Load Shift in the Power Grid (includes small discussions on other energy storage systems EES)

Dear Viewers,

This presentation is an outcome of a short study on "Analysis of Load Shifting in Power Grid" for the New England Region. The study includes cost analysis of the methodology employing one or more technologies. The objective is to destress the power grid by reducing the peaking in the energy generation curve and answering questions such as

a. Cost of Shifting Energy ( in MWh)
b. Cost of Shifted Energy per MWh
c. Technology comparison for selecting one or weighing of the technologies on various economic, technology reliability parameters to go for a mix of such techs.
d. Policy Decisions

Rise in the number of Electric Vehicles (EVs) in the New England region fused with consumer charging patterns for daily office commuters, business owners etc would be considered as a use case to see if the employed method is able to negate the effects of the peaking, regulators may find it interesting to base policy decisions.

For cost optimisation- I have created a simple algorithm in order to calculate the energy required to be stored, this how-ever takes into account the cost of the individual components of the mix only.

The cost analysis for the storage systems as I had come to understand is a very straight forward thing, steps:

a. Find "cheapest and most reliable" source of electricity
b. Find "second cheapest and second most reliable"
c. and so on

As per our AVERAGE energy need,

if (the cheapest and most reliable satisfies the average energy requirement):
STOP
else-if (the cheapest and most reliable does not satisfy the requirement):

Use the second cheapest and or the third cheapest and reliable source to cover the requirements.

Now at lower utilization (load) curve, we may use this energy from the cheapest and the most reliable source to charge batteries, pump water, or thermal storage etc. The algorithm would be as follows:

if ((lower bound of the utility curve less than max of cheapest and most reliable) && AVERAGE energy need is greater than max of (cheapest and most reliable + second cheapest and second most reliable +...))
Store Energy = AVERAGE energy - Instantaneous Lower Bound of the Utility curve.

Some other key parameters that influence the cost optimisation would be MoUs between stakeholders favouring a particular component from the energy mix like say solar or onshore wind etc, state regulations encouraging a particular technology etc. These additional parameters can be very well accommodated by making slight changes to the algorithm.

This part has special talks from Mr Vinh Dao and Mr Kunal Rajput.
For any queries/clarifications or further information, please feel free to contact me on my personal email address [email protected].

Regards,
A