ARPA-E Archived Funding Opportunities

On November 7, ARPA-E hosted a webinar on this RFI. To view a recording of the webinar and the slides presented, please visit https://www.youtube.com/watch?v=PbOBzwbxXCwAs.

ARPA-E seeks input on the design of a competition (carried out in multiple phases) to accelerate the development and comprehensive evaluation of new solution methods for grid optimization. Specifically, ARPA-E seeks to provide a platform for the identification of transformational and disruptive methods for solving power system optimization problems including Security Constrained Optimal Power Flow (OPF) and Security Constrained Unit Commitment (UC). Algorithms that perform well in the proposed competition will enable increased grid flexibility, reliability and safety, while also significantly increasing economic and energy security, energy efficiency and substantially reducing the costs of integrating variable renewable generation technologies into the electric power system in the United States.

With this RFI, ARPA-E is soliciting opinions regarding various details of the competition design—including the baseline problem specifications, competition rules, eligibility for participation, scoring metrics, criteria for winning, prize structure and online competition computational platform design details. ARPA-E is anticipating total prize money in this competition of $3,500,000, subject to the availability of appropriated funds. Designing a competition that identifies and validates the most promising new grid optimization solution methods in a fair and transparent manner is critically important.

Background:

Reliable operation of electric power systems requires the real-time matching of instantaneous electricity generation and demand. Achieving a continuous match between supply and demand requires utilities, grid operators, and other stakeholders to use a variety of sophisticated optimization algorithms operating across a wide range of timescales. A number of emerging trends, including the integration of high penetrations of renewable electricity generation, changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources (including storage), will substantially alter the operation and control of electric grids over the next several decades. This expected growth in system complexity will require the development of substantially improved software optimization and control tools to assist grid operators, and deliver the societal benefits of improved grid performance.

Many new grid optimization methods have been proposed in the research community in recent years.[1],[2],[3],[4] In addition, many claims have been made regarding the possible practical benefits that these new algorithms might offer utilities and grid system operators. Today, it is extremely difficult to compare strengths and weaknesses of different proposed approaches. The vast majority of reports only test new algorithms on relatively small-scale models that often must be heavily modified to satisfy the modeling requirements for each algorithm. Computational experiments are also typically conducted on a wide range of computational systems (ranging from commodity laptops to large-scale clusters with many thousands of nodes). Variations in modeling assumptions further complicate the comparability of algorithm testing results (for example, what types of contingency constraints are included and/or how normal vs. emergency ratings are considered). Even small changes in how specific constraints are modeled or which constraints are considered can have significant implications for algorithm performance and solution quality. A new paradigm for the testing and evaluation of emerging grid optimization algorithms is needed to accelerate the adoption of these transformational techniques by industry.

This competition seeks to lay the foundation for that change. In particular, ARPA-E is considering filling this gap through the establishment of a prize competition, executed in multiple phases, using a common computational platform for the fair and consistent evaluation of new algorithms. The existence of this platform will accelerate the use and widespread adoption of new power system optimization and control approaches. As currently envisioned, success will require competitors to demonstrate the applicability and strength of new algorithms across a wide range of system operating conditions.

Initially, the competition is expected to focus on the central optimization challenge underlying a wide range of grid planning and operations tools: the security constrained Optimal Power Flow (OPF) problem. Simply stated, the OPF problem is that of finding the optimal dispatch settings for power generation, flexible customer demand, energy storage, and grid control equipment that maximize one or more grid objectives.[5],[6],[7] In order to be deployable, the recommended settings must satisfy all physical constraints of electric power infrastructure and applicable operating standards (including, for example, minimum/maximum voltages at each bus, minimum/maximum power generation from all generators, thermal transmission constraints, and constraints related to the security of the system when contingencies occur). For a more complete history and formal problem formulation, we refer the reader to a history authored by the Federal Energy Regulatory Commission (FERC).[8]

The core OPF solution methods predominantly used in industry today were designed in an era when computers were far less capable and more costly than they are currently and formal general purpose optimization solvers were in their infancy. Grid operators, power system software vendors, and the research community were required to make a range of simplifying assumptions, most commonly a set of linearizing assumptions which ignore voltage and reactive power optimization, referred to as “DC-OPF.”[9] Many proprietary variations on these algorithms have been developed over the past several decades by industry vendors. Despite improvements in DC-OPF formulations and solvers, there are no tools currently in widespread use in industry that use the full AC power flow equations (without linearizing assumptions) and simultaneously co-optimize both real and reactive power generation (known as “AC-OPF”).

The OPF tools in use today often result in conservative solutions that additionally must be iteratively checked for physical feasibility before implementation. The development and demonstration at scale of OPF solution methods providing physically feasible solutions and capable of optimizing both real and reactive power generation and demand within the time limits required for practical application remains an open, unsolved problem. Achieving these capabilities are expected to become increasingly critical in the future as electricity systems evolve, especially as OPF becomes increasingly important in the context of electric distribution systems.

Improved OPF algorithms could yield significant benefits. For example, recent studies have suggested that enhanced OPF algorithms could offer as much as 5–10% reductions in total U.S. electricity cost due to the alleviation of grid congestion (corresponding to $6–$19B saved depending on energy prices).[10] In addition, the full realization of the potential benefits of renewable generation as well as recently developed electric transmission power-flow controllers, distribution automation technologies, distributed generation, energy storage, and demand-side control will require more complex grid operation optimization and dispatch algorithms. Further, as the number of controllable resources connected to electric power systems (at both transmission and distribution voltages) grows substantially, distributed or decentralized versions of OPF algorithms could become increasingly important. The importance of new “AC-OPF” methods was also recently recognized by the National Academies.[11]

There are reasons to believe that recent advances could enable significantly improved OPF software. Dramatic improvements in computational power and advancements in optimization solvers in recent years have prompted research on new approaches to grid operation and new approaches to solving OPF and other grid optimization problems.[12] Since the turn of the millennium, the performance of the most powerful supercomputers has increased by almost four orders of magnitude (while the cost per computational step has dropped by approximately the same factor).[13],[14] Improvements in optimization and search methods have evolved similarly, especially those related to Mixed Integer Programming (MIP) and heuristic-based optimization methods. The relative speed of commercial general-purpose solvers such as CPLEX and GUROBI has also increased by over three orders of magnitude on fixed hardware.[15],[16] Cloud computing which can be used to leverage many of these gains, has also started to gain more widespread interest within the power system engineering community.[17]

In tandem, many new approaches to solving OPF problems have been proposed in the literature in recent years; it appears increasingly likely that scalable and more accurate approaches to solving the OPF problem may be within reach. For example, fast and accurate convex relaxations have been formulated where the global minimum can be found efficiently using semi-definite and second order cone programming.[18],[19],[20],[21] Often it can be shown that these relaxations give global solutions to the original, non-convex problem.[22],[23] Distributed and parallelizable OPF algorithms have also been proposed, for example, using the Alternating Direction Method of Multipliers (ADMM), suggesting that OPF solution algorithms can be designed that leverage more advanced computational hardware.[24],[25],[26] These same algorithms could enable the real-time coordination and/or optimization of large numbers of distributed energy resources. Finally, many unique methodologies using techniques such as genetic algorithms, neural networks, fuzzy algorithms and holomorphic embedding have also emerged, claiming, in many cases, to revolutionize solution methods for OPF. [27],[28]

Looking beyond OPF, the Unit Commitment (UC) problem is also critically important and relies, in part, on an OPF solver.[29] The UC problem focuses on making multi-period (typically 24-72 hour ahead) generation commitment decisions such as generator start-up and shutdown while also respecting generation ramp and other intertemporal constraints. Similar to OPF, Unit Commitment has also been the subject of intense research over the past decade and many new solution methods have been proposed, particularly focusing on solving the problem in the context of higher uncertainty due to growth in renewable generation.[30],[31] Traditionally, the UC problem has been viewed as a more difficult problem to solve since it involves binary decisions. Though, as more equipment with discrete controls are taken into account by OPF algorithms, the differentiation between those two problems is becoming less distinct. ARPA-E envisions that a UC algorithm competition would naturally follow and extend an OPF competition.

Despite numerous recent research projects and papers on improved OPF and UC solution strategies, most new advances have struggled to mature past the early-research stage. Few mechanisms currently exist to allow for the direct comparison of different solution methods; most recent advances remain non-validated on realistic, large-scale test models. It is difficult to know the precise relative strengths, weaknesses and operational limits of different algorithms.

Formal prize competitions appear to be an attractive mechanism for facilitating the development and comprehensive evaluations of new OPF and UC algorithms. Many other optimization and algorithm-intensive technical domains have successfully employed prize competitions to accelerate algorithm development and validation.[32],[33],[34] When objectives are clear and measurable and there exists a large population of potential solution providers, competitions have a number of advantages over traditional research grants. When employed properly, they can result in better solutions, more efficient use of funding, and engagement across broad communities of stakeholders. [35] Indeed, research at Harvard Business School has provided strong evidence that prize competitions can lead to faster, more efficient, and more-creative problem solving.[36] Prizes often also attract surplus investment, time, and talent from motivated participants. For example, teams competing for the $10 million Ansari X PRIZE collectively spent over $100 million to develop reusable manned spacecraft. Successful prize competitions that produce vetted solutions can also create momentum towards more ambitious programs and greater financial involvement from the private sector. Since the Ansari X PRIZE concluded in 2004, $1.5 billion has been invested in the nascent space taxi industry.[37] Prize competitions can also increase the number and the diversity of entities that are addressing difficult challenges.

By bridging across disciplines and involving the private sector through problem definition, financial sponsorship, judging, and commercialization, prize competitions create communities in ways that grants cannot achieve.

Purpose and Need for Information:

The purpose of this RFI is solely to solicit input for ARPA-E consideration to inform the possible formulation of a future competition related to grid optimization algorithm development.[1] ARPA-E will not provide funding or compensation for any information submitted in response to this RFI, and ARPA-E may use information submitted to this RFI on a non-attribution basis. This RFI provides the broad research community and industry stakeholders with an opportunity to contribute views and opinions regarding the design of multiple phases of a grid optimization algorithm focused competition. Based on the input provided in response to this RFI and other considerations, ARPA-E may decide to launch a substantial prize competition and/or decide to release a separate “Proposal Track” FOA related to this competition (to support algorithm development). If a separate FOA is published related to the competition, it will be issued under a new FOA number. No FOA or competition exists at this time. ARPA-E reserves the right to not issue a FOA in this area and not initiate a prize competition in this area.

REQUEST FOR INFORMATION GUIDLINES:

ARPA-E is not accepting applications for financial assistance or financial incentives, or competition entries under this RFI. Responses to this RFI will not be viewed as any commitment by the respondent to develop ideas discussed or enter any future competition. ARPA-E may decide at a later date to issue a FOA or initiate a prize competition based on consideration of the input received from this RFI. No material submitted for review will be returned and there will be no formal or informal debriefing concerning the review of any submitted material. ARPA-E reserves the right to contact a respondent to request clarification or other information relevant to this RFI. All responses provided will be taken into consideration, but ARPA-E will not respond to individual submissions or publish publicly a compendium of responses. Respondents shall not include any information in the response to this RFI that might be considered proprietary or confidential.

Responses to this RFI should be submitted in PDF or Word format to the email address ARPA-E-RFI@hq.doe.gov by 5:00 PM Eastern Time on November 22, 2016. ARPA-E will not review or consider comments submitted by other means. Emails should conform to the following guidelines:

Please insert “Responses for Grid Optimization Competition RFI” in the subject line of your email, and include:

Responses to this RFI are limited to no more than 50 pages in length (12 point font size). Though, shorter, concise responses are encouraged.Responders are strongly encouraged to include preliminary results, data, and figures that support their perspectives but shall not include any information that might be considered proprietary or confidential. Responses to this RFI may be shared with organizations supporting ARPA-E’s efforts in designing the competition including national laboratory partners and academic subcontractors.[1]