EMK:Improvements to Water Values

Aims

Water Value Series

Currently a run is restricted to the use of a single annual water value profile. This means sequences where the water values change significantly require multiple runs to model. I can see two ways to deal with this.

The first method is to produce annual water values starting at the end of a run, and then projecting these values backwards over the run. This method would produce a single dated water value sequence that is valid over the length of a run regardless of its length.

The second method would use annual water values always, but to allow them to be assessed starting at any date in the run. This method would produce a series of annual water value profiles. The run would then switch between profiles as it progressed.

The second method may allow more flexibility for user judgement on when profiles are likely to be reassessed. However, I have a preference for the first method as it is simpler, more widely understood from a theoretical viewpoint, and would not produce artefacts from switching from one profile to the next. This paper briefly describes water values calculated using the first method which is based around Stochastic Dynamic Programming (SDP) techniques.

Multi-Dimensional Water Values

The current system of controlling water release in EMarket operates each hydro system only with regard to its own stored energy. This method is clearly unrealistic in some situations, despite the fact that it manages total storage in the country fairly reasonably, with Waitaki dominating the control of marginal storage release. However, the other systems tend to be operated more loosely than would be expected in the real market as they are unable to anticipate system-wide shortage or excess.

With five major controlled reservoirs and two uncontrolled, the levels of which all potentially affect water values at any reservoir, a comprehensive multi-dimensional analysis of water value is quickly overloaded with complexity, inevitably resulting in excessive computation time for often marginal gain.

I propose a compromise consisting of a two dimensional profile for each reservoir, where two values determining water values are the local storage and the total of all other storage in the country. A water value profile could consist of, for each hydro system, $/MWh values for a set of storage values, e.g. 20 levels from empty to full for the local reservoir, 5 levels for the other national storage levels, giving 100 values in total. Water value at any level would be interpolated between these points

SDP can then be used to determine the two dimensional matrices for each hydro-system. It is still possible to use 1-in-N security factors to drive the cost of shortage, but this would probably need to be done by iterating the SDP and leading to excessive run times, so the design outlined in this paper uses another approach.

Retail/Hedge Commitment

Hydro operators will inevitably offset their generation earnings risks with wholesale purchase agreements and hedges. The effect of these pressures can be allowed for in water value modelling, but care needs to be taken that this is done in a balanced manner. In as far as retail and hedge commitment is required by the water value algorithm one average commitment power value (MW) per reservoir is all that is required for each weekly time step and scenario (A scenario is assumed to be one inflow sequence, but in could in theory extend to include multi-dimensional scenarios, for example, an inflow-demand scenario. But typically, runs and their respective WVs will be set up with approximately 80 historical inflow scenarios).

EMarket currently allows for the input of company portfolio data. This information could be used to generate commitment values, but there are two difficulties with this approach:

1. extra care is required of the user to enter reasonable portfolio figures;
2. where two or more reservoirs are owned by the same entity, the commitment should be shared – which would require a complication of the algorithm - or divided, which requires some assumptions about the reservoirs.

I don’t think co-optimising Manapouri and Waitaki (because they are both owned by Meridian) would impact greatly on the outcome, and to do so would add considerable complication to the algorithm.

Instead I suggest that a certain level of retail commitment is assumed, based on the total rated output of the hydro system, and then profiled using the average demand profile. While this may be far from accurate as an estimate of commitment, it is an easily generated and robust value that can be used at least to complement the response of hydro management to the provision of retail demand. The extent to which inaccuracies in this approach are unacceptably high will need to be tested.

Other Operating Considerations

Some reservoirs have strategic values that fall outside the profit maximisation objective of the normal water value relationships. Most importantly, low storage levels can be seen as undesirable without consideration of the lack of future revenue they represent. This is because a greater level of management, both operationally and politically, is required when security of supply is perceived to be threatened. Examples could include the analog of the Waitaki storage buffer currently used in EMarket, or perhaps the cost of risk aversion which acts to keep storage higher going into winter due to the risk of low winter inflows.

In order to include these considerations in water value calculation an additional marginal value of storage can be added. For example, if storage below 600GWh was considered undesirable to the extent that every GWh below 600 incurred a cost of $50 per week this can be expressed in the water values by adding a $50 * 0.168 = $8.4 cost per MWh to the water values after each weekly back projection (The figure of 0.168 is obtained by dividing 168 hours in a week by 1,000 MWh).

Another example is the mitigation of flood protection costs. This consideration may put a negative premium on high storage level, reducing their marginal value, possibly even below zero.

These ‘strategic’ costs may vary with the time of year as their nature can be a matter of perception and policy. In general the addition of costs such as these to the water value algorithm provides a flexible and fairly meaningful way of adapting the algorithm to mimic real life policies.

I propose that a number of additional marginal costs can be specified for each reservoir, expressed as a single $/MWh figure, a lower or higher limit to apply to the cost and a week from/week to range to apply the cost to.

Smaller Reservoirs

The current water value algorithm uses deterministic water values to evaluate smaller reservoirs. This will change with the new approach as these reservoirs need risk aware management to behave realistically. Accordingly, all reservoirs will be treated the same in the water value SDP algorithm.