RULE-BASED ENERGY MANAGEMENT SYSTEM APPLIED TO LARGE INDUSTRIAL FACILITIES

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ABSTRACT

Deregulation of electricity and rising fuel costs are causing renewed interest in Energy Management Systems (EMS). This paper details the results of integrating a rule-based EMS controller at a Pulp and Paper Mill and additional findings from several other large industrial power complexes. It is a PC-based supervisory system that is interfaced to a Distributed Control System (DCS). The EMS has been applied on powerhouse complexes as large as 433 MW of electricity and 7500 KPPH of steam. The EMS may, as required, include boiler load allocation, steam turbine load allocation, combustion turbine and HRSG load allocation, real-time pricing (RTP) tie-line control, coordinated header pressure control, bus voltage and plant power factor control and electric and steam economic load shed systems. It optimizes the powerhouse operations to meet rapidly changing steam and electrical requirements of the plant at minimum cost subject to all of the operating constraints imposed on the generation equipment.

Steady state optimization methods, such as linear and non-linear programming, are not suited for on line optimization of power complex operations since the process is rarely in steady state. Instead, it is critical to control the trajectory of the power generation for optimal steam and electric moves while satisfying multiple constraints. The optimization strategy applied here is reduced to a fairly small number of prioritized rules. It has proven itself capable of optimizing large powerhouse complexes while keeping the powerhouse and process units within a safe operating envelop.

BACKGROUND

The purchased energy (fossil fuel and electricity) cost component for producing a product can be significant and small incremental changes can make a big impact on the profitability of a plant. The plant studied here minimizing purchased fuel and maximizing waste fuel usage to reduce energy cost and emissions. As a direct result of the application of a Rule Based EMS to the powerhouse, this plant became one of the lowest energy cost per air-dried metric tonne (admt) dry pulp paper mill in its class. Table 1 will show that the outside purchased energy to the plant was reduced 13% during softwood pulp production runs and 21% during hardwood pulp production runs.

Table 1 - Combustion Results

The object is to maximize the amount of steam supplied by their self-generated waste fuel sources such as hog and black liquor. They can also work to optimize boiler loading and boiler fuels to produce the mill's steam requirement from the power boilers at the lowest cost. An energy management system (EMS) is a tool that can be used for this task.

The powerhouse has a number of environmental, equipment and process constraints that must be adhered to as the powerhouse equipment is maneuvered to meet the mill's energy demand at the lowest possible cost. Balancing the optimization function with all those constraints is a difficult task requiring a significant amount of operator intervention. In developing the project requirements, Daishowa Marubeni International determined the following functionality was required:

EMS Requirements:

1. Minimize Power Boiler outside purchased Natural Gas use while:

• Maximize Power Boiler stability (eliminate/reduce trips)
• Maximize steam from hog fuel (Higher % Steam from Hog)
• Eliminate the use of outside purchased fuels for Header Pressure Control

2. Reduce the steam venting and excessive steam condensing

• Maintain steam header stability
• Reduce Natural Gas use, avoid venting while burning Natural Gas

3. Maximize electrical Power Contract Production

• Increase power production when import power cost are high
• Decrease power production to ration hog fuel usage during shortages
• Generate power in the most cost effective manner
• Only burn Natural Gas when import power costs are high
• Support demand and Real Time Pricing (RTP) rates for optimal generation

EMS Project Schedule:

The project developed rapidly. The drag and drop nature of the EMS configuration allowed rapid control system development. Engineering review was started in January 1999 and completed by April of 1999. All systems were installed and operational with formal operator training completed by October of 1999.

EMS Benefits:

The EMS has been in continual automatic operation for operation since 1999. Several significant benefits have been determined:

1. Steam users report more stable header pressure

• Smoother operation of the pulp dryers caused by consistent dry air availability
• More stable evaporator operation

2. Greater Power Boiler Operating Stability

• Operations can run without Natural Gas
• Reduced natural gas usage
• Higher percentage of steam from hog fuel
• Considerable cost savings

3. Lower Venting/Dump to Condenser Flows

• Reduced natural gas usage
• Reduced power boiler steam production
• Reduced steam production overall
• Considerable cost savings
• Reduced stack emissions

4. Stable operation identified bottlenecks

• Units ran consistently on the edge of optimal performance
• System indicators identified constraints

5. Accelerated Return on Investment

• Original ROI estimate was 1 year
• Actual ROI was less than 6 months

The advantage of a rule-based system is to take the best engineering and operational knowledge and insert it into the control system to be operational 24 hours a day, seven days a week. It can perform the optimization function while adhering to all constraints. Like the operator, the EMS sacrifices cost optimization whenever a constraint is reached. This results in robust process control. The control priorities are:

1. Meet all environmental constraints (stay out of jail).
2. Meet all equipment constraints (avoid equipment damage).
3. Meet all process constraints (Assure utility delivery to process units).
4. Meet energy requirements at minimum costs ($).

STEAM AND ELECTRICAL NETWORK

The steam and electrical network for the powerhouse is shown in Figure 1. More than half of the steam that is generated comes from burning process byproducts (black liquor and hog). Most of the process steam demand is for low-pressure steam (in this case 1100 and 400 kPa steam). Steam is generated at a higher pressure (in this case 6380 kPa) and throttled through the turbine-generator to the lower pressure headers. A significant amount of electrical power, termed "extraction power" or "cogeneration", is generated as a result of this throttling action. Pressure reducing valves (PRVs) offer an alternative way to throttle the steam to the lower pressure headers. However, since no power is generated, the steam flow through PRVs, condenser or atmospheric vent should be minimized.

Figure 1 - Simplified Powerhouse PCD.

There is significant variability in the process steam and electrical power demand. Batch digester operation, wood yard log chippers, soot-blowers, paper machine disturbances and pulping process upsets all contribute to this variability. A "sheet break" on a large paper machine and subsequent rethreading of the sheet can result in large sudden steam demand swings in a period of less than a minute. Sometimes the power boilers must go from maximum load to minimum load and back again to maximum load within several minutes. Power boilers seldom operate at steady state conditions unless they are base loaded (i.e. the boiler master is placed in "manual"). It is this variability that makes realtime optimization of the powerhouse operations so challenging. Steady state optimization methods simply do not provide the solution when the process is rarely at steady state.

ENERGY MANAGEMENT SYSTEM

A new type of EMS has been developed and implemented to minimize the total cost of energy required by a mill. It coordinates and optimizes the generation and distribution of steam as well as the generation and purchase of electricity. It also controls the main steam header pressure. The EMS is a supervisory control system that works in tandem with regulatory controls residing in the powerhouse DCS and PLCs.

The EMS subsystems include boiler load allocation, turbine load allocation, hog optimization and demand or real-time pricing tie-line control. Each subsystem can be operated independently. The operator selects the subsystem and places it on EMS control. For boiler load allocation, the operator selects which boilers and fuels are to be used. The EMS control software resides in a Windows NT personal computer or can be installed directly in the DCS. It has been designed specifically for implementing fuzzy logic control. There are interfaces to the powerhouse DCS, turbine controllers and various PLCs.

Boiler Load Allocation

A schematic of the 6380 kPa header pressure control with the embedded boiler cost optimizer is shown in Figure 2. The plant master is implemented with a fuzzy matrix controller that offers some significant advantages over a PID version. Fuzzy matrix controllers can exhibit superior control performance compared to a PID controller, especially for nonlinear, complex processes. But the tuning of fuzzy controllers is a trial and error procedure that involves adjusting many parameters. A simple method to help with the tuning of fuzzy controllers has been developed. By overlaying a phase-plane plot on the rule matrix and analyzing the phase-plane trajectories, it becomes relatively easy to adjust membership functions and modify the rules to obtain the desired trajectories.

Figure 2 - 6380 kPa Header Pressure Control with Embedded Boiler Cost Optimizer.

The fuzzy controller executes once per second and sends a request to the boiler cost optimizer for an incremental steam change. The boiler load optimizer design involves integration of three distinct functions. A safe operating envelope representing prioritized environmental, equipment and process constraints are defined which the allocator must respect. An optimization method is used which adjusts multiple boilers and fuels to obtain the most economical operating solution. The issue of header pressure control stability is addressed so power boilers with widely varying response capabilities can work in concert. Balancing these three functions is key to a successful design.

The boiler load allocator observes all predefined constraints before adjusting boiler fuel flows. These constraints create a safe operating envelope. Observing constraints prevents boiler damage and keeps the process out of undesirable operating regions. Constraints are prioritized in order of importance. Typical constraints for a boiler are (listed in order of priority):

1. Maintain opacity (6-minute average) below maximum.
2. Keep ID fan speed within control range.
3. Prevent furnace draft from going positive.
4. Maintain drum level in safe range.
5. Prevent excess oxygen from going too low.
6. Keep boiler steam generation within limits.

The boiler load allocation problem is analogous to the economic dispatching problem faced by an electric utility company whenever transmission losses can be ignored. For optimal allocation, the utilities must operate the units at equal incremental generating costs. Often the boiler load allocation problem has been posed as a static optimization problem (1). But in reality, the allocation function is embedded in the header pressure control loop that transforms it into a dynamic control problem. Since there are continuous disturbances to header pressure (caused by variations in steam demand), boiler load allocation also takes place on a continuous basis. Direct application of steady state optimization methods does not work for a process that is never at steady state. Instead, a dynamic boiler allocation method is used. In this solution, the optimization method has been converted to an optimization rule set that is integrated into the overall rule set.

An incremental steam generation cost ($/tonne of steam) is continuously calculated for each boiler (fuel) based on the fuel cost ($/QJ), the selected "swing" fuel and incremental boiler efficiency for the selected fuel. This efficiency number is entered based on historical data or online calculations.

For incremental steam increase requests, boilers and fuels with lower incremental steam costs are favored more than boilers and fuels that have higher costs. All of the boilers move in concert to prevent one boiler from taking all of the load swings. For incremental steam decrease requests boilers and fuels with higher incremental steam costs are favored. In the long run, the most economical boilers and fuels take most of the steam load. The more expensive steam producers are kept at the minimum value. In the short term, if more expensive steam is required for good header pressure control, it is used. If the control is properly tuned the penalty for better control is usually not significant.

Hog Optimization

Hog optimization is incorporated in the boiler load allocation function. The operator enters a minimum and maximum hog rate limit. It is desired to keep the hog rate for each boiler at its maximum value as much as possible.

In a multi-fuel boiler, each fuel is treated as if it were a separate boiler by the boiler load allocator. The cost of hog ($/QJ) is entered as a very low value.

There is a significant lag time (several minutes) associated with the transport of hog from the hog bin to the boilers. This lag time prevents hog from being an "effective" swing fuel. However, an operator adjustable "aggressiveness" factor is used to allow hog to be treated as a pseudo "swing" fuel and maintain stable header pressure control.

Normally, hog flow will remain at the maximum limit (entered by the operator) and header pressure control is accomplished by adjusting gas flows. However, there are periods of low steam demand when hog flow must be reduced to prevent excess venting of steam to the atmosphere. When the fossil fuel is at minimum limits and further steam generation reduction is needed, the boiler allocator will reduce the hog flows of the power boiler. When the process demand increases, hog starts to increase. Hog is considered "somewhat" base loaded, since it always works its way back to the maximum limit.

Steam System Management

The next area of concentration is steam usage management. The primary focus is the proper allocation of the generated steam to satisfy steam header and system electric generation requirements. Table 2 Steam System Management shows a 16% reduction in steam losses (vent & condenser) for hardwood and a 36% reduction in losses for softwood while maximizing the electric generation requirements and improving steam header reliability. The Steam System Management components are described below.

Table 2 - Steam System Management

Header Pressure Control Stability

One of the major challenges of implementing boiler load allocation is to maintain stable header pressure control for all combinations of boilers, fuels and equipment conditions. Boilers have different response times. Variable fuel quality and moisture content can effect the boiler's response time. Mechanical problems can limit the rate of load changes for a boiler.

Multi-fuel boilers, where hog is burned on a traveling grate, seem to create problems. Wet hog, long lag times in the hog feed system and hog "piling" on the grates can make using the boiler for header pressure control quite challenging.

An "aggressiveness" factor is assigned to each boiler fuel. It determines how much a boiler fuel is asked to participate in header pressure control. The factor varies from 0 to 1. When set to zero (0), the fuel does not participate in header pressure control. It becomes base loaded. When set to one (1), the boiler fuel has full participation in header pressure control. For any value in between, there is partial participation. Matching the "aggressiveness" factor to the responsiveness of each boiler is important for achieving stable header pressure control. Reducing the participation of boiler fuels that have poor steaming response is essential. However, there must be at least one boiler fuel (in large plants, preferably two) that has a fast steam response if satisfactory header pressure control is to be obtained.

Sometimes boiler constraints reduce header pressure control effectiveness. Each boiler's constraints are checked once per second to insure process limits are not being violated. As a boiler approaches a limit, its participation in header pressure control is reduced to zero. When some limits are exceeded, such as boiler steam generation, constraint controllers may make counter control moves to place the boiler back inside the safe operating envelope. Counter control moves are usually to the detriment of good header pressure control. This means that the header pressure is not the highest control priority. In fact, it is the lowest priority.

An extension to the above steam generation fuzzy controller was applied to the excess steam utilization condenser and atmospheric vent systems. This facility had unique constraints in the application of steam header control using a common steam condenser system for generation and steam balance. The flexibility of the Rule Based Control System provided seamless integration into the balance of the control system the rules shown in Figure 3 - 400 kPa Steam Header Condenser & Venting Optimizer.

Figure 3 - 400 kPa Steam Header Condenser and Venting Optimizer.

Turbine Load Allocation

This subsystem provides supervisory control for 1100 kPa and 400 kPa extraction flows of the TG to minimize PRV flows and maximize the total power that is generated. The TG is assigned primary responsibility for control of the 1100 kPa header pressure, 400 kPa header pressure and purchased power. RTP tie-line control adjusts the load to the condenser via the TG and throttling valves.

A safe operating envelope for turbine load allocation has been defined that will:

1. Maintain all TG parameters (V1, V2, V3, MWs ... etc.) within the minimum & maximum limits.
2. Provide override control for 6380 kPa header pressure (outside of minimum & maximum limits).
3. Provide override control for 1100 kPa header pressure (outside of minimum & maximum limits).
4. Provide override control for 400 kPa header pressure (outside of minimum & maximum limits).
5. Maintain extraction flows on TG in control range for extraction pressure control.
6. Maintain sufficient swing range for TG's condensing flow to accommodate RTP tie-line control.

The turbine load allocator adjusts the 1100 kPa and 400 kPa flow setpoints in TG controller to achieve turbine load allocation. Both flow setpoints continue to be "bumped" up until a constraint is reached.

ELECTRIC UTLILITY RATE SCHEDULES

Most industrial customers purchase power from an electric utility company on a 15 or 30 minute interval. This type of rate schedule has a demand component and fixed energy charges for on and off-peak periods. The demand charge is usually based on the highest (peak) interval demand in the last 11 or 12 months. Interval demand is the average purchased power over an interval. Exceeding a previously set peak demand may cost hundreds of thousands of dollars since this new peak demand is usually ratcheted in as the minimum demand charge for the following 12 months.

Real Time Pricing (RTP) is a new type of rate schedule offered to industrial customers by many electric utilities. The utility provides tomorrow's hourly prices based on the grid load and generating capability. Under the RTP rate schedule there is no demand charge or demand interval. Instead, the price of electricity varies on an hourly basis. Customers can purchase all the power they need without worrying about setting a new peak demand. During summer periods, when the power demand becomes high, the midday hourly price is usually quite expensive. On some days it may even exceed $1000/megawatt hour. The customer obviously doesn't want to buy any more of this expensive electricity than is absolutely necessary.

RTP TIE-LINE CONTROL

The ability to select the most attractive electric rate schedule is critical for today's energy manager. In this application the Tie line control has three modes:

1) RTP
2) Demand MW
3) Constant Purchase MW

However, it is the RTP mode that is becoming more important in the deregulated business environment. In some instances the utility faces utility generation or transmission constraints and will provide attractive economic incentives for excess power generation or demand side management during peak periods. The objective is to reduce the mill electric demand or at some mills, generate power onto the grid during periods of high utility demand. This becomes a "Win-Win" for both the plant and the utility. The primary control objective for the mill is to adjust TG's steam flow to the condenser or vent to minimize the cost of providing the mill electrical deficit while staying within a predefined safe operating envelope.

Mill Electrical Deficit

To implement an RTP or Demand tie line controller, it is necessary to focus on the Mill Electrical Deficit shown in Figure 4. The electrical deficit is defined as the mill's total electrical power demand minus the power being generated due to the turbine's extraction flows and minimum flow to the condenser.

Figure 4 - Mill Electric Demand Deficit.

There are 3 sources of power that can be used to meet the deficit:

1. Purchased power
2. Forced condensing power
3. Venting (400 kPa steam) power

The function of the RTP control algorithm uses the mill electrical deficit to the selects the proper operating mode and to minimize utility cost.

RTP Control Algorithm

A schematic of the RTP tie-line control algorithm is shown in Figure 5. The prioritized constraints are shown in the top part. They define the safe operating envelope. Using this constraint boundary, the turbine is "herded" to stay within the envelope while the tie-line control function is performed. It does not allow condensing to increase when:

1. Condensing flow is high.
2. Condenser vacuum is low.
3. Purchase power is at low limit.
4. TG generated MWs is high.
5. TG 1100 kPa extraction flow is high.
6. TG 400 kPa extraction flow is high.
7. TG throttle flow is high.
8. Total power boiler steam generation is very high.
9. 6380 kPa header pressure is very low.

Figure 5 - RTP Algorithm.

It does not allow condensing to decrease when:

1. Condensing flow is at minimum.
2. Purchased power is at a high limit.
3. TG generated MWs is low.
4. TG 1100 kPa extraction flow is low.
5. TG 400 kPa extraction flow is low.
6. TG throttle flow is low.
7. Swing PB steam generation is low.
8. 6380 kPa header pressure is very high.

Each day the utility provides tomorrow's hourly prices by electronic mail or Internet to each RTP customer. Around 5:00 PM each day, tie-line control automatically downloads these prices (see Figure 6). At midnight the prices are automatically transferred into a buffer for use.

The control system continuously calculates the incremental cost to generate the next megawatt hour by forced condensing. It is based on the incremental cost of steam generation and the amount of steam required to generate a megawatt from forced condensing. The price of condensing power is compared to the cost of purchased power. When it is less expensive to buy power, the control decreases turbine condensing until it encounters a process constraint.

When it is less expensive to make power, the control increases condensing until it encounters a process constraint. When the cost to buy versus make is nearly the same, condensing is controlled to mid-range the loads on the power boilers for maximum operating flexibility. The control adjusts the turbine's load to minimize electrical costs only when all variables are within the safe operating envelope. The control sacrifices minimum cost for safe process performance.

Figure 6 - RTP Rate Schedules.

Additional Benefits

Operations worked closely with engineering in the development of the EMS operator interfaces. The resulting control system has almost "human characteristics" that makes the system easy to understand. This has also resulted in a control system that is very easy to troubleshoot and modify. A second benefit is the ability to use the system to identify bottlenecks in the process. Two characteristics of the EMS provide this ability:

1) The process units are always operating in on the edge of the optimizing envelope.
2) As the process moves between these optimal operating points, the constraining criteria are highlighted on the operating displays.

This allows operations to identify both the magnitude and frequency of operating constraints.

CONCLUSIONS

The rule-based EMS described in this paper has been designed and implemented in several powerhouses. All projects have demonstrated substantial savings. The savings attributed to this powerhouse was a minimum reduction in gas purchase of 14% and a total reduction in purchased energy of 13% while improving steam and electric generation quality and reliability.

The design is based on fuzzy logic control. A new inference engine and defuzzification method is employed. It is the heart of this new supervisory software package. This methodology integrates online optimization and a set of prioritized constraints. A list of process, equipment and environmental constraints are converted to a set of linguistic variables (fuzzy variables), which are used to define a safe operating envelope. When the process is operating inside the envelope, the EMS optimizes the powerhouse to provide process steam and electrical power at the lowest cost possible. The EMS usually operates the process on the boundary of the constraints.

This new control technology is applicable for many other online process optimizations in pulp and paper mills and other industrial facilities. Current applications outside Pulp and Paper have included CO and Waste Gas management in Petrochemical Complexes, multiple gas turbine and steam generation dispatch in large utilities.

ACKNOWLEDGMENT

The effort of Dr. Frederick Thomasson is greatly appreciated in the original "Rule-Based Energy Management System" [2] and Jim Robinson for compiling this data.

REFERENCES

1. Thomasson, F.Y. , 1979 Proceedings of the American Power Conference, AMERICAN POWER CONFERENCE, Chicago. P. 853.
2. Thomasson, F.Y. , RULE-BASED ENERGY MANAGEMENT SYSTEM, 2000 IETC