Optimal Bidding Strategy in the Reserve Capacity Market
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Good balancing services is a prerequisite for a well-functioning power market. Additionallyto the day-Ahead market (DA) for electricity, there is a balancing market (BM)which provides the necessary buffers to handle short visibilities and uncertainties,suchas frequency deviations, in the grid. To ensure that enough balancing reserves are available,a reserve capacity market(RKOM) has been created as an incentive for participantsin the power market to reserve capacity exclusively for the BM. As part of the project"Integrating Balancing Markets in Hydro Power Scheduling Methods" was a short termhydro power scheduling model implemented,by SINTEF Energy, in the mathematicalprogramming tool AMPL. It is a multi stage, multi scenario stochastic optimizationproblem.The main purpose of this thesis has been to evaluate how participating in RKOM affectsthe decision making of a hydro power producer compare to only participating in theDA and BM. The weekly time resolution of the reserve capacity market makes it diffi-cult to analyze in already excising hydro power scheduling models. Consequently, themodel implemented in AMPL by SINTEF Energy has in this thesis been expanded andaltered to incorporate the reserve capacity market. The main changes, in the model,were done considering the model s time horizon, and the scenario tree input used inthe model.Stage wise scenario reduction was used to handled some of the challengesconsidering the model expansion. The model expansion was called AMPLWeek andis meant as decision-support for a hydro power producer considering bids in RKOM.Additionally, a simulation method, that includes AMPLWeek, was created to observehow simulation over multiple weeks, and seasons, affects short term scheduling. Thiswas done by incorporating the changes in the reservoir level and water values, createdby ProdRisk, over several weeks of operation. The study sought to answer howdecision-making changes by participating in RKOM. A case study was done consideringStatkraft s Tokke-Vinje hydro power plant in the NO2-area of Norway. To evaluatethe efficiency of RKOM, and to see if there are any gains for a hydro power schedulingto participate in this market, the model was evaluated for different RKOM prices anddifferent seasonal simulations. This thesis also intents to evaluate the benefits of usinga weekly scheduling plan compared to a daily time horizon, and how doing seasonalsimulations affects the decision making for the hydro power producer. It is also consideredto which extent scenario reduction affects the approximation done consideringDA and BM prices in this thesis.The work presented in this thesis demonstrates that a hydro power producer s willingnessto reserve capacity in RKOM increases with an increased RKOM price. The averagepower dispatch in the DA and the down regulation dispatch in BM decreases witha higher RKOM price. Smaller changes could be observed in the average up regulationdispatch, in form of a bell shaped curve, with increasing RKOM price. The up regulationdispatch is higher for a low RKOM price than for a high RKOM price. Results from seasonalsimulations indicates that participating in RKOM is most profitable during springand summer, when day-ahead prices and reservoir levels are low. Comparative analysisof a weekly and daily time horizon demonstrates that the optimal reserved capacity iniiRKOM changes based on the time horizon. A lower objective value is obtained with aweekly time horizon.The main finding of this thesis is that RKOM is a profitable market for a hydro powerproducer. Further, the bell-shaped curve of average up regulation dispatch with respectto RKOM price might indicate that incentive created by RKOM not necessarilyincrease the participant s dispatch in BM. Whether this is due to model simplificationsor also can be observed in the real market is unknown, and recommended asfurther work. It was also concluded that seasonal simulations provides useful informationabout changes in hydro system parameters, like reservoir levels, not observedby a weekly optimization. It also follows from the results of this thesis, that schedulingmodel with a weekly time horizon provides better decision support than a model witha daily time horizon.