AGENT-BASED MODELLING OF RESIDENTIAL ENERGY GENERATION WITH MICRO-CHP Michiel Houwing, Ivo Bouwmans Section of Energy and Industry Faculty of Technology, Policy and Management Delft University of Technology
Jaffalaan 5, 2628 BX Delft, the Netherlands Phone (+31) (0)15-27812340, Fax (+31) (0)15-2783422 E-mail:
[email protected]
Keywords: agent-based modelling; distributed control; distributed generation; distributed energy resources; micro-CHP; stirling technology. ABSTRACT Distributed energy resources are expected to pervade the energy system in the future and will contribute to the EU’s policy goals of energy market liberalisation and decreasing environmental impacts from energy use. This modelling study focuses on residential power and heat generation via micro combined heat and power units (µCHP), coupled with heat storage possibilities. The impact of the application of residential µCHP on operational energy flows to and from households, energy costs and CO2 emissions has been researched. We show that these operational impacts are significantly dependent on the adopted control mode applied in the total µCHP system. However, nearly every control mode showed cost and emission reductions in different seasons. The design of the heat-led, electricity-led end leastcost control modes are presented and their implementation in an agent-based model is discussed.
1
INTRODUCTION
1.1
Distributed energy resources
Distributed energy resources (DER; including energy generation, energy storage and load management options) play a crucial role in supporting the European Union's key policy objectives of market liberalisation, combating the greenhouse effect, increasing the amount of electricity generated from renewable sources and saving energy. Much literature states that distributed generation (DG) of electricity (e.g. via photovoltaics or wind turbines) has a good chance of pervading the energy infrastructure in the future. DG units provide power on-site and feed in power at the low voltage level. Much literature deals with the advantages of DG. Advantages mentioned in [1-3] include:
Environmental benefits (emission reductions via the use of renewable sources and the efficient use of fossil fuels); Reduced investment risks; Diversification of energy sources (needed with a view to the depletion of fossil fuels); Energy autonomy and fuel diversification (less geo-political dependency on fossil fuel rich countries); Energy efficiency increase: less line losses and combined heat and power (CHP) applications.
Large-scale diffusion of DER will probably have a profound impact on energy infrastructure functioning: it will bring radical changes to the traditional model of generation and supply as well as to the business model of the energy industry. DER will affect actors’ performance and decision making due to changing technical and social dynamics within the infrastructure. Besides electricity generation, other novel technologies to convert, exchange and store electricity and heat at a decentralized level are under development (e.g. fuel cells, heat pumps, aquifers). The focus of this paper is residential DG, heat generation and heat storage. Electricity storage and load shift possibilities are not considered here. 1.2
Smart power systems
An important issue in the application of DER is increased intelligence in the system. Intelligent metering and advanced ICT options in the infrastructure could lead to more sophisticated management and control of distributed generators, storages and loads at the supply and demand side of the infrastructure, as well as of the network itself. This enables active network management. In this work we do not look at the physical network part of the infrastructure in terms of cables, transformers
and power electronics. We focus on intelligent control of residential DG, creating smart power systems and active generation management. 1.3
Distributed generation control concepts
With residential power generation and increased intelligence in the energy system, actors in the electricity infrastructure obtain additional options of organising their power generation and supply activities. Figure 1.1 shows different control concepts between residential DG operators (households) and external parties, such as energy suppliers or a network operators.
Figure 1.1: Different control concepts between an external party (e.g. energy supplier, network operator) and a number of households. Dotted lines represent contractual electricity sales which are possible, but less significant in that specific control concept. Residential power generators could first of all be operated in a stand-alone way (Figure 1.1, left). Households then only contractually interact in electricity sales with external parties. Households themselves could adopt intelligent modes to control their generators and storage systems. Another option is that households fulfill their energy demand mainly by contractual interactions amongst each other (Figure 1.1, middle). This is the microgrid concept. A third option is that, according to contracted electricity sales, an external party controls the settings of residential generators or other parameters influencing these settings (Figure 1.1, right). This is the virtual power plant (VPP) concept. Suppliers can, for example, trade residentially generated power to optimise their own economic performance. Virtual power plants can be operated for technical or economic purposes: technical or economic VPPs, respectively. Furthermore, within the
economic VPP concept two additional possibilities can be discerned: including or excluding electricity trade between the households. In this paper we focus on the stand-alone control concept. 1.4
Objective of paper
In [4] we described our conceptual framework to study the operational impact of residential energy generation, storage and exchange on actors in the electricity infrastructure. In our research we regard this infrastructure as a socio-technical system and the focus is on three actors: households, energy suppliers and distribution network operators. As described in [4], we view this system as being complex, which provides a suitable way of observing and understanding system behaviour. The objective of this paper is to provide quantitative insight into the operational impacts on households due to the application of micro combined heat and power (micro-CHP or µCHP) units based on Stirling prime mover technology. Stirling µCHP units are expected to pervade the Dutch energy system on a large scale in the medium to long term. In a thorough study presented in [5], around 1.7 million units are expected to be operating in the Netherlands in 2020 and around 3.5 million in 2030. Of main interest in this paper are the impacts of different µCHP control modes on energy flows to and from households (heat, gas, electricity), associated operational energy costs and CO2 emission levels. Various designs of control modes have been developed and will be presented. The least-cost control mode is specifically new with respect to previous work in the literature. An agent-based model was developed and simulations with the model provide useful insight into the aforementioned impacts when households apply Stirling µCHP under different control modes. In the analysis, the technnical characteristics and constraints of the total Stirling µCHP system and the necessary control system received special attention. 1.5
Paper lay out
In section 2 the Stirling µCHP system and its balance of plant equipment will be described. Section 3 deals with the design of control modes pertaining to Stirling µCHP. We present our designs with respect to what has been done in the literature. Section 4 shows the model build-up and in section 5 the simulation results are presented. The article ends with conclusions and options for further research in section 6.
2
RESIDENTIAL MICRO-CHP BASED ON STIRLING TECHNOLOGY
2.1
System description
Many configurations of a µCHP unit could be thought of in relation to its balance of plant equipment (BoP; heat storage, piping, pumps, heat exchangers). See, for example, [6,7]. The presence of heat storage in the system decouples heat production from demand. In the Netherlands heat storage is almost exclusively used in providing sanitary hot water. Other configurations could also be conceived of, however. Figure 2.1 shows how we envisage the system configuration in a household. This configuration is in use in the UK and is based on [8].
Figure 2.1: Configuration of a residential µCHP system and its balance of plant equipment. H.X. = heat exchanger. The unit supplies heat to a central hot water storage from which hot water for both space heating and sanitation purposes are obtained. The fact that all heat is obtained from one storage facilitates the modelling of residential heat provision. Apart from heat, the µCHP unit also produces electricity. This can be used in the household and, when produced in excess of the electric load, can also be exported to the grid. When the self-generated power is insufficient to meet all demands, additional power can be obtained from the grid. In principle, the µCHP concept is technology independent. Prime mover technologies that could be incorporated in µCHP units are Stirling, fuel cells, gas engines, gas turbines, organic rankine cycles and steam cells [5]. In this paper we focus on Stirling technology. In future work other technologies will be considered as well.
Figure 2.2 shows the conceptual model of a household and all the involved energy flows within that household and between the household and its environment. In the Dutch electricity infrastructure the network management activities are separated from the commercial activities of power generation, trade and supply. Households are physically connected to the network and contractually interact with their supplier. The system under study consists of groups of households (j, j+1, j+2…) fulfilling their energy (electricity and heat) demand by using their µCHP system.
Figure 2.2: Conceptual model of energy flows in a household and between a household and the environment. The physical interactions within household j are shown in detail. Conversion 1 represents the Stirling engine. Primary fuel (f1) is converted into heat (h1) and electricity (g1). An auxiliary burner, also inside the µCHP unit, can deliver extra thermal power at a variable power level [5,9,10]. Conversion 2 represents this auxiliary burner, converting primary fuel (f2) into heat (h2). Heat can be stored in the heat storage (hs). All heat consumption (hc) is taken from the heat storage. Generated electricity can be used directly in the household (g2). Power generated in excess of residential load (ec) could be sold to an external party (e). Imported electricity (i) can be bought from external parties and can be transmitted through the grid. In this paper we consider this external party to be the energy supplier of the household. The supplier sells primary fuel for fuelling the µCHP unit as well as additionally required import electricity for households. Furthermore, the supplier buys any electricity that is produced by households in excess of their own consumption.
2.2
Technical assumptions regarding Stirling µCHP and BoP
In modelling households operating µCHP units with their BoP many assumptions were made about technological system characteristics. These assumptions are presented in this section.
Space heating is realised with hot water, not by heating air. No electric heating is considered.
Produced heat cannot be dumped; all heat should be used.
Each household has one µCHP unit.
In [5] efficiency values for current and future high efficiency condensing boilers are stated. For space heating purposes 105% efficiency is mentioned (all efficiency values are based on fuel Lower Heating Value, LHV). Current heating efficiencies for hot sanitation water are about 78% and future values (2015) are predicted to be around 90%. We take the aggregate of sanitation and space heating demand as total heat demand input for our models (this will be described in more detail in section 4.5.1). Thereby we assume a general heating efficiency of 105% for highefficiency condensing boilers.
High-efficiency condensing boilers (modelled in a ‘Non-CHP’ reference case) have a power output level between 11 and 36 kWth [11].
We assume space heating to be only high-temperature heating.
A µCHP unit comprises a Stirling engine and an auxiliary burner. The Stirling engine can operate at full and at partial load. The auxiliary burner can deliver extra thermal power at a variable power level. The Stirling engine and the auxiliary burner both have the same total efficiency as a highefficiency condensing boiler: 105%.
The Stirling engine of the µCHP unit produces heat and electricity with fixed electrical and thermal efficiency values over varying power output levels. The electric efficiencies of current state-of-the-art Stirling engines lay around 15% [5]. This gives a thermal efficiency of Stirling engines of 90%.
Two Stirling engine types were considered as a basis for our two modelled systems: the WhisperGen® and the Microgen® system [9,10]. The Microgen system is reported to have a rated electrical power output of 1 kWe. It cannot operate in part-load. The associated maximum thermal output of the Stirling engine is 5 kWth. The auxiliary burners have thermal outputs between 10 and 30 kWth. The WhisperGen system can deliver 1.4 kWe as maximum power. In normal mode it delivers 0.85 kWe. Associated thermal outputs of the total system lie between 4.2 and 10.5 kWth.
We have not implemented the exact technical specifications as given in the manufacturer’s product specifications. The modelled Microgen and WhisperGen systems both have a maximum electric power output level of 1.1 kWe. Part-load capacity is assumed at 0.55 kWe. With the efficiency values mentioned in the previous assumption a maximum thermal output of the Stirling engine of 6.6 kWth is assumed (part-load: 3.3 kWth). The modelled systems differ in their auxiliary burner power levels. For the Microgen system these lie between 3 and 30 kWth and for the WhisperGen system between 0 and 7 kWth.
Stirling engines have a so-called minimal up-time. On-off switching of the engine should not happen too frequently as this could damage the engine. Setting a minimal amount of running time limits the number of on-off switchings. A minimal up-time of half an hour is assumed [12].
Stirling engines have a warm-up and cool-down time of around three minutes [12] . As will be explained in detail in section 4.5.1 the time step of our simulations is 15 minutes. Therefore we can safely neglect the warmup and cool-down periods of Stirling engines.
The Stirling engine and the auxiliary burner will be activated and deactivated according to temperature levels of the water in the hot water storage. These levels were chosen as follows. At a minimum level (TStirling,min = 60ºC) the Stirling engine is activated. It will operate to a maximum, TStirling,max = 75ºC. If the heat demand is so high that this still leads to a decreasing water temperature when the Stirling engine is running, the auxiliary burner is switched on at Taux,min = 58ºC. The auxiliary burner operates to Taux,max = 68ºC. We assume a uniform temperature distribution in the heat storage tank. System control according to these temperature levels will be discussed in more detail in section 3.
Water temperature should not drop below 60 ºC because of legionella bacteria [13].
Primary fuel for the µCHP unit is natural gas. We have assumed this natural gas to purely consist of methane.
There are no thermal losses in the conversion and storage systems.
Combustion in the µCHP unit and in conventional high efficiency boilers is complete.
All households have a hot water storage. The volume is assumed to be 100 liters.
Parasitic load from BoP equipment (compressors, pumps, etc.) is neglected.
3
DESIGN OF µCHP CONTROL MODES
In controlling residential µCHP units and their BoP equipment we have designed several control modes and implemented them in the model. Before explaining the details of each control mode, we present a brief literature review. 3.1
µCHP control modes in the literature
dit is een bewerkte alinea, waarin de referenties niet meer ge-Endnote zijn: Not too much has been written on µCHP control. The main scientific publications on the topic come from Hawkes and Leach [14] and from Peacock and Newborough [15-18]. Hawkes and Leach [14, chapter 2] made a relatively comprehensive literature review on µCHP control modes. In these works, heat-led, electricity-led and least-cost control modes are discussed. Other prime mover technologies besides Stirling are considered, mainly fuel cells and gas engines. Peacock and Newborough exclude cost calculations in their work. In [18] Peacock and Newborough describe microgrid control, which is outside the scope of this paper. In [14] it is mentioned that the industry point of view is that µCHP control is usually assumed to be heat-led. hieronder de oorsponkelijke alinea, voor de referenties: Not too much has been written on µCHP control. Hawkes and Leach [14] have made a relatively comprehensive and interesting literature review on µCHP control modes. For that review, we refer to chapter 2 of their publication. The main scientific publications on the topic thus come from Hawkes and Leach [14] and further from Peacock and Newborough [15-18]. In all these works, heat-led, electricity-led and least-cost control modes are discussed. Other prime mover technologies besides Stirling are considered, mainly fuel cells and gas engines. Peacock and Newborough exclude cost calculations in their work. In [18] Peacock and Newborough describe microgrid control, which is outside the scope of this paper. In [14] it is mentioned that the industry point of view is that µCHP control is usually assumed to be heat led. Although some control modes have been developed before, our designs differ substantially from the literature on prominent points, which will be indicated in the following sections. 3.2
Heat-led control mode
The first control mode is a heat-led control mode. In reality such a control mode will operate on set temperature levels for the heat storage and the Stirling engine and auxiliary burner will be activated when there is a need for thermal energy. This means that the temperature in the heat storage will frequently be measured by a unit that controls the heat-generating equipment.
As will be explained in detail in section 4.5.1 our simulations follow a time step of 15 minutes, so the continuous control occurring in reality must be translated in discrete events. The heat-led control mode is represented by the decision flow chart (algorithm) of Figure 3.1. The heat demand of a household is fulfilled, resulting in a lower heat content in the storage, which is translated into a new storage temperature (Theat storage). If the Stirling is still in its up-time, it will keep running the next simulation step. The activation rule of the Stirling engine is as follows. The engine is activated when the new Theat storage falls below TStirling,min or when Theat storage is below TStirling,max and the engine was running in the previous period. This is to ensure that the engine will keep running until it reaches the maximum set point temperature. All electricity production, import and export flows are calculated. Then again a new Theat storage is calculated, arising from the heat generated by the Stirling engine. If this new Theat storage is below Taux,min the auxiliary burner will be activated. A check is performed on the required auxiliary burner power Paux,required, so that the minimum and maximum power levels of the burner are respected. At the end of each simulation step all operational energy flows are calculated, just as the operational costs and CO2 emissions (the calculation structures are explained in section 4. Our design resembles the heat-led control described in [15-17], although the design in [16,17] is more sophisticated than the one in [15]. The main difference with our design is that our Stirling activation happens on the basis of temperature levels, whereas in [16,17] this is done on the basis of needed thermal power. If the required thermal power is larger than zero, the engine is activated. This could lead to different outcomes of the activation rule compared to our control, because the required power level could be higher than zero and still the temperature in the heat storage could be sufficiently high to keep the Stirling engine deactivated. In [14] the heat-led control is also based on thermal power requirements. More general differences with our work and [14] are the following. In [14] lower total system efficiencies were assumed (without explanation) and modulation of power output was possible. This is in contrast with our assumptions, which are based on technical information of Stirling system manufacturers and personal communications with field experts [9,10,12]. In [10] one part load power mode is given additional to the full load value. In [14] system efficiency values vary over the power output range, which is a valid assumption that is a little more precise than ours. In [14] the warm-up and cool-down periods are much larger than the three minute periods we use. Further, [14] assumes heat dump to be possible. As energy savings is one of the objectives behind CHP application, we differ on this issue.
Figure 3.1: Heat-led control mode.
3.3
Electricity-led control mode
The decision flow chart of the electricity-led control mode is shown in Figure 3.2. The Stirling engine is activated whenever there is an electricity demand in the household and if the temperature level in the heat storage is below TStirling,max. This is simulated by checking if the heat produced by the Stirling engine in the coming tick of 15 minutes does not result in a too high temperature of the water in the heat storage. The main difference with the electricity-led control in [14] is that in [14] there is no temperature check and that there is a part-load option. 3.4
Least-cost control mode
In the previous two control modes the continuous control occurring in reality is translated into discrete control in the simulations. The least-cost control mode is different in that in reality actions to be taken by a controller are also discrete. Based on the information that a controller has at a specific moment, a prediction is made and the control settings are subsequently decided on. The least-cost control mode is explained on the basis of Figure 3.3, which shows a decision flow chart of the least-cost control mode in which part load operation of the Stirling engine is possible. The first part of the activation rule of the Stirling engine is similar to that of the heat-led control. Then a prediction of costs and revenues is made when 1) operating the Stirling in full load, 2) operating the Stirling in part load, or 3) importing all electricity. In the second part of the activation rule it is checked if operating the Stirling engine (at full or part load) results in less costs than importing all electricity. Also, storage of the produced heat should not lead to too high temperatures. This type of control can be regarded as a simple form of ‘model predictive control’ (MPC). With the help of a model of household technologies and with predictions of certain parameters a decision is arrived at. In this specific case the energy demand (electricity, heat) for the coming 15 minute time period is assumed being equal to the value at the start of that time interval. In our simulations we further assume the actual demand in the 15-minute period to be equal to the predicted demand. Another variation of this control mode is when there is a variable instead of a fixed electricity import price. Hawkes and Leach [14] are the only ones to have implemented a least-cost control mode. Their model minimises annual costs, which is a fundamentally different approach to ours. In [14] the activation rule of the Stirling engine was not based on a cost prediction as described above.
Figure 3.2: Electricity-led control mode.
Figure 3.3: Least-cost control with part load option for Stirling engine.
4
MODEL DESCRIPTION
After describing our system, the main assumptions and the control modes, we will now further discuss the structure of the simulation models. In section 4.1 our modelling paradigm and the simulation time scale are described. Section 4.2 gives the main general modelling assumptions. The economic and CO2 emission calculation are shown in sections 4.3 and 4.4. The values of model input parameters are given in section 4.5 4.1
Model structure
The modelling paradigm of the present research is agent-based modelling (ABM). For some basics on ABM and the agent concept, see [4]. Households in the system shown in Figure 2.2 are represented by agents in the developed ABMs. Household agents fulfill their electricity and heat demand and their technologies behave according to the control mode adopted by that agent. This main action of energy fulfillment is scheduled and executed each simulation step. Viewing households as agents is an elegant way to include the diversity of household technologies and their control. Our simulation model is implemented in Java and is based on the Repast agent simulation toolkit [19]. Each simulation step represents a time interval of 15 minutes. Electricity and heat demand profiles with time precision of 15 minutes were used (discussed in more detail in 4.5.1). Modelling the performance of µCHP systems is highly dependent on the temporal precision with which both the thermal and electrical demand is considered. For instance, the environmental outcomes of µCHP deployment have been reported to vary by up to 40% depending on the temporal resolution of the analysis [20]. In [20] a relatively small difference in outcomes was observed for demand data precisions between 5 and 10 minutes. Results varied more significantly with 30-minute precision data. A 15-minute temporal precision was shown to produce accurate economic and environmental results in (optimisation) modelling of µCHP. In [20] it was concluded that demand data in 10-minute blocks were sufficient to obtain results that reflect the assumed constraints on the model of µCHP technology. We therefore assume that 15 minute time intervals will lead to reliable simulation outcomes. 4.2
Main modelling assumptions
In addition to the technical assumptions given in section 2.2. there are some important general modelling assumptions.
4.3
We consider the electricity infrastructure to be hybrid; central as well as distributed generation co-exist. Therefore there is also a hybrid economic electricity system, meaning that there are several wholesale markets for electricity trade. Wholesale market price formation is assumed not to be influenced by the large-scale application of DER, not even when more advanced control modes anticipating future energy demand data would be used. In reality, price formation will of course be influenced by anticipative control. In this work no local electricity markets between households exist, but these could be added in the future.
Intelligent metering and data exchange are present in system. These meters can communicate energy demand data and can receive price information. They further encompass the control units having computational capabilities.
Electric load shift (shifting electricity usage in time) is not possible. A household will always use electricity and heat when it likes to.
Import price of primary fuel (natural gas) is fixed. The electricity import price can vary per simulation step.
There are no technical constraints in the physical electricity network. We do not focus on network effects and network topology.
The simulations distinguish three different days: a spring day, a summer and a winter day. Economic calculations
Several items are considered in the calculation of the operational costs that households incur when fulfilling their energy needs. 4.3.1 Electricity and gas import prices Statistics Netherlands states a total electricity tariff for small consumers for 2006 of 194 €/MWh [21] (household class: single tariff, 3000 kWh). The variable part of the total tariff (incl. energy and VAT taxes) is around 90 % of the total tariff [22], so this becomes 0.1746 €/kWh. In [21] a total gas tariff for small consumers of 552 €/1000 m3 is given (for a consumer class of 2000 m3). According to [22], 91% of the gas tariff is variable (including taxes). This leads to a value of 0.50232 €/m3. 4.3.2 Feed-back tariff According to the Dutch government, , the tariff received by a small consumer when feeding back into the grid less than 3000 kWh annually is equal to the variable
electricity costs [23]. However, we assume the feed-back tariff that the federation of energy companies in the Netherlands (EnergieNed) applies to electricity which is fed back into the grid by households (even when this is less than 3000 kWh per year). The average 2006 value (no day/night distinction) mentioned by EnergieNed is 0.0601 €/kWh [24]. The effect of different tariffs will be subject of further study. 4.3.3 Subsidy for RES-E In the Netherlands there is a feed-in tariff, which is a subsidy for electricity generated from renewable energy sources (RES-E). This subsidy is currently 0.033 €/kWh for CHP [25]. This subsidy is only applicable for CO2-neutrally generated electricity. The amount of electricity that is generated CO2-neutrally is calculated as follows. For a certain power, the CHP uses an amount of primary fuel with which heat and electricity are generated. The same amount of heat could also be generated by a conventional condensing boiler. The difference in primary fuel needs from the CHP and the conventional boiler, when converted by central power generation with an assumed average efficiency of 45% [5,26], will result in a certain amount of conventionally produced electricity. The amount of electricity the CHP produced additional to the conventionally produced electricity is considered as CO2-neutrally generated power. 4.3.4 Tax compensation A tax compensation is needed to avoid double taxation. The electricity delivered to the network will ultimately be subject to energy tax and VAT, whereas the gas used to produce this has already been taxed. We therefore assume that a household will not have to pay energy tax and VAT on the gas that has been used in generating exported electricity. Energy tax and VAT are about 32% of total gas price in 2006 [22]. 4.4
CO2 emission calculations
CO2 emissions arising from energy generation with a µCHP unit follow from the complete combustion equation of methane with air. For an overall calculation of the CO2 emitted due to residential energy consumption it is necessary to calculate the average CO2 emissions resulting from central power generation. Total CO2 emissions of Dutch electricity production are assumed to be equal to all tradable CO2 emissions of the Dutch power generation sector. The Dutch government has stated that small players (yearly emission smaller than 25 kton CO2) may decide for themselves to participate in the emission trading system for the first period (2005 - 2008). There are 164 potential players with this option. Little less than 40% of these players voluntarily joined the system. They are expected to emit
1.15 Mton CO2 [27]. With total CO2 emission due to power generation under the cap of 51 Mton in the Netherlands in 2001 [28], total emissions amount to 52.15 Mton. With a total Dutch electricity generation in 2005 (central + DG, no import) of 100.2 TWh [21] we arrive at an average CO2 emission per unit of centrally generated power of 0.5205 kg/kWh. (In [5] values around 0.6 kg/kWh are mentioned, so this calculated number is relatively reliable.) 4.5
Model input
4.5.1 Electricity and heat demand profiles In the simulation, each household has a slightly different energy demand profile. Energy demand data were chosen from a normal distribution around average values. These average values were taken from residential energy demand profiles in [17]. In [17] electricity and gas consumption data are presented, which were recorded on a 1-minute time base for 30 detached dwellings in England. The gas consumption data apply only to central heating boilers and excludes gas fires and gas cookers. Within the constraints of their database, a dwelling dataset was sought that exhibited a reasonable match for average English requirements. The dwelling chosen for further investigation had an annual gas consumption of 17.5 MWh and an electrical demand of 5.8 MWh. It was noted that the actual heat demand could not be derived from the gas consumption. Computation of the time-varying boiler efficiency was not feasible, because boiler supply and return temperatures were not monitored. For the purposes of their investigation it was assumed that the annual heat demand of the dwelling was equivalent to the annual gas consumption. This overestimates the actual heat demand, probably by 15 to 25% depending on the average efficiency of the existing boiler [17]. Thus, gas consumption was considered indicative of the aggregate space heating and hot-water demands. We further note, that the dynamics in daily heat consumption and gas consumption could differ substantially due to the delay effects of heat storages. However, it seems that the gas consumption data in [17] come from households that have no heat storage. In this work, we also assume the heat demand profile of a household to be equal to the gas consumption profile mentioned in [17]. In addition, we adopt the electricity consumption profiles mentioned in [17]. The heat and power demands of single dwellings show considerable minute-by-minute fluctuations. As we work with 15minute time steps we aggregated data to average values for each 15-minute period. We do not have the original dataset from [17] at our disposal, but we obtained our demand patterns by carefully examining their profiles. The daily energy demand profiles for three different seasons from [17] are shown in Figure 4.1.
Figure 4.1: Daily electricity and heat demand profiles on 1 household in three different seasons (top: 29th April, center: 21st August, bottom: 25th January) [17]. 4.5.2 Fixed and variable electricity import price In the least-cost control modes we used fixed electricity import prices as described in section 4.3.1. We also ran least-cost simulations with variable import prices. This variable tariff was obtained by varying the variable supply part of the total tariff (accounting for 32% of the total tariff [22]) according to Dutch power exchange values. We used a random day on the Amsterdam Power Exchange: 21st September 2005 [28]. In this way we got a variable import price as shown in Figure 4.2.
electricity import price variation over a day [€/kWh] 0,2 0,19
price [€/kWh]
0,18 0,17
fixed import price, 0,175 variable import price
0,16 0,15 0,14
23
21
19
17
15
13
9
11
7
5
3
1
0,13
hour of the day
Figure 4.2: Fixed and variable electricity import price used in the simula tions.
5
SIMULATION RESULTS
In this section the outcomes of the simulations are presented. These can be regarded as emergent properties of the modelled system. We simulated seven different control modes; one ‘Non-CHP’, conventional, reference case and six CHP modes. In each simulation thousand household agents fulfilled their energy demands as explained in section 4.5.1. 5.1
Results
The results are presented in Table 5.1. Only six control modes are given, because the outcomes of least-cost control with fixed electricity import price gave exactly the same result as heat-led control. The table gives the performance results for one average household, representative for the total group of thousand, for one day of each season. A positive value for ‘ext’ means that a household imports a net amount of electricity. The total daily heat consumption of one average household is 100 kWh (spring), 12.5 kWh (summer) and 170 kWh (winter). Further, it is interesting to mention that also a least-cost control mode was simulated in which a WhisperGen Stirling engine was implemented. The outcomes of this mode are not shown in the table, as the outcomes were similar to the outcomes of the least-cost control mode with the Microgen Stirling. Apparently a different auxiliary burner capacity has no influence on simulation outcomes.
Table 5.1: Model Outcomes: one average household, one day. The notation of the last control mode, ‘L.C. – p.l. – variable import’, means least cost control, with a part load option of the Stirling engine and a variable electricity import price. ‘cons’ = electricity consumption, ‘prod’ = electricity production, ‘imp’ = electricity import, ‘exp’ = electricity export, ‘ext’ = electricity from external grid, ‘gas’ = gas consumption, ‘el. c’ = electricity costs, ‘g.c’ = gas costs, ‘t.c’ = total operational costs, ‘CO2’ = CO2 emissions. Energy values are in kWh, costs in € and CO2 emissions in kg. Control mode 0. Conventional
1. Heat Led
2. Electricity Led 3. Least-cost – variable import 4. Least-cost – part load option 5. L.C. – p.l. – variable import
Season cons prod imp
exp
ext
gas
el.c
g.c
t.c
CO2
spr
43
0
42.5
0
42.5
96
7.5
5
12.5
42
sum
25
0
25
0
25
12.1
4.3
0.7
5
15.5
wi
28
0
28
0
28
165
4.8
8.6
13.4
48
spr
43
9.5
37
3
34
107
5.8
5.2
11
41
sum
25
2.7
23.5
1
22.5 16.9 3.75 0.75
4.5
15.7
wi
28
18
19
7.5
11.5 182
2.5
8.8
11.3
47
spr
43
2.5
41.5
1
40.5 64.5
7.1
3.3
10.4 34.5
sum
25
0
25
0
25
11.5
4.3
0.6
4.9
wi
28
3.5
26
1
25
118
4.5
6.1
10.6 37.5
spr
43
9.5
37
3
34
107
5.5
5.2
10.7
41
sum
25
2.7
23.5
1
22.5 16.9
3.4
0.8
4.2
15.7
wi
28
18
19
7.5
11.5 182
2.2
8.8
11
47
spr
43
8.5
37.5
3
34.5
6.3
4.2
10.5
37
sum
25
2.2
23.2
0.7
4
0.7
4.7
15.2
wi
28
14
20
5
15
141
3
6.7
9.7
39
spr
43
8.5
37.5
3
34.5
86
6.1
4.2
10.3
37
sum
25
2.2
23.2
0.7
3.8
0.7
4.5
15.2
wi
28
14
20
5
2.9
6.7
9.6
39
86
22.5 14.8
22.5 14.8 15
141
15.2
Figure 5.1 shows an example of the daily electricity flows on a spring day for the thousand households. The energy balance makes that production + import = consumption + export. The ‘from external grid’ line represents the difference between electricity import and export. A negative value for ‘from external grid’ means that electricity is exported to the external grid (this is the environment defined in Figure 2.2)
Figure 5.1: Daily electricity profiles of thousand households on the 29th of April (spring). 5.2
Analysis of the results
5.2.1 Operational costs From Table 5.1 it can be seen that in all control modes (1-5), for each season, the total operational costs as well as the electricity costs are less than for the conventional household (0). Cost savings range from 2 to 28% as compared to mode 0. The gas costs in the summer, however, are bigger than in mode 0 for control modes 1 and 3. Mode 5 has the least total costs in spring and winter. Mode 3 has the least total costs in the summer. Mode 3 has the least electricity costs in all seasons and mode 2 has the least gas costs in all seasons. A variable electricity import price leads to operational costs that are a little lower in comparison with a fixed import price (compare mode 5 with 4, and mode 3 with 1). The variable import price is found to have no effect on the Stirling activation rule, however. So, predictions of expected costs are never lower for the case of a running Stirling as compared to when all power would be imported (part 2 of the Stirling activation rule in Figure 3.3 engine never gave ‘yes’ as an answer). 5.2.2 CO2 Emissions For all control modes (1-5), for the spring and winter seasons, the CO2 emission levels are less than for the conventional household (0). Only in summer, the emissions for control modes 1 and 3 are somewhat larger than in mode 0.
5.2.3 Energy flows Modes 1 and 3 result in the highest amounts of produced electricity, in each season. These modes also show the lowest electricity import and the highest electricity export values. In summer, only the part load modes (4, 5) give even smaller import values. In summer (when very little heat is needed) relatively very little electricity is produced. In the spring the ratio of electricity export to production is highest in mode 2 (0.4); the summer ratio is highest in modes 1 and 3 (0.37); in winter, the highest ratio occurs in mode 3 (0.42). In modes 1 and 3 the least electricity needs to be imported from the external grid (true for all seasons). Modes 1 and 3 have higher gas consumption values in all seasons compared to mode 0. Mode 4 and 5 only have higher gas consumption in summer. The electricity-led mode (2) has lower gas consumption values than mode 0 in all seasons. Before each simulation step it is checked if temperature values in the heat storage are not below 55ºC (legionella) or above 90ºC. These limits are exceeded relatively often. In the partload control modes much better temperature management in the heat storages is observed: minimum and maximum temperature levels are exceeded far less often. 5.2.4 Comparison of the results with previous work In [17] the authors state that their heat-led ‘unrestricted thermal surplus – Stirling’ control mode resulted in 37% of power consumption being met by the µCHP system (63% was imported) on the spring day (29th April). On that day, 4.7 kWh was exported, which was equivalent to 20% of that demanded. These numbers differ substantially from our outcomes. Our outcomes show 6 to 22% of power consumption being met by the µCHP in spring. Also export was 1 to 3 kWh on the spring day, equivalent to about 2 to 7% of demanded power. The conclusions of [17] state that a 1 kWe system would achieve CO2 savings of between 9 and 16%. Our results show savings between 2 and 22%, depending on control mode and season. The results published in [16] for the 1 kWe Stirling engine show an import of 7.6 kWh for the winter day (January 25th), which is 43% of consumption (17.8 kWh). We saw import values of 19 to 26 kWh, on a total consumption of 28 kWh (this is 68 to 93%). (Here we see that averaging minute data over a 15-minute period can give different results: consumption of 18 kWh originally, and 28 kWh in our simulation). For the spring and summer days there is again a relatively wide discrepancy between absolute consumption and import values, but relative to each other (import/consumption) there is much more similarity between their and our work (see Figure 5 of [16]). Moreover, in [16] an export of 6.2 kWh on the winter day is mentioned, 35% of consumption. Our results show an export between 1 and 7.5 kWh (between 4 and 27% of consumption). At the end of [16] it is stated that demand from the external grid was reduced by 25% for the simulated 1 kWe Stirling system. Our results show reductions of 0 to 59%, depending on control mode and season.
As the Stirling engine in [14] has a capacity of 2 kWe, which is about twice the value used here, comparing simulation outcomes is not meaningful.
6
CONCLUSIONS AND FURTHER RESEARCH
6.1
Conclusions
With technological progress in DER and with the penetration of more intelligence in the energy system, smarter power systems and more active infrastructure management can be arrived at. The operational performance of µCHP systems is highly seasonal, linked as they are to the thermal demands occurring within households. Furthermore, there are large differences in system performance between different adopted µCHP control modes. The decision flow charts representing the control modes result in a decision about what to do per simulation step. However, operational costs, as we have defined them, and CO2 emissions are lower in each modelled control mode as compared to a ‘Non-CHP’ reference household. Cost savings are in the range of 2 to 28%. Only in summer slightly higher CO2 emissions are observed when compared to the reference case. An important point to note is that in the least-cost control modes a variable electricity import price has no effect on the activation decision of the Stirling engine. This may have to do with the fact that the import price was not high enough to encourage self-generation of electricity by households. 6.2
Further research
Further research that follows from this work is the following:
A sensitivity analysis on model inputs will be made, investigating the sensitivity of heat storage temperature levels used in system control, the electric efficiency of the Stirling engine and the volume of the heat storage.
New household types using other types of DG technologies (fuel cells, heat pumps, photovoltaics and wind turbines) will be developed.
The operational influence on other actors than only households, especially energy suppliers and distribution network managers, will be looked into. Also other control concepts than ‘stand-alone’ will be modelled.
The operational costs of ICT systems needed to arrange the communication between households and external parties will be included in the cost calculation.
A feed-back tariff equal to the variable electricity import costs will be implemented in the model as an alternative for remunerating electricity exported to the grid by households.
We will research if a variable import price for electricity influences control outputs (DG activation) differently when electricity storage and electricity sales between households will be enabled. This is especially interesting when we will look into fuel cells , as fuel cells have different technological characteristics than Stirling engines. Different days on the power exchange will also be experimented with.
Daily residential electricity and heat demand profiles will be studied in more detail and will be incorporated in the model. These profiles should resemble average households and not just one household, as done in this paper. Average daily residential energy demand should better match the average Dutch values. Table 5.1 shows that electricity and heat consumption data used are relatively high compared to the Dutch average, which is 3400 kWh of electricity and 1736 m3 natural gas annually per household in 2004 [22].
As in [18], we will investigate daily load profiles more in depth on characteristics such as ‘after diversity maximum demand’ (ADMD; indicates expected peak loading at an LV distribution transformer) and daily load factors.
We will develop more sophisticated model predictive control (MPC). We will study if there is a difference in system outcomes when predicted demand for a certain interval differs from actual demand. Up till now we have assumed predicted and real demand to be equal. In the MPC we will extend the prediction horizon beyond one simulation time step and we will include predictions of import prices. Furthermore, we will use an optimisation solver to find the optimal setting. Such a solver becomes necessary when decision freedom in households increases due to the inclusion of electricity storage options and possibilities for power trade between households. Applying more advanced MPC in the other control concepts of Figure 1.1 will then also be looked into.
ACKNOWLEDGMENTS This project is supported by the Next Generation Infrastructures Foundation (http://www.nginfra.nl).
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