Bogdan Vesovic Siemens Smart Grid Solutions, Minneapolis, USA

Transcription

1 Evolution of Restructured Power Systems with Regulated Electricity Markets Panel D 2 Evolution of Solution Domains in Implementation of Market Design Bogdan Vesovic Siemens Smart Grid Solutions, Minneapolis, USA bogdan.vesovic@siemens.com

2 Introduction Market based operations were the drivers for solution domain improvements. Solution domain includes accurate modeling of physical characteristics of grid and optimization technique. With flexible software architecture, these solutions domains are configurable both for vertical utility or for market based operations Solution domain maturity, partly yielded to higher demands made by Market Design (MIP, look ahead formulation, co optimization). Over the past decade significant improvements were made in Hardware Architecture and hardware computing performance, Mathematical algorithms Problem modeling Constrained Optimization became the core of Electricity Markets, making it possible to achieve more economic and more secure solution for a variety of timeframes

3 Current State Large Power System such as Mexico can be solved: for longer horizon (a week) for large number of time intervals with: Full model for all supply and demand resources Full network model Within strict performance requirements for execution speed and solution optimality criteria

4 Constrained Optimization Market Core

5 Evolution of Optimization in Control Centers Generation Control SCADA AGC LFC ED To Power Plants Transmission Security Generation Planning SE UC Historical SCADA, SE Data CONTINGENCY ANALYSIS Start/Stop To Power Plants User Interface, Trending SECURITY CONSTRAINED DISPATCH t(n,1) t(n,2) Single interval SCED or SC OPF SCUC/ SCDD t(n,m) m # of multi intervals at time tn Day Ahead : 24 hourly intervals Intra day and Intra hour configured intervals 5

6 System Operators: Optimization and Reliability Functions COST/BID Optimization/Reliability Billing Vertical System Operator: Generator cost data ISO (Market 1): Tariff dependent bid structure and data Day Ahead, Intra day and Intra hour look ahead functions (configure interval size, duration) SCUC, SCDD: (configure selected constraints) UC and Network Analysis in tandem for co optimization of energy and reserves with resource, environment and network constraints, preventivecorrective and dynamic dispatch Vertical System Operator: Billing and statements ISO (Market 1): Tariff dependent Settlement ISO (Market 2): Tariff dependent bid structure and data After the fact Energy Accounting: Allocation and expected energy calculation of Energy and Reserves of resources ISO (Market 2): Tariff dependent Settlement Dynamic data: SE Solution Static data: Resource data, Network Model Quasi static data: Forecast, schedules Reserve requirements. Limits

7 Computing Hardware Improvements Parallel processing Simultaneous Multiprocessing system contains more than one such CPU on same motherboard, allowing them to work in parallel Chip level Multiprocessing a multi core CPU has multiple execution cores on one CPU Simultaneous Multithreading so that the core looks like multiple separate "processors" to the operating system Multi Interval Power Flow, Multiple Contingency Analysis, Parallel search in UC Mathematical Problem Optimization were made possible and scallable

8 Computing Hardware Scallability We end up with a multiprocessor, multicore, multithreaded system, where for example we would have a motherboard with Two CPUs Each CPU is quad core Each core is hyperthreaded That would give you 2x4x2 = 16 logical processors from the point of view of the operating system

9 Computing Hardware Improvements continuted Number of other improvements as: 64 bit architecture replacing 32 bit allowed optimization problem to increase in size Faster memory architectures and bigger memory capacity are facilitating MIP scalability Solid state drive allow for fast Database I/O needed for mission critical applications

10 Solver Optimization Technology

11 Unit Commitment for Longer Horizons

12 Unit Commitment and Dynamic Dispatch in RT

13 Optimization of Energy and A/S

14 Power Flow solution and Contingency Analysus

15 Realistic Physical Model Realistic Schedules

16 Resulting Advancements of Last Ten Years UC solution and full network model AC solutions are iterated for accuracy: accurate congestion and loss modeling multi interval look ahead solutions (PF and CA) realistic and optimal commitment/dispatch is achieved as a result enables entire set of network analysis tools (e.g., HVDC, phase shifters, remedial actions, VAr dispatch) full network model accuracy enables managing loop flows and counter flows in shorter time frames (e.g., 5 or 15 ) Cost, price or mixed inputs to constrained optimization Configured time periods: Ability to move to five minute, fifteen minute or hourly market is a settlement topic and NOT a solution issue Locational energy and regional reserves marginal cost/price available and aggregated as needed

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