Computational Modeling for Resilient Electricity-Gas Infrastructures (CMREX)

Transcription

1 Computational Modeling for Resilient Electricity-Gas Infrastructures (CMREX) Milos Cvetkovic, Neha Nandakumar, Dylan Shiltz, Anuradha Annaswamy TAI Conference, October 30, 2015, Washington DC Interdependence between Natural Gas & Electricity Infrastructures High penetration of natural gas Implications of high dependancy [1] ISO-NE fuel mix on October 29, 2015, 7:10 PM September 25, 2014 Gas-fired units are increasing due to: o reduced footprint and increased efficiency compared to coal, o higher penetration of volatile renewable power, and o the need to reduce energy imports from foreign sources. [2] Much needed are operations and market solutions that increase coordination of interconnected natural gas and electricity grids Cold weather 1. Newsham, Jack. Electric rates in Mass. Set to spike this winter. Boston Globe 25 Sept. 2014:. Print

2 CMREX goals and team Goal Assess the interdependency of ING-EI and propose actions for improving resilience Challenges: Implications of Gas network constraints on EI Gas market structure on EI Growing dynamic intermittency and uncertainty due to high penetration of renewables New resiliency issues due to o ING-EI interactions o cyber-physical interactions Team Anuradha Annaswamy (MIT) Ignacio Perez-Arriaga (MIT) Christopher Knittel (MIT) Alefiya Hussain (USC) In this talk Implications of NG market structure on EI Natural gas market modeling to address unequal access to gas issue Dynamic intermittency and uncertainty due to high penetration of renewables Dynamic market mechanism for higher adaptability of the electricity grid operations 4

3 Uncertainty in Obtaining Natural Gas Gas is the generally greatest fuel source for electricity Uncertainty in obtaining this fuel instigated Winter Reliability Program in past couple winters More uncertainty could exist in gas bids with increased intermittent renewable penetration into electric grid Uncertainty in Obtaining Natural Gas Misalignments in real-time market can introduce uncertainties as NGPPs are on nonfirm contracts Less uncertainty in DAM due to FERC 809 ruling Gas Day New Timely Nomination Evening Nomination Intraday Nomination 1 Re-bid for gas Intraday Nomination 2 Intraday Nomination 3 12:00 am 6:00 am 12:00 pm 6:00 pm 12:00 am 6:00 am 12:00 pm 6:00 pm 12:00 am Electric Day Bids to ISO Schedule Posted Schedule Reposted Gas Dispatch Model examines different levels of access to spot market

4 Quantity of gas Price for gas Valuation function of customer provided to each seller Preferenc e type Pipeline Company Producer Price setter for firm capacity WTP for secondary release capacity Price setter for different contract types Consumer Marketer 1 2 Consumer 2 Marketer 3 Consumers/LDCs can be on interruptible or firm contracts for commodity and capacity Marketer-consumer bilateral transactions (with or without bidding) Transactions regarding sale of capacity Transactions regarding sale of commodity Natural Gas Infrastructure Pipeline Operators Local Distribution Companies (LDC) RCI Consumers Commodity Contracts Capacity Contracts Distribution Pipelines Gas Producers Interstate Pipelines Main Service Allocations to GenCos Marketers/Third Party Shippers Bids based on ISO dispatch Natural Gas Power Plant/ Generation Companies Electricity ISO Local Distribution Companies (LDC) RCI Consumers Development of computational model at intersection between GenCo and NG marketers Proposed Computational Gas Dispatch Model Marketer 1 8

5 Overall Structure of Analysis NG Dispatch model to each NGPP Spot price, Allocated quantity Electricity Market Electricity Dispatch Massachusetts Case Study M1 M2 M3 M4 M5 Gas Node 2 Gas Node Gas Node Gas Node Gas Node Gas Node NGPP1 NGPP2 NGPP3 NGPP4 NGPP5 From actual monthly gas demand, daily gas demand (LDC and NGPPs) calculated for state of MA from Sept 2014 April 2015 Actual data from five main receipt point nodes from the Algonquin pipeline Assume 5 marketers of natural gas and 5 NGPPs owned by one generating firm The generating firm has equal or unequal access to gas with the marketers (as LDCs) Each marketer is tied to one node, and each of the 5 NGPPs are tied to one node where each marketer can sell gas to any of the 5 NGPPs Gas Dispatch Model Marketer-NGPP interaction M1 M2 M3 C1 C2 Posed as a constrained optimization problem Optimization objective: Marketers profit Constraints: Capacity, Feasibility Mass balance Demand: From NGPPs

6 ISO-NE Electric Dispatch Bilateral transaction between marketer and NGPP Day Ahead Market NGPP bids to ISO Renewable Penetration Real Time Operations NGPP gets dispatched amount to produce Equal vs. Unequal Access to Secondary Release Capacity Market (SRCM) More curtailment on interruptable contracts Computational Modeling Gas topology (MA data) and gas dispatch model Matpower Case118 electricity market model Gas Marketer, Natural Gas Power Plant (NGPP)

7 Implications of Renewable Intermittency given Unequal Access to SRCM More curtailment during realtime intraday market if renewables do not produce 13 Remaining Challenges Accurate data for NGPP curtailments how often do NGPPs get curtailed the gas they bid for and can t produce what the ISO schedules them to produce? Complete or partial curtailment? How often do NGPPs submit re-offers to the ISO in the realtime market? Does this happen often after knowledge of gas procurement? What are the implications for uncertainty in gas procurement?

8 In this talk Implications of NG market structure on EI Natural gas market modeling to address unequal access to gas issue Dynamic intermittency and uncertainty due to high penetration of renewables Dynamic market mechanism for higher adaptability of the electricity grid operations The need for higher adaptability of real-time operations 1. Volatile, uncertain and intermittent renewables 2. Variable amount of responsive demand 3. Fuel-uncertain natural gas units Real-time operations must be highly adaptable to the change in operating conditions Our solution: Dynamic Market Mechanism (DMM)

9 Current practice economic dispatch Collect cost curves Find optimal dispatch Communicate set-points Generation ISO Flexible demand Economic dispatch is: 1) Periodic with a regular interval, 2) Single iteration process, 3) Computed centrally by the ISO. Inflexible load Automatic generation control is: 1) Faster process than economic dispatch, 2) Centralized control computed by ISO. Automatic generation control Generation set-points Economic dispatch interval Time 17 Our Solution: Dynamic Market Mechanism (DMM) Negotiate and converge to an optimal solution DMM characteristics: 1) Most recent information is included. 2) Individual constraints remain private. Start negotiations Inflexible load Sufficiently long period for convergence Automatic generation control Implement set-points Benefits when addressing: o Fuel uncertainty Wind Solar Natural gas o Change in operating conditions of components Saturation limits Protection tripping Emergency conditions o Dynamic price response Close-loop price control Generation set-points Economic dispatch interval Time

10 Approximated Hessian Increases rate of convergence Preserves privacy Stability Convergence is guaranteed when a unique equilibrium exists, with a small step size, and the use of barrier functions to accommodate constraints [*] D. Shiltz, M. Cvetkovic, and A.M. Annaswamy, An Integrated Dynamic Market Mechanism for Real-time Markets and Frequency Regulation, submitted to IEEE Transactions on Sustainable Energy, Distributed gradient updates A single cost/utility bid per iteration Preserves privacy Modified power balance Integrates real-time market and AGC Conventional generation Renewable generation Demand response Voltage angles Iterative negotiations with DMM ISO compute optimal price Aggregated error DMM communication time-scales 30 ms DMM negotiations 30 s DMM dispatch Marginal Cost/Bid [$/MW] Suggested price [$] Suggested dispatch [MW] Cleared price [$] Cleared dispatch [MW] 4 s AGC updates AGC update ConCos maximize utility GenCos minimize cost Adaptability: DMM includes information on the most recent grid conditions at 30 ms intervals Consumed power Generated power Electrical frequency Power grid DMM Iterates Final Form* State and price update equations 20

11 Modified IEEE 118 Bus Test Case Wind Generator (30%) Demand Response (10%) DMM Market Clearings (50 clearings) s Generation [MW] Flexible Demand [MW] Time [s]

12 Negotiations over a single 30 second period Flexible Consumption [MW] Conventional Generation [MW] Conventional generation Renewable generation Demand response Voltage angles Time [s] Time [s]

13 Impact on Area Control Error Peaks less severe using DMM than OPF Adding feedback shifts ACE closer to zero Number of iterations to Convergence Matpower test cases Number of iterations does not increase with decision variables The convergence time depends on: Step size Congestion Cost curves Demonstrates the scalability of the DMM

14 DMM - Dispatch vs. Control Implementation Dispatch implementation [1] Control implementation [2] Set-point Set-point Aggregated error Secondary control Characteristics Negotiations period 30 ms Max convergence time 30 s Limitations Requires new communication channels No DR energy payback (no look ahead) No optimal equilibrium for AGC Secondary control Characteristics Negotiations period 2 s Iterates used as control input during negotiations Benefits Uses AGC dedicated channels DR energy payback guaranteed Economically efficient AGC [1] D. Shiltz, M. Cvetkovic, A.M. Annaswamy, An Integrated Dynamic Market Mechanism for Real-time Markets and Frequency Regulation, submitted to IEEE Transactions on Sustainable Energy, 2015, [2] D. Shiltz, A. Annaswamy, A Practical Integration of Automatic Generation Control and Demand Response, IEEE American Control Conference, Boston, MA, July 2016, submitted, Remaining Challenges Integrate Natural Gas markets into DMM Allows simultaneous optimization of both networks Model the dynamics of NG networks in detail Actions taken on the electricity side (by NG generators for example) may have effects on the gas network, affecting other generators and users Ultimate goal: detailed dynamic analysis of coupled networks using real-time decision making

15 Recent publications D. Shiltz, M. Cvetkovic, and A.M. Annaswamy, An Integrated Dynamic Market Mechanism for Real-time Markets and Frequency Regulation, (submitted to IEEE Transactions on Sustainable Energy). D. Shiltz, A. Annaswamy, A Practical Integration of Automatic Generation Control and Demand Response, IEEE American Control Conference, Boston, MA, July 2016, submitted, M. Cvetkovic, A. M. Annaswamy, Coupled ISO-NE Real-time Energy and Regulation Markets for Reliability With Natural Gas, IEEE Power and Energy Society General Meeting, July N. Nandakumar, A. M. Annaswamy, M. Cvetkovic, Natural Gas-Electricity Market Design Utilizing Contract Theory, IEEE Power and Energy Society General Meeting, Poster session, July A. M. Annaswamy, A. Hussain, A. Chakrabortty, M. Cvetkovic, Foundations of Infrastructure CPS, IEEE American Control Conference, Boston, MA, July 2016, submitted, M. Cvetkovic, A. Annaswamy, Frequency Control using Cooperative Demand Response through Accumulated Energy, IEEE American Control Conference, Boston, MA, July 2016, submitted, J. Hansen, J. Knudsen and A. M. Annaswamy. "A Dynamic Market Mechanism for Integration of Renewables and Demand Response, IEEE Transactions on Control Systems Technology, to appear, DOI /TCST S. Jenkins and A.M. Annaswamy, A Dynamic Model of the Combined Electricity and Natural Gas Markets, IEEE PES Innovative Smart Grid Technologies Conference, February A. Kiani and A.M. Annaswamy. A Dynamic Mechanism for Wholesale Energy Market: Stability and Robustness, IEEE Transactions on Smart Grid, 5(6): , November A. Kiani, A.M. Annaswamy, and T. Samad, A Hierarchical Transactive Control Architecture for Renewables Integration in Smart Grids: Analytical modeling and stability, IEEE Transactions on Smart Grid, Special Issue on Control Theory and Technology, 5(4): , July A. Kiani and A. M. Annaswamy. Equilibrium in Wholesale Energy Markets: Perturbation Analysis in the Presence of Renewables, IEEE Transactions on Smart Grid, 5(1): , January mcvetkov@mit.edu nehan@mit.edu supported by NSF grant no. EFRI A snapshot of our multi-timescale approach 1. Allows flexible consumers to act as price-setters at the realtime market 2. Admits the most recent weather predictions in market clearing (ex. every 30 seconds) 3. Enables feedback from AGC layer into the market layer, reducing regulation requirements 4. Preserves privacy of market players sensitive information e.g. cost curves, generation/consumption bounds Is this scalable?

16 Line 59 congestion Lines 31,32 congestion Transmission line flows Generation and price increase at bus 3010 once three transmission lines reach their limits. Power generation Locational marginal prices Polish 3120 Bus Test System The system consists of: 3120 buses 3693 transmission lines with line capacities of 250 MW 505 generators with linear cost curves and capacities in the range 10MW- 150MW Extension to renewable energy resources and demand response is straight forward. Data source: Matpower Figure source: Paul Hines, Estimating and Mitigating Cascading Failure Risk, JST-NSF-DFG-RCN Workshop, April 2015 Single DMM Clearing =30 s 30ms per iteration

17 Integrated DMM (economic dispatch + AGC) Conventional architecture Proposed approach Energy Market Regulation Market Energy Market Regulation Market Automatic Generation Control Automatic Generation Control Assumption of magnitude and time-scale separation between OPF and AGC. Large penetration of intermittent energy represents a challenge. Aggregated feedback from AGC Simultaneous decisions at both markets. Natural Gas-Electric Interdependence Increasing Supply of natural gas growing Demand/consumption for natural gas growing in electric sector More curtailments of natural gas Is there an optimal contract design that has all players satisfied (maximized utility of attaining gas)?