Algorithm to Control Power Production from Solar Panels Kumar Saurav, Sambaran Bandyopadhyay, Pratyush Kumar, Vijay Arya IBM Reseach {kr.saurav.010, sambband, pratyush.kumar, vijay.arya} @in.ibm.com
ABSTRACT
ally zero maintenance costs. However, despite its simplicity it has its own sets of challenges. Power generation from solar follows the daily irradiance profile which results in a mismatch of demand and supply of energy. A lot of surplus power generation during middle of the day when the demand may not be that high and little energy is produced in the mornings and evenings when the demand is highest. Further, no power is produced during the night. Consequently, this may result in back flow of the power from prosumer towards the conventional production source. Our power transmission grids may not be compatible for this reverse power flow and may results in over voltage and instability. The problem of mismatch between demand and supply from solar energy is well known in literature. Several different methods have been proposed to mitigate this mismatch and also to address the issue of variability in generation. One commonly proposed approach is to store the excess energy in battery banks [3], [4] and [5]. While batteries will definitely reduce the wastage of energy, they are extremely expensive solution especially for multi-mega-watt installations. Further, batteries have a running cost and need to be replaced every few years. Also, we still need to account for the cases when the batteries are fully charged. In that case, the problem is only partially solved. Another problem in integration of large scale solar into the power grid is that it may led to back flow of power. To address this, authors have proposed to cutoff some prosumers from the grid [6] and [7]. This approach will ensure that an upper limit on the renewable intake is always met. However, this may result in reduced intake of renewable energy. Additionally, this approach is highly unfair to prosumers who have been barred from feeding in. Another approach to solve the problem of over production during the peak hours is to somehow reduce the power production/feed-in across the prosumers, i.e., to curtail all the prosumers equally. This will ensure that upper limit on renewable intake is never violated and it will not suffer from the fairness problem since every one will get an equal chance to produce power. In this paper, we propose an algorithm which controls the power output from solar panels. When demand is less than the maximum production capacity of the panels, the algorithm curtails the output to match the demand. And when demand is more, the algorithm does a best effort to produce maximum possible power.
With the cost of renewable energy sources like photo-voltaic solar progressively declining, it is expected that contribution from these sources in overall power generation will increase. However, these newer sources have their own set of drawbacks. First and foremost is the variability in generation, then, controlling the power output from solar is also not trivial. Furthermore, there is a mismatch between the demand profile from the consumer and the supply profile of renewable generation. This increased penetration of renewable sources may lead to grid related problems such as over voltage, power back flow and instability. In this paper we propose a curtailment based solution to ameliorate the problem of over production. We provide an algorithm which controls the power output from solar panels. When demand is less than the maximum production capacity of the panels, the algorithm curtails the output to match the demand. And when demand is more, the algorithm does a best effort to produce maximum possible power.
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INTRODUCTION & RELATED WORK
Cost of energy production from renewable sources such as solar and wind is progressively declining. This declining prices and strong encouragement from the government in the form of investments and subsidies has resulted in aggressive increase in the overall deployment of these new energy sources. Many countries have set ambitious target for their share of energy coming from renewable sources. For instance, Indian government targets to get 175 GW from renewables by 2022 as compared to 46GW in 2016 which approximately 300% increase in just 6 years [1]. On the similar lines, Germany wants 60% of total energy and 80% of electrical energy from renewable sources by 2050 [2]. A major fraction of the share of renewable energy will be generated from solar photo-voltaic (pv). Solar pv is quite simple and does not have any moving parts. It presents an attractive market since after installation, there is virtuPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
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e-Energy ’17, May 16–19, 2017, Shatin, Hong Kong
PROPOSED ALGORITHM & RESULTS
A charge controller is required to operate the solar panels at the optimal operating point which is typically the
c 2016 ACM. ISBN 978-1-4503-5036-5/17/05. . . $15.00
DOI: http://dx.doi.org/10.1145/3077839.3081672
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Figure 1: Left: I–V and P–V characteristics of a solar cell Center: Comparing the power output from both algorithm under clear sky irradiance Right: Comparing the power output from both algorithm under non clear sky irradiance start
voltage where the maxima occurs in power-voltage curve of the pv panels. The charge controller is programmed with maximum-power-point-tracking (mppt) algorithms to ensure that the panels are always operating near its peak power production capability. However, this setup leads to power production which is significantly higher than the actual demand around afternoon. To address this issue we are proposing to replace the existing mppt algorithm with a new algorithm which has the capability to control the power output from solar panels. The proposed algorithm has several advantages as compared to the existing literature described in section 1. Firstly, since we are proposing a software based solution, there is no hardware requirements. Existing charge controllers already have the mppt implemented in them and we just need a firmware update to re-program the charge controller to implement the proposed algorithm. Secondly, the closed loop feedback control is generally robust to disturbances such as changing solar irradiance or cloud cover. Figure 1(Left) shows the I-V and P-V characteristics of a typical solar panel. We divide this into two region, a) Power control mode and b) mppt mode. In power control region we implement a closed loop feedback control system to track the desired power and in mppt mode we do a best effort to produce maximum possible power. We are using a simple proportional-integral (PI) controller for the power control mode and hill climbing for mppt mode. The choice of mode of operation is decided by the desired input power. This algorithm is summarized briefly in Figure 2. We now compare the simulation results obtained from our proposed algorithm against a traditional mppt implementation. First, we take simple clear sky irradiance for our simulation. In Figure 1(Center ), the black curve shows the power output from a typical mppt algorithm and the colored lines are the power output for different desired outputs. For the case where desired power is more than the production capacity of solar panels, the new proposed algorithm delivers the same power as the mppt thus doing a best effort to deliver the required power demand. We can observe this best effort during morning and during evening hours. Further, in the middle of the day, when panels can produce more than the desired value, the algorithm curtails the output to the desired value. Next, we added a high frequency noise and a low frequency disturbance over the clear sky irradiance to simulate a more realistic solar profile. For this case also, when available power is more than desired, the algorithm keeps the power output near the desired value and does a best effort in other case as illustrated in Figure 1(Right).
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Figure 2: The proposed control algorithm around the world have lead1 to increased share of solar pv in the energy market. Solar pv is a lucrative energy sources however it have its own limitations of variability and over production during peak hours. In this paper, we have tried to address the problem of overproduction in solar pv by proposing a new algorithm to replace the existing mppt controllers. Our simulation results shows that the algorithm sets the voltage of the solar panels such that the power output matches the demand. For the times when the demand is more than the solar panel can generate, the algorithm does a best effort to produce maximum possible power. In future, we plan to integrate this algorithm into a small scale grid to observe the response of the grid in presence of our power control algorithm in contrast to a normal mppt controller. Next, we will physically implement this into a solar charge controller and see the differences between the proposed algorithm and mppt controller in a real world implementation.
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REFERENCES
[1] MNRE, India. (2015, Mar) Tentative state wise breakup of renewable power target. mnre.gov.in/mission-and-vision-2/achievements/. [2] The Federal Ministry for Economic Affairs and Energy Berlin. (2015, Nov) The energy of the future: Fourth energy transition monitoring report. [3] Wang, Guishi et. al., “Power smoothing of large solar pv plant using hybrid energy storage,” IEEE Transactions on Sustainable Energy, 2014. [4] CV Nayar et.al, “A grid-interactive photovoltaic uninterruptible power supply system using battery storage and a back up diesel generator,” IEEE Transactions on Energy Conversion, vol. 15, 2000. [5] Cody A Hill et. al, “Battery energy storage for enabling integration of distributed solar power generation,” IEEE Transactions on smart grid, vol. 3, 2012. [6] S. Bandyopadhyay et. al, “Planning curtailment of renewable generation in power grids.” in ICAPS, 2016. [7] S Rongali et. al, “iplug: Decentralised dispatch of distributed generation,” in 8th International Conference on Communication Systems and Networks, 2016.
CONCLUSIONS AND FUTURE WORK
Declining costs and aggressive targets set by governments
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