters occur, it is not uncommon to have extensive interruptions in service to the end-use customers. Because it is not cost effective to make the existing electrical infrastructure 100% reliable, society has come to expect disruptions during abnormal events. However, with the increasing number of abnormal weather events, the public is becoming less tolerant of these disruptions. One possible solution is to deploy microgrids as part of a coordinated resiliency plan to minimize the interruption of power to essential loads. This paper evaluates the feasibility of using microgrids as a resiliency resource, including their possible benefits and the associated technical challenges. A use-case of an operational microgrid is included.