Modeling Hydro Power Plants in Deregulated Electricity Markets: Integration and Application of EMCAS and VALORAGUA

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1 Modeling Hydro Power Plants in Deregulated Electricity Markets: Integration and Application of EMCAS and Prakash Thimmapuram 1, Thomas D. Veselka 1, Vladimir Koritarov 1 Sónia Vilela 2, Ricardo Pereira 2, Rui Figueiredo Silva 3 1 Argonne National Laboratory Decision and Information Sciences Division 97 S. Cass Avenue Argonne, IL 6439 USA Phone: prakash@anl.gov tdveselka@anl.gov koritarov@anl.gov 2 Rede Eléctrica Nacional, S.A. Rua de Sá da Bandeira, 567-4º Porto, Portugal Phone: sonia.vilela@ren.pt ricardo.pereira@ren.pt 3 Energias de Portugal Rua Sá da Bandeira, 567-2º Porto, Portugal Phone: Rui.FigueiredoSilva@edp.pt Keywords Hydro Power Plants, Deregulated Power Markets, Agent-Based Modeling, Iberian market Abstract In this paper, we present details of integrating an agent-based model, Electricity Market Complex Adaptive System (EMCAS) with a hydro-thermal coordination model,. EMCAS provides a framework for simulating deregulated markets with flexible regulatory structure along with bidding strategies for supply offers and demand bids. provides longer-term operation plans by optimizing hydro and thermal power plant operation for the entire year. In addition, EMCAS uses the price forecasts and weekly hydro schedules from to provide intraweek hydro plant optimization for hourly supply offers. The integrated model is then applied to the Iberian electricity market which includes about 111 thermal plants and 38 hydro power plants. We then analyze the impact of hydro plant supply offers on the market prices and ways to minimize the Gencos exposure to price risk. O I. INTRODUCTION peration and management of hydro power plants is quite complex, with a unique requirement to satisfy not only the needs of power generation but also those of irrigation, transportation, recreation, and ecological balance. The recent trend of deregulating electricity markets poses challenges to the already complex operation of hydro power plants. Hydro power plant models and tools that were developed and used for decades are not quite suitable for the optimization of shortterm bidding of hydro capacity in today s deregulated electricity markets. Therefore, new tools are required to model today s complex systems with interaction between several physical and business entities. These new tools aim to coordinate the short-term strategies with the longer-term plans and vision. In the last few years, agent-based modeling and simulation (ABMS) has been gaining importance as a modeling methodology to study restructured electricity markets. The Electricity Market Complex Adaptive System (EMCAS) is one such tool developed to model the deregulated electricity markets, while is a hydro-thermal coordination model used for the optimization of power systems operations and management of hydro power plants and water reservoirs. The combination of these two models provides capabilities to improve the simulation and optimization of hydro power plant operation in deregulated electricity markets. In this paper, we first present a brief overview of EMCAS and, followed by the details of integrating the two models. Next, a case study of the soon-to-be-implemented

2 Iberian electricity market is presented in which hydropower accounts for nearly 31% and 25% of total electricity generation in Portugal and Spain, respectively. Finally, we provide examples of short-term hydro plant optimization in deregulated markets based on longer-term plans and vision. II. EMCAS Electricity Market Complex Adaptive System (EMCAS) uses a novel agent-based modeling approach to simulate the operation of deregulated electricity markets. EMCAS can be used as an electronic laboratory to probe the possible operational and economic impacts on the power system by various external events. Market participants are represented as agents with their own sets of objectives, decision-making rules, and behavioral patterns. Agents are modeled as independent entities that make decisions and take actions using limited and/or uncertain information available to them, similar to how organizations and individuals operate in the real world. Some of the capabilities of EMCAS include the simulation of day-ahead and real-time markets, modeling of take-or-pay fuel contracts, bilateral financial contracts, demand response, carbon emissions and allowances, etc. EMCAS can also handle congestion management either through locational marginal pricing or system marginal price and uplift charges. For a comprehensive description of EMCAS and its capabilities please refer to [1-3]. III. has been in use for several decades as a hydro-thermal coordination model with the objective of optimizing overall system operation over a period of up to 1 year. The model optimizes the operation of hydro and pumped-storage power plants and management of hydro reservoirs and hydro cascades. The objective function minimizes the overall system operating costs based on the calculated expected value of water in each time period (52 weeks). This model takes into account system configuration, projected loads, thermal and renewable capacity, reservoir characteristics, hydro cascading, and historical water in-flows and generates weekly schedules for each of the hydro power plants based on stochastic dynamic programming and non-linear programming algorithms. For a comprehensive description and capabilities of please refer to [4]. IV. INTEGRATION The interaction between EMCAS and occurs primarily through information exchange of projected hydro data for wet, average, and dry conditions with their associated probabilities, as well as the actual simulated hydro data for the selected hydro conditions (see Fig.1). This interaction occurs on two time scales, annual and weekly. In the annual loop, EMCAS creates an annual supply curve based on the previous year s supply offers. uses this annual supply curve, system load, and other pertinent hydrological data to update the hydro patterns file for the next year (see Fig. 2). In addition, an initial spin up is necessary where is executed to generate initial hydrological data for use in the EMCAS model and to create a bid-based supply curve for. Fig. 1. Information Flow between EMCAS and START Current System Configuration Historical Hydro Inflows (Initial Spin Up) Hydrological Pattern HYDCOND Hydro Conditions and Probabilities VALHYDRO Projected Hydro Data for Wet, Avg. & Dry Conditions VALPATTERNS Actual Hydro Data for the Selected Hydro Condition SUPPLYCURVES Resulting Bid Prices of Generating Units Hydro Data (W, A & D Conditions) EMCAS (Annual Run) Resulting Bids (weekly data for entire year) To weekly cycle Input Data (loads, hydro, thermal, etc.) from and other sources Maintenance Scheduler Maintenance Schedule Supply Curve Generator Supply Curves (52 weeks) Fig. 2. Annual Interaction between EMCAS and EMCAS In the weekly cycle (see Fig. 3), provides weekly targets for generation and pumping for each of the hydro and pumped-storage plants. EMCAS uses these weekly targets to schedule (i.e., create supply offers) hydro power plants within the week. In turn, EMCAS provides weekly supply curves to to re-optimize the operation of hydro power plants for the next week taking into account the latest electricity prices achieved on the market during the last week. The weekly target data that are then passed from to EMCAS include maximum and minimum hydro capacity, weekly total generation for the turbining plants, and additionally maximum and minimum pumping capacity, weekly pumping load, and efficiency for the pumped-storage plants. In EMCAS, the generating companies schedule their hourly hydro generation based on a dynamically updated price projection. In EMCAS, the next-day hourly Annual Loop

3 market prices are projected based on the rolling average of the preceding five days. A mixed integer linear programming (MILP) problem is formulated for each individual hydropower plant, where the objective is to maximize profits while meeting various constraints of the plant s operation, including the maximum and minimum hydro plant capacity and s specified weekly target for generation and/or pumping. (wrapping 52-week runs) Updated Supply Curves Supply Curve Generator Updated Initial Conditions (e.g., res. levels) Hydrological Pattern Hydrological Condition Generator Resulting Bids (current week) From annual run Updated Hydro Data (W, A & D Conditions) EMCAS (Simulation Run - week by week) Fig. 3. Weekly Interaction between EMCAS and In EMCAS, the hydro power plants are modeled as pricetakers, and hence, bid their supply offers at zero price. In the basic implementation, most of the hydro generation will be placed in hours with the highest projected prices. Because of the uncertainty in the projected market prices, this can increase price risk, and hence, profit risk for Gencos. In addition, in markets where hydro generation is a significant contributor, the supply offers from the hydro plants can influence the market prices, and high price forecasts and high profits may not be realized. Therefore, an alternative methodology is implemented which distributes the hydro generation more evenly between the hours in order to reduce the risk exposure. In this alternate methodology, the plant capacity is divided into a base block and several peak blocks, with the constraints that the peak block is not loaded unless the base block is loaded, and that the value of each peak block decreases as a function of block loading. In addition, base blocks are either on or off and the peak blocks can be partially loaded. The block sizes and the perceived value of blocks relative to the price forecast can be specified by the user for each of the hydro power plants. Other techniques can also be employed to minimize the price risk or profit risk. These techniques depend on the fact that there may be several generation patterns that may yield the same objective value. However, a generation pattern that minimizes the number of start-ups and shutdowns is more desirable. Adding start-up and shutdown costs increases the probability of such a generation pattern being selected by the No End of Yes Updated System Configuration To annual loop optimizer. Similarly, a generation pattern that minimizes the daily fluctuation and maintains a somewhat uniform average daily generation level is more desirable for minimizing price fluctuations from hour to hour. V. IBERAIN ELECTRICITY MARKET The European Union, in its directives 96/92/EC, and 23/54/EC, defined the rules for the implementation of an internal electricity market. Anticipating the creation of this internal electricity market, Portugal and Spain agreed on the creation of a regional electricity market, named MIBEL. As shown in Fig. 4, the Iberian electricity market is modeled with four nodes: one node each for Portugal, Spain, France, and Morocco. Transfer capabilities are modeled in aggregate. The nodes in Portugal and Spain have both loads and generation capacity, whereas the node in France has only generation capacity (limited to the available transfer capacity into Spain), and the node in Morocco has only load (again limited to the available transfer capacity from Spain). The forecasted hourly load for each of the nodes for the year 28 is shown in Fig. 5 and the generation capacity by Genco and by technology is given in Table I. Even though generic Genco names are used, they accurately represent actual generation companies in Portugal and Spain. Our case has about 111 thermal power plants and 38 hydro power plants, including 5 aggregated hydro power plants in Spain. Fig. 4. Four-Node Representation of Iberian Electricity Market VI. EXAMPLES OF HYDRO PLANT OPERATIONS The integrated model has been applied to the Iberian market as described in Section V. Several scenarios were run for the year 28 as follows: Base Case: Basic hydro optimization where the Gencos maximize profits for given price forecasts and s weekly targets for generation and pumping, and subject to unit maximum and minimum capacity constraints.

4 Load (MW) 5, 45, 4, 35, 3, 25, 2, 15, 1, 5, Portugal Spain Morocco Fig. 5. Hourly Load Profiles for Portugal, Spain and Morocco Table I Portugal and Spain Capacity and Imports from France (MW) Genco Thermal Hydro Cogen Wind Other Genco A 4,1 4,4 Genco B 1, Genco C 6 Genco D 11,4 Genco E 11,8 Genco F 5, Genco G 1,4 Genco H 3,4 Genco I 1,6 Genco J 6,7 Genco K 4 Genco L 25 Genco M ,9 325 Genco N 1,6 11,5 8,25 Genco O 13,25 Import 6 France Total 48, 19, ,4 8,575 Case A: Start-up and shutdown costs are included to minimize the cycling of the plants, while still subject to the same constraints as in the base case. Case B: Using parameters to split hydro capacity into one base and several peak blocks with a decreasing expected price as a function of block loading, while still subject to the same constraints as in the base case. Case C: Using parameters to maintain a uniform daily average generation and daily average pumping while still being subject to the same constraints of the base case. The alternate hydro optimization in Cases A through C is applied to only one hydro power plant (referred to as Plant A). All of the other hydro power plants still employed the basic hydro optimization. In the following analysis, supply offers and demand bids in week 3 from Plant A are analyzed in detail. The plant characteristics and the targets for generation and pumping in week 3 for base case are given in Table II. These targets slightly differ in cases A to C. Table II Plant A Capacity and Week 3 Targets Pmax (MW) Pmin (MW) 6 66 Energy (GWh) Efficiency (%) N/A 75 Fig. 6 shows the supply offers from the base case. It is clear that most of the generation is offered during the period when the forecasted prices are higher, and most of the pumping load bid when the forecasted prices are lower. This generation pattern satisfies all the constraints of plant maximum and minimum capacity limits and -imposed weekly generation and pumping targets. However, the actual dayahead prices turn out to be much different than the forecasted prices. In fact, the actual prices are lower than the forecasted prices in the hours for which hydro power plants offered their generation capacity into the market, and the actual prices are higher than the forecasted prices in the hours pumped-storage hydro plant bid load in to the market. This influence of hydro generation and pumping load on the market prices can be clearly seen in Fig. 7, which presents the data for a single day. and (MWh) Fig. 6. Base Case Hydro Bidding for Plant A Fig. 8 shows the supply offers from Case A for Plant A. In this scenario, the hydro optimization includes start-up and shut-down costs to minimize the cycling of the plant s capacity. The resultant number of start-ups and shut-downs is minimized compared with the base case and the plant s operation still meets all the constraints as before. Fig. 9 shows the supply offers from Case B for the same Plant A. In this scenario, the hydro optimization includes blocking the capacity into a base block and peak blocks with the expected value of peak blocks decreasing as the blocks are loaded. Again, we can see that the hydro optimization distributes generation offers over a broader time period to

5 minimize the price risks while still meeting the operational constraints of the plant and targets. and (MWh) Hour Fig. 7. Day-6 Base Case Hydro Bidding for Plant A offered into the market. In cases where alternate hydro optimization is employed, Plant A schedules its generation over a broader time period, and hence, has a higher chance of realizing the forecasted prices. The expenditure for pumping load is also given in Table IV for each of the cases. There is less variation in pumping costs as the plant is pumping at its maximum capacity in this particular week. and (MWh) and (MWh) Fig. 8. Start-up and Shutdown Hydro Bidding for Plant A and (MWh) Fig. 9. Capacity Blocking Hydro Bidding for Plant A Prices ( /MWh) Fig. 1 shows the supply offers from Case C for the same Plant A. In this scenario, the hydro optimization includes a parameter to maintain a somewhat uniform average daily generation and average daily pumping. Again, we can see that the alternate hydro optimization distributes generation over broader time periods to minimize the price risks, while still meeting the operational constraints of the plant and targets. The daily generations for the base case and Case C are compared in Table III. The annual expected and actual generation-revenue for Plant A are given in Table IV. The expected revenue is calculated based on supply offers, and Genco s forecasted prices, and the actual revenue is computed based on actual plant generation and realized day-ahead prices. Similarly, the expected and actual pumping expenditure are computed using demand bids and load served instead of supply offers and actual generation. The base case has lower revenue than any other case. This is due to the fact that in the base case, the actual day-ahead prices are suppressed whenever the hydro generation is Fig. 1. Attempt to Maintain Uniform Average Daily Table III Daily and Pattern for Plant A Day (MWh) (MWh) Base Case Case C Base Case Case C ,287 1, ,3 1, ,3 1, , ,456 1, ,373 1,155 1, ,947 1, Total 7,165 7,46 6,776 6,728

6 Table IV Plant A Expected and Actual Revenue and Expenditure Case Year 28 Revenue (million ) Year 28 Expenditure ( million ) Expected Actual Expected Actual Base Case Case A Case B Case C VII. CONCLUSIONS The operation and management of hydro power plants is a very complex problem that requires unique strategies in deregulated electricity markets. Supply offers from hydro power plants affect market prices and hence the Genco profits. Therefore, the short-term bidding decisions and strategies should coordinate with a longer-term vision or plan. The combination of EMCAS and offers such a mechanism. In the present analysis, the parameters used for alternate scenarios are specified by the user. Our future work will explore the possibility of endogenously estimating these parameters by the Gencos as they learn through repeated interaction with the markets. hydro-thermal coordination, reliability and production cost analysis, marginal cost calculations, risk analysis, and electric sector deregulation and privatization issues. Before joining Argonne, Vladimir worked as senior power system planner in the Union of Yugoslav Electric Power Industry. He is a graduate of the School of Electrical Engineering, University of Belgrade. Sonia Vilela did graduate work in Applied Mathematics at the University of Porto, Portugal, and post-graduate work in Quantitative Methods Applied to Management Science at the Escola de Gestão do Porto, Portugal. Since 1999, she has worked at REN (Rede Eléctrica Nacional) in the Generating System Planning Directorate. Her research interests are development of mathematical models for optimization and simulation of hydro-thermal and renewable generating systems. Ricardo Pereira graduated in Mechanical Engineering from the University of Porto, Portugal, and received a Master of Business Administration (MBA) from Escola de Gestão do Porto, Portugal. Since 2, he has worked at the Planning Directorate of REN. His research interests include hydro-thermal generating systems modeling and optimization, power system expansion studies, and assessment of security of supply and reliability of power systems. Rui Figueiredo Silva graduated in Electrical and Computer Engineering, with an Electrical Power Systems specialization from the University of Porto, Portugal. Since 23, he has worked at the Planning and Control Department of EDP (Energias de Portugal) in the Prospective Studies of Strategic Planning Group. His work has been centered on hydro-thermal generating systems modeling and optimization, medium- and long-term power system planning, technical and economic evaluations of new hydro power plants, and assessment of power systems security of supply and reliability. Most of his recent focus is on wind generation impact in the hydro pumping system and wind and hydro pump power plant complementarities. VIII. REFERENCES [1] R. Cirillo, P.R. Thimmapuram, T.D. Veselka, V. Koritarov, G. Conzelmann, C. Macal, G. Boyd, M. North, T. Overbye, and X. Cheng, Evaluating the Potential Impact of Transmission Constraints on the Operation of a Competitive Electricity Market in Illinois, Argonne National Laboratory, Argonne, IL, USA, ANL-6/16, 26. [2] V. Koritarov, Real-World Market Representation with Agents: Modeling the Electricity Market as a Complex Adaptive System with an Agent-based Approach, IEEE Power & Energy Magazine, Vol. 2, No. 4, pp , 24. [3] A. Botterud, V. Koritarov, P.R. Thimmapuram, Multi-agent simulations of the electricity market in Central Europe, in Proc. of the 26th USAEE/IAEE North American Conference, Ann Arbor, MI, USA, 26. [4] Electricidade de Portugal,, A Model for the Optimal Management of a Hydro Thermal Electric Power System, Electricidade de Portugal, IX. BIOGRAPHIES Prakash Thimmapuram is with the Decision and Information Sciences division of Argonne National Laboratory. He received an M.S. in Chemical Engineering from the University of Illinois at Chicago. He has more than 13 years of experience developing models for energy, environmental, and economic systems analysis. His current research interests include agent-based modeling and simulation, electricity markets, power system analysis, and carbon trading. Thomas D. Veselka is an Energy Systems Engineer in the Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) at Argonne National Laboratory. He has provided technical support and managed projects related to electric utility systems and the environment for more than 2 years. His work includes extensive hydropower systems analyses as related to power markets. Vladimir S. Koritarov joined Argonne National Laboratory in 1991 and is presently the Deputy Director of CEEESA. He has 24 years of experience in the analysis and modeling of electric and energy systems in domestic and international applications. He specializes in the analysis of power system capacity expansion options, modeling of hydroelectric and irrigation systems,