Introducing Plasmo.jl

Transcription

1 Introducing Plasmo.jl A Package for Graph-Based Modeling using JuMP Jordan Jalving and Victor Zavala Department of Chemical and Biological Engineering University of Wisconsin-Madison JuMP Developers Meetup June 13 th, 2017 Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

2 Plasmo.jl - What is it? Platform for Scalable Modeling and Optimization A Graph-based modeling and optimization framework Key Features: Component models associated with nodes and edges Facilitates construction of hierarchical graphs (uses subgraphs) Modularization of component models Manipulate graph structure for solver interface Ease of modeling complex systems Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

3 Overview Motivation - Complex systems Modeling Systems with Components (Graphs) Applications Design considerations Goals right now Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

4 Group Research Theme:Complex Systems x2 x1 x3 Multi-stage stochastic programs Asynchronous systems Multi-scale systems Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

5 Types of Problems Nonlinear (nonconvex) optimization Stochastic programming Model predictive control Some Applications Enery storage systems Connected infrastructure Microgrids Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

6 Hierarchical Networks Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

7 Technology Landscapes Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

8 Challenges with complex physical systems Millions of constraints and variables can make the computation intractable I Generally apply ad-hoc methods to perform some model reduction Millions of system connections makes model instantiation non-trivial I I Multiple scenarios Solution inspection Modeling asynchronicity in large communicating systems is non trivial I Jalving (UW-Madison) Plasmo.jl Decentralized control JuMP Meetup June / 25

9 Some existing modeling frameworks Modelica Components, hierarchies, architectures (highly abstracted) Designed for simulation (Optimica extension does some optimization) Write connectors for coupling (I always found this difficult) gproms Equation oriented chemical flowsheeting software Custom modeling language Commercial Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

10 Revisiting our Goals Model encapsulation Modularity and reuse Navigate solutions to complex optimization problems Facilitate modeling of communicating systems Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

11 The Power of Abstraction - Graphs Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

12 Relevant Graph Concepts Graph Definition A graph (G) is a finite set V(G) of vertices (nodes) and a finite family E(G) of pairs of elements of V(G) called edges Vertex Edge A A is a subgraph of B B The degree of a node is specific to its graph Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

13 Graph Based Modeling Plasmo associates model components with nodes and edges Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

14 Plasmo.jl - Key Features Key Features (some in progress): Associates models (JuMP Models) and linking information (constraints) with nodes and edges within a graph Exploits a subgraph abstraction to enable hierarchies of models (multiple graphs defined on a set of nodes) Uses LightGraphs.jl as the graph backend Accesses model information on nodes and edges Provides interfaces with structured solvers (PIPS, etc...) Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

15 Plasmo - Old syntax (still supported) using Plasmo graph = PlasmoGraph ( ) # Create a graph n1 = add_node! ( graph ) n2 = add_node! ( graph ) n3 = add_node! ( graph ) edge1 = add_edge! ( graph, n1, n2 ) edge2 = add_edge! ( graph, n1, n3 ) #Set component models setmodel! ( n1, simple_model ( ) ) setmodel! ( n2, simple_model ( ) ) setmodel! ( n3, simple_model ( ) ) # provide l i n k i n g i n f o r m a t i o n setcouplingfunction! ( graph, edge1, couplenodes ) setcouplingfunction! ( graph, edge2, couplenodes ) model = generate_model ( graph ) s e t s o l v e r ( model, I p o p t S o l v e r ( ) ) solve ( model ) function couplenodes (m: : Model, graph, edge (m, getconnectedfrom ( graph, edge ) [ : x ] == getconnectedto ( graph, edge [ : x ] ) ) end Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

16 New Syntax - In Progress using Plasmo # Create a graph m = GraphModel ( s o l v e r = I p o p t S o l v e r ( ) ) n1 = add_node! (m) n2 = add_node! (m) n3 = add_node! (m) edge1 = add_edge! (m, n1, n2 ) edge2 = add_edge! (m, n1, n3 ) #Set component models setmodel! ( n1, simple_model ( ) ) setmodel! ( n2, simple_model ( ) ) setmodel! ( n3, simple_model ( ) ) # l i n k the two ( edge1, getconnectedfrom (m, edge1 ) [ : x ] == getconnectedto ( edge1 ) [ : x ] ( edge2, getconnectedfrom (m, edge2 ) [ : x ] == getconnectedto ( edge2 ) [ : x ] ) solve (m) # solve w ith I p o p t # solve_pips (m, n1, [ n2, n3 ] ) # solve with PIPS NLP Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

17 Gas Networks θ l2 p l2 (L l2 ) = θ n3 θ l3 θ l11 f l1 (0) = f in l11 d θ n1 n 1 n 2 n 3 n 11 n 12 n 13 l 2 s l 1 θ n2 p l2 (0) = θ n2 + θ l2 l 11 f l1 (L l1 ) = f out l11 l 12 θ n13 N : Set of nodes in the gas network (junctions) L: Set of links (pipelines) S N : Set of gas supplies D N : Set of gas demands L a L: Set of active links (pipelines with compressors) L p L: Set of passive links (pipelines without compressors) Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

18 Gas Networks Mass and Momentum Balances on a Network p l (t, x) t f l (t, x) t + c2 f l (t, x) = 0, l L A l x + 2c2 f l (t, x) f l (t, x) c2 f l (t, x) 2 p l (t, x) p l (t, x) A l p l (t, x) x A l p l (t, x) 2 + A l x x = 8c2 λa l π 2 D 5 l f l (t, x) p l (t, x) f l(t, x), l L Compressor Power P l (t) = c p T f in,l ( (pin,l (t) + p l (t) p in,l (t) ) γ 1 γ Node Conservation f out,l (t) f in,l (t) + g i (t) l L rec n i Sn j Dn l L snd n Supply and Demand f deliver,n (t) f demand,n (t), n D 1 ), l L a d gas j (t) = 0, n N Boundary Conditions p l (0, t) = p in,l (t) + p l (t), l L a p l (0, t) = p in,l (t), l L p p l (L l, t) = p out,l (t), l L Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

19 Graph Based Modeling - Gas Networks The subgraph abstraction allows multiple couplings on the same node This can be used to build modular systems and couple them at higher levels Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

20 Graph Based Modeling - Coupled Networks m = GraphModel ( ) graph = getgraph (m) add_subgraph! ( graph, power_network ) add_subgraph! ( graph, gas_network ) generator = getnode ( power_network, : gen ) demand = getnode ( gas_network, : demand ) l i n k = add_edge! ( graph, generator, demand ( l i n k, getconnectedfrom ( graph, l i n k ) [ : Pgend ] <= getconnectedto ( graph, l i n k ) [ : f d e l i v e r ] ) Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

21 Graph Based Modeling - Coupled Networks Key Findings: Infrastructure models (graphs) can be developed independently and coupled within larger systems (graphs) Illinois Case Study: 7% more gas delivered to generators; 27% revenue increase versus uncoordinated case Uncoordinated case simulated by solving successive optimization problems Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

22 Multistage Stochastic Programming Our graph abstraction corresponds to the node-based abstraction in multistage stochastic programming Component models associated within nodes (scenarios) Link constraints propogate transition from stage to stage Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

23 Embedding Graph Models Graph models can themselves be embedded as models in nodes or edges Simplifies construction of multiple layers in systems Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

24 Future Direction Generalize the model interface if possible (strictly uses JuMP) Find suitable abstraction for computational workflows Decentralized control Algorithmic strategies (e.g. scheduling and operations) Graph partitioning and model reduction Initialization strategies Simulation interfaces Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25

25 Goals right now Finalize physical model abstraction Push first version to github Figure out a suitable graph communication abstraction Jalving (UW-Madison) Plasmo.jl JuMP Meetup June / 25