Utilizing Spinning Reserves as Energy Storage for Renewable Energy ...

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Utilizing Spinning Reserves as Energy Storage for Renewable Energy Integration Muhammad Usman Usama, Student Member, IEEE, David Kelle, Student Member, IEEE, and Thomas Baldwin, Fellow, IEEE

Abstract—Energy Storage Systems (ESS) show much promise for mitigating the dynamics introduced by nondispatchable variable generation. By taking advantage of spinning reserves as a form of flywheel energy storage, the fossil fuel power plant becomes a form of ESS. The spare power capacity in the generator and prime mover represent the throughput of the ESS and the stockpile of fuel defines the energy capacity. The ESS discharging and charging behavior can be seen as increasing and decreasing the fuel pile usage. Similar forms of energy storage have been recently implemented using gas turbines with fast dynamic responses, which are necessary for renewable generation. Criticism of fossil fuel generation focuses on high operational costs of thermal power plants. However, the cost of operating a thermal power plant compares favorably to the cost of implementing other forms of ESS such as battery storage. Utilizing thermal plants in this way allows for reliable increase of renewable penetration, and reduces capital and operating cost. This paper presents analysis tools necessary to properly evaluate the economics of energy storage provided by thermal plants. These tools also balance the available energy storage against spinning reserve requirements for system reliability. Index Terms—Energy storage, wind energy generation, power system reliability, power system economics

I. Introduction Variability and uncertainty are inherent to many forms of renewable generation technologies. As dependence upon variable power generation increases in the grid, the unpredictability of meeting demand also increases. This poses several challenges such as reliability, cost effectiveness of generation and power fluctuations which can lead to over and under voltages, off-nominal frequency operations and possible unstable grid conditions. Grid-scale energy storage systems (ESS) have the potential to manipulate this unpredictable generation and provide controllable power from an otherwise uncontrollable source. Modeling the interaction between different generating technologies and the utility grid is an underlying factor for understanding performance and optimizing functionality. Energy storage systems permit a temporal decoupling of generation output, network power flow and load demand. Prior work in [1] discusses using ESS for variable generation on both the generation and load side of the transmission line in order to better utilize the transmission line capacity and reduce the need for additional transmission lines due to peak loading. ESS placed on the generation side of the transmission system stabilizes variable power production and maximizes the transmission line capacities. ESS on the load side of the utility system smooths the power flow required to meet the demand profile. The energy capacity size is dependent on several key factors including percentages of penetration from all types of generation, transmission power capacities, demand, etc. The ESS size is proportional to variable generation dependence.

Batteries are a popular form of proposed energy storage and can be charged during low demand intervals and excess generation, and discharged for high demand and low generation intervals. This charging and discharging behavior is shown in Table I. The primary barrier of large scale implementation of battery ESS is their high cost of storage compared to fossil fuel. Batteries also require installation of additional infrastructure. TABLE I: ESS charging and discharging behavior during high and low generation and load situations. Gen High High Low Low

Load High Low High Low

Gen ESS Charge Charge Discharge Discharge

Load ESS Discharge Charge Discharge Charge

Gas turbines and some coal-fired plants have been recently utilized as an alternative to traditional energy storage. Often these plants have been operated at low power levels which allow the spinning reserves to respond to drops in renewable generation. These thermal plants are often selected due to their fast dynamic responses. The cost of operating a thermal power plant compares favorably to the cost of implementing other forms of ESS such as battery storage. Utilizing thermal plants in this way allows for reliable increase of renewable penetration, and reduces capital and operating cost. Another concept discussed in [1] is the reclassification of generators according to dynamic performance. These categories include base, dispatchable and variable generation. Ptotal = Pbase + Pdispatchable + Pvariable

(1)

Base generation includes nuclear and large fossil fuel power plants, which are optimized for constant power delivery. Classical intermittent and peaking generators fall under the classification of dispatchable generation which is comprised of gas turbines, small fossil fuels and hydroelectric power plants due the their quick response times and load following ability. These three categories of generation technologies sum to the total generated power. A. Common Methods for Determining Spinning Reserves Failure of a generating unit can have severe impacts on a system when operating near its peak capacity. Spinning reserves allow the system to continually meet demand during loss of generator scenarios. Spinning reserve is defined as the unused capacity of connected and synchronized generation, which can be instantaneously utilized in response to a disturbance or activated on decision of the system operator [2]. The traditional method for determining the spinning reserve T amount PSR for a given demand forecast d and time t is to set

c 978-1-4799-3960-2/14/$31.00 2014 IEEE

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it equal to the capacity of the largest online generator: T PSR ≥ max(uti Pimax )

A. The Fuel Pile as Energy Storage (2)

where uti is unit commitment for generation unit i at time t, and Pimax is the capacity of generating unit i. This method ensures that no load has to be reduced if any single generator becomes unavailable. It does not account for the probability of outages or the cost of providing spinning reserve. Several alternate methods have been proposed that address issues with the traditional method. In [3], Black and Strbac discuss the following lower bound for spinning reserve: T PSR ≥ 3.5σdt

(3)

where σdt is the standard deviation of the net demand forecast. Ortega-Vazquez and Kirschen [4] develop a stochastic optimization approach for calculating the amount of spinning reserve for systems with wind power integration. Spinning reserve requirements implemented by the industry are reviewed in [2]. Standards in the state of California divide generation resources into several categories in order to calculate the spinning reserve needs. T PSR

max (0.05 · PH + 0.07 · PO , PLC ) = + PN F I 2

(4)

where PH PO PLC PN F I

The scheduled generation from hydroelectric sources The scheduled generation from non-hydroelectric sources The value of the power imbalance due to the largest contingency The total of all the interruptible (non-firm) imports

Alternatively, the European Network of Transmission Systems (UCTE) specifies a method by incorporating load: T PSR =

p

10LM Z + 1502 − 150

(5)

where LM Z is the maximum load of the UCTE control area during a given period. This paper presents a method to optimize renewable capacity by utilizing spinning reserve in traditional power plants as a form of generation side energy storage. A general calculation approach is introduced which develops the requirements of spinning reserves as bounds on generator scheduled operating point. The method is analyzed in several scenarios of wind penetration, and answers the question of how much spinning reserve is necessary for integrating variable renewable generation.

II. Spinning Reserves as Generation Side Energy Storage Before developing requirements for spinning reserves, it is helpful to motivate the perspective of spinning reserves as a form of energy storage that exhibits charging and discharging behavior. This is best illustrated by describing the energy content of the fuel pile in terms of the rate of fuel consumption. From this viewpoint it is possible to state conditions that should be satisfied by an allocated amount of spinning reserve and discuss potential benefits of utilizing spinning reserves as energy storage.

The fuel pile of a fossil fuel power plant contains a significant amount of stored energy. Compared to other storage forms, chemical bonds generally provide a safe, stable and economical configuration of energy storage. The fuel stockpile emulates the charging and discharging functions seen in other storage forms. Compared to other systems, this moves the energy storage to the front end of the firebox instead of within the electrical grid. This concept can be illustrated by briefly considering a simplified expression of a generator’s time varying rate of fuel mass consumption r(t) = m(t). ˙ By decomposing this rate into two components, a demand based component r¯(t) and a time-varying perturbation component δr(t), the utilization rate becomes: r(t) = r¯(t) + δr(t)

(6)

A small deviation in the rate of fuel use from that demanded by the load creates or uses surplus fuel in the pile. As a negative δr(t) represents an increase in fuel surplus, the surplus fuel mass at time τ is given by

Zτ msurplus (τ ) = −

δr(t)dt.

(7)

0

Considering these perturbations about the scheduled rate of fuel utilization, the changes in the stored energy in the fuel pile behave equivalently to charging and discharging behavior in an energy storage system with more typical forms of storage. B. Guidelines for Spinning Reserve Amounts It is now possible to formulate guidelines for calculating the spinning reserve amounts using the perspective of generation side storage. The two components of spinning reserve must satisfy the following conditions: 1) Spinning reserve must be sufficient to handle demand during worst case generation loss. This amount of spinning T . reserve is denoted PSR 2) Spinning reserve must be sized appropriately to allow charging and discharging behavior to serves as generation ESS . side storage. This amount is denoted PSR These component conditions will be treated separately in this section and combined into a total amount of spinning reserve T in the next section. Setting PSR equal to the size of the largest online generator automatically satisfies the first condition. For simplicity and without loss of generality, only wind energy is considered in this paper as the renewable generation source. Similar to Eq. 6, the total generated power can be decomposed into a demand-based p¯(t) and perturbation δp(t) components. Let the demand based power component be p¯(t) = Pdemand (t) − Pˆwind (t)

(8)

where Pˆwind is the expected or forecast wind power and Pdemand (t) is the power demand of the load. The perturbation component will be allowed to vary according to the actual production of the variable sources, Pwind : δp(t) = Pdemand (t) − Pwind (t).

(9)

By specifying that p¯(t) follow the load and allowing the perturbation component to vary according to the production of the variable sources, production will always be sufficient to meet demand as long as there is sufficient spinning reserve

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available. The spinning reserve size requirement for energy storage system (ESS) operation is chosen to be the maximum possible value of the perturbation parameter.

Max Max

Max Min SR

TSR

ESS PSR = max(δp(t))

(10)

Max − TSR

−

SOP + ESS

Max − TSR

SOP

C. Benefits and Considerations

+

Since shutting off a generator is equivalent to charging the fuel pile, generator shedding may reduce the total operating cost of the generation system. Natural gas and diesel generators are good choices for this shedding operation due to their short turn-on times. It may also be possible to reduce the utilization of base generation. Due to high capital cost and virtually zero operating cost, it is economically desirable to operate wind and solar farms at their maximum capacity. This means that in planning system operations renewable generation sources will not be the source of spinning reserves. Because of this outcome, the needed amount of spinning reserves as calculated from traditional T methods (PSR ) will be distributed entirely among the fossil fuel generators. III. Determining the Spinning Reserve Requirement Spinning reserve can simultaneously address both the reliability concerns due to loss of generation as well as the unpredictable dynamics due to forecasting errors for renewables. Traditional spinning reserve allocation methods mitigate the impact of low probability occurrences such as loss of a generator or a power importing tie-line. However, system dynamics introduced by variability of renewables represent high probability events and require a different approach. In order for the spinning reserve to effectively function as energy storage for variable sources, a combined approach must be develop to take into account both high and low probability events.

SOP − ESS

Max SR (−)

SOP + ESS SOP

(+) Min

Min

SOP − ESS

(a) At the maximum (b) At the minimum allowable SOP allowable SOP

Min

(c) Maximum and minimum SR

Fig. 1: The (−) arrow indicates equivalent discharging behavior viewing the fuel pile as energy storage, while the (+) arrow T indicates equivalent charging. The label TSR represents PSR ESS and ESS represents PSR .

Version of MATLAB AOP is expected toStudent vary due to changingStudent wind and solar Student Version of M Version of MATLAB availability and is in the interval:

ESS ESS (SOP − PSR , SOP + PSR ).

(14)

The scheduled operating point takes on a maximum value when ESS T SOP + PSR = M AX − PSR .

(15)

The minimum of SOP occurs when ESS SOP − PSR = M IN.

(16)

This yields the acceptable range of SOP as:

A. Definitions First it is necessary to specify several definitions: The wind power prediction error, δ Pˆwind , is defined as δ Pˆwind = Pwind − Pˆwind

(11)

The scheduled operating power SOP is the sum of the scheduled operating powers of the generators in the system and is set equal to the demand based power component, p¯. SOP = Pdemand − Pˆwind

(12)

The actual operating power AOP is the sum of actual power levels of the generators due to the discrepancy between predicted and actual wind power production. AOP = SOP − δ Pˆwind

(13)

The maximum operating power M AX is the maximum possible generation level the system is capable of producing and the minimum power M IN is the minimum total power level required to keep generators operating. B. Constraints on Scheduled Operating Power This section presents a method to calculate the constraints on SOP from an energy storage viewpoint. This range does not depend on parameters of fossil fuel generators. For a given scheduled operating point SOP , the actual operating point

ESS T ESS M IN + PSR ≤ SOP ≤ M AX − PSR − PSR .

(17)

And the range of acceptable SR is given by T ESS ESS PSR + PSR ≤ PSR ≤ M AX − M IN − PSR .

(18)

IV. Examples The analytical approach above is applied to two examples ESS T showing that the PSR and PSR amounts are sufficient for mitigating dynamics introduced by wind prediction errors as well as handling a loss of generator event, respectively. The first scenario investigates the system’s ability to meet demand with the wind prediction errors approximated from the 3-day hourly average and does not consider the reliability case of the loss of a generator. Each case is analyzed for three renewable generation penetration levels: 15%, 20% and 30%. The second scenario analyzes the generation system’s reliability during a loss of a generator event while assuming zero wind prediction error. For these examples, the wind power data is provided from the DOE-NREL Western Wind Dataset [5], [6]. From sampled dataset sequences, resources for creating the wind power prediction and actual wind power values are obtained. The wind power prediction is obtained by calculating the hourly mean value over three days from January 01-03, 2006. For each of

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Effect of Renewable Penetration 20% with Total Generation = 200 MW

Effect of Renewable Penetration 15% with Total Generation = 260 MW 180

240 MaxOp 220 200

SOP AOP ESS Bounds

140 Power (MW)

180 Power (MW)

MaxOp

SOP AOP ESS Bounds

160

160 140

120 100

120 80

100 80

60 MIN

60 40 0

MIN 40 0

5

10 15 Time (Hours)

20

5

25

Fig. 2: Effects of wind prediction error: 15% penetration. Peak demand is 202 MW.

10 15 Time (Hours)

20

25

Fig. 3: Effects of wind prediction error: 20% penetration. Peak demand is 146 MW Effect of Renewable Penetration 30% with Total Generation = 170 MW 140

the example cases, the size of the wind farm remains fixed at 30 MW, while the load demand and thermal power plants sizes are varied to fit the penetration levels.

ESS is chosen to equal the installed wind The value of PSR capacity and is used to determine the curves ESS(−) and ESS(+), which bound the upper and lower possible values of SOP. Several criteria are used to evaluate each system.

1) The upper limit (MaxOp) should never be crossed by the upper bounding curve ESS(−). 2) It is not desirable for the lower bounding curve, ESS(+), to cross the lower limit (MIN).

100 Power (MW)

A. Wind Prediction Error

MaxOp

SOP AOP ESS Bounds

120

80 60 40 MIN 20 0 0

5

10 15 Time (Hours)

20

25

Fig. 4: Effects of wind prediction error: 30% penetration. Peak demand is 101 MW.

Three Scenarios: i. If ESS bounds fit within maximum operating point (M ax− SRT ) and minimum operating point (M IN ) then the system meets the criteria. ii. If lower bound ESS(+) crosses the minimum operating point M IN , but the upper bound ESS(−) is still always below the maximum operating point (M ax − SRT ), then the system will be suitable but may require spillage. iii. If the upper bound ESS(−) crosses the maximum operating point regardless of the lower bound ESS(+) behavior, the system will not be able to meet demand during generation loss scenarios. The system behavior is evaluated for the three penetration levels shown in Figures 2, 3, and 4. The ESS bounds represent the worst case behavior of the system. If predicted output wind power is 30MW and the actual output from wind turbine is 0MW, the AOP will touch the ESS(−) bound. Likewise, if predicted output wind power is 0MW and the actual output from wind turbine is 30MW, the AOP will touch the ESS(+) bound. Hence in the worst cases we are not exceeding the ESS bounds. All three cases meet the first criterion. However in Fig. 4, the ESS(+) bound crosses the minimum operating point and does not meet the second criterion. Therefore, the 15% and 20% cases are an example of scenario (i) and the 30% penetration case is an example of scenario (ii). If wind spillage is not acceptable, it would not be desirable to exceed 20% wind penetration for the chosen example system.

B. Loss of a Generator For this case, peak demand is set at 1000MW in a system with five thermal generators and a wind farm. If the generator with largest generation (G1) fails, then the load will be shared on the remaining four generators. The effect on each generator’s loading is shown in Table II. The % net load is the percent of the remaining power system load minus the wind farm which is met by the given generator during normal operating conditions. The % Failure Load is the percent of the net system power met by the generator during the loss of generation unit 1. TABLE II: Generator sizes and percent of total load. Gen. # G1 G2 G3 G4 G5 WF

Size (MW) 300 200 200 150 150 30

% Normal Load 30% 20% 20% 15% 15% –

% Failure Load 0% 28.57% 28.57% 21.43% 21.43% –

Figure 5 shows that the system can handle the loss of the largest generator and still operate below maximum. Only the effect of the biggest generator loss is plotted as the other generators will have similar effect. In this case, AOP is equal to SOP because we are assuming no error in wind prediction. The figure shows that before the failure of G1, the system behaves

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Effects of Generator Loss on Reliability MAX

200 180

The analysis and results show that the modeled integrated system can work appropriately to handle the cases of 100% prediction error in forecasting and the failure of the largest generation unit, or both situations simultaneously.

160

Power (MW)

140

MaxOp

120 100 80 60 MIN 40

AOP (loss) SOP (normal)

20 0 0

5

10 15 Time (Hours)

20

Fig. 5: Generator 1 goes offline at 12 hours. The actual operating power of generator 2 utilizes spinning reserve and remains below capacity.

normally. After 12 hours, when the G1 turned off, the AOP T but it is still goes above the max SOP in the region of PSR below the maximum capacity of the generator. V. Interpretations and Follow Up Work Because of the construction of the bounds on PSR in Eq. 18, the system is able to handle maximum wind power power prediction error and a loss of a generator event simultaneously. If a wind or solar farm provides variable generation below the maximum rated capacity, this is referred to as spillage as opposed to spinning reserves. Nevertheless, spinning reserve for variable generation can be treated as a form of reliability insurance, and the variable generation providers could be compensated for this spinning reserve ancillary service. Methods for estimating this actuarial cost of reliability insurance is currently lacking and is an opportunity for further work. It may be possible to price the spinning reserve ancillary service for reliability independently from the spinning reserve service for energy storage. From this separate pricing it could be determined whether the service should be provided by fossil fuel plants or renewable plants according to the specific plant economics. This method will be incorporated into a more detailed method taking into account timing and generator time constants and modifying the unit commitment formula. The challenge is that there will always be statistical uncertainty. VI. Conclusion T The traditional calculation method of spinning reserve, PSR , is augmented to account for the mitigation of variable source dynamics resulting from variable power prediction errors. This method calculates the amount of spinning reserve from the size of the variable generation source and the size of the largest generator in the system. This method was shown to satisfy both the requirements of dynamics mitigation and reliability assurance. Future work includes classifying variable spinning reserve as an ancillary service provided by the wind farm in addition to power delivery. It may be beneficial if the wind farm is compensated for this service. It may be fruitful to pose this problem using insurance premium methods to price the variable spinning reserve provided by wind facilities.

References [1] M. West and T. Baldwin, “Energy storage and supergrid integration,” North American Power Symposium (NAPS), 2013, pp. 1–6, 2013. [2] Y. Rebours and D. Kirschen, “What is spinning reserve,” The University of Manchester, 2005. [3] M. Black and G. Strbac, “Value of Bulk Energy Storage for Managing Wind Power Fluctuations,” IEEE Transactions on Energy Conversion, vol. 22, no. 1, pp. 197–205. [4] M. A. Ortega-Vazquez and D. S. Kirschen, “Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 114–124. [5] National Renewable Energy Laboratory. Western Wind Dataset. [Online]. Available: http://www.nrel.gov/electricity/ transmission/western wind methodology.html [6] 3TIER, “Development of Regional Wind Resource and Wind Plant Output Datasets,” National Renewable Energy Laboratory, Tech. Rep. NREL/SR-550-47676, Mar. 2010.