**5. Concluding remarks**

We have supplied a partial proof of concept for two methods of reducing supply-demand imbalance. First, we have shown that the effect on power production of distributing solar plants - the coefficient of deviation is reduced by 50% in our example. If we were to consider covariance of energy production on different locations, our optimization problem for distribution could be extremely complicated, given that the covariance varies from day to day and from season to season. We will address this in future work, as the issue will obscure presentation of the basic idea here. Our calculations show that solar production and AC power consumption are strongly correlated. Hence, solar production could penetrate the grid to the extent of replacing the peaker gas turbine plants that the utilities use for peak usage in summer. Finally, we have shown by use modeling of aggregate demand of household appliances that it is possible to use solar or wind power as it gets produced. Whenever there is availability of solar power on the grid, smart appliances can switch on and use it.

Our ultimate objective is to reduce the unpredictability of supply-demand in the grid as solar power penetration increases, with minimal use of expensive, limited life grid storage. While the solution approaches we have proposed show how this can be done in theory, they ignore the transients that depend upon the speed of sensing supply and matching it with demand. The main requirement for stability in the grid is the matching of phase from various sources over a reactive time scale and load scheduling over the tactical time scale (minutes to hours). High speed measurements are available for both grid voltage and current. The movement of clouds is also reasonably predictable in short interval of less than hours. This will ensure that a utility can fire up a base load coal or steam turbine 3 hours before it is necessary or a gas turbine 15 minutes before it is necessary.

The utility industry is extremely conservative and will not make changes that can destabilize the grid. Even in Germany, solar penetration has not exceeded 2% inspect of significant taxpayer subsidies. What we have shown is that specific guarantees of safety can be constructed for various levels of solar penetration–whether distributed or centralized. Once we construct these guarantees, grid penetration of solar power could perhaps reach 10% even without advances in battery or thermal storage technology.

#### **6. References**

16 Will-be-set-by-IN-TECH

<sup>0</sup> 0.5 <sup>1</sup> 1.5 <sup>2</sup> 2.5 <sup>3</sup> 3.5 <sup>4</sup> 4.5 <sup>5</sup> 0.2

We have supplied a partial proof of concept for two methods of reducing supply-demand imbalance. First, we have shown that the effect on power production of distributing solar plants - the coefficient of deviation is reduced by 50% in our example. If we were to consider covariance of energy production on different locations, our optimization problem for distribution could be extremely complicated, given that the covariance varies from day to day and from season to season. We will address this in future work, as the issue will obscure presentation of the basic idea here. Our calculations show that solar production and AC power consumption are strongly correlated. Hence, solar production could penetrate the grid to the extent of replacing the peaker gas turbine plants that the utilities use for peak usage in summer. Finally, we have shown by use modeling of aggregate demand of household appliances that it is possible to use solar or wind power as it gets produced. Whenever there

is availability of solar power on the grid, smart appliances can switch on and use it.

Our ultimate objective is to reduce the unpredictability of supply-demand in the grid as solar power penetration increases, with minimal use of expensive, limited life grid storage. While the solution approaches we have proposed show how this can be done in theory, they ignore the transients that depend upon the speed of sensing supply and matching it with demand. The main requirement for stability in the grid is the matching of phase from various sources over a reactive time scale and load scheduling over the tactical time scale (minutes to hours). High speed measurements are available for both grid voltage and current. The movement of clouds is also reasonably predictable in short interval of less than hours. This will ensure that a utility can fire up a base load coal or steam turbine 3 hours before it is necessary or a gas

Fig. 15. Probability of system failure with different levels of solar penetration

Load

0% solar peneration 1% solar peneration 10% solar peneration 50% solar peneration 100% solar peneration

0.3

turbine 15 minutes before it is necessary.

**5. Concluding remarks**

0.4

0.5

0.6

0.7

Probability of failure occurs

0.8

0.9

1

1.1

1.2


http://www.nerc.com/files/IVGTF\_Report\_041609.pdf

Solar Intensity Data is from *National Solar Radiation Data Base*, available at: http://rredc.nrel.gov/solar/old\_data/nsrdb/

