Indian Journal of Science and Technology, Vol 8(33), DOI: 10.17485/ijst/2015/v8i33/73237, December 2015
ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645
Demand Response Programs in Optimal Operation of Multi-Carrier Energy Networks Samaneh Pazouki1* , Shahab Ardalan2 and Mahmoud-Reza Haghifam3 Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran;
[email protected] 2 Department of Electrical and Electronic Enigineering, San Jose State University, California, USA;
[email protected] 3 Department of Electrical and Computer Enigineering, Tarbiat Modares University, Tehran, Iran;
[email protected] 1
Abstract Smart grid worldwide project provides integration of Distributed Energy Resources (DERs) to electrical power systems, especially electrical distribution networks. Combined Heat and Power (CHP) and Demand Response (DR) programs are considered as prominent resources of DERs. CHP allows integration of different energy infrastructures and DR enables customers to participate in energy market. In this paper, effect of DR programs is evaluated on multi-carrier energy networks based on the Energy Hub (EH) approach. Time-based and incentive-based programs of DR are taken into account. GAMS software is used to solve Mixed Integer Linear Programming (MILP) models of the proposed EH. Results demonstrate that different DR programs have different effects on load shape and hub operation costs. The results confirm that load participation factors by customers provide more operation cost reductions for EH owners. The results also demonstrate possible purchasing distributions of energy carriers from the networks for operation cost minimization in the presence of DR programs. In addition, the results show, in order to operation costs reduction, which technology should be optimally operated when different DR programs are applied on the multi-carrier network.
Keywords: Demand Response Programs, Energy Hub, Multi-Carrier Energy Networks, Optimal Operation, Smart Grid
1. Introduction Smartgrid enables and empowers DERs in distribution networks through advanced technologies. CHP is a great example of the technologies with efficiency, reliability, and economic improvement, allowing the integration of multi-carrier energy networks such as electricity, gas, and district heat. Integration of DERs such as wind, energy storage, and DR to existing electrical networks can help companies eliminate the investment costs related to transmission line expansion and establishment of new conventional power plants. It also improves network power loss, voltage power, power system reliability, and reduce operation costs for customers. Integrated energy systems have been considered
* Author for correspondence
recently as “Micro-Grid”1, “Hybrid Energy Hub”2, and the latest approach originates in the Vision of Future Energy Network (VOFEN) project3 which defines “EH.” EH is defined as a super node in electrical systems receiving different energy carriers, such as gas and electricity, which then schedules timeslots and quantity of each carrier to be purchased and stored to provide for the hub demands. EH simplifies multiple-system optimization both in operation and planning. References4,5 provides insight on optimization of hubs and interconnected hubs. The EH approach is not restricted to a predefined concept. The model can be flexibly expanded by DERs in large buildings, industrial plants, bounded geographic areas, and island systems6.
The Effects of Core-focused Yoga and Yoga-stabilization Combined Exercise on Isokinetic Trunk Strength and Body Balance
A hybrid energy system with economically-scheduled interruptible programming of DR is introduced in7. Multi-carrier networks are operated in the presence of curtailment programs for DR in8. A commercial EH is scheduled based on operation cost and energy not supplied in a hub in9, but the effect of the DR program is not studied in the paper. Effects of the demandshifting program of DR in residential EH is examined as energy storage and renewable complement in10. A financial analysis of EH equipped with demand-side management of heat load is modeled and evaluated in11. Heat load management of DR and energy storage in EH are evaluated in12. DR is applied on a residential EH with Model Predictive Control (MPC) in order to manage household micro-CHP in13. Curtailment programs for DR are employed for operation cost reduction and load shape management of an EH in14. This paper is organized to evaluate the effect of different DR program implementations in multi-carrier energy networks based on the EH approach. The proposed EH is introduced in Section 2. The problem is mathematically formulated in Section 3. Section 4 evaluates simulation results and concludes in Section 5.
2.1 Time-based Program of Demand Response
2. Proposed Energy Hub
Real Time Price (RTP) service is related to the volatility of hourly electricity prices within 24 hours, in either minutes or seconds, which is revealed in smart grids projects. The tariffs are continuously varied throughout the day. Despite predefined TOU and CPP programs, the tariffs are related to the volatile power market.
The proposed EH in Figure 1 can be scheduled as a hub central unit via intelligent information technologies in order to minimize operation costs to satisfy the hub demands through DR programs. The EH receives grid electricity to supply the hub-required electricity demand. The hub also imports network gas to supply its CHP and boiler in order to satisfy hub-required heat demand. A heat exchanger is used to convert the high temperatures of produced heat into normal heat for consumption. Thermal storage is employed to store additional heat to be used as later required. DR is described by the electricity consumption pattern change from normal usage in response to spot market of electricity price changes over time via smart technologies in response to costsaving and power system reliability maintenance15. The previously described DR programs are applied on the EH in order to evaluate the effects of different DR programs on the hub operation cost reduction16. The EH approach facilitates the complex interaction of the technologies towards minimizing operation costs.
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Time-based programs of DR are related to different tariffs for different times, described as follows:
2.1.1 Time of Use (TOU) The Time-Of-Use (TOU) program is referred to by three levels of electricity prices: peak, off peak, and average. The demands are synchronized by the prices. This type of DR is common and appropriate for residential customers. The prices at each level may be varied in different seasons, but the three level steps will remain constant. Advanced Metering Infrastructures (AMI) makes it possible to read customer consumptions remotely in a smart grid scheme.
2.1.2 Critical Peak Price (CPP) Critical Peak Pricing (CPP) program defines two levels of electricity price. One level is related to high price tariffs and other level is for off-peak times. This pricing occurs in some days in year (i.e., days with critical peak).
2.1.3 Real Time Price (RTP)
2.2 Incentive-based Program of Demand Response
Incentive-based program have predefined incentives and penalties to encourage customers into participating in electricity markets in response to power system reliability needs:
2.2.1 Direct Load Control Management (DLC) System operators adopt incentives for customers which can interrupt customer load (e.g., warmer days) in peak or critical times remotely based on predefined incentives in the contract. Frequency and duration of interruptions are limited in the program.
Indian Journal of Science and Technology
Heun-Joo Park, Yong-Kweon Kim, Song Joo and Yong Hong
2.2.2 Interruptible Demand (I/C)
gas from boiler P (H ) , and prices for electricity e(H) and gas g(H). The hub can be scheduled to operate advanced technologies for minimizing operation costs. B g
This contract is formed under the assumption that customers will reduce their consumption during critical times announced by system operators. If customers fail to follow the contract, they are penalized. Customers who consume 200kW to 3MW can participate in this program. When the system operator announces a load interruption, customers are obliged to reduce their load for 30 to 60 minutes. Schools, hospitals, and 24/7 continuous process are excluded by the program.
2.2.3 Emergency Demand Response Programs (EDRP) The service is optionally implemented by customers who receive incentives by the system operator for contributing in critical conditions when system reliability is threatened. Customers are not penalized if they choose not to participate.
Min :
OF =
H =24
å éëe (H ) * P
T e
H =1
H =24
H =24
H =1
H =1
(H )ùû + å éëê g (H ) * Pgc (H )ùûú + å éëê g (H ) * PgB (H )ùûú
(1)
In Equation (2a), hub electricity demand Pe(H) is supplied by imported electricity for transformers from the network P (H ) through transformer efficiency h . It could also be supplied by CHP through imported network gas for CHP P (H ) and its efficiency, h . Part of the demand is supplied by the DR program P (H ) , which is dependent on load participation factors from customers. T e
T ee
C g
C ge
DR e
Pe (H ) = h eeT PeT (H ) + h Cge PgC (H ) + PeDR (H ) (2a)
Hub heat demand is provided by Equation (2b) and Equation (2c) where heat exchange power is P (H ) and efficiency is h , CHP power represented by P (H ) through its efficiency h , boiler power is P (H ) and its efficiency h , and discharge power of thermal storage is P (H ) . Additional produced heat is saved in thermal storage P (H ) . Imported gas power for CHP and boiler is restricted by gas network capacity in Equation (2d). HE H C g
HE Hh
C gh
B g
B gh
2.2.4 Load Reduction Acting as Capacity (CAP) The program is offered by ISO/RTO who pays an incentive to customers curtailing the loads (e.g., 100kW) during critical times, but they are not penalized for not interrupting demands. This program is similar to insurance, which requires a fee even if crucial events do not occur.
2.2.5 Demand Bidding/Buyback Program (DB) Large customers are allowed to offer their prices for participating in the program or they are offered their load reduction amounts based on predefined price by ISO.
2.2.6 Ancillary Services (AS) Customers are allowed to offer prices for load reduction in critical events when system reliability is threatened. Customers should quickly implement load reduction (i.e., within a few minutes) in comparison to peak shaving programs (i.e., hours).
3. Problem Formulation
dis h
ch h
Ph (H )
P (H ) (2b)
HE HE Hh H
B PHHE (H ) = h Cgh PgC (H ) + h gh PgB (H ) - Phch (H ) + Phdis (H )
Equation (3c) shows how much customers should consume electricity power in order to achieve maximum revenue in 24 hours while they take part in DR programs17, where PeDR (H) denotes customer demand changes in 24 hours and d0 (i) denotes load participation factor. E(i,i) shows self-elasticity of each DR program price in a 24hour interval. E(i,j) represents cross elasticity of each DR program price in a 24 hour interval, where ρ0(i) and ρ0(j) denote base default prices. Thermal storage energy is restricted by its remaining energy Ph (H-1), charge P (H ) and discharge P (H ) energy, and thermal storage loss factor a in (4a). In Equations (4b)-(4d), thermal storage is limited between its minimum and maximum P energy, charge P (H ) and discharge of ch P (H ) thermal storage, and charge hh and discharge h efficiencies of thermal storage. Binary variables of charge I and discharge I are used to prevent simultaneous charge and discharge. dis h
ch h
loss h
ch h
dis h
T e
(2c)
Pg (H ) = PgC (H ) + PgB (H ) (2d)
max h
The EH is economically scheduled based on minimum operation costs. The objective Function (1) is related to purchased electricity P (H ) , purchased gas Pg(H), purchased gas from the network for CHP P (H ) , purchased
ch h
dis h
dis h
C g
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Indian Journal of Science and Technology
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The Effects of Core-focused Yoga and Yoga-stabilization Combined Exercise on Isokinetic Trunk Strength and Body Balance
Ph (H ) = P (H -1) + Phch (H ) - Phdis (H ) - ahloss .Ph (H )
(4a)
0 £ Ph (H ) £ Phmax (4b) 0 £ Phch (H ) £ I hch (H ) * (1/ h hch ) * Phmax (4c) 0 £ Phdis (H ) £ I hdis (H ) *
dis h
* Phmax (4d)
0 £ I hch (H ) + I hdis (H ) £ 1 (4e)
Gas and electricity networks are constrained based on their maximum amounts in Equation (5a) and Equation (5b). 0 £ Pg (H ) £ Pgmax (5a) 0 £ PeT (H ) £ Pemax (5b)
Sizes of transformers ST, boilers SB, CHP SC, and heat exchanger SHE are constrained in Equations (6a)-(6d) for importing electricity, gas and heat from network.
Table 1. The proposed hub parameters Symbol αhloss ηTee ηCge ηCgh ηBgh ηHEHh ηchh ηdish g Pemax Pgmax Phmax SB SC SHE ST
Quantity Values Loss factor of thermal storage 0.05 Electricity efficiency of transformer 0.98 Gas to electricity efficiency of CHP 0.35 Gas to heat efficiency of CHP 0.4 Gas to heat efficiency of boiler 0.9 Heat efficiency of heat exchanger 0.9 Charge efficiency of heat storage 0.9 Discharge efficiency of heat storage 0.9 Gas price 5(cent/kWh) Electric network capacity 1000kW Gas network capacity 1700kW Thermal storage capacity 400kWh Boiler capacity 1500kW CHP capacity 800kW Heat exchanger capacity 1200kW Transformer capacity 1200kW
h eeT PeT (H ) £ ST (6a) B h gh PgB (H ) £ S B (6b)
h cgh PgC (H ) £ SC (6c) HE HE h Hh PH (H ) £ S HE (6d)
4. Simulation Results Simulation is carried out on the proposed EH in Figure 1. Required information (i.e., hub parameters, hub demands, and hourly prices with incentives and penalties) are respectively depicted in Table 1, Figure 2, and Table 2. Simulation results are applied on the MILP model of the objective Function (1) with its constraints in Equation (2a) to (6d) by the GAMS software. Load participation factor is assumed to be 10% of electric demand.
Figure 2. The hub required electricity and heat demands.
Table 2. Demand response programs with incentives and penalties DR Programs CPP TOU RTP EDRP I/C DLC CAP
Figure 1. Proposed energy hub. 4
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Hourly Price (cent/kWh) I n c e n t i v e Penalty (cent/kWh) (cent/ kWh) 9 Average, 18.9 Peak 0 0 4.3 Valley, 9 Average, 18.9 0 0 Peak 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 4.3, 0 0 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 18.9, 18.9, 18.9, 18.9 9 Flat Rate 15.9 0 9 Flat Rate 10.5 4.5 10 Flat Rate 10.5 0 11 Flat Rate 5.5 4.5
Simulation results demonstrate effects of time-based (i.e., CPP, TOU and RTP) and incentive-based (i.e., EDRP, I/C, DLC and CAP) programs of DR on load shape Indian Journal of Science and Technology
Heun-Joo Park, Yong-Kweon Kim, Song Joo and Yong Hong
of the EH in Figure 3 and Figure 4, respectively. Effects of time-based and incentive-based programs of DR on the hub operation costs are illustrated in Figure 5 and Figure 6. Hub operation costs are evaluated in Figure 7. Table 3 shows operation costs of EH with DR, without applying DR, and with increasing load participation factor of customers in DR programs.
3 and Figure 4 that CPP programs are more effective on load shape than other time-based programs.
Figure 6. Operation costs as a result of incentivebased programs of DR.
Figure 3. Load shape as the result of time-based programs of DR.
Figure 4. Load shape as the result of incentive-based programs of DR.
CPP can smooth load shape by filling up valley and shaving peak demands. DR incentive-based programs are most affected by considering incentive and penalty amounts. Based on applying incentive and penalty amounts in Table 2, the EDRP program provides the best result of load shape smoothing in comparison to other incentive-based programs. It can be also inferred from Figure 5 and Figure 6 that the RTP program is the most effective time-based program for hub operation cost reduction and EDRP is the most effective for incentive-based programs. Operation costs are directly affected by hourly electricity prices; timebased programs CPP and TOU increase hub operation costs, causing purchased electric power to affect load shape more than DR programs. Incentive-based programs follow flat rates of electricity price, allowing EDRP to affect load shape are more than purchased electricity power. The incentive and penalty amounts determine the efficiency of incentive-based programs. It should be noticed that tradeoffs are required in choosing time-based programs for smoothing load shape and hub operation cost reduction. The TOU program provides optimized results. Table 3. Evaluation of the hub operation costs without applying DR, with applying DR, and with increasing DR by Time-Base and Incentive-Based programs of DR
Figure 5. Operation costs as a result of time-based programs of DR.
Finally, the effect of DR on hub scheduling to satisfy the hub required demands is evaluated in Figure 8 through Figure 10. The CAP and EDRP incentive-based DR programs are compared for economic scheduling of the proposed EH. It can be observed from load shape changes in Figure
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DR Programs CPP TOU RTP EDRP I/C DLC CAP
Without DR 300082 272486 251554 252163 252163 252163 252163
With DR 295086 269009 250786 239900 241725 250934 251959
With increase of LPF of DR 292633 267293 250425 233822 236557 250320 251856
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The Effects of Core-focused Yoga and Yoga-stabilization Combined Exercise on Isokinetic Trunk Strength and Body Balance
As shown in Figure 7 and Table 3, applying DR programs reduce the hub operation cost, which are even further reduced by increasing load participation factors from customers.
Figure 7. Evaluation of hub operation costs without applying DR, with applying DR, and with increasing LPF of DR through time-based programs.
Figure 8 shows that applying the incentive-based EDRP program decreases purchasing electricity for the proposed EH more than the incentive-based CAP program.
Figure 8. Purchased electricity power from network in EDRP and CAP programs.
Figure 9. Purchased gas power from network for CHP and boiler and heat storage power in CAP program.
Figure 9 and Figure 10 show that EDRP causes less CHP consumption compared to CAP since EDRP participation supplies more electricity than the CAP program. The boiler is employed more in EDRP than CAP
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in order to provide for heat demands, causing more CHP utilization in CAP to simultaneously supply electricity and heat demands in order to reduce operation costs. Heat storage while applying CAP is employed more to store additional produced heat by CHP and is discharged when heat demand is higher.
Figure 10. Purchased gas power from network for CHP and boiler and heat storage power in EDRP program.
5. Conclusion Smart grid enables integration of distributed energy resources to cope with the problems of emission and growing energy needs. Combined heat and power is a great example of the technologies with the significant benefit of coupling electricity and gas networks. Smart grid allows customers to participate in an electricity market to decline their operation costs through Demand Response programs. In this paper, the effects of different DR programs (Time-Based and Incentive-Base) on multicarrier energy networks (operation costs, load shape and the proposed hub scheduling) were evaluated by employing EH approach. GAMS software was employed to solve the MILP model of the proposed EH. Results conclude that Time-Based Programs of DR have an inverse relationship between hub operation costs and load shape smoothing. CPP provides the smoothest load shape among Time-Based DR programs and reduces operation costs the least, while RTP has the lowest operation cost while providing the least smoothing. Purchasing network electricity is directly affected by hourly electricity prices. Time-Based TOU of DR programs can provides the desired result in electrical load shape and the hub operation costs; consequently, TOU of Time-Based DR programs is suggested to achieve the best overall performance and a balance between load shape smoothing and the hub operation costs.
Indian Journal of Science and Technology
Heun-Joo Park, Yong-Kweon Kim, Song Joo and Yong Hong
Furthermore, the results indicate that Incentive-Based DR programs directly affects hub operation costs and load shape smoothing as purchasing electric power from the network follows a flat rate of electricity price. It is also demonstrated that the Incentive-Based EDRP program (a voluntary program of DR) provides the best results for operation cost reduction and load shape smoothing among all DR programs. Incentive-Based IC program (a mandatory program of DR) can support a desired result for the hub operation costs and load shape smoothing. In addition to applying DR programs (Time-Based and Incentive-Based), results confirm that increasing load participation factors from customers further reduces operation costs in the proposed EH. Finally, scheduling the proposed EH by IncentiveBased EDRP and CAP programs of DR verifies that CHP is used more when the used DR program has less effect on load shape smoothing and operation costs so that CHP can supply electricity and heat demand simultaneously. In contrast, applying a DR program with more effect on load shape smoothing and operation costs reduction, the proposed EH employs boiler more than CHP in order that the hub operation costs minimized. Last but not least, the thermal storage is utilized as CHP is operated more than boiler to minimize the hub operation costs.
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