INTELLIGENT AGENT APPLICATION FOR HYDRO-WIND ELECTRICITY GENERATION CONTROL Cameron Potter and Michael Negnevitsky School of Engineering - University of Tasmania E-mail:
[email protected] Abstract Intelligent applications in power systems are becoming increasingly prevalent as engineers realise the potential these systems have. However, scheduling of renewable power sources – especially hydro – is a difficult task and no system as yet has been created without flaws (or is ever likely to be). This paper investigates some of the issues generation control is posing in Tasmania and seeks to determine the best available technology to operate a hydro-wind generation system. 1.
INTRODUCTION
This paper provides a survey of the most common power generation technologies and the systems most often used to control them. The paper has a specific aim of investigating the use of renewable power as the primary power source and finding ways to schedule it efficiently. The paper compares the different scheduling technologies with the requirements of an efficient scheduling system and finds that only one of the available technologies manages to solve each of the issues presented. This tool is the “intelligent agent” approach – discussed and described later in this paper. Tasmania is one of the few places throughout the world that generates its power requirements almost solely from renewable sources. Although much literature is available on hybrid power systems that include fuel-based units as the majority of their production capability, there is a distinct lack of literature representing cases such as Tasmania’s. The cost of operating a modern power system is great and thus research that yields improvements in system operation is beneficial. However, as Tasmania is in an unusual situation regarding generation, much of the useful research must be specifically produced. The unusual situation of predominantly renewable power sources produces various problems, but also provides solutions to other issues as well. Power system operation includes such issues as security, reliability, quality of power supply and, of course, the cost of running the system. This is then all underpinned by the need to minimise costs and maximise return. Moreover the scheduling must also adhere to the specific technical constraints of the power system. In a fuel-based system, the major cost of day-to-day usage is the fuel itself. Thus, such a system attempts to reduce its
fuel consumption at the expense of ‘free’ hydro units [1]. However, if the system is no longer using fuel (in the conventional sense) then the operation of the system is entirely changed. The controllers are not worried about limiting purchased fuel (such as gas, oil or coal) but are instead concerned with maximising the effectiveness of the natural resources. There is a subtle, but important, difference between this approach and the “save fuel” approach. This difference stems from the fact that fuelbased power stations tend to use the renewable sources as peak-load sources and such are not concerned about “spill”. The operators of renewable-based systems know that lost energy might mean a power shortage later – causing a need to operate very expensive backup measures. 2.
OVERVIEW OF POWER SYSTEM OPERATION
This paper addresses power generation as coming from two sources so far, fuel-based and renewable. However, the distinction is somewhat blurred. For the purposes of this paper it will be convenient to use these two titles as allencompassing categories. The fuel-based systems destroy the fuel they use, but can purchase more when needed. The renewable energy systems do not destroy the fuel, but cannot purchase more when needed. The operation of power systems has time horizons normally divided into four categories: long-term, medium-term, short-term scheduling and real-time operations [2]. Furthermore, the load can be divided into three types: base load, middle load and peak load [3]. The different time horizons must be planned for through modelling. However, there can still be variations in the load. These variations must be accounted for using spinning reserve and fast/immediate response generators.
2.1.
Fossil Fuel Based Systems
Fuel based systems are diverse and include many variations that can solve most generation issues. There is the high initial expense, low running cost, option of nuclear power and the low initial expense, relatively high running cost option of gas. In the middle of these extremes is coal-fired generation. However, the generators with low running costs also tend to have slow ramp-rates (rate at which the level of operation can be altered). This means that in order to have a power station that can rapidly respond to changes in demand, gas-fired stations must be used. The other alternative is to consider a renewable system. 2.2.
Renewable Systems
Renewable systems also have diverse potential for generation. Hydro generation has the advantage of quick response, while wind and geothermal energy have the advantage of providing a reliable, non-exhaustible (within practical limits) supply of power. Furthermore, the energy sources used to produce the electricity are free. However, these systems are not free of shortcomings. Renewable systems have a high installation cost and so have been prohibitively expensive to nations with closely limited resources. Wind and geothermal energy are not able to be stored in their own right. Hydro power, which is storable, is not always available. If there is a drought, or even a period with less rain than forecast, this can cause problems with the operation of a hydro system. However, even with these negative aspects, in developed countries, most locations that could be used to generate renewable power are being utilised [2]. Thus, most countries rich enough to afford the initial outlay agree that the advantages of free energy in the long term outweigh the negatives of renewable power – and the fact it is environmentally sustainable is becoming more important too. Hydro power has many technical constraints (such as differing catchments) that cause difficulties, but has the large advantage of being capable of supplying instantaneous, fast and slow reserve power [3]. This availability of reserve power from a large system is a keystone in the efficiency of hydro power. Furthermore, hydro power has the advantage of free fuel costs. However, the power that can be generated by hydro power is only as plentiful as the rainfall it relies upon. In dry weather, the water in storages becomes scarce and hence valuable. Thus a hydro power system needs support from another system in order to function reliably. This is commonly achieved with a thermal generator that can be used as a backup source in times of emergency.
Wind power is another system that is used in Tasmania; though wind power is very different from hydro power. Wind cannot be stored, and it is impractical to store energy in the form of electricity (ie batteries). Thus, some other manner of storing energy is desirable. Wind energy also has the problem of being unpredictable. There is little hope of wind power being able to be efficiently used to supply peak power loads. Any power that is not used, will either destabilise the power system or must be dispersed somehow. Thus wind power must function as a base load supply [3]. The wind power turbines are set to try to achieve the most power that they can and the rest of the system is operated around that idea. Unfortunately, the random behaviour inherent in wind power dictates that it is impossible to perfectly predict the power production, and hence, there must be significant reserves to accommodate this need for change [3, 4]. Finally, a wind-hydro hybrid system is an excellent combination, providing that the environment is suitable to create such a system. The two technologies: hydro and wind generation, behave drastically differently, but result in a symbiosis that makes the combination of the two generation methods an innately successful pair. The wind power can provide a reasonably steady base load, while the hydro generators can provide the peak power and respond very rapidly to changes in demand and changes in wind generation. There is also another major advantage of using these technologies together; each helps to overcome the other’s disadvantages. The hydro storages can efficiently store the wind power. If large wind power facilities are present, any surplus wind power can be used to pump water into higher storages, increasing the potential energy. The wind power can provide a second source of power, meaning that the power system is no longer solely reliant on the water needed for hydro power – in fact, the system can replenish its water storages with the excess wind power. The other problems associated with the operation of the generators can also be overcome by the symbiotic relationship. It costs little to keep a hydro generator as a spinning reserve and hence the unpredictable wind conditions are well accounted for. The hydro generator’s reliance on rainfall is reduced, as the wind will now assist in dry times. Thus, hydro power provides a cheap, efficient and powerful solution to the problems associated with wind power storage and quality. Meanwhile, the wind power overcomes the
reliance on wet periods that define the operations of hydro generation. 3.
cross-section. Changes to the cross-sections also mean changes in the measured amount of water being supplied to storages by that channel.
CONTROL SYSTEM REQUIREMENTS
To be effective, a power control system must be able to supply its customers with a frequency and voltage within the acceptable range [2]. This is only possible if the energy management system (EMS) is capable of reacting to changes in conditions rapidly. Additionally, the EMS must be able to operate according to local parameters and restrictions. For a utility operating in a bidding environment it is desirable to have a system that reacts rapidly to generation allowances from the power pool management. The EMS must also be capable of altering the generators’ output fast enough to allow for deviations in power demand and also to allow for auxiliary power supply issues.
Figure 1 shows a simple example of a change in the channel section due to a landslide. The “rating” of water height to flow volume has changed and thus the EMS must operate in a different manner. If the EMS remained unchanged, the inflow to the downstream storage would be less than expected and inefficiencies would occur. In times of little water, the storage may be run until it is below acceptable limits and possibly even worse, at high water periods, the EMS may predict an overflow that would not have occurred. This predicted overflow, or “spill” would mean that the station would be operated at maximum capacity and any power it produced would be undervalued, since the water was simply used because it was supposed to be excess, not because it was needed.
Renewable sources add further constraints to the EMS. Unlike with a fossil-fuel based system where the fuel supply is readily available, with a renewable source of power, the operation heavily depends upon external factors. The EMS must be capable handling these changes in operation too. Thus, the EMS for a renewable system must be extremely fast. However, speed of response is not the only issue to decide upon when selecting a power system control tool. It is important that the EMS is capable of being understood and altered. The electricity generation industry is experiencing rapid change and has been since the advent of deregulation [5]. Power systems are being operated with new methods and new expectations are being placed upon utilities. Accordingly, these requirements are needing to be met by the EMS. Given that changes do occur, it is imperative that the EMS is easily adapted to new situations. Renewable sources add further complication in the requirements of an EMS regarding its adaptability too. Since the weather cannot be perfectly predicted, it is impossible to perfectly allow for future changes in the weather patterns. However, it is also vital that the renewable sources’ output is optimised. Thus a system that is easily changed becomes a major consideration. This is even more pronounced in the case of a hydro system, especially if it uses natural waterways. Overtime, the continual rush of water down a channel will affect its cross-section. This can occur slowly over many years, or can happen suddenly, such as during a flood. It is even possible to have external influences (such as rockslides) making a significant difference to the channel
Before Landslide Area = 10m2
After Landslide Area = 7m2
Figure 1: Effect of changing channel section. 4.
GENERATION CONTROL TECHNOLOGIES
Various techniques have been used to try to handle power system economic dispatch, some faring better than others do. However, economic dispatch is a complex problem, and no solution has been (or is likely to ever be) perfect. There is a lot of literature available supporting each of the contending technologies. The major technologies are detailed in this section. 4.1.
Programming Techniques
A variety of (mathematical) programming techniques have been used to try to achieve a good EMS. These include Lagrangian relaxation, linear programming and dynamic programming as the most successful examples. The classic tools for dealing with constrained problems are Lagrangian techniques [6]. However, the classic approach is not always the best approach. The method of Lagrangian relaxation depends upon the system to be modelled being continuous and also approximating linear behaviour. In many fossil-fuel based systems, these approximations are acceptable; not so with the renewable systems. Hydro power often has minimum release schedules and the incoming flow is usually far from linear. The average
height to flow relationship is actually close to logarithmic, such as in Figure 2.
far from accurate, or alternatively constantly being remodelled. 4.2.
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Genetic Algorithms
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Figure 2: Typical rating curve Linear programming is variation of equation development that is extremely prevalent [7]. It is a form of utilising Lagrangian techniques, resulting in a clear symmetric form of Lagrangian theory. It can be very powerful, but has the same inherent errors as the more basic Lagrangian techniques; the system must be continuous and approximate linear behaviour. Dynamic programming tends to improve upon the accuracy of the linear method and can handle some of the problems associated with the less sophisticated programming techniques [8]. Dynamic programming can handle nonlinear systems and can even cope with non-continuous systems. However, this extra capability comes at the cost of speed. Dynamic programming utilises the “principle of optimality”. This is a statement of the fact that an optimal solution can be considered as a series of smaller optimisation problems to make up the whole [9]. This means that dynamic programming uses a method similar to integrating along a line to approximate the power system. This is a better approximation, but needs much more computational power as there are many more equations to solve. The computational requirements for a dynamic solution depend upon the size of the discrete step size. With a step size of 1MW (a very basic accuracy requirement), even a system that is only 20MW has many states and thus significant computational draw [10]. This delay in processing is usually unacceptable [7, 10, 11]. Another issue that all programming techniques have in common is the fact that they are difficult to update [12, 13]. A change in the system operation will not simply introduce extra information to the model; it requires complete remodelling. This means that a change such as a new power station – regardless of its size – would require the entire process to be started afresh. Worse still, in the case of hydro and wind systems which rely on the constantly changing weather patterns, such a system would either be
Genetic algorithms (GA) and evolutionary computation (EC) are both optimisation techniques based upon the process of natural selection[14, 15]. It is a fast and efficient way of finding maxima, however it does not always find the global maxima – many of the problems associated with programming are equally associated with GAs. The genetic algorithms approach the maximisation of efficiency differently to programming, but the final result is still not entirely reliable.
Figure 3: Example of Evolutionary Computing In power systems, there are likely to be local minima and maxima as well as a global minimum and maximum. Figure 3 shows an example set of data that might represent a power system, albeit a very simple one. The genetic algorithm may well find a local maximum (B or C), but not the global maximum (A). There are ways of trying to eliminate this problem; however, they are not perfect. To find the global maximum in a complex case, the mutation rate would have to be high and would also require a long breeding period [16]. Furthermore, evolutionary computing has the problem of being subjective, both in breeding time and also in population size. There is no way to ensure the best population size or breeding time. Unless every possible place on the “map” is checked, there is never a guarantee that the maximum has been reached. Genetic algorithms are difficult to update, as any change would require the remodelling of the entire the system. This can be a time consuming task for the same reasons mentioned regarding programming. The final and most significant issue is with the ability of evolutionary computing to convey its decision process. Genetic algorithms can’t explain the decision made and thus, cannot be checked. Thus, they are susceptible to failing in the exceptional cases – and failing spectacularly.
4.3.
Neural Networks
Artificial neural networks (ANNs) are potent tools for power system operation, especially regarding forecasting and diagnosis. Most implementations of ANNs have been employed in classification, or “taxonomy” [15]. However, even in this field, ANNs are not acknowledged as being error free. Neural networks are not easily updated and must be entirely retrained to accommodate new system features [13]. Furthermore, it has been found that neural networks can be susceptible to bad data, resulting in a system that gives too much credence to single “spikes” in the data [17]. However, neural networks are also very powerful and the issues above would not be sufficient to draw interest from this technology. However, neural networks do have another major flaw. Similar to programming and genetic algorithms, ANNs have no capability to explain their reasoning. This means that neural networks operate as “black boxes” [13] and cannot be adequately checked. This may lead to catastrophic results in very unusual cases. 4.4.
Intelligent Agents
Intelligent agents are the next step of evolution from expert systems. In essence, a software agent is an abstraction for a set of code and rules [5] that enable the program to function as a sophisticated “servant” for some limited set of tasks. To understand the relatively new paradigm in control systems, it is best to consider the meaning of the two parts of the name, intelligent and agent. To be an agent, the software must be capable of autonomous execution, capable of communication and capable of observing its environment. The question of intelligence is more difficult. What is essential for some systems to operate intelligently is redundant to others. This leads to a fuzzy interpretation of intelligence, where there is no defined line between “smart” and “dumb”. The abilities of intelligent agents are best thought of as a melding of expert systems with its knowledge base, conventional programming with its ability to interact with other media and also a blend of other intelligent technologies. Intelligent agents must be capable of rule based reasoning, capable of operating on the internet and also capable of calling other (external) programming methods. The ability to call other methods means that an intelligent agent can use any software technology in its pursuit of a solution. If the best tool to use would be a neural network, the intelligent agent could implement a neural network to solve that part of the problem. It could then still use the
same program that and include rules and explanation facilities to inform the user what steps were taken in reaching the solution. Thus, an intelligent agent can be considered an intelligent system that is capable of being a hybrid system consisting of several technologies. Using hybrids has been acknowledged to be an effective way to overcome problems with each of the sub-systems [16], making the whole greater than the sum of its parts. Finally, intelligent agents also overcome the issue of lengthy retraining or re-programming. As an intelligent agent operates similarly to an expert system, new conditions can be met and allowed for with the simple addition of extra rules. This means that the system can be updated with much more ease than the other power control systems. This means that intelligent agents overcome the two major stumbling blocks found in the other systems – ability for explanation and rapid re-training. 5.
A PROBLEM FOR FURTHER STUDY
Tasmania produces most of its power from hydro generation and is investing heavily in increasing its supply of wind power. This means that it has an unusual and also difficult to schedule power generation system. There are also developments presently occurring to construct an underwater power cable between Tasmania and mainland Australia [18]. This presents with it new challenges that must be met by Tasmania’s power utility – Hydro Tasmania. It has been acknowledged that artificial intelligence applications are especially useful in the optimising of cascading hydro operation [18, 19]. There are several good papers on this subject, especially Rux [11] and Soder [3]. However, these are both flawed in that they don’t utilise the hydro system to its full effect. Soder does not allow for hydro power being used to store wind generated power and Rux only focuses on a system that has fairly few storages (Lower Colorado River Basin) and hence, allow for variation of water value in different storages. To operate effectively in Tasmania the intelligent system must be able to handle these factors. With the implementation of the underwater cable, Hydro Tasmania also has to consider how to trade effectively on a price-dominated market. This is another influencing factor in trying to design the power scheduling system. The scheduling system must be easily integrated with a bidding system, which in turn must be easily integrated with the online marketplace. This all supports the use of intelligent agents.
6.
CONCLUSION
An intelligent agent should be an excellent method of power system operation. There are many possible systems that can be used (and then further variations on these again) however, no single system offers as much promise as intelligent agent technology. Intelligent agents operate rapidly; fast enough to be considered a “real time” operation. This further improves the usefulness of Hydro Tasmania’s hydro resources as they can schedule and re-schedule rapidly to take advantage of market swings once the underwater cable is completed. With an increasing tendency towards demand-side management, such operations will become progressively more important. The usefulness of the intelligent agent also being used to improve bidding and selling of the generated power cannot be overestimated. A coordinated approach will work far more efficiently. Intelligent agents have the capability to schedule power rapidly, can be changed and updated with relative ease, can account for non-operating zones and differential water values and can also operate over the internet (remotely) which will be another major consideration in years to come. 7.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
ACKNOWLEDGEMENTS
The authors would like to thank Mr Mico Skoklevski, Mr Peter Clark and Dr Roger Allen for their assistance in focussing this paper and promised aid in the future. We would also like to thank Hydro Tasmania for their generous support and access to their databases. 8.
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