Engineering topics
Publications and source records attributed to Stephen, Gordon.
This tool provides interfaces for ingesting real-time unit commitment data (e.g., from a grid operator's control room energy management system) and calculating system inertia estimates based on the H-constant method, before relaying those estimates to customizable visualization or logging outputs.
As our power grids transition toward a decarbonized energy mix, ensuring reliability and provision of grid services remains paramount. The power system has always been heavily influenced by the weather - extreme temperatures determine the timing of peak demand, winter cold snaps can limit natural gas supply, gas turbine reliability and output are affected by ambient conditions, and hydro output varies seasonally and annually. However, as the grid increasingly relies on variable renewable energy (VRE), like wind and solar, the attention to reliability and weather conditions is increasingly important. The implications of changing reliability are large. The Electric Reliability Council of Texas (ERCOT) rolling blackouts from earlier this year impacted millions of people across the state and could be seen from space (Figure 1).
Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.
In competitive wholesale electricity markets, significant effort is devoted to designing markets that set efficient prices for maintaining supply-demand balance. One factor that can impact prices is administratively-set scarcity pricing, which sets prices to a preset level when the market is not able to meet operating reserve or energy requirements. When energy and operating reserves are co-optimized, assumptions surrounding operating reserve requirements and scarcity pricing can impact system-wide price outcomes for both operating reserves and energy. This study uses production cost modeling of an ERCOT-like system to evaluate the impact of operating reserve eligibility, scarcity pricing, and quantity rules on electricity prices, and therefore also on generator revenues. Results reveal economic and operational benefits with allowing open participation in reserve markets, as this enables greater access to the full set of capable resources at lowest cost. Furthermore, both energy and reserve prices are strongly impacted by reserve scarcity pricing events, which reveals that reserve scarcity pricing assumptions can impact price outcomes even for units not providing reserves. This study highlights the importance of operating reserve scarcity pricing rules because of the strong coupling between energy and reserve prices and because these rules serve as proxies for true price responsive demand.
This presentation provides summaries of three reports selected from a body of work that examines potential reliability challenges related to planning future power systems with higher solar photovoltaic (PV) penetrations in the western United States. The three topics covered are 1) Power system flexibility requirements and supply; 2) Resource adequacy and the capacity credit of solar; 3) Simulating distributed energy resource responses to transmission system-level faults considering IEEE 1547 performance categories on three major WECC transmission paths. The full reports and related resources are available at the project website, https://www.westernenergyboard.org/western-interstate-energy-board/barrier-mitigation-to-enhanced-distributed-solar-photovoltaic/. This presentation is targeted toward a technical audience. A companion policy presentation provides higher-level summaries of the same material.
This study examines the impact of reserve margin-based reliability assessment, as commonly used in capacity expansion models, on planning resource-adequate power systems under high penetrations of solar photovoltaics (PV). As a generation resource, PV is operationally different from the conventional dispatchable resources for which most capacity expansion models were designed. The question this study attempts to answer is whether large amounts of PV on a system (in this case, the Western Interconnection of North America) would bias the results of conventional reserve margin-based capacity expansion modeling towards an over- or under-provisioning of resource adequacy. This analysis used NREL’s Resource Planning Model (RPM) for capacity expansion modeling and NREL’s Probabilistic Resource Adequacy Suite (PRAS) for resource adequacy assessment. RPM uses a reserve margin requirement to enforce resource adequacy. PRAS, a collection of tools for studying the resource adequacy of power systems and the adequacy contributions of individual resources on a probabilistic basis, was used to compute multiple resource adequacy metrics across a number of simulated scenarios and system representations with differing regional detail. In all cases, including high PV penetrations (up to 33% annual generation from PV, interconnection-wide), RPM was able to produce resource-adequate systems as measured by normalized expected unserved energy and loss-of-load expectation results from PRAS. The accuracy of reserve margin approaches depends heavily on the underlying assumptions informing the capacity credit assigned to variable and energy-limited resources, particularly when such resources are abundant in the modeled system. RPM’s standard methodology for estimating variable and flexible resources’ capacity contributions, which is based on the top 100 hours of net load, did not appear to systematically undervalue or overvalue variable generation relative to a more rigorous equivalent firm capacity assessment using PRAS, although both over- and under-valuations were observed in specific scenarios. In the worst cases, the top 100 hour method underestimated the equivalent firm capacity of PV by two percentage points, and overestimated the equivalent firm capacity of PV by five percentage points. Calculating capacity contributions based on the top 10 hours of net load systematically underestimated equivalent firm capacities at more modest PV penetrations, but was often a better approximation of equivalent firm capacity than the existing 100-hour approach in scenarios with higher PV penetrations.
The Electricity Markets Investment Suite Approximate Equilibrium (EMIS-AE) package is developed at NREL and provides a solution capability to solve generation expansion equilibrium problems in the electricity markets. EMIS-AE is designed to capture the evolution of the electricity generation portfolio resulting from the interactions of heterogeneous investors under different policy and market designs. The investment problem for each generation company (GENCO) is a bi-level problem with the investment decision made in the upper level and market clearing condition in the lower level, which traditionally is represented as a Mathematical Program with Equilibrium Constraint (MPEC). EMIS-AE provides a predictive model to be trained for estimating the system-wide revenues for each technology type across energy, ancillary services and capacity markets given the amount of installed capacity on the grid. The profit maximization investment problem for each GENCO is solved using a global search algorithm, which uses the predictive model to evaluate the objective function. To solve for the strategic equilibrium, each GENCO’s problem is plugged into a diagonalization algorithm that is generally used in multi-leader, single-follower bi-level problems.
The Electricity Markets Investment Suite Agent-based Simulation (EMIS-AS) model is an agent-based model developed at NREL for simulating annual investment and retirement decisions of heterogeneous investors in the electricity sector. EMIS-AS is designed to capture the evolution of the electricity generation portfolio resulting from the interactions of heterogeneous investors under different policy and market designs. EMIS-AS not only allows end-users to customize market products and rules, but also to capture investors' heterogeneous financing parameters, technology preferences, beliefs about the future (forecasts), ability to update those forecasts, and risk preferences under uncertainty.
PLEXOSUtils.jl provides functionality for loading in PLEXOS result zipfiles on disk into in-memory Julia data structures. The data structures mirror the relationships defined in the original PLEXOS result XML and provide a convenient starting point for writing application-specific code.