Dynamics of supply-chain and market volatility of networks

Fernanda Strozzi

Cattaneo University-LIUC

Italy

WP5

Work package number

5 Start date or starting event: Month 0

Work package title Dynamics of supply-chain and market volatility of networks

Participant ID QMUL JRC COLB MASA LIUC NESA GEME

Person-months per participant:

6 3 18 4 24 Ad hoc

Ad hoc

Objectives: To understand and measure how the volatility, in the time series of energy market spot prices affects congestion and its links to frequencies and length of blackouts trends in European synchronously connected grids.To connect the Electricity grid with supply – chain networks and electricity market spot prices.To develop and implement an algorithm for Early Warning Detection of blackouts.

WP5: Tasks overview

Supply chain Model

T5.1, T5.5Energy spot pricesVolatility

BlackoutsVolatility

Correlations(T5.2)Analysis(T5.3)

Electric power ModelT5.1

Electricity price ModelT5.1

EWDS of Blackouts T5.4

Coupling modelsTask5.5

Interaction RiskT5.6

• D5.1. Report on supply-chain logical model by means of the Petri nets formalism (M12), completed.• D5.2. Report on market dynamics model (M12), completed.• D5.3. Report (paper) on Cross Recurrence Quantification Analysis between markets volatility and the dynamics of power systems dynamic (M24), 50% Done• D5.4.Report (paper) on coupled market dynamics - power systems- supply chains (M30) • D5.5. Report on early warning detection algorithm and suggestions on how implement it in real systems (M36) EWDS developed for a one level supply-chain.

Deliverables

D5.1 (M12) Impact of the electric power supply on a logistic-production system

Fault generation ModelMonte Carlo

System Dynamic Model

One supply chain levelPetri Net

service level

Faults generation model:• Model of Medium Voltage (2400-34500 volts) Power Distribution System in Presence of faults and restoration events.• Monte Carlo Simulation to generate a Random Walk to simulate the faults.• Simulation of the protection device intervention and reconfiguration of the system.System Dynamic logical modelIdentification of the important variable in one supply chain level and their relationships.Model of one supply chain levelDiscrete model using Petri Net

D5.1Supply-Chain and electric model

D5.2 (M12)Electricity Markets and Spot Price Models

The available studies are classified in terms of the applied methodology . The proposed models can be broadly divided into three classes:

• Statistical: technical analysis, simple autoregressive models.• Econometric: more sophisticate models with jumps, peak over the threshold and regime switching. Other models are focused on price volatility evaluation. • Structural fundamental methods, including the impacts of important physical and economic factors on the spot price (Economic Cycles)

The available models are mostly either for univariate or uniequational analysis.

Not agreement on the models to utilize and on the main variables to be considered

Dynamic Factor ModelsStock and Watson (2002a,b, 1999)

• Factor Model (Principal Component): to identify latent (not measured)variables + Dynamical model (Dynamical Factor) to study the relationships between variables.

• They can cope with many variables without running into scarce degrees of freedom.

• They can manage large data set at a high disaggregate level.• They have not been explored for the electricity price dynamics

D5.2 (M12)Electricity Markets and Spot Price Models

Fault generation ModelMonte Carlo

System Dynamic Model

One supply chain levelPetri Net

service level

Electricity priceFactor Dynamic Model

?

D5.2 (M12) Future Work

Global service activity

D5.3 (M24) RQA analysis of electricity prices and blackout

Recurrence Plots (RPs) represent the distance between state space points of a time series.RPs use state space reconstruction techniques (normally delayed vectors of only one measured variable)RQA extracts quantitative information from Recurrence Plots, in terms of several parameters: DET(%determinism), RR(% recurrence), LAM (laminarity)

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RQA parameters are able to distinguish between spot electricity prices dynamics and Gaussian linear correlated noise with the same autocorrelation function (the same FFT)

RQA parameters gives a new measure of the dispersion of data (volatility) and dynamical information.

D5.3 (M24)

[1] Application of non-linear time series analysis techniques to the Nordic spot electricity market data.F. Strozzi, E.Gutiérrez, C. Noè, T. Rossi, M.Serati and J.M. Zaldívar. LIUC Paper 200, october 2007.

[2] Application of RQA to Financial Time Series, F. Strozzi, J.M. Zaldivar, J. Zbilut, Second International workshop on Recurrence Plot, Siena, 10-12 September 2007.

[3] Measuring volatility in the Nordic spot electricity market using Recurrence Quantification Analysis. F. Strozzi, E.Gutiérrez, C. Noè, T. Rossi, M.Serati and J.M. Zaldívar . Submitted to Physica D

D5.3 (M24)RQA analysis of electricity prices and blackout

D5.5 (M36)Early Warning Detection System (EWDS)

for Blackouts

• Divergence gives a filtered measure of the acceleration of the measured variable.• Divergence can be obtained analytically from the model of the system (the trace of its Jacobian).• Divergence can be reconstructed on-line using only one measured variable

On-line Safety and optimization of chemical reactionscontrolling temperature to prevent runaway reactions

On-line Trading startegy applied to high frequencies stock exchangebetter results than RSI (Relative Strenght Index)

Divergence control

[2] Strozzi, F., Zaldivar, J.M., Noè, C., 2007, The Control of Local Stability and of the bullwhip effect in a supply chain. International Journal of Production Economics (In press).

[3] Caloiero, G., Strozzi, F., Zaldívar, J.M., 2007. A supply chain as a serie of filter or amplifiers of the bullwhip effect. International Journal of Production Economics (Accepted).

D5.5 (M36) Early Warning Detection Algorithm for Blackouts

Bullwip control in supply-chainapplication off-line and comparison with a

proportional control

O1 D1=O0D0

Bullwhipi =var(Di)/var(Oi)

D5.5 (M36) Early Warning Detection Algorithm for Blackouts

Bullwip control in supply-chainon-line application and comparison with a proportional control

[4] Strozzi, F., Noè, C., and Zaldivar, J.M. 2007, Control and on-line optimization of a supply chain. (In preparation).

Cost reduction with the new control technique based on the divergence of the system in case of a periodic noisy demand

LIUC Collaborations

• Qeen Mary (Physica A)• JRC (Physica A, Physica D)• COLB (under discussion)• MASA (under discussion)

LIUC Gender Action

2 female PhD students started to work on:

• Models of Supply Chain

• Ranking Risk in Supply Chain