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Journal Paper Accepted: An A Priori Analytical Method for the Determination of Operating Reserve Requirements

We are happy to announce that our recent paper entitled: “An A Priori Analytical Method for the Determination of Operating Reserve Requirements”, has been accepted at the International Journal of Electrical Power & Energy Systems (IJEPES). This study comes as a result of collaboration between three universities; Masdar Institute, Dartmouth, and MIT. The work is authored by Aramazd Muzhikyan (Masdar Institute), Prof. Amro M. Farid (Dartmouth) and Prof. Kamal Youcef-Toumi (MIT).

As renewable energy becomes an ever present resource in power systems, so called “operating reserves” become increasingly important instruments for reliable power grid operations. One can think of operating reserves as additional generation capacity scheduled to compensate for real-time power supply and demand imbalances due to the existing uncertainties in forecasting not just demand but also renewable energy. On the one hand, the amount of operating reserves should be sufficient to successfully mitigate the real-time imbalances and maintain power system reliable operations. On the other hand, operating reserves are a costly commodity and they should not exceed the minimum required amount to avoid unnecessary expense. This makes accurate assessment of the operating reserve requirements vital for reliable, economic, and environmentally friendly operation of the power grid.

Currently, the necessary amount of the operating reserves is assessed based upon the power system operator experiences and the assumption that the circumstances of power system operations remain relatively unchanged. However, growing integration of renewable energy sources (RES), implementation of demand side management and transportation electrification alter the overall structure and the dynamics of the power grid. High penetration of RES brings new levels of variability and uncertainty to the grid which challenges the established practices of power system operations and the operating reserve requirement assessment methods. This newly published article provides closed-form analytical formulae that tells grid planners how much reserves to procure as they plan for more renewable energy without sacrificing economics or reliability.

While RES integration can potentially reduce the grid’s CO2 emissions and operating costs, it also brings new challenges that power grid operators need to address in order to maintain reliable operations. Wind power, for example, is known to have high intermittency; that is, the output power of a wind turbine may vary uncontrollably in a wide range. This, combined with comparably low wind forecasting accuracy, requires careful scheduling of traditional power plants and their operating reserves. Integration of solar power, on the other hand, has its own challenges. As shown in the figure below, the net load profile (the power demand minus the solar generation) of a system with integrated solar generation has a distinctive profile. It is often called the “Duck Curve” for its resemblance to the side-profile of a duck. The figure presents the net load profiles of the California Independent System Operator (CAISO) for the day of March 31 for forecasted from 2014 to 2020. The “belly” of the curve corresponds to the day time when the solar generation is at its maximum and is expected to grow with new solar power installations. With an estimated demand of 22,000MW in the year 2020, the solar generation accounts for 10,000MW or 45%; leaving only 12,000MW for the traditional generation. This situation increases the risk of overgeneration and solar generation curtailment. Another challenge is the steep jump of the net load around 6pm as solar generation wanes with the sunset and demand picks up for evening home life. Such severe variations of the net load require more careful consideration of the ramping capabilities of the scheduled generation.

duck

The CAISO duck chart (source: P. Denholm, M. O’Connell, G. Brinkman, and J. Jorgenson, “Overgeneration from Solar Energy in California: A Field Guide to the Duck Chart,” National Renewable Energy Laboratory, Nov. 2015)

This publication has developed analytical formulae for calculation of the requirements for each type of operating reserves; namely, load following, ramping and regulation. The derivations show that the operating reserve requirements are effectively defined by a set of dimensionless parameters related to the RES characteristics and the operations of the power grid. Those parameters are the penetration level, renewable energy capacity factor, variability, day-ahead and short-term forecast errors of the integrated RES, and the power grid day-ahead scheduling and real-time balancing time steps. Such analytical expressions reveal how the requirements of each type of reserve will change when, for instance, more renewable energy is integrated, renewable energy forecasting accuracy is improved, and the day-ahead scheduling time step is reduced. This study show that higher RES variability significantly increases the requirements of all three types of reserves. Also, while the impact of the RES forecast error on the ramping reserve requirement is negligible, its impact on the load following and regulation reserve requirements can dominate that of the variability. On the other hand, reducing the day-ahead scheduling time step can mitigate the impact of the variability on the load following reserve requirement while having negligible impact on the ramping and regulation reserve requirements. Also, changing the balancing time step has no noticeable impact on the load following reserve requirement, it has opposing impacts on the ramping and regulation reserve requirements. Reducing the balancing time step reduces the regulation reserve requirement but increases the ramping reserve requirement.

These formulae can be used for renewable energy integration studies, such as those conducted in NE-ISO and PJM-ISO, to assess the required amount of reserves for the planned RES installation. They can also be adapted by the state and federal standards organizations to establish reserve procurement standards that reflect the evolution of the power grid.

In depth materials on LIINES smart power grid research can be found on the LIINES website.

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Prof. Amro M. Farid contributes to World Wind Energy Association Report

The World Wind Energy Association (WWEA) technical committee has recently published a report entitled “Wind Energy 2050: On the shape of near 100% RE grid”, which studies the challenges of wind energy integration into the power grid and discusses some of the solutions to address these challenges. Chapters 5 and 6 of this report are based upon the work of Dr. Amro M. Farid and discuss the evolution the power grid as it accommodates increasing capacities of wind energy.

Wind and solar energy have already become mainstream energy sources in some regions of the world. While the integration of wind energy has numerous benefits, it also creates new challenges for power system operations. Wind energy is inherently variable and, in order to successfully accommodate it, the power system has to undergo a dramatic change.   Furthermore, and in contrast to the traditional thermal generation units, wind energy sources are non-dispatchable in the traditional sense, meaning their outputs cannot be set to the desired value. As a result, the integration of wind energy requires new approaches to power grid planning and management, including investments into improved wind forecasting techniques and reconsidering operating reserve requirements.

A conventional power system consists of relatively few centralized and dispatchable generation units, and a large number of distributed and stochastic (but accurately forecastable) loads. The electricity is delivered from the centralized and predominantly thermal power plants to the distributed electrical loads. During many decades of operations, power system operators and utilities have developed improved methods for performing their tasks. Generation scheduling and dispatch, reserve management and control technologies have matured. Load forecasting accuracy has improved significantly, reducing forecast errors to as low as a few percent. Power system security and reliability standards have also evolved accordingly.

Six key drivers currently govern the evolution of the grid, namely environment protection, reliability concerns, renewable energy integration, transportation electrification, consumer participation and power market deregulation. This evolution will lead to a diversification of the power grid energy portfolio to include more solar, wind, energy storage and demand-side resources. Thus, the newly emerging operation procedures will not only engage with generators but also with consumers and other ancillary units. As a result, the already existing control technologies and procedures will expand significantly in both number and type.  This will challenge the basic assumptions of power system design and operations. Therefore, the question is not how to mitigate wind variability, but rather how the power grid should evolve to successfully accommodate a high penetration of wind energy.

Governed by these drivers, power system generation and consumption will evolve towards more equal roles in grid operations.  First, from the perspective of dispatchability, wind energy sources resemble traditional consumption in that they are non-dispatchable and forecasted. On the other hand, the introduction of demand response creates makes some portion of the energy consumption dispatchable much like traditional power generation facilities. These two trends change the balance of dispatchability and forecastability as shown in Table 1. Second, the integration of wind energy, like most renewable energy sources, changes the spatial distribution of the generation. Wind energy sources can vary from several kWs to hundreds of MWs.  While larger facilities will continue to be installed centrally into the transmission system, the smaller facilities will be installed at the power grid periphery as distributed generation.  (See Figure 2).  This creates the potential for upstream flow in the distribution system, which was not generally allowed before, and requires the redesign of the protection system accordingly.

GridPortfolio

Table 1: Future grid generation and demand portfolio

gridstructure

Figure 2: Graphic representation of the evolving power grid structure

While many power grid phenomena overlap, the literature has traditionally treated them strictly separately. The evolution of the power grid necessitates reconsidering the distinction between  timescales.   It also requires revisiting the distinction between the transmission and distribution systems. In advocating for power grid enterprise control, our work encourages holistic approaches that work across time scales as well as the fully supply chain of electricity including both the transmission as well as the distribution system.

This work also moves away from the traditional classification of technical and economic control objectives and utilizes the concept of integrated enterprise control as a strategy for enabling holistic techno-economic performance of wind integration. As shown in Figure 3, the power system is modeled as a cyber-physical system, where the physical integration of wind energy and demand-side resources must be assessed in the context of the control, automation, and information technologies. The horizontal axis represents the energy value chain from the generation to the consumption. Finally, the third axis classifies both the generation and the consumption into dispatchable as well as stochastic units. This graph represents the scope of the power system that must address a complex mix of technological, system and societal objectives.

gridcyberphysical

Figure 3: Electrical power grid as a cyber-physical system

This work also moves away from the traditional classification of technical and economic control objectives and utilizes the concept of integrated enterprise control as a strategy for enabling holistic techno-economic performance of wind integration. As shown in Figure 3, the power system is modeled as a cyber-physical system, where the physical integration of wind energy and demand-side resources must be assessed in the context of the control, automation, and information technologies. The horizontal axis represents the energy value chain from the generation to the consumption. Finally, the third axis classifies both the generation and the consumption into dispatchable as well as stochastic units. This graph represents the scope of the power system that must address a complex mix of technological, system and societal objectives.

In depth materials on LIINES smart power grid research can be found on the LIINES website.

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