Real-time and Intraday Optimization of Multiple Power Generation Units

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1 , ID T6S4P3 Real-time and Intraday Optimization of Multiple Power Generation Units Dr. Rüdiger Franke Hansjürgen Schönung Marcel Blaumann Dr. Alexander Frick Stephan Kautsch ABB AG, Germany

2 1 INTRODUCTION BRINGING MATHEMATICAL OPTIMIZATION TO REAL-TIME CONTROL INTERNAL OPTIMIZATION OF LARGE MULTI-UNIT PLANTS COMBINED HEAT AND POWER PRODUCTION REAL-TIME OPTIMIZATION OF POOLS OF BIOGAS PLANTS RENEWABLE SUPPLY MANAGEMENT INTRADAY OPTIMIZATION OF MUNICIPAL POWER CONCLUSIONS Introduction 2 / 15

3 1 Introduction Facing increasing penetration of renewable energy, new opportunities arise for the trading of power. Particularly the increased participation in grid services and intraday trading becomes crucial for the economical result. This raises the requirement for increased flexibility on the power generation side: Frequent updates during the day complement traditional day-ahead plans for conventional power generation. Combined heat and power generation is changed from heat driven to electricity driven, exploiting storage capacities on the heat side. The controllability of renewable generation units is increased. This paper introduces a new optimization method that is placed in the real-time control of power plants. This enables the pooling of individual power generation units and their management like one large plant. The real-time optimization receives overall set points and distributes them to each individual power generation unit, considering actual efficiencies, process constraints and temporary limitations. The introduced hierarchy reduces overall complexity and increases the flexibility. Moreover, it solves the power generation task at the best point for the considered units. The paper introduces the optimization method and lists five different application examples. 2 Bringing mathematical optimization to real-time control Traditional unit commitment optimization focuses on day-ahead plans for power production and trading. Well-established optimization tools find on top of plant information management systems and interface with trading systems. Introduction 3 / 15

4 Engineering & Maintenance Traditional Optimization Fleet Management Trading, Accounting PIMS Scanner Unit 1 DCS 1 Unit 2 DCS 2 Unit Other DCS/PLC Figure 1: Classical system layout for plant optimization, e.g. unit commitment Figure 1 shows the evolved classical system layout. Multiple production units are connected through an automation network. A Scanner reads real-time data from the automation network and provides it for the Plant Information Management Systems (PIMS). The PIMS provides a process historian database and analysis functionalities. This primarily improved the engineering & maintenance. Optimization algorithms traditionally run on top of the PIMS system. Typically, human operators use such traditional optimization tools interactively. Four new trends arose during the previous years: 1. The number of power production units significantly increases with the use of renewable energy 2. The power production needs to be re-planned frequently during a day, in order to account for fluctuations Bringing mathematical optimization to real-time control 4 / 15

5 3. The required optimization cycle times reduces from daily planning cycles down to seconds, e.g. for pooling of secondary frequency control 4. The role of human operators changes from being part of the loop to supervision Moreover, the mathematical optimization technology has maturated during the last years. Modern optimization solvers treat large linear optimization programs, including also integers, reliably in fractions of seconds. Even quadratic and nonlinear optimization algorithms perform well in model predictive control applications in real-time. The new trends require a shift from interactive use of optimization tools to automated optimization in the real-time control system. Engineering & Maintenance Fleet Management Trading, Accounting PIMS Online Optimization Scanner Unit 1 DCS 1 Unit 2 DCS 2 Unit Other DCS/PLC Figure 2: New system layout with online optimization Bringing mathematical optimization to real-time control 5 / 15

6 Figure 2 shows the new system layout. The optimization now directly accesses the automation network through the Scanner. It only communicates data exchanged with the fleet management, like planning data, through the PIMS. This system layout provides for significantly improved reliability. For instance, the optimization can still run if the PIMS goes offline just updates of planning data would not work anymore in this case. The direct communication also allows for significantly reduced optimization cycle times down to about a second. All communication between the Scanner, the PIMS and the Optimization goes through TCP/IP sockets. This provides a lot of flexibility for different implementations, depending on specific needs. In the simplest case, one server PC can run all functions (Scanner, Optimizer, PIMS). Two redundant server PCs provide for higher availability. Moreover, the Scanner and the PIMS can run on separate server PCs, in order to increase the throughput of data. Finally yet importantly, the Optimization can share the use of already existing functions, like an already installed Scanner or PIMS or of already existing hardware, e.g. for running the Scanner on servers already existing in the plant control or SCADA system. Traditionally a private automation network provides the communication with the power generation units. Alternatively, virtual private networks (VPN) using the Internet or GPRS enable the communication to small, distributed power generation units. The new system layout particularly suits for the real-time optimization of actual plant set points and for the intraday optimization of plant schedules. The following sections give an overview about important use cases and exemplary installations. 3 Internal Optimization of large multi-unit plants Two large power plants with an installed capacity of 6x500MW and 2x500MW/1x890MW/1x600MW, respectively, run a real-time optimization for plant set points and secondary frequency control. The idea is to pool and optimize multiple units per plant locally. At the same time, the optimization system automates the communication from Internal Optimization of large multi-unit plants 6 / 15

7 the load dispatcher to the control systems of the power generation units, in order to enable the reaction on increasingly frequent updates, see Figure 3. Figure 3: Principle of the Internal Optimization for a 6x500MW power plant The optimization model covers efficiency curves and own consumption of the plant units, besides many constraints ensuring the appropriate provision of primary frequency control, secondary frequency control and required load ramps. The Modelica technology provides for graphical structuring of the model equations and generation of efficient executable code, see Figure 4. Internal Optimization of large multi-unit plants 7 / 15

8 Figure 4: Graphical formulation of the optimization model using Modelica The graphical model editor exports compiled executable models to the online optimization. In this case, two online optimizations are running in parallel in each plant. One optimization adjusts the set points for the provision of secondary frequency control automatically in realtime. The second optimization covers the plant schedule and proposes new set points. The operator can influence the optimization by adjusting min/max constraints per plant unit. Once acknowledged, the set points are transferred to all units automatically. Standard operator graphics hides the optimization details from the operators. For instance, standard faceplates serve as input for max and min bounds for feasible operating points. The online optimization automatically determines the overall best point within the ranges specified by human operators. Internal Optimization of large multi-unit plants 8 / 15

9 Figure 5: Operator graphics visualizing the status of all plant units together with optimization results and linking to faceplates for operator inputs 4 Combined Heat and Power production Combined heat and power production is key to improving energy efficiency. The physical interaction between different components on heat side and on electricity side results in complex constraints for the plant operation. Combined Heat and Power production 9 / 15

10 Figure 6: Plant model and Operator screen for combined heat and power production Figure 6 shows the application to real-time optimization of plant set points. The plant model, shown on the left hand side, considers: Efficiency curve and own consumption for each relevant plant component Couplings through steam headers Appropriate assumptions need to be made, in order to enable online optimization in real-time. The optimization builds on a well-tuned base control system for each unit. This means in particular that optimal coordination of multiple plant aggregates builds on well-controlled header pressures and temperatures. Real-time optimization treats a mathematical optimization program based on the physical plant model. The optimization constraints and the objective cover: Demands for steam and electricity Externally given set points for the recovery boilers Constraints per plant component (e.g. min and max load) Fuel costs for coal and biomass The real-time optimization of set points integrates with the regular plant operation in a straightforward way. Standard operator screens visualize optimized set points besides actual values. The base control systems take over optimization results either automatically or after confirmation by human operators. Combined Heat and Power production 10 / 15

11 5 Real-time optimization of pools of biogas plants Comparable fast load ramps make renewable generation units well suited for the provision of grid services. This is why the direct marketing of renewable power is becoming increasingly attractive. Individual renewable power plants are too small though. The pooling of small renewable power plants is required, in order to achieve an overall capacity that is sufficient for participation in the electricity market. Online optimization manages pools of biogas plants for the provision of secondary frequency control. The real-time optimization implemented on a central redundant server receives set points from load dispatchers and distributes them to pooled plants. The IEC protocol is used in Virtual Private Networks (VPN) for the communication over the Internet. Figure 7 gives an overview of the system structure. 4 Grid Operators Redundant Process Database Real-Time Optimization IEC Scanner Operation & Management Up to 500 production units, typically 500 kw per unit Service Client Operations Client Engineering Client Import / Export for operational management Figure 7: Pooling of biogas plants for secondary frequency control Real-time optimization of pools of biogas plants 11 / 15

12 6 Renewable supply management Many renewable production units, like wind and solar, do not participate in the direct trading. Instead, they are supplying an increasing amount of unmanaged power to the grid. This can harm the grid operation and stability. Renewable supply management controls the amount of renewable power fed to particular distribution networks. Online optimization offers proven technology to break down overall set points to individual power generation units, fulfilling legal requirements (like EEG Einspeisemanagement) and plant constraints. As a result, the renewable generation units receive limits for their supply to the grid (see Figure 8). Figure 8: Overall system layout for renewable supply management (EEG-Einspeisemanagement) Renewable supply management 12 / 15

13 7 Intraday Optimization of municipal power Storage capacities enable the temporal decoupling of power production and consumption. Typical storages are latent heat stores of combined heat and power production and pump stores. Moreover, the emerging electric mobility offers a huge potential for future storage capacities. From a control point of view, storages require a shift from real-time optimization of actual set points to predictive planning of load trajectories. The planning needs to consider a multitude of constraints. For instance, heat storages primarily need to ensure the supply of heat and car batteries primarily have to power the engine. Intraday optimization enables the optimal use of power generation units and storage capacities, considering constraints and maximizing the economic result. It reacts on new conditions by re-planning the power production. This maintains the overall balance, in order to avoid the purchase of expensive regulating power from the transmission net. Figure 9 shows the optimization model of multiple producers, consumers and storages that form one regional balancing zone. Figure 10 shows exemplary intraday optimization results. Triggered by an update of weather forecast data, a new prediction for the wind power arrives. The wind power (thick green line) decreases after 12:00 and increases after 14:00, compared to the original day-ahead plan (thin green line). The intraday optimization can avoid a misbalance by re-planning the combined electricity and heat production (red lines). The heat storage buffers excess heat during overproduction of electricity and releases it during underproduction of electricity (black line). Intraday Optimization of municipal power 13 / 15

14 Figure 9: Graphical formulation of an intraday optimization model Figure 10: Exemplary results of intraday optimization Intraday Optimization of municipal power 14 / 15

15 8 Conclusions Traditional optimization tools run on top of process databases. They are used interactively and the optimization results are typically transferred manually to the control systems of the optimized power production units (e.g. via or ftp to the plant and manual input into the control system). These tools and procedures run into limitations when facing an increasing number of small renewable power generation units and/or increasingly frequent re-planning cycles. Online optimization connected directly to automation networks enables the flexible reaction on frequently changing production tasks. The plant control systems either receive optimized set points automatically. Alternatively, standard operator graphics may present the optimization results to human operators for acknowledgement. In both cases, the online optimization not only increases flexibility, but also maintains the overall power production at the economic best point. Real-time optimization treats current plant set points. The solution time is short (down to fractions of a second) thanks to the availability of mature numerical optimization solvers and powerful computing hardware. This is why the optimization can even be placed into the communication and sub-division of set points, e.g. for secondary frequency control or for renewable supply management. Moreover, this allows an interactive use. For instance, a human operator can adjust bounds or constraints using regular process graphics and immediately see the effect on the optimization results. Intraday optimization not only covers current set points, but also predicts into the future. This is required in the case of available storages, like heat buffers, pump storages or batteries. This decouples the production from the consumption of electricity up to the extent of available storages. ABB has implemented the real-time and intraday optimization in OPTIMAX PowerFit, now basing on the online platform ABB Dynamic Optimization. The new optimization method has proven successful in a number of different application projects. Conclusions 15 / 15

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