Distributed Power Generation Units Research Articles

ANALYSIS OF MICRO GAS TURBINE PERFORMANCE USING MODELLING APPROACH

Micro-gas turbine (MGT) is compact and self-sufficient distributed power generation units that exhibit reliability and has the capability to mitigate carbon monoxide (CO) emissions while conserving energy together with biomass used as a fuel sources. It is expected that they will have a noteworthy impact on securing the provision of upcoming energy resources for isolated areas, disaster condition and off-grid zone regardless of their connection to the power grid. However, the MGT performance using the various fuel still questionable. Databases and procedure of the MGT performance analysis should be established well to understand the overall MGT performance. In this study, MGT performance is determined by utilising the computer aided design (CAD) and solver software such as SOLIDWORKS and ANSYS-FLUENT. CAD drawing is used to design the combustion chamber and simulation performance is visualised in specific inside the combustion chamber (CC) of the MGT. The solver software was employed for the purpose of conducting computational fluid dynamics (CFD) analyses. The optimal configuration contained a flame holder with a diameter of 50 millimetres, a chamber height of 60 centimetres, four holes with diameters of 6, 8, and 10 millimetres, and a dead zone separating the combustion and dilution zones. The air inlet boundary conditions were established by utilising the specifications and compressor maps of the Garrett GT25 turbocharger in the simulation. The simulation employed the standard k-ε model of viscous model and energy equation to establish an optimal maximum airflow rate of 0.15 kg/s at a pressure of 1.4 bar, resulting in a compressor efficiency of approximately 70%. Three different fuels, diesel-air, wood-volatiles-air, and coal-hv-volatile, were used to determine the MGT's optimal performance. Successful investigation of this study opts to significant contribution to understand the MGT performance thus, give the awareness to choose as one of the alternative power generations which can be used in the future.

Full Operating Range Optimization Design Method of LLC Resonant Converter in Marine DC Power Supply System

The marine DC power supply system is the key to a ship’s power supply, which needs to convert the energy from storage batteries or distributed power generation units into stable DC voltage for electric propulsion or the ship’s electronics. The LLC resonant converter can be used as the key power conversion link in the marine DC power supply system due to its ability to realize electrical isolation in a high-power environment and soft switching within a wide load range. Aiming at the problem of sudden changes in voltage gain at a high switching frequency under light load conditions and the problem of insufficient voltage gain under heavy load conditions due to the parasitic parameters of power devices (mainly referring to the junction capacitor), this paper first proposes a full operating range performance optimization design method. By adding an auxiliary circuit that can be opened according to the operating conditions and a multi-objective particle swarm parameter optimization method that considers the converter loss and voltage gain under heavy load conditions, the performance of the LLC resonant converter can be improved in a full range of operating conditions. Finally, the effectiveness of the proposed method is verified by an experimental prototype and compared with the conventional methods and existing solutions to highlight the superiority of the proposed method in this paper.

Real-Time Machine Learning-based fault Detection, Classification, and locating in large scale solar Energy-Based Systems: Digital twin simulation

The current study considers numerous renewable energy resources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) in order to propose a reliable large-scale energy management framework that can be applied to islanded and grid-connected operations of renewable hybrid AC-DC microgrids (MGs). The framework uses a bat optimization algorithm (BOA) for minimizing the operating costs of the network and in addition introduces an intrusion detection system (IDS) according to the sequential hypothesis testing (SHT) method for detecting identity-enabled cyber-attacks (i.e classification of Sybil attacks, masquerading attacks) on the wireless-enabled advanced metering infrastructures (AMI). The suggested IDS uses the received signal strength (RSS) amount for distinguishing various signal resources and detecting cyberattacks. An IEEE 33-bus testing system has been used to construct a real-time hybrid MG in order to determine the reliability and efficiency of the suggested framework.

Distributed carbon-aware energy trading of virtual power plant under denial of service attacks: A passivity-based neurodynamic approach

With the popularity of distributed power generation units, traditional consumers become prosumers. Meanwhile, the emerging information and communication technology brings new opportunities to the energy trading service by providing various platforms. Thus, the energy trading problem of prosumers quickly ascends to the spotlight. Nevertheless, the personalized small-scale prosumers bring challenges of coordinated operation, supply-demand balance and carbon emission control. In this paper, based on an edge-cloud platform, a distributed carbon-aware energy trading mechanism is proposed for coordinating the prosumers within the virtual power plant. Moreover, the personalized characteristics including generation cost and the carbon emissions are handled without revealing the private information under the distributed formulation. In this way, the coordination effects of the prosumers are fully explored, and a multi-agent network is employed to achieve the supply-demand balance and the carbon emission awareness. Moreover, to deal with the denial of service attacks in the cyber layer, a passivity-based neurodynamic approach is proposed, which is resilient to denial of service attacks. A set of realistic case studies on energy trading problem under denial of service attacks are given, demonstrating the effectiveness of the proposed mechanism in coordinated operation, supply-demand balancing and carbon-awareness under denial of service attacks.

Co-optimizing transmission and active distribution grids to assess demand-side flexibilities of a carbon-neutral German energy system

In modern power system planning and operation, the relevance of distributed power generation units and demand-side flexibility options grows. As a consequence, smart planning and operation routines become necessary also in the distribution grid. We therefore develop a co-optimization framework to consider transmission and distribution system constraints and their interactions together. This enables bidirectional flows between both systems, supporting a consistent and cost-efficient overall energy system. The key feature of this paper is an automated coupling of two independently and modularly defined systems. It allows a bottom-up modelling approach of energy demands, conversion and storage technologies from both system levels. However, this approach increases the model size significantly. Thus, timeseries aggregation methods are applied to reduce the computational complexity. The results for the optimal planning of the coupled German energy system show that the electrification of heat and mobility sectors in the distribution grid is not only beneficial to achieve carbon neutrality in 2050, but also to particularly make use of additional flexibility potentials. Our results underline a significant positive correlation between a smart operation of heat pumps and charging stations with the maximum integrable capacities of low-cost rooftop photovoltaics in the distribution system.

Energy Management in the Microgrid and Its Optimal Planning for Supplying Wireless Charging Electric Vehicle

The ongoing research work on electric vehicles (EVs) as well as the growing concern around the world to ensure a pollution-free environment is sure to lead to a significant increase in the number of EVs in the near future. The electrification of automobiles is an inevitable trend of future development. However, the growth of EVs relies on several elements: autonomy, the charging practice and infrastructure, the price, and the high amount of energy needed for supplying EV. This tendency impacts several points in transportation such as the road infrastructure and electrical power network. The aim of this article is the integration of new energy power sources as a part of the microgrid (MG) to supply EV with dynamic wireless charging. The main goal is to establish an energy management strategy reducing the running cost. The purpose is suggested for two kinds of operation mode: relying only on the MG (island mode) or relying on the MG and the large grid (grid-connected). The optimization problem is solved on the basis of the particle swarm optimization (PSO) algorithm. We could note that the stability of the microgrid in the off-grid mode is better, when the load is close to the output power of the distributed power supply. Through the coordination and cooperation of the battery output and the other two distributed power generation units, the microgrid can achieve its autonomy and maximize the economy of the system operation. Thanks to our methodology, a better revenue and an enhanced flexible dispatching of the system were met in the grid-connected mode as well.

Fault Detection in Smart Grid Networks by Optimizing the Sensor Network for Distributed Decision Guided by Machine Learning

A smart grid network allows the existence of distributed power generation units. These units generate power through renewable or non-renewable means and supply it through the distribution networks. A major problem with these distributed power generation units is that they introduce harmonic components and affect power flow, creating high impedance faults (HIF) in the distribution network. HIF detection is difficult because the associated current has a low amplitude, rendering overcurrent safety devices ineffective. Wireless communication is one of the solutions for fault detection and feeder reconfiguration. This proposed work has an effective sensor network employed to determine and localize the HIF faults in the distribution network supporting distribution generation units. Fast Independent Component features are clustered in each area, and a SVM classifier is constructed to recognize faults. The learnt knowledge represented in SVM is converted to decision rules and disseminated into the sensor network nodes for effective distributed detection and localization of faults. Due to distributed detection, faults can be localized in less time. This improves the accuracy of fault detection as well as improves the network performance.

Trading platform for cooperation and sharing based on blockchain within multi-agent energy internet

With the release of the electricity sales side, large-scale small-capacity distributed power generation units are connected to the distribution side, forming multi-type market entities such as microgrids, integrated energy systems, and virtual power plants. With the large-scale integration of distributed energy, the energy market under the energy internet is different from a traditional transmission grid. It is currently developing in the direction of diversified entities and commodities, a flat structure, and a flexible and competitive multi-agent market mechanism. In this context, this study analyzes the value of combining blockchain and the electricity market presents the design of a blockchain trading framework for multi-agent cooperation and sharing of the energy internet. The nodes in market transactions are modeled through power system modeling in the physical layer and the transaction consensus strategy in the cyber layer; moreover, the nodes are verified in a modified IEEE 13 testing feeder of a distribution network. A transaction example is demonstrated using the multi-agent cooperation and sharing transaction platform based on the Ethereum private blockchain.

A Digital Delay Compensation Method to Improve the Stability of LCL Grid-Connected Inverters

Grid-connected inverters are an important part of the connection between distributed power generation units and the large grid, and their stability is the basis for ensuring the safe operation of distributed power generation units. This study found that there is an inherent digital control delay in the three-phase LCL grid-connected inverter system. This characteristic causes the effective range of capacitive current feedback active damping to be reduced, the selection range of the active damping coefficient to be limited, and the phase at the open-loop cutoff frequency to be reduced. In order to reduce the impact of digital delay, this article conducts a detailed analysis of the characteristics of the first-order lead link that can be used as delay compensation, pointing out that its infinite gain when it obtains the optimal compensation effect will bring noise to the inverter system. This paper proposes a method of cascading digital filters for the first-order leading link to suppress its infinite gain. An improved delay compensation link that is more suitable for numerically controlled inverter systems is constructed. Finally, the effectiveness and necessity of the proposed improved delay compensation link are verified by a simulation platform and an experimental platform.

Research on Control Strategy of Isolated DC Microgrid Based on SOC of Energy Storage System

With the rapid development of renewable energy technologies, islanded DC microgrids have received extensive attention in the field of distributed power generation due to their plug-and-play, flexible operation modes and convenient power conversion, and are likely to be one of the mainstream structures of microgrids in the future. The islanded DC microgrid contains multiple distributed power generation units. The battery energy storage system (BESS) is the main controlled unit used to smooth power fluctuations. The main parameter of concern is the state of charge (SOC). In order to maintain the stability of the microgrid, this paper takes the islanded DC microgrid as the research object and designs a control strategy based on the SOC of the BESS. Additionally, in the control strategy, the BESS’s energy balance control strategy and the microgrid’s operation control strategy are emphatically designed. The designed BESS control strategy adjusts the droop coefficient in real time according to the SOC of the battery energy storage unit (BESU), and controls the charge and discharge power of the BESU to achieve the SOC balance among the BESUs. The microgrid operation control strategy takes the energy storage system (ESS) as the main controlled unit to suppress power fluctuations, and distributes the power of distributed power sources according to the SOC of the BESS to achieve power balance in the microgrid, and control the DC bus voltage fluctuation deviation within 4.5%.

Hybrid machine learning based energy policy and management in the renewable-based microgrids considering hybrid electric vehicle charging demand

In this paper, a secured energy management architecture for both islanded and grid-connected operation modes of renewable hybrid AC-DC microgrids considering various renewable energy sources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) is proposed. In this architecture, whale optimization algorithm (WOA) is employed to minimize the operation cost of the grid and also an intrusion detection system (IDS) based on the sequential hypothesis testing (SHT) approach is introduced to detect identity-based cyber-attacks (i.e. Sybil attack, masquerading attack) on the wireless-based advanced metering infrastructures (AMI). The proposed IDS makes use of the received signal strength (RSS) value to distinguish different signal sources and detect cyber-attacks. The feasibility and performance of the proposed architecture are tested on a practical hybrid microgrid, which is constructed based on the IEEE 33-bus test system. the results are provided for both islanded and grid-connected operation modes.

Internal Model Based Smooth Transition of a Three-Phase Inverter Between Islanded and Grid-Connected Modes

Recent technical advances in control, protection and interconnection of distributed power generation units imply that it is practically viable and economically profitable to keep them as backup generators in isolated operating modes. Therefore, along with the development of islanding detection techniques, seamless operation in transition between islanded and grid connected modes is required and more sophisticated control strategies are needed to recognize the existing working condition and adjust the performance to meet the strict standards of grid interconnection. This paper presents a new adaptive control structure, based on internal model control (IMC), which uses multiple models and an inherent islanding detection method through an optimized switching mechanism to tune the operation of a three-phase inverter under transitions between islanded and grid-tied conditions. By applying a power synchronization method, the system emulates the operation of a synchronous machine which is needless to rely on a phase-locked loop to synchronize during the transitions. Hardware co-simulation environment in Simulink/PLECS and Xilinx System Generator have been utilized to evaluate the transient behavior of the controller in discretized domain and verify its robustness during parameter variations and load switching conditions. Various switching rules have been applied and a comparison of their effect in transient response is demonstrated. The results, taken from several case studies, confirm the significant robustness of the proposed control methodology.

Optimal energy management strategy in microgrids with mixed energy resources and energy storage system

The continued growth of distributed generation (DG) in the electrical grid has led to the expansion of microgrids. Microgrids contain distributed power generation units, energy storage devices, and controllable loads with the capability to operate in both grid-connected and island modes. The economic operation of a microgrid is achieved through an energy management system that optimally schedules DGs and storage devices and continuously balances supply and demand. In this study, a formulation of optimal unit commitment (UC) and dispatch scheduling of DGs in a grid-connected microgrid system is presented. Mixed-integer linear programming is used to implement the optimal UC and dispatch scheduling model. The objective is to minimise the overall operating cost of the system by optimally utilising an energy storage device and a combined heat and power (CHP) generation unit using load and renewable energy generation prediction. Operational constraints such as generation limits of DGs, battery charging/discharging limits and state-of-charge limits are to be satisfied during all intervals of operation. Simulation results indicate that the operational cost of the system is significantly reduced through optimal scheduling of an energy storage system and a CHP unit using the proposed strategy.

Demand Response Management in the Smart Grid in a Large Population Regime

In this paper, we introduce a hierarchical system model that captures the decision making processes involved in a network of multiple providers and a large number of consumers in the smart grid, incorporating multiple processes from power generation to market activities and to power consumption. We establish a Stackelberg game between providers and end users, where the providers behave as leaders maximizing their profit and end users act as the followers maximizing their individual welfare. We obtain closed-form expressions for the Stackelberg equilibrium of the game and prove that a unique equilibrium solution exists. In the large population regime, we show that a higher number of providers help to improve profits for the providers. This is inline with the goal of facilitating multiple distributed power generation units, one of the main design considerations in the smart grid. We further prove that there exist a unique number of providers that maximize their profits, and develop an iterative and distributed algorithm to obtain it. Finally, we provide numerical examples to illustrate the solutions and to corroborate the results.

Powering PA: A Case Study of Distributed Power Generation from Marcellus Shale

A distributed power system consists of a series of small, geographically distributed power generation units, each of which provides power to a single or small set of dedicated customers in the vicinity of the generation unit. Typically, the electricity is sold behind the meter as opposed to being provided through the electric distribution grid, although any excess capacity might be sold as usual through the grid. In this report we study a distributed power generation unit that is powered by natural gas and is also located in the vicinity of a gas well. In such a scenario it would be possible for the power producer to buy the gas locally and use it to satisfy the demand for a dedicated local customer. Beyond the obvious environmental and social benefits from localizing production and usage, such an arrangement could result in economic benefits to (1) the gas producer, from selling close to the source and saving the costs associated with gas transportation, (2) the electricity producer, from cheaper fuel and higher margins in the electricity they sell behind the meter, and (3) to the final consumer, in the form of electricity prices that are lower than the grid price. We study this arrangement using an actual distributed power generation unit along with demand and cost data from an actual potential customer, in order to estimate the overall supply chain savings that could be shared between the various entities. Based upon our analysis we estimate that on the upstream side the overall supply chain efficiency increases by around 22% and for the specific producer we studied, the value of this is estimated to be between approximately $10 and $20 million over a twenty year period, depending on the operating time of the unit. These savings could potentially be shared between the gas producer and the power generator. On the downstream side the overall supply chain efficiency increases by around 42% and for the customer that we studied, the value of this could average approximately $6 million over a twenty year period (for a customer with larger demand this could be higher). These savings could potentially be shared between the electricity generator and the dedicated customer.

Strategies for the Connection of Distributed Power Generation Units to Distorted Networks

Connection of distributed power generators (DPGs) to a utility grid requires the synchronization between the DPG and the grid voltages with the goal of controlling the power flow and limiting the currents at instant that the switch or breaker closes. Synchronization methods typically match the positive sequence voltage component of the two systems being connected, the connection being realized when the error is smaller than a preset threshold. While synchronization using the positive sequence voltage provides adequate results in the case of undistorted grid voltages, it can be inadequate if the grid is polluted, e.g., in case of weak grids. Instantaneous voltage mismatches between the DPG and the grid, e.g., due to the harmonic content or unbalances in the grid and/or DPG voltages $\ldots$ , at the instant that the breaker closes, can cause overcurrents and result in unwanted protection tripping or equipment damage, even if the corresponding positive sequence voltages perfectly match. This paper analyzes the synchronization and connection of DPGs to highly distorted grids with the goal of limiting the overcurrent due to voltage mismatch. Once the connection is realized, a transition to the conventional positive sequence voltage based synchronization occurs, as this is adequate to control the power flow in steady state.

Stability Enhancement Based on Virtual Impedance for DC Microgrids With Constant Power Loads

In this paper, a converter-based dc microgrid is studied. By considering the impact of each component in dc microgrids on system stability, a multistage configuration is employed, which includes the source stage, interface converter stage between buses, and common load stage. In order to study the overall stability of the above dc microgrid with constant power loads (CPLs), a comprehensive small-signal model is derived by analyzing the interface converters in each stage. The instability issue induced by the CPLs is revealed by using the criteria of impedance matching. Meanwhile, virtual-impedance-based stabilizers are proposed in order to enhance the damping of dc microgrids with CPLs and guarantee the stable operation. Since droop control is commonly used to reach proper load power sharing in dc microgrids, its impact is taken into account when testing the proposed stabilizers. By using the proposed stabilizers, virtual impedances are employed in the output filters of the interface converters in the second stage of the multistage configuration. In particular, one of the virtual impedances is connected in series with the filter capacitor, and the other one is connected at the output path of the converter. It can be seen that by using the proposed stabilizers, the unstable poles induced by the CPLs are forced to move into the stable region. The proposed method is verified by the MATLAB/Simulink model of multistage dc microgrids with three distributed power generation units.

A New Synchronization Algorithm for Grid-Connected Inverters

Synchronization algorithms are of great importance in control of grid-connected inverters as an integral part of distributed power generation units such as photovoltaic systems. A new all-digital closed-loop phase-locked algorithm for the synchronization signals of three-phase grid-connected inverters is presented even considering seriously distorted and variable-frequency utility conditions. The proposed synchronization algorithm can suppress the negative sequence utility voltage at fundamental frequency and high-frequency harmonic components effectively, and lock the positive sequence phase at fundamental frequency accurately. Moreover, a frequency adaptation algorithm is also proposed for applications where large frequency variations are often expected. Finally, digital simulation and experimental results both prove the feasibility of the proposed phase-locked method. The introduced synchronization method provides higher degree of immunity and insensitivity to harmonics and other types of pollutions in the utility when used to synchronize inverters.