- Email: [email protected]
Design of Combined Heat and Power Microgrids
Mario R. Eden, John D. Siirola and Gavin P. Towler (Editors) Proceedings of the 8th International Conference on Foundations of Computer-Aided Process Design – FOCAPD 2014 July 13-17, 2014, Cle Elum, Washington, USA © 2014 Elsevier B.V. All rights reserved.
Design of Combined Heat and Power Microgrids Michael Zachar,a Milana Trifkovic,b Prodromos Daoutidisa* a
Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, MN 55455, USA b Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive N.W., Calgary, AB T2N 1N4, Canada [email protected]
Abstract This paper deals with the design of community-scale microgrids. A generic system is considered consisting of solar photovoltaics, wind turbine, microturbine, battery array, and electric boiler. The microgrid is grid-connected and designed to supply both heat and power. It is constrained to meet 100 % of the energy demand. An optimal design is found to minimize the cost of energy supply over a 20 year lifespan. The optimal design is analysed with respect to technology cost and environmental policy in the form of CO2 taxation. Use of combined heat and power is found to lower cost of energy supply in all cases. CO2 taxation is found to be ineffective in promoting greener power supply for this system unless coupled with reductions in renewable technology cost. Keywords: Distributed generation, Combined heat and power, Renewable power
1. Introduction Over the coming decades, the level of distributed electricity generation is expected to rise dramatically (Carl et al., 2013). This distributed generation (DG) will come in the form of renewables (e.g. wind and solar), small fuel-fired units (e.g. microturbines and internal combustion engines), and new technologies (e.g. fuel cells) (Lasseter, 2007). This DG will typically be paired with distributed storage technologies, such as batteries, so that peak demand can be met economically and to account for the intermittency of supply in the case of renewables. These multiple local DG and DS units are often viewed as an autonomous microgrid (G) that interacts with the macrogrid (MG) through a single connection point. ɊGs allow for better control of distributed units, ensure that minimal changes to MG architecture are needed to incorporate large amounts of DG, and are able to island themselves and continue power supply in the event of a MG outage (Lasseter, 2007). Due to its proximity to the end user, these local Gs can also capture waste heat from units like microturbines (MTs) for use in satisfying thermal loads. This practice is known as combined heat and power (CHP). MTs designed for CHP can achieve an overall fuel efficiency as high as 80 % with a ~2:1 ratio between useful heat and power (Lasseter, 2007). All fuel efficiencies are given in terms of the lower heating value. Several approaches have been proposed for the economic sizing of G units. Chedid et al. (1998) is typical of early work in this area, where DG and DS are sized separately. In such approaches, DG is sized so that annual or monthly average production is equal to (or proportional to) average demand. DS is then sized to meet a certain level of power supply reliability. However, this is only applicable to stand-alone systems (not
658
M. Zachar et al.
connected to the MG), and oversizing of DG may be a less costly way of improving reliability in some cases. More recent works, such as Yang et al. (2008), have focused on the simultaneous sizing of DG and DS. In these formulations, when renewables power supply is insufficient to meet load, assumptions must be made about dispatch priorities (e.g. always deplete batteries before using generators) rather than letting the magnitude and duration of power needs dictate which unit to use. This approach is suboptimal since fuel-based units tend to perform more efficiently close to their maximum power point and when operated for prolonged periods of time. Mehleri et al. (2011) addressed the design of Gs for combined heat and power supply. Their results indicate that the optimal G design is able to supply energy at 59% of the cost of the conventional system (macrogrid power and gas-fired boiler). However, only 12 days of data (one representative day for each month) are used. Since the full stochasticity of load and weather are not accounted for with such a small data set, the probability of unmet demand in their design might be significant. This paper considers a G that incorporates CHP, multiple renewable technologies, and DS. An optimization problem is formulated to determine the optimal design, i.e. the included units and their sizes. A sensitivity analysis is also performed to analyse changes in the optimal design based on renewables capital cost and CO2 taxation rate.
2. Microgrid Description and Optimization Problem The goal of the optimization is to find the least costly way to satisfy the electrical and heat demand of a small community. The problem is formulated as a mixed integer linear program (MILP) with 5 possible types of units: solar photovoltaics (PV), wind turbine (WT), CHP microturbine, lead-acid battery, and electric boiler. Weather and demand data are based on an average year for Sarnia, ON, CA. The demand is scaled to correspond to a community of approximately 175 households (a total land area of approximately 1 km2) (Statistics Canada, 2012). The size of the G was limited so that distribution losses for both heat and power would be negligible. 2.1. Optimization Problem Formulation The objective function is formulated to minimize the net present value (NPV) of energy supply costs. Factors included in this calculation were initial capital costs; unit replacement costs; operating and maintenance (O&M) costs; MT startup cost; fuel costs; purchased power costs; and CO2 emission taxation. All annual costs are assumed to escalate at 2.5 %/y. The discount rate for NPV calculations was 8.3 %. Design variables are the number of MTs to include, the capacity of the battery, and the rated outputs of the PV array, WT, and boiler. The resulting MILP was formulated in GAMS and the optimal design was found using the CPLEX 12 solver. G peripherals such as flywheels and inverters are not accounted for since their cost is assumed to be independent of the actual design. Cost of electricity from the MG is 10.8 ¢/kWh. Nominal capital and O&M costs for each unit are shown in Table 1 (Carl et al., 2013). 2.2. Microturbines Microturbines were selected as a source of dispatchable generation due to their low number of moving parts (less maintenance), their ability to supply CHP, and their fuel flexibility (Lasseter, 2007).
Design of Combined Heat and Power Microgrids
659
Due to decreasing efficiency at low set points, they have a minimum output of 50 % of rated power when on. The efficiency of MTs in this range is assumed to be constant. Thus, the heat output, Qm, and the fuel consumption of the microturbines is directly proportional to the power output, Pm. In addition, MT startups are explicitly accounted for, and their cost is based on fuel consumption during a 6 minute startup time. Three identical MTs rated at 165 kW are available to be installed in the G. MTs have an electrical efficiency of 23 %, and a heat-to-power ratio of 2. 2.3. PV Array A single horizontal PV array is used to harvest solar energy. A maximum power point tracker is assumed to keep the array operating at optimal voltage and current. Thus, the power production of the PV array, Ps, is directly proportional to incident irradiation. Ps is equal to rated power at a solar intensity of 1000 W/m2. Lifespan of the PV themselves is assumed to be 20 years, but the inverter (which account for ~11 % of the capital cost) only has a 12 year lifespan (Carl et al. 2013). 2.4. Wind Turbine The ɊG utilizes a single variable-speed wind turbine to harvest wind energy. A typical power curve as described in Chedid et al. (1998) is used for the WT power output, P w. Because wind speed data for the location was not available at hub height, 10-m data was extrapolated to hub height using a power law relationship with a scaling factor of 1/7 based on previous literature for open, flat areas (Yang et al. 2008). Cut-in, rated, and cut-out speeds are 3 m/s, 12 m/s, and 25 m/s, respectively. 2.5. Lead-Acid Battery A lead-acid battery bank is used to store excess electricity and dispatch it as needed. Other types of batteries, i.e. nickel-cadmium or lithium-based batteries, are not considered due to their higher cost, fragility with respect to operating conditions, and more intensive maintenance requirements (Hadjipaschalis et al., 2009). Power sent to the battery, Pb-, is stored with an efficiency of 90 %. Power discharged from the battery, Pb+, is utilized at 95 % efficiency. The energy stored in the battery is bounded by its capacity and the maximum permissible depth of discharge of 80 %. Cycling of the batteries is not directly accounted for in order to limit the number of binaries in the problem. Instead, a low end value for lifespan, 5 years, is assumed to account for the expected behavior of 1 charge/discharge cycle per day. Table 1 - Unit nominal costs and lifetimes. Microturbine kW and kWh refer to electrical power.
Unit
Capital Cost (including balance of systems)
Annual O&M Cost
Lifetime
Wind Turbine
3400 $/kW rated
0.8 ¢/kWh usage
20 years
PV Array
5000 $/kW rated
52 $/kW rated
20 years
Microturbine
3600 $/kW rated
2 ¢/kWh usage
20 years
Battery
132 $/kWh capacity
0.143 ¢/kWh usage
5 years
Electric Boiler
60 $/kW rated
0.75 ¢/kWh usage
15 years
660
M. Zachar et al.
2.6. Boiler The electric boiler is used to convert electric power to heat. The heat output of the boiler, Qe, is directly proportional to the power consumption, Pe. The minimum partial load of the boiler is 0 % and conversion efficiency is assumed to be a constant 0.9 since these boilers can be cycled on a sub-hour scale. 2.7. Energy Balance Equations The G system is constrained to meet 100 % of both electricity and heat load, either through local generation or through power purchased from the MG. ܲ݉ ሺݐሻ ܲ ݓሺݐሻ ܲ ݏሺݐሻ ܲ ܩܯሺݐሻ ܾܲ ሺݐሻ ݉
݈ܲ ݀ܽሺݐሻ ܾܲെ ሺݐሻ ܲ݁ ሺݐሻ
ܳ݉ ሺݐሻ ܳ݁ ሺݐሻ ݈ܳ ݀ܽሺݐሻ
ݐ
(1)
ݐ
(2)
݉
where Pload and Qload are the heat and power demands, respectively. 2.8. Policy Considerations One important policy consideration taken into account is CO2 emission taxation rate. Although no current national carbon tax exists within the U.S., it has been suggested as a method for curbing greenhouse gas emissions. This research helps to analyze what effects such a policy might have on G design. For the purpose of emission calculations, the MG is assumed to produce CO2 at a rate of 0.5 t/MWh based on 37 % coal-fired and 30 % natural gas-fired power plants. Any extra tax incurred by the macrogrid is passed directly on to consumers. Table 2 - Base case optimal design Parameter
Value
Parameter
Value
WT Rating (kW)
0
Battery Capacity (kWh)
0
PV Rating (kW)
0
EIR
0.183
Total MT Rating (kW)
165
CO2 Emissions (t)
1850
Boiler Rating (kW)
107
NPV cost (million $)
6.15
Policy incentive such as tax rebates for renewable generation units are not explicitly taken into account since they can vary greatly by region. However, they may be considered as an implicit factor in capital cost of applicable units.
Design of Combined Heat and Power Microgrids
661
In addition, the energy index of reliability, EIR, is calculated as shown below: ܴܫܧൌ
σ ܩߤܲ ݐሺݐሻ σ ܩߤܲ ݐሺݐሻ σ ܩܯܲ ݐሺݐሻ
(3)
where PMG is the power purchased from the macrogrid, and PG is power produced by the G. EIR is the fraction of power that would be available in a MG outage, and thus is a measure of energy security.
3. Results and Discussion Using the nominal cost values and a CO2 taxation rate of 0 $/t, the base case shown in Table 2 was found to be the optimal design. In this design, a single MT, an electric boiler, and power from the MG are used. CHP is used to supply the majority of the heat demand and ~18 % of the power demand. These results indicate that renewables are not economically competitive at this time. In addition, the base case supports the assertion by some authors that CHP can be used to lower the cost of energy, though these results do not compare its effectiveness to systems in which heat is supplied through the combustion of natural gas. The model inputs for renewables capital cost and CO2 taxation rate were then varied and the optimization was resolved for each new set of parameter values. Some representative results (due to space limitations) are discussed below. In all cases, one CHP microturbine was selected to be installed. The effect of CO2 taxation on G design economics is shown in Fig. 1. For a CO2 taxation rate up to 90 $/t, the optimal design does not change. Thus, CO 2 taxation alone cannot be used to reduce emissions. It must be paired with a complementary reduction in renewables price for the effects of this policy to be seen. Fig. 2 shows the relationship between renewables contribution and G economics. Each point represents a unique set of values for WT cost, PV cost, and CO2 taxation rate. These results indicate that Gs with significant levels of renewable generation (~20 %) do not cause a significant reduction in NPV. This is problematic for the adoption of renewable technology as consumers may not be willing to invest in this newer, riskier technology for such small expected gain.
Figure 1 - Effect of CO2 taxation on microgrid economics under nominal capital costs
Figure 2 - Relationship between NPV and renewables usage. Each point represents a unique optimization run. Relative NPV refers to NPV as compared to a design with no renewables.
662
M. Zachar et al.
Figure 3 - Effect of CO2 taxation and renewables capital cost on CO2 emission levels.
Fig. 3 shows the dependence of annual CO2 emissions on renewables capital cost and CO2 tax rate. CO2 taxation is seen to not have a strong effect on actual emission levels in general. Taxation does have a weak effect so long as renewables are installed in the system. However, even with a very low renewables cost (WT at 500 $/kW) and a high tax (90 $/t), emissions are only reduced by ~50%. Thus, GHG are expected to remain significant as long as the sole factor in power system designs is cost of energy supply.
4. Conclusions Currently, renewable energy is not viable on a purely economic basis. However, the results presented in the paper indicate that a local MT used for CHP may result in economic gains while at the same time providing for limited supply of heat and power in the event of a MG outage. Lowering the cost of renewable energy technology can lead to much more utilization of renewable power as well as decreased cost and decreased CO2 emissions. Current proposals for taxing greenhouse gas emissions will not lead to greener power supply by themselves. However, such taxes can be used in conjunction with other cost reduction policies to improve their effectiveness. Future work will focus on expanding the model to incorporate more technologies, such as natural gas fired boilers and thermal storage. The robustness of promising designs with respect to variations in annual demand and weather variables will also be analyzed.
References J. Carl, G.P. Shultz, and S. Talbott, 2013, Distributed Power in the United States: Prospects and Policies, Hoover Institution Press publications, Standford University, Stanford, CA, USA R. Chedid, H. Akiki, and S. Rahman, 1998, A decision support technique for the design of hybrid solar-wind power systems, IEEE Trans. Energy Convers., vol. 13, no. 1, pp. 76-83 I. Hadjipaschalis, A. Poulikkas, and V. Efthimiou, 2009, Overview of current and future energy storage technologies for electrical power applications, Renew. Sust. Energ. Rev., vol. 13, no. 67, pp. 1513-1522 R. H. Lasseter, 2007, Microgrids and Distributed Generation, J. Energ. Eng., vol. 133, no. 3, pp. 144-149 E. D. Mehleri, H. Sarimveis, N. C. Markatos, and L. G. Papageorgiou, 2011, Optimal design and operation of distributed energy systems, Comput. Aided Chem. Eng., vol. 29, pp. 1713-1717 Statistics Canada, 2012, Census Profile: Sarnia, ON, Focus on Geography Series, 2011 Census H.Yang, W. Zhou, L. Lu, and Z. Fang, 2008, Optimal sizing method for stand-alone hybrid solarwind system with LPSP technology by using genetic algorithm, Sol. Energy, vol. 82, no. 4, pp. 354-367
Design of Combined Heat and Power Microgrids Michael Zachar,a Milana Trifkovic,b Prodromos Daoutidisa* a
Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Ave. SE, Minneapolis, MN 55455, USA b Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive N.W., Calgary, AB T2N 1N4, Canada [email protected]
Abstract This paper deals with the design of community-scale microgrids. A generic system is considered consisting of solar photovoltaics, wind turbine, microturbine, battery array, and electric boiler. The microgrid is grid-connected and designed to supply both heat and power. It is constrained to meet 100 % of the energy demand. An optimal design is found to minimize the cost of energy supply over a 20 year lifespan. The optimal design is analysed with respect to technology cost and environmental policy in the form of CO2 taxation. Use of combined heat and power is found to lower cost of energy supply in all cases. CO2 taxation is found to be ineffective in promoting greener power supply for this system unless coupled with reductions in renewable technology cost. Keywords: Distributed generation, Combined heat and power, Renewable power
1. Introduction Over the coming decades, the level of distributed electricity generation is expected to rise dramatically (Carl et al., 2013). This distributed generation (DG) will come in the form of renewables (e.g. wind and solar), small fuel-fired units (e.g. microturbines and internal combustion engines), and new technologies (e.g. fuel cells) (Lasseter, 2007). This DG will typically be paired with distributed storage technologies, such as batteries, so that peak demand can be met economically and to account for the intermittency of supply in the case of renewables. These multiple local DG and DS units are often viewed as an autonomous microgrid (G) that interacts with the macrogrid (MG) through a single connection point. ɊGs allow for better control of distributed units, ensure that minimal changes to MG architecture are needed to incorporate large amounts of DG, and are able to island themselves and continue power supply in the event of a MG outage (Lasseter, 2007). Due to its proximity to the end user, these local Gs can also capture waste heat from units like microturbines (MTs) for use in satisfying thermal loads. This practice is known as combined heat and power (CHP). MTs designed for CHP can achieve an overall fuel efficiency as high as 80 % with a ~2:1 ratio between useful heat and power (Lasseter, 2007). All fuel efficiencies are given in terms of the lower heating value. Several approaches have been proposed for the economic sizing of G units. Chedid et al. (1998) is typical of early work in this area, where DG and DS are sized separately. In such approaches, DG is sized so that annual or monthly average production is equal to (or proportional to) average demand. DS is then sized to meet a certain level of power supply reliability. However, this is only applicable to stand-alone systems (not
658
M. Zachar et al.
connected to the MG), and oversizing of DG may be a less costly way of improving reliability in some cases. More recent works, such as Yang et al. (2008), have focused on the simultaneous sizing of DG and DS. In these formulations, when renewables power supply is insufficient to meet load, assumptions must be made about dispatch priorities (e.g. always deplete batteries before using generators) rather than letting the magnitude and duration of power needs dictate which unit to use. This approach is suboptimal since fuel-based units tend to perform more efficiently close to their maximum power point and when operated for prolonged periods of time. Mehleri et al. (2011) addressed the design of Gs for combined heat and power supply. Their results indicate that the optimal G design is able to supply energy at 59% of the cost of the conventional system (macrogrid power and gas-fired boiler). However, only 12 days of data (one representative day for each month) are used. Since the full stochasticity of load and weather are not accounted for with such a small data set, the probability of unmet demand in their design might be significant. This paper considers a G that incorporates CHP, multiple renewable technologies, and DS. An optimization problem is formulated to determine the optimal design, i.e. the included units and their sizes. A sensitivity analysis is also performed to analyse changes in the optimal design based on renewables capital cost and CO2 taxation rate.
2. Microgrid Description and Optimization Problem The goal of the optimization is to find the least costly way to satisfy the electrical and heat demand of a small community. The problem is formulated as a mixed integer linear program (MILP) with 5 possible types of units: solar photovoltaics (PV), wind turbine (WT), CHP microturbine, lead-acid battery, and electric boiler. Weather and demand data are based on an average year for Sarnia, ON, CA. The demand is scaled to correspond to a community of approximately 175 households (a total land area of approximately 1 km2) (Statistics Canada, 2012). The size of the G was limited so that distribution losses for both heat and power would be negligible. 2.1. Optimization Problem Formulation The objective function is formulated to minimize the net present value (NPV) of energy supply costs. Factors included in this calculation were initial capital costs; unit replacement costs; operating and maintenance (O&M) costs; MT startup cost; fuel costs; purchased power costs; and CO2 emission taxation. All annual costs are assumed to escalate at 2.5 %/y. The discount rate for NPV calculations was 8.3 %. Design variables are the number of MTs to include, the capacity of the battery, and the rated outputs of the PV array, WT, and boiler. The resulting MILP was formulated in GAMS and the optimal design was found using the CPLEX 12 solver. G peripherals such as flywheels and inverters are not accounted for since their cost is assumed to be independent of the actual design. Cost of electricity from the MG is 10.8 ¢/kWh. Nominal capital and O&M costs for each unit are shown in Table 1 (Carl et al., 2013). 2.2. Microturbines Microturbines were selected as a source of dispatchable generation due to their low number of moving parts (less maintenance), their ability to supply CHP, and their fuel flexibility (Lasseter, 2007).
Design of Combined Heat and Power Microgrids
659
Due to decreasing efficiency at low set points, they have a minimum output of 50 % of rated power when on. The efficiency of MTs in this range is assumed to be constant. Thus, the heat output, Qm, and the fuel consumption of the microturbines is directly proportional to the power output, Pm. In addition, MT startups are explicitly accounted for, and their cost is based on fuel consumption during a 6 minute startup time. Three identical MTs rated at 165 kW are available to be installed in the G. MTs have an electrical efficiency of 23 %, and a heat-to-power ratio of 2. 2.3. PV Array A single horizontal PV array is used to harvest solar energy. A maximum power point tracker is assumed to keep the array operating at optimal voltage and current. Thus, the power production of the PV array, Ps, is directly proportional to incident irradiation. Ps is equal to rated power at a solar intensity of 1000 W/m2. Lifespan of the PV themselves is assumed to be 20 years, but the inverter (which account for ~11 % of the capital cost) only has a 12 year lifespan (Carl et al. 2013). 2.4. Wind Turbine The ɊG utilizes a single variable-speed wind turbine to harvest wind energy. A typical power curve as described in Chedid et al. (1998) is used for the WT power output, P w. Because wind speed data for the location was not available at hub height, 10-m data was extrapolated to hub height using a power law relationship with a scaling factor of 1/7 based on previous literature for open, flat areas (Yang et al. 2008). Cut-in, rated, and cut-out speeds are 3 m/s, 12 m/s, and 25 m/s, respectively. 2.5. Lead-Acid Battery A lead-acid battery bank is used to store excess electricity and dispatch it as needed. Other types of batteries, i.e. nickel-cadmium or lithium-based batteries, are not considered due to their higher cost, fragility with respect to operating conditions, and more intensive maintenance requirements (Hadjipaschalis et al., 2009). Power sent to the battery, Pb-, is stored with an efficiency of 90 %. Power discharged from the battery, Pb+, is utilized at 95 % efficiency. The energy stored in the battery is bounded by its capacity and the maximum permissible depth of discharge of 80 %. Cycling of the batteries is not directly accounted for in order to limit the number of binaries in the problem. Instead, a low end value for lifespan, 5 years, is assumed to account for the expected behavior of 1 charge/discharge cycle per day. Table 1 - Unit nominal costs and lifetimes. Microturbine kW and kWh refer to electrical power.
Unit
Capital Cost (including balance of systems)
Annual O&M Cost
Lifetime
Wind Turbine
3400 $/kW rated
0.8 ¢/kWh usage
20 years
PV Array
5000 $/kW rated
52 $/kW rated
20 years
Microturbine
3600 $/kW rated
2 ¢/kWh usage
20 years
Battery
132 $/kWh capacity
0.143 ¢/kWh usage
5 years
Electric Boiler
60 $/kW rated
0.75 ¢/kWh usage
15 years
660
M. Zachar et al.
2.6. Boiler The electric boiler is used to convert electric power to heat. The heat output of the boiler, Qe, is directly proportional to the power consumption, Pe. The minimum partial load of the boiler is 0 % and conversion efficiency is assumed to be a constant 0.9 since these boilers can be cycled on a sub-hour scale. 2.7. Energy Balance Equations The G system is constrained to meet 100 % of both electricity and heat load, either through local generation or through power purchased from the MG. ܲ݉ ሺݐሻ ܲ ݓሺݐሻ ܲ ݏሺݐሻ ܲ ܩܯሺݐሻ ܾܲ ሺݐሻ ݉
݈ܲ ݀ܽሺݐሻ ܾܲെ ሺݐሻ ܲ݁ ሺݐሻ
ܳ݉ ሺݐሻ ܳ݁ ሺݐሻ ݈ܳ ݀ܽሺݐሻ
ݐ
(1)
ݐ
(2)
݉
where Pload and Qload are the heat and power demands, respectively. 2.8. Policy Considerations One important policy consideration taken into account is CO2 emission taxation rate. Although no current national carbon tax exists within the U.S., it has been suggested as a method for curbing greenhouse gas emissions. This research helps to analyze what effects such a policy might have on G design. For the purpose of emission calculations, the MG is assumed to produce CO2 at a rate of 0.5 t/MWh based on 37 % coal-fired and 30 % natural gas-fired power plants. Any extra tax incurred by the macrogrid is passed directly on to consumers. Table 2 - Base case optimal design Parameter
Value
Parameter
Value
WT Rating (kW)
0
Battery Capacity (kWh)
0
PV Rating (kW)
0
EIR
0.183
Total MT Rating (kW)
165
CO2 Emissions (t)
1850
Boiler Rating (kW)
107
NPV cost (million $)
6.15
Policy incentive such as tax rebates for renewable generation units are not explicitly taken into account since they can vary greatly by region. However, they may be considered as an implicit factor in capital cost of applicable units.
Design of Combined Heat and Power Microgrids
661
In addition, the energy index of reliability, EIR, is calculated as shown below: ܴܫܧൌ
σ ܩߤܲ ݐሺݐሻ σ ܩߤܲ ݐሺݐሻ σ ܩܯܲ ݐሺݐሻ
(3)
where PMG is the power purchased from the macrogrid, and PG is power produced by the G. EIR is the fraction of power that would be available in a MG outage, and thus is a measure of energy security.
3. Results and Discussion Using the nominal cost values and a CO2 taxation rate of 0 $/t, the base case shown in Table 2 was found to be the optimal design. In this design, a single MT, an electric boiler, and power from the MG are used. CHP is used to supply the majority of the heat demand and ~18 % of the power demand. These results indicate that renewables are not economically competitive at this time. In addition, the base case supports the assertion by some authors that CHP can be used to lower the cost of energy, though these results do not compare its effectiveness to systems in which heat is supplied through the combustion of natural gas. The model inputs for renewables capital cost and CO2 taxation rate were then varied and the optimization was resolved for each new set of parameter values. Some representative results (due to space limitations) are discussed below. In all cases, one CHP microturbine was selected to be installed. The effect of CO2 taxation on G design economics is shown in Fig. 1. For a CO2 taxation rate up to 90 $/t, the optimal design does not change. Thus, CO 2 taxation alone cannot be used to reduce emissions. It must be paired with a complementary reduction in renewables price for the effects of this policy to be seen. Fig. 2 shows the relationship between renewables contribution and G economics. Each point represents a unique set of values for WT cost, PV cost, and CO2 taxation rate. These results indicate that Gs with significant levels of renewable generation (~20 %) do not cause a significant reduction in NPV. This is problematic for the adoption of renewable technology as consumers may not be willing to invest in this newer, riskier technology for such small expected gain.
Figure 1 - Effect of CO2 taxation on microgrid economics under nominal capital costs
Figure 2 - Relationship between NPV and renewables usage. Each point represents a unique optimization run. Relative NPV refers to NPV as compared to a design with no renewables.
662
M. Zachar et al.
Figure 3 - Effect of CO2 taxation and renewables capital cost on CO2 emission levels.
Fig. 3 shows the dependence of annual CO2 emissions on renewables capital cost and CO2 tax rate. CO2 taxation is seen to not have a strong effect on actual emission levels in general. Taxation does have a weak effect so long as renewables are installed in the system. However, even with a very low renewables cost (WT at 500 $/kW) and a high tax (90 $/t), emissions are only reduced by ~50%. Thus, GHG are expected to remain significant as long as the sole factor in power system designs is cost of energy supply.
4. Conclusions Currently, renewable energy is not viable on a purely economic basis. However, the results presented in the paper indicate that a local MT used for CHP may result in economic gains while at the same time providing for limited supply of heat and power in the event of a MG outage. Lowering the cost of renewable energy technology can lead to much more utilization of renewable power as well as decreased cost and decreased CO2 emissions. Current proposals for taxing greenhouse gas emissions will not lead to greener power supply by themselves. However, such taxes can be used in conjunction with other cost reduction policies to improve their effectiveness. Future work will focus on expanding the model to incorporate more technologies, such as natural gas fired boilers and thermal storage. The robustness of promising designs with respect to variations in annual demand and weather variables will also be analyzed.
References J. Carl, G.P. Shultz, and S. Talbott, 2013, Distributed Power in the United States: Prospects and Policies, Hoover Institution Press publications, Standford University, Stanford, CA, USA R. Chedid, H. Akiki, and S. Rahman, 1998, A decision support technique for the design of hybrid solar-wind power systems, IEEE Trans. Energy Convers., vol. 13, no. 1, pp. 76-83 I. Hadjipaschalis, A. Poulikkas, and V. Efthimiou, 2009, Overview of current and future energy storage technologies for electrical power applications, Renew. Sust. Energ. Rev., vol. 13, no. 67, pp. 1513-1522 R. H. Lasseter, 2007, Microgrids and Distributed Generation, J. Energ. Eng., vol. 133, no. 3, pp. 144-149 E. D. Mehleri, H. Sarimveis, N. C. Markatos, and L. G. Papageorgiou, 2011, Optimal design and operation of distributed energy systems, Comput. Aided Chem. Eng., vol. 29, pp. 1713-1717 Statistics Canada, 2012, Census Profile: Sarnia, ON, Focus on Geography Series, 2011 Census H.Yang, W. Zhou, L. Lu, and Z. Fang, 2008, Optimal sizing method for stand-alone hybrid solarwind system with LPSP technology by using genetic algorithm, Sol. Energy, vol. 82, no. 4, pp. 354-367