Low-Carbon Power System Dispatch Incorporating ... - IEEE Xplore

IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 28, NO. 4, NOVEMBER 2013

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Low-Carbon Power System Dispatch Incorporating Carbon Capture Power Plants Zhen Ji, Chongqing Kang, Senior Member, IEEE, Qixin Chen, Member, IEEE, Qing Xia, Senior Member, IEEE, Changming Jiang, Zhixu Chen, and Jianbo Xin

Abstract—In a carbon-constrained world, emissions will become a new concern in power system dispatch. Meanwhile, carbon capture power plants (CCPPs), which are a critical low-carbon power generation option, will have a significant impact on power system operation and dispatch. This paper presents research on low-carbon power system dispatch (LCPSD) incorporating CCPPs. The operating mechanism of CCPPs is investigated first. Then, the operating characteristics of CCPPs in power system dispatch are analyzed, including feasible power output limits, ramping rates and relationships between the power outputs and the carbon emissions. A comprehensive LCPSD model is formulated, in which the carbon emissions of power plants are treated as a new set of decision variables, and low-carbon-related cost terms are considered. The dispatch features of CCPPs are elaborately formulated and incorporated into the LCPSD model. The effectiveness and the validity of the proposed LCPSD mode and model are demonstrated using numerical examples based on an IEEE 118-bus tested system. emissions, Index Terms—Carbon capture power plant, low-carbon power system dispatch, operating characteristics.

I. INTRODUCTION

A

T present, there is consensus on the need for emissions abatement as a crucial measure for tackling global warming. As the largest sectional emission source, the power industry will be confronted with great challenges in terms of both external environments and internal operating mechanisms, especially in the traditional power system dispatch mode. Carbon emissions will become a new concern in the process of power system dispatch. Given the differences in the carbon emission intensities of various types of power plants, the scheduling of power system dispatch will have a significant impact on the overall carbon emissions of the power system. Moreover, different power dispatch schedules will lead to different distributions of carbon emissions among the relevant power plants, which will also affect their operating profits, whether in the form of a carbon tax or an emissions trading scheme (ETS). As a pioneer, California set an emission performance standard of 499 kg Manuscript received January 11, 2013; revised April 15, 2013 and July 05, 2013; accepted July 17, 2013. Date of publication August 15, 2013; date of current version October 17, 2013. This work was supported by Specialized Research Fund for the Doctoral Program of Higher Education (20100002110007), National Natural Science Foundation of China (51107059), and the National High Technology Research and Development Program of China (863 Program No.2011AA05A101). Paper no. TPWRS-00034-2013. Z. Ji, C. Kang, Q. Chen, and Q. Xia are with the State Key Lab of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China (e-mail: [email protected]). C. Jiang, Z. Chen, and J. Xin are with the State Grid of China, Beijing 100031, China. Digital Object Identifier 10.1109/TPWRS.2013.2274176

per MWh, which was roughly half of the normal emission level, on some of the power plants within the state in 2006 [1]. Though the actual implementation effects were still not quite satisfying, similar policies are expected to draw attentions to carbon emission issues that relate to power system dispatch. Thus, in a carbon-constrained world, system operators should consider both power system dispatch and “carbon emission dispatch” at the same time. This new dispatch mode is referred to as “low-carbon power system dispatch” (LCPSD) in this paper. A major challenge for LCPSD is to incorporate the new lowcarbon power sources into the power system dispatch scheduling. Given the significant emission mitigation effects, carbon capture power plant (CCPP) is undoubtedly one of the most capture syspromising low-carbon power sources. With that is contems, CCPPs separate and store the bulk of tained in the flue gas, guaranteeing the continued utilization of fossil fuels with low carbon emissions. In recent years, CCPPs have become a hot topic. In 2009, the U.K. government proposed that new coal-based power plants built in the U.K. should have demonstration carbon capture facilities on at least 300 MW of their capacity or be ready for capture retrofit when it becomes technically and economically viable in the future [2], [3]. The European Commission has also proposed similar policies. In October 2012, the UK government released 1 bn of carbon capture and storage (CCS) competition funds to support the development of CCS technology [4]. Therefore, CCPPs may become an important component of the future power mix. The introduction of a carbon capture system introduces great complexities to the operation of a CCPP. First, a CCPP is capable of independently controlling its power output and its carbon emissions. This technical feature might increase the adaptability of CCPPs in combined power and carbon markets and improve the overall economics of their use by taking advantage of the price differences [5]. In comparison, for the conventional non-capture power plants, it is almost impossible to control power outputs and carbon emissions separately because carbon emission levels are roughly proportional to the power outputs. Moreover, by coordinating the states of the carbon capture system and the generation system, CCPPs could have more flexible operating characteristics in power system dispatch, which might also have a significant impact on LCPSD and thus should be discussed and analyzed. Much research has already been performed on LCPSD issues. Many studies have been conducted to explore the operation of power systems toward achieving low emission targets, respectively on issues of optimal power flow (OPF) [6], unit commitment (UC) [7]–[9], environmental economic dispatch [10], economic emission dispatch [11], etc. The major focus

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of these studies is to introduce emission constraints [8], [9] and costs [7], [10], [12], [13] into traditional power system dispatch problems in a multi-objective coordination scheme [14]–[16]. However, these studies did not treat carbon emission as a new type of controllable resource in power system dispatch; additionally, CCPPs were not considered. However, several studies [17], [18] have analyzed the flexible operating potential of CCPPs in power system operation. The operating mechanism of CCPPs was discussed and formulated in [19]; several other studies [5], [20], [21] considered the operating optimization issues of CCPPs as an individual generation operator. Two further studies [22], [23] discussed the impact of CCPPs on the operating characteristics of power systems. The synergy between CCPPs and intermittent energy sources was explored in [24], [25], and the potential of CCPPs to provide ancillary services in power system operation was addressed in [18]. One study [26] developed a mixed integer linear programming (MILP) model, which formulated the operating characteristics of CCPPs from the aspect of unit commitment. Thus, although parts of detailed studies have been conducted on the operation of CCPPs, few of these studies have incorporated CCPPs into dispatch issues from a system-wide perspective. These two aspects of the literature strongly suggest that the topic of LCPSD incorporating CCPPs deserves more attention. In this paper, the operating mechanism of CCPPs is investigated first; on this basis, the operating characteristics of CCPPs in power system dispatch are analyzed, including feasible power output limits, ramping rates, and relationships between power output and carbon emissions. Then, a comprehensive LCPSD model is formulated, in which the carbon emissions of power plants are treated as another set of decision variables, and lowcarbon-related cost terms are considered. The dispatch features of CCPPs are elaborately formulated and incorporated into the LCPSD model. This paper is organized as follows: the operating characteristics of CCPPs are analyzed in Section II. Formulation of the comprehensive LCPSD model is presented in Section III. A numerical case study is described in Section IV, and the conclusion of this paper is provided in Section V. II. OPERATING CHARACTERISTICS OF CCPP capture, exAccording to the principles and process of isting CCPPs can be divided into three major types: post-combustion, pre-combustion and oxy-fuel combustion CCPPs [19]. Post-combustion types are among the technologies that are relatively technically mature and close to commercial deployment. Hence, this paper is focused on post-combustion-based CCPPs using generic amine solvents. A. Operating Mechanism of CCPPs The post-combustion capture system has three main components: the absorber, the stripper and the compressor. In a typical capture process, the flue gas contacts the amine solvent in the absorber, where approximately 90% of the in flue gas is removed. The solvent containing , referred to as “rich solvent”, then flows through a heat exchanger and enters the stripper, where the solvent is heated to separate the and

regenerate the solvent for absorption again. The regeneration energy required in this process is provided by extracting a certain proportion of the steam flow from the crossover pipes between the turbines. The captured pure is then compressed for the subsequent transport and storage processes. The regenerated solvent from the bottom of the stripper, referred to as “lean solvent”, returns to the absorber for the next cycle of capture [21]. This process of capture will incur a large amount of additional energy consumption, referred to as the “energy penalty”, and the net power output of the CCPP will also be reduced. The energy penalty mainly arises from the solvent regeneration and the compression. Several studies [17]–[20] have investigated the operating mechanisms of CCPPs. An important conclusion was that, by introducing two types of auxiliary devices, CCPPs could be operated flexibly, with the carbon emissions and the net power output as independently controlled variables. One method is to add venting facilities between the generation system and the absorber. In this case, the volume of treated is independent of the operating level of the generation system. Another method is to add two solvent storage tanks between the absorber and the stripper; thus, the operating levels of the absorber and the stripper can be separately controlled. Then, the steam extracted from the turbines can be adjusted, which means that the energy penalty can be temporarily mitigated and the net power output can be temporarily recovered. Because CCPPs with solvent storage tanks show better operation performance than CCPPs with venting facilities [20], this paper focuses only on CCPPs with solvent storage. B. Relationships Between the Carbon Emission and the Power Output As discussed above, in LCPSD, system operators should pay attention to the power dispatch and the “carbon emission dispatch” at the same time. Thus, it is important to explore the relationships between the carbon emission and the power output. is the gross power output of the CCPP, indicating the equivalent power output of the generation system. is the net power output of the CCPP, which indicates the electric energy actually delivered into the power grids from the plant. is the energy penalty. Thus, we have (1) consists of two parts: the basic energy penalty the operating energy penalty :

and (2)

is considered as a constant. , which includes the where energy penalty for absorption, desorption and compression, can be treated as proportional to the amount of being treated. can be expressed as (3) where unit of

denotes the operating energy penalty to treat each and denotes the amount of being treated. is the net emission, which is defined as (4)

JI et al.: LOW-CARBON POWER SYSTEM DISPATCH INCORPORATING CARBON CAPTURE POWER PLANTS

where denotes the amount of in the flue gas generated by the generation system. denotes the amount of being captured. denotes the emission intensity per unit of gross power output, and denotes the capture rate of , which is roughly in the range of 80%–95%. According to (1)–(4), we can deduce the following equation:

(5) , and are controllable variables, whereas In (5), the other parameters are technical parameters related to the specific CCPP. (5) is a linear equation by ignoring the nonlinear factors, including variation of , and . This approach will largely reduce the complexity of modeling, while not impose significant impacts on the kernel and framework of modeling. Actually, (5) indicates the manner of independent control of the net power output and the net carbon emission. Setting as the abscissa and as the ordinate, the CCPP can be operated not only at a specific operating point, but in a certain operating zone, as shown in Fig. 2. Brief introduction is included as below. For a given , when increases from 0 to , the operating points will form a line, and, when increases, the line will move to the upper right while the length and slope will remain unchanged according to (5). So, when varies from to and varies from 0 to independently, all possible operating points form a parallelogram. C. Operating Characteristics of CCPPs in Power System Dispatch Compared with conventional non-capture power plants, CCPPs show different operating characteristics in power system dispatch, which can be attributed to the following two aspects: 1) Faster Ramping Rates: For conventional non-capture power plants, the ramping rates of the power output, , can be expressed as (6) where is the rated capacity and denotes the maximum ramping ratio of the generation system. The net power output of the CCPP can be ramped up and down by controlling the load of the generation system as well. Moreover, the net power output can be ramped by adjusting the level of energy consumption of the capture system, which is essentially controlled by controlling the flow rate of the extracted steam between the turbines. Thus, CCPPs can be ramped faster and more flexibly than conventional power plants [27]–[29]. With as the ramping rate of the CCPP, we have (7) where denotes the maximum ramping ratio of the capture system, which reflects the changing rate of the extracted steam flow. denotes the rated energy penalty of the capture system, where the absorber, the stripper and the compressor are operated in the rated condition of the generation system.

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It should be noted that the availability of fast ramping of the CCPP is dependent on the steam turbine size. If the steam turbine is not able to accept steam that the capture plant no longer needs, then flexibility of the CCPP is substantially reduced. 2) Lower Minimum Power Output Limits: Denoting the minimum output limit of the generation system as , the net power output of the CCPP, , can be further lowered by extracting steam flow to the capture system: (8) where denotes the maximum operating energy penalty incurred by the capture system. The relationship between and can be reflected by a proportion coefficient, , which is (9) represents the maximum treating ability of the stripper and the compressor. When , the stripper and the compressor can treat both current process stream and the stored rich solvent. III. FORMULATION OF THE LCPSD MODEL The LCPSD model is formulated in this section, which describes the day-ahead power system dispatch problem in the form of DC power flow. Unit commitment is incorporated into the LCPSD model. Wind power is considered as a type of scheduled generation source, allowing curtailment. The errors between day-ahead forecasting and the real-time situation are neglected. emissions variations during unit start-up and shutdown processes are reasonably neglected [20]. A. Decision Variables power output of non-capture unit at time ; gross power output of CCPP unit at time ; net power output of CCPP unit at time ; net

emission of CCPP unit at time ;

scheduled power output of wind power at time ; on/off state of generation unit (including CCPP) at time (“1” indicates “on”, and “0” indicates “off”); starting up state variable of generation unit at time , indicates that the unit has just started up at time ; shutting down state variable of generation unit at time , indicates that the unit has shut down at time . To formulate the LCPSD model as a standard MILP problem, some new decision variables need to be introduced. As discussed above, the operating zone of the CCPP is a convex polygon, more specifically, a parallelogram. The coordinates of the four vertices represent four typical operating states (as shown in Fig. 2), which are formulated as follows:

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where is the set of dispatching time intervals. is the basic time interval. is the set of CCPP units. is the set of non-capture units. indicates the generation costs of unit , which is a function of . Similarly, indicates the generation costs of CCPPs. denotes carbon tax/price, and and denote the start-up and shut-down costs. C. Constraints 1) System Power Balancing Constraints: Fig. 1. Structure of the post-combustion carbon capture system.

(13) is the set of wind power units, is the set of where buses, and denotes the load demand of node . 2) System Reserve Constraints:

Fig. 2. Operating zone of CCPPs.

In this case, the coordinates of any point within the operating zone can be expressed as linear combinations of the coordinates of the vertices. The four coefficients for linear combinations, which are denoted , , and , can serve as another form of decision variables for the CCPP, and determine the values of and . Thus, we have (10) and

can be expressed as

(11)

B. Objective Function

(14) where and are the maximum and minimum power outputs of non-capture units and and correspond to and denote the upward and downward the CCPP units. system spinning reserve requirement. 3) Power Flow Constraints: (15) is the power flow limit of transmission line . where can be expressed as

(16) where indicates the element of the generation shift distribution factor (GSDF) matrix at row , column . , and denote the elements of the node-relation matrix at row , column , of non-capture units, CCPP units and wind power units, respectively. 4) Unit On/Off State Constraints: The relationships among the state variables are shown as (17)

The objective is to minimize the overall operating costs over the whole dispatch period, more specifically, over one day. The overall operating cost is the sum of the generation cost, the unit start-up and shut-down costs, and the carbon-emission-related costs, which might arise from various emission-related policy alternatives, such as a carbon tax and carbon cap-and-trade [30]. The objective function can be formulated as follows:

5) Power Output Constraints: The power outputs of noncapture units and the gross power outputs of CCPPs must remain within their upper and lower limits, whereas the scheduled wind power outputs should be less than the forecasted dispatch-able outputs: (18) is the forecasted dispatch-able wind output. where 6) Ramping Constraints: For non-capture units, we have

(12)

(19)


For CCPP units, we have

(20)

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where , as defined by (1) and (2). and indicate the initial volumes of solvent in the tanks. 12) CCPP Solvent Storage Volume Balancing Constraints: It should be guaranteed that the volumes of the solvent stored in the tanks at the end of the scheduling period are the same as the initial states. Thus, we have

7) Unit Commitment Duration Constraints:

(24) (21)

and represent the minimum running time where after start up and the cooling time after shut down. 8) Unit Commitment Time Constraints: (22) and denote the allowed maximum start-up where and shut-down times during the scheduling period. 9) Constraints on the Relationships Between the Power Outputs and Emissions of CCPPs: The relationships among , and are defined by (5), which should be taken as an equality constraint in this model. 10) Constraints on the Operating Zones of Net Power Outputs and Net Emissions of CCPPs: As shown in Fig. 1, it should be guaranteed that CCPPs are operated within the zones that are described and defined by (10) and (11). 11) Capacity Constraints of the CCPP Solvent Tanks: Because the amount of contained in the flue gas is large, it would be unrealistic to design these tanks with volumes that allow long-term storage [5]. Thus, a constraint must be incorporated to ensure that the amount of solvent stored does not exceed the capacity of the tanks. The net flows of rich solvent pumped into the rich solvent storage tank from the absorber could be expressed as the amount of rich solvent (in the form of contained ) transferred from the absorber to the rich solvent tank minus that drained from the rich solvent tank to the stripper. Thus, the net flows can be expressed as . This expression can also be implemented for the net flows of the lean solvent drained from the lean solvent storage tank. is introduced to describe the maximum capacity of the tanks, which is defined as the number of hours required to completely fill an empty rich solvent tank under the condition that and . The capacity constraints of the solvent tanks are formulated as

(23)

IV. CASE STUDY The LCPSD model, which is formulated in (10)–(24), is a standard MILP problem and can be solved by CPLEX [31]. A case study was conducted based on the standard IEEE 118-bus tested system, with slight modifications. The LCPSD model was implemented in MATLAB R2011a and solved by CPLEX 12.4 on a PC with an Inter Core 2 CPU (2.1 GHz) and 2.0 GB RAM. A. Basic Data The dispatch period is one day (24 hours), and the basic time interval . The IEEE 118-bus system includes 54 generating units. 19 of them are coal-fired units and the others are gas-fired units. Generation cost functions of the units are assumed to be quadratic: (25) Then, the cost is transformed into piecewise linear functions in the program. The parameters of all generation units are listed in Table I. The gas-fired units are simply assumed to have the same parameters, while each coal-fired unit have particular parameters. The parameters for the transmission lines are referenced from standard examples in Matpower [32]. The upward and downward system spinning reserve rates are both set as 20%. The price is set as . Fig. 3 shows the 15-min system load curve. Two wind farms are added on bus 13# and 16#. The forecasted dispatch-able wind power outputs are also given in Fig. 3. The wind data come from the real operating data of two wind farms. It should be noted that a “bad” day scenario is implemented in this case, in which wind power outputs are relatively low during peak periods and high during off-peak periods. This setting would be helpful to better illustrate the characteristics of CCPPs in peak load regulation. The four largest coal-fired units (on bus 10#, 69#, 80# and 89#) are retrofitted as post-combustion CCPP units with solvent storage tanks. For each of the four units, is set as 0.269 [21], and is set as 0.5% of . The capture rate is set as 90%, and the maximum load of the stripper and the compressor, , is set as 120%. is assumed to be 4 hours. and , indicating the initial states of the tanks, are set as 0.5. The maximum ramping ratio of the capture system, , is set as 5% of per minute. With these data, and , the ramping rate and the minimum net output of each CCPP unit, can be calculated. For example, for unit on bus 10#, , . When it’s retrofitted as a CCPP unit, , .

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PARAMETERS

TABLE I GENERATION UNITS

OF THE

Fig. 3. 15-min system load curve and forecasted dispatch-able wind power outputs.

B. Results The LCPSD model based on the tested system was solved with the CPLEX tool kit. Fig. 4 shows the scheduled power outputs of the CCPP on bus 69# and the amount of solvent in the two solvent storage tanks. In the left figure of Fig. 4, the dashed line represents the gross power output, and the solid line represents the net power output of the CCPP. The gaps between the two lines indicate the energy penalty consumed by the capture system. The operating state of solvent storage tanks can be clearly identified from the right figure of Fig. 4. During peak load periods, the CCPP is scheduled to increase its net power output by increasing the gross power output and reducing the operating level of the capture

Fig. 4. Scheduled power outputs and operating states of the solvent storage tanks of the CCPP on bus 69#.

system, and the volume of stored rich solvent increases; by contrast, during off-peak load periods, the capture system switches to higher operating load levels to treat the stored rich solvent with in the rich solvent storage tanks and contribute to the emissions abatement for the entire power system. Owing to limitations on the length of this paper, the schedules of the other generation units are not shown in detail here. The overall emission curve of the system is shown in Fig. 5. Because emissions are treated as a type of dispatch-able resource, this parameter can be scheduled by several means, for example, by controlling the operation of the CCPP and the scheduling of wind power and fossil-fuel power plants with different generation intensities. As a result, the shape


Fig. 5. Overall

emission curve of the system.

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Fig. 6. Wind power outputs and wind curtailment under LCPSD with and without CCPPs.

TABLE II COMPARISONS OF THE RESULTS UNDER DIFFERENT SCENARIOS

Fig. 7. Total system costs and overall prices.

of the system curve.

emission intensities under different

emission curve deviates from the system load Fig. 8. Payback periods under different

C. Comparison and Analysis To assess the effects of LCPSD and CCPPs, a traditional power system dispatch mode (TPSD) is introduced as a benchmark for comparison. The objective function of TPSD is the same as LCPSD but without taking into account the carbonemission-related costs. Meanwhile, a comparison is made between scenarios with and without CCPPs. The results under different dispatch modes and different numbers of CCPPs are presented in Table II. As shown in Table II, compared to TPSD, the implementation of LCPSD would lead to reductions on the total system costs and the emissions. Furthermore, as CCPPs can be operated with lower minimum power outputs and faster ramping rates, they can be better implemented in peak load regulation of the system, thus helping to avoid additional start-ups or shutdowns of the non-capture units. The CCPPs can also be flexibly controlled in response to the intermittent wind power outputs, helping to reduce wind curtailment significantly. Fig. 6 shows the total forecasted dispatch-able power outputs and the scheduled power outputs of the two wind farms on 13# and 16# under LCPSD with no CCPP and with 4 CCPPs. The solid parts in the figure indicate the wind curtailment. D. Sensitivity Analysis of

Prices

The price is an important factor affecting the operating performance of the power system under LCPSD. Fig. 7 presents

prices and CCPPs numbers.

the total system costs and the overall system emission intensities under different prices in the TPSD and LCPSD modes, respectively: Increasing prices will drive CCPPs to increase their operating loads of capture, which will lead to an increase in the generation costs while reducing the overall emission intensities. With increasing prices, emission related costs will become a larger part of the total system costs, and the benefits of the LCPSD mode will become more obvious. The trends for the total system costs and the overall emission intensities are reflected in Fig. 7, demonstrating the differences between the LCPSD and TPSD modes, as well as the significant effects of incorporating CCPPs into power systems. E. Cost-Benefit Analysis of

Capture Systems

The reduction on system operation costs incurred by CCPPs should be weighed against the capital costs of capture systems (including solvent storage tanks, etc.). This tradeoff could be investigated by means of cash flow analysis. The discount rate is set as 10.3% [20]. The capital costs of capture systems and solvent storage tanks are assumed to be $908/kW and $23/kW [21], [33]. prices are assumed to increase by 5% per year [21]. Fig. 8 presents the payback periods under different scenarios with various prices and numbers of CCPPs:

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It could be observed that when the price in the first year is set as , it will take 10 years for the system with 1 CCPP to recover the capital costs of the capture system. The payback periods will be increased with more plants retrofitted as CCPPs, while increasing of prices could help to reduce the payback periods significantly. V. CONCLUSION In a carbon-constrained world, emissions will become a new type of dispatch-able resource in power system dispatch. Meanwhile, CCPPs might become an important component in the future power mix given their promise and importance as a low carbon power generation option. This paper analyzed the operating mechanism and technical characteristics of CCPPs. A comprehensive LCPSD model was formulated, which incorporates the operation of CCPPs. A numerical case based on the IEEE 118-bus tested system was studied. The results suggest that LCPSD might reduce the total system costs and the emissions, especially when prices are relatively high. Moreover, the effects of incorporating CCPPs into the operation of power systems were identified and assessed as well. Because of their impressive technical characteristics in power system dispatch, CCPPs might be critical for avoiding additional start-up/shut-down operations on generation units and for reducing wind curtailment. At last, a cost-benefit analysis of capture systems was performed, taking capital costs into account. On this basis, payback periods of capture systems under different scenarios with various prices and CCPP capacities were demonstrated and compared. REFERENCES [1] The California Energy Commission and the California Public Utilities Commission, SB 1368 Emission Performance Standards, 2006. [Online]. Available: http://www.energy.ca.gov/emission_standards/. Capture, [2] Parliamentary Office of Science and Technology, U.K., Transport and Storage, 2009. [Online]. Available: http://www. parlia ment.uk/documents/post/postpn335.pdf. [3] Department of Energy and Climate Change, U.K., Carbon Capture Readiness, 2009. [Online]. Available: http://www.decc.gov.uk/en/cont ent/cms/meeting_energy/consents_planning/electricity/electricity. aspx. [4] Department of Energy and Climate Change, U.K., Short List for UK’s 1 bn CCS Competition Announced, 2012. [Online]. Available: http:// www.decc.gov.uk/en/content/cms/news/pn12 _136/pn12_136.aspx. [5] Q. X. Chen, C. Q. Kang, Q. Xia, and D. S. Kirschen, “Optimal flexible capture power plant in a combined energy and operation of a carbon emission market,” IEEE Trans. Power Syst., vol. 27, no. 3, pp. 1602–1609, Aug. 2012. emission-incorporated ac optimal [6] M. Shao and W. T. Jewell, “ power flow and its primary impacts on power system dispatch and operations,” in Proc. IEEE PES General Meeting, Minneapolis, MN, USA, Jul. 2010. [7] D. Yamashita, T. Niimura, R. Yokoyama, and Y. Nakanishi, “Thermal reduction including significant wind power unit scheduling for penetration,” in Proc. IEEE PES General Meeting, Detroit, MI, USA, Jul. 2011. [8] Y. M. Chen and W. S. Wang, “Economic dispatch with environmental considerations using marginal rate of substitution decision approach,” J. Quality Vol., vol. 16, no. 2, p. 109, 2009. [9] D. N. Simopoulos, Y. S. Giannakopoulos, S. D. Kavatza, and C. D. Vournas, “Effect of emission constraints on short-term unit commitment,” in Proc. IEEE Mediterranean Electrotechnical Conf., Malaga, Spain, May 2006. [10] S. Faias, J. de Sousa, and R. Castro, “Environmental dispatch of the emissions reduction,” in Proc. 8th portuguese power system for Int. Conf. European Energy Market, Zagreb, Yugoslavia, May 2011.

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Zhen Ji received the B.Eng. degree from the Department of Electrical Engineering at Tsinghua University, Beijing, China, in 2011. He is currently a Postgraduate Student at Tsinghua University. His research interests include low-carbon electricity, low-carbon power system dispatch and low-carbon power planning.

Qing Xia (M’01–SM’08) received the Ph.D. degree from the Department of Electrical Engineering at Tsinghua University, Beijing, China, in 1989. He is currently a Professor at Tsinghua University. His research interests include electricity market, generation scheduling optimization and power system planning.

Chongqing Kang (M’01–SM’07) received the Ph.D. degree from the Department of Electrical Engineering at Tsinghua University, Beijing, China, in 1997. He is currently a Professor at Tsinghua University. His research interests include low-carbon electricity, power system planning, power markets, power system reliability and load forecasting.

Changming Jiang is a Senior Engineer in State Grid of China. His major is power system operation.

Zhixu Chen is a Senior Engineer in State Grid of China, Beijing. His major is power system operation.

Jianbo Xin is a Senior Engineer in State Grid of China, Beijing. His major is power system operation. Qixin Chen (M’10) received the Ph.D. degree from the Department of Electrical Engineering at Tsinghua University, Beijing, China, in 2010. He is currently an Associate Professor at Tsinghua University. His research interests include low-carbon electricity, power system economics and optimization, power markets and power generation expansion planning.