Smart Meters and Variable Prices Could Cause Chaos and Blackouts

A seemingly obvious way to make the electricity market better may actually make it worse
Smart Meters Can Destabilize Grid, Study Says | EE Times

The simulation shows that in such a competing situation the market behaviour will tend to be wild, erratic and chaotic. An example: If the energy supply is low and therefore the price is high, most users simply will tend to postpone their energy consumption. But they wont be able to do this at infinitum, explains Bornholdt. The more machines are waiting to get started, the higher the potential demand: A bubble forms. This bubble will burst at latest in the moment the price level falls slightly. Because many consumers have postponed their washing schedule, countless washers will start to wash suddenly at the same time. This triggers a collective avalanche mechanism that charges the grids extremely, Bornholdt says. This situation makes black-outs much more probable.


According to the conclusion of the research team, the massive deployment of smart meters is a quick shot that has not been thought through. In our computing model we reproduced with various variables
what real humans would logically do in such situations. In such situations, the individual does not know which consequences arise from his actions if it is multiplied. And unfortunately those who supply the energy do not know either.

Smart Meter Time Varying Pricing Can Lead to “Catastrophic Consequences” for the Grid | Smart Grid Awareness
A new study has been released based upon research at Bremen University that reveals that the use of time varying rates implemented through the mass deployment of smart meters can lead to consumer demand avalanches resulting in smart grid blackouts.

Density of prices for the time series as described in the caption of Fig. 2 (blue solid line). The average price is indicated with a vertical line together with multiples of one standard deviation (dotted lines). The density of highest acceptable prices pi(t) [Eq. (4), black circles] shows a concentration at prices far below the average price. The density of load bought at certain prices [Eq. (5), red squares] shows a maximum at rare price events more than two standard deviations below the average price. Simulation results are shown for N=106 and f=10−3 (as in Fig. 2) and T=107 time steps.

The study of the Bremen scientists has been published in the Physical Review of the American Physical Society.
For more details, see http://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.012815

Abstract

The average economic agent is often used to model the dynamics of simple markets, based on the assumption that the dynamics of a system of many agents can be averaged over in time and space. A popular idea that is based on this seemingly intuitive notion is to dampen electric power fluctuations from fluctuating sources (as, e.g., wind or solar) via a market mechanism, namely by variable power prices that adapt demand to supply. The standard model of an average economic agent predicts that fluctuations are reduced by such an adaptive pricing mechanism. However, the underlying assumption that the actions of all agents average out on the time axis is not always true in a market of many agents. We numerically study an econophysics agent model of an adaptive power market that does not assume averaging a priori. We find that when agents are exposed to source noise via correlated price fluctuations (as adaptive pricing schemes suggest), the market may amplify those fluctuations. In particular, small price changes may translate to large load fluctuations through catastrophic consumer synchronization. As a result, an adaptive power market may cause the opposite effect than intended: Power demand fluctuations are not dampened but amplified instead.

Related/Background:

• Active Adaptive Control Laboratory
• » Smart Meters Navigant Research 
• Modeling and simulation of consumer response to dynamic pricing
• Dynamic Electricity Pricing for Smart Homes - University of ...
• smart meters, real time pricing, and demand response ...