• CSS

    Coal Stockpile Simulator

    Coal Stockpile Simulator

    A simulation model to estimate coal stockpile levels, provide 'what-if' scenarios and inform coal stockpile level management decisions.

    A simulation model to estimate coal stockpile levels, provide 'what-if' scenarios and inform coal stockpile level management decisions.
  • Coal-fired power stations have stockpiles that act as buffers for any variations in the coal usage rates, coal delivery rates, and coal supply interruptions. The level of the stockpile is influenced by how much coal is delivered and how much coal is burnt. This is in turn affected by a range of factors, including coal quality, planned and unplanned outages, load forecast uncertainty and the merit order scheduling interactions between stations.

    With the ever increasing load growth in the country, a shrinking reserve-margin and insufficient installed capacity, the load factor at the power stations has to increase which, by inference, places pressure on the coal supplier contractual limitations. Ancillary suppliers are therefore required, which in turn come with their risks such as availability, reliability and costs, increasing the risks on the stockpile.

    The decisions required in managing the stockpiles need to come from an informed position, not only from a historical perspective, but also from a forward looking one, supplemented with ‘what-if’ scenarios. These aspects are particularly important for Eskom’s Primary Energy Division (PED), responsible for the planning, procurement and delivery of coal to the power stations.

    A stochastic simulation model was required to further inform stockpile level management decisions, specifically so that the stochastic interaction of stations, due to planned and unplanned outages, could be included in purchasing additional, or moving coal around. The specific contribution sought was the ability to cater for situations where planned coal usages are altered on the fly, in the future, due to the fact that when one station has an outage, another station must ‘take-up’ the production. As a result, additional coal is used at the second station, where normal linear planning modules (e.g. linear optimisation in terms of reducing transport costs, contract purchase cost) are not able to cater for these stochastic interactions.

  • In order to support decision making regarding coal stockpile management, Enerweb developed a Coal Stockpile Simulator as a dynamic tool to estimate the expected 95th and 5th percentiles of the stockpile level, taking specific consideration of the stochastic failure distributions of the different stations, and building in the dependency models such that when one station cannot produce, another takes it place.

    This model uses statistical parameters based on historic data as well as budget and projections for coal station parameters from the Eskom Generation Production Plan and the PED Coal Supply Plan.

    At the heart of the simulation model is a cause-and-effect diagram, constructed to understand the inter-relationship between the different components of the system and used as a roadmap for improvements to the model.

    The model, implemented in an open source decision support tool framework, was developed iteratively to accommodate changes in focus and ensure rapid delivery of features.

    The simulation model allows planners to simulate and understand complex station interactions about the variations in movements in merit order, planned and unplanned maintenance, coal quality and coal delivery logistics. It provides a scenario planning enhancement framework set for decision making and is based on:

    • The energy sent out forecasts (including monthly and time-of-use profiles);
    • The maintenance plan for the power stations;
    • The modelled forced outages (failures);
    • The despatch plan, according to merit order and station interdependencies;
    • The international imports; and
    • The interrelationships between Eskom’s nuclear, hydro, pumpstorage, open cycle gas turbines and coal-fired power stations.

    The outputs can also be used as control limits, i.e. alarm level to compare actual performance against planned performance.

  • The CSPS has demonstrated its value, especially in contributing to the plans of the new generation of coal fired plant in South Africa, in the following ways:

    • Works by posting processes to an advanced linear optimiser (transport costs, contractual costs, coal quality constraints per station);
    • Models the effects of coal supplier unreliabilities;
    • Incorporates planned maintenance into a future-looking coal stockpile maintenance plan;
    • Models the unreliability of power stations themselves (unplanned outages);
    • Models the interaction between stations due to the above factors;
    • Implements energy balance constraints (i.e. all demand must be satisfied as electricity cannot be stored); and
    • Models the interdependency schedule rules between stations, i.e. control of merit order due to failures of any kind.

    The system summarises results in an ‘over’ and ‘under’ delivery plan, to incorporate all of the above effects. It can be deployed as a desktop or server based technical solution, and provides graphical, tabular and visual feedback in a rich graphical user interface. It is based on open source platforms, languages and tools to facilitate integration and interoperability.