1. Introduction

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.

1.1 Competition Rules

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.