Browsing by Author "Chowdhury, Sunetra"

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    Open Access
    Creating additional network capacity on constrained medium voltage networks utilizing distributed generation (specifically PV technology)
    (2024) Ramdhin, Avinash; Chowdhury, Sunetra
    Medium voltage (MV) networks are designed for forward power flow and they radially distribute power to various types of electrical loads. With electrical load growth driven by economic growth, aging networks have limited network capacity to supply the increase in load demand and non-technical losses, such as theft, further exacerbate the problem. This incremental load growth results in the network feeder having unacceptable voltage regulation and/or thermal limitations and such a network is defined as a constrained network where no new load or increase of existing load can be connected. Short-term and long-term mitigation solutions are implemented on these constrained networks to create more electrical capacity to meet the rising load demand. These solutions and investment thereof are also influenced by strategic load forecasting and may include the installation of voltage regulators, shunt compensation, new substations and/or various other network strengthening solutions. Long-term solutions are generally quite costly and the timeline for implementation is extremely long (>3 years). Short-term solutions are limited by equipment ratings and as such the network capacity is improved but by a relatively lower percentage. Due to the fixed and limited output of these devices, an alternate, additional un-constraining mechanism is required. Integrating distributed generation (DG) to medium voltage (MV) networks can improve or worsen the operating level of the network. However, linking this balance of network improvement to the amount of generation would improve the operating level. This research therefore utilizes the integration of DG, specifically solar photovoltaic (PV) installations, as an alternate approach to improve the capacity of constrained electricity networks. Solar PV technology is becoming practically feasible in its installation and cost; and is being supported by industrial and residential load types. The literature review compiled in this thesis highlights the various network improvement solutions utilized to assist MV networks in operating within their grid code regulations. The research then develops a coded method to be utilized for network analysis in DIgSILENT Powerfactory supported by a data analytic interface in Microsoft Excel. This analysis optimally places PV to the network to maximize network capacity and is quantified by defining an objective function that relates network capacity improvement to DG power generation. Technical guides, policies, distribution standards and grid codes that govern the integration of DG to MV and LV (low voltage) networks determine the mathematical constraints of the objective function. The non-linear solutions to the function results in optimizing the amount and allocation of distributed DG along the MV feeder hence creating additional capacity on constrained networks. Particle Swarm Optimization (PSO) was found to be best suited for this research due to its efficiency to solving non-linear optimization problems and is proposed as the appropriate method of optimally integrating DG to un- constrain MV networks. Suitable applications of the assessment tool are placing microgrids and electric vehicle charging infrastructure. Two realistic and practical electrical networks (11 kV and 22 kV) supplying rural, commercial and industrial type loads were chosen as test networks. These networks were modelled in DIgSILENT Powerfactory by firstly using the manual method of connecting DG to the network and then secondly by applying the developed DPL script to the same network. Then results were compared to investigate what optimal PV magnitude and point of connection led to what increase in network capacity for both methods. These results are summarized below. For Network 1, a very interesting relationship was seen when calculating the objective function to achieve the percentage improvement per MW of PV generation added to the network. Different scenarios were used such as, a constrained feeder during a light load scenario was modelled using the above methodology. The network capacity to PV generation ratio (objective function) indicates that the network capacity improvement of 67% could be achieved per MW of generation added. Similarly, for the normal feeder peak load scenario, the objective function (%/MW) was approximated around 40%/MW. The developed tool results correlated closely with the results from the manual method of placing PV on MV networks to maximize network capacity. The applicability of the derived results can be, for example interpreted as such, if one is to install 400 kW of PV on Network 1, the objective function for the peak times is 39%/MW so for 0.4 MW, the network capacity improvement is calculated by 39% x 0.4 = 15.6%. A similar approach was applied to Network 2 and similar results were derived albeit Network 2 having a voltage regulator as the voltage controlling device on the network. It was concluded from the analysis that there is no linear relationship on the amount of generation and position on a network to which it may be installed. The many tee-off points on the backbone and the variation of load with respect to its location, speaks to the uniqueness of every network and no general rule can be made for PV integration. However, the ratio of the network capacity increase to the amount of generation is more or less consistent for every scenario.
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    Electrical performance and economic feasibility analyses of hybrid and battery storage devices used in remote area islanded renewable energy systems
    (2016) Chotia, Imran; Chowdhury, Sunetra
    South Africa has the fifth largest coal based utility grid in the world, unfortunately many regions in the country are simply too remote for connection with this grid thus have no electricity access [1]. Many remote areas possess high wind speeds and solar irradiance exposure, which makes them ideal for Renewable Energy Systems (RES) but the electrical and economic viability of this deployment, is still in question. Based on these observations, an electrical performance analysis and economic feasibility study based on islanded RES deployment in remote areas of SA is conducted. RES growth is restricted to the effectiveness of its energy management strategy. Pumped Hydro Storage (PHS) is the cheapest islanded large scale storage option but its assignment is restricted to applicable an landscape and terrain [2], [3]. After conducting a critical review, the Lead Acid Battery Storage System (BSS) and Hybrid Battery Supercapacitor Storage (HBS) were over the PHS. A theory development study on established generations systems and storage models was used to compare software designs which resulted in the selection of Matlab software for electric performance analysis and HOMER for the economic feasibility study. The electric performance analysis was divided into three case studies based on the input power supply, viz. ideal voltage source, Solar Photo Voltaic (PV) and Wind Energy Conversion System (WECS), with each case being connected to a BSS and HBS. A load profile and solar and wind resource investigation was conducted using the NASA, Wind Atlas of South Africa (WASA) and Solar GIS database. Electrical cases were modelled in Matlab and evaluated in terms of power security, load matching, power response and charge algorithm accuracy. The results showed that deploying an islanded RES in South Africa is indeed electrically feasible based on the high power security, load matching accuracy, and disturbance response seen in the solar-RES cases. The wind-RES maintained an uninterruptable power supply but failed to match the load as accurately. Cases which used the HBS showed improvements in power stability; load fluctuation response and an extension of storage device lifespan when compared to the BSS connected cases. This was due to the supercapacitor high power density which made it ideal for the compensation of RES and load fluctuations. Three new cases were established for the economic study as follows; solar, wind and hybrid solar-wind generation all tested under BSS and HBS conditions once again. A socio economic study established the region of deployment, natural resources, terrain, landscape as well as the price of WECS, PV, storage, and converter components. These findings were used in HOMER to construct an optimised combination of components required for the supply of a 5MWh/d average load. This was followed by a sensitivity analysis which conducted 14 different optimisations at loads ranging from 1-10MWh/d. Economic benefits of the supercapacitor power density was uncovered through a reduction of the required RES Peak Operating Reserve (POR) capacity. This is especially significant in islanded RES, as they demand large POR in order to maintain autonomous power supply. This amounted to substantial NPC savings ranging from $1 - $7.5 million for the 25 year project. What was more interesting was the hybrid wind-solar generation results of the last case which extended total NPC savings, by up to $10 million. The hybrid-HBS does show some POR reductions which brought the COE to 0.3$/kWh on average, with the hybrid-BSS at 0.35$/kWh. The hybrid-BSS is slightly more expensive but has a reduced complexity which can be more inviting to project engineers therefore both hybrid cases are exceptionally feasible for local RES deployment. Single source RES is indeed electrically and economically feasible and shows extended sizing and performance benefits when implementing HBS. However, the cost reductions and performance benefits of hybrid generation make it the most practical solution to islanded RES in South Africa.
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    Intelligent voltage dip mitigation in power networks with distributed generation
    (2014) Ipinnimo, Oluwafemi; Chowdhury, Sunetra
    The need for ensuring good power quality (PQ) cannot be over-emphasized in electrical power system operation and management. PQ problem is associated with any electrical distribution and utilization system that experiences any voltage, current or frequency deviation from normal operation. In the current power and energy scenario, voltage-related PQ disturbances like voltage dips are a fact which cannot be eliminated from electrical power systems since electrical faults, and disturbances are stochastic in nature. Voltage dip tends to lead to malfunction or shut down of costly and mandatory equipment and appliances in consumers’ systems causing significant financial losses for domestic, commercial and industrial consumers. It accounts for the disruption of both the performance and operation of sensitive electrical and electronic equipment, which reduces the efficiency and the productivity of power utilities and consumers across the globe. Voltage dips are usually experienced as a result of short duration reduction in the r.m.s. (r.m.s.- root mean square) value of the declared or nominal voltage at the power frequency and is usually followed by recovery of the voltage dip after few seconds. The IEEE recommended practice for monitoring electric power quality (IEEE Std. 1159-2009, revised version of June 2009), provides definitions to label an r.m.s. voltage disturbance based upon its duration and voltage magnitude. These disturbances can be classified into transient events such as voltage dips, swells and spikes. Other long duration r.m.s. voltage variations are mains failures, interruption, harmonic voltage distortion and steady-state overvoltages and undervoltages. This PhD research work deals with voltage dip phenomena only. Initially, the present power network was not designed to accommodate renewable distributed generation (RDG) units. The advent and deployment of RDG over recent years and high penetration of RDG has made the power network more complex and vulnerable to PQ disturbances. It is a well-known fact that the degree of newly introduced RDG has increased rapidly and growing further because of several reasons, which include the need to reduce environmental pollution and global warming caused by emission of carbon particles and greenhouse gases, alleviating transmission congestion and loss reduction. RDG ancillary services support especially voltage and reactive power support in electricity networks are currently being recognized, researched and found to be quite useful in voltage dip mitigation.
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    Metering and adaptive protection for a microgrid with distributed generation
    (2013) Buque, Claudio; Chowdhury, Sunetra
    The main objective of this project is to develop an adaptive relaying system that will protect the microgrid both in connected and isolated modes. Therefore the settings for the different relays will be observed for the two modes of operation. This will determine whether they are correctly coordinated in order to operate as an adaptive relaying system. A secondary but also important objective is to identify load management techniques through smart metering that could facilitate power system operation and in turn power system protection. To achieve the goal of this project the proposed relaying system will have to prove appropriate in all the test cases. Based on the results obtained in the simulations, conclusions about the relaying scheme were drawn. Based on cases where the scheme seemed inappropriate or could be improved, recommendations were made. The relaying scheme proposed in this project proved highly successful in detecting abnormalities and protecting the power system when necessary.
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    Modelling and macroeconomic analysis of a Solar PV/diesel hybrid power plant
    (2015) Mthwecu, Sabatha; Chowdhury, Sunetra
    This research thesis covers the latest research on renewable energy globally and focuses on the solar panel and biofuels market. A full macroeconomic analysis is done on the Chinese Taipei, and this results in some parameters which then become the basis of this research. The macroeconomic parameters are then put into a tabular form and applied to India, Turkey and Australia to see how much weight the analysis can hold and if there is enough data per country on the macroeconomic parameters chosen. This research thesis conducts a shorter, custom version of a macroeconomic analysis on a South African area, and considers the national Gross Domestic Product, pollution, length of transmission lines, weather factors such as sunlight and temperature and more. Following from this, a hybrid power system is developed under these circumstances and the information is compared with past research. A very informative discussion is then had as to what the model means on a macroeconomic scale and how it performs technically. The technical solution at this point has no economic barriers. Economics can be a tool and not a financial hurdle in the face of technological advancement.
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    Optimal energy management for a grid-tied solar PV-battery microgrid: A reinforcement learning approach
    (2022) Muriithi, Grace; Chowdhury, Sunetra
    There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases.
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    Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach
    (2021-05-08) Muriithi, Grace; Chowdhury, Sunetra
    In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.
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    Optimal sizing and placement of independent power producers on MV, HV and EHV networks for minimum power line losses using neural networks
    (2024) Lombard, James; Chowdhury, Sunetra
    In June 2021, the South African president announced the adjustment of schedule 2 of the Electricity Regulation Act. The effect of this adjustment was to increase the National Energy Regulator of South Africa's (NERSA) licensing limit for renewable projects applying for direct connection to the national grid, having export capacities ranging from 1 MW to 100 MW. This increased the number of Independent Power Producers (IPPs) seeking connection to the South African electricity transmission system (TS) or distribution system (DS) – which, as a mandatory requirement, are to comply with the minimum technical and design grid connection regulations stipulated within the South African Grid Code (SAGC) covering renewable energy. The contribution of this thesis is therefore the design and testing of an ANN model that locates and sizes IPPs of Category B (1 MW to 20 MW) and Category C (> 20 MW) of the SAGC seeking connection to MV/HV/EHV backbone feeders from geographical locations far from the Point of Connection (POC). In the ANN model developed using MATLAB, the user is prompted to enter specific network parameters applicable to the grid connection study. These parameters include backbone voltage (kV), backbone length (km), interconnecting feeder length (km), maximum load seen at the receiving end (MW), distance from the IPP at the POC to the sending end busbar as a percentage of the total backbone length (km) and the load power factor at the receiving end. Based on these inputs, the model determines the most suitable IPP location, IPP size, interconnecting conductor and IPP power factor in order to achieve the lowest overall power line losses for the network. The algorithm consists of 7 ANN models, with each ANN model applied specifically to a unique nominal voltage network undergoing IPP interconnection. Seven cases are presented starting from the lowest voltage test case (11kV) to the highest voltage test case (400kV). Case 1 and Case 2 test the 11kV and 22kV ANN models on modified IEEE 13– bus systems, while Case 3 to Case 7 test 66kV, 132kV, 220kV, 275kV and 400kV ANN models on modified IEEE–14 bus systems. For the 11kV test case, the user enters input parameters: backbone conductor length of 5km, receiving end power of 4.05MVA and receiving end power factor of 0.85 (lagging). The 11kV ANN model returns an IPP size of 1.5MW operating at 0.975 (lagging) power factor, located 4.5km from the sending end substation using an ACSR Chickadee conductor. The 11kV ANN model also returns a total line loss value of 0.05351MW, while the true loss value is shown to be 0.05343MW (when compared to DIgSILENT Powerfactory simulations). This translates to an error of 0.1684%. The 22kV case is presented using the same network parameters as the 11kV case but uprated to nominal voltage of 22kV. The same trend is seen for the 22kV case but with total losses significantly less than the 11kV case due to the increased network voltage. For the 66kV test case, the backbone conductor considered is a 15.6 km ACSR Kingbird with receiving end power 30MW operating at 0.95 lagging power factor. The 66kV ANN model recommends an optimal IPP size of 12MW operating at 0.975 (lagging) power factor, located 14.04km from the sending end substation using an ACSR Kingbird conductor. The 66kV ANN model also returns a total line loss value of 0.0193MW, while the true loss value is shown to be 0.01868MW when compared to DIgSILENT Powerfactory. This translates to an error of 3.29%. The 132kV test case achieves a prediction error of 0.775% and returns an optimal IPP size of 67.5MW, located 31.5km from the sending ending busbar on the 35km backbone feeder, operating at 0.95 lagging power factor. For the EHV cases (220kV – 400kV), the same trend is seen. For the 220kV network, the lowest losses are seen for an IPP connected furthest away from the sending end (120.9km) along the 134km backbone with receiving end power 201MW at 0.95 lagging power factor. This requires a 110MW IPP at operating at 0.95 lagging pf resulting in 3.7MW of line losses using a Single ACSR Zebra interconnecting conductor. It is shown that for an IPP operating at 0.95 leading power factor, the total system losses increase to 5MW, indicating that the algorithm predicted correctly. The 275kV case has lowest losses for a 110MW IPP size operating at a lagging power factor of 0.95. This generates 1.8MW of losses (approximately 500kW lower than the capacitive case), but also is significantly lower than the 220kW case since a twin conductor Zebra bundle is used for the interconnecting feeder. v Since the 400kV network is modelled using quad Zebra backbone conductors, losses are significantly smaller than the 220kV and 275kV cases, which only used a Twin bundle conductor geometry per phase. This increased the geometric mean radius which increased the maximum power transfer of 150MW required at the receiving end. Since the power factor at the 400kV receiving end load is unity, the required reactive VAR support, in addition to the high voltage level (400kV at 1.04pu), saw an optimal IPP power factor setpoint of 0.95 (leading) resulting in a surplus of VARs. For a 138km backbone feeder with receiving end load of 150MW at unity power factor, the 400kV ANN model returns a total loss value of 179kW. The model developed can be used as a tool for providing additional support to network engineers and independent power producers (IPPs), especially for performing grid application studies. DIgSILENT PowerFactory power system simulation software is used to verify the accuracy of the algorithm. This tool is especially relevant for current needs and caters specifically to IPP units that fall under Category B and Category C of the SAGC, since these are rapidly growing in today's South African Energy Sector
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    Open Access
    Optimal sizing and placement of independent power producers on MV, HV and EHV networks for minimum power line losses using neural networks
    (2024) Lombard, James; Chowdhury, Sunetra
    In June 2021, the South African president announced the adjustment of schedule 2 of the Electricity Regulation Act. The effect of this adjustment was to increase the National Energy Regulator of South Africa's (NERSA) licensing limit for renewable projects applying for direct connection to the national grid, having export capacities ranging from 1 MW to 100 MW. This increased the number of Independent Power Producers (IPPs) seeking connection to the South African electricity transmission system (TS) or distribution system (DS) – which, as a mandatory requirement, are to comply with the minimum technical and design grid connection regulations stipulated within the South African Grid Code (SAGC) covering renewable energy. The contribution of this thesis is therefore the design and testing of an ANN model that locates and sizes IPPs of Category B (1 MW to 20 MW) and Category C (> 20 MW) of the SAGC seeking connection to MV/HV/EHV backbone feeders from geographical locations far from the Point of Connection (POC). In the ANN model developed using MATLAB, the user is prompted to enter specific network parameters applicable to the grid connection study. These parameters include backbone voltage (kV), backbone length (km), interconnecting feeder length (km), maximum load seen at the receiving end (MW), distance from the IPP at the POC to the sending end busbar as a percentage of the total backbone length (km) and the load power factor at the receiving end. Based on these inputs, the model determines the most suitable IPP location, IPP size, interconnecting conductor and IPP power factor in order to achieve the lowest overall power line losses for the network. The algorithm consists of 7 ANN models, with each ANN model applied specifically to a unique nominal voltage network undergoing IPP interconnection. Seven cases are presented starting from the lowest voltage test case (11kV) to the highest voltage test case (400kV). Case 1 and Case 2 test the 11kV and 22kV ANN models on modified IEEE 13– bus systems, while Case 3 to Case 7 test 66kV, 132kV, 220kV, 275kV and 400kV ANN models on modified IEEE–14 bus systems. For the 11kV test case, the user enters input parameters: backbone conductor length of 5km, receiving end power of 4.05MVA and receiving end power factor of 0.85 (lagging). The 11kV ANN model returns an IPP size of 1.5MW operating at 0.975 (lagging) power factor, located 4.5km from the sending end substation using an ACSR Chickadee conductor. The 11kV ANN model also returns a total line loss value of 0.05351MW, while the true loss value is shown to be 0.05343MW (when compared to DIgSILENT Powerfactory simulations). This translates to an error of 0.1684%. The 22kV case is presented using the same network parameters as the 11kV case but uprated to nominal voltage of 22kV. The same trend is seen for the 22kV case but with total losses significantly less than the 11kV case due to the increased network voltage. For the 66kV test case, the backbone conductor considered is a 15.6 km ACSR Kingbird with receiving end power 30MW operating at 0.95 lagging power factor. The 66kV ANN model recommends an optimal IPP size of 12MW operating at 0.975 (lagging) power factor, located 14.04km from the sending end substation using an ACSR Kingbird conductor. The 66kV ANN model also returns a total line loss value of 0.0193MW, while the true loss value is shown to be 0.01868MW when compared to DIgSILENT Powerfactory. This translates to an error of 3.29%. The 132kV test case achieves a prediction error of 0.775% and returns an optimal IPP size of 67.5MW, located 31.5km from the sending ending busbar on the 35km backbone feeder, operating at 0.95 lagging power factor. For the EHV cases (220kV – 400kV), the same trend is seen. For the 220kV network, the lowest losses are seen for an IPP connected furthest away from the sending end (120.9km) along the 134km backbone with receiving end power 201MW at 0.95 lagging power factor. This requires a 110MW IPP at operating at 0.95 lagging pf resulting in 3.7MW of line losses using a Single ACSR Zebra interconnecting conductor. It is shown that for an IPP operating at 0.95 leading power factor, the total system losses increase to 5MW, indicating that the algorithm predicted correctly. The 275kV case has lowest losses for a 110MW IPP size operating at a lagging power factor of 0.95. This generates 1.8MW of losses (approximately 500kW lower than the capacitive case), but also is significantly lower than the 220kW case since a twin conductor Zebra bundle is used for the interconnecting feeder. v Since the 400kV network is modelled using quad Zebra backbone conductors, losses are significantly smaller than the 220kV and 275kV cases, which only used a Twin bundle conductor geometry per phase. This increased the geometric mean radius which increased the maximum power transfer of 150MW required at the receiving end. Since the power factor at the 400kV receiving end load is unity, the required reactive VAR support, in addition to the high voltage level (400kV at 1.04pu), saw an optimal IPP power factor setpoint of 0.95 (leading) resulting in a surplus of VARs. For a 138km backbone feeder with receiving end load of 150MW at unity power factor, the 400kV ANN model returns a total loss value of 179kW. The model developed can be used as a tool for providing additional support to network engineers and independent power producers (IPPs), especially for performing grid application studies. DIgSILENT PowerFactory power system simulation software is used to verify the accuracy of the algorithm. This tool is especially relevant for current needs and caters specifically to IPP units that fall under Category B and Category C of the SAGC, since these are rapidly growing in today's South African Energy Sector
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    Open Access
    Power quality enhancement in electricity networks using grid-connected solar and wind based DGs
    (2017) Matlokotsi, Tlhoriso; Chowdhury, Sunetra
    The integration of DG into utility networks has significantly increased over the past years primarily as a result of growing energy demand, coupled with the environmental impacts posed by conventional fossil fuel-based power generation. The prominent DG technologies which are capable of meeting bulk energy demands and are clean energy sources are wind and solar energy sources. The resources for solar and wind based DG are available in abundance in most geographical locations in South Africa and the rest of Africa. Through the Renewable Energy Independent Power Producer Procurement Programme (REIPPPP) introduced by the South African government in 2011, 3 920 MW of renewable energy has been procured to date. Out of this, solar and wind energy constitute 2 200 MW and 960 MW, respectively. Grid integration of solar and wind-based intermittent DGs may however pose negative impacts on the quality of power supplied by the utility network. Some of the detrimental impacts of DG include voltage fluctuations, flicker, etc. which are in general categorised as power quality (PQ) problems. The proper planning of DG integration is required to mitigate the negative impacts they pose on system's PQ to ensure that the performance of the utility network is enhanced in terms of the overall PQ improvement of the system. This dissertation reviews general PQ problems in utility networks with DG integration and whether poor planning of DG integration affects PQ negatively. The work emphasizes on the impacts of grid integration of wind and solar PV sources on power quality. It investigates the manner in which wind and solar energy systems differ in their impacts and capacity to improve PQ of the network in terms of a number of factors such as point of integration and capacity of DG, type of DG, network loading, etc. The role of grid-integrated DG in PQ improvement in electricity network is also investigated by exploring different PQ improvement techniques. The networks considered for the grid integration of DG for PQ improvement in this work are the IEEE 9-bus sub-transmission network at the nominal voltage of 230kV and the IEEE 33-bus distribution network at the nominal voltage of 12 kV. The aspects essential for facilitating proper planning of DG integration for PQ improvement and total loss reduction are investigated and the comparative analysis is made between grid integration of wind and solar PV based DGs. The simulations of different case studies in this work are done using DIgSILENT PowerFactory version 14.1 as well as coding in MATLAB. The cases studies conducted are aimed at facilitating the proper planning of grid integration of wind and solar PV-based DGs by comparing their PQ improvement capabilities under different scenarios. First the investigation of how their location and capacity affect the network voltage profiles and active power losses is conducted. Their ability to improve the system's PQ is also studied by observing PQ improvement strategies such as voltage control, installation of energy storage and the optimal placement of DGs under different scenarios. In order to account for the weakness of most South African utility grids, PQ improvement in weak networks with DG integration is also studied by investigating how DG integration in networks with different grid strengths affect the system's PQ. The results provide an understanding of the role of grid integration of wind and solar based DGs on PQ which is useful in the planning of grid integration of RE, particularly in South African electricity networks. The results revealed that the location and capacity of integrated DGs indeed affect the quality of power as well as active power losses in the grid. It is also established that a significant improvement in network's PQ and line loss reduction can be achieved in networks with wind and solar integration. The results however indicated that wind and solar PV based DGs differ in their impacts and capacity to improve the quality of power in the network. Furthermore, the results revealed that wind and solar plants integration into weak utility grids may pose adverse impacts on the system's PQ. It was however established that including reactive power control devices such as STATCOM and SVC at the PCC can successfully improve the system's PQ and enable grid code compliance in electricity networks with DG integration.
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    Techno-economic comparison of standalone microgrids for rural electrification in South Africa
    (2018) Patel, Himal; Chowdhury, Sunetra
    Rural electrification is a global problem that primarily affects developing countries. The people worst affected are people living in sub- Saharan Africa. There are number of reasons why rural electrification is generally low. People in rural areas generally live in small communities, located far away or from the grid or in geographically tough terrain. As a result, it is not financially viable to extend the grid to these areas and therefore they remain unelectrified. Another dictating factor, is the fact that people in these areas are generally poor, and therefore this discourages any investment from the private sector. This dissertation focuses on rural electrification in South Africa specifically. Most people in South Africa affected by not being electrified live in rural areas on the border between the Eastern Cape and Kwa-Zulu Natal. As it is too expensive to extend the grid to these areas, off-grid options, such as microgrids were investigated. A large amount of research has been carried out on hybrid microgrids as a solution to rural electrification. However, a limited amount of research has been carried out on single source microgrids. Furthermore, South Africa is fortunate to have an abundance of solar, wind and microhydro resources, however, it is unclear which resource would be cheapest based on the location of the rural area. As a result, the aim of thesis was to analyse the impact of the strength of the resource when implementing a microgrid and comparing the three different renewable resources systems against one another. In order to carry out this analysis, three unelectrified villages were selected with each village located in an area of a strong resource, whether it be wind, solar or microhydro. i.e. one village was selected in an area with a strong solar resource, the second in an area with strong wind resource and the third in an area with strong microhydro resource. Once selected, a load for each village was modelled and the resource data for each village was obtained using open source sites. Solar-battery, wind battery and microhydro-battery systems were modelled for each village using HOMER. From the results it was clear that when comparing the same resource in each of the villages, then the strength of the resource did affect the levelised cost of energy i.e. the stronger the resource, the less the lower the cost of energy which was as expected. However, when comparing the solar, wind and microhydro system in each village against each other, it was apparent that the strength of the resource did not dictate the type of technology to be used in that area. It was found that wind systems were not suited to small scale generation, whilst microhydro was the cheapest technology in each village, however, its implementation may be deterred by non-technical issues such as the social and environmental impacts of constructing a dam. The cost of the solar system was comparable to microhydro only when the irradiation was above a certain level. As solar systems are easier and quicker to implement it is possibly the best system in general for rural areas in South Africa. Implementation of off-grid systems for rural electrification in South Africa is a viable option however, as the private sector is not incentivised to implement these systems, then government back in the form of grants and subsidies are required to implement these systems. However, as renewable technologies improve and get cheaper with time, this option to electrify rural areas is always becoming cheaper.