Introduction
The current energy landscape will undergo a fundamental redesign within the
next decade. There are several major forces driving the change process: First,
fossil energy prices are continuously rising, second the reduction of green
house gas emissions has become a top priority for governments all over the
world, and third the concentration of the remaining fossil energy reserves
within rather few, politically unstable countries, drives the desire of many
western governments to become more independent from energy imports. These
challenges have to be addressed within the next few years. On the generation
side small-scale distributed and oftentimes intermittent energy
production capacities will be installed. On the consumption side,
demand-side-management technologies (DSM) will be established that enable
consumers to better observe, actively influence and consciously shift their
energy consumption loads adding flexibility to the system as a whole but also
making demands harder to predict on an aggregated level. Additionally, the
expected transition to electric mobility poses a serious threat of
overcharging the current grid infrastructures if introduced without
intelligent charging and coordination strategies in place but also promises
significant amounts of storage capacity that can be used for load balancing.
All these trends conflict with current centralized power grid control
infrastructures and strategies, where a few control centers manage limited
numbers of large power plants to adjust their output to expected and observed
demands in real time.A more flexible, decentralized, and self-organizing approach towards energy
management will have to be developed that allows for an active management of
all grid hierarchy levels from local distribution grids to the pan-European ENTSO-E wide area transmission grid. One approach is to apply the
distributed resource-allocation power of markets to achieve near-optimal
balance between producers and consumers of power at lower levels of the grid
hierarchy and to connect the different hierarchical levels through market
coupling. So far, only limited guidance is available on how such markets
should be designed and operated. Kambil and van Heck point out that
poorly-designed markets can do more harm than good; the California energy
market breakdown in 2000 demonstrated the problems that can occur if economic
incentives of market participants are not clearly understood in the design of
such markets.For these reasons, the TAC Energy Competition was created. From a scientific
point of view it will be a laboratory to evaluate proposed market designs and
different types of trading strategies in a competitive simulation environment
similar to the one of the Trading Agent Competition for Suppy Chain Managment.
TAC Energy models a market-based management structure for local and regional
energy networks at multiple levels of complexity using real historic data on
energy production and consumption, weather, and consumer preferences.In its current release TAC Energy provides an environment that allows the
simulation of intraday energy trading, which is modeled very similar to the
one that takes place every day e.g. at the European Stock Exchange (EEX). For
more details on related work and on the concept of TAC Energy please refer to
the TAC Energy White Paper. Also a short
prezi presentation with an introduction to TAC Energy is available
online.Balancing Power Calculation
Balancing Power is the difference between the amount of energy (ore more precisely the amount of one particular product)
a participant acquired through trading in the market and the true demand for this product. The true demand is ex-ante
unknown but approximated with gradually increasing precision over time forecasts the participant receives on each simulation
time shift.Balancing power demand is calculated for all products (timeslots) after the competition has finished.From a energy consumer's point of view, two cases for balancing power provisioning have to be distinguished:Case 1: Insufficient energy in stock
Example: A Participant had a true demand of 100 units of a certain product but managed to acquire only 50 units.
In this case the difference of 50 units is booked into the participants depot after the competition is finished
in parallel with a cash deduction for the missing 50 units, that are billed at a balancing power price.
The balancing power price for excess demand is calculated as the highest historic price that occurred for this
particular product during trading and is further increased by Competition.balancingCostUnder
, which is freely
defineable for each competition (Default: 0).If no trade occurred in this product during the competition runtime the average trade price across all other
products is calculated instead as balancing power price.If not trade occurred at all during the competition runtime (which should never be the case in reality) an
arbitrarily chosen price of 111 is used as balancing power price.
Case 2: Excess energy in stock
Example: A Participant had a true demand of 100 units of a certain product but acquire 110 units.
In this case the difference of 10 units is deducted from the participant's depot after the competition is finished.
If Competition.balancingCostOver
is set to a value > 0, the amount of money for each unit of excess energy is
additionally deducted from the participant's cash account as balancing power fee for excess demand.From an energy supplier's point of view the rules are applied vice versa.