An agent-based modelling approach for domestic load simulation

Transcription

1 An agent-based modelling approach for domestic load simulation Ana Soares INESC Coimbra University of Coimbra Portugal Carlos Henggeler Antunes INESC Coimbra University of Coimbra Portugal Álvaro Gomes INESC Coimbra University of Coimbra Portugal Keywords smart grid, electricity consumption, demand response, energy management system, residential sector Abstract The evolution of power systems towards smart grids will expectedly foster the implementation of dynamic tariffs and provide the technological basis for a broader dissemination of local generation and electricity storage. These trends will require a more proactive attitude from typical domestic end-users, namely determining the best demand response strategies: when using, storing or selling electricity back to the grid in face of dynamic variables such as electricity prices, weather conditions, comfort requirements, and local generation availability. This is a very challenging decision process that requires some form of automated support to achieve optimal decisions. This process may be simplified using a decision support tool in the form of energy management systems (EMS) endowed with algorithms with the capability of simulating consumption and able to manage different loads, generation and storage resources. The objective of the EMS is to minimize domestic end-user s electricity bill without degrading the quality of the energy service provided. The design of adequate management algorithms requires a robust simulation tool able to compute the electricity consumption within a home and the impact of the management algorithms on the endusers electricity bill and quality of service. This simulation tool must consider the uncertainty associated with the use of certain appliances, end-users habits and preferences besides having also some forecasting capability. Agent-based models (ABM) can be used in this context to build a simulation environment where the behaviour of loads without interference of any management system and the behaviour of loads managed by an EMS is replicated. The aim of this paper is to present an ABM simulation tool able to reproduce end-user s electricity consumption profiles. This tool is an essential step to develop and test the robustness and acceptance of management algorithms to optimize the use of multiple endogenous energy resources. Introduction In a smart grid scenario, demand-side management (DSM) can be used to help matching demand with supply to reduce demand peaks and emissions and optimize energy generation, storage and use. From the domestic user point of view, DSM may be used as a tool to reduce the electricity bill without degrading the quality of the energy services provided. Since the intervention of end-users to make decisions concerning which loads to use in each time period according to multiple input signals namely the kwh price is not realistic, EMS are required to optimally manage load, generation and storage resources. Hence EMS should respect end-user s preferences and exploit the flexibility that users generally have in the timing of their electricity usage to better design and schedule automated demand response (ADR) actions. To achieve that goal, EMS must communicate with the surrounding environment (end-use loads, meters, sensors, consumer, electricity provider), simulate electricity consumption and make decisions concerning the ADR actions. In order to adequately design ADR actions and test them under different scenarios extensive simulations of load consumption patterns before and after ADR actions are implemented should be carried out. Agent based modelling is an adequate methodology that can be used for this purpose. An agent may ECEEE SUMMER STUDY proceedings 1631

2 Soares et al 6. Appliances, product policy and ICT be defined as a software or hardware entity in a certain environment able to autonomously react to changes in that environment (Lavinal, 1999). The three basic characteristics of an agent are reactivity, pro-activeness and social ability. While reactivity refers to the ability of an agent to react to changes in its external environment, pro-activeness indicates its goal-directed behaviour. The social ability of an agent allows him to interact with other agents based on an agent communication language (Yang and Wang, 2012). Agent based models (ABM) can be used to determine different demand profiles according to users preferences and then to compute the impacts caused by ADR actions implemented. ABM for domestic (or small consumer) load simulation can be implemented in two layers: a lower layer concerning the reproduction of the consumption patterns of end-use loads and an upper layer consisting in the design and tuning of ADR actions. Different types of agents may be used for representing end-users behaviours and preferences, determining the power profile of appliances, and designing ADR actions in the everchanging operating conditions. Multi-agent based simulation has already been used in previous studies to model different automated demand management strategies and their effect on occupants comfort levels and satisfaction (Guo et al., 2010). The type of ADR actions considered was switching on and off the power supply to the loads to decrease and/or postpone electricity consumption during certain time periods. The methodology was intended to be used by energy suppliers to simulate and determine the optimal strategy for DSM programs. The study presented in this paper is focused on using ABM to mimic electricity consumption before an EMS is available to make optimized decisions. Further work will include the EMS to generate optimal ADR actions over appliances as well as optimal management of generation and storage systems. This version of the simulation environment allows reproducing domestic endusers responses concerning electricity consumption based on a model that comprises several components, namely: appliances, relevant household building characteristics and typical behaviours. In future work inputs coming from the utility (for instance, price signals) and ADR actions tailored to each scenario (enduser s preferences, price of kwh, state of local generation and storage systems) will be tested using the simulation environment. The capability of interacting with other agents and respond to changes in environment, operating parameters or any other change imposed, namely by the management algorithms, must be an essential characteristic of the agents. Domestic electricity demand Domestic electricity consumption is determined by the operation of various types of electric equipment and appliances (e.g. lights, cold appliances, washing machines, dishwashers, electric water heaters, HVAC systems, computing and entertainment equipment), which in turn are controlled by the users needs and behaviours (Zhang et al., 2011). In a first stage, ABM can be used to build a simulation environment to replicate domestic electricity demand, and other endogenous energy resources, like storage and microgeneration systems. This will allow the development of management algorithms to be implemented in the EMS and to test their robustness and impact on end-users satisfaction. Considering the characteristics of domestic loads and the need to manage a part of demand without jeopardizing users comfort, some issues must be considered when deciding which loads can be the target of ADR actions and how these actions should be designed. The categorization of loads allows identifying those which are more adequate to be managed, the models that should be used to reproduce their power profile and the ADR actions that may be implemented (Soares et al., 2012). Accordingly, the end-use loads more suitable for ADR actions are: end-use loads that have some kind of storage associated, therefore a dissociation existing between the energy service provided and electricity consumption (e.g. thermostatically controlled loads); loads that provide energy services whose level can be slightly changed during short periods of time leading to changes in the energy consumption without noticeable changes in the quality of the energy service provided (e.g. new temperature settings for thermostatically controlled loads); loads that may provide the energy service in different periods of the day when there are economic advantages for the end-user without decreasing their quality (e.g. washing machines, dishwashers, clothes dryers) (Albadi and El-Saadany, 2008; Meyers et al., 2010). Simulation results For the purpose of simulation, loads were divided into two main groups: non-controllable loads and manageable loads. Non-controllable loads are appliances directly managed by the end-user and therefore cannot be the target for scheduling nor used under a different parameterization, for instance, lights, vacuum cleaners, ovens, microwaves, televisions and personal computers. Since these appliances are not suitable for being controllable, their electricity consumption was simulated using data gathered during audits. Concerning manageable loads, what really matters to the end-user is their end result: heating, cooling, conserving food, washing clothes or dishes, etc. Two different methods have been used to reproduce their consumption. In the case of thermostatically controlled loads, like cold appliances, air conditioners and electric water heaters, physical-based models are the most adequate modelling approach (Gomes, 1999; Gomes et al., 2007; Shao et al., 2012). For washing machines, dishwashers and clothes dryers data gathered in audits were used. The profile consumption selected for these appliances respects end-users preferences, namely the service request in a prescribed time and the preferred program. In this context, and in order to have suitable data to test the simulation environment, audits for the appliances mentioned above have been carried out and a database has been built. In this database, besides the type of appliance, other important features were included namely the temperature range in the case of thermostatically controlled loads, program used in dishwashers, clothes washing machine and clothes dryers. Using that database with the measured power profiles of clothes 1632 ECEEE 2013 SUMMER STUDY RETHINK, RENEW, RESTART

3 6. Appliances, product policy and ICT Soares et al washing machines, clothes dryers and dishwashers and according to end-users preferences in terms of preferred scheduling (including preferred days of the week) and program chosen, the electricity consumption pattern has been computed using the simulation environment. The models used to obtain the power profile of fridges/freezers, electric water heaters, conventional air conditioners and inverter air conditioners (thermostatically controlled loads) have been built according to the technical and operational characteristics of those appliances plus external information, like outside temperature and indoor temperature, to reflect the real-world situation. A range of comfort temperature was defined for air conditioning systems since when the indoor temperature in the conditioned rooms goes outside that comfort range end-users may feel uncomfortable thus decreasing their satisfaction when implementing ADR actions. Also for cold appliances, the minimum and maximum temperature was also set to assure that food is adequately conserved. End user s preferred temperature for hot water was obtained by filling the preferred temperature range. Since not all the domestic end-users own the same appliances nor use them in an environment with the same characteristics, the simulation is flexible enough to allow the introduction of different parameters. At any moment of the simulation some of that information may be changed, namely: Figure 1. Aggregate load diagram Sunday. indoor heat load variation; internal and external temperature for cold appliances; temperature of the water coming from the supply system and external temperature for electric water heaters; Figure 2. Aggregate load diagram Monday. utilisation of the energy service. The simulation platform is able to reproduce the aggregate load diagram of each individual house according to different factors (Figure 1 to Figure 6). Besides giving information about the aggregate load diagram, it is also possible to disaggregate the consumption and clearly identify the manageable loads working at each minute. From the diagrams presented in Figure 1 and Figure 2 it is possible to conclude that on Sunday end-users have a different behaviour, using some appliances that are not used on Monday and thus the peak power is higher. Since end-users do not have the same electricity consumption behaviours every single day, preference patterns have been assigned to the use of washing machines, dishwashers and clothes dryers. For the same reason, the base of the load diagram also varies from weekends to working days and even among working days (Figure 2 and Figure 3). Additionally, for other end-users the consumption also varies as well as the appliances owned and their number, habits, preferences and even thermal characteristics of the house, thus influencing, for example, air conditioner electricity consumption. Therefore, the simulation platform has been designed as flexible as possible to allow the simulation of multiple scenarios, namely considering users with different habits, preferences and appliances. Hence several features of the appliances, namely of thermostatically controlled loads, and thermal characteristics of specific rooms may be changed through a user-friendly interface before beginning the simulation or even during the simulation (Table 1). Figure 3. Aggregate load diagram Thursday. Ongoing Work The next step of this work consists in the design of management algorithms to optimize electricity use and minimize domestic end-user s electricity bill without degrading the quality of the energy service provided. The goal is to have a smart system able to manage energy resources to optimize energy use and end-users wellbeing, thus achieving both energy savings and customer satisfaction. Based on this simulation environment for domestic electricity consumption, the next step includes microgeneration and storage systems, namely plug-in hybrid electric vehicles. The algorithms must be responsible for the optimal manage- ECEEE SUMMER STUDY proceedings 1633

4 Soares et al 6. Appliances, product policy and ICT Figure 4. Disaggregate load diagram Sunday. Figure 5. Disaggregate load diagram Monday. Figure 6. Disaggregate load diagram Thursday ECEEE 2013 SUMMER STUDY RETHINK, RENEW, RESTART

5 6. Appliances, product policy and ICT Soares et al Table 1. Information to be inserted for thermostatically controlled loads. Thermostatically controlled load Fridges/Freezers Electric water heaters Air conditioner (conventional and inverter) Specific information Internal temperature at the beginning of the simulation COP (coefficient of performance) Water temperature at the beginning of the simulation Temperature of the water coming from the supply system Hot water consumed Room temperature at the beginning of the simulation COP/EER (energy efficiency rating) Volume of the room Characteristics of the room (insulation material, windows, doors, solar radiation, orientation, air renewal rate, etc.) Common information Power Capacity Desired temperature (minimum and maximum) External temperature Characteristics of the insulation Figure 7. Example of a solution found to schedule domestic electric loads (Soares et al., 2013). ment of all the available energy resources and assure the optimization of the use of endogenous resources while respecting technical constraints and users preferences. An approach using a genetic algorithm to optimize the scheduling of domestic electric loads, according to technical and user-defined constraints and input signals, has been developed in (Soares et al., 2013) although using a different method for the simulation environment (Figure 7). The ADR actions implemented comprise time deferral and the load scheduling was done for the next 36 hours assuming a dynamic pricing structure known in advance. In order to provide the EMS with some kind of intelligence, end-users preferences need to be well interpreted and learnt through the feedback behaviour of end-users when presented with the ADR generated by the EMS. This learning process can be carried out interactively based on the reinforcement mechanism through learning the behaviours taken by the agents representing end-users, the ADR actions and their degree of acceptance/refusal by the occupants (Yang and Wang, 2012). From the power systems side a responsive demand will allow an improved management of the grid, mitigating the adverse effects of the variability associated with renewable generation and also reducing the potential undesirable impacts of electric vehicle charging (for instance, the creation of a local high peak demand) besides contributing for an improved load factor, lower losses and increased reliability. All of these benefits can be translated into a reduction of costs from imported energy and minimization of blackout events (Huang and Billinton, 2012; Koutitas, 2012). Conclusion The implementation of ADR actions while assuring the quality of the energy services provided and end-users comfort requires a sophisticated EMS to deal with the information flow and manage all the different energy resources. Since end-users behaviours have a direct impact on the electricity consumption, the EMS should be able to predict end-users preferences and ECEEE SUMMER STUDY proceedings 1635

6 Soares et al 6. Appliances, product policy and ICT needs, respond to their requests and simultaneously deal with the uncertainty associated with some energy resources, weather conditions and information coming from the grid. Additionally, the EMS should be able to quickly react to unpredicted behaviour and make decisions concerning new ADR actions. To face those unexpected events, the EMS should be endowed with algorithms able to learn end-user s particular behaviours (Doctor, Hagras, 2005). The methodology presented in this paper is able to reproduce the aggregate load diagram of an individual house according to information previously included concerning the features of different appliances, thermal characteristics of the house, insulating materials of cold appliances, and the end-user s preferences for end-use load operation, among other information. The relevance and practical use of the simulation tool implemented is twofold: in the development of management algorithms and as a consumption simulator to be used by the EMS. The accuracy of this simulation environment, namely of the appliance-level load models, has been assessed by comparing the models output and the actual electricity consumption data of domestic end-users previously gathered during audits. The diagram computed uses a small time step to capture the power peaks of turning on appliances like microwaves, ovens, clothes washing machines and dishwashers. This feature allows checking whether the contracted power is exceeded and also the implementation of the next step of this work: the design of management algorithms to be implemented in the EMS. The integrated management of the residential energy resources will bring advantages to multiple stakeholders, and, besides reducing end-user s electricity bill, contribute to (Strbac, 2008; Guo et al., 2010): relief the energy network infrastructure; postpone or even avoid the investment needed to increase the system capacity; allow a better and increasing integration of renewables; reduce peak demand; decrease the operation of less efficient peaking plants. With the implementation of EMS, an immediate predictable effect is the reduction of power load during high electricity price time periods and thus energy savings to the end-user (Koutitas, 2012). This type of systems is therefore an attempt to control fluctuations in energy requirements at the appliance level reducing the operational costs of energy provision (Guo et al., 2010). Using adequate ABM it is possible to reproduce the real world setting in order to optimally manage consumption, generation and storage at the individual domestic level, essentially changing energy usage when the demand for electricity and prices are high. References Albadi, M.H., El-Saadany, E.F., A summary of demand response in electricity markets. Electric Power Systems Research 78, Lavinal, E., Weiss, G., Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press Cambridge, MA, USA. Doctor, F., Hagras, H. Callaghan, V., A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments,. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 35, Gomes, A., Simulation-based assessment of electric load management programs. 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Energy and Buildings 43, Acknowledgements This work has been framed under the Energy for Sustainability Initiative of the University of Coimbra and supported by EM- SURE Energy and Mobility for Sustainable Regions Project (CENTRO FEDER ) and Fundação para a Ciência e a Tecnologia (FCT) under grant SFRH/BD/88127/ 2012, and project grants MIT/SET/0018/2009 and PEst-C/EEI/ UI0308/ ECEEE 2013 SUMMER STUDY RETHINK, RENEW, RESTART