ENERGY PORTFOLIO MAN AGEMENT. Hydro and pump storage modelling in ABB Ability PROMOD Benchmark report

Transcription

1 ENERGY PORTFOLIO MAN AGEMENT Hydro and pump storage modelling in ABB Ability PROMOD Benchmark report

2 TABLE OF CONTENTS Table of Contents 1 ABSTRACT INTRODUCTION Hydro Modelling Pump Storage Modelling ANALYSIS CONCLUSION ABOUT THE AUTHOR...9 List of Figures Figure 1 Hydro Dispatch Logic... 3 Figure 2 PJM Hourly Hydro Generation Benchmark System Level...5 Figure 3 PJM Hourly Pump Storage Generation Benchmark System Level... 6 Figure 4 PJM Average Hourly Trading Hub LMP Benchmark Comparison... 7 Figure 5 PJM Average LMP by Trading Hub {Average (e7 Legacy) = 0.33 $/MWh}... 7 Figure 6 PJM Average LMP by Bus {Average (e7 Legacy) = $/MWh}... 8 List of Tables Table 1 Benchmark Result at System Level...5

3 ABSTRACT HYDRO & PUMP STOR AGE MODELING 1 ABSTRACT The PROMOD Hydro & Pump Storage optimisation modules provide the capability to determine the optimum release schedules and storage strategies for systems of water based generating units. These scheduling optimisations help with improving system operation and efficient resource utilisation. These modules have the ability to model the limitations of water using energy availability limits and capacity levels whilst automatically bias the generating schedules to peak load hours to reduce the load shape served by other units. New enhancements have been developed in PROMOD on the e7 data management platform which leverages cutting edge technology to enable faster development of new features and reach high quality standards. The Introduction section of this report covers the foundation of hydro and pump storage modelling capabilities that exist in both legacy and PROMOD on e7. The Analysis section discusses the differences between these two versions for hydro and pump storage modules. The benchmark results using MISO s MTEP16, PJM RTEP, and SPP databases are also included in the Analysis section. A summary of the modelling differences between legacy and e7 are provided in the Conclusion section. Future work will include further enhancements that will be implemented in the PROMOD on e7. These enhancements will target more sophisticated business needs. 2 INTRODUCTION PROMOD analysis has been widely received with high acknowledgement by industry leaders and experts over the last 41 years. For example, PROMOD analysis is a critical component of the PJM Regional Transmission Expansion Process (RTEP). It drives the Market Efficiency RTEP planning component. Also, MISO leverages PROMOD to provide MTEP reports not only for planning but also for setting the rules to coordinate with their members. To use the latest technology platform and address the changing needs of the modern customer, PROMOD on e7 is developed utilising Xpress Solver and Microsoft SQL databases for input and output data along with providing web services and grid computing. It provides striking interface capabilities such as in-application reporting, charting, formula tool (to create custom variables), and flexible visualisation of the resolved data or input/output together. It can perform daily/weekly commitment with support for hourly or sub-hourly (30, 15, 10, 5 minute) dispatch. It can also automatically execute a defined set of tasks to reduce repeatable steps, data input errors, and the time to obtain valid results. This report focuses on the hydro and pump storage modelling aspects of PROMOD and highlights the differences between the legacy and e7. The following two subsections covers the detail modelling for these two modules separately. 2.1 Hydro Modelling Hydro power is used worldwide to provide relatively low marginal cost and low emission electricity. Hydro power facilities are some of the most flexible types of generators in the power sector. Flexibility is a key resource for the electric grid in balancing demand with generation despite normal and unexpected variability in load or resource availability and from forecast errors. In general, large hydro power facilities have the capability to provide significant flexibility to the electric grid because of their fast ramping capabilities and innate storage in the reservoirs behind large dams. 9AKK107046A5365-A4 1

4 ABB ABILIT Y PROMOD INTRODUCTION In PROMOD, the hydro units are scheduled before the thermal units, subject to minimum and maximum capacity constraints, energy limits, ramp rate constraints, and minimum up and down time limits. Hydro units may be scheduled against either a location area, control area (pool), or system load. To schedule the energy, PROMOD provides options to define hydro type as Run of River or Peak Shave. For the Run of River option, the hydro units will be scheduled such that the provided energy limit is spread uniformly. For Peak shave option, the hydro energy will be allocated to cover the upper-most part of the load. Basically, the hydro unit will be scheduled with a run of river portion (based on the min capacity) and the rest of the energy will be applied to level the peak hourly load. To adjust the hydro dispatch for economic purpose i.e. hydro-thermal co-optimisation (HTC), PROMOD provides an option to specify hydro unit response P factor. The multiplication of this factor and the difference between the maximum and the minimum capacity will adjust the hydro unit schedule based on the locational marginal price (reference a bus or hub) and the unit s cost. The hydro energy remains the same after HTC schedule. PROMOD also allows the users to configure the weekly max energy limit beside the monthly max energy limit. If the weekly max energy limit is set to zero, Proportional Load Following (PLF) schedule will be performed according to the provided monthly max energy. The PLF logic determines the load target based on the benefit to control area (pool) or bene-fit to location area depending on the defined dispatch location selected by the user. This logic is used in calculating the schedule based on an iterative approach. This will result in using less hydro energy during low load periods compared to the energy used during the high load periods. If the minimum capacity is set to zero, the minimum capacity percentage is multiplied by the weekly energy limit (either provided by direct input or calculated from Monthly energy limit using PLF) and used as input to the run of river component, and the rest of the weekly energy contributes to the peak levelling (Peak Shave). The sum of the resulted sub period hydro schedule will equal the weekly and monthly energy limits. Figure 1 illustrates how hydro dispatch in PROMOD is modelled where: PLF stands for Proportional Load Following R stands for Run of River PS stands for Peak Shave 2 9AKK107046A5365 -A4

5 INTRODUCTION HYDRO & PUMP STOR AGE MODELING Figure 1 Hydro Dispatch Logic 2.2 Pump Storage Modelling A pump storage unit consumes power to pump water into a reservoir during off-peak hours and generates during on-peak hours based on the price signals. PROMOD provides flexibility to dispatch these storage units against the locational marginal price (LMP). This dispatch takes place after the preliminary dispatch of the thermal units in the system (end of the first iteration). Preliminary scheduling is always done at the end of the first iteration of the unit dispatch to get the price signals. During this step, unit operating constraints such as min runtime and min downtime are factored into the resulting unit commitment. In each hour/sub-hour, for both pumping and generating, the incremental cost is determined in terms of $/MW change in generation, in a discrete function (LMP cost curve). User can define the number of steps to be included in the LMP cost curve for pump storage optimisation. For ex-ample with input of 4 LMP Cost Curve Steps, for a pump storage generator with maximum capacity of 200MW, there will be specific MW levels of -200, -150, -100, -50, 0, 50, 100, 150, and 200. If there are multiple units in the same station mapped to the same power flow bus, those units will share the same LMP cost curve. The MW levels will be determined by the sum of all those pump storage units; maximum capacities divided by the number of MW levels. 9AKK107046A5365-A4 3

6 ABB ABILIT Y PROMOD ANALYSIS The obtained pump storage schedule after the preliminary dispatch is then used in the following iterations to adjust the corresponding load. The operating constraints for storage include a requirement that the value of the energy displaced on-peak must off-set the cost of storing the energy off-peak. PROMOD allows user to define a final energy storage schedule at the end of n-1 iteration against the corresponding LMP which corresponds to the most accurate locational marginal price. In other words, the dispatch of pump storage units takes place before the final thermal dispatch of remaining units in the system. This schedule is essentially locked in and treated as a modification to the load for the thermal dispatch of all remaining units and subsequent transactions. In the pump storage optimisation process, the following constraints will be enforced: The reservoir s starting net level at midnight of the prescribed calendar day. Ending reservoir level is assumed to be the same as the starting level. Unit can have both daily refill cycle and weekly refill cycle. If the reservoir level at 6 AM is entered, it is treated as daily refill cycle and the reservoir level at 6 AM must be enforced. The generating maximum and minimum capacities for each unit of the re-source. The minimum and maximum reservoir levels. The pumped storage reservoir may undergo water/energy inflow, ex-pressed in GWh per week or month. This energy will be generated in the course of the week in which it arrives even if the pumped storage station is not otherwise operated. The maximum weekly generation will not exceed the input value. The objective of pump storage dispatch is to minimise the cost of meeting load whilst: In each hour, the pumping level must be no greater than the maximum pumping capacity and no less than the minimum pumping capacity. Similarly, the generating level must be no greater than the maximum generating capacity and no less than the minimum generating capacity. At no time may the quantity of stored energy exceed the maximum or fall below the minimum. Between any two hours, the change in stored energy must be equal to the difference between the energy input to the storage system and the energy output from it, accounting for the round-trip input-output efficiency. In the final dispatch for nodal transmission simulation, the pumped storage generation is included as the fixed generation at a bus. Therefore, it will influence active constraints such as flows, interfaces, and control area (pool) load balance. The pumped storage pumping is factored in as a fixed load at a bus, and will contribute to the underlying flows due to load. 3 ANALYSIS This section covers the modelling differences between the legacy and e7 for hydro and pump storage modules. To identify these differences, the benchmark results using MISO s MTEP, PJM RTEP, and SPP databases are examined in terms of hydro and pump storage generations. For these comparisons, the third week of July (peak week) forecast results are used. The 2024 forecast for MISO, 2020 forecast for SPP, and 2019 forecast for PJM are used for these comparison. Table 1 shows total energy comparisons between the legacy and e7 for these databases in terms of total generation (hydro, pump storage, thermal, and price stations) and total cost. The price stations cover renewables and transactions. The total hydro generations are provided to highlight the databases with the highest difference. 4 9AKK107046A5365 -A4

7 ANALYSIS HYDRO & PUMP STOR AGE MODELING Table 1 Benchmark Result at System Level Since PJM hydro comparison shows the largest difference, the remaining of this section is focused on PJM database. As shown in Figure 2, hydro units at the system level are running more during peak hours in e7 except for the weekends (low load hours versus to the high load hours of weekdays) comparing to the legacy PROMOD. Note that the X-axis shows the number of hours from 1 to 168 to cover the whole week starting at Monday 7/15/2019. The Y- axis shows the hydro generation in MW. Figure 2 PJM Hourly Hydro Generation Benchmark System Level Here are the main modelling differences between the e7 and the legacy for hydro dispatch: 9AKK107046A5365-A4 5

8 ABB ABILIT Y PROMOD ANALYSIS 1. In the legacy PROMOD, hydro units are dispatched from high capacity factor to low capacity factor. In PROMOD on e7, they are dispatched from high capacity to low capacity. 2. In PROMOD on e7, Proportional Load Following logic works similar to the legacy PROMOD with the exception that, instead of saving the hourly schedule for the month, it will sum up the hydro energy for each week and use that weekly energy through the normal weekly logic to come up with the hourly schedule for the week. 3. The cost of hydro unit is zero, unless the user input set as an adder ($/MWh) provided which will be applied to the dispatch lambda of all committed minimum segments including hydro units in the Legacy PROMOD. PROMOD on e7 also has this input but it is by unit. Note that for this database, this user input was zero. 4. In the legacy PROMOD, there isn t initial loading and ramp rate applied for the start of the study (hour 1). In PROMOD on e7, the initial status determined based on generation from hour 1. If in hour 1, unit is off, hours off is min down time + 1. If in hour 1, unit is on, hour on is min up time + 1. Note that the provided benchmark comparison is for the third week of the study and this differences shouldn t impact the result differences. For pump storage generation differences, Figure 3 shows that e7 is running pump storage more than the legacy. In terms of average LMP hub price difference between the e7 and the legacy for this week, e7 price is 0.33$/MWh higher than the legacy obtained price. Figure 3 PJM Hourly Pump Storage Generation Benchmark System Level Figure 4 shows the hourly locational marginal prices average over all the hubs. In average, the e7 LMP is 33.78$/MWh and the legacy LMP is 33.45$/MWh. Note that in general the e7 LMP for high load hours is less than the legacy LMP. The X-axis shows the number of hours from 1 to 168 to cover the whole week starting at Monday 7/15/2019. The Y-axis shows LMP in terms of $/MWh. The LMP price differences by trading hub are provided in Figure 5. Figure 6 shows average LMP differences at different bus. In average over all the buses, the e7 LMP is 34.28$/MWh and the legacy e7 LMP is 34.82$/MWh. 6 9AKK107046A5365 -A4

9 ANALYSIS HYDRO & PUMP STOR AGE MODELING Figure 4 PJM Average Hourly Trading Hub LMP Benchmark Comparison Figure 5 PJM Average LMP by Trading Hub {Average (e7 Legacy) = 0.33 $/MWh} 9AKK107046A5365-A4 7

10 ABB ABILIT Y PROMOD CONCLUSION Figure 6 PJM Average LMP by Bus {Average (e7 Legacy) = $/MWh} The only difference between the e7 and the legacy PROMOD for pump storage dispatch is an extra option provided for the user to do final pump storage scheduling at the end of n iteration against the corresponding price. The legacy PROMOD will do final pump storage scheduling at the end of n-1 iteration. Note that e7 has this option as well and the provided benchmark results are based on scheduling at the end of n-1 iteration. The number of iteration for these studies is 3, i.e., n = 3. Since pump storage scheduling is a function of the obtained prices, these results need to be examined further in terms of profits, flows and flowgate sensitivities for monitored flowgates and the prices at key generator buses as well as for hubs. Further investigation is in progress and will be reported in near future. 4 CONCLUSION This report provides the basic modelling algorithm for scheduling hydro and pump storage units in PROMOD. To enable faster development of new features and reach high quality standards for addressing the changing needs of the modern customer, the PROMOD is upgraded to PROMOD on e7 utilising Xpress Solver and Microsoft SQL databases for input and output data along providing web services and grid computing capabilities. To identify modelling differences between the legacy and PROMOD on e7, the benchmark results using MISO s MTEP16, PJM RTEP, and SPP databases for hydro and pump storage modelling are provided. The benchmark results are examined in terms of total generation and cost. The cur-rent results show a very comparable generation and cost at the system level for these different databases between the e7 and the legacy. Hourly hydro schedule and pumped storage run more especially during peak hours in e7 comparing to the legacy PROMOD. Also the resulting LMP prices for high load hours from the e7 is lower compared to the output from the legacy. 8 9AKK107046A5365 -A4

11 ABOUT THE AUTHOR HYDRO & PUMP STOR AGE MODELING Further investigation is in progress which examines profits, flows and flowgate sensitivities for monitored flowgates and the prices at key generator buses whilst looking at the generation by different unit types (other than hydro and pump storage units). Meanwhile, enhancements to PROMOD on e7 will continue with the purpose of meeting more sophisticated business needs. 5 ABOUT THE AUTHOR Batool Nekooie, Ph.D., Product Manager, ABB Enterprise Software Dr. Nekooie is a market analysis product manager at ABB Enterprise Software. She has held various management and senior engineering positions in the energy industry during the last 25 years. She has worked with large regulated investor owned utilities as well as unregulated merchant energy companies in the United States. Her research interests and experience includes optimisation, forecasting, and data analysis applied to utility system planning. Batool holds a Doctorate degree in Electrical Engineering from the Georgia Institute of Technology. 9AKK107046A5365-A4 9

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