Comparative and Cost Analysis of a Novel Predictive Power Ramp Rate Control Method: A Case Study in a PV Power Plant in Puerto Rico
Comparative and Cost Analysis of a Novel Predictive Power Ramp Rate Control Method: A Case Study...
Patarroyo-Montenegro, Juan F.;Vasquez-Plaza, Jesus D.;Rodriguez-Martinez, Omar F.;Garcia, Yuly V.;Andrade, Fabio
2021-06-22 00:00:00
applied sciences Article Comparative and Cost Analysis of a Novel Predictive Power Ramp Rate Control Method: A Case Study in a PV Power Plant in Puerto Rico Juan F. Patarroyo-Montenegro * , Jesus D. Vasquez-Plaza , Omar F. Rodriguez-Martinez, Yuly V. Garcia and Fabio Andrade Sustainable Energy Center, University of Puerto Rico, Mayagüez Campus, Mayagüez, PR 00680, USA; jesus.vasquez@upr.edu (J.D.V.-P.); omar.rodriguez27@upr.edu (O.F.R.-M.); yuly.garcia@upr.edu (Y.V.G.); fabio.andrade@upr.edu (F.A.) * Correspondence: juan.patarroyo@upr.edu Abstract: One of the most important aspects that need to be addressed to increase solar energy penetration is the power ramp-rate control. In weak grids such as the one found in Puerto Rico, it is important to smooth power fluctuations caused by the intermittence of passing clouds. In this work, a novel power ramp-rate control strategy is proposed. Additionally, a comparison with some of the most common power ramp-rate control methods is performed using a proposed model and real solar radiation data from the Coto Laurel photovoltaic power plant located in Ponce, Puerto Rico. The proposed model was validated using one-year real data from Coto Laurel. The power ramp-rate control methods were compared in real-time simulations using the OP5700 from Opal-RT Technologies considering power ramp rate fluctuations, power ramp-rate violations, fluctuations Citation: Patarroyo-Montenegro, J.F.; in the state-of-charge, among other indicators. Moreover, the proposed power ramp-rate control Vasquez-Plaza, J.D.; strategy, called predictive dynamic smoothing was explained and compared. Results indicate that Rodriguez-Martinez, O.F.; Garcia, the predictive dynamic smoothing produced a considerably reduced Levelized Cost of Storage Y.V.; Andrade, F. Comparative and compared to other power ramp-rate control methods and provided a higher lifetime expectancy for Cost Analysis of a Novel Predictive lithium batteries. Power Ramp Rate Control Method: A Case Study in a PV Power Plant in Puerto Rico. Appl. Sci. 2021, 11, 5766. Keywords: power ramp-rate control; photovoltaic energy; large scale solar power plant; levelized https://doi.org/10.3390/app11135766 cost of storage; battery degradation Academic Editors: Reinaldo Tonkoski and Rodrigo D. Trevizan 1. Introduction Received: 27 May 2021 Electrical energy in Puerto Rico has been mainly produced using imported fossil Accepted: 12 June 2021 fuels. For the fiscal year of 2020, petroleum-based power plants supplied almost half of Published: 22 June 2021 the total power generation, while coal-based power plants supplied 29%, and renewable energies only supplied 2.5% [1]. The Puerto Rico Electric Power Authority (PREPA) is Publisher’s Note: MDPI stays neutral aimed to increase renewable energy penetration to 40% by 2025, 60% by 2040, and 100% with regard to jurisdictional claims in by 2050 [2]. Particularly, solar energy is the renewable resource that has grown the most published maps and institutional affil- with an increase in supply from 0.3% in 2015 to 1.4% in 2020 [3]. Additionally, PREPA is iations. aimed to add up 2740 MW and 1440 MW in solar power and batteries, respectively in the following years [4]. Due to the inherent stochastic nature of solar irradiance, factors such as frequency and voltage stability of a converter-dominated power system (CDPS) may be critically affected on cloudy days. To overcome this issue, some local power authorities Copyright: © 2021 by the authors. across the world have imposed solar power plants to include power ramp-rate control Licensee MDPI, Basel, Switzerland. (PRRC) methods in their power injection controllers. A maximum power ramp-rate (PRR) This article is an open access article of 10% of the contracted power capacity per minute is allowed in Germany, whereas distributed under the terms and Hawaii, Ireland, and Denmark allow a maximum PRR of 2 MW/min, 30 MW/min, and conditions of the Creative Commons 100 kW/s respectively [5,6]. Attribution (CC BY) license (https:// The Puerto Rico Energy Bureau (PREB) has developed a regulation on renewable creativecommons.org/licenses/by/ energy development—including its registration and operation—as a strategy for rebuilding 4.0/). Appl. Sci. 2021, 11, 5766. https://doi.org/10.3390/app11135766 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 5766 2 of 30 and strengthening the electric power system. The Bureau’s purpose is to increase the local renewable energy generation and provide a regulatory framework for the implementation of renewable energy across the island. One of the most important requirements described in the regulation is the Power Purchase and Operation Agreement (PPOA) or electric power contract. The PPOA is a document that establishes an energy contract between two parties (the seller and the buyer). This contract contains mutual obligations between the parts involved in the power sale, that governs the rights and duties of the parties [7]. One of the most important requirements for third-party photovoltaic (PV) power plants stipulated in the PPOA is the Minimum Technical Requirements for the Interconnection of Photovoltaic Installations (MTR). MTRs are mandatory requirements that the third-party seller must meet to help the main grid in case of contingencies. In the MTR, the ramp rate requirement is addressed, which sets the maximum rate of change for the system’s output power. This maximum rate applies for both, increasing and decreasing the output power with a requirement of 10% of the contracted capacity. Additionally, the current MTR requires the third-party systems to be able to deliver 30% of their measured AC capacity for at least 25 min using their storage units [8]. To fulfill PRR requirements and smooth power fluctuations, large-scale solar power plants (LSSPP) rely on the use of energy storage systems (ESS). The state-of-charge (SOC), the output power P , and the depth-of-discharge (DoD) of the ESS through a day may out vary drastically depending on the PRRC method and the type of battery used. These factors directly impact battery degradation and the levelized cost of storage (LCOS) [9–11]. Thus, LSSPP may incur extra costs in ESS depending on the implemented PRRC method. The ramp-saturation (RS) is the simplest PRRC method [12,13]. In this method, the battery absorbs or injects power when the PRR at the point of interconnection (POI) exceeds the requirement. The Simple Moving Average (SMA) and the Exponential Moving Average (EMA) are also two popular methods due to their low computational cost and ease of implementation [14–17]. However, the RS, MA, and EMA methods require a large ESS storage capacity [13,18,19]. This implies an increase in costs and battery degradation. PRRC methods such as the Enhanced Linear Exponential Smoothing (ELES) [20–23], the First Order Low-Pass filter (FOLPF) [24–27], and the second-order LPF (SOLPF) have been proposed to reduce power fluctuations in the ESS [28]. A solution to avoid the use of ESS is the Active Power Curtailment (APC) method [5,29–34]. This method moves the power converter away from the maximum power point to reduce the power injection to the main grid. Though, the APC is only valid for ramp-up events. For this purpose, the Forecasted APC (FAPC) method has been proposed in [35,36]. This method determines whether a ramp-down event will occur and reduces the injected power by moving the power reference away from the maximum power point. However, as both APC and FAPC are aimed to avoid the use of ESS, their reliability is highly affected by the accuracy of the forecast. As each of the aforementioned PRRC methods has its own advantages and disad- vantages, it becomes of high interest to study how they affect the operational costs and how they affect the battery degradation in LSSPP. Furthermore, it becomes important to study the performance of each PRRC method in an LSSPP in Puerto Rico, which is characterized to have a special solar radiation behavior and different grid characteristics compared to other territories. Other studies about the impact of PRRC have been made in Puerto Rico [5,37,38]. However, none of them have tested the impact of different PRRC methods nor their impact on battery degradation. This work is intended to propose a predictive PRRC method and to perform a study comparing the aforementioned PRRC methods applied to a case study in a highly detailed model of the Coto Laurel solar power plant. Coto Laurel is a solar farm in Ponce, a town located on the southern coast of Puerto Rico. The installed capacity is approximately 14 MW DC, distributed in fifteen (15) groups of solar panels, for a total of 55,416 panels. The contracted capacity at the POI is 10 MW. Figure 1 shows the satellite view of the solar farm, each of the fifteen groups is depicted with a white box. Moreover, each group was Appl. Sci. 2021, 11, 5766 3 of 30 labeled with a black capital letter to simplify the discussion. The green boxes depict the location of the inverters and their respective transformers, whereas the orange box shows the farm’s battery bank and its respective inverters and transformers to connect to the main grid. Figure 1. Coto Laurel Satellite View. Photo from Map Data © 2021 Google. The model of the Coto Laurel solar power plant was developed, tested, and validated using an OP5700 simulator from OPAL-RT technologies. Furthermore, the proposed predictive PRRC method was compared against others PRRC methods to analyze the effect in the LCOS and the battery degradation. The rest of this document is organized as follows: In Section 2, a brief overview of the most common PRRC methods is made. Then, in Section 3, the proposed predictive PRRC method is presented. Section 4 presents the methodology for the economic analysis of each of the PRRC methods. Section 5 presents the case study in Coto Laurel, Puerto Rico. Section 6 presents daily and yearly simulation results and a comparative analysis Finally, discussions and highlighting are presented in Section 7. 2. Overview of the Power Ramp-Rate Control Methods A simplified scheme of an LSSPP with PRRC, similar to Coto Laurel, is shown in Figure 2. The DC voltage of each PV array is transformed to AC using three-phase voltage- source converters (VSC). The output of each converter is connected to PREPA using a central feeder transformer. Similarly, the ESS is composed of a set of battery banks. The voltage of each battery bank is transformed to AC using a VSC. The ESS has its own transformer to inject or absorb power from the AC bus. The PRRC block reads the power in the PV feeder and generates a power reference to the VSC so that the power injected into the main grid fulfills the PRR requirements. Appl. Sci. 2021, 11, 5766 4 of 30 Figure 2. Basic scheme of an LSSPP with PRRC. The output power in the ESS feeder can be positive (battery charge) or negative (battery discharge). As seen in Figure 3, when the PRR in the PV violates the requirement, the battery absorbs or injects energy to smooth the output power to the main grid. Figure 3. Battery behavior during PRR events. Depending on the PRRC method, the battery may achieve a higher or lower DoD, and the SOC at the end of the day may vary as well. These two factors drastically affect the battery degradation and the cost associated with ESS for PRRC. Following, a brief description of each of the PRRC methods analyzed in this work is presented. To simplify notation, the power injected into the main grid is represented by: P = P +P . (1) out pv ess Furthermore, the PRR of the power injected by the PV array at time t = kT is repre- sented by: P [k] P [k 1] pv pv PRR [k] = , (2) pv where T is the sampling period. Additionally, the maximum allowed PRR at the POI by regulation is defined as PRR . max Appl. Sci. 2021, 11, 5766 5 of 30 2.1. Ramp Saturation (RS) Method The RS is one of the most common and easy-to-implement PRRC methods. This method evaluates the PRR of the PV and absorbs or delivers energy from the ESS to saturate the PRR at the output. The RS method can be expressed as [12,13]: P [k 1]PRR P [k], when PRR [k] > PRR < pv max pv pv max P [k] = P [k 1]PRR P [k], when PRR [k] <