decentralized secondary frequency control in an optimized

, STOCKHOLM SWEDEN 2018
Decentralized Secondary Frequency Control in an Optimized Diesel PV Hybrid System
ALICE VIEIRA TURNELL
KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
Decentralized Secondary Frequency Control in an Optimized Diesel PV Hybrid System
Sekundärreglering i ett Optimerat Diesel PV Hybrid System
Alice Vieira Turnell
Examiner: Dr. Patrik Hilber Supervisors: M.Sc. Kateryna Morozovska (KTH) M.Sc. Peter-Philipp Schierhorn (Energynautics GmbH)
A thesis presented for the degree of M.Sc. in Electric Power Engineering
School of Electrical Engineering and Computer Science KTH Royal Institute of Technology
Stockholm, Sweden August 2018
Abstract
This research argues that a diesel-based isolated electrical system can be optimized by integrating a high share of solar photovoltaic (PV) generation and that the frequency stability of such system can be improved by including the PV participation in frequency regulation. A case study is developed in order to explore an island’s expansion of the installed generating capacity and its optimization. This study uses the tool HOMER to solve the optimization problem and PowerFactory to verify the frequency stability of the proposed system. The PV integration allows for a reduction of diesel fuel consumption, emissions and generation costs. Additionally, in high PV penetration scenarios, the reduced inertia in such systems can lead to high frequency deviations that may trip the system protection. The study demonstrates that the instantaneous frequency deviation after a load and generation imbalance can be reduced by designing the PVs to operate with an allocated reserve and a decentralized time-based secondary frequency control. The frequency stability was achieved after different disturbance scenarios under high PV penetration and reduced available inertia, indicating that high PV integration is economically and technically feasible in small island grids.
Keywords:Solar photovoltaic, frequency stability, decentralized secondary frequency control, reserve allocation, hybrid system, island system, HOMER, PowerFactory.
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Sammanfattning
I detta examensarbete studeras hur ett dieselbaserat och isolerat elsystem kan optimeras genom att integrera en hög andel solceller (PV) i elproduktionen och att frekvensstabilitet kan förbättras när PV användas i regleringen. En fallstudie har utvecklats under denna forskning för att analysera en ökning av den installerade generationskapacitet vid en ö samt hur detta kan optimeras. I denna studie användas verktyget HOMER för modeloptimering och PowerFactory för att testa den optimerade systemfrekvens stabilitet. Med PV generation kan diesel konsumption, utsläpp och kostnader minskas för hela systemet. En hög andel PV i generationen reducerar elsystemet totala svängmassa vilket kan ledda till avvikelser i systemfrekvensen som kan ursaka att skyddsystem aktiveras. Studien demonstrerar att den momentana systemavvikelsen efter en obalans kan reduceras genom att designa PV i systemet med en allokerad reserv och en decentraliserad och tidsbaserad sekundär frekvensreglering. Frekvensstabiliteten nåddes i olika obalans scenarier med hög andel solcellgeneration och misnkat svängsmassa. Detta tyder på att en hög andel PV integration är både ekonomisk- och tekniskt möjligt i mindre elsystem.
Nyckelord: Solceller, frekvensstabilitet, decentraliserad sekundär frekvenskontroll, reservallokering, hybridsystem, ösystem, HOMER, PowerFactory.
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I dedicate this thesis to my parents, who have always encouraged me. Especially to the memory of my father, who has seen the beginning of this thesis journey and who has always
pushed me to aim higher and try harder, even if it took me far away from home.
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Acknowledgements
I would like to express my sincere gratitude to my supervisors Msc. Kateryna Morozovska and Msc. Peter-Philipp Schierhorn, as well as to my examiner Dr. Patrik Hilber, for their support and knowledge sharing during this research work.
My sincere thanks also goes to Dr. Thomas Ackermann and Dr. Eckehard Tröster, who provided me with an opportunity to join their team as a master student at Energynautics and develop my knowledge skills in the area. I would also like to thank Daniel, Pablo and all my colleagues at Energynautics, for their feedback and cooperation.
Last but not least, I would like to thank my parents, David and Maria de Fatima, my siblings Mariana, Mathew and Carolyn, my stepfather Ian, as well as my other family members and friends, for their motivation and support throughout this Master programme and my life in general.
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Common Abbreviations
AGC Automatic Generator Control CRF Capital Recovery Factor DAPI Distributed-Averaging Proportional-Integral DSL DIgSILENT Simulation Language DSM Demand Side Management ENTSO-E European Network of Transmission System Operators for Electricity ESS Energy Storage System FCR Frequency Containment Reserves FRR Frequency Restoration Reserves FSM Frequency Sensitive Mode HOMER Hybrid Optimization for Multiple Energy Resources IRENA International Renewable Energy Agency LCOE Levelized Cost of Energy MPP Maximum Power Point MPPT Maximum Power Point Tracking NREL National Renewable Energy Laboratory NPC Net Present Cost O&M Operation and Maintenance PV Photovoltaic ROCOF Rate of Change of Frequency RR Replacement Reserves STC Standard Test Conditions TSO Transmission System Operator
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List of Tables 2
List of Figures 4
1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Research Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Integration of a High Share of Renewables into Small Island Power Systems 3 2.1 Design of the Hybrid Off-Grid System . . . . . . . . . . . . . . . . . . . 4
3 Introduction to Solar PV Participation in Frequency Regulation 9 3.1 Frequency Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Introduction to Solar PV Technology . . . . . . . . . . . . . . . . . . . . 12 3.3 Solar PV Contribution to Frequency Regulation . . . . . . . . . . . . . . 14
4 Research Methodology 21 4.1 Research Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2 Methodology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3 Introduction to Case Study . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.4 Optimization of Generation Expansion . . . . . . . . . . . . . . . . . . . 26 4.5 PV Frequency Control Strategy . . . . . . . . . . . . . . . . . . . . . . . 33
5 Results and Discussion 40 5.1 Optimal Generation Installed Capacity . . . . . . . . . . . . . . . . . . . 40 5.2 Frequency Stability Analysis of Optimal Solution . . . . . . . . . . . . . 43 5.3 Feasibility Analysis of Results . . . . . . . . . . . . . . . . . . . . . . . 57
6 Conclusions and Future Work 60
Bibliography 62
Appendix 67
List of Tables
2.1 Optimization problem’s structure for a hybrid power system design. . . . 5 2.2 Off-grid systems in islands: successful application examples. . . . . . . . 8
4.1 Characteristics of the pilot island. . . . . . . . . . . . . . . . . . . . . . 23 4.2 Optimization problem structure. . . . . . . . . . . . . . . . . . . . . . . 27 4.3 Load forecast scenario with 20% load increase per year. . . . . . . . . . . 28 4.4 Component’s technical parameters. . . . . . . . . . . . . . . . . . . . . . 29 4.5 Rates for the system’s cost parameters. . . . . . . . . . . . . . . . . . . . 29 4.6 Capital cost forecast of the hybrid system components. . . . . . . . . . . 30 4.7 Operational costs of the hybrid system components. . . . . . . . . . . . . 30 4.8 Optimization constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.9 Comparison of features from different frequency control strategies. . . . . 34 4.10 Definition of test scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.1 Installed generation capacity in PowerFactory for the 2025 island’s system. 43
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List of Figures
3.1 Example of frequency quality metrics. . . . . . . . . . . . . . . . . . . . 11 3.2 Cumulative solar photovoltaic installed capacity worldwide. . . . . . . . 13 3.3 PV operation outside maximum power point. . . . . . . . . . . . . . . . 16 3.4 PV control topologies for secondary frequency control, (a) centralized and
(b) decentralized. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.5 Primary and secondary control block diagram. . . . . . . . . . . . . . . . 18 3.6 Time-dependent controller gain for frequency control in single event and
multievent detection scenario. . . . . . . . . . . . . . . . . . . . . . . . 20
4.1 Flowchart of the optimal scenario selection methodology. . . . . . . . . . 22 4.2 Island’s demand measured sample during a partial supply regime. . . . . 24 4.3 Island’s demand measured sample during a 24 h supply regime. . . . . . . 24 4.4 Daily mean capacity factor of a sample solar PV system in case study location. 25 4.5 Daily mean capacity factor of a sample wind turbine in case study location. 25 4.6 Step-wise simulation inputs and outputs in HOMER. . . . . . . . . . . . 32 4.7 Step-wise simulations’ cost and load inputs. . . . . . . . . . . . . . . . . 32 4.8 Single line diagram of island hybrid diesel-PV system of 2025. . . . . . . 38
5.1 Optimized installed capacities: system with and without renewable sources. 41 5.2 Minimum renewable fraction achieved. . . . . . . . . . . . . . . . . . . . 42 5.3 Optimized levelized cost of energy: system with and without renewable
sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.4 Optimized fuel consumption: system with and wihtout renewable sources. 42 5.5 Percentage of solar production curtailed. . . . . . . . . . . . . . . . . . . 43 5.6 Overview of the PV participation in frequency regulation during a 6,5%
load variation and medium irradiance values (420 W/m2). . . . . . . . . . 45 5.7 Overview of the PV participation in frequency regulation during a 6,5%
load variation and high irradiance values (1000 W/m2). . . . . . . . . . . 46 5.8 Total active power output of the PV units under different frequency control
modes of operation, during a load variation of 13% and irradiance 600 W/m2. 47 5.9 Total active power output of the diesel generators for the PV operation
under different frequency control modes, during a load variation of 13% and irradiance 600 W/m2. . . . . . . . . . . . . . . . . . . . . . . . . . . 48
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LIST OF FIGURES
5.10 Frequency response to a 13,5% load disconnection, for the PV operation under different frequency control modes, during irradiance 600 W/m2. . . 48
5.11 Frequency response to a 13,5% load reconnection, for the PV operation under different frequency control modes, during irradiance 600 W/m2. . . 49
5.12 Total active power output from the PVs, for different allocated reserve levels, for a load variation of 30,1% and irradiance of 600 W/m2. . . . . . 50
5.13 System’s under-frequency response for load reconnection event of 30,1%, for different allocated reserve levels and irradiance of 600 W/m2. . . . . . 50
5.14 Active power output of one PV plant per affected group during irradiance variations simulating a cloud movement. . . . . . . . . . . . . . . . . . . 51
5.15 Active power and frequency response during an irradiance variation in different PV units simulating a cloud movement. . . . . . . . . . . . . . 52
5.16 Active power and frequency response to a 30,1% load variation during high PV penetration (Irradiance 1000 W/m2). . . . . . . . . . . . . . . . . . . 53
5.17 Active power and frequency response to a disconnection of one PV plant during a high PV penetration scenario (Irradiance 1000 W/m2). . . . . . . 54
5.18 Active power and frequency response to a diesel unit disconnection during a medium PV penetration scenario (Irradiance 600 W/m2). . . . . . . . . 55
5.19 Active power after the disconnection of one diesel unit, during a medium PV penetration scenario (Irradiance 600 W/m2). . . . . . . . . . . . . . . 56
5.20 Active power and frequency response to a large feeder disconnection during a high PV penetration scenario (Irradiance 1000 W/m2). . . . . . . . . . 57
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1 | Introduction
1.1 Motivation Amidst the current worldwide actions towards less polluting energy systems, countries are setting ambitious targets for the expansion of their renewable generation. The effects of having a high share of renewable energy must be analyzed for each individual system, in order to guarantee a successful generation capacity expansion. Within this context, the Indonesian government has set a renewable generation target of 23% of renewables in produced energy by 2025.
Indonesia comprises several islands, many of which are isolated and with electrical systems relying fully on diesel generators, resulting in high generation costs. Designing the optimal generation expansion for such isolated island systems, based on a high share of renewable generation, will greatly reduce the generation cost, contribute to achieving the aforementioned national target and contribute to reducing greenhouse gas emissions. Nonetheless, due to the lower availability of inertia in systems with a high share of renewable generation, the design phase must include a thorough analysis of the system stability and reliability under different operating conditions.
The fast operation of inverter based generation technologies, such as solar photovoltaic (PV)1 and wind power, despite not providing inertia to the system, has the potential to improve the system’s stability through the participation of such technologies in frequency regulation, allowing for frequency stabilization and restoration. Whereas the secondary frequency reserves in larger systems are usually automatically dispatched, in smaller systems a manual dispatch might occur. Methods are being researched for a PV decentralized secondary frequency control, with no communications required for the dispatch. Decentralized methods represent an alternative strategy for systems with unreliable communication channels, in order to avoid further instabilities caused by communication delays or interruptions in the channels which are used for the centralized generation dispatch.
1The term PV, used hereafter in this report, comprises the conversion of solar energy into DC power and the conversion of the resulting DC power into AC power.
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Chapter 1. Introduction 2
1.2 Research Problem Feasible methods must be sought to successfully integrate high shares of renewable generation in isolated electrical systems.
The technical and economic impacts of having high shares of renewable generation in isolated small, or very small, grids need to be further investigated, taking into account each system’s unique characteristics and targets. For isolated communities, the design of the capacity expansion must also account for the cost of transporting equipment and fuel to remote locations, which directly impacts the final generation cost. Additionally, the reduced inertia in small island grids with a high share of renewables requires further system stability analysis to ensure that the designed system is capable of maintaining stability under different disturbance scenarios. Finally, isolated systems with unreliable communication channels require additional measures for frequency regulation. For optimal system design, a balance must be sought between the stability, reliability and costs of the proposed system.
1.3 Research Goal The primary objective of this Master’s thesis is to propose a technically and economically feasible generation capacity expansion plan for a very small off-grid system, which will yield the targeted share of renewable generation whilst ensuring frequency stability.
In the first step of this case study, specific economic and technical data is used to structure, and solve, an optimization problem yielding an optimal capacity expansion plan for a small tropical island. In the second step the optimization problem solution is further analyzed using dynamic simulations in order to verify the system’s frequency stability under different scenarios in which the PVs are contributing to frequency regulation.
The specific research goals are:
• to structure and solve a capacity expansion optimization problem for an isolated hybrid power system that will meet a proposed renewable target
• to identify the frequency stability challenges encountered with the expansion of a small island hybrid system with a high share of renewables
• to propose a decentralized frequency regulation strategy for off-grid systems with unreliable communication channels and verify if the frequency stability can be improved when PVs participate in frequency regulation
2 | Integration of a High Share of Renewables into Small Island Power Systems
Small off-grid electrical power systems, such as those in small islands, are still predom- inantly dependent on fossil-fuelled generators, more specifically oil-based. Utilizing oil-based generation impacts on current emission reduction targets and results in high generation costs. Fuel transportation to remote locations further increases generation costs. The reduction of technology costs of renewable generation combined with the emission reduction targets make hybrid off-grid systems with a high share of renewable generation complemented by fossil-fuelled generation an attractive solution for isolated electrical systems.
Integrating a high share of intermittent renewable energy sources, such as solar and wind energy, into small scale off-grid systems poses a time compatibility challenge to meeting the demand distribution. In addition to the intermittent characteristic of these sources, the lack of inertia provision due to the power electronic interface represents a stability challenge for large-scale renewable integration. Nonetheless, when adequately combined with other generation sources, a high share of renewable generation can yield an efficient and reliable power supply [1].
Hybrid off-grid systems must be designed to optimize the available resources, according to the economic and technical characteristics of each specific system and location. In this case study, the optimization of the generation expansion will be performed for the off-grid system of a remote tropical small island in Indonesia. The island, with 13000 inhabitants and 0,8 MW peak consumption, is currently supplied by two diesel generators during 12 hours per day, and has a (high) cost of 0,21 EUR/kWh. The expansion plan is part of a pilot study in Indonesian islands, to contribute to the country’s renewable target. Further characteristics of the case study system will be addressed in Section 4.3.
This chapter addresses considerations for the design of an off-grid hybrid power system comprising PVs and fossil fuel-based generation. It includes an introduction to the challenge of maintaining system stability in small hybrid power systems which is further explored in Chapter 3 along with the latest research on PV participation in frequency regulation. Solar PV is the renewable technology at focus in this research, as the potential for wind energy is low in the case study.
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Chapter 2. Integration of a High Share of Renewables into Small Island Power Systems 4
2.1 Design of the Hybrid Off-Grid System Efficient design and planning of a hybrid power system requires optimization of the system’s Net Present Cost (NPC) whilst ensuring reliability of the system [2]. The NPC is defined as the ratio of total annual costs of the system to annual electricity delivered. It equals the total discounted present values of all the future costs during the system’s lifetime [3]. The NPC includes the initial costs of components, replacement costs and maintenance costs.
An optimization problem must be structured and solved in order to obtain the optimal solution for the hybrid system design. It must consider the installed capacity per generation source which will yield the lowest system NPC whilst meeting (i) the load, (ii) the renewable energy production target and (iii) the spinning reserve constraints. The level of details in the optimization problem structure and the accuracy of the input data will determine the quality of the obtained solution. Moreover, decisions regarding energy storage systems, demand side management and reliability requirements, must be made when structuring the optimization problem. A brief overview on these topics is given in the following subsections, as well as a summary of successful application cases in which a high share of renewable energy has been integrated into island off-grid systems.
2.1.1 Optimization Problem for the Generation Expansion A variety of computational tools exist for structuring and solving optimization problems. The overall problem structure comprises an objective function containing the variable to be optimized either through minimization or maximization, as well as the definition of parameters, variables, constraints and limits. The optimization problem for a hybrid system’s generation expansion can be structured as illustrated in Table 2.1.
2.1.2 System Stability and Reserve Requirements Hybrid systems must be carefully designed to ensure system stability. The stability of a power system corresponds to its ability to regain a state of equilibrium after the occurrence of a physical disturbance. When a disturbance occurs, the kinetic energy stored in the rotating masses of machines is the immediate system inertial response and contributes to reducing the frequency instability. Frequency controllers then act through the adjustment of the generators’ production in order to stabilize the frequency variation and return it to the system’s nominal value. An increase in the system’s load will reduce the system’s frequency and, in order to adjust their production upwards, the generators participating in frequency regulation must operate with a reserve capacity.
Small isolated power systems have an additional challenge regarding system stability because the loss of a generating unit represents a greater share of the total generation. The reduced inertia in such systems results in a higher rate of change of frequency (RoCoF) on the occurrence of a disturbance than would occur in interconnected systems [4].
Fossil-fuelled generators can provide the response required for primary frequency control when their ramping capability is well-dimensioned for the application. A fast ramping capability allows for compensation of fast load or generation variations such as those caused by cloud movements in PV energy production. Therefore, higher ramping
Table 2.1: Optimization problem’s structure for a hybrid power system design.
Category Items
Objective function
Meteorological data (wind speed, solar irradiance and temperature) Load Profile (hourly demand profile, seasonal and daily variability) Costs (initial capital costs of equipment, operation and maintenance, replacement and fuel costs) Equipment parameters (efficiency, lifetime, minimum power output limit, minimum operating hours) Project lifetime Load consumption projection (kWh) Economic parameters (expected inflation rate and nominal discount rate)
Variables to Installed capacity per generation type be optimized Production per generation type
Levelized Cost of Energy System’s Net Present Cost Renewable energy fraction Total fuel consumption Curtailed PV generation
Constraints Minimum share of renewable generation target Maximum annual capacity shortage
Limits Available resources on-site
capability of fossil-fuelled generators allows for higher renewable energy integration of PVs and wind power.
In contrast to PVs, wind power plants can provide synthetic inertial response when operating at their maximum power point, due to the kinetic energy stored in the turbine blades. However, this initial response can only last a few seconds as the kinetic energy must be restored quickly. Nevertheless, both PV and wind power plants can provide frequency regulation by allocating part of the installed capacity as a reserve.
Different frequency control strategies have been proposed in order to allow renewable generation to participate in frequency regulation. According to Liu et al. (2017) in [5], due to its simplicity and robustness, strategies with droop-based control are more likely to be first applied in the market. The latest strategies for PV participation in frequency regulation will be introduced in Chapter 3.
Energy Storage Considerations
In hybrid systems containing PVs, the utilization of Energy Storage Systems (ESS) can aid in improving system performance. The excess of PV generation can be stored in the ESS, thus reducing or avoiding a curtailment. The ESS also contributes to balancing short term fluctuations from the load/generation balance.
Storage systems can be included in the generation optimization problem and the optimal solution will indicate the need for such storage based on the system’s technical and economic characteristics. ESS can be of chemical nature, electrical or thermal, among others [6]. The optimal ESS will be determined based on the application. Currently, batteries are the most commonly used storage technology in off-grid hybrid systems. This is due to the maturity of the technology and market costs. The battery system must be specified in order to provide adequate system stability with minimum resulting LCOE [7].
In PV generating units, power fluctuations occur in the inverter output due to PV intermittency. A storage unit can be connected directly to the DC link of the PV inverter in order to limit the power fluctuations. Nonetheless, a centralized energy storage system is preferred for PV plants with more than one inverter, since PV power fluctuations at the Point of Common Coupling (PCC) with the grid will reduce with increasing PV plant site. Therefore, when choosing to limit the fluctuations at the PCC with the system, the energy storage requirement is lower than when connecting it at the inverter level [8].
2.1.3 Demand Side Management Potential in Remote Areas In order to increase the system’ stability in off-grid small scale energy systems based on a high share of renewable energy, Demand Side Management (DSM) can be used to obtain flexibility on the demand side to meet the fluctuations of the intermittent generation share. Demand response technologies offer the possibility of improving the system’s reliability, reducing peak demand and reducing corresponding capital investments.
Different techniques for DSM exist, in which the demand can be controlled directly through controllers or by a customer response to variations in price. For systems located in tropical weather areas, there is a great potential for DSM through the control of space cooling and refrigerating machines, as well as water supply pumping hours.
2.1.4 System Reliability Requirements A hybrid system must be designed to supply the loads at the lowest system’s cost whilst still ensuring a reliable system. The intermittent characteristics of solar and wind resources represent a risk for the system’s reliability and must be compensated for in the system’s design.
Different methods exist in order to analyze the reliability of hybrid systems. These include: loss of power supply probability (LPSP), loss of load probability (LOLP), unmet load (UL) and loss of load risk (LOLR). LPSP is the ratio of the power supply deficits to the electric load demand during a time period. LOLP is the ratio between power failure time period and the total working time of the system. Unmet load is the load that is not served regarding the total load of a certain time period. LOLR is the probability of the generating system not meeting the daily demand due to deficit of energy from the renewable power supplies used [9].
In [10], LPSP has been used to assess the reliability of a power supply for a hybrid off-grid system and determine the size of the system’s components that would yield the highest economically attractive and reliable system.
2.1.5 Successful Application Cases: Island Hybrid Systems with High Renewable Share
Pilot projects for hybrid systems with a high share of renewables have been conducted in different islands. Their results and learning outcomes are a useful source for new projects. Four successful application projects which have published results will be briefly discussed in this section. These are the systems of the islands: King Island, St Eustatius, South Tarawa and Madeira. Further characteristics of these island systems are summarized in Table 2.2.
King Island’s generation was fossil-fuelled until the year of 1998. The island’s hybrid wind-biodiesel system with energy storage and demand side management allowed for a 45% fossil fuel reduction. Its wind power supplies up to 70% of the demand and an occasional operation occurs with zero diesel generation due to the Uninterruptible Power Supply (UPS) and flywheel system. Adequate battery sizing and type selection was one of the challenges reported in the project. The initial batteries selected – Vanadium Redox Batteries – presented cell stack failures as well as several inverter failures. These were later replaced by lead-acid batteries. An additional challenge identified was the high cost of electricity supply when compared to the income from the electricity sale. The difference is currently reimbursed by the government and charged as a community service cost [11].
St Eustatius Island’s generation was fossil-fuelled until 2015. A solar-diesel hybrid system with energy storage was commissioned there in 2016, reducing the fossil fuel consumption by 23%. Periods with up to 88% of the load being supplied by the solar PV production were observed [12].
South Tarawa’s hybrid solar-diesel system was commissioned in 2015 and 227000 liters of fossil fuel consumption reduction was achieved, coupled with a contribution to the electricity production of 8% from solar panels [13]. With no ESS, a high level of curtailment occurs. An analysis performed by the International Renewable Energy Agency (IRENA) estimated that a level of contribution to the island generation of approximately 35% would be achieved by adding 2,5 MW of Photovoltaic (PV) capacity and 2,64 MW/
5,6 MWh of ESS [14]. Madeira island has a target of achieving 50% contribution from renewable energy
generation to the energy mix by 2020. In 2017, a 30% contribution was achieved with 17,96 MW of installed PV systems [15], [16].
These examples serve as lessons learned for high renewable integration projects in islands. A balance between the technical and economic characteristics of the system design must be sought, in order to avoid high generation costs such as the ones observed in King Island. An optimization problem can be solved to determine an optimal capacity expansion for each system. The PV production curtailment can be reduced by adding an ESS, as indicated in South Tarawa Island. Madeira island indicates the possibility of achieving a high renewable target with a hybrid system diversified with different generation sources. St Eustatius island is similar to the case study in this research and indicates that high PV penetration is possible in a hybrid diesel-PV-ESS system. The ESS in St Eustatius compensates for small load/generation imbalances with no frequency regulation contribution from the PV system.
Table 2.2: Off-grid systems in islands: successful application examples.
King Island St Eustatius South Tarawa Madeira Island
Population 1 800 (2013) 3 877 (2015) 50 180 (2010) 262 456 (2011) Country Australia Caribbean Kiribati Portugal Load 3,3 MW peak 2,3 MW peak 17,3GWh (2011) 820,8 GWh Wind Turbines 2,45 MW - - 45,11 MW PV System 100 kW 1,9 MWp 1,5 MWp (2016) 17,96 MW Diesel Genera- tors
6 MW 4 MVA 5 MW Thermal: 218,7 MW
Other generation sources
ESS (batteries) 3MW/ 1,6MWh 1MW/ 580kWh; - - Dynamic resis- tor
1,5 MW - - -
3 | Introduction to Solar PV Participation in Frequency Regulation
Whilst rotating machines accomplish the secondary frequency control within a few minutes, in PVs, the converter based technology has the potential to perform this operation within a few seconds [17]. A literature review of the contribution of PVs to frequency regulation will be presented in this chapter, focusing on the recent research area of PVs’ contribution to decentralized secondary frequency control. The theoretical basis, and terminology, of frequency regulation will also be introduced.
3.1 Frequency Stability
3.1.1 Frequency Regulation Dynamics Using the terminology from the European Network of Transmission System Operators for Electricity (ENTSO-E), frequency control can be performed in three stages, each of which with a designated reserve allocation, namely: Frequency Containment Reserves (FCR), Frequency Restoration Reserves (FRR) and Replacement Reserves (RR). The terms primary, secondary and tertiary control are also used.
In the first few seconds after a disturbance in the active power balance occurs, the dynamic behaviour of the system is given by the Inertial Frequency Response (IFR). The IFR comes from the kinetic energy stored in the rotating masses of the synchronous generators connected to the system. This response opposes the fast frequency deviation and thus reduces the Instantaneous Frequency Deviation (IFD) reached. The IFD should not exceed the maximum permissible limit as it may trigger protection systems, causing further system instabilities which can lead to black outs. Maintaining an adequate amount of inertia in the system is important as it takes a few seconds before the activation of FCR and thus, within this time, IFR is the only opposition to the frequency deviation. Therefore, the inertia available in a system directly impacts the rate of change of frequency in the system as well as the IFD [18].
FCR is activated in order to stabilize the system frequency within the maximum allowed Steady-State Frequency Deviation (SSFD) value, whereas FRR is utilized to regulate
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Chapter 3. Introduction to Solar PV Participation in Frequency Regulation 10
the frequency error to zero and replace the activated FCR. RR control has the target of restoring FRR progressively and supporting FRR activation [19]. RR control coordinates the operation of the power flows in a grid, in order to achieve the optimal economic dispatch among the generating units as well as meeting grid constraints such as reactive power flows and corresponding voltages. The control layers have different response speeds and thus have separate dynamics [20].
The activation process for FCR is performed within seconds and corresponds to an automatic response, commonly from the mechanical speed governor control in the rotating generators. It utilizes a pre-defined frequency droop in order to achieve active power sharing among the generators participating in the control stage. The activation process for FRR takes between a few seconds and a few minutes and can either be manually activated or performed by the Automatic Generation Control (AGC), by activating a supplementary control loop which integrates the frequency error. The FCR full activation time requirement varies for different synchronous area sizes. For instance, for continental Europe, [19] requires the activation time to be within 30 seconds, whilst for Great Britain it is within 10 seconds.
3.1.2 Frequency Regulation Requirements for Generators Network Codes specify guidelines for the connection of generators, which will be a function of the generator type. Different units may have different droop and activation times in order to reach the overall droop required by the system. Published guidelines for the Network Code, given by the ENTSO-E in [21], discuss the requirements for frequency regulation, some of which will be introduced in this section.
Limited Frequency Sensitive Mode - Over-frequency (LFSM-O)
In the LFSM-O, the PVs would be required to automatically reduce the active power output in case of over-frequency, due to a large loss of demand or a sudden production increase, in order to reduce the IFD. The size of the synchronous area impacts the frequency threshold settings, with higher values for smaller areas in the LFSM-O. The frequency threshold for the LFSM-O, mentioned in [21], corresponds to a value within the interval [50,2, 50,5] Hz, thus [0,004 , 0,01] p.u., and a droop within [2, 12]%.
Limited Frequency Sensitive Mode - Under-frequency (LFSM-U)
In the LFSM-U, generating units must increase their active power output in case of under- frequency due to a severe loss of generation or load increase. The generation increase is proportional to the frequency variation and activated as fast as technically possible. Different power generating units can have different droop settings and activation times, in order to reach the system’s required droop without unnecessary protection tripping and loss of load. Failure to stabilize the frequency will lead to frequency collapse, with consequences such as cascade tripping, load shedding and black outs. The operation of PVs in the LFSM-U is possible if they operate below their maximum power point, i.e. with a reserve capacity. The frequency threshold for the LFSM-U, mentioned in [21], corresponds
to a value within the interval [49,8, 49,5] Hz, thus [0,004 , 0,01] p.u., and a droop within [2, 12]%.
Rate of Change of Frequency (RoCoF) Withstand Capability
The RoCoF is the time derivative of the system frequency (df/dt), being used as a metric of system inertia by network operators to guide the control actions to maintain the stability of the system. Networks with a high penetration of inverter-based generation have a larger RoCoF because the inertia is low.
The RoCoF withstand requirement is to ensure that PVs will remain connected to the system in case of abrupt frequency variations up to a set-point df/dt (determined by the TSO). This must be done by neglecting short term electro-mechanical effects, and thus allowing the primary frequency control to react, avoiding a cascading effect of generation loss. This requirement must be carefully coordinated with the protection settings [22].
The RoCoF withstand capability is determined by the TSO, based on the characteristics of the entire synchronous area. In small systems with low inertia, the capability can be determined based on the loss of the largest power generator unit when the inertia in the system is low [22].
RoCoF thresholds of ±2Hz/s for a moving average of 500ms window are suggested by ENTSO-E in [22]. Power generation modules are allowed to disconnect if the frequency varies beyond this threshold. These limits are listed as a general reference and are not applicable to small systems with low inertia.
3.1.3 Frequency Control Quality Metrics Three parameters are commonly used to evaluate the frequency response in a system: RoCoF (δf/δt), IFD (called frequency nadir for under-frequency events or zenith for over-frequency events) and Steady State Frequency Deviation (SSFD) [23]. These are exemplified in Figure 3.1 for an under-frequency scenario.
c© 2017, IEEE
Figure 3.1: Example of frequency quality metrics. Source: [23].
3.1.4 Requirements for an Adequate Frequency Measurement The electrical frequency in a system can be measured for different purposes, such as for power frequency control and power system protection. The frequency measurement is a fundamental stage of the frequency control and thus must adequately reflect the system frequency for this purpose.
Different phenomena have different time constants and require a specific measurement setting that results from a compromise between speed and accuracy. In [24], ENTSO-E has published reference parameters that cover compliance tests and monitoring needs according to the intended use of the measurement. The recommended measurement window for decentralized generation control is 100 to 200 ms, with an accuracy of 10 mHz.
Some phenomena, such as short circuits and transformer energization, can lead to errors in the local frequency measurement, due to phase jumps resulting from discontinuities near the frequency measurement point. In applications that require a fast response, less filtering is required causing these local events not to be distinguished from the global events. In a decentralized frequency regulation process, the localized frequency error will be contained in the PV plant near the measuring point. Erroneous measurement may however lead to the disconnection of PVs plants [24].
In a system with synchronous generators, the frequency variations are limited by the inertial response from the rotating masses, which allows the frequency to be calculated over a longer period thus obtaining smoother measuring results [24]. In an island system with a high share of PVs and thus reduced inertia, the frequency measurement sampling rate must be sufficiently high to allow fast reaction of these generators without causing unnecessary reactions to small frequency variations. Although the frequency measurement technique is not the focus of this research, the effects of frequency measurement errors can be considered in future analysis.
3.2 Introduction to Solar PV Technology With the rapid expansion of solar photovoltaic installed capacity worldwide, reaching 384,6 GW1 in 2017 [25], as depicted in Figure 3.2, the impact of their penetration is significant on power systems. Several grid codes include guidelines for PV connection in order to maintain system stability and reliability. Fault ride through capability, reactive power compensation and frequency regulation performed by solar PV systems must be addressed.
A PV module can be described by a set of equations, which will be briefly introduced in this section and later used in the design of the PV model in the power system analysis tool.
Photovoltaic cells contain p-n junctions which generate an electric current when exposed to light. The photo-electric current of a PV module, Iph, equals the short circuit current of one string2 corrected by a temperature correction factor in order to incorporate scenarios which differ from the standard test conditions (STC). Iph is given by Equation 3.1 [26]:
Iph = [Isc +KI(T − Tref )]E, (3.1)
1Does not include concentrated solar power installed capacity. 2A string of PV corresponds to the PVs which are connected in series.
c© IRENA
Figure 3.2: Cumulative solar photovoltaic installed capacity worldwide. Image generated from the IRENA database. Source: [25].
in which T is the temperature [kelvin], Isc is cell short circuit current [A], Tref is the reference temperature [kelvin], KI is a temperature coefficient and E is irradiance [W/m2].
The PV array current can be sized by adjusting the number of cells in parallel and the voltage can be sized by adjusting the number of cells in series. The output current of the PV array, Ipv is given by Equation 3.2 [26]:
Ipv = Np
( Iph − Irs
) − 1 )) , (3.2)
in which Np is the number of cells in parallel, Ns is the number of cells in series, Irs is the reverse saturation current [A], q is the electron charge [1,602 × 10-19 C], k is Boltzmann’s Constant [1,381 × 10-23 J/K], A is the ideality factor and Vdc is the open circuit DC voltage [V].
The output DC voltage at maximum power point, VMPP , can be modelled by Equation 3.3 [26]:
VMPP = VMPPO
) (1 + αv(T − TSTC)), (3.3)
in which αv is the temperature correction factor and VMPPO is the output DC voltage [V] at maximum power point for standard test conditions (STC), i.e. temperature TSTC of 25C and irradiance of 1000 W/m2.
Among the data needed for modelling the PV, only a few are available on manufacturer’s technical data sheets. The remaining values must be calculated analytically or via
optimization techniques [27]. The voltage and current of the PV depend on the temperature and irradiance at each
moment. Under uniform test conditions of ambient temperature and solar irradiance, there is a unique operation point that yields maximum power and thus maximum efficiency, regardless of the load [26]. Because this Maximum Power Point (MPP) is continuously varying with temperature and irradiance, PV’s are equipped with a Maximum Power Point Tracking (MPPT) control technique to track the MPP and thus output the maximum power available at each moment. The MPPT control will find the optimal value of the DC output voltage (or current), based on measurements of the module current and voltage which reflect the updated weather conditions, and send it as a reference to the main controller so that it regulates the DC voltage (or current), in order to achieve the MPP.
Different MPPT techniques exist. In [27] the most common and recent techniques are classified into conventional and soft computing techniques. The authors provide a comparative review of these techniques, regarding tracking speed, algorithm complexity, tracking under partial shading and hardware implementation.
Regarding the connection to the electrical system, PV panels are connected to a DC bus, to which they transfer the generated DC power converted from the solar energy. A DC-AC inverter connected to the DC bus and to an AC bus, will then convert the DC power into AC power, in order to connect the PV system to the AC electrical system. The MPPT control is performed by the inverter, through the control of the DC voltage. A capacitor is used to smoothly adjust power fluctuations between the AC and DC sides.
According to how the PV array is configured, different topologies for inverter connection can be used. Among these, a central inverter is commonly used which has high efficiency and low cost. This is because a single converter is used, with several PV modules connected in series (representing a string) and several strings connected in parallel.
3.3 Solar PV Contribution to Frequency Regulation The most recent grid codes are starting to require, or to recommend, frequency regulation participation from PVs, through operation reference points set by the transmission system operator [28]. The control settings of the frequency response are crucial in order to achieve a fast frequency response without causing further instability in the system, especially in low inertia systems.
In droop-based control methods, the inverter measures the grid’s frequency on the AC side of its terminals and modulates the output power according to the droop settings, which can be pre-programmed with the TSO droop curve reference. This will re-establish the balance between generation and load. However, this will not restore the frequency to the nominal value.
The NREL has performed power hardware-in-the-loop (PHIL) tests on different PV inverters currently available in the market, connected to the grid system of the island of Oahu in Hawaii. The tests performed confirmed that these inverters are currently capable of providing frequency support for over-frequency events, using a droop setting method. However, different inverters perform it differently, since this is often not a requirement in grid codes and therefore there is not one standardized method. Regarding under-frequency
control, it was identified as technically possible but, requires part of the PV capacity to be allocated as reserve, with the PVs operating below their maximum power point [29].
Furthermore, as described in [29], it was found through simulations and tests that:
• Most inverters are not configured to provide frequency support, therefore already installed PVs would have to be reconfigured whereas it is recommended that future installed PVs have their droop control activated upon commissioning.
• When PVs are configured with stepper droop curves, the nadir frequency reduces. However, simulation results indicated the occurrence of higher oscillations, especially when the amount of PVs contributing to frequency regulation is high.
• When using droop-based control with PVs a dead-band must be used in order to avoid excessive actions from the governor control due to small variations in frequency during normal operation. This dead-band must be similar or smaller than the dead- band of synchronous generators’ governor [5]. Simulations in [29] indicated that narrower dead-bands yield a better frequency response. However, the dead-band must be sufficiently sized so that the function is not activated by typical frequency fluctuations.
• PV inverters have demonstrated in the tests that they are capable of fast power ramping, thus the settings on the time response must be fast regardless of the magnitude of the power change, especially in islands with low inertia. The droop recommendation of the study was between 5% and 3%. These values are aligned with the droop settings of synchronous generators therefore yielding a sharing of the primary frequency response. The time response of the droop control for the inverter to perform 90% of the power change is commonly recommended to be below 2 seconds. Lower values are indicated to yield better results. However the interaction with the synchronous generator controls should be carefully analyzed.
Operation Below the MPP (Reserve Capacity)
When PV systems are set to operate at the point of maximum power extraction from the modules, this results in no additional capacity available for frequency control in under- frequency events. In [17], a strategy is proposed to allocate part of the PV-generated power as reserve capacity, which can be injected into the system for frequency control, improving the system’s stability and reducing the need for a storage system or reducing the charging/discharging cycles of existing storage. The power of the PV module is a function of three main parameters: temperature, irradiance and output voltage (or current), as seen from Equations 3.2 and 3.3 in Section 3.2. The strategy proposes to change the output power by adjusting the duty cycle of the converter so that the output voltage is a fraction of the total possible voltage. The remaining fraction is allocated as reserve. The research results showed less stress on the conventional power plants due to reduced power variations as well obtaining faster frequency response with the contribution of PVs to both primary and secondary frequency control.
Figure 3.3 exemplifies the shift in the operating point of the PV from the point of maximum power (with voltage and power: VMPP and PMPP ) to the de-loaded operation
point (at VMPP + V and P1). In this figure, the power reduction was obtained through a voltage increase V. The available reserve power will thus correspond to the subtraction of the new operating point power P1 from the maximum extractable power PMPP.
When using the reserve capacity of the PVs for frequency control combined with fast reaction of PV plants, the conventional generators will only be used for further frequency regulation once the PVs capacity is fully used. This prioritization of the PV generation is designed to reduce the conventional generators’ ramping and emissions. The authors in [26] propose a control strategy to de-load the PV considering the amount of reserve capacity of each PV plant, so that frequency control is performed proportionally to the available power in each PV plant.
Figure 3.3: PV operation outside maximum power point. Source: [26]1.
Secondary Frequency Control in an Unreliable Communication Network Scenario
The most common secondary frequency control relies on a centralized controller to send the frequency correcting signals to each of the generating units that participate in secondary frequency regulation and thus requires a reliable communication network. A supervisory control and data acquisition (SCADA) system can be used in a centralized strategy and connect the on-site equipment to the off-site regulators such as utilities and grid operators.
In remote islands, typically no communication system exists for the secondary control dispatch, due to high costs. In such cases the dispatch is performed manually. A less reliable channel could be used, for instance the internet, however the reliability of such communication networks may be compromised. Therefore, alternative methods have been proposed for a decentralized secondary frequency control, in which each participating generating unit has a local secondary frequency controller, reducing the communication requirement for the secondary control layer and thus increasing the system’s reliability. Nevertheless, even in secondary controls methods without a communication layer such as the one proposed in [20], communication is still required for black start coordination and real-time monitoring. In Figure 3.4, the centralized and decentralized topologies for the secondary frequency regulation can be seen [30].
1Reprinted from International Journal of Electrical Power Energy Systems, 60, P.P. Zarina,S. Mishra,P.C. Sekhar, Exploring frequency control capability of a PV system in a hybrid PV-rotating machine-without storage system, pp. 258-267, Copyright (2014), with permission from Elsevier.
(a) (b)
Figure 3.4: PV control topologies for secondary frequency control, (a) centralized and (b) decentralized. Source: [30].
The main challenge of a decentralized secondary frequency control is to adjust multiple controllers to restore the nominal frequency in an efficient and orderly manner, without causing further system instability. Decentralized control methods based on a distributed- averaging proportional-integral (DAPI) controller and a consensus technique are proposed in the literature. In the former, the generating units will estimate their frequency, send it to some, or all, of the other generating units and calculate the average of the received information in order to determine the set-point for its secondary controller. In this method, although a central controller is not required, there is still a large communication exchange in the network. The "consensus" or continuous-time distributed averaging equation introduced in [31] can be used to calculate a diffusive averaging term. This term can be included in the secondary control, so that the inverters shift the droop characteristic by the same amount and obtain accurate active power sharing. In the consensus technique, communications are required only with neighbouring generating units and are based on multiagent systems theory [32].
In [33], a decentralized secondary control scheme is proposed for a microgrid, composed of a grid-forming controlled inverter and grid-following (PQ controlled) inverters. The former restores the frequency by modifying their frequency reference and the latter by modifying the active power reference in proportion to the frequency error. The inverters are configured so that an active power sharing ratio is maintained between the inverters and no unit is overloaded. A grid-forming inverter is necessary when there is no grid connection or other element in the system that regulates and controls the frequency.
In [20] a control strategy for grid-forming inverters is proposed with two configurations in order to achieve a fast transient response as well as an accurate steady-state frequency restoration, based on a time-dependent protocol. In droop-based primary control, the angular frequency, ω, is reduced by increasing the active power supplied, P , as per Equation 3.4 in which ω0 is the reference angular frequency and m is the droop coefficient.
ω = ω0 −mP (3.4)
The instantaneous active and reactive powers are filtered using a low-pass filter in order to decouple the droop control from the voltage and current control systems [30]. The
filtered active power, P , can be thus expressed as per Equation 3.5 [20]:
P = HLPF p = ωc
s+ ωc p, (3.5)
where ωc is the cut-off frequency, p is instant active power and HLPF is the filter’s transfer function. Because the frequency is a common variable shared by all generating units in the system, local measurements are sufficient to ensure accurate active power sharing in steady-state. This is not the case for reactive power sharing due to different voltage amplitude throughout the grid. Therefore uniform reactive power sharing may lead to undesired reactive power flows in the grid [20].
A secondary control term, δ, is added to Equation 3.4 in order to correct the steady- state error introduced by the primary control, yielding Equation 3.6 and the control block diagram represented in Figure 3.5 [20]. As can be seen in Equation 3.6, the secondary control contribution is in parallel with the primary control contribution.
ω = ω0 −mP + δ (3.6)
c© 2017, IEEE
Figure 3.5: Primary and secondary control block diagram. Source: [20].
In primary frequency control, when using the same droop control slope in different PVs, equal active power sharing will be obtained in all PV units. The droop coefficients must be selected proportionally to ensure active power sharing, as per Equations 3.7 and 3.8. This indicates that although the operation of the frequency control is decentralized, the droop coefficients must be selected having a global knowledge of the system [32]. Due to the proportionality represented in Equation 3.7, a smaller droop coefficient implies a steeper droop curve and thus yields a higher active power reduction. The droop coefficient is thus a measure of each generator’s participation in frequency regulation.
Pimi = Pjmj ,∀i, j ∈ νI (3.7)
Pi
Pimax
= Pj
Pjmax
,∀i, j ∈ νI (3.8)
where νI is the set of inverter nodes, mi and mj are the droop constants of inverters i and j, Pimax and Pjmax are the ratings of inverter i and j and Pi and Pj are the I and j inverters’ nominal active power injection.
The transfer function for δ was initially proposed as a P controller, based on a low pass filter with an additional pole for high frequency attenuation and control gains k0 and k1, given by Equation 3.9 [20]. A low-pass filter is used in order to decouple the primary and secondary control loops [30].
δ = k1
s+ k0k1 (ω0 − ω) (3.9)
With this control model, the frequency error in steady-state is given by Equation 3.10 [20].
e0 = ω0 − ω = k0mP
1 + k0 (3.10)
A modified time-dependent control scheme has been proposed in [20], in order to overcome the trade-off observed in Equations 3.9 and 3.10, for which a faster frequency response yields a higher error. The controller, represented in Equation 3.11, with gains k1 and k(t), will switch between a filtered proportional controller and an integral controller.
δ(t) = k1
The sign function, sgn, is defined in Equation 3.12 [20].
sgn =
0, k (t) = 0. (3.12)
Equation 3.11 can be represented in the Laplace domain as shown in Equation 3.13. For positive values of the cut-off frequency of the low-pass filter, given by k(t), the control is performed by a proportional controller. The low steady-state error is also achieved for values of k(t) close to zero, for which the controller will behave like an integral controller. If the switching characteristic of the proposed control given by sgn was not present, the integral control might lead to an unstable response due to small frequency errors leading to an cumulative value of δ. For values of k(t) equal to zero, δ will assume a constant value C, which corresponds to the last value calculated [20].
δ =
{ k1
C, k (t) = 0. (3.13)
The time-dependent value k(t) will determine the cut-off frequency of the low-pass filter and thus the steady-state frequency error. Higher values of k(t) leads to faster dynamics but higher frequency errors. With the proposed switch control in [20], higher values of k(t) are used immediately after frequency deviation event detection, in order to obtain a fast transient response, and decrease linearly to zero after a total time of ct+ramp, to reduce the steady-state error to negligible values, as shown in Figure 3.6. The constant section of the gain curve allows time for fast active power sharing, whereas the ramp section allows for a smoother transaction between primary and secondary frequency control actions. The
c© 2017, IEEE
Figure 3.6: Time-dependent controller gain for frequency control in (top) single event and (bottom) multievent detection scenario. Source: [20].
event is detected when the frequency or the active power signal deviates by more than a predetermined limit, as suggested by [20]. Four parameters must be chosen: k1, kmax ct
and ramp.
4 | Research Methodology
4.1 Research Hypothesis The research hypothesis is that a small island hybrid system can be optimized with a high share of PV generation in order to meet a renewable target, and that the frequency stability of such a system can be improved by including PV participation in primary and secondary frequency regulation, without communication requirements between the generator units in the system.
The economic optimization of the installed capacity of generators in the hybrid system, as well as the parameters of the PV frequency control are selected for the case study, using real economic and technical data from the existing system. Different operation scenarios are used in order to verify the system stability and test the research hypothesis.
4.2 Methodology Overview The case study consists of an Indonesian island’s electrical system. The research was conducted in the two stages described below.
In the initial stage, data from the island adopted as a case study was collected and analyzed, including the costs specific to the island as well as technical specifications of the existing electrical system. This data was used as input to the tool HOMER, which was used in order to optimize the system’s future installed capacity of generators aiming to meet the 23% renewable target by 2025.
The second stage of the research corresponded to a frequency stability analysis of the optimal scenario resulting from the first stage. The island’s 2025 electrical system was designed in PowerFactory using data from the current system as well as the installed capacity optimized in the first stage. Frequency control for the PVs was designed in PowerFactory and added to the PVs in the island system. The system frequency stability was analyzed in order to validate the designed expansion scenario or to demand a new scenario selection from the first stage. The flowchart of the optimal scenario selection methodology is shown in Figure 4.1. It depicts a combined analysis using the tools HOMER and PowerFactory.
In this chapter, the case study characteristics will be introduced, and each of the research steps will be described in details.
21
Collect economic and technical data from the island
Optimize the 2025 generation to meet renewable target,
using HOMER
Model 2025 island’s grid in PowerFactory, using optimized
installed generation capacity
Perform frequency stability analysis of the island grid, using PowerFactory
Is the scenario feasible?
Optimal generation expansion scenario found
Design PV control strategy, using PowerFactory
Add controller to the PV model on the 2025 island system
no
yes
Chapter 4. Research Methodology 23
4.3 Introduction to Case Study Indonesia comprises over 17 000 islands, out of which approximately half are inhabited, and in which half of the population lives in rural areas [34]. The percentage of households connected to the grid (electrification ratio) has improved significantly in the past few years, rising from 73,7% (2011) to 92.8% (mid 2017) [35]. The remaining percentage represents the greatest challenge, as it corresponds to households in remote locations, thus representing higher investment costs. The majority of rural areas are supplied by local diesel generators, as the low demand in these areas does not justify the investment cost of an infrastructure for an electricity grid.
Within the renewable energy technologies available, solar photovoltaic is still a small percentage of the generation installed capacity in Indonesia, representing 0,63% of the renewable generation capacity installed in 2017. Nevertheless, the installed capacity of this technology has increased in the past 10 years, rising from 5,7 MW in 2007 to 58,06 MW in 2017. In 2017, 71% of the installed capacity of solar photovoltaic technology in Indonesia was located in off-grid systems, mainly PV-battery systems powering small villages [25].
The Indonesian government has set targets for the renewable energy generation to achieve 23% of renewable energy sources by 2025 and 31% by 2050. Renewable energy sources include geothermal resources, hydropower, bioenergy, solar, wind and ocean energy. Within the 23% target of renewable energy utilization, the Ministry of Energy and Mineral Resources (MoEMR) has set subtargets of 10% bioenergy, 7% geothermal, 3% hydropower, and 3% others [36]. In case the renewable target is not met, the remaining renewable generation target will be fulfilled by gas. In order to meet the targets, the “35 GW Programme” was created at the end of 2014 and aims to complete 35 GW of power generation projects within 5 years.
An Indonesian island was selected for a large-scale renewable integration scenario analysis, in which the expansion of the generation will be optimized in order to meet the 2025 national renewable target. The results from the technical-economic analysis performed can be used as lessons learned in the generation expansion planning of similar isolated islands. Although the island’s identity must remain undisclosed in this report, this must not impact the reader’s understanding of it. The selected island will be referred to as "the island" throughout this report. The characteristics of the island’s system are described in Table 4.1.
Table 4.1: Characteristics of the pilot island.
Parameter Value
Population 13 000 (with 4000 Households) Load 0,8 MW peak
3 GWh (2017) Supplied 12 hours/day
Classification Very small grid Power plants 1,312 MW (100% Diesel) Fuel consumption and cost 94 500 liters/month average
0,589 EUR/Liter Generation cost 0,21 EUR/kWh (2017)
Today, the electricity supply of the island is limited to 12 hours per day due to its high cost, with a 13,5 h supply on Sundays. Two new Cummins generators (model C900D5, engine QSK23G3, 656kW/820kVA Prime Rating) have been installed recently, which replaced the previous less efficient and aged generators. A sample power curve of a Sunday during the partial supply regime in 2017 can be seen in Figure 4.2.
0
100
200
300
400
500
600
700
800
900
7 8 9 10 11 12 13 14 15 16:30 18:00 19:30 21:45 23:45 04:30 06:00
D e
m an
d [
kW ]
Hour
Demand in the partial supply regime (Sunday 13,5h supply in 2017)
NO SUPPLY
Figure 4.2: Island’s demand measured sample during a partial supply regime.
During the commissioning of the new generators, trial periods were run with a 24 h supply. A load data sample during the 24 h regime can be seen in Figure 4.3. This load sample will be used as base data to project the load increase for the year of 2025, in which a 24 h supply will be in place.
0
100
200
300
400
500
600
700
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
D e
m an
Demand in the 24h regime (January 2018)
Figure 4.3: Island’s demand measured sample during a 24 h supply regime.
Renewable Generation Potential
The overall renewable energy potential in the island region can be analyzed using data available in [37]. The capacity factor of a power plant represents the ratio of the expected production over a period of time to the maximum production if operating at nameplate
capacity. The daily mean capacity factor for the solar energy in the case study island area is depicted in Figure 4.4, with a total mean capacity factor of 16,5%1.
Corresponding data can be found on Figure 4.5 for the wind potential, and illustrates a very low total mean capacity factor of 2,90%2. The low capacity factor observed for wind energy in this region makes the installation of wind turbines in the island economically unfeasible.
PV System Daily Mean Capacity Factor
Figure 4.4: Daily mean capacity factor of a sample solar PV system in case study location. Annual average of 16,5%. Source: [37]3.
Wind Turbine Daily Mean Capacity Factor
Figure 4.5: Daily mean capacity factor of a sample wind turbine in case study location. Annual average of 2,90%. Source: [37]3.
Solar irradiance measurements from an Indonesian location with similar conditions to those of the case study, shown in Appendix A1, indicated variations of up to 810 W/m2
within one minute, with an average daily irradiance of 420 W/m2. These will be used as reference for the rate of irradiance change per minute in the simulations.
1No tracking, zero tilt and 10% system losses were considered in order to estimate the output. 2The small wind turbine Enercon E40 600 kW, with maximum hub height of 65 meters [38] was used to
estimate the results. 3License to reproduce image granted under the public license: https://creativecommons.org/licenses/by-
nc/4.0/legalcode
4.4 Optimization of Generation Expansion
4.4.1 Structure of the Optimization Problem As shown in the flowchart of Figure 4.1, in order to obtain an optimal scenario for the generation installed capacity, an optimization problem must be structured using the island’s specific technical and economic parameters. An optimization problem structure contains parameters, variables, constraints, limits and an optimization function. The tool selected to model and solve the optimization problem in the case study was the Hybrid Optimization for Multiple Energy Resources (HOMER) software, originally developed by the NREL.
The software HOMER is an optimization tool to select the optimum generation mix based on selected grid components and input parameters such as discount rate, generation costs, fuel costs, capital costs, load characteristics (annual average [kWh/day], daily profile characteristics), energy storage characteristics, among others. The tool searches for the minimum value of the optimization variable, in this project defined as the net present cost of all the costs in the system.
The optimization problem will minimize the Levelized Cost of Energy (LCOE) in the system by choosing the generation sources and corresponding dispatch that will meet the load demand with the lowest cost, whilst meeting the constraints defined by the user. The tool allows setting constraints for the operating reserve as a percentage of load or renewable generation, emission limits as well as for the minimum renewable energy contribution to the total yearly energy production. The structure of the optimization problem in HOMER can be found in Table 4.2.
The values selected for the parameters and constraints of the optimization problem were defined based on the Indonesian national targets, as well as on data collected from the island. These values are input to the tool HOMER and are summarized in the following sections (4.4.2 and 4.4.3).
Table 4.2: Optimization problem structure.
Type Object
Parameters Equipment’s initial capital cost Equipment’s O&M cost Equipment’s replacement cost (replacement at the end of its lifetime) Equipment’s lifetime Generator’s minimum load ratio Diesel fuel price Fuel curve (input as liters/hour, per output power in kW) Equipment’s installed capacity (if already installed in the system) 24 h load profile (kW) Day to day variability (%)1
Nominal discount rate (%) Expected inflation rate (%) Project lifetime 2
Constraints Minimum renewable energy fraction (%)3
Operating reserve (as a percentage of load and/or solar power output) 4
Emissions
Variables Installed capacity of different system components (diesel generators, PV panels, converter and ESS) LCOE, NPC, operating cost, initial capital cost Total fuel consumption (liters/year) Dispatch and operating hours per source Percentage of renewable fraction achieved in the production Dispatch and operating hours per source
4.4.2 Optimization Parameters
Renewable Resource Parameters
In HOMER, the user can insert data for the renewable resources available or select the geographical location of the project. The latter option was used, in which the tool collects the location’s renewable resource data from the NASA Surface Meteorology and Solar Energy Database. This data can also be accessed online at [39].
Section 4.3 introduced the potential of the renewable resources available in the island’s region, highlighting that the wind potential on the island area is unfavorable to the installation of wind turbines. This was confirmed from initial simulations results, after which wind
1Variability randomly added to the load to obtain unique daily load profiles. The load value entered by the user is multiplied by a random value from a normal distribution with mean 1 and standard deviation equal to the day to day variability.
2Duration of the project during which costs occur. Salvage values of components are accounted for at the end of the project’s lifetime.
3Annual share of the generation supplied to the load that was originated from renewable energy. 4Surplus of operating capacity that can be used instantly in case of load or generation sudden variation.
turbines were removed from the optimization problem, in order to reduce the simulation time.
Load Parameters
The load was scaled to 2025 based on the growth forecast of the island’s population, using the sampled data for the demand when the supply is 24 h (shown in Figure 4.3). An additional load increase was accounted for, because the loads and consumption profile are expected to change when moving from a partial electricity supply to a 24 h supply. For example, during the 24 h supply trial period consumers already bought new appliances. A 20% load increase per year was considered. Although this value might seem high it would result in a consumption of only half the Indonesian average by 2025. The load forecast data can be found in Table 4.3. This load considers a variability of 5%, in order to introduce a randomness to the daily load.
Table 4.3: Load forecast scenario with 20% load increase per year.
Year Consumption [GWh/year] Demand [MW]
2018 3,8 0,8 2019 4,6 1 2020 5,5 1,2 2021 6,6 1,4 2022 7,9 1,7 2023 9,5 2,1 2024 11,4 2,5 2025 13,7 3
ESS, PVs and Diesel Generator Parameters
The technical parameters of the system’s components are listed in Table 4.4. The new generators are modelled with the same characteristics of the existing generators.
Economic Parameters
In HOMER, the Levelized Cost Of Energy (LCOE) is calculated by dividing the annualized cost of the electricity production in the system by the total load served. The annualized cost is the annual value of the net present cost, calculated by the product of the total NPC and a the Capital Recovery Factor (CRF), the latter a function of the nominal discount rate and the expected inflation rate parameters [40], [41]. These parameters were selected as the average of historical Indonesian values available at [42] and [43]. These values, the transportation cost considered for total equipment costs and the conversion rate to Indonesian Rupiah (IDR) used for quotations obtained in Euros are listed in Table 4.5.
Table 4.4: Component’s technical parameters.
Component Parameter Value
GENERATOR
Model Cummins C900-D5, engine QSK23G3 Minimum load ratio 10% Prime rating 656 kW/ 820 kVA Standby rating 720 kW/ 900 kVA Lifetime 25 years
Fuel curve 1 Output [kW] Consumption [L/hour]
164 46 328 85 492 121 656 161
PVs
Lifetime 25 years Derating factor2 80% Ground reflectance3 20% No tracking system Temperature effects on power3 -0,5 [%/C ] Nominal operating cell temperature3 47 [C] Efficiency at STC3 13%
LI ION INVERTER 3
Inverter input Lifetime: 15 years Efficiency: 95%
Rectifier input Relative capacity: 100% Efficiency: 90%
LI ION 3 Lifetime 15 years Initial state of charge 100% Minimum state of charge 20%
Table 4.5: Rates for the system’s cost parameters.
Cost parameter Value
Inflation rate [%] 4 Nominal discount rate [%] 8 Conversion rate (on 14.03.18) [IDR/EUR] 16973 Transportation cost to the island4 [EUR/kg] 1,473
1Data from the manufacturer’s equipment datasheet (Cummins C900D5). 2The derating factor reduces the PV output based on different aspects, including aging, inverter and trans-
former, shading, wiring, etc. 80% is used in the installation year (approximated from the 0,77 suggested by NREL’s PVWatts [44]). An additional 1% loss per year was considered for the PV panels, based on aging.
3HOMER default parameters. 4The transportation cost of the existing generators to the island was used as reference for future equipment,
added to their capital cost.
Research was conducted on current and forecasted costs for PVs, Li Ion battery systems and diesel generators. The data gathering focused on current prices in Indonesia as well as on international price forecasts. The conversion rate given in Table 4.5 was used when necessary and the transportation cost shown was used in order to obtain component costs specifically for the island. The economic parameters obtained, and used as inputs to HOMER, are listed in Tables 4.6 and 4.7. The capital cost forecast is based on both technology and installation cost reductions.
Table 4.6: Capital cost forecast of the hybrid system components.
Year PV 1 Battery Inverter4 Batteries (Li Ion) 1 Diesel Generator 5
CC2 CC+tr36 CC CC+tr6 CC CC+tr7 CC+tr 2018 810 990 389 429 272,84 283,15 243,24 2019 758 939 364 404 263,03 273,34 243,24 2020 710 891 341 381 253,57 263,88 243,24 2021 665 846 320 359 244,45 254,76 243,24 2022 623 803 299 339 235,66 245,97 243,24 2023 584 764 280 320 227,19 237,50 243,24 2024 547 727 263 302 219,01 229,32 243,24 2025 512 692 246 286 211,14 221,45 243,24
Table 4.7: Operational costs of the hybrid system components.
Year PV1 Battery Inverter Battery Li Ion1 Diesel Generator5
2018 - 2025 O&M8 O&M O&M Fuel (EUR/L) O&Mv9 O&Mf10
12,16 0 5,7 0,589 0,0097 15000
1Costs calculated based on data from [45]. 2CC: Capital Cost in [EUR/kW] for PV, inverter and diesel generator and in [EUR/kWh] for the batteries. 3CC+tr: Capital Cost including transportation cost to the island, same unit as component’s CC. 4Costs from the quotation of an existing PV plant in the vicinity of the island. 5Costs obtained from the purchase and maintenance contracts of the existing units on the island. 6The year reduction considered was 9%[46] on equipment cost (67% of total, excluding transportation costs)
and -1% on the remaining percentage (installation costs)[45]. 7The year reduction considered was 4,5%[47] on equipment cost (74% of total, excluding transportation
costs) and -1% on installation costs[45]. 8O&M: Operation and maintenance Cost in [EUR/kW,yr] except for Li Ion batteries, in [EUR/kWh,yr]. 9O&Mv: Operation and maintenance variable cost [EUR/kW,hr].
10O&Mf: Operation and maintenance fixed cost [EUR/yr], corresponding to staff costs.
4.4.3 Optimization Constraints The constraints selected for the optimization problem are summarized in Table 4.8. The first constraint in the system is that no capacity shortage is allowed, in order to guarantee that every peak load can be met by the generation installed capacity.
The minimum renewable fraction desired to be achieved is the second constraint defined by the user. This corresponds to the fraction of the energy delivered to the load that is produced from renewable sources. For the year 2025, this value corresponds to the Indonesian target.
The third constraint corresponds to the operating reserve. In a system with a high share of renewable production it is important to have a higher operating reserve in order to cover sudden variations in the renewable production in addition to the load variation, increasing the reliability of the system. This operating reserve can be provided by energy storage systems, rotating machines, the grid, among others. In HOMER simulations, the tool attempts to respect the constraint set for the reserve requirements at each time step, resulting in the operation of an installed capacity larger than would be required to meet the load [48].
The operating reserve as a percentage of the solar power, was defined based on the irradiance variations of up to 80% within a minute, obtained from the data depicted in Figure 6.1, Appendix A1. Higher values for the operating reserve will result in higher costs. However, if the PVs are geographically distributed along the island it is less likely that a cloud movement will affect all units simultaneously with this irradiance variation, thus the operating reserve constraint could be reduced. In the simulations, a conservative approach was used by considering a value of 80%. A constraint that at least one of the diesel generation must be running at all times was also included.
Table 4.8: Optimization constraints.
Optimization Constraints
Maximum annual capacity shortage [%] 0 Minimum renewable fraction [%] 23 Operating reserve as a percentage of load [%] 10 Operating reserve as a percentage of solar power output [%] 80 Diesel-off operation Not allowed
4.4.4 Simulation Structure in HOMER The optimization problem was solved for a system with renewable energy as well as for a system with diesel generators only, for comparison. In order to account for decreasing prices in PV, ESS and converter technologies until 2025 (forecasted as per Table 4.6), a step-wise simulation structure was developed in HOMER, allowing for the optimization of a gradual installation of the renewable energy in the system until 2025. Two year steps were considered for each installation period, resulting in 4 simulations per load scenario with renewable penetration, as well as one additional simulation for the scenario comprising diesel generators only.
The step-wise simulations were structured in a cascade, with the optimal output of each simulation representing inputs to the next simulation as shown in Figure 4.6. The optimized additional installed capacities of the generation at the end of each 2 year period becomes a fixed capacity in the following simulation, with the new load increase to be covered by new installed generators or by a capacity factor increase of the already installed generation.
Figure 4.6: Step-wise simulation inputs and outputs in HOMER.
The cost input parameters for each 2-year step-wise simulation will be the cost of the component in the first year of each simulation period (installation year of that portion of renewable energy). The step-wise simulations are optimized to supply the projected load of the second year of each simulation period (maximum load of that simulation period). The reference years for the cost and load values used in each simulation can be seen in Figure 4.7. Intervals of two years have been used in order to simplify the optimization process. The consequences of these simplifications were higher operation costs, due to the highest load (second year) being used also for the first year of each simulation. The constraint of 23% renewable generation was only included in the last simulation step (year 2025) thus, in the remaining simulations, the installed renewable generation was optimized based on costs only.
Simulation 1
1Includes installed capacity of all previous steps.
4.5 PV Frequency Control Strategy Whereas the tool HOMER can be used to optimize the installed generation capacity for the island grid accounting for economic parameters and system constraints, the behaviour of the electrical system with the resulting installed capacities must be further analyzed using a power system analysis tool. In this section, the methodology utilized for the PV secondary frequency control will be introduced, as well as the test scenarios chosen for the frequency stability analysis of the island system.
4.5.1 Proposed PV Model Features The designed PV control model is based on the method proposed by [20], which was introduced in Section 3.3, as well as in a previous model made by the company Energynautics, which consists on an adaptation of the generic three-phase PV model in Powerfactory. In Table 4.9, a comparison is made between the main features of each model/method.
In the model previously adapted by Energynautics, the PVs were designed to participate in LFSM-O, by curtailing their power output in case of an over-frequency event that exceeded the frequency dead-band. The PVs return to their maximum power point output once the frequency decreases to a value within the hysteresis dead-band. The power curtailment is performed based on a user pre-defined droop curve and frequency dead-band setting. The PVs have no active power reserve and thus operate by default on their MPP. This model also includes reactive power control.
Compared to the previous Energynautics model and to the method proposed by [20], the proposed model has the main advantages of:
• Improvement of the system’s frequency response to under-frequency events, by enabling the PV contribution to such events through the allocation of a fraction of the PV’s installed capacity as a reserve.
• Differentiation of the active power sharing between PVs and diesel generators during the over and under-frequency regulation process, through different over and under- frequency control parameter settings of the PV units. During under-frequency events, PVs should contribute more to the required generation increase, wh

decentralized secondary frequency control in an optimized

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