Sub-Title: Achieving a “Net Zero” or “Net-Negative” Capacity Build
In previous dialogs we have analyzed and forecast the capital investment requirements of the power system through 2030 ($400 billion for SG applications and $1.6 trillion for power system infrastructure), and presented strategies for reducing those costs such as the 80/20 rule for SG applications deployment, and the potential for SG asset management applications to optimize the replacement of aging infrastructure.
We have also written about the shift to a “net” load forecast for dispatching decisions, and the increasing uncertainty of the “net” load forecast as self-optimizing customers make autonomous decisions about local energy use and distributed resource activation.
While replacement of failing aged equipment can’t be avoided, what about capacity needed for demand growth? A major longer-term benefit of certain SG applications is the deferral of capital expenditures for new power plants through better use of existing plants and equipment.
How does that work? Many of us who have worked in the power sector look at the average utilization rates of 50% - 65% for existing capacity (generation, transmission, and distribution) and, even allowing for peak to average demand ratios, can’t help but think that the utilization rates could be increased, maybe substantially.
Could we even achieve a “net-zero” capacity build for the next decade or two with the help of SG applications? Could we reduce the amount of replacement required with a “net-negative” capacity build? What would the implications be for the current utility business model?
The New Load Curve
Dispatchers do not dispatch to the traditional load forecast any more, i.e., the gross demand of the customer, they use the “net” load forecast: the load that the utility sees at the point of common coupling.
The utility or ISO can itself shape/manage the “net” load through dispatchable demand response, distributed generation (DG), distributed storage (DS), and Virtual Power Plant programs.
However, self-optimizing customers will shape their own load curve autonomously (with little to no visibility to the utility or ISO) by dispatching DG or DS, programming price-responsive devices (including high-speed M2M control loops), and/or charging EVs.
The transactive energy concept aims to create dynamic equilibrium in an integrated power market for all participants including end-use customers, where prices will clear at all nodes in the power system simultaneously (or close to real-time), resulting in a continuing matching of power supply and demand. We are very far from that capability at present (both technologically and policy-wise), and some less optimal and possibly more practical approaches may intervene, such an orderly backlog management/queuing approach based on traffic engineering, see here.
By How Much Can Future Gross and Net Load Curves Differ? Continue reading