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Goal-driven Automation of a Deep Space Communications Station: A Case Study in Knowledge Engineering for Plan Generation and Execution
This paper describes the application of Artificial Intelligence techniques for plan generation, plan execution, and plan monitoring to automate a Deep Space Communication Station. This automation allows a Communication station to respond to a set of tracking goals by appropriately reconfiguring the communications hardware and software to provide the requested communications services.
PLAN-IT-2: The next generation planning and scheduling tool
PLAN-IT is a scheduling program which has been demonstrated and evaluated in a variety of scheduling domains. The capability enhancements being made for the next generation of PLAN-IT, called PLAN-IT-2 is discussed. PLAN-IT-2 represents a complete rewrite of the original PLAN-IT incorporating major changes as suggested by the application experiences with the original PLAN-IT. A few of the enhancements described are additional types of constraints, such as states and resettable-depletables (batteries), dependencies between constraints, multiple levels of activity planning during the scheduling process, pattern constraint searching for opportunities as opposed to just minimizing the amount of conflicts, additional customization construction features for display and handling of diverse multiple time systems, and reduction in both the size and the complexity for creating the knowledge-base to address the different problem domains.
SU-E-T-308: Dosimetric Comparison of SBRT VMAT Treatment Plans Generated for 6 MV, 6 MV FFF, and 10 MV FFF Photon Beams
Purpose: To assess differences in treatment plan quality between VMAT stereotactic body plans generated using the 6 MV, 6 MV FFF, and 10 MV FFF modalities available in our clinic. Plans for lung, spine, and other sites were compared to see if there is any advantage of one modality over the other. Methods: Treatment plans done for actual SBRT patients were selected. Groups of ten lung plans, five spine plans, and five plans from other sites were selected. New treatment plans were generated for each plan using the Varian Eclipse AAA algorithm. The constraints were kept the same as used in the actual plans, but the same version of software was used to generate plans for the three modalities. In addition, because there are natural variations in plans re-done with the same dose constraints, one of the lung plans was repeated ten times to assess those differences. Volumes of the 100%, 90%, 50%, 20% and 10% isodose surfaces were compared. Maximum dose two centimeters from the PTV were compared, as well as the volume of the 105% isodose surface outside of the PTV. In addition, the 20 Gray lung volume was compared for the lung plans. The values of these parameters were divided by the values for the 6 MV plans for comparison. Average and standard deviations were obtained for quantities in each group. The Student t test was done to determine if differences were seen at the 95% confidence level. Results: Comparison of the treatment plans showed no significant differences when assessing these volumes and doses. There were not any trends seen when comparing modalities as a function of PTV volume either. Conclusion: There is no obvious dosimetric advantage in selection of one modality over another for these types of SBRT plans.
A Scalable Solution Framework for Stochastic Transmission and Generation Planning Problems. Draft
Current commercial software tools for transmission and generation investment planning have limited stochastic modeling capabilities. Because of this limitation, electric power utilities generally rely on scenario planning heuristics to identify potentially robust and cost effective investment plans for a broad range of system, economic, and policy conditions. Several research studies have shown that stochastic models perform significantly better than deterministic or heuristic approaches, in terms of overall costs. However, there is a lack of practical solution approaches to solve such models. In this paper we propose a scalable decomposition algorithm to solve stochastic transmission and generation planning problems, respectively considering discrete and continuous decision variables for transmission and generation investments. Given stochasticity restricted to loads and wind, solar, and hydro power output, we develop a simple scenario reduction framework based on a clustering algorithm, to yield a more tractable model. The resulting stochastic optimization model is decomposed on a scenario basis and solved using a variant of the Progressive Hedging (PH) algorithm. We perform numerical experiments using a 240-bus network representation of the Western Electricity Coordinating Council in the US. Although convergence of PH to an optimal solution is not guaranteed for mixed-integer linear optimization models, we find that it is possible to obtain solutions with acceptable optimality gaps for practical applications. Our numerical simulations are performed both on a commodity workstation and on a high-performance cluster. The results indicate that large-scale problems can be solved to a high degree of accuracy in at most two hours of wall clock time.
21st Century Power Partnership Fellowship Program: Supporting Next-generation Planning Modeling Practices at South Africa's Power Utility Eskom
This presentation details the 21st Century Power Partnership's fellowship program accomplishments from 2016. This fellowship brought two fellows from South Africa's power utility, Eskom, to the U.S. Department of Energy's National Renewable Energy Laboratory. The fellows spent two weeks working to improve the fidelity of Eskom's PLEXOS long-term and short-term models, which are used in long-term generation planning exercises and capacity adequacy assessments. The fellows returned to Eksom equipped with a new suite of tools and skills to enhance Eksom's PLEXOS modeling capabilities.
Does Risk Aversion Affect Transmission and Generation Planning? A Western North America Case Study
Here, we investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We also show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk-averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. Furthermore, this model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models.
New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints
A novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems is proposed and it considers a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen’s inequality to define a lower bound, which we extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improves upon the standard Benders’ algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen’s inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. But, the decomposition phase is required to attain optimality gaps. Moreover, use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately.
SU-E-T-303: Dosimetric Comparison of a LINAC Fallback Plan Generated From Tomotherapy System
Purpose: Quantitatively evaluate the Multi Criteria Optimization (MCO) based MLC step and shoot (sMLC) fallback plan derived from Tomotherapy of multiple lesions lung SBRT Methods: Inter-comparison of various IMRT planning systems tends to be difficult due to the vendor-specific functionalities. The methodology of defining dose constraints and beam geometries is challenging. Raysearch™ planning system offers an alternative replanning to deliver same intensity map from Tomotherapy without modifying original fluence. This intuitive comparison comes from the final fluence map converted without any embedded system dependent dose optimization. This planner independent approach could be utilized to study the merits of individual machines. The term “fallback” was utilized to (A) transfer plans in among treatment delivery systems; (B) maintain acceptable plan qualities; and (C) minimize the biological dose impact due to machine breakdown. The Tomotherapy specific DICOM RT dose and plan are retrieved into Raystation’s pre-defined sMLC protocol. Based on specific machine characteristics, same fluence maps were converted to generate equivalent deliverable segments. Therefore, the treatment plans were evaluated among two planning tools, Tomotherapy and MCO based sMLC delivery plans. Results: By converting the fluence map with the pre-defined machine characteristics, the 9-fields fallback plan has similar ITV coverage compared to the original Tomotherapy plan. ITV average doses, the D95 consisted of 0.9% variation. The total lung doses of fallback plan drifted from 17.4% to 30.5% which represents the limitations of the static beam delivery. D2 of fallback spinal cord increased from 22.4% to 36.4% but still within tolerances. Ipsilateral lung changed from 11.0% to 22.6%. Low dose region between ITVs presented increased dose to the normal lung tissues. Conclusion: Acceptable fallback plan for Tomotherapy SBRT has similar ITVs coverage, but lack of the normal tissues avoidance due to the limitation of geometrical factors to reduce doses in between ITVs.
Insights for Canadian electricity generation planning from an integrated assessment model: Should we be more cautious about hydropower cost overruns?
Hydropower accounts for approximately 60% of electricity generation in Canada, with growth expected in the coming decades as part of renewable energy transitions; however, frequent cost overruns threaten the viability of this growth. Here, using the integrated assessment model GCAM, we develop an endogenous representation of hydropower for Canada that accounts for market dynamics, thus permitting analysis of hydropower competition with other electricity generation technologies, both with and without cost overruns. Results show that modelling hydropower resources endogenously increases Canadian hydropower deployment relative to an assumption of fixed hydropower production, from 417 to 495 TWh annually by 2050. In scenarios that apply cost overruns at historical levels, hydropower loses market share to more easily scalable technologies like wind power. When including high cost overrun assumptions, the model determines that hydropower falls from about 73% to 65% of Canadian electricity generation by 2050, while wind power increases from about 8% to 11%. Countries may be better able to achieve electrification and renewable energy targets at lower cost by avoiding large-scale, overrun-prone hydropower and nuclear generation projects. Model results support that cost overruns are important considerations for policy decisions related to electricity sector development in Canada and elsewhere.
Mixed-initiative activity plan generator for Mars Exploration Rovers mission (MER)
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Repurposing an energy system optimization model for seasonal power generation planning
Not Available
A scalable solution framework for stochastic transmission and generation planning problems.
Abstract not provided.
Identifying Executable Plans
Generating plans for execution imposes a different set of requirements on the planning process than those imposed by planning alone. In highly unpredictable execution environments, a fully-grounded plan may become inconsistent frequently when the world fails to behave as expected. Intelligent execution permits making decisions when the most up-to-date information is available, ensuring fewer failures. Planning should acknowledge the capabilities of the execution system, both to ensure robust execution in the face of uncertainty, which also relieves the planner of the burden of making premature commitments. We present Plan Identification Functions (PIFs), which formalize what it means for a plan to be executable, md are used in conjunction with a complete model of system behavior to halt the planning process when an executable plan is found. We describe the implementation of plan identification functions for a temporal, constraint-based planner. This particular implementation allows the description of many different plan identification functions. characteristics crf the ~xec~~tieonfvii r~nm-enft,h e best plan to hand to the execution system will contain more or less commitment and information.
Challenges in building intelligent systems for space mission operations
The purpose here is to provide a top-level look at the stewardship functions performed in space operations, and to identify the major issues and challenges that must be addressed to build intelligent systems that can realistically support operations functions. The focus is on decision support activities involving monitoring, state assessment, goal generation, plan generation, and plan execution. The bottom line is that problem solving in the space operations domain is a very complex process. A variety of knowledge constructs, representations, and reasoning processes are necessary to support effective human problem solving. Emulating these kinds of capabilities in intelligent systems offers major technical challenges that the artificial intelligence community is only beginning to address.
A Comparison of Declarative and Hybrid Declarative-Procedural Models for Rover Operations
The MAPGEN [2] (Mixed-initiative Activity Plan GENerator) planning system is a great example of a hybrid procedural/declarative system where the advantages of each are leveraged to produce an effective planner/scheduler for Mars Exploration Rover tactical planning. We explore the adaptation of the same domain to an entirely declarative planning system (ASPEN [4] Activity Scheduling and Planning ENvironment), and demonstrate that, with some translation, much of the procedural knowledge encoding is amenable to a declarative knowledge encoding.
Avoiding Selection Bias in Generating Examples of Plans in the Presence of Heuristic Error
It is generally understood that heuristic error hurts the performance of search algorithms, measured in terms of search effort. Hence there is an interest in understanding how to reduce heuristic error. One way to do this is to learn a heuristic from a set of examples of plans generated offline, e.g. bootstrapping methods. In this paper, we consider how some methods for generating examples of plans may skew the training set in the presence of heuristic errors. Initial theoretical results show that duplicate detection is one source of selection bias in the canonical A* algorithm. We introduce a duplicate selection scheme for A* that avoids selection bias in generating cost-optimal examples, without compromising memory efficiency, and develop ideas in the satisficing setting. We evaluate our approach on n x m grids with multiple cost-optimal solutions and synthetic heuristic error. Finally, we attempt to extend these ideas to the problem of generating extreme examples of plans.
Automated Generation of Antenna Tracking Plans for a Deep Space Communications Station
This paper describes the application of Artificial Intelligence planning techniques to the problem of antenna track plan generation for a NASA Deep Space Communications Station.