Capacity Optimization and Control Method of Battery Energy Storage System in AGC

4. Simulation Results

We analytically discuss the optimization of the regulation capacity of generators and ES so that

AGC signals are dispatched appropriately to generators and ES. Under these optimal parameters, we

discuss the control strategy with practical considerations, including amplitude limiting and remaining

energy management. In this section, we make simulation experiences to show the optimization process of

regulation capacity and validate the control strategy.

4.1 Simulation Experiments

AGC orders in simulation come from the real regulation data of PJM from 18th, Dec in 2012 to 18th,

Jan in 2013, as shown in figure 4.9. The arrival interval of AGC orders are 2s. As the data provided by

PJM is normalized, so that we assume the base value of AGC orders is 10MW in simulation.

ES parameters in simulation come from ref [57]. ES discharging efficiency is , while

charging efficiency is , too. The lifetime of energy converting devices is , and the

cycle times of storage battery is . ES power costs are $/MW, and energy costs

are $/MWh.

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The rated power of generators is MW in simulation, and the upper and lower ramping

rate is 10% of the rated power. We take advantage of the method in ref [1] to estimate the extra costs of

generators involving in AGC, i.e. involving in AGC leads to generator operation efficiency decreases,

while decreases in operation efficiency lead to increased costs to generate the same amount of energy,

which is regarded as the extra costs that generators participated in AGC. Frequent operations and variation

of operation points both lead to decreases in generator efficiency, with the cost per MWh by frequent

operations is set to $/MWh. Since the maximum value of AGC power orders is 10% of

generator rated power, thus the operation point of generators is around 90%. Relatively, the decreases of

generator efficiency is not that large, thus the extra cost related to change of operation points is set to

constant of $/MWh. In general case, can be estimated through generator efficiency

curve.

4.2 Capacity Optimization Results

4.2.1 Daily Regulation capacity results: Given α, the computation of daily regulation capacity is the basis

of the whole optimization method. The following is the computation of generators and ES regulation

capacity of 1st, Jan in 2013 according to the AGC orders of PJM of the same day under the method

introduced in 4.3.1.2. Figure 4.10 displays the original AGC orders waveform of that day, and the

filtering results with α equal to 0.996.

Based on the low-frequency portion and high-frequency portion separated from the

original AGC orders, we can furtherly obtain the relationship curves between generator regulation capacity

, ES power capacity and regulation cover rate p1, which is shown in figure 4.11. ES energy

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capacity is influenced by ES power capacity, but the latter is related to p1. Hence, when analyzing the

relationship between and regulation cover rate p2, p1 should be assigned a value first. Figure 4.12

display the relationship between and p2 given p1 is equal to 100%.

It is obvious that from figure 4.11 and 4.12, as p1 and p2 increase, the power capacity and energy

capacity will increase as well. There is a sharp increase in capacity when p1 and p2 are close to 100%. To

eliminate the influence of short-time sharp power and sharp energy, p1 and p2 are set to 95%.

4.2.2 Multi-day Regulation capacity results: Based on regulation data of PJM from 18th, Dec in 2012 to

18th, Jan in 2013, when p1 and p2 are 95%, and filtering coefficient α is 0.996, we get the relationship

curve between regulation capacity and p3 shown in figure 4.13 and 4.14. As we can see from these two

figure, when p3 is close to 100%, as p3 increase, there is a phenomenon of sharp increases in capacity. As

for the same consideration to eliminate the influence of sharp power and sharp energy, we set p3 to be

95%.

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4.2.3 Regulation results under different α: With different value of α, regulation tasks for both generators

and ES are also different, and so is regulation capacity and costs. Figure 4.15 and 4.16 show the

relationships between individual cost, total cost and α for generators and ES respectively.

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For generators, frequent operation daily cost decreases as ramping amount for generators

decreases as α increase. As we assumed that stay constant in the problem field, so the daily cost

caused by changes of operation point is determined by average output power of generators, and

changes are not obvious. The total daily costs is mainly influenced by , so that it decreases as well.

With respect to ES, as α increase, the regulation tasks get heavier, and power capacity needed

increases, leading to increases of daily power costs . Though energy capacity increases as well, but it

is smaller after smoothing, so that get smaller. ES daily average total costs is mainly influenced

by , thus it increased with .

The optimal α solved using generic algorithm is 0.9941, and correspondingly, , , ,

are 495.2$, 424.8$, 684.9$ and 502.2$, and regulation capacity is 9.40MW, ES power and energy capacity

are 6.25MW and 8.15MWh, respectively.

4.3 Secondary Frequency Regulation Control Results

In the following simulations, α take the value of 0.9941, is 9.40MW, and and are

6.25MW, 8.15MWh, respectively. What’s more, and are 95% and 5% of , respectively.

The initial remaining energy is set to be 50% of . AGC orders of 1st, Jan in 2013 are used.

4.3.1 Comparison between AGC orders and real output: he difference between AGC orders and real

output power reflects the response capacity of generators to AGC, with small differences indicate strong

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response capacity. Figure 4.17 shows the difference between AGC orders and real output powers in

remaining energy management of ES under cases with and without ES.

4.3.2 Comparison of generators ramping: The involvement of ES, not only improve the response

capacity of system to AGC orders, but also relief the ramping burdens of generators as ES share portion of

the AGC orders. Figure 4.18 display the variations of generators ramping power under conditions with and

without ES. Without ES, generators frequently execute ramping operations to adjust output power. The

ramping power are greatly reduced with ES.

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4.3.3 Remaining energy management effects: ES can keep continuous regulation capacity when the

remaining energy stay in a reasonable interval. Once the remaining energy reaches around the extreme

point, ES loses its regulation capacity. Figure 4.19 the remaining energy variation under conditions with

and without remaining energy management given the initial remaining energy is 50%. Figure 4.19 shows

the differences between AGC orders and real output power under these two conditions.

From figure 4.19, without remaining energy management, the remaining energy will decreases to

10% at the end of the day. While after remaining energy management, the energy is controlled in a

reasonable interval of 30% to 70%. At the same time, the difference between AGC orders and output

power are 316.8MW and 29.7MW before and after management, respectively. Total generators ramping

power are 547.0MW and 573.4MW. Remaining energy management make it convenient for ES track AGC

orders, which is shown in figure 4.20.

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5. Conclusion

Our paper study the coordination of ES in AGC with generators. The quick response of ES makes it

suitable for frequency regulations. Through simulation experiences, we show that with ES, the differences

between AGC signals and real output power is significantly reduced, which indicates that the reliability of

the power grid has been greatly improved through the utilization of energy storage. Similarly, the ramping

power of generators experience a reduction with the energy storage in the regulation system, which is the

consequence of ES burdening a portion of regulation tasks, especially fast load fluctuations.

The hybrid regulation system with both generators and ES can take advantages of the benefits of

both the devices. The dispatch of regulation tasks between these two participators are optimized in form of

the filtering coefficient and the sharp increases phenomenon when the cover rate close to 100% is noticed.

The optimization method is put into practice in simulation experience with real data.

When analyzing the control strategy, we introduced practical constraints that are of great importance

in the power grid practices, such as amplitude limiting and remaining energy management of ES. The

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simulation results indicate that the remaining energy of ES avoid huge variations when the remaining

energy management is considered.

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