guest post by Todd McKissick
The last few years, in energy circles, people have begun the public promotion of what they call the smart grid. This is touted as providing better control, prediction and utilization of our nation’s electrical grid system. However, it doesn’t provide anyone except the utilities more benefits. It’s expected to cost much more and to actually take away some of the convenience of having all the power you want, when you want it.
We can do better.
Let’s investigate the benefits of some changes to their so called smart grid. If implemented, these changes will allow instant indirect control and balance of all local grid sections while automatically keeping supply in check with demand. It can drastically cut the baseload utilization of existing transmission lines. It can provide early benefits from running it in pseudo parallel mode with no changes at all by simply publishing customer specific real-time prices. Once that gains some traction, a full implementation only requires adding smart meters to make it work. Both of these stages can be adopted at any rate and benefits only as much as it is adopted. Since it allows Demand Reduction (DR) and Distributed Generation (DG) from any small source to compete in price fairly with the big boys, it encourages tremendous competition between both generators and consumers.
To initiate this process, the real-time price must be determined for each customer. This is easily done at the utility by breaking down their costs and overhead into three categories. First, generation is monitored at its location. Second, transmission is monitored for its contribution. Both of these are being done already, so nothing new yet. Third, distribution needs to be monitored at all the nodes and end points in the customer’s last leg of the chain. Much of this is done and the rest is being done or planned through various smart meter movements. Once all three of these prices are broken down, they can be applied to the various groups of customers and feeder segments. This yields a total price to each customer that varies in real time with all the dynamics built in. By simply publishing that price online, it signals the supply/demand imbalance that applies to them.
This is where the self correction aspect of the system comes into play. If a transmission line goes down, the affected customers’ price will instantly spike, immediately causing loads to drop offline and storage systems and generation systems to boost their output. This is purely price driven so no hard controls are sent to the customer equipment to make this happen. Should a specific load be set to critical use, like a lifeline system for a person or business, they have less risk of losing power completely but will pay an increased amount for the duration of the event. Even transmission rerouting decisions can be based on the price, allowing neighboring local grids to export their excess to aid a nearby shortfall. Should an area find its price trending higher or lower over time, the economics will easily point to whatever and wherever something is needed to be added to the system. This makes forecasting the need for new equipment easier at both the utility and the customer level.
If CO2 or some other emission charge was created, it can quickly be added to the cost of individual generators, allowing the rest of the system to re-balance around it automatically.
Once the price is published, people will begin tracking their home and heavy loading appliances to calculate their exact electrical bill. When they learn they can adapt usage profiles and save money, they will create systems to automatically do so. This will lead to intelligent and power saving appliances, a new generation of smart thermostats, short cycling algorithms in HVAC and even more home automation. The result of these operations is to balance demand to supply.
When this process begins, the financial incentive becomes real for the customer, attracting them to request live billing. This can happen as small as one customer at a time for anyone with a smart meter installed. Both customer and utility benefit from their switchover.
A truly intelligent system like this eliminates the necessity of full grid replacement that some people are proposing. Instead, it focuses on making the existing one more stable. Incrementally and in proportion to adoption, the grid stability and redundancy will naturally increase without further cost. The appliance manufacturers already have many load predictive products waiting for the market to call for them so the cost to advance this whole system is fully redundant with the cost of replacement meters which is already happening or planned soon. We need to ensure that the new meters have live rate capability.
This is the single biggest solution to our energy crisis. It will standardize grid interconnection which will entice distributed generation (DG). As it stands now, most utilities view DG in a negative light with regards to grid stability. Many issues such as voltage, frequency and phase regulation are often topics they cite. In reality, however, the current inverter standards ensure that output is appropriately synchronized. The same applies to power factor issues. While reducing power sent via the grid directly reduces the load, it’s only half of the picture.
DG with storage and vehicle-to-grid hybrids both give the customer an opportunity to save up their excess and sell it to the grid when it earns the most. By giving them the live prices, they will also be encouraged to grow their market. It is an obvious outgrowth for them to buy and store power from the grid in the middle of the night and sell it back for a profit during afternoon peaks. In fact this is already happening in some markets.
Demand reduction (DR), or load shedding, acts the same as onsite generation in that it reduces the power sent via the grid. It also acts similar to storage in that it can time shift loads to cheaper rate periods. To best take advantage of this, people will utilize increasingly better algorithms for price prediction. The net effect is thousands of individuals competing on prediction techniques to flatten out the peaks into the valleys of the grid’s daily profile. This competition will be in direct proportion to the local grid instability in a given area.
From material utilization perspectives significant hardware is manufactured and installed for this infrastructure often to be used at less than 20-40% of its operational capacity for most of its lifetime. These inefficiencies lead engineers to require additional grid support and conventional generation capacity additions when renewable technologies (such as solar and wind) and electric vehicles are to be added to the utility demand/supply mix. Using actual data from the PJM [PJM 2009] the work shows that consumer load management, real time price signals, sensors and intelligent demand/supply control offer a compelling path forward to increase the efficient utilization and carbon footprint reduction of the world’s grids. Underutilization factors from many distribution companies indicate that distribution feeders are often operated at only 70-80% of their peak capacity for a few hours per year, and on average are loaded to less than 30-40% of their capability.
At this time the utilities are limiting adoption rates to a couple percent. A well known standardization could replace that with a call for much more. Instead of discouraging participation, it will encourage innovation and enhance forecasting and do so without giving away control over how we wish to use our power. Best of all, it is paid for by upgrades that are already being planned. How's that for a low cost SMART solution?
 Peter Mark Jansson and John Schmalzel, Increasing utilization of US electric grids via smart technologies: integration of load management, real time pricing, renewables, EVs and smart grid sensors, The International Journal of Technology, Knowledge and Society 7, 47-60.