Abstract—Since renewable energy is well known as a clean source of power, the incentive for providers and customers to join the renewable energy boom is coming in different ways. Under the forcing policy, the wind generation will be guaranteed to be purchased in the wholesale market if wind generators bid at their marginal price. However, a forcing policy may lead to infeasibility if wind spillage is not allowed. The numerical results in this paper show that integrating more wind generation may lead to market inefficiency or more greenhouse gas emissions, e.g. . To test this hypothesis a deterministic unit commitment was solved by AMPL by modeling power markets and calculating operating costs. With the consideration of the start-up costs and ramping emissions, the cost of emissions is compensated by the generators.

Index Terms—Economic analysis, emissions, forcing policy, power markets, unit commitment, wind energy.

I. NOMENCLATURE

A. Indices and sets Set of generators

Set of generators at node Set of transmission lines

Set of transmission lines with as the “from” bus Set of transmission lines with as the “to” bus

Set of buses (nodes) Set of time periods

Set of wind generators at node

B. Parameters Susceptance of transmission line

Emission cost of unit Linear operating cost of unit No load cost of unit Shut-down cost of unit Start-up cost of unit

System demand at bus n in period Minimum down-time of unit

Linear emission rate of unit Start-up emission rate of unit

Ramping emission rate of unit Maximum production level of unit

Manuscript received June 2, 2013. This work was supported in part by Dr. Kory Hedman. Integration of Renewable Energy: Managing Renewables in Power Markets.

Minimum production level of unit Line rating of transmission line

Forecasted wind peak output of generator w at period

Ten-minute ramp-rate of unit Hourly ramp-rate of unit Minimum up-time of unit

C. Variables Ramping emission at unit at period Start-up emission at unit g at period

Power produced by unit in period Power flow variable of transmission line in

period Wind generated of unit at node in period

Spinning reserve provided by unit g in period Shut-down variable of unit g in period Start-up variable of unit g in period

Unit commitment variable of unit in period (1: committed; 0: decommitted)

Bus angle variable at node in period

II. INTRODUCTION XPANDING renewable energy sources benefits consumers with stable or lower prices and minimal environmental

impacts due to emissions. Also a vast and inexhaustible energy supply is a plus, and rapid deployment can provide a significant share of future electricity needs. Job opportunities and other economic benefits will continue. Energy system will be more reliable and resilient because distributed and modular renewable systems are less prone to large-scale failure [1].

However, increased emissions and higher costs may occur when centralized planners try to manipulate market choices by forcing providers to purchase no matter how much renewable energy is produced. It happened in Germany when Chancellor Angela Merkel expanded Germany`s renewable energy push in the wake of the 2011 Fukushima nuclear disaster in Japan by ramping up the government’s plan to phase in renewable energy and take the country`s nuclear power industry offline [2]. As shown in Fig. 1, the production of wind changes dramatically over time. As a result, operating costs due to ramping may increase. The variability of wind output may cause problems if system operators try to buy all the wind

Yuwei Bai School of Electrical, Computer and Energy Engineering

Arizona State University Tempe, Arizona USA

ybai17@asu.edu

Integration of Renewable Energy: Managing Renewables in Power Markets

Chun-Yi Lee, Jung-Chun Lu and Zongfei Wang School of Electrical, Computer and Energy Engineering

Arizona State University Tempe, Arizona USA

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978-1-4799-3656-4/14/$31.00 ©2014 IEEE

output at the time.

Fig. 1. Example of wind power forecasts 3 and 36 h ahead of operation compared to realize wind power production for one week, 8 GW [3].

In this paper, four models with wind energy are used to demonstrate the drawbacks of a forcing policy which requires operators to reduce wind output. Contrary to a forcing policy is the option policy in which a power system takes a reasonable amount of wind energy that the system needs.

The models estimate the least-cost dispatch of available generation resources to meet the electrical load [4]. This is why unit commitment is used to establish the objectives and constraints for the models of the power system with 24 buses, 24 hour load profiles and 33 generators as they are shown in Fig. 2. The generator types include oil/steam, oil/CT, hydro, coal/steam and nuclear. The Details are shown in TABLE I. Some data is from Power Systems Test Case Archive [5]. Wind generators are added at Bus 7 in Fig. 2. The wind generation out puts based on different time periods are shown in TABLE II. The formulation of a mixed integer programming (MIP) problem is based on unit commitment and is implemented by A Mathematical Programming Language (AMPL [6]) to obtain the operating cost with dispatched data. Analysis on the models is accomplished by comparing the operating costs.

Fig. 2. The test model of power systems.

TABLE I. GENERATION TYPE AND ITS MAXIMUM OUTPUTS

Generation Type Generation Number and Maximum

Output (MW) Oil/ Steam 16-20 (12MW) 9-11 (100MW)

12-15 (197MW) Oil/ CT 1, 2, 6 (20MW) Hydro 25-30 (50MW)

Coal/ Steam 3, 4, 7, 8 (76MW) 21, 22 31,32 (155MW) 33 (350MW)

Nuclear 23, 24 (400MW) TABLE II. WIND GENERATION OUTPUTS

The amount of emissions and emission costs are compared

between the option policy and the forcing policy. Based on the simulation results and discussion, the forcing policy`s drawbacks are revealed. A forcing policy may increase power system emissions and higher operating costs.

III. UNIT COMMITMENT AND ITS IMPLEMENT Mathematical formulations of the unit commitment problem

are used to find the optimal cost plan of on/off states and power supplies for a set of distributed power generators. As the data for the variables has decimals, the problem should be mixed integer programming (MIP). Also the output of wind plants are assumed by experience and the unit commitment problem uses a deterministic model. System operators normally solve this problem when generators bid for new prices or want more information for day-ahead markets. Unit Commitment

(1) Min:

Subject to:

(5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) 0 (16 )

(16 )

(21)

Objective (1) minimizes the start-up, shut-down, no-load, and fuel costs. Constraints (2)-(4) can model the start-up and shut-down variables. Constraint (5) guarantees the net injection, including wind, into a bus. Constraint (6) linearizes the real power line flow equation with assumption of zero line resistance. Constraint (7) describes the transmission line rating. Constraint (8) imposes the minimum and maximum production of each generator. Constraint (9) and (10) are the minimum up and down time constraints. Constrains (11) and (12) represent the hourly ramp-up and ramp-down rates of each generator. Constraints (13)-(15) are the spinning reserves constraints. Constraint (16a) identifies the minimum and maximum wind capacity of each wind unit. Constraint (16b) forces the wind capacity to take all the natural wind generation. Objective (1,em) minimizes the operating cost, including start-up, shut-down, no-load and the linear costs, and the emission cost, containing linear emission, start-up emission and ramping emission costs. To model the emission cost, the ramping emission variable is constrained by constraint (17) and (18), which determine the ramping capacity of committed generator for calculation of ramping emission. Also, the start-up emission variable is shown by constraint (19) and (20) that tabulates the start-up capacity of each generator for calculation of start-up emission. Formulation (21) is the binary variable [7].

IV. ANALYSIS ON THE POWER SYSTEMS WITH AND WITHOUT FORCING POLICY

Two models are established by AMPL with the formulations of unit commitment. The first system model without forcing policy of wind energy contains Objective, (1), Constraints (2)-(16 ) and Formulation (21). The second system model with forcing policy of wind energy contains Objective, (1), Constraints (2)-(15), (16 ), and Formulation (21).

Basically, the wind generators that are dispatched for the two models have significant differences due to the forcing policy in Fig. 3. From this figure, the curve of wind generation under the option policy is much smoother than the one under the forcing policy. Results show that taking all the wind power into the grid is not the best solution for the model. The optional solution only dispatches some of the wind power, instead of all of it.

The operating cost of the first model is $1,030,562 while the cost of the second model is $1,146,008. The forcing policy makes the operation cost of the power system higher than the one with option policy. The total cost difference is about 10%, which is a significant percentage. The total cost of those two policies is illustrated in Fig. 4

Fig. 3. Wind power with and without forcing policy.

Fig. 4. Operating cost with two policies. A review of the comparative results shows most of the generators have minor changes between the two models. Large changes happen to generators 13, 14 and 15 that might be the most crucial reason that makes those two objective solutions different. Those three generators are oil/stream generators and they have the highest start-up and shut-down costs, $6,510, among all the generators in this model. For these units, the linear cost is $74.75/hour, and the no-load cost is $1,159.3, which is also rather high. Fig. 5, Fig. 6, and Fig. 7 show the difference of generation amounts of units 13, 14, 15 under the two policies, respectively. With more wind power injected in the power grid, those high total cost generators produce more power. This counterintuitive situation can be explained that under forcing policy, the system must consume more wind power. The smaller generators will stay off otherwise will pay more because of start-up and shunt-down costs. Comparing to the linear cost of those three oil/steam generators, the start-up and shut-down costs of those smaller generators are much more and that the optional solution will choose to increase the generation of the oil/ steam plants into the grid. The forcing policy. Therefore, the supply will be forced to generate the amount to compensate the constraints.

Fig. 5. Power dispatched with two policies.

Fig. 6. Power dispatched with two policies.

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Fig. 7. Power dispatched with two policies. Based on the results in Fig. 8, the sum of linear and no-load costs with a forcing policy is about $1,100,000. If a certain transmission line is congested under the option policy, there is power generated by wind in it and there is also power generated by other generators. When it is converted to a force policy, this transmission line has to take more power generated by wind. As a result, the other generators may not be committed or have to decrease their output. Under such situation, the generators with large capacities will be chosen to turn on for a longer period of time. Since their linear and no-load costs are rather high, the sum of no-load and linear costs under a force policy is, therefore, higher than the one under an option policy.

Fig. 8. Total linear and no-load cost with option and forcing policy. The sum of total start-up and shut-down cost is higher under option policy. The difference is about $455, which is a small amount. The sum of total linear and no-load cost under two policies has rather enormous differences, $127,167. It supports the point of view mentioned before. The start-up and shut-down costs are restricted under force policy in order to have minimum total cost. The system operators keep the oil/ steam generators always on and generate more to support the load. Essentially, the operation cost with forcing policy is more than the cost with option policy.

V. EMISSIONS ON THE POWER SYSTEMS WITH AND WITHOUT FORCING POLICY

For renewable energy, it is clean and should be helpful for the environment with less emission. The models are made to illustrate that forcing policy can cause more emissions by consuming more wind energy, the carbon dioxide emission is considered into the optimization’s objective. The third model is established with Objective, (1, em), Constraints (2)-(16a), (17)-(20) and Formulation (21). The forth model is made by Objective, (1, em), Constraints (2)-(15), (16b), (17)-(20) and Formulation (21).

The following concepts help develop the emission in the objective. When someone drives the vehicle, starting up the engine, stepping on the accelerator and keeping same speed are associated with the emission; also, the emission cost is same if one gallon of gasoline is consumed.

The total operation cost is obtained after the model associating the emission. It shows the total cost under two polices and the system requires higher cost under force policy

as expected. The total cost increase approximately from $1.25 million to $1.36 million comparing from the option policy model and forcing policy model. Both of the operation cost with two models is more than the ones without considering the emissions. From Fig. 9, the wind generation under option policy is lower than wind generation under forcing policy. In the power system engineering, a smooth wind generation is preferred Power quality needs to be maintained. The customers require the continuity of power services, which is one of the power quality’s concerns.

Fig. 9. Wind power dispatched with two policies.

The wind generation influences the amount the dispatched generator. The oil/steam type generators (Gen 12, 14, 15) are reexamine shown in Fig. 10, 11, 12, respectively. Again, the oil/steam generator produces more under force policy comparing to production under option policy. In intuitive thinking, the generation should be less if all the wind is taken, but this is not the case. The AMPL simply chooses oil/steam type generator to dispatch more rather than turning the generators on and off occasionally. Also, the cheap generators (hydro and coal) has deducted on dispatched amount, making more dispatched quantity on oil/steam type generator.

Fig. 10. Power dispatched with two policies.

Fig. 11. Power dispatched with two policies.

Fig. 12. Power dispatched with two policies. AMPL captures the nuclear unit (Gen 24) which impacts on the total cost with the higher generation under the force policy. The unit’s productions is smooth as the load demand increases after period 7, shown in Fig. 13. The reason of higher nuclear

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production under force policy is similar to the case of oil/steam type generators.

Fig. 13. Power dispatched with two policies. The objective consists of the operating and the emission costs. The operating cost includes linear, no-load, start-up and shutdown costs. In the force policy, the total linear and no-load costs are higher than the option policy, shown in the Fig. 14. However, the total start-up and shutdown cost is lower in force policy, shown in Fig. 15. Since the system chooses to enforce the oil/steam and nuclear generator dispatch in the force policy, the cheap generators do not turn on and off periodically. This is the reason that Fig. 15 shows the counter intuitive results.

Fig. 14. Total linear and no-load cost between two policies.

Fig. 15. Total start-up and shutdown cost between two policies. The carbon dioxide emission is assessed and the total quantity

of linear emission is shown with the forcing policy improves from 8,626 tons to 8,657 tons. The system emits thirty tons more in the force policy. The average emission of Toyota Camry is around 115g/km and, if the Toyota emits same amount of carbon dioxide, it can travel six times around the Earth. The test case of this model only incorporates a zone with 24 bus and 33 generators. If larger scale of zone such as whole U.S. with thousands of generation units is considered, the emission is substantial. As a result, the carbon dioxide emission not only influences the quantity of greenhouse gas, but also impacts global warming. The locational marginal prices (LMPs) are examined in the model with wind injection and emission consideration. From the data obtained from the day-ahead model, the results are appealing. The Fig. 16 shows the LMP for each bus under period 15. With the option policy, the LMPs fluctuate because the system has to capture a different generator at specified node to supply additional increased or decreased MW. Unlike the LMPs in option policy, the LMPs in force policy are the same everywhere in each period. The system shows that a generation unit (not only limited to marginal unit) has the capability to supply all the nodes under this period. Moreover, the test case does not involve the congestion rent, which is the LMP difference between two nodes multiplied by the line flow. The zero congestion rent means the model is lossless, suggesting

that the model simply resembles the economic dispatch that does not consider the transmission loss.

Fig. 16. LMPs comparison of model with emission under two policies

VI. CONCLUSION Taking all the free wind was considered to reduce the cost

and alleviate the emission in the power system in Germany; however, the mandatory policy imposed a huge problem on not only economic perspective but also the environmental aspect. With the two models illustrated in this paper, the total cost and carbon dioxide emission are aggravated under the force policy. The system has the choice to dispatch a certain amount of wind generation under option policy, but the system is mandated to take all the renewable resource under force policy. Unlike the system in option policy, it is restricted to take whatever the wind produced, so the system cannot be better off considering cost and emissions under force policy.

People consider renewable energy as a kind of free and green energy. It cannot be ignored that renewable energy is variable and unpredictable. Consequently, it is advised to weigh both the advantages and disadvantages of utilizing renewable energy.

With the analysis of the project, the idea of relaxation and restriction can be better applied to cases in power market area in the future. An option means a chance to better the result. Two seemingly different choices may actually have a deep relation between each other. One choice can be one part of the other choice. Such insight can even help making decisions outside the power area.

REFERENCES [1] Anonymous (2013, April 8). Benefits of renewable energy use (1st ed.)

[Online]. Available: http://www.ucsusa.org/clean_energy/our-energy-choices/renewable-energy/public-benefits-of-renewable.html#vastenergy

[2] H. Rich. (2013, March 14). Germany's green energy disaster: a cautionary tale for world leaders (1st ed.) [Online]. Available: http://www.forbes.com/sites/realspin/2013/03/14/germanys-green-energy-disaster-a-cautionary-tale-for-world-leaders/

[3] B. C. Ummels, M. Gibescu, E. Pelgrum, W. L. Kling, and A. J. Brand, “Impacts of wind power on thermal generation unit commitment and dispatch,” IEEE Transactions on Energy Conversion, vol. 22, no. 1, Mar. 2007.

[4] Wikipedia [Online]. Available: http://en.wikipedia.org/wiki/Power_system_simulation#Unit_commitment

[5] Power Systems Test Case Archive [Online]. Available: http://www.ee.washington.edu/research/pstca/

[6] A Mathematical Programming Language (AMPL) [Online]. Available: http://www.ampl.com/

[7] F. Wang and K. W. Hedman, “Dynamic reserve zones for day-ahead unit commitment with renewable resources,” IEEE Transactions on Power Systems, submitted for publication.

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