Deregulation demands smarter trading systems

23 April 1998

The inadequacies of early IT systems for trading electricity and gas as commodities in a newly deregulated and privatized market have caused some of the early commercial products to be taken off the market. The difficulties of devising adequate systems are providing a significant challenge for the major system developers which is only likely to be met by international collaboration. An important initiative under the EU ESPRIT programme promises to answer some of the key issues.

Short and long term optimization of electricity generation and trading" is the title of a major computer systems research and development initiative under the European Union's (EU) ESPRIT 4 programme that is becoming increasingly urgent for the imminent intoduction of competitive electricity dealing.

Rejoicing under the project acronym of 'Sloegat', it is identified as the subprogramme area: 'high performance computing and networking – HPCN simulation and design'. Its objective is to develop and test, on a high performance computing platform, a software system to simulate and optimise the energy generation and trading coordination planning process in large electricity generating systems, both in the short term and medium to long term.

Special consideration is to be given to the growing importance of the energy trading problem in a deregulated market.

The aim of the work, for which the main contractor is the Spanish utility Iberdrola, is to develop and implement suitable mathematical algorithmic approaches for a parallel high performance environment so that the solution times for a 'large size case' would be in the range of:

  • 10 to 15 minutes for the short term planning problem

  • 0.5 to 1 hour for the medium to long term planning problem.

    The current dilemma is that available systems have been unable to solve the problems adequately in terms of speed, risk management and bid price optimisation. They are only capable of addressing the problems as a sequence of subproblems which, requiring computing effort that should be affordable with present solutions, cannot guarantee global optimisation of the problem.

    No futures market

    The problems are particularly evident in post-privatization UK – the new trading and futures market operations in Scandinavia are probably a better model to follow. According to Bob Thurlby of ICL, pursuing an important part of the ESPRIT programme, "After privatization of the gas and electricity industries in the UK, trading in gas and electricity was predicted by some analysts to become the next big commodity and futures market. This has not happened for a number of reasons:

  • natural caution among the players

  • a regulatory environment that did not encourage trading

  • lack of real competition in generation and supply

  • severe loss of load penalties.

    "Even in the area of contracting, caution was the order of the day. Most players concentrated on contracts for differences (CFDs) and ignored opportunities to use other contract types frequently used in futures markets such as swaps, collars and hedges.

    "Another significant factor was the lack of suitable software to support a dynamic trading environment. Whilst software to manage a portfolio of contracts was readily available, the key piece was not. This was a capability to manage risk dynamically in an energy trading environment. Such a piece of software would give the trader the ability to evaluate and optimise his position in response to changes in the market and new offers to buy being made."

    ICL utilities, with Imperial College, London, have embarked on a software development project to meet this need using an advanced constraint logic programming platform: ECliPSe-2. This package will allow the trader to assess the risks associated with trading energy contracts.

    The CHIC solution

    Managing the financial risk involved in the electricity spot market needs a good hedging strategy, a main part of which involves the management of balanced portfolios of electricity futures contracts. In CHIC-2 a decision support prototype for the purpose of portfolio management is being developed. The system is designed to compute an optimal portfolio of futures contracts in accordance with the level of risk which the trader is willing to take. It is a scenario planning tool which offers best and worse case assessments to a set of forecast data.

    CHIC-2 is the successor to CHIC (Creating Hybrid Solutions for Industry and Commerce), which has resulted in the construction of a generalized constraint handling software environment called ECLiPSe. The project is run by a consortium led by IC-Parc – a planning, scheduling and decision support research centre of Imperial College – specialising in the use of constraint logic programming.

    The consortium includes seven partners, of which some are from industry and others are specialists in solving optimization problems and developing decision support systems.

    The CHIC-2 research project is funded partly by the ESPRIT programme administered by the EU. It is intended to be a three year programme; the first year of work is now complete and the results reported.

    With the help of an electricity supplier, the team has input one year's worth of half yearly demand forecast data into the first prototype, and has obtained 15 standard CFD profiles which are commonly available in the EFA market. The business rules of the electricity supplier concerned were adopted in order to test the prototype, and the supplier's normal choice of contracts was set against the given set of data to provide the baseline for comparison.

    The main lessons learnt so far seem to be that new forms of CFD are needed, and that the complexity of the choice of energy contracts to hedge risks increases exponentially depending on the time granularity to be used. The choice of contracts was very different when the data was considered using daily average, weekly average and monthly average.

    Taking the UK as an example, given that the total electricity market is about $37 billion per annum, even an improvement of 0.1 per cent in risk management is significant.

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