As described in our previous dialog, a number of new market factors are stressing utilities’ ability to deliver 3 x 9s reliability.
These factors fall into four categories: (1) new or expanded energy policies and regulations, (2) deployment of SG applications absent reliability-enhancing SG controls, (3) imperfect coordination between electricity market-clearing processes and the physical control processes of the power system, and (4) aging power system infrastructure.
Evolving Energy Policies and Regulations Have the Potential to Negatively Affect Reliability
Utilities dispatch power generation based on a net load forecast, where net load equals the native customer load minus any power generated by (1) a self-optimizing individual customer (e.g., distributed generation or energy storage discharge), (2) an aggregated self-optimizing set of customers, or (3) a micro-grid.
RPS energy policies as well as regulatory policies encouraging DG, EVs, distributed storage, CHP, and micro-grids are having increasingly significant effects on the shape of the net load and on the first derivative of the shape. For example, Mike Niggli, CEO, SDG&E, speaking in Distributech 2013’s plenary session, referred to expected load ramp rates in March 2020 of 4,500 MW down in two hours and 12,500 MW up in two hours, on a 25,000 MW system.
In most cases, the utility does not have visibility into customers’ distributed generation decisions ahead of time. The challenge for the utility is to maintain its target level of service reliability despite the uncertainty associated with the ensuing net load.
To a certain extent, short-term volatility in the net load caused by intermittent generation (distributed PV) may threaten system stability, especially if aggregated. Some utilities have established rules of thumb for the maximum percentage of PV they will allow on a feeder, e.g., 15%. However, it appears that these rules of thumb/heuristics are overly conservative. One private study simulating a typical distribution system found that its feeders, even in low load situations, could tolerate PV capacity of more than 50% of the load when appropriate (and not too complicated) control equipment is put in place.
To decrease the uncertainty in the net load forecast, and to access additional existing capacity next to the load center that can help maintain reliability in tight supply situations, some utilities offer a “virtual power plant (VPP)” program to their customers. For example, ConEd, PGE, CPS Energy/San Antonio, Duke’s Microgrid Program, AEP, and Europe’s FENIX program offer VPP programs of different types.
In some of these VPP programs, the utility interconnects, maintains, and operates the customer-owned generation/demand reduction applications as a bundle of dispatchable capacity, in return for which the utility provides the customer with certain tariff concessions.
Jurisdictions offering dynamic pricing, e.g., TOU, CPP, and RTP, also create uncertainty in the load forecast. Automated customer price responses can produce large, rapid, swings in the net load. If the consumer’s price response is not automated, i.e., not “smart”, the net load forecast uncertainty can likely be reduced over time based on increasingly accurate (“learned”) estimates of the price elasticity of customer segments -- it helps that price responses will likely be diversified across the service area.
To incentivize an acceptable level of service reliability, state regulators in over 50% of states have mandated penalties for SAIDI or CAIDI performances above a predetermined acceptable range, or have instituted service quality mandates with quantitative metrics. The penalties can be costly -- they provide a strong incentive for utilities to install equipment that improves reliability.
Naturally, these equipment costs are subsequently reflected in customers’ bills. However, the solutions simultaneously improve utility asset utilization and can even prolong the lifetime of some utility assets.
Somewhat Surprisingly, Initial Deployment of SG Applications Can Have a Negative Impact on Reliability
While SG applications can help enhance reliability through smart sensors and increased automation, it appears that the initial SG applications could negatively impact system reliability before subsequent D.A. applications provide ameliorating automation, i.e., SG can be first a sword against, and later a shield for, reliability.
Here we will address the negative impacts and follow-up below with some business opportunities for SG applications that mitigate these negative effects on reliability. Continue reading