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Relying on DER penetration in distribution network planning Statistical methods in planning

Relying on DER penetration in distribution network planning Statistical methods in planning. Hungarian National Seminar 08. Mai 2008 Dr.-Ing. Christine Schwaegerl. Planning required for DER integration. Network analysis

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Relying on DER penetration in distribution network planning Statistical methods in planning

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  1. Relying on DER penetration in distribution network planning Statistical methods in planning Hungarian National Seminar 08. Mai 2008 Dr.-Ing. Christine Schwaegerl

  2. Planning required for DER integration • Network analysis • probabilistic steady state and dynamic calculation of dispersed generation system including network loading, voltage, short circuit and fault ride through behavior • DER impacts on reliability and protection • optimized siting and sizing of distributed energy resources (DER) to minimize loss, improve reliability and provide alternatives for investment • Energy management • optimized configuration of thermal and electrical generation units considering economics, environmental impacts as well as energy aspects • calculation of optimum supply scenarios to minimize costs, optimize energy portfolio, or participate in energy trading • optimized operation strategies based on daily schedules • Further analysis • economical analysis (cost / benefit ratio, return of investment, payback times, ... ) • analysis of environmental impact for different planning and operation strategies

  3. Application Areas of Stochastic Methods • Why are stochastic methods required in case of high DER penetration? • -> Traditional network analysis not further sufficient: • Load Flow: boundary conditions do not necessarily occur at generalized extreme case studies • Reliability: availability and contribution of RES units cannot be described with a deterministic generator model • Control and Operation Strategies:uncontrollability and intermittency of RES units have to be considered to have always equal energy balance

  4. Stochastic Methods for Network Analysis • Two major approaches: • Simulation (Sequential Monte Carlo Method) Pros: Close description of reality, good simultaneity model, suited for real-time control Cons: High computational requirement, high RES modeling requirement • Analytical (Modeling Based on Probability Distributions) Pros: Efficient calculation algorithm, limited data volume, clear results, accurate math model Cons: Difficult to simulate impact of DER output variation with same (normal) distribution function; simultaneity among varied DER units difficult (impossible) to model

  5. The Analytical Approach: Basic Concepts • Basic idea: Shift time-domain calculation to (statistical) frequency domain calculation Load Load + PV generation PV generation Time Domain relative power p.u. relative power p.u. relative power p.u. hours per year hours per year hours per year Frequency Domain probability density function probability density function probability density function relative power p.u. relative power p.u. relative power p.u.

  6. actual simulated actual analytical approximation 4 approximation 8 The Analytical Approach: Modeling Errors • Problems: • (1) Errors caused by conversion into • standard probability distribution • functions (such as high-order • normal distribution) • (2) Errors caused by combining two or • more random variables that are not • statistically independent from each • other Actual, simulated, and analyzed PDF outputs under 100% PV penetration Actual and approximated pdf profiles 0,9 1,4 0,7 1 0,5 Probability density function (pdf) probability density function (pdf) probability density function (pdf) 0,6 Probability density function (pdf) 0,3 0,2 0 -3 -2 -1 1 2 3 0 -3 -2 -1 1 2 3 Sum load + PV generation curve Linearily transformed load curve

  7. WT Wind Turbine 1 Markov Markov 0.9 Sample WT Sample WT Simulated Simulated 0.8 Transition Transition 0.7 Profile Profile WT Profile WT Profile 0.6 Matrix Matrix 0.5 0.4 0.3 0.2 0.1 0 1 1001 2001 3001 4001 5001 6001 7001 8001 Trend Curve Trend Curve PV Photovoltaic Array + + Annual Annual 1 0.9 Markov Chain Markov Chain Data Data 0.8 0.7 Simulated Simulated 0.6 0.5 PV Profile PV Profile 0.4 Beta Distribution Beta Distribution 0.3 Daily Daily 0.2 0.1 + + Data Data 0 1 1001 2001 3001 4001 5001 6001 7001 8001 Statistical Transf. Statistical Transf. CHP Combined Heat and Power 1 0.9 Truncated Truncated Simulated Simulated 0.8 + + Deterministic Deterministic 0.7 Normal Normal CHP/Load CHP/Load 0.6 0.5 Schedule Schedule Distribution Distribution Profile Profile 0.4 0.3 0.2 0.1 0 1 1001 2001 3001 4001 5001 6001 7001 8001 The Simulation Approach: Modeling of DER power p.u. power p.u. power p.u. yearly profiles

  8. Max Max Max Max Max Max Min Min Min Min Min Min Simulation results – DER impact on network Simulations prove necessity of stochastic simulation methods • -> Errors due to deterministic methods

  9. Simulation results – DER impact on network Real voltage in meshed network can even be higher than maximum calculated by deterministic worst case simulation! Nodal voltage bands under deterministic and stochastic load flow analysis

  10. Simulation Results – Impact of active DER operation Peak Loading Ratio Loss Ratio Passive Passive Operation Operation 1 Active 1 Operation Active Operation Dispatchable DER Units Penetration Level 0 0 Penetration Level Voltage Drop Boundaries of Voltage 1 Max Passive Min Active Operation Operation 0 Penetration Level Intermittent DER Units - 1 Voltage Rise

  11. Simulation Results – Optimumpenetration levels for minimum peak and loss ratio Average optimum penetration level depend on load and DER type Optimum penetration level A: Household B: Business C: Commercial D: Industrial E: Agricultural Load Type A B C D E A B C D E A B C D E Wind Turbines Photovoltaics CHP Units DER Type

  12. Simulation results - Application to Scheduling • Simulation approach is suitable for real-time analysis of DER-penetrated networks

  13. Simulation results – Test network to demonstrate impact on reliability

  14. Comparison of Reliability Improvements by Different DER Types Without DG PV Wind Frequency of supply interruption (1/a) CHP DG with constant output Reliability increases with increasing ratio of microgeneration (highest for CHP, lowest for PV) ! Intermittency needs to be considered -> otherwise results are too good ! Assumption: Installed DER capacity equals maximum network load (100 % DER penetration)

  15. Simulation result - Reliability analysis Real German LV network • Scenarios: • A: original network without any DG • B: network with 100% PV penetration • C: network with 100% CHP penetration

  16. Summary Uncertainty in energy output Impact on loading, losses, voltage, reliability Impact on profits of different stakeholders Stochastic Simulation Technical Analysis of Network Impact Economic Evaluation Probability distr. Markov Chain Optimum DER Location and Operation Analysis of Energy Management Large amount of data Environmental Evaluation Load Flow Estimation Impact on greenhouse gas emissions, particular matter Impact on energy balance and supply costs Save statistically critical information

  17. Thank you for your attention!

  18. Simulation results – Microgrid operation with DER owned by one VPP

  19. Simulation results – Microgrid operation with DER owned by one VPP Passive Active Operation Operation Average Generation Cost 54,4 € /MWh 40,9 € /MWh CO2 Emission Level 450 kg/kWh 342,8 kg/kWh Daily Grid Energy Loss 535,9 kWh 440,5 kWh Worst Voltage Variation +7,7 % - 5,0 % Highest Line Loading 93,6 % 95,5 % Daily VPP Profit / Loss Loss of 197,3 T € Profit of 96,4 T €

  20. Why Stochastic Methods Are Needed? • Superiority: Better Comparison Criteria for Different Control Options

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