Isolated Power System Research Articles

Frequency Control of Isolated Power System Integrated with Renewables using Biogeography based Krill Herd Migration Optimized Controllers

Frequency Control of Isolated Power System Integrated with Renewables using Biogeography based Krill Herd Migration Optimized Controllers

Coordinated Control of Demand Response for Large Installation of Renewable Energy Sources in Isolated Islands

Renewable energy sources are attractive for supplying electricity to isolated island power systems. When the penetration rate of Photovoltaic Systems (PVs) becomes large, the electricity demand cannot consume all of the PV output, but the PV output needs to be curtailed. Demand Response (DR) of heat pump water heaters and battery energy storage systems can reduce the curtailment. Waterworks systems are also suitable for DR resources because many of the waterworks systems have large tanks or dams as water storages. In order to utilize the large flexibility of waterworks systems fully, multi-daily coordinated control of the DR resources will be needed. This paper constructs the optimization model of the isolated power system with several DR resources as a first step of making coordinated control method. Comparing the operation of DR resources between 2-weeks optimization and 1-day optimization, the effect of long-term planning is analyzed with 5 PV capacity settings. Simulation results indicate that the suitable rules of DR coordinated control differ according to the seasons and installed PV capacity.

Coordinated frequency control for isolated power systems with high penetration of DFIG-based wind power

Coordinated frequency control for isolated power systems with high penetration of DFIG-based wind power

Model-Based Control Strategy to Reduce the Fault Current of a Gas Turbine Synchronous Generator under Short-Circuit in Isolated Networks

The fault protection devices that are used in high power isolated power systems require attention because of the high current levels. Such installations might benefit if their short-circuit current levels are reduced by control, rather than by inserting series impedance to the generators that would otherwise worsen regulation and increase size. This work proposes a strategy for controlling the fault current by actuating on the excitation system of a synchronous generator. The aim is to reduce fault levels during, both, transient and steady-state. The proposed strategy utilizes an adaptive algorithm working as an optimal model-based controller with current feedback, whenever a fault is detected and until its clearance. The used methodology comprises the modeling of the synchronous generator, as well as of the main sub-elements that are dynamically involved in the system, namely the exciter and the gas turbine models. The analyzes and simulations results are obtained for a round-rotor synchronous generator with a nominal power of 31.25 MVA. The proposed strategy effectively reduces the fault current amplitudes during transient dynamics compared to the system operating with a typical automatic voltage regulator (AVR).

Isolated and Interconnected Multi-Area Hybrid Power Systems: A Review on Control Strategies

Concerned with the increasing greenhouse gases in the atmosphere due to fossil fuels, the entire world is focusing on electricity generation through renewable energy resources. The most advantageous aspect of the distributed renewable sources is to provide the electricity to remote, scattered and the deprived rural areas by developing the hybrid power system at the smaller scale where power transmission through grid extension is not viable due to some economical, technical or environmental constraints for building new transmission lines. An accurate and adequate control strategy becomes inevitable to uphold the smooth operation by restraining the frequency and voltage deviation within its limit ensuring the highest degree of reliability of hybrid power system to provide an adequate power quality. In this paper, a comprehensive review of different control strategies adopted in isolated and interconnected multi-area hybrid power systems is presented.

The role of modified diesel generation within isolated power systems

Traditional engine load limits restrict a diesel engine's range and its ability to load share. Accordingly, to achieve high renewable penetrations, diesel generation is conventionally turned off, positioning diesel in competition with variable renewable generation. While this approach maximises renewable penetration (environmental gain), it is uneconomic, requiring costly and complex enabling technologies such as battery storage. For many isolated communities the commercial, technical and social barriers associated with storage integration prove insurmountable. A stalemate results, with owners unable to transition to renewable generation due to inflexible diesel technologies. In this respect, it is easy to view diesel generation as an obstacle to renewable integration. Thankfully, modified diesel technologies can offer improved flexibility without the cost or complexity of energy storage. This paper details the development and testing of both low load and variable speed technologies, each able to improve the performance of diesel generation. Two isolated power system case studies are modelled, with Cape Barren island increasing renewable penetrations to between 41.3% to 58.0% compare to conventional penetrations of 7.7% to 10.1%. For Lady Elliot island the modelled range is 26.7% to 28.5% in comparison to a base of 5.3%. The results serve to recommend modified diesel application as a precursor to battery storage integration.

Medium-term load forecasting in isolated power systems based on ensemble machine learning models

Over the past decades, power companies have been implementing load forecasting to determine trends in the electric power system (EPS); therefore, load forecasting is applied to solve the problems of management and development of power systems. This paper considers the issue of building a model of medium-term forecasting of load graphs for EPS with specific properties, based on the use of ensemble machine learning methods. This paper implements the approach of identification of the most significant features to apply machine learning models for medium-term load forecasting in an isolated power system. A comparative study of the following models was carried out: linear regression, support vector regression (SVR), decision tree regression, random forest (Random Forest), gradient boosting over decision trees (XGBoost), adaptive boosting over decision trees (AdaBoost), AdaBoost over linear regression. Isolation of features from a time series allows for the implementation of simpler and more overfitting-resistant models. All the above makes it possible to increase the reliability of forecasts and expand the use of information technologies in the planning, management, and operation of isolated EPSs. Calculations of the total forecast error have proved that the characteristics of the proposed models are high quality and accurate, and thus they can be used to forecast the real load of a power system.

Adaptive ensemble models for medium-term forecasting of water inflow when planning electricity generation under climate change

Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of electricity at hydroelectric power plants, taking into account the regulation in the medium term. Medium-term regulation is necessary to amplify the load in the peak and semi-peak portions of the load curve. The solution to such problems is aggravated by the lack of sufficiently reliable information on water inflow and prospective power consumption, which is of a stochastic nature. In addition, the mid-term planning of electricity generation should consider the seasonality of changes in water inflow, which directly affects the reserves and the possibility of regulation. The paper considers the problem of constructing a model for medium-term forecasting of water inflow for planning electricity generation, taking into account climatic changes in isolated power systems. Taking into account the regularly increasing effect of climate change, the current study proposes using an approach based on machine learning methods, which are distinguished by a high degree of autonomy and automation of learning, that is, the ability to self-adapt. The results showed that the error (RMSE) of the model based on the ensemble of regression decision trees due to constant self-adaptation decreased from 4.5 m3/s to 4.0 m3/s and turned out to be lower than the error of a more complex multilayer recurrent neural network (4.9 m3/s). The research results are intended to improve forecasting reliability in the planning, management, and operation of isolated operating power systems.

State Transitions Logical Design for Hybrid Energy Generation with Renewable Energy Sources in LNG Ship

In terms of energy generation and consumption, ships are autonomous and isolated power systems with energy requirements related to the type and kind of power demands and according to ship types: passenger ships, or commercial ships. Power supply on ships is traditionally based on engines thermal generators, which use fossil fuels, diesel, or natural gas. Due to the continuous operation of thermal generators in ships, this ends up increasing polluting gas emissions for the environment, mainly CO2. A combination of Renewable Energy Sources (RES) with traditional ship thermal engines can reduce CO2 emissions, resulting in a ‘greener’ interaction between ships and the environment. Due to the varying power needs for ship operation, considering the varying nature of load demands during long distance travels and during harbor entry, the use of RES must be evaluated. This paper presents a new control method to balance LNG ship load demands and power generation from RES, based on an accurate model and solution in real conditions. The Energy Management System (EMS) is designed and implemented in a Finite State Machine structure using the logical design of state transitions. The results prove that the reduction of consumption of fossil fuels is feasible, and, if this is combined with RES, it reduces CO2 emissions.

A Peer Survey on Load Frequency Contol in Isolated Power System with Novel Topologies

Electrical Power systems are paramount intricate system which built by human beings, therefore this type of systems should maintain stable and to get upgrading for upcoming days need multiple control techniques. In these convoluted power systems voltage frequency plays a major role .Hence frequency has to control proper. To control frequency of voltage has three control techniques are primary, secondary and also tertiary frequency control techniques .Thereby second technique also known as Load Frequency Control[1], It is to maintain the desirable frequency even after occurrence of disturbance. Several techniques have been used (like classical, adaptive) to mitigate the power flow disturbances, but drawbacks (parameters tuning, cyber-attacks) are having in these methods. This paper proposes soft computing techniques to build up the operation, control and then stability of the electrical power system.

Bilayered fault detection and classification scheme for low-voltage DC microgrid with weighted KNN and decision tree

ABSTRACT DC microgrids (DCMGs) are gaining popularity mainly in shipboards and isolated power systems for their modest control and higher efficiency. However, the newer conception and less literature are forcing the operators to take up tough challenges for smoother operation of the overall system. Protection areas of DCMGs are still immature because of the inexperience of handling DC faults and less availability of DC circuit breakers (DCCBs) in outsized range. In this work, the fault detection and classification issue in low-voltage-DC-microgrid (LVDCMG) has been solved with a bilayered machine learning scheme based on weighted K-nearest neighbor (WKNN) and decision tree (DT). WKNN accurately detects the fault in the line, and DT classifies the fault as PG or PP for further corrective measures. The developed technique has been implemented in an LVDCMG with PV and battery energy sources. Voltage and current measurements from the network have been used as training samples to the classifier models. It has been noticed that the proposed bilayered protection scheme is giving 100% detection accuracy. The outcome of the proposed structure seems to be promising and has been compared with some of the existing machine learning (ML)-based fault detection techniques for microgrids (MGs).

Optimizing the power quality by reducing harmonic distortion in the power supply network for isolated territories

New technologies of electric power generation, such as generation based on renewable energy sources, distributed generation, make extensive use of modern power electronics. All these non-linear elements generate harmonic components in the supply voltage. This leads to a deterioration in the quality of the supplied energy. In this regard, improving the quality of supplied energy is an urgent task. The article proposes a method of continuous distributed monitoring of the quality of electrical energy in the isolated power systems. This method is based on the use of the PMU infrastructure to continuously obtain information on instantaneous values of currents and voltages. It is proposed to use a smart meter proposed earlier by the authors as a primary measuring instrument for monitoring the level and direction of harmonics in various sections of the power system. This procedure is carried out in the current time mode, and thus there is a continuous adjustment of the power supply system to a mode close to optimal in terms of the content of harmonic components (harmonics) in the supply voltage at the selected points of connection.

Simplified Event-Based Load Shedding Scheme for Frequency Stability in an Isolated Power System With High Renewable Penetration. El Hierro: A Case Study

This study presents a novel approach to calculate the load to be shed in El Hierro isolated power system in generation tripping events. The proposed shedding law is based on a linear regression model. The regression model is obtained by means of offline dynamic simulations in a set of representative test cases. The proposed shedding law has been compared to the under-frequency load shedding scheme currently utilized in El Hierro, considering three different implementation schemes. The results of this study demonstrate that the proposed shedding law can contribute to reduce the average amount of load shed in generation tripping events and meet the requirements of the system’s frequency with a moderate investment in smart power management devices.

Combined hydro-wind frequency control scheme: Modal analysis and isolated power system case example

Wind speed fluctuations and the lack of Variable Speed Wind Turbines (VSWTs) synchronous inertia make difficult grid frequency control, mainly in isolated power systems with high penetration of wind energy. To solve this drawback, a combined wind-hydro frequency control scheme is proposed and evaluated. The VSWT rotational speed variations are included as an additional input of the hydro-power governor units, determining the hydro-power frequency control response in accordance with such VSWT rotational variations and grid frequency oscillations. This new combined control scheme is introduced in two cases: VSWTs providing or not inertial control. A modal analysis based on a linear reduced-order model is used to discuss the oscillation modes, participation factors and power system stability. An isolated power system located in El Hierro (Canary Island, Spain) is proposed as case example. Results, discussion and comparative to previous frequency control approaches based on twenty-two different schedules are included in the paper. The proposed combined frequency control scheme reduces both VSWTs rotational speed and frequency deviations, regardless VSWTs frequency contribution. Additionally, VSWTs inertial control becomes more effective under imbalances, mainly due to the minor rotational speed excursions.

Total power deficiency estimation of isolated power system network using full-state observer method

<span>An isolated electrical network with an independent local distributed generator is very sensitive towards the contingencies between load demand and supply. Although the network system has less complexity in term of structure, its stability condition is crucial due to its stand-alone operating condition. The total power deficit in the network gives the important information related to the dynamical frequency responses which may directly affect the system’s stability level. In this paper, the approach to estimate the total power deficiency for the isolated electrical network was presented by utilized the Luenberger observer method. Although the power deficit is not the state variable in the network mathematical model, the solution of estimation problem was feasible by introducing the new variable using additional dummy system. The simulation was carried out by using MATLAB/Simulink environment and the designed estimator was verified using multifarious load demand changes. The results show that the estimated signal was successfully tracked the expected actual signal with minimum error.</span>

Integration of energy storage with diesel generation in remote communities

HighlightsBattery energy storage may improve energy efficiency and reliability of hybrid energy systems composed by diesel and solar photovoltaic power generators serving isolated communities.In projects aiming update of power plants serving electrically isolated communities with redundant diesel generation, battery energy storage can improve overall economic performance of power supply system by reducing fuel usage, decreasing capital costs by replacing redundant diesel generation units, and increasing generator system life by shortening yearly runtime.Fast-acting battery energy storage systems with grid-forming inverters might have potential for improving drastically the reliability indices of isolated communities currently supplied by diesel generation.This paper will highlight unique challenges and opportunities with regard to energy storage utilization in remote, self-sustaining communities. The energy management of such areas has unique concerns. Diesel generation is often the go-to power source in these scenarios, but these systems are not devoid of issues. Without dedicated maintenance crews as in large, interconnected network areas, minor interruptions can be frequent and invasive not only for those who lose power, but also for those in the community that must then correct any faults. Although the immediate financial benefits are perhaps not readily apparent, energy storage could be used to address concerns related to reliability, automation, fuel supply concerns, generator degradation, solar utilization, and, yes, fuel costs to name a few. These ideas are shown through a case study of the Levelock Village of Alaska. Currently, the community is faced with high diesel prices and a difficult supply chain, which makes temporary loss of power very common and reductions in fuel consumption very impactful. This study will investigate the benefits that an energy storage system could bring to the overall system life, fuel costs, and reliability of the power supply. The variable efficiency of the generators, impact of startup/shutdown process, and low-load operation concerns are considered. The technological benefits of the combined system will be explored for various scenarios of future diesel prices and technology maintenance/replacement costs as well as for the avoidance of power interruptions that are so common in the community currently.Graphic abstractDiscussionIn several cases, energy storage can provide a means to promote energy equity by improving remote communities’ power supply reliability to levels closer to what the average urban consumer experiences at a reduced cost compared to transmission buildout. Furthermore, energy equity represents a hard-to-quantify benefit achieved by the integration of energy storage to isolated power systems of under-served communities, which suggests that the financial aspects of such projects should be questioned as the main performance criterion. To improve battery energy storage system valuation for diesel-based power systems, integration analysis must be holistic and go beyond fuel savings to capture every value stream possible.

On the road to a sustainable transport mobility in isolated power systems: The role of light-duty powertrain electrification

Plug-in vehicles are seen as a promising path to reduce the road transport greenhouse gas (GHG) emissions. However, the singularities of small isolated power systems in terms of the high dependence of fossil fuel-based generation and the relative short distances travelled do require a particular analysis to evaluate the potential emission reduction. This paper describes a simulation study for estimating the energy consumption and the corresponding Well-to-Wheels GHG emissions of different light-duty powertrain architectures subjected to real-world driving conditions, taking the island of Tenerife (Canary Islands) as a test case. The simulations have been carried out with a high-level vehicle powertrain system analysis tool, capable to estimate second-by-second vehicle energy consumption. Road gradient, GHG grid intensity and battery capacity impact on the GHG emission reduction were analysed in detail.Based on the results, the current high carbon content of the grid and the additional weight of the large battery packs limit the potential benefit of Battery Electric Vehicles (BEVs) with respect to hybrid architectures (HEV). The simulations also reveal that Plug-in Hybrid Electric Vehicles (PHEVs) currently offer a great potential to reduce the GHG emissions. Unlike other geographical areas, the high Utility Factors derived from the short distances travelled ensure the proper use of this technology.

Coordinated intelligent frequency control incorporating battery energy storage system, minimum variable contribution of demand response, and variable load damping coefficient in isolated power systems

Maintaining a generation-demand balance becomes more challenging nowadays due to the limited availability of traditional automatic generation control (AGC) and spinning reserves. In response to this concern, the purpose of the proposed paper is to bridge the gap by introducing battery energy storage system (BESS) control loop in the load frequency control (LFC) system. Furthermore, a minimum variable share of demand response (DR) is utilized as an additional mean to regulate the system frequency. The growing intermittency of controllable loads calls the awareness to incorporate frequency-sensitive loads by considering a variable load-damping coefficient in LFC problem. The presented control scheme demonstrates a stable power operation with the optimal sharing of BESS, dynamic DR, and the supplementary control loop. Moreover, because of the continuous and sudden variations in the demand, adaptive LFC controller is designed using fuzzy logic based on particle swarm optimization (PSO) tuning method. The proposed controller ensures a stable and robust frequency regulation of the system under load fluctuations. In this paper it is revealed that adding the BESS control loop is very effective in enhancing the performance of LFC as it can deliver fast power compensation. The obtained results prove the capability and effectiveness of the proposed method in an isolated power system.

Potential of data centers for fast frequency response services in synchronously isolated power systems

Grid frequency support is one of the most challenging issues in wind rich islanded power systems. This problem becomes critical with the displacement of synchronous generators and their associated services (i.e., inertia and primary operating reserve). The services that are lost can be replaced by other sources, such as demand response schemes to enhance the resiliency and security of power system operations. Demand response based on internet data centers is expected to become an increasingly important asset to make a significant contribution to frequency ancillary services. To exploit this resource, internet service companies are expected to combine the capabilities of a variety of data centers to participate as a single provider similar to a virtual power plant. In this context, this work develops a novel framework for cooperative participation of data centers delay-tolerant workloads and backup power supply units to provide effective fast frequency response service. This is achieved by employing the model predictive controller to initiate reference signals to data center resources while respecting device operating conditions and constraints. Various case studies are run on the modified linear model of the 39 Bus system via dynamic simulations for the projected 75 % system non-synchronous penetration. Simulation results demonstrate the potential of different data center configurations to stabilize grid frequency during signal delays and severe cascade failures. The analysis shows that the proposed framework is critical to the adoption of renewable energy and reduces the requirement for an expensive spinning reserve used in a typical power system.

Theoretical Investigation of Combustion and Performance Analysis of Diesel Engine under Low Load Conditions

Diesel engine is using prominently in islands and remote areas due to its reliability and stability for power generation. In recent years, most of the isolated power systems (e.g., islands and remote areas) have integrated renewable energies to reduce both the cost and pollution in diesel power generating system. However, due to intermittent and stochastic behaviour of renewable sources (e.g., solar and wind), it is unable to eliminate diesel generation entirely. In that case, low-load diesel operation (operation < 30% of maximum rated load) is particularly relevant for its ability to support higher levels of renewable penetration. In this paper, a thermodynamic model was developed using MATLAB for diesel engine combustion and performance. This model includes sub models such as heat release rate, heat transfer, double-Wiebe function, and ignition delay correlation. Engine thermal efficiency (TE), brake power (BP), indicated mean effective pressure (IMEP) and brake specific fuel consumption (BSFC) has been taken into consideration for performance analysis. The simulation results show that at 25% load, in-cylinder pressure and temperature are 168 bar and 2300 K which are the cause of lower heat release rate (74 J/deg) and longer ignition delay (0.25 ∼0.5 ms higher than that of conventional mode) and significantly responsible for lower efficiency (18%), brake power (4kW) and higher brake specific fuel consumption (1.2 g/kWh).