the distributed nature of most renewable energy sources, and. ⢠a wide range of potential benefits for electric transmission and distribution systems. (Lamarre ...
MODELLING AND SIMULATION OF THE OPERATIONAL IMPACTS AND VALUE OF DISTRIBUTED RENEWABLE RESOURCES IN ELECTRIC POWER SYSTEMS I.F. MacGill and R.J. Kaye Centre for Photovoltaic Devices and Systems School of Electrical Engineering University of New South Wales Sydney 2052
Abstract Distributed renewable resources are, together with other novel decentralised power technologies, likely to play an increasingly important role in electric power systems. There are however, a number of operational issues that need addressing before their widespread use. These resources can have variable and somewhat unpredictable power outputs, while the presence of numerous small, and probably independently owned, plants distributed throughout the network has important implications for overall system coordination. This paper outlines the development of an object oriented software modelling and simulation tool for exploring power system operation with distributed resources. Initial studies with the tool have focussed on price-induced coordination and storage optimisation.
INTRODUCTION
Distributed resources are likely to play an increasingly important role in electric power systems. They include: • renewable energy from photovoltaics, solar thermal, wind, small hydro and biomass, • novel fossil fuelled generation including small gas turbines and fuel cells, • energy storage with a range of battery and other technologies, and • electrical end-uses that can respond to system conditions; for example, ‘smart’ houses which control loads and exploit inherent energy storage such as space heating and cooling. The real costs of distributed resources continue to fall and they are increasingly cost effective, as well as environmentally friendlier, alternatives to conventional power generation options. There are numerous reasons why these technologies are likely to be implemented in small scale installations distributed throughout the power system network rather than as large centralised plants. These include (Lovins, 1993): • their inherent modularity which provides economies of mass production rather than of scale, and reduces investment risks through smaller plant sizes and shorter lead times, • opportunities for sharing infrastructure; for example, incorporating photovoltaics in commercial, industrial and domestic buildings,
• •
the distributed nature of most renewable energy sources, and a wide range of potential benefits for electric transmission and distribution systems (Lamarre, 1993) including reduced energy losses, the capability to defer T&D investment, and improved supply quality and reliability.
In Australia and elsewhere, the trend for electricity distributors to focus more on energy services will create new opportunities for distributed resources. Before this can happen however, a range of power system interconnection, operational and planning issues need addressing. Gilbert and Morrison (1994) amongst others have investigated the direct impacts of distributed resources on power system protection and safety. This paper reports on work into the broader impacts and potential value of distributed resources for power system operations. The focus is on developing methodologies for coordinating and optimising the operation of these resources. This is a key step in planning the development of distributed power systems. The paper discusses some of the major operational issues with distributed resources, describes the development of a computer modelling and simulation tool for exploring these questions, briefly outlines some initial applications of the software, and then considers possible future research directions.
DISTRIBUTED RESOURCES AND POWER SYSTEM OPERATION
The principal objectives of power system operation are to: • maximise the net value between the benefits derived from consuming electricity and the variable costs of supplying it (including the value of environmental externalities), and • maintain quality of supply; ensuring that supply equals demand at all points within the power network, the system is stable and reliable and the voltage waveform is acceptable. Conventional power system operation is based on centralised utility control of a relatively small number of large and remote thermal generators, delivering power through an extensive transmission and distribution system, to meet the demands of widely dispersed users. The presence of distributed resources throughout the network will clearly alter this arrangement. The operational characteristics of these resources are very different from those of conventional plant. Most renewables tend to have low operating costs but poor dispatchability. Power output is variable and somewhat unpredictable depending on, for example, daily and seasonal cycles and natural fluctuations in the weather. Some renewables including stored hydro and biomass plants can be constrained more by storage capacity than power output. There is then considerable system value, and potential synergies with other distributed resources, in the flexibility of their wide output range. Where distributed resources are connected to the grid through power electronics, it is now possible to improve supply quality through reactive power compensation and harmonics reduction (Spooner, 1992). Generation within distribution systems intended for centralised generation and unidirectional power flows raises numerous issues. The direction of energy flows could now change according to the availability of renewable resources. Conventional radial networks may no longer be the most appropriate distribution topology.
Independent ownership and management of distributed resources has perhaps the greatest implications. The present trend in electricity industries world wide is towards greater, often market based, competition and a larger role for independent participants. Power systems may end up neither under monopoly control or necessarily tightly coupled, and their operation a matter of coordination rather than control. A key question for all these issues is the level of penetration of distributed resources. At low overall levels, and for small plants, they can effectively be operated as negative load and hence independently of any system wide coordination (Grubb, 1991). However, at some point this becomes technically infeasible as well as economically inefficient. This leads to a choice between decentralised coordination and the existing centralised control of power system operations. The latter will require that detailed information from all distributed resources is made available to a central controller. There are questions as to whether this is either technically feasible or acceptable to independent owners. An intermediate approach might be through a hierarchy of monitoring and decision making. This multi-level structure would change the nature and reduce the quantity of necessary information flows (Bahrami and Caldwell, 1979). With decentralised coordination, the operational objectives of the electricity industry as a whole are achieved by coordinating the decision making of all major participants. Pricing induced coordination is seen as one approach for having participants’ operating decisions, made on the basis of their own personal goals, accord with the overall objective of optimising power system operation (Murphy et al., 1994). There remains however, a range of network, storage and other inter-temporal issues that need resolving.
COMPUTER MODELLING AND SIMULATION OF DISTRIBUTED RESOURCES
A software tool for modelling and simulating power systems with distributed resources has been developed to investigate these issues. The existing tools for exploring and optimising power system operation do not tend to cater for distributed resources. Further, their optimisation strategies are largely structured around, and take advantage of, the traditional generation model of a small number of large units (Bose and Anderson, 1984). This new software package is intended to: 1) model the key operational features of a wide variety of distributed resources and possible distribution system configurations, and then 2) simulate their operation under different operating conditions for a range of novel coordination strategies.
Object Oriented Programming
Object Oriented Programming (OOP) is a relatively new approach to software which aims to make programming more closely resemble the way people appear to actually view the world (Whitehorn, 1992). Conventional programming separates facts about ‘real world’ objects from descriptions of their behaviour. Object oriented programs are, instead, organised as a collection of discrete software models of ‘real’ objects, incorporating both their characteristics and behaviours. OOP has its origins in simulation and would seem to offer advantages in software management, flexibility and expandability (Nielsen, 1989). It seems particularly
appropriate for modelling power systems made up of a range of discrete and autonomous, yet interconnected, ‘resource’ objects with communal as well as personal objectives. The step between the power system model and software model is relatively small. OOPS is also amenable to numerous Artificial Intelligence strategies, and these may be applied to the task of power system coordination. The code itself is written in C++ with all input and output data managed in a commercial spreadsheet package.
Distributed Power System Modelling
A generic operational model for distributed resources has been developed (Figure 1). It is capable of representing resources ranging from grid connected photovoltaics, where the primary resource is variable and somewhat unpredictable solar insolation and there is no storage, through to controllable loads, where the end-use service might be heating and cooling for a building and there is effective energy storage in its thermal mass. Similarly, the distribution system is described using a generic model of distribution links. For a given power system configuration, each resource and distribution link has its own software object which encodes its characteristics as data, and its range of behaviours as operations (Figure 2).
Communications
Coordinator
.
Primary energy resource or end-use energy energy service flow
Energy Conversion
Storage
Electrical Conversion
electricity flow
Power system node
Figure 1. Generalised operational model for a distributed resource.
Distributed Power System Simulation
System analysis requires the capability to simulate the ongoing operation of this overall power system model. Each distributed resource object has a public interface which defines its range of possible communications and information flows. Running the simulation involves resources and, perhaps, a central coordinator making and servicing ongoing requests amongst themselves to determine overall power system operation over time. Resources can choose whether to respond to requests for particular information or, more importantly, requests that the resource operate in a particular manner. This approach is clearly suited to exploring decentralised coordination strategies and information flows between participants. Initial investigations are being restricted to the time scale of economic dispatch (eg. 15 minute periods looking ahead over several weeks). This captures the distributed resources’ operating benefit/cost structures, and also mid-term supply continuity problems with variable renewable energy inputs and dispersed storage.
Distributed Resource (DR) Object Public Interface Communications operations through Coordinator with • other DRs • distribution system links • a central coordinator DR can choose to service or ignore requests • for information on DR operational data, intended behaviour • that the DR operate in a given way Power flow operations onto or off the distribution network Private data and operations Physical plant characteristics including • plant capacity, flow constraints • storage and associated energy conversions (possibly) DRs benefit/cost relationship with primary resource • time, energy flow dependence Primary energy resource or end-use characteristics • time dependency • stochastic variation (possibly) Internal Coordinator operations including • pursuit of DR objectives • energy balance, other inter-temporal states • calculation of internal variables
Figure 2. Software object for a distributed resource.
RESEARCH DIRECTIONS
Initial simulation studies focussed on market based ‘Short Run Marginal Cost’ strategies for optimising the net value of power system operations while maintaining overall supply-demand balance (Outhred, 1993). Network effects were not considered. More recently, research has been directed towards optimising the operation of storage within power systems; a key factor in the integration of distributed renewable resources. A number of centralised optimisation and control techniques have been explored including discrete dynamic programming (Wood and Wollenberg, 1984) and genetic algorithms (Goldberg, 1989). These provide performance benchmarks for the next research stage which will explore decentralised strategies for optimising and coordinating the overall operation of large numbers of independently owned resources. Candidate approaches include distributed versions of conventional optimisation algorithms as well as genetic algorithms and ‘intelligent’ software agents (Chaib-Draa et al., 1992). With further work into network modelling, these coordination strategies can then be assessed for a range of different distribution system configurations.
CONCLUSION
Distributed resources, and particularly those based on renewable energy sources, show great potential yet pose important questions for power system operation. This paper has outlined the development of a modelling and simulation tool for exploring the operational impacts and possible value of these resources. The tool is novel in its focus on the wide range of distributed resources, and decentralised approaches to coordinating their operation. The intention is that this tool assist in both immediate questions arising from the appearance of distributed resources within existing power system distribution networks, and also broader examination of future power systems where distributed resources may play a far greater role. It has been argued that distributed power systems are an unsuitable concept for electricity supply. Distributed resources will certainly add to the complexity, as well as capabilities, of power system operation under the present control paradigm. These resources may well, however, represent less a problem and more an opportunity to move towards highly decentralised, loosely coordinated and largely renewable power systems offering greater operational efficiency and supply quality with far lower environmental impacts.
REFERENCES
Bahrami, K A and Caldwell, R W (1979) Electricity utility systems application of dispersed storage and generation, Proceedings of the IEEE PES Summer meeting, Vancouver, Canada, July 1979 Bose, A and Anderson, P (1984) Impact of new energy technologies on generation scheduling IEEE Transactions on Power Apparatus and Systems, Vol. PAS-103, No. 1, Jan. 1984 Chaib-Draa, B, Moulin, B, Mandiau, R, and Millot, P (1992) Trends in distributed artificial intelligence, Artificial Intelligence Review, Vol. 6, Iss. 1, pp. 35-66, 1992 Gilbert, D M and Morrison, I F (1994) Protection of Utility Connected Photovoltaic Sources, SOLAR’94 Proceedings of the Annual Conference of the Australian and New Zealand Solar Energy society, 30 Nov. - 3 Dec., Sydney, Australia Goldberg, D E (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Grubb, M J (1991) The integration of renewable electricity sources, Energy Policy, Sept. 1991 Lamarre, L (1993) The Vision of Distributed Generation, EPRI Journal, April/May 1993 Lovins, A B (1993) Renewables in Integrated Energy Systems, SOLAR’93 - Proceedings of the Annual Conference of the Australian and New Zealand Solar Energy Society, 1-5 Dec. 1993, Fremantle, Australia Murphy, L, Kaye, R J and Wu, F F (1994) Distributed spot pricing in radial distribution systems IEEE Transactions on Power Systems, Vol. 9, Iss. 1, Feb. 1994 Nielsen, N R (1989) Application of Artificial Intelligence Techniques to Simulation Artificial Intelligence Simulation and Modelling, New York: Wiley, 1989 Outhred, H R (1993) Principles of a Market-Based Electricity Industry and Possible Steps Toward Implementation in Australia, Proceedings of the International Conference on Advances in Power System Control, Operation and Management, Hong Kong, 1-10 Dec. 1993 Spooner, E D (1992) Power Conditioning Units, Proceedings of Workshop on PV in grid-connected and RAPS systems, Centre for Photovoltaic Devices and Systems, University of NSW, Sydney, Australia, May 1992 Whitehorn, M (1992) An object lesson in programming New Scientist, pp. 37-40, 8 Feb. 1992 Wood, A J and Wollenberg, B F (1984) Power Generation, Operation and Control, New York: Wiley 1984
