Simulation results
The simulation was done for the IEEE 30 bus system. The system data is presented Table 1.
The load variation was represented by a Load Scaling Factor (LSF) which is shown in table 2 for 24 hours load curve.
Base case simulation results are shown in figure 5.
The price of the base case and the LSF are represented as equilibrium points during each interval. The figure 5 shows clearly that electricity prices are sensitive to increase in demand. By removing line 3 – 4 from network data a contingency was introduced in the system. The figure 6 shows the base case price and contingency case price. The contingency causes a large increase in market price during peak period of hour 16 to 18 which is clearly shown in figure 6. These high prices will be announced 24 h ahead. Note that we assume that the operator is aware that such a contingency will happen the next day and will last for the whole day.
Now we introduce a self elasticity value of -0.1 for all loads. That means a 100% change in price will reduce the load by 10%. Consider all customers are SR type. By using the formula of elasticity new demand values are calculated. That new demand values for each hour is shown in table 3.
Figure 9 and table 5 clearly shows that prices of elasticity case were below than base and contingency case prices.
Moreover, due to the assumption that customers react in response to the day-ahead published prices, the expected high prices of the peak periods were actually reduced even below the base case prices. However, this will not happen in practice because elasticity is not constant, and it has a lower value during peak periods.
Conclusion
The normal patterns for electricity consumption of customer is changed by demand response. The price of electricity is changed over time. The payment of incentive for the lower use of electricity at high market price time and when system reliability is in danger reduces power consumption in peak periods. All electricity consumers are profited because of Demand Response program. Demand Response program improves system reliability and reduces electricity price and price volatility. Demand Response program’s execution is evaluated by reduction of peak load and demand elasticity. The above case study simulating the effect of demand elasticity in electricity prices demonstrates the effect of Demand Response program in cases of system contingency.
…concluded
I. Arul Doss Adaikalam is T.J.S
Engineering College, Chennai, Tamil
Nadu, India
C. K. Babulal is Thiagarajar College
of Engineering, Madurai, Tamil
Nadu, India
