Comparison between sequential selection and co-optimization between energy and ancillary service markets

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1 Comparison between sequential selection and co-optimization between energy and ancillary service markets Date: Tuesday, November 7, 2017 Draft for discussion Click here to enter names of people copied on this document.

2 Table of Contents Background... 1 Hypothesis... 1 Definitions... 1 Methodology... 2 Assumptions... 4 Results... 4 Example 1 Co-optimization results in lowering price and revenues... 5 Example 2 Co-optimization results in increasing price and revenue... 2 Aggregate Statistics... 3 Conclusion... 4 Appendix... 5 Enter Footer AESO Internal

3 Background The Ancillary Services market in Alberta is a separate market that runs in advance of the real time market for energy. The design of the market links energy and operating reserve (OR) prices through an indexing that provides for some degree of optimization between the markets or at least creates some degree of indifference between the products. In offering into the active operating reserve (OR) market, participants submit their price and volume offers into the day ahead market for each product at a discount or a premium to the pool price, referred to indexing to the pool price. When the market closes, the last offer that satisfies the amount of volume required is the marginal or clearing price, and all offers that are lower in price or below the clearing price will receive equilibrium price calculated at that clearing price. Equilibrium pricing is calculated as the half-way point between the AESO s bid price and the marginal offer. Depending on if the equilibrium price is a positive or negative, this is either added to or subtracted from the pool price during the associated settlement interval. The AESO s bid price varies based on reserve type in the Active market, and establishes the offer cap for that particular reserve type. For transparency, the AESO bid is posted on Watt-ex and is set as a static value that can change based on analysis by the AESO to ensure the market is working. This indexing as described above, provides market participants the assurance that should pool price increase in the energy market, the active operating reserve market will see the same increase. In certain situations where, for example, the equilibrium adjustment is small, this may make participants nearly indifferent between participating in the energy and operating reserve markets. Co-optimization between the energy and OR markets was considered when the separate market was designed but for many reasons including concerns about a lack of transparency for product value and pricing, the markets were kept separate. Hypothesis This paper compared the AESO s sequential selection method in the energy and ancillary services market and compares it to a co-optimized solution. - Sequential selection in the Energy Only Market where offers are locked down in the T-2 hour timeframe in the Energy market and Contingency Reserve (CR) offers are locked down day ahead based on a forecast of volumes. CR offers are accepted first followed by energy offers. - Co-optimization where offers in the energy and AS markets are evaluated at the same time Definitions Offer curves are as submitted to the AESO for energy and Ancillary Service (AS) Energy Used the Block size and Block price from the energy market merit order Enter Footer Page 1 AESO Internal

4 Ancillary Service Used offers as submitted and treated active supplemental and spinning reserves as the same. System Marginal Price in the energy market is the highest dispatched block Marginal Offer (Ancillary Service) is the highest offer block that meets the procurement volume for active Contingency Reserves (CR) Equilibrium Price (Ancillary Service) is the average of the contingency reserve marginal offer and the bid. Bid is $40 Bid set by AESO Equilibrium Price =Average of Marginal offer and bid Marginal Offer Clearing Price (Ancillary Services) is the equilibrium price plus system marginal price, or zero if the result is negative. Clearing price is also the marginal price in the AS market. Energy Revenue is the system marginal price * dispatched quantity Ancillary revenue is the clearing price * contingency reserves required. The CR required volume is the CR procured for that hour. Methodology Data over two time periods was analyzed (2013 and 2016) to evaluate two different market conditions. Out of this aggregate data, we extrapolated two specific days to drill down into merit order curves for both energy, ancillary services and to compare to a co=optimized state. The methodology is summarized in the graphic and table below. Enter Footer Page 2 AESO Internal

5 1. Offer Curves Load Energy Offer curves Load AS Offer Curves Combine blocks by lowest prices and assets where possible Include all blocks 2. Co-optimize Actual energy dispatch levels determines energy demand CR procured levels determines AS demand Apply linear programming algorithm 3. Create sets of merit orders for two options Co-optimized Sequential 5. Summarize costs and revenues Multiple price and quantity to find costs and revenues 4. Calculate Prices Energy Market Marginal AS equilibrium and clearing Figure 1 - Methodology 1. Offer curves from energy and AS were combined. For the energy market the energy market merit order was used with block price and block size. There is no differentiation between active spinning and supplemental reserves and they were treated the same. For those assets with offers in only one market, dummy 0 MW $0 price blocks were created. This is required to set up the optimization formulation. 2. Perform a linear optimization algorithm to allocate MW in the energy and ancillary services market. Dispatch level for the hour is used as the demand number and CR procured is the contingency demand number. 3. Once the optimization is done, the energy and ancillary services MW are compared to the original market merit orders. These merit orders help determine marginal assets and prices. Enter Footer Page 3 AESO Internal

6 4. Calculate marginal prices for merit orders. With marginal prices, it is now possible to calculate costs and revenues. Metric System Marginal Price Methodology Highest Dispatched block that meets demand Clearing Price AS Clearing price for highest block that meets procurement volume. Energy Revenues AS Revenues AS Marginal Offer System Marginal Price * Energy Demand Clearing price * contingency reserves required. Negative values were floored at zero. The CR required volume is the CR procured for that day. Highest offer to meet CR requirement Figure 2 - Metrics 5. Summarize results is the final step for drawing magnitudes. Assumptions - Offer curves are as is, historically taken - Only active contingency reserves are optimized with the energy market, spinning and supplemental were treated equally - Prices are calculated as per today s market methodology, that is, AS prices are indexed to pool price. Same methodology for calculating equilibrium and clearing prices. Marginal prices were calculated after sequential selection and/or co-optimization. - Assets can participate in either market, but must have offers in both (or offers will be created at zero) - Constraints/Congestion not taken into account. No engineering concepts typically found in a cooptimization algorithm considered - Contingency requirements are volumes procured in D.A.M - Assumes blocks are flexible - Virtual assets were not considered, to reduce analytical complexity and do not change indicative results Results The results are shown in a series of figures to consider the change in merit orders for January 8, 2016 HE 9 and January 16, HE12 as well as in aggregate for 2013 and As indicated by the graphics and examples chosen, the results varied depending on overall market conditions. Enter Footer Page 4 AESO Internal

7 Example 1 Co-optimization results in lowering price and revenues On Jan 8, 2016 HE 9, the following were the offer curves into the energy and AS market. Actual pool price was $474.76/MWh. Figure 3 - Offer Curves for Jan 8, 2016 HE 9 Once the offer curves were combined and co-optimized the allocation of MW changed the merit order as shown in Figure 5. In the example, Energy was removed from lower priced blocks in the energy market and placed in the AS market. The new marginal block also had more MWs allocated to it compared to the original curve. The resulting curves had lower system marginal price and there resulted in lowering overall energy revenues. Enter Footer Page 5 AESO Internal

8 Figure 4 Energy Market Merit Order comparison Jan 8, 2016 HE 9 Enter Footer Page 1 AESO Internal

9 On the ancillary services side, the MW shifted the curve to the left and raising the marginal offer price by $8. Figure 5 Ancillary Service Merit Order comparison - Jan 8, 2016 HE 9 Metric* 1 Cooptimized Sequential Change from Seq System Marginal Price($/MWh) Clearing AS ($/MWh) Energy Revenues (P*Q $) 4,980,150 5,533, ,350 AS Revenues (Clearing Price*Q $) 211, ,702-22,499 AS Marginal Offer ($/MWh) Figure 6 - Metrics Jan 8, 2016 HE 9 The net result on the clearing price was still an overall reduction, as the magnitude drop in the energy market was bigger than the increase in the Ancillary Services market. 1 Small differences in clearing prices due to rounding, applies to all tables Enter Footer Page 1 AESO Internal

10 Example 2 Co-optimization results in increasing price and revenue The following energy merit order and Ancillary Services merit order cover January 16, 2016 HE 12. Figure 7 Energy Market Merit Order Comparison for Jan 16, 2016 HE 12 Figure 8 Ancillary Service Merit Order for Jan 16, 2016 HE 12 Enter Footer Page 2 AESO Internal

11 Metric Cooptimized Sequential Change from Seq System Marginal Price ($/MWh) Clearing AS ($/MWh) Energy Revenues (P*Q $) 2,440, ,069 1,560,178 AS Revenues (Clearing Price*Q $) 99,704 24,567 75,137 AS Marginal Offer ($/MWh) Figure 9 Metrics Jan , HE 12 Aggregate Statistics On average the results over the two years as shown indicate that co-optimization increased system marginal prices and AS clearing prices from $1 to $4 depending on price volatility (more volatility higher the changes). These price changes translated to higher revenues Metric Cooptimized Sequential Change from Seq Average System Marginal Price ($/MWh) Average Marginal Offers ($/MWh) Average Clearing AS ($/MWh) Total Energy Revenues (P*Q $) 1,612,850,271 1,532,486,849 80,363,421 Total AS Revenues (Clearing Price*Q $) 34,586,837 32,069,638 2,517,199 Figure 10 - Aggregate results 2016 Metric Cooptimized Sequential Change from Seq Average System Marginal Price ($/MWh) Average Marginal Offers ($/MWh) Average Clearing AS ($/MWh) Total Energy Revenues (P*Q $) 3,046,360,623 2,929,152, ,208,157 Total AS Revenues (Clearing Price*Q $) 142,114, ,319,630 8,794,724 Figure 11 - Aggregate results 2013 Only 4122 observations in 2013 were processed until the algorithm reached an hour in June which could not be resolved via linear optimization. The results show that some opportunities for co-optimization exist when prices are higher and only provide efficiency if the system is selectively optimized. Enter Footer Page 3 AESO Internal

12 Figure 12 - System Marginal Price vs total revenue changes in 2013 Year Sample size % of hours that could be cooptimized % % The percentage of hours that allow co-optimization is less than 20% in both sets of samples. Conclusion Under the current assumptions and methodology, preliminary analysis of co-optimization does not yield overall benefits, however the analysis shows that there are specific situations especially when system marginal prices are higher, that co-optimization can lead to more efficient outcomes. Enter Footer Page 4 AESO Internal

13 Appendix Technical Details. Objective function and constraints Objective is to minimize production costs of energy and ancillary services by finding the ideal mix of price and quantity pairs in the combined energy and ancillary services markets. This ideal mix can be achieved by using linear programming optimization using an lpsolve 2 package. Objective function is to minimize cost such that: NN zz = min [pppp ii qqqq ii ] + [pppppp ii qqqqqq ii ] ii=1 Where pe and qe are energy price and quantity pairs and pas and qas are ancillary price and quantity pairs. In this case offer price is known and quantity is to be determined by the algorithm. N is the number of offers in the market Constraints: NN DDDDDDDDDDDDDDDDDDDD CCCCCCCCCCCCCCCCCCCC = BBBBBBBBBB SSSSSSSS ii ii=1 NN 0 ii=1(qqqqqq ii + qqqq ii ) DDDDDDDDDDDDDDDDDDDD CCCCCCCCCCCCCCCCCCCC The offered energy in either or both the Energy or AS cannot exceed the unit s capability. Quantity of energy must meet demand NN qqqq ii = DDDDDDDDDDDD ii=1 NN qqqqqq ii = CCCCCCCCCCCCCCCCCCCCCC RRRRRRRRRRRRRRRRRRRRRRRR ii=1 Quantity of AS offers must meet contingency requirement 2 Enter Footer Page 5 AESO Internal