A novel hybrid agent-based model predictive control for advanced building energy systems

Energy Conversion and Management 178 (2018) 415–427

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

A novel hybrid agent-based model predictive control for advanced building energy systems

T

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Roozbeh Sangia, , Dirk Müllerb a b

Bosch Thermotechnik GmbH, 73243 Wernau, Germany Institute for Energy Efficient Buildings and Indoor Climate, E.ON Energy Research Center, RWTH Aachen University, Mathieustr. 10, 52074 Aachen, Germany

A R T I C LE I N FO

A B S T R A C T

Keywords: Building energy systems Agent-based control Model Predictive Control (MPC) Multi-agent systems Exergy Hybrid control Advanced controls HVAC systems

The development of new energy efficient components and complex energy concepts in recent years has heightened the need to design advanced control strategies. The main objective of this research is to develop a control strategy for building energy systems to save primary energy by applying the concept of multi-agent systems. Most of the advanced control strategies have been developed to be energy efficient, while their objectives are obtained from energy analysis. However, an energy analysis is unable to provide information on the quality of energy streams flowing through a system. In this study, exergy is selected as the objective of the optimization. To reach the goal of this research, an agent-based control for building energy systems using the exergy cost functions is developed. Agent-based control, which takes into account the interactions among the components of the system, offers a promising solution to the need for more advanced control strategies for complex building energy systems. The classical agent-based control developed in this study is combined with model predictive control, which leads to a novel hybrid agent-based model predictive control for the optimization of advanced building energy systems from an exergy point of view. For evaluation purposes, a case study is defined and modeled, which is controlled by a reference control and the agent-based under the same circumstances through softwarein-the-loop simulations. The results show that the agent-based control is able to reduce the primary energy consumption by 2 percent while maintaining the room air temperature at the same level of the reference case.

1. Introduction Recent legislation on the reduction of carbon dioxide emission encourages the improvement of building energy systems’ efficiency as well as minimizing the usage of primary energy resources and damaging impacts on the environment. This fact has attracted the attention of energy system engineers to the importance of developing control strategies to use energy resources more efficiently. Moreover, the development of new energy efficient components and complex energy concepts in last decades has heightened the need to design advanced control strategies to reach the full potential of the recently-developed complex energy systems even more. Most of the advanced control strategies have been developed to be energy efficient, while their objectives are obtained from energy analysis. Energy analysis is a traditional method to determine the effective use and to assess the way energy is consumed in a system. This method consists of writing energy balances based on the first law of thermodynamics for the system being analyzed, which may be applied to reduce energy waste. However, energy is independent of environmental

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properties and can be neither produced nor destroyed. As a result, energy analysis is unable to provide information on the quality of energy streams flowing through a system. Exergy analysis, which is based on both the first and second laws of thermodynamics, overcomes the aforementioned problem. Exergy is defined as the maximum available work that can be extracted from a system during a process that brings the system into equilibrium with its environment [10]. In contrast to energy, exergy can be destroyed, and the fact that something can be destroyed can be usefully applied in the design of energy systems [10]. Exergy analysis helps to identify which components of the system are responsible for irreversibilities. It can be further applied as a powerful thermodynamic technique for assessing and optimizing the performance of complex energy systems [10]. Furthermore, exergy analysis takes into account the environmental conditions, such as ambient temperature, which has a significant effect on the performance of building energy systems. A lot of research has been carried out on the exergy analysis of energy systems, but they are typically based on steady-state assumptions. These studies may be helpful to identify or rank the sources of

Corresponding author. E-mail address: [email protected] (R. Sangi).

https://doi.org/10.1016/j.enconman.2018.08.111 Received 9 April 2018; Received in revised form 14 August 2018; Accepted 31 August 2018 Available online 25 October 2018 0196-8904/ © 2018 Elsevier Ltd. All rights reserved.

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steady state conditions. Torio and Schmidt [65] performed the dynamic energy and exergy analyses on different configurations of a solar thermal system for the heating and cooling purposes of a hotel building. Sakulpipatsin [40] developed knowledge into the applicable domains and potential added values of exergy analysis in the built environment, by studying under what conditions exergy could function as a useful concept for the built environment. Angelotti et al. [4] compared the results of the steady state and dynamic analyses to identify the cases when the dynamic methodology should be preferred. The yearly behavior of a reversible air source heat pump providing heating, cooling and dehumidification to a simple building was simulated by means of the dynamic energy simulation software TRNSYS, taking into account different climates. Jansen et al. [26] described a framework for a semi-dynamic exergy analysis method and addressed assumptions to make. Torío et al. [64] presented a comprehensive and critical view on the most recent studies on exergy analysis of renewable energy-based climatisation systems for buildings. Pons [35] studied how exergy must be defined in cases when ambient conditions fluctuate, which led to proposing proposed the idea of associating the Carnot cycle to an ideal heat storage, i.e. infinitely large and equipped with infinite heat transfer areas. The function of that storage consists of, first receiving heat from the Carnot cycle exactly whenever required, and second releasing that heat toward ambient air exactly at the moment when the ambient temperature is the most favorable for the process. Sakulpipatsin et al. [41] presented an extended method for exergy analysis of buildings and HVAC systems, according to an energy demand build-up model from the building side to the energy supply side. The Guidebook of ECBCS Annex 49 gives a description on the first unitary methodology for performing dynamic exergy analysis on building systems [16]. Yokoyama [72] investigated the performance of a CO2 heat pump water heating systems from the exergy viewpoint. Xiang et al. [70] performed the dynamic exergy and exergy cost analyses of a heating system with groundwater source heat pump in a whole heating season by the use of structural theory of thermoeconomics and the software TRNSYS. Torio [63] presented a method for dynamic exergy analysis and showed that exergy analysis is strongly dominated by the highquality fossil fuel input, similarly as conventional (primary) energy analysis. Hoh [19] proposed a new exergy-based evaluation method for building energy systems based on the theory of ideal heat storage suggested in Pons [35]. Campos-Celador et al. [13] applied a thermoeconomic analysis to the annual operation of a micro-cogeneration installation in a tertiary sector building, combining the capabilities of dynamic simulation with the thermoeconomic analysis. Sangi et al. [55] developed a dynamic model of central heating system in Modelica and analyzed it from an exergy point of view. Sangi et al. [56] evaluated the performance of the intelligent decentralized hydronic heating of “Wilo-Geniax” pumping system using the exergy principles. Gonçcalves et al. [17] compared the overall energy and exergy performance of eight space heating options for different outdoor environmental conditions. Keçebaş and Yabanova [28] proposed an exergy efficiency-based control strategy to ensure the maximum exergy efficiency by means of the flow rate control of a large-scale geothermal district heating system. Jansen [25] presented a research on the added value of exergy for the assessment and development of energy systems for the built environment. Sangi et al. [48] developed a Modelica-based tool, consisting of two sub-models, to systematically perform dynamic exergy analysis of energy systems. Jahangiri et al. [24] applied the exergy analysis tool developed by Sangi et al. [48] in the city district model and dynamically optimized the system from an exergy point of view by a parameter study. Details of the optimization scenarios were presented in Sangi et al. [47]. Sangi et al. [58] proposed three approaches for a fair comparison of renewable and non-renewable building energy systems from an exergy point of view. The three exergy evaluation approaches were also

irreversibilities in an energy system, but usually, the main challenge that is faced in real-life is how to efficiently control the energy systems, which are naturally dynamic. Most of the time, the inefficient components of a system are either known or can readily be found, but due to financial concerns, replacing those components with efficient ones or even changing the current system layout in order to maximize the efficiency is not feasible, while achieving the most efficient operation of an imperfect system is always a desire. Despite being common, the steady-state analyses are not capable of offering practical solutions to the real life problems. In contrast to the steady-state exergy analysis, in a dynamic exergy analysis the effects of the storage term and the time-dependent inputs are taken into account, giving it the potential to be applied for control purposes. These two terms play important roles, especially in the analysis of building energy system, which are usually equipped with storage and also operate at the temperatures close to the fluctuating ambient temperature. Therefore, theoretically, dynamic exergy analysis can be used to efficiently control energy systems, but there is still a real need to investigate and evaluate the practical application of exergy from a control point of view, especially on the building scale. The main objective of this study is to develop an exergy-based control strategy for building energy systems. The control strategy is supposed to regulate a building energy system in a way that the most exergy efficient operation of the system in question is achieved. The challenges of applying the theoretical concept of exergy into the practical control of energy systems should then be realized, and methodologies and solutions to tackle possible problems should be proposed. A proper control technique for the implementation of the concept is among the other requirements. In order to be capable of properly evaluating the developed exergy-based control strategy, a case study with a reference control as the baseline also needs to be defined and modeled. The objectives of the study are obtained in six sections. The first section introduces the objectives and the scope of the research followed by the literature review, where the previous studies on the application of exergy in building energy systems are reviewed. Challenges of developing the exergy-based control strategy are discussed in Section 2, where approaches to tackle problems are proposed. The case study of this research is introduced in Section 3. Section 4 explains how the controller is developed. Section 5 presents the results of the implementation of the agent-based control strategy. Conclusions from the results and discussions accompanying with the topics of future research are presented in Section 6. 1.1. Literature review A lot of research has been carried out on the exergy analysis of energy systems, but they are typically based on steady-state assumptions, which make them impractical for control purposes. Although dynamic exergy analysis has recently attracted the attention of researchers, not many studies have been performed on this topic, among them only a few are really control-oriented. Therefore, there is still a real need to evaluate the practical application of exergy in control, especially on a building scale. In the following, a brief overview of studies performed on the transient, semi-dynamic and dynamic exergy analysis in buildings is presented in chronological order. Bejan [9] stated that the maximization of energy delivery is the fundamental problem in solar collector thermal design. He examined the trade-off between the storage and the immediate use of solar exergy, with the objective of maximizing the long-term exergy output from a solar collector installation. Badescu [5] considered an operation strategy for open loop flat-plate solar collector systems and implemented a direct optimal control method. Angelotti and Caputo [3] conducted a research for comparing different technologies for heating and cooling, from both the energy and the exergy perspective, assuming 416

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2.1. Selection of the reference environment

discussed in Sangi and Müller [51]. Huber et al. [20] compared and evaluated different control strategies for solar cooling systems according to their energy and exergy efficiencies using a software-in-theloop test bench, where the original controller of a solar thermal system is connected to a simulated building model. Gürel and Ceylan [18] designed a heat pump fluidized bed dryer with a PID temperature control. Their analyses included the energy analysis, energy utilization ratio, exergy analysis and uncertainty analysis. Using a medium model for the refrigerant propane developed by Sangi et al. [46] and the geothermal heat exchanger models developed by Sangi and Müller [52], dynamic modeling, simulation and exergy analysis of geothermal heat pump systems were performed in Sangi et al. [45]. Sangi et al. [50] presented a systematic approach to perform a thermoeconomic analysis of building energy systems. The approach was tested by implementation in simulation models developed by Sangi et al. [57]. An algorithm for step-wise exergy-based MPC of building HVAC supply chains was presented by Baranski et al. [6]. The algorithm was also introduced in Baranski et al. [7]. Sayadi et al. [59] formulated dynamic exergy and exergoeconomic analyses for the building envelope. Sayadi et al. [60] addressed the advantages of exergy-based control strategies for HVAC systems by comparing different control strategies for heating systems in the built environment based on the minimization of supplied energy, minimization of supplied exergy and minimization of operation costs. To investigate the functionality of the ideal heat storage concept proposed by Pons [35], Sangi Müller [53] implemented the concept in a test case. Regardless of the figures, it was found that the proposed approach is applicable, however, it would not be suitable, when the reallife applications of exergy, such as exergy-based control strategies, are investigated. Sangi et al. [49] developed an exergy-based MPC for advanced building systems using mixed integer linear programming. Sangi [42] demonstrated the real-life implementation of the exergybased mixed integer linear MPC in an advanced building system. To sum up, the literature review on the reference environment reveals that there is no agreement on this issue and the selection of the reference environment is still up to the analyzer. Moreover, the literature review shows that since 2013, a movement from the steady-state exergy analysis, which had mostly been used for design purposes, to the dynamic exergy analysis has started with the aim of developing exergybased control strategies. Despite the fact that the significance of exergy had already been realized, until that time, exergy had stayed on a theoretical level, while recent studies on the dynamic analysis have unfolded a practical prospective by finding real-life applications of exergy. However, only a few of them are really control-oriented. The literature review also reveals that among the common control techniques applied to develop exergy-based control strategies, MPC is the most favorite one with six studies followed by ANN with three papers. Optimal and PID controls have been applied twice, while the application of fuzzy logic and parameter study has been reported only once. Moreover, three control parameters, entitled, control-perfect index, eco-efficiency factor and relative exergy array have been defined during the studies performed on application of exergy in control. The significant identified gap is a lack of real-life use cases demonstrating the applicability of the exergy-based control strategies.

The definition and selection of the reference environment for the exergy analysis in buildings has always been a challenging issue, which deserves a special attention, as the considered reference environment strongly influences the results of the exergy analysis. There are several options for choosing the reference environment. Most researchers take the outdoor air as the reference environment. Others use the indoor air temperature as the reference temperature for the exergy analysis of thermal energy flows in buildings. The mean temperature of universe and undistributed ground temperature are other choices, which are not as common as the air temperature. The literature review on the reference environment reveals that there is no scientific agreement on this issue, and the selection of a reference environment is still up to the analyzer. Following the discussions in Sangi [42], the actual ambient temperature has been selected as the reference environment of this research. 2.2. Approach to calculate the exergy destruction and efficiency In general, there are two ways to calculate the exergy destruction and loss, which are regarded as exergy destruction in the following, and subsequently the exergy efficiency of the overall system. In the first approach, the boundary of the whole system can be considered as the control volume. In this case, the exergy of fuel of the control volume is the sum of all exergy of fuels flowing through the whole system. Similarly, the exergy of product can be defined as the exergy of products of the whole system. The second approach that can be applied to determine the exergy destruction and efficiency of a system is to calculate the exergy destruction in each component of the system individually. In this case, the total exergy destruction is then obtained by the sum of the exergy destructions occurred in each single component of the system. For this purpose, the exergy of fuel and the exergy of product of each component should be defined individually and the exergy balance should be written over each one of them. Following the discussions in Sangi [42], it was concluded that in this study the second approach should be selected to calculate the exergy destruction and efficiency of the system. The component by component analysis approach presents a clear insight into the internals of system so the behavior of all components can be taken into account in order to make control actions. By applying this approach, not only the effect of changing a parameter on the exergy destruction of a particular component is obtainable, but also the effect imposed on the other components of the system, as a result of decrease or increase in exergy destruction of that component can be observed. This granular control can be very helpful for control purposes presented in this study. 2.3. Selection of a control technique Attempts to develop efficient and environmentally friendly building energy systems have led to modern complex energy concepts for buildings. This complexity has consequently initiated a need for new control strategies and techniques. The conclusion of Section 1.1 nominates MPC and agent-based, among the investigated control techniques, as candidate for the implementation of the proposed exergy-based control strategy. MPC has been used to control building energy systems more often than the other advanced controllers, however, agent-based control has been successfully applied in the areas of logistics and telecommunication [66]. Besides, agent-based control is worth paying more attention to, as it could offer a promising solution to complex systems with uncertain interactions between various system components. Classical controls are most common, but they are too simple to be applied on complex energy systems, specially if the overall performance of the system is supposed to be optimized, as in this kind of analysis the objective cannot be achieved only by regulating variables with discrete

2. Methods A few challenges should be tackled in order to develop an exergybased control strategy. First, a reference environment for exergy that suits control applications should be selected. Then, it should be defined how the exergy destruction and efficiency of the system in question are obtained. Finally, a control technique is required to implement the developed exergy-based algorithm in a building energy system. The aforementioned three steps are explained in the next three sections, respectively. 417

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economics [30]. In recent years, the field of energy generation and distribution has become much more complex due to the increase of renewable energies and the concept of smart- and micro-grids. MAS depict a promising technology to control the described energy systems. Regarding the use of MAS to control classical smart- and micro-grids, a lot of research has been conducted, for instance [27]. Khan et al. [29] also reports the use of MAS to control. Rahman et al. [38] also discusses the use of MAS to control classical smart- and micro-grids. The use of MAS for complex energy systems for the generation and distribution of heat or cold, such as building energy systems, HVAC systems and district heating grids, has also gained growing attention recently. In Huberman and Clearwater [22], a market-based MAS is used to distribute warm and cold air in an office building. Qiao et al. [37] introduce an MAS which combines the control of a building energy system with user interaction. A similar system based on personal agents, local agents and central agents is used in Yang and Wang [71]. In Wang et al. [68], a system using only central and local agents is used. The central agent contains the main intelligence of the system. In Wang et al. [67], an indoor energy and comfort management system based on information fusion and a multi-agent control system is proposed. Davidsson and Boman [14] uses an MAS consisting of personal comfort agents, room agents, environmental parameter agents and badge system agents to control temperature and illumination in a building. In Mokhtar et al. [33], an already existing MAS for building heat distribution control is updated with an ARTMAP, a type of artificial neural network with learning capabilities. Mokhtar et al. [32] use a similar MAS based on ARTMAP to control a building energy system based on learned user preferences. Their reported simulation results show that the system provides better energy control and thermal comfort management than a reference rule-based MAS. In Hurtado et al. [23], an agent-based approach to optimize the interaction of smart-grids and building energy systems is developed. van Pruissen et al. [36] present a solution based on electronic market principles called HeatMatcher. HeatMatcher is a peer-to-peer (P2P) system based on PowerMatcher [31], which is a general purpose coordination mechanism for balancing supply and demand in electricity networks. In Huber et al. [21], an MAS based on consumer agents and supply agents is introduced. In Skarvelis-Kazakos et al. [62], the structure of a hierarchical multi-agent system with four different types of agents is introduced. The developed MAS controls and coordinates multiple energy carrier systems with the objective to minimize the overall cost and/or emissions while adhering to technical and commercial constraints, such as network limits and market contracts. Cai et al. [12] present a multi-agent control methodology that can be applied to the optimization of building energy systems in a ”plug-and-play” manner. In Shaikh et al. [61], an agent-based control system for building energy systems is embedded with an evolutionary multi-objective genetic algorithm and a hybrid multi-objective genetic algorithm.

on or off values. Agent-based control has been used in renewable energy power generation units with electrical energy storage systems to improve operation efficiency and stability. However, it has rarely been used in buildings with thermal storage, which could result from the fact that its application entails developing control strategies for the integration of thermal storage into building systems within the framework of overall building control strategies. Despite difficulties of applying agent-based control, it was concluded to use agent-based control as the main control technique, as the strong point of agent-based control perfectly suits the objective of this research, where the exergy performance of the system is evaluated with regard to interactions between system’s components. However, it was later found that agent-based control on its own does not meet all the criteria of an exergy-based control strategy. Therefore, the initiallydeveloped agent-based platform in this study has been combined with MPC to cover the weak point of agent-based control, which leads to a novel hybrid agent-based MPC for the optimization of advanced building energy systems from an exergy point of view. The concept of agent-based control is a concept that allows to control complex systems by splitting the main objective of the system into smaller objectives, which so-called agents try to fulfill by interacting with each other. Although the concept is widely spread in the scientific area, especially in the field of computer science and information technologies, there is no unified definition of the term agent. After the term first appeared in the context of a dissertation in 1985, in which the term agent is connected with the attributes of autonomy and problem-solving behavior [39], further attributes such as proactivity and the ability to work towards higher goals [69], the ability to perceive the changes of their surroundings and to react on them [15], the ability of rational calculation and organization of actions to achieve higher aims as well as permanent activeness [30], socialness and truthfulness [11] were defined by various authors. In VDI-2653 [66], agents are defined as encapsulated entities, hardware or software, with specified objectives. An agent attempts to achieve these objectives through its autonomous behavior, in interacting with other agents and their surroundings. In addition, several characteristics such as autonomy, scope of action, interaction, encapsulation, persistence, goal-orientation and reactivity are defined. VDI-2653 [66] describes Multi-Agent Systems (MAS) as a set of agents interacting to fulfill one or more tasks. [11] describe MAS as entities that can model complex systems and introduce the possibility of agents having common or conflicting goals. These agents are able to interact with each other, both indirectly, by acting on the environment, or directly via communication and negotiation. Depending on their task, they may cooperate to reach a common goal or compete to achieve their own aims [11]. An MAS can be used to control complex systems. One advantage over a holistic control concept is the possibility of splitting the often very complex control problem into sub-problems and -tasks and dividing them between the agents. This approach is beneficial for the developer, as the analysis of those sub-problems is more accessible than the analysis of the holistic problem, and thus also the implementation of the systems solving these problems. Furthermore, an agent-based approach has the advantage of being more easily adjustable during the runtime of the system, as new agents can be implemented and added to the system. MAS have received growing recognition in various fields over the past few years. Beginning in the sub-fields of computer science, such as human computer interaction, where agents help the user depending on their already existing experience with the software, or information retrieval, where agents search the internet for specific information for their user, now agent-based systems have also reached the field of logistics and telecommunication [66]. As a consequence of growing complexity in the various fields of science, MAS also received growing attention in the fields of chemistry, biology, physics, sociology and

3. Case study In this section, a generic model for building energy systems is developed as the case study for this research. A mode-based control strategy is also defined for the developed case study as the reference control to be a baseline for comparison purposes. 3.1. A generic building model The case study of this research is a generic model that is designed to represent the working principle of the HVAC and control systems of the main building of E.ON Energy Research Center in Aachen, Germany [54]. The central energy supply system of the main building of E.ON Energy Research Center is presented in Fig. 1. The simulation model of this advanced building has been further 418

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Fig. 1. The central energy supply system of the main building of the E.ON Energy Research Center.

meeting rooms. The heart of the system is a heat pump with a thermal capacity of 2.5 kW, which allows heat displacement from the cold water cycle into the warm one. To damp the demand and supply, each cycle possesses a storage tank. The cold storage tank has a volume of 900 ∼ l and the volume of the hot storage tank is 600 ∼ l. If cold and heat demands are not balanced out, which occurs in case of very cold and very hot days, a Geothermal Field (GTF) can be used in both the evaporator and the condenser sides of the heat pump either as a heat source or as a seasonal storage. The cold storage tank is directly connected to the demand side. The forward flow from the cold storage tank to the consumers is then called the cold forward flow of the energy system. The hot storage tank is connected to a high temperature cycle through a heat exchanger. The high temperature cycle is fed by a CHP and a boiler to cover peak loads. The boiler has the capacity of 4 kW and the thermal and electrical capacity of the CHP is 4.1 and 2.5 kW, respectively. Additionally, the hot storage tank is equipped with a heating rod with a capacity of 4 kW, which can also be used during peak demands. Table 1 shows the technical specifications of the energy supply components. The forward flow of the heat exchanger to the consumers is also referred to as the hot forward flow of the energy system.

developed in a way that it meets the specifications required to be controlled from an exergy point of view. Therefore, the energy demand can be met by a variety of energy suppliers while the temperatures and mass flow rates in the system can be regulated. These special features have led to an advanced energy system with an innovative modern energy concept. Despite being complex, the model still represents a generic building. Fig. 2 shows the schematic illustration of the developed generic building. The generic building model and all the simulation component models developed in the course of this research, like pipe, three-way valve, hydraulic separator, facade ventilation unit and their controllers are publicly available as an open source library [1]. In the modeling process, the heat consumption level of E.ON Energy Research Center is reduced to keep the computational effort low. Therefore, the simulation model features two meeting rooms, each 132 m2 , which are equipped with Facade Ventilation Units (FVU). The facade ventilation units are able to condition the room with fresh outdoor air or by circulating the filtered inside air. For heating and cooling of the air, each facade ventilation unit features a separate heating and a separate cooling heat exchanger, which are supplied by cold and hot water, respectively. Furthermore, a recuperator can be used in order to heat up or cool down the fresh air with the exhausted air. The facade ventilation units are equipped with a controller that maintains a certain air quality and a set room temperature. The set point temperature of the room depends on whether the room is used or not. If the room is occupied, the set point is 20 °C, otherwise, the set point is allowed to be violated by 1 K. The CO2 concentration is also controlled to ensure a permanent high air quality. The fresh air is conducted to the room, as soon as the CO2 concentration in the room exceeds 700 ppm. The occupation of the room is simulated by CO2 generation and internal gains during working hours. The required hot and cold water for the facade ventilation units are provided by the building energy supply system. In this study, the capacities of the energy suppliers of E.ON Energy Research Center are adjusted corresponding to the heat demand of the two aforementioned

3.2. Reference control The reference control of the system is mode-based, which splits the operation of the controlled system into different operation modes. In general, an operation mode is defined by a certain state of the HVAC system, such as “cooling”, “heating”, “on” or “off”. Depending on the energy demand of the controlled system, the system goes either in heating or cooling mode. Adopted from Pauly [34] and Sangi et al. [43], the main operation mode of the case study is determined by comparing the power taken from the hot storage tank, PHS , with that of the cold storage tank, PCS . Based on the temperature gradient of the hot and cold storage tanks, the controller decides whether the system is in 419

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Fig. 2. Schematic illustration of the developed generic building model.

help the heat pump, if the hot forward flow temperature of the energy system is lower than the ambient-based set point temperature, which occurs due to high loads. This is the second heating mode, which is divided into three sub-modes. If the requested heat can be totally covered by the CHP, the CHP is turned on. In case the heat demand is higher than the maximum capacity of the CHP, the boiler is also used. If the requested demand is lower than the minimum capacity of the CHP, only the boiler is activated. These three sub-modes are not shown in the figure for ease of readability. If the temperature of the hot storage tank drops below 39 °C and the ambient temperature is below 5 °C , the heating rod is turned on. To avoid a frequent switching on and off, the heating rod is controlled by a hysteresis and is deactivated, if the average temperature of the hot storage tank reaches 41 °C. In the above three modes, heat is displaced from the cold storage tank to the hot storage tank as long as the temperature of the cold storage tank is above 7 °C. As soon as this condition is violated, the geothermal field takes over the role of the cold storage tank and the heat pump gets connected to the geothermal field instead of the cold storage tank on the evaporator side. This is done to ensure a reasonable COP and to prevent the evaporator from freezing. This transition leads to another three heating modes. In total, eight main heating and cooling modes for the system have been defined. The defined mode-based control strategy controls the operation of the system at a superior level. However, the respective power of the energy suppliers is defined by a subordinate controller. The power of the heat pump is a function of the temperatures of the condenser and the evaporator. In the cooling mode, the heat pump is

Table 1 Technical specifications of the energy suppliers adjusted for the meeting rooms. Component

Power

Boiler CHP Heat pump Heating rod

0–4 kW thermal 1.23–4.1 kW thermal 0.315–0.63 kW electrical 4 kW electrical

the cooling mode or in the heating mode. When the temperature in the hot storage tank falls faster than the temperature rises in the cold storage tank, the system goes into the heating mode (∣ΔPHS ∣ > ∣ΔPCS ∣). When the temperature in the cold storage tank rises faster than the temperature falls in the hot storage tank, the system goes into the cooling mode (∣ΔPHS ∣ < ∣ΔPCS ∣). Both the heating and cooling modes are further divided into sub-modes (Fig. 3). Within the cooling mode, the system can again be in two different states. If the average temperature of the hot storage tank is below 51 °C, the heat pump runs by absorbing heat from the cold storage tank and releasing it to the hot storage tank. As soon as the temperature of the hot storage tank exceeds 51 °C , which means the hot storage tank has no more storage capacity due to very low heat demand, the system goes into the second cooling state. The second cooling state connects the condensing side of the heat pump to the geothermal field. Within the heating mode, there are six different states. The first state is running the heat pump by heat displacement from the cold storage tank to the hot storage tank. A boiler and a CHP may come to 420

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Fig. 3. Mode-based control as the reference control for the generic building model.

the CHP and the boiler are simultaneously used. The heating rod in the hot storage tank is rarely utilized. In Fig. 5, the operation modes of the mode-based control are shown (see Fig. 3 for operation mode numbers). Most of the time, the system runs in modes six and seven of the heating mode. The cooling mode is activated for short periods of time, however, there is no need for heat or cold at these times and the heat pump is off. The reason behind this unexpected behavior is that the switching to the cooling mode is unrelated to the heat demand of the consumers, and it in fact resulted from the heat loss in the storage tank. The details of energy consumption, exergy destruction, power of the components, deviation from the set point and deviation from the set point range are presented in Section 5, where the comparisons are made.

switched off, if the average temperature of the cold storage tank drops below 10 °C and it is activated with the help of a hysteresis, whenever the temperature goes higher than 14 °C. In the heating mode, a hysteresis turns off the heat pump, if the temperature of the hot storage tank exceeds 50 °C and turns it on, whenever the temperature of the hot storage tank drops below 40 °C . The CHP and the boiler are controlled by a PID and P controller, respectively, to maintain a forward flow temperature of 90 °C . The opening of the three-way valve is regulated by a PID controller in way that the hot forward flow temperature of the energy system remains at the set point. 3.3. Performance analysis of the mode-based control 3.3.1. Simulation period The generic building model regulated by the mode-based control is simulated in a 28-day period, which starts at the beginning of January. The weather data of Aachen has been taken from BBSR [8], which is considered to be an authoritative source. The results of this simulation are used as the baseline for the performance evaluation of the proposed exergy-based control strategy.

4. Agent-based control Following the discussion in Section 2.3, this section presents the application of agent-based control as the control technique candidate for the implementation of the exergy-based control concept. The case study, introduced in Section 3, is then regulated by the developed agent-based control while exergy is defined as the cost function. The advantages and disadvantages of agent-based control for the implementation of the exergy-based control concept are discussed.

3.3.2. Simulation results As shown in Fig. 4, in the mode-based control, the heat pump is almost permanently in operation. If the capacity of the heat pump is insufficient to cover the heat demand, the boiler is first switched on for demands slightly higher than the capacity of the heat pump. If the heat demand requested from the high-temperature circuit is high enough to reach the minimum capacity of the CHP, the CHP operates, while the boiler is off. In case the demand is higher than the capacity of the CHP,

4.1. Cost functions Cost functions are used by the producer agents to calculate a corresponding price for a requested capacity adjustment. In the developed 421

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Fig. 4. Power of the components for the mode-based control in January.

Fig. 5. Operation modes of the mode-based control in January.

HVAC Agent-based Control Library, which is a Modelica-based library for the agent-based control of HVAC systems [44]. Using the Modelica HVAC Agent-based Control Library, the exergybased control strategy is implemented in the case study, where the agents attempt to run the system with a minimum exergy destruction. In Fig. 6, the scheme of the agent-based control in the generic building is shown. The consumers are represented by one consumer agent and two room agents. The consumer agent represents the storage tank of the warm water cycle. It is set to keep the temperature of the storage tank within a range of 40–50 °C . The two rooms are represented by one room agent each. The room agents are set to keep the room temperature at 20 °C, however, they only get active when the actual room temperature differs from the set one by more than 2 K. The consumer and room agents address their requests to the first broker agent. This broker agent knows four producer agents, which represent the heating rod, the condenser side of the heat pump and the evaporator side of the heat pump. The broker agent also knows the intermediate agent, which represents the heat exchanger and the threeway valve. The intermediate agent acts as a consumer for the broker of the high temperature circuit. This broker of the high temperature circuit knows the producer agents of the boiler and the CHP as producers. The control signals are the powers of the energy suppliers. One can notice that the cold storage tank is not represented by any

agent-based library, producer agents and cost functions are separate components, which means that the cost functions are easily exchangeable, depending on the optimization goal of the MAS. In the developed platform, cost functions depending on fuel cost, exergy destruction cost and primary exergy destruction cost are available. To develop an exergy-based control strategy applying the agentbased concept, the exergy efficiency or exergy destruction may be defined as the target of the optimization. Since a cost function should connect high values of the function with high costs, and low values of the function with low costs, the exergy destruction is better suited than the exergy efficiency for this purpose, and it is the reason behind choosing the exergy destruction as the cost function in this study. The general exergy balance is solved to obtain the exergy destruction in each single component. Every time when the broker agent is contacted by a consumer agent, the broker asks all the energy producers for the exergy cost. The offer with the less exergy destruction is then taken. This process repeats every time when a consumer calls the broker asking for energy. 4.2. Implementation of the agent-based control in the generic building To control a system based on the MAS concept, an agent-based platform is required. The platform used in this research is the Modelica 422

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Fig. 6. Control scheme of the agents.

temperature at the set point. Therefore, the capability of maintaining the room air temperature at the set point is calculated for the evaluation of the controllers. For this purpose, the Root-Mean-Square deviation of the room air temperature, T, from the room set point temperature, TSet = 20 °C, (RMSESet ) is used for comparison:

consumer agent. This is the case, because the temperature inside the cold storage tank is dependent on the behavior of the temperature in the hot storage tank, as they are linked by the heat pump. A separate agent is therefore not necessary. Such an agent would furthermore require a separate broker, as only the heat pump can provide cooling, but not the other producer agents. By neglecting the cold storage agent, a large amount of computational time can be saved without compromising the functionality of the control system. For the facade ventilation units, the default control that simulates the actual controller of a facade ventilation unit model is kept. This is also the case for the control of the valves that connect and disconnect the heat pump to and from the geothermal field. In principle, the integration of these components into a complete agent-based control system is possible with the developed Modelica library, however, their integration to the agent-based control is contingent on how fast the agent-based control can react to changes.

RMSESet =

1 n

n

∑t=1 (T (t )−TSet )2

(1)

where n is the number of time discrete temperature values over the entire period. In order to check whether the room air temperature lies within the permissible range, the Root-Mean-Square deviation from the upper and lower limits (RMSESet,lim ) are also calculated:

RMSESet,lim=

1 n

n

∑t = 1 (ΔT (t ))2

T (t )−Tlow ∀ T (t ) ⩽ Tlow ΔT (t )=⎧ ⎨ ⎩T (t )−Tup ∀ T (t ) ⩾ Tup

(2)

4.3. Software-in-the-loop simulation 5. Results and discussion This section deals with the implementation of the developed agentbased control in the generic building model. Therefore, the performance of the agent-based control is evaluated using a software-in-theloop simulation. The results of the agent-based control are compared with those of the mode-based control.

Table 2 comprehensively compares the results of the mode-based control with those of the agent-based control for the period of January. The exergy destruction and the energy consumption of the system for the mode-based control are 1050 kWh and 1191 kWh , and for the agentbased control are 1013 kWh and 1182 kWh , respectively. In addition to reducing the exergy destruction by 3.5 %, it is evident that the agentbased control is able to reduce the energy consumption, but the improvement is limited to 0.7% . In Table 2, the temperature deviations according to Eqs. (1) and (2) for both rooms are reported. As can be seen, the difference between the controllers is not very significant. In both cases, the deviation from the set point temperature is nearly the same, but the deviation from the set point range is slightly lower in the mode-based control. The important result is that the agent-based control is able to maintain the room air temperature at the set point while slightly reducing the energy consumption.

4.4. Simulation period To evaluate the performance of the agent-based control, the 28-day period of January, which was simulated in the mode-based control, is simulated. 4.5. Reliability of the controller In addition to a reduction in the total energy consumption of the system, the exergy-based controller should be able to keep the room air 423

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generation process. The electricity required to run the heat pump and the heating rod is either taken from the grid or provided by the CHP. Since the grid electricity is generated outside of the system’s boundary, which is different from the electricity produced by the CHP, from an exergy point of view, it can be evaluated in two different ways:

Table 2 Comparison of the mode-based control with the agent-based control in January.

Energy consumption Exergy destruction Heat pump power CHP power Boiler power Heating rod power RMSESet Temperature of room 1 RMSESet Temperature of room 2 RMSESet,lim Temperature of room 1 RMSESet,lim Temperature of room 2

Mode-based control

Agent-based control

[kWh] [kWh] [kWel ] [kWth ] [kWth ] [kWel ] [–] [–] [–]

1191.0 1050.9 0.5762 0.4573 0.4199 0.1467 1.1517 1.2045 0.6121

1182.5 1013.8 0.7171 0.0017 0.1067 0.9162 1.1501 1.1987 0.6561

[–]

0.7166

0.7652

1. Scenario 1 (S1): The exergy of the electricity is the electrical power taken from the grid: Eel̇ = Pel 2. Scenario 2 (S2): The generation process in the power plant is also taken into account to calculate the primary exergy: Eel̇ = Pel/ ζ el The exergy efficiency, ζ el , of a combined cycle power plant is reported to be ζ el = 44 % [2]. With the help of auxiliary variables PCHP,HP, PG,HP, PCHP,HR and PG,HR , the contributions of the CHP and the grid (G) in feeding the heat pump and the heating rod are determined:

PCHP,HP + PG,HP= Pel,HP PCHP,HR + PG,HR= Pel,HR PCHP,HP + PCHP,HR⩽ Pel,CHP

Despite having nearly the same energy consumption, the controllers regulate the operation modes of the heat pump, the boiler, the CHP and the heating rod differently. The average power of the components is also presented in Table 2. In the agent-based, the heat pump is used more often than in the mode-based control (see Fig. 7). Whenever the heat pump cannot cover the demand on its own, the heating rod is often used by the agent-based control, while the heating rod is rarely used in the mode-based control. The more frequent use of the heating rod resulted from the higher exergy efficiency of the heating rod (ηen = 100%) compared to the boiler (ηen = 88%) and CHP (ηen = 88% at full load). The boiler is used less frequently in the agent-based than in the mode-based control. The CHP is activated rarely in the agent-based control, while its usage in the mode-based is even higher than the boiler’s. In spite of the fact that the exergy-based control strategy slightly reduces the energy consumption of the system, its performance is not justifiable from a primary energy point of view, since it gives priority to the heating rod, which consumes electricity. This occurs because in the presented scenario the exergy content of the electricity feeding the heat pump and the heating rod is equal to the electrical energy. However, the calculation of the exergy content of the electricity can be treated differently by including the exergy destructed in the electricity

(3)

The exergy content of the electricity used by the heat pump and the heating rod is then obtained as:

̇ Eel,HP = ζ el PG,HP + PCHP,HP ̇ Eel,HR= ζ el PG,HR + PCHP,HR

(4)

The results of scenario 2 are presented in Table 3. As can be seen, the local exergy destruction and the local energy consumption in scenario 2 of the agent-based control increase compared to the mode-based control, however extending the boundary of the system to the power generation level decreases the primary exergy destruction and the primary energy consumption by 6.1% and 1.8%, receptively. This reduction results from the change in the operation of the system in scenario 2. Compared to scenario 1, it is evident that in scenario 2 the boiler and CHP are used more frequently while the heating rod is activated rarely. Regardless of performing the exergy analysis or the primary exergy analysis, a deeper analysis of the case study’s behavior regulated by the agent-based control revealed that more energy is produced by the energy suppliers than required. For example, it would be desirable, if the

Fig. 7. Power of the components for the agent-based control. 424

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a result, in this case, the controller acts less aggressively and the requested capacity increases gradually in each heating period. In the first heating period, only the heat pump is used. In the second one, the heat pump and the heating rod are used. As the demand is relatively low during the first two days with outdoor temperatures above 0 °C , such a behavior is logical. Fig. 8 also proves the correct selection of producer agents in terms of exergy destruction costs. The heat pump with a COP ranging between 3 and 4 is always chosen first. The heating rod is chosen second, because it has a higher thermal efficiency than the boiler. In most cases, the CHP is the last option considering its low efficiency at part-load operations. The analysis of the case study with two different PID control configurations reveals a weak point of agent-based control, whenever the cost functions are not easy to predict. It was found that an agent system alone does not solve the whole control problem, as the behavior of the system is strongly dependent on the behavior of the conventional PID controllers, which determine the magnitude of the capacity request. The agent system itself only determines which device supplies the requested capacity with the lowest cost generation at the point of time when the request occurs. In terms of system theory, the agent system acts as a dead time element between the PID controller and the behavior of the physical system. In order to get a heating system running under optimal conditions, the determination of the right capacity requests needs to be as accurate as possible. This weak point of the agent-based control does not cause any problems when the cost functions are constant, simple to predict (such as the case reported by Huber et al. [21]) or totally independent of the controlled system’s conditions, but when a property like exergy is defined as the cost function, it results in a considerable uncertainty. The performance of an exergy-based control implemented by agents strongly depends on how accurate the exergy cost prediction is, which is not readily predictable, as exergy is a function of thermodynamic properties of the controlled system and the ambient temperature at the moment when the system is being analyzed. One solution to this problem could be the integration of MPC into agent-based control so that each agent would be able to predict the exergy destruction accurately. This idea led to a novel hybrid agent-based MPC technique for the proper implementation of the exergy-based control strategy.

Table 3 Comparison of the mode-based control with the agent-based control S2 in January.

Local energy consumption Primary energy consumption Local exergy destruction Primary exergy destruction Heat pump power CHP power Boiler power Heating rod power RMSESet Temperature of room 1 RMSESet Temperature of room 2 RMSESet,lim Temperature of room 1 RMSESet,lim Temperature of room 2

Mode-based control

Agent-based control S2

[kWh] [kWh] [kWh] [kWh] [kWel ] [kWth ] [kWth ] [kWel ] [–] [–] [–]

1191.0 1943.7 1050.9 1566.8 0.5762 0.4573 0.4199 0.1467 1.1517 1.2045 0.6121

1569.4 1908.5 1265.1 1471.1 0.6134 0.7629 0.6529 0.0380 1.1502 1.1985 0.6560

[–]

0.7166

0.7650

boiler could shut down faster in some cases, as it generates unnecessary high temperature in the storage tank. The overshooting is governed by the aggressiveness of the PID controller, which determines the magnitude of the capacity requested by the consumer agent of the storage tank. This phenomenon will be further discussed in the following. In Fig. 7, the behavior of the power outputs of the producer agent for the default configurations of the PID controller are shown for the first two simulation days in scenario 1. In this case, the control parameters are: k = 1, Ti = 20, Td = 1. In Fig. 7, aggressive behaviors can be observed, which lead to higher capacity requests, causing all the heat producer agents to switch on immediately when a demand occurs. Only in the first heating period, it can be seen that the capacity of the heat pump is increased gradually before the heating rod and finally the boiler are switched on. In Fig. 8, the behavior of the power outputs of the producer agent for different configurations of the PID controller is shown. In this case, the PID-controller, which determines the capacity request of the consumer agent representing the heat storage tank, is tuned to k = 0.1, Ti = 200, Td = 0.1, based on a trial and error tuning process. As

Fig. 8. Power of the components for the agent-based control with the tuned PID. 425

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software-in-the-loop simulations. The results show that the hybrid agent-based MPC control is able to reduce the primary energy consumption while maintaining the room air temperature at the same level of the reference case. The hybrid control also increased the stability of the controller by avoiding aggressive actions. The performance of agent-based control depends on the number of available sub-systems and accordingly the number of agents. The presented system in this study was only an example to prove the concept. The application of agents in bigger and more complex systems such as smart cities could lead to more energy saving. As a topic for future research, implementation of the developed exergy-based control strategy in bigger scales of energy systems, for example, district heating/cooling systems should be considered to assess the dependency of the improvement potential of the exergy-based control strategy on the system’s scale. The proposed exergy-based control strategy could be further developed by integrating the approach of exergy costing. Exergoeconomics applies the concept of exergy to the economic theory. The combination of the exergy analysis with the economic principles may be applied in the control of energy system to obtain an exergyefficient operation of a cost-effective system. In this case, rather than an energy-based control strategy, an economic cost-based control strategy should also be defined as the baseline for evaluation purposes. Investigation of other advanced control techniques would reveal the most suitable control technique for the implementation of the exergybased control strategy. However, the control technique should be selected among already well-established ones, as the integration of exergy concept into any control techniques would increase the level of complexity and optimization time, which could hang a question mark over the functionality of the exergy-based control strategy to the real-life applications.

Table 4 Comparison of the mode-based control with the agent-based and hybrid controls in January. Mode-based control

Energy consumption Exergy destruction Heat pump power CHP power Boiler power Heating rod power RMSESet Temperature of room 1 RMSESet Temperature of room 2 RMSESet,lim Temperature of room 1 RMSESet,lim Temperature of room 2

Agent-based control Classic

Hybrid

[kWh] [kWh] [kWel ] [kWth ] [kWth ] [kWel ] [–]

1191.0 1050.9 0.5762 0.4573 0.4199 0.1467 1.1517

1182.5 1013.8 0.7171 0.0017 0.1067 0.9162 1.1501

1166.4 999.8 0.7077 0.0016 0.1056 0.9041 1.1409

[–]

1.2045

1.1987

1.1923

[–]

0.6121

0.6561

0.6075

[–]

0.7166

0.7652

0.7091

5.1. Hybrid agent-based MPC The work mechanism of the proposed hybrid control is quite complicated in comparison with the solely agent-based control. As soon as a consumer agent calls the broker asking for heat/cold and its corresponding exergy price, the simulation of the generic building model takes a pause. The same simulation model, which was running before the agent’s call, is then initiated for 15 min by each agent. These simulations-within-the-simulation reveals how much exergy would be destructed, if each of the energy producers would satisfy the requested demand. The exergy costs are then calculated based on these simulations-within-the-simulation results. As soon as the offer with the least exergy destruction is determined, the simulation of the generic building model is restarted. The same process is repeated every time the broker is called. The results of the mode-based, the classic agent-based and the hybrid agent-based MPCs are presented in Table 4. As can be seen, the exergy destruction in the hybrid control is reduced to 999.8 kWh . The energy consumption of the system for the hybrid control is 1166.4 kWh , which is 2.06% and 1.31% lower than its value in the mode-based and the classic agent-based controls, respectively. There is no significant difference between the classic and the hybrid agent-based control in the selection of the energy suppliers. The deviation from the set point temperature is nearly the same in both kinds of agent-based controls, but the deviation from the set point range is slightly lower in the hybrid agent-based model-predictive control. The results show that the hybrid control acts better than both the mode-based and the classic agent-based controllers with respect to energy saving. Considering the room air temperature, the hybrid control performs as well as the mode-based control does, while its performance is slightly better than the classic agent-based.

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6. Conclusions and outlook In this study, the challenges of applying the theoretical concept of exergy into the practical control of energy systems were identified and methods and solutions to them were proposed. An agent-based control for building energy systems using the exergy cost functions was developed. The classical agent-based control developed in this study was combined with MPC to cover the weak point of agent-based control, which led to a novel hybrid agent-based MPC for the optimization of advanced building energy systems from an exergy point of view. For evaluation purposes, a case study with a reference control as the baseline was defined and modeled, which was controlled by a reference control and the agent-based under the same circumstances through 426

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