Solar Power Capacity Value Evaluation- A Review

Transcription

1 Solar Power Capacity Value Evaluation- A Review Sarah Awara saawara@ucalgary.ca Hamidreza Zareipour hzareipo@ucalgary.ca Andy Knight aknigh@ucalgary.ca Abstract With the increase in renewable generation in power systems, it is critical to accurately determine the capacity value of renewables during generation planning to maintain system reliability. This review paper is intended for readers who are seeking to understand the basics of reliability evaluation and gives an insight on the factors that affect the capacity value of solar resources. The capacity value estimation methods are discussed briefly, followed by a discussion of the factors that may affect the capacity value of solar resources. The methodology explained here is applicable to any renewable generation. However, the focus of the discussion is on the factors that affect the capacity value of solar resources. Also, the impact of input data on the solar capacity value is included. Potential future work is also included. Keywords solar, capacity value, capacity credit, reliability, renewables. I. INTRODUCTION Given increasing environmental concerns, electric power systems around the world are integrating more renewable generation and trying to shift away from conventional generation. However, unlike conventional generators, renewables are not dispatchable. This introduces challenges during generation planning as renewable resources are intermittent and unpredictable due to their dependence on weather characteristics. The reliability of supply is a major concern in electric power systems. This reliability will increase when more renewables are added to the current electric power system. However, when renewables replace dispatchable generators, there is a higher concern about the reliability of the system [1]. Higher amounts of reserve capacity are needed to maintain the system s reliability. Therefore, it is important to determine the CV of generation resources to ensure that the system reliability is met during power generation planning. The capacity value (CV) of any generator is the contribution that a given generator makes to overall system adequacy [2]. CV is also referred to as capacity credit in some of the literature. Based on the standard reliability-based method used for determining the CV of any generation unit, the CV of a generator is the additional amount of load that can be added to the system with the added generator served at the target reliability level [3]. However, the CV of generator resources depends on the technology of the resource. In other words, the risk model characteristics used to calculate the CV of solar is different than the risk model characteristics used to calculate the We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), CRDPJ (Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG), CRDPJ ). This project was funded in part by Alberta Innovates, which supports and accelerates research, innovation and entrepreneurship. This project is also supported by Rocky Mountain Power. CV of wind. Based on the literature, the CV of a renewable facility can have a wide range between 5% and 95% of the nameplate capacity and some of the factors that contribute to this range are related to geography, penetration levels of the technology and the correlation of generation and demand [3]. The methodology to determine the CV of a unit when added to a system is illustrated in Figure 1. However, even when calculating the CV of a specific technology, the literature includes several different risk models with different assumptions. For example, certain risk models developed to calculate the CV of solar consider the system component failures of the PV system in the generation model while other models do not. Hence, there is inconsistency in the CV methodology for intermittent generation units. Multiple review papers have discussed the CV of renewables. To list a few, Pudaruth and Li discuss previous work on reliability models and relates it to capacity credit evaluation [4]. Dent et al. provide a review of the work done by the IEEE PES task force on CV of solar in [5]. Keane et al. provide the best practices for wind CV determination in [2]. Dent et al. provide a critical review of simplified methods for renewable generation capacity credit calculation in [6]. This review paper will focus on the factors that affect the CV of solar resources. Fig. 1. Capacity Credit Determination Methodology This review paper focuses on the factors accounted for in the risk model to analyze their impact on the solar resources CV based on the case studies in the literature. Section II presents a short background on the reliability evaluation of power systems. Section III presents the CV estimation methods. Section IV presents the literature that studies the CV for solar resources followed by the conclusion. This review paper is intended for readers who are seeking to understand the basics

2 of reliability evaluation and gives an insight on the factors that affect the CV of solar resources. II. RELIABILITY EVALUATION OF POWER SYSTEMS A. Adequacy vs Security Assessments Power system reliability assessments are based on the concepts of adequacy and security. Adequacy is the existence of sufficient facilities within the system to satisfy the consumer demand but does not consider the ability of the system to respond to disturbances [7]. Security is the ability of the system to respond to disturbances arising within that system [7]. This paper focuses on the adequacy assessment for generation capacity planning. The question that should be answered in this assessment is how much generation capacity is required to ensure an adequate power system. During generation planning both the static and operating capacity requirements are evaluated. The major difference between the static and operating capacity is the evaluation time interval. The static capacity requirement is a long-term evaluation of the system s requirement that considers the installed capacity that must be planned and constructed in advance of the system requirements [8]. The operating capacity requirement is a short-term evaluation of the actual capacity required to meet a given load level [8]. B. Developing the Risk Model As illustrated in Figure 1, the generation model is convolved with the load model to develop the risk model. The reliability indices Loss of Load Expectation (LOLE) and the Expected Energy Not Served (EENS) are derived from the risk model. The LOLE gives the duration (number of hours or days per year) in which the demand is not met during a specific time interval. The EENS (MWh/year) expresses the energy not met by generation in a specific time interval. The standard LOLE used in North America is 0.1 days/year [9]. There are two approaches to calculate the reliability indices: analytical and simulation. The analytical approach uses a mathematical model and solves the problem using direct numerical solutions by simplifying the model to reduce the computation [7]. The simulation approach can be computed either randomly or sequentially depending on whether the history of the system impacts the behaviour of the system. The random approach chooses the time intervals in a random manner, then examines them. This approach is used when the history of the system does not impact the behaviour of the system [8]. The sequential approach analyzes the time intervals in a chronological manner. This approach is used when the history of the system impacts the behaviour of the system [8]. C. Generation System Model The unit s forced outage rate (FOR) or unit unavailability is a key parameter to analyze when evaluating the static capacity of a unit. Billinton and Allan define FOR as the probability of finding the unit on forced outage at some distant time in the future [8]. It does not include the scheduled outages. For the FOR, a multiple-state model may be used to calculate the probability of the FOR of a generating unit. However, with the equivalent forced outage rate (EFOR), accommodating a large number of states for a unit may not be feasible. Therefore, the number of states is reduced to the minimum number of states possible that will not result in considerable inaccuracy and then the weighted-average is used. The capacity outage probability table (COPT) represents the generation model in the loss of load approach and this table represents the probability of the capacity levels being in existence. The COPT should include all the generation units in the system; however, to reduce the computing time in the analytical approach, if the cumulative probability is less than a specific cumulative probability value, then these units can be neglected in the evaluation. When evaluating the adequacy of a generation configuration, there are certain assumptions that are usually considered when calculating the reliability indices. It is usually assumed that the transmission constraints and the generation deficiencies at any specific load point are not included in the reliability indices calculation [8]. D. Load Model The risk model results depend on the load model used. If the load model only uses the daily peak load to represent the load in a single day, then the loss of load index is expressed in days/year. This is the most commonly used load model: the individual daily peak load can be arranged in descending order to form a cumulative load model which is known as the daily peak load variation curve (DPLVC) [10]. If the load model uses the individual hourly load values in a day, then the loss of load index is expressed in hours/year (sometimes expressed as Loss of Load Hours (LOLH). When these hourly loads are arranged in descending order, the load duration curve (LDC) is created. The area under this curve is the energy required for this given period and this area can also be used to calculate the EENS due to shortage in generation. E. Evaluation methods on period bases The results from the LOLE approach for different systems cannot be compared. It is important to understand the model assumptions before comparing the risk indices for the different systems. Billinton and Allan discuss three ways that use the LOLE for calculating the annual risk index [8]. The first approach is the monthly or period basis considering maintenance. For this approach, the COPT for each month or period is convolved with the load model to develop the risk model. The LOLE is calculated for each month or each period within each month, if the maintenance is not constant during the month, then the summation of the monthly LOLE is the annual LOLE. The second approach is the annual basis neglecting the maintenance: the annual forecast peak and system load characteristic are combined with the system capacity outage probability table to give an annual risk level [8]. In this approach, the assumption is that the capacity is constant over the entire period. The period is divided into a light load and peak load season and the maintenance and unit additions are planned in a way to maintain a constant capacity over the timeperiod. The third approach is the worst period basis. In this period, the period with the highest risk is used. For example, if a

3 specific month has a high load level, then the risk level of this month is calculated and then this risk level value is multiplied by 12 to get the annual risk level. After determining the loss of load indices from the risk model, the next step is to determine the CV of the generating unit, which is discussed in the following sections. III. CAPACITY VALUE ESTIMATION METHODS There are multiple methods for estimating the CV of generators. These methods are applicable to all technologies of generation units and can be used to describe the CV of a conventional generator or a renewable generator. Most of the literature uses the Equivalent Firm Capacity (EFC) or Effective Load Carrying Capability (ELCC) to estimate the CV. The EFC is the amount of firm capacity from a 100% reliable unit that can provide the same capacity as the added unit under the same LOLE. The ELCC is the amount of additional load that can be added to the system due to the added generating unit. The Equivalent Conventional Power (ECP) and Guaranteed Capacity are other CV estimation methods based on arbitrarily chosen parameters [1]. The ECP uses the same procedure as the EFC except that instead of using a 100% reliable unit, the baseline unit is not 100% reliable. Guaranteed capacity is the amount of capacity that power plants can deliver at a defined probability level [11]. Unlike the EFC, ELCC and ECP, which consider how the added unit reduces the risk of load shedding, the Guaranteed Capacity method measures the impact of the new unit on the total available generation capacity in the system [1]. The methods for estimating the CV of a unit is divided into two classes: reliability and approximate-based methods. Reliability-based methods are based on probabilistic parameters. EFC, ELCC, ECP and Guaranteed Capacity are classified as reliability-based methods [12]. Reliability-based methods require detailed data of the system such as outage rate data and nameplate capacities of generators and load data. Approximate-based methods use simpler algorithms compared to reliability-based methods. Approximate-based methods require less data and analytical effort and are typically used by utilities and system operators for capacity planning purposes [13]. The approximate-based methods usually analyze the system during stress periods such as peak load hours reducing the computational challenges faced when using the reliabilitybased methods [13]. Capacity Factor Approximation Method [14], Garver s Approximation Method [13], Garver s Approximation Method for Multi-State Units [13] and the Z- method [13] [15] are classified as approximate-based methods. IV. CAPACITY VALUE OF SOLAR POWER Two major types of solar technologies are used in the literature: photovoltaic (PV) power plants and concentrated solar power (CSP) plants. The focus of this paper is on solar PV. An insight on the CV of CSP can be found in [16] [17] [18]. Several factors affect the CV of solar power plants: solar radiation at the plant location, the failure rate of solar plant components, the penetration level of solar power and the load are a few [19]. The literature determines the CV of solar by setting assumptions based on these factors. As mentioned in Section II, the LOLE approach results vary depending on the assumptions used in the generation model and the load model. Therefore, this section will focus on discussing how accounting for certain factors in the risk models impact the CV of solar. A. ELCC Reliability-based Approach to estimate CV of PV As previously mentioned in Section III, there are several methods to compute the CV. However, the most widely used method in the literature is the ELCC to express the CV of a PV unit [20]. This section describes the ELCC approach to calculate the CV of PV. First, the reliability of the base power system before adding any PV unit is computed by determining the loss of load probability (LOLP). The LOLP is the probability that the generation units will not be able to meet the demand at a given time. The LOLE is then computed, which is the summation of the LOLPs of the base power system over a specific time-period (the blue curve in Fig. 2). After the PV unit is added to the system, the same computation is done for the LOLPs and LOLE (the green curve in Fig. 2). After the addition of the PV unit, the LOLE of the system decreases compared to the base case which implies that the system has the capability to handle additional MW of load. Therefore, additional MW of load is added until the LOLE of the base power system and the LOLE of the power system with added PV unit are the same. This additional MW value is the CV (ELCC) of the PV unit. Figure 2 shows a graphical representation of ELCC. The 400 MW is the ELCC for the example in Fig. 2. This work assumes that the CV metric used in the case studies in section B is ELCC unless otherwise stated. Fig. 2. A graphical representation of ELCC [21] B. Reliability Risk Models and CV Assessment Multiple studies in the literature have focused on finding models that would give a good estimate of the CV of solar resources. These models differ in the factors that are accounted for in each model. This section gives an insight into how the CV is impacted by several factors such as the choice of the reliability indices, component failure, penetration level of solar power, load and sun-tracking capability. As discussed in section II, depending on the load model used (LDC or DPLVC), the LOLE is expressed differently. Using the LDC gives the LOLE in hours/year. Using the DPLVC gives the LOLE in days/year. It is not possible to calculate the LOLH from LOLE since the load model used is different [8]. Ibanez et al. compare the resource adequacy metrics LOLH (hours/year), LOLE (days/year) and Expected

4 Unserved Energy (EUE) (MWh/year) for different wind and solar penetration levels to see its impact on the CV [22]. The ELCC is used as the CV metric. The conclusion of this work is that there is a linear relationship between the adequacy metrics and that the CVs calculated based on the different adequacy metrics are very close in value. Therefore, the CV is insensitive to which adequacy metric is used. The literature shows that depending on the factors considered in the generation model, there may be a variation in the CV of solar resources. Failure of components impacts the CV of solar. Sulaeman et al. show that if the failure of components of a solar PV farm is not considered, the CV of solar PV is higher than when the failure of components is considered [23]. This work shows that using the proposed analytical method and comparing it with Monte Carlo simulation method both give very close results. It shows that the analytical method can produce accurate results with less modeling complexity and computation cost than the Monte Carlo simulation method. Ghaedi et al. also propose an analytical reliability method that studies the impact of the probabilistic behaviour of component failure of a solar farm and the solar radiation uncertainty on the output power of solar PV farms [24]. Zhang et al. also perform a sensitivity analysis of the PV system reliability to PV panel failure rate [25]. Increasing the penetration levels does not always improve the capacity credit of solar resources. Ghaedi et al. show that increasing the penetration levels of solar PV does improve the reliability indices [24]. However, Samedi et al. show that when increasing the penetration levels of solar, the LOLE increases; therefore, the CV of PV and CSP decreases with higher penetration solar levels [19]. At a higher percentage of solar power resources, uncertainty is higher and hence the effective capacity is lower [19]. Mosadeghy et al. also show that with increasing solar penetration levels, the CV of solar decreases as a percentage of the installed solar PV plant capacity [26]. Correlating the solar radiation and load can improve the capacity credit of solar plants. Due to the lack of studies that address this topic, a study that uses the Garver approximation to evaluate the capacity credit instead of ELCC is used for discussion purposes [27]. Richardson et al. show how different strategies to improve the correlation between solar production and demand can impact the capacity credit of each strategy [27]. The three strategies evaluated are: optimally orienting modules in a multi-module array (Multiple modules in Fig. 3), use of geographically dispersed sites (Geography in Fig. 3), and use of energy storage (Storage in Fig. 3) [27]. The energy storage strategy gives the highest CV at all grid penetration levels. The sun-tracking capability of the PV system can also improve the CV of solar PV. Madaeni et al. compare the CV of solar PV with different sun-tracking capability (fixed-axis, single-axis and double-axis capability) [13]. The double-axis capability gives the highest CV for solar PV. In a study by Xcel Energy for the Public Utility Commission of Colorado to study the impact of location and the tracking capability, the tracking solar system was found to have a higher CV than the fixed axis solar system [28]. Fig. 3. The effect of PV grid penetration for the different strategies [27] Other factors also affect the CV of solar resources such as the interconnections between power systems, location of the solar farm, the maintenance schedule of the solar farm and unit ramping. C. The Impact of Data on the CV The input data of the reliability model plays a major role in the CV estimate of solar PV. Gami and et al. study the robustness of solar CV to three data issues [3]. First, the study performed sensitivity analyses to determine the affect of 1-min interval solar insolation data vs. average hourly solar insolation data when calculating the CV of solar PV. The study shows that the CV of solar PV can range up to ±10% using the hourly averaged data interval solar insolation data in a given year. However, usually the ELCC is biased upward when using the hourly averaged data. The study shows that when multiple years of solar insolation data is used when using the average hourly solar insolation data, the CV of solar is improved. Second, the study shows the effect of errors in recording and interpreting load data. The study shifts the load profiles backward and forward to see its affect on the CV. The study shows that the load shifts can affect the CV of solar by more than 35% and the load shift direction will not necessarily affect the CV estimate in the same direction. The third part of the study shows the effect of using modeled data as opposed to measured data. The model shows that using modeled data can increase the errors in the ELCC CV. Unlike the first part of the study, using multiple years of data will not improve the CV estimate caused by load errors or caused by using modeled data. D. Other Works Studying the CV of Solar Munoz et al. compare the CVs calculated from a probabilistic model, a standard deterministic planning model and a modified deterministic planning model [29]. Sigrin et al. validate treatment of PV in the ReEDS model by comparing it with the results of two other models. [30]. Ding et al. propose an empirical model to calculate the CV of a PV plant in [31]. Minimal research work acknowledges the relationship between the available variable generation capacity and the load. An insight into this topic is discussed in [32]. Discussion on the different CV estimation methods and their impact on the CV can be found in [13], [6] and [33].

5 V. CONCLUSION This review paper shows the impact of the choice of the reliability indices, component failure, penetration level of solar power, load and sun-tracking capability on the CV of solar PV resources. Potential future work involves studying the effect of weather on the relationship between generation and demand, studying the integration of storage and demand response in improving the CV of intermittent resources and studying mechanisms to reliably calculate the capacity credit for fair remunerations in the capacity market. VI. REFERENCES [1] M. Amelin and L. Söder, "Taking Credit: The Impact of Wind Power on Supply Adequacy- Experience from the Swedish Market," IEEE Power and Energy Magazine, vol. 8, no. 5, pp , [2] A. Keane, M. Milligan, C. J. Dent, B. Hasche, C. D'Annunzio, K. Dragoon, H. Holttinen, N. Samaan, L. Soder and M. O'Malley, "Capacity Value of Wind Power," IEEE Transactions on Power Systems, vol. 26, no. 2, pp , [3] D. Gami, R. Sioshansi and P. 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