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Coordinated Scheduling, Dispatch and Control of Demand Response, Distributed Generation and Storage Resources

Emerging distributed and renewable sources of energy present challenges in operating power systems. At the distribution level, generalizing the functional model for balancing areas and adapting it to lower hierarchical levels of the distribution grid can address some of these challenges. Ubiquitous and inexpensive sensing, control and communication technologies allow the coordinated scheduling, dispatch and control of demand response, distributed energy resources and storage assets to address energy balance and phase balance issues in the distribution grid.

The increasing penetration of renewable energy resources is raising operational issues in both wholesale power/transmission and retail/distribution. The inherent variability of renewable generation results in increased load and generation imbalances at the local as well as the system level. In retail and distribution, operational issues are aggravated inasmuch as little coordinated planning is done to locate renewable resources and, in North America, most residential customers are connected to single-phase laterals. That leads to an imbalance in the phases of distribution circuits which, in turn, result in increased losses in the distribution grid.

To some extent, demand response (DR) and energy storage can be used to compensate for the variability introduced by renewable energy resources. Through coordinated scheduling and dispatch of DR and distributed energy resources (DER), including energy storage, both energy and phase balance issues can be mitigated.

To address both phase balance as well as power balance issues, the functional model defined and in use for balancing areas at the bulk power level can be generalized and applied to lower hierarchical levels of the power system down to individual microgrids and even to individual customers. Adopting the balancing authority model as a universal model and applying it to all layers of power systems hierarchy has a number of advantages: It is well understood, field proven over the years and accepted by power system operators.

Moreover, using a common model will help address seams issues from the customer side to distribution, transmission and markets.

Using the balancing authority functional model, the problem can be divided into the following sub-problems:

  • Data acquisition and supervisory control (SCADA), to provide the required visibility into and control of generation, load, storage and grid assets. The time frame of concern for this sub-problem is real-time or near real-time. The capability is required to gather information from the field every several seconds and to provide the capability to control assets within the same time frame.
  • Short-term forecast and real-time dispatch, to economically dispatch and control the controllable assets (generation, storage, DR and distribution grid) in the short term (minutes) and with adequate headroom for second-by-second control, while taking into account resource capabilities as well as limitations imposed by the grid.
  • Forecasting and scheduling, for overall optimization of the resources for a forecasted time period (several minutes to hours), while taking into account resource constraints (max/min capabilities, DR program constraints, ramp and charge/discharge rates, and so on), as well as operating modes (such as charge, discharging and idling for storage resources).

Given the increasing impact of DR and DER at the local/distribution circuit level, and to reduce the balancing problem to smaller and more manageable sub-problems, it may be prudent to define balance zones or Micro Balancing Areas (µBA). A µBA can be defined as a collection of generation, storage and load assets that are electrically close to one another (on the same phase, circuit, feeder, substation or electrical zone).

The entire distribution grid can be divided into a number of µBAs. Every µBA needs to be equipped with adequate sensing and control devices to provide visibility to the grid assets as well as to loads, generation and storage assets. The control devices will provide the capability to control all controllable assets including DR assets, distributed generation, storage and controllable grid assets such as capacitor banks and voltage regulators.

Sensing and control capability can be provided through inexpensive sensing and control devices using powerline communications, Wi-Fi, cellular and Internet technologies, among others. Telemetered data from sensors coupled with a wealth of data available from existing Advanced Metering Infrastructures (AMI), will enable visibility into the state of the distribution grid without a need for complex mathematical algorithms and software applications such as Three-Phase Power Flows or State Estimators.

The problem of dispatch and real-time control is akin to automatic generation control. Controllable DR and DER devices are instructed to minimize imbalance, thus reducing the area control error (ACE). This process of generation/load balancing is performed in real time or in a near real-time frame. In this process storage assets can be used in a dual role both as load and generation resources, but with additional sets of constraints including state of charge and charge/discharge rates.

Forecasting and scheduling requirements include both forecasting of variable generation and load as well as their uncertainty (either in the form of confidence levels, or as average, min and max forecasts). For scheduling (hourly/sub-hourly resolution) and dispatch (minutes resolution) purposes, the load and generation uncertainties are used to determine the amount of ramping/load following, and regulation/headroom capacity that must be set aside in the process of optimizing commitment and dispatch schedules for load, generation and storage assets.

Forecasting will take on a new dimension as the focus is more on the very short term, minutes to several minutes, rather than longer time frames. This is predicated by the variability and relative unpredictability of renewable generation assets such as solar PV and micro wind turbines. For scheduling, the objective function may no longer be only the cost of generation but also may include minimizing generation/load imbalance, flattening the net load curve and minimizing phase imbalances; that will subject to a wider set of constraints such as charge/discharge rates for storage devices, line flow limits, tolerable phase imbalances, voltage limits, availability of dispatchable load assets and pre-set initial and terminal requirements for storage devices’ state of charge.

With the emerging distributed and renewable energy technologies, low cost control and communication devices, and active demand-side participation, the electric power system of the future will have a very different architecture than that of today’s. The focus will be more on zero-net-energy buildings, µBAs, microgrids, small/distributed sources of energy located near the consumer and the capability of loads to serve as controllable assets.

Contributor

  • Farrokh AlbuyehFarrokh Albuyeh, an IEEE life member, is Vice President, Smart Grid Projects, at Open Access Technology International (OATI).

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About the Smart Grid Newsletter

A monthly publication, the IEEE Smart Grid Newsletter features practical and timely technical information and forward-looking commentary on smart grid developments and deployments around the world. Designed to foster greater understanding and collaboration between diverse stakeholders, the newsletter brings together experts, thought-leaders, and decision-makers to exchange information and discuss issues affecting the evolution of the smart grid.

Contributors

Farrokh AlbuyehFarrokh Albuyeh an IEEE life member, is Vice President, Smart Grid Projects, at Open Access Technology International (OATI).
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Amro M. FaridAmro M. Farid is an IEEE member and assistant professor of engineering systems and management.
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Jianhui WangJianhui Wang is a computational engineer with the Decision and Information Sciences Division at Argonne National Laboratory.
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Zhaoyu WangZhaoyu Wang is working towards a Ph.D. degree in the School of Electrical and Computer Engineering, Georgia Institute of Technology.
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Mahmud HasanMahmud Hasan is currently pursuing a Ph.D. in electrical and computer engineering at the University of Ottawa.
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Hussein T. MouftahHussein T. Mouftah is a university distinguished professor in the School of Electrical Engineering and Computer Science at the University of Ottawa.
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