Distributed Energy Generation Systems Research Articles

Micro Gas Turbines in the Global Energy Landscape: Bridging the Techno-Economic Gap with Comparative and Adaptive Insights from Internal Combustion Engines and Renewable Energy Sources

This paper investigates the potential of Micro Gas Turbines (MGTs) in the global shift towards low-carbon energy systems, particularly focusing on their integration within microgrids and distributed energy generation systems. MGTs, recognized for their fuel flexibility and efficiency, have yet to achieve the commercialization success of rival technologies such as Internal Combustion Engines (ICEs), wind turbines, and solar power (PV) installations. Through a comprehensive review of recent techno-economic assessment (TEA) studies, we highlight the challenges and opportunities for MGTs, emphasizing the critical role of TEA in driving market penetration and technological advancement. Comparative analysis with ICE and RES technologies reveals significant gaps in TEA activities for MGTs, which have hindered their broader adoption. This paper also explores the learning and experience effects associated with TEA, demonstrating how increased research activities have propelled the success of ICE and RES technologies. The analysis reveals a broad range of learning and experience effects, with learning rates (α) varying from 0.1 to 0.25 and experience rates (β) from 0.05 to 0.15, highlighting the significant role these effects play in reducing the levelized cost of energy (LCOE) and improving the net present value (NPV) of MGT systems. Hybrid systems integrating MGTs with renewable energy sources (RESs) and ICE technologies demonstrate the most substantial cost reductions and efficiency improvements, with systems like the hybrid renewable energy CCHP with ICE achieving a learning rate of α = 0.25 and significant LCOE reductions from USD 0.02/kWh to USD 0.017/kWh. These findings emphasize the need for targeted TEA studies and strategic investments to unlock the full potential of MGTs in a decarbonized energy landscape. By leveraging learning and experience effects, stakeholders can predict cost trajectories more accurately and make informed investment decisions, positioning MGTs as a competitive and sustainable energy solution in the global energy transition.

Optimal cost predictive BMS considering greywater recycling, responsive HVAC, and energy storage

A crucial aspect of a sustainable city is ensuring that energy and water supplies can adequately meet urban demand. With the scarcity of natural resources and growing electricity and water demand, it becomes increasingly important for consumers to manage their resource usage more efficiently. This paper proposes a novel perspective of demand-side management coordination strategy for a building's water-energy nexus to enhance the resilience and efficiency of the overall electricity-water-heating system. The model is formulated to optimally coordinates the onsite greywater recycling system, heating, ventilation, air conditioning (HVAC) loads, and distributed energy generation systems with a bidirectional grid connection in a residential building. All subsystems are controlled by a model predictive controller (MPC) receiving real-time time of use (ToU) pricing from electricity and water utilities. The presented mixed integer linear programming model is verified to meet the customers' demands while reducing the operational costs. Results are compared with benchmark systems lacking the water recycling or energy storage system showing 8.3 % operational cost reduction while reducing potable water consumption by 21.5 %. The effect of increased MPC control horizon is also studied showing reduction in cost with increased horizon. Detailed analysis of the proposed framework computational burden and effect of prediction errors is performed to prove the MPC adaptability and robustness. Testing under increased room size and different user preferences further validate the efficacy of the proposed scheme in reducing the operational costs.

THD Minimization and Reliability Analysis of Cascaded Multilevel Inverter

Inverters are DC to AC power converters that are widely used in AC motor drives and distributed energy generation systems. Multi-Level Inverters (MLIs) have emerged as the preferred inverter technology because of their advantages of reduced switching losses and better harmonic profile. This paper deals with the Minimization of Total Harmonic Distortion (MTHD) of Symmetric Cascaded H-bridge Multi-Level Inverter (SCMLI) and Asymmetric Cascaded H-bridge Multi-Level Inverter (ACMLI). A hybrid memetic algorithm composed of heuristic PSO (Particle Swarm Optimization) algorithm and traditional MAS (Mesh Adaptive direct Search) is proposed to optimize the switching angles. In Photo Voltaic (PV) system, the input DC voltage generally varies from its nominal value due to the change in temperature and irradiance. Thus, the sensitivity analysis of THD and harmonics is also carried out considering the variations with non-integer magnitudes of input DC sources. The experimental prototype of SCMLI and ACMLI topology is developed and validated with simulation results. The reliability and Mean Time to Failure (MTTF) of SCMLI and ACMLI are investigated based on power losses of the MOSFET and thermal parameters.

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model to enable accurate SoC estimation. The proposed method is evaluated using two dynamic battery tests: the dynamic street test (DST) and the federal urban driving schedule (FUDS) test. The simulation results show that the proposed method achieved high estimation accuracy and fast response speed. Additionally, real-time battery information, including battery output voltage and SoC, was acquired and displayed by an automatic monitoring system. The designed system is valuable for all battery application cases.

Photovoltaic distributed generation integrated electrical distribution system for development of sustainable energy using reliability assessment indices and levelized cost of electricity

AbstractReliability assessment index is one of the main performance indicator for analyzing the impact of distributed energy generation system in the utility distribution system and minimize the costs associated with energy interruptions. In this study, sustainable energy development by using integrated photovoltaic distributed generation (DG) system considering reliability assessment indices and levelized cost of energy are presented. Electrical transient and analysis program simulation software are used for evaluation and assessment of the reliability indices based on the failure rate, average annual power interruption/outage time and repair time. The analytical method are used to evaluate the expected interruption cost (ECOST) of the system. HelioScope software is a photovoltaic simulation tool are used to design and calculate the energy generation (kWh) as well as global horizontal irradiation (kWh/m2) of the photovoltaic system. Experimental test system is a radial distribution network consisting of an 11.0 kV feeder emanating from 132.0 kV distribution grid station Satiana. According to the reliability evaluation and assessment of experimental test system before integration of photovoltaic distributed energy generation, the average service availability index (ASAI) is 0.9648 per unit. Then, the expected energy not supplied (EENS) is 772.0308 MWh per year and ECOST is 386,652,468.18 PKR per year. After integration of Photovoltaic DG, ASAI is 0.9741 per unit, then the EENS is 563.8584 MWh per year and integration ECOST is 227,087,143.39 PKR per year. The results showed that integrated PV DG system improve the reliability of conventional energy distribution systems.

The Potential of Ecological Distributed Energy Generation Systems, Situation, and Perspective for Poland

Poland needs to fulfill its climate goals and become “climate neutral” by 2050. The plan is intricate for the Polish Government because coal-powered power plants generate about 80 percent of electricity in the country. Although policymakers are making an effort to redesign the energy sector, a lot still remains to be done. The viral trend in that transformation involves installing photovoltaic (PV) panels by private, corporate, and self-government investors. For example, the “My energy” support program of the National Fund for Environmental Protection and Water Management has helped finance 220,000 micro-PV installations. The achievement is significant but constitutes only partial success. PV powerplants will not simply replace coal powerplants. That is why the research on the ecological distributed energy generation systems has to be executed. The article presents the research results on ecological distributed energy generation systems, making the transformation of the Polish energy sector possible. The study’s primary objectives were to review the energy situation with particular attention paid to the technologies that could be used as the ecological distributed energy generation systems and draw the scenarios for the sector development. The authors used Desk research, the Delphi method supported with the Computer Assisted-Web Interview (CAWI) technique, and the Weighted SWOT analysis to fulfill the objectives. The findings showed that photovoltaic (PV) systems would be the fastest-growing energy sector even in the perspective of doubling the energy consumption by 2050. Private investors investing in ecological distributed energy generation systems, especially the PV systems mentioned above, and biomass or biogas systems, would significantly help policymakers, including those in Poland, fulfill the climate goals.

Design and implementation of virtual laboratory for a microgrid with renewable energy sources

AbstractGrowing demand for energy consumption worldwide has led to an increase in carbon emissions, which have significant effects on climate change. There is an urgent need for clean and renewable energy sources. However, most renewable energy sources, such as solar and wind, have very high initial costs, especially when used as a principal source. Distributed power generation using solar and wind power provides an effective solution to reduce the dependency on conventional power generation and to increase the reliability and quality of power systems. A mixture of distributed energy generation systems and loads can then be connected to form a microgrid. Virtual and remote laboratories are becoming widely accepted to conduct experiments in higher education. A virtual laboratory is especially suitable for the renewable energy‐based microgrid to overcome cost, space, and time restrictions. In this paper, the design and implementation of a web‐based virtual laboratory for a microgrid with renewable energy sources is presented. The virtual laboratory was developed using LabVIEW, Microsoft.Net Core, and Matlab/Simulink. Innovative courseware was developed around the virtual laboratory and used in several engineering technology courses to teach topics, including solar and wind power, dc‐dc converters, battery management, and microgrid. The assessment shows that the virtual laboratory greatly increased students awareness, interest, and knowledge in renewable energy. It provided an interactive and convenient tool for students to experiment with renewable energy and microgrid.

Power quality and transient analysis for a utility-tied interfaced distributed hybrid wind-hydro controls renewable energy generation system using generic and multiband power system stabilizers

Due to its inherent ability of augmenting power system stability limit while maintaining good electric Power Quality (PQ), Power System Stabilizer (PSS) is envisaged to be an effective device in numerous Distributed Generation (DG) applications. It becomes a paramount work to maintain stable frequency, and alleviate the utility-grid collapse. Owing to this, this paper evaluates the performance of generic, and Multiband (MB)-PSSs for a hybrid 9 MW wind farm connected with mini/micro hydro power plant. The proposed system is interfaced with 200 MVA, 25 kV utility-grid system. A comparative analysis in the performance of a generic-PSS is presented with a MB-PSS at various voltage level points and power flows at various Point of Common Couplings (PCC) are analysed. Transient analysis is carried out during the presence of various unsymmetrical faults. It is envisaged that transients and unbalanced voltage sag-swells are mitigated at various PCCs while maintaining uninterruptable real and reactive power flows. The superiority of MB-PSS is established over generic-PSS in maintaining optimal PQ flow. Consequently, the reactive power requirement of the connected load is compensated by wind and mini/micro hydro controlling schemes using MB-PSS. Overall, the accurateness of the developed simulink models is demonstrated and satisfactory results are obtained in comparative analysis of both stabilizers.

APPLICATIONS OF DISTRIBUTED ELECTRICITY GENERATION SYSTEMS IN HOSPITALS

Distributed energy generation systems have currently increasing applications in many sectors due to the resulted benefits. In the current study the application of distributed electricity generation systems in hospitals is investigated. The energy consumption in hospitals in many countries varies between 254.9 KWh/m2 and 738.5 KWh/m2. Various distributed energy generation systems have been examined and their characteristics are mentioned. The fuels used in them are either natural gas or renewable energy sources. Some energy systems generate only power while others co-generate heat and power. Our results indicate that various distributed generation systems are mature, reliable and cost-effective and they are currently used in health care centers. Others could be used in the future after improvements in their technology and reduction of their cost.  Use of the abovementioned energy systems in hospitals would result in the increase of their sustainability, decrease of conventional fuels used as well as in lower carbon emissions into the atmosphere. Taking into account that the use of unconventional green energy sources in hospitals is currently rather limited our results could trigger the increasing use of low or zero carbon emission energy sources in them contributing in the global effort for climate change mitigation.

Energy Management Strategy for Grid-Networks of a Distributed Generation

Energy management and control are major factors of importance in this revolutionary era of power system. The revolution in renewable energy generation and its utilization imposes to develop and adopt energy management strategies. Considering the dynamic behavior of renewable energy sources and load, a systematic control and energy management strategy is developed. The aim of this paper is to implement real time supervision and energy management on a hybrid system for efficient utilization of renewable energy resources. The developed energy management strategy can be imposed on any size of off-grid and on-grid distributed renewable energy generation system. A grid connected distributed generation is considered with solar, wind and biomass energy sources with battery as a storage. MATLAB is used to develop energy management strategy and different cases are resulted successfully. The effectiveness of energy management strategy on hybrid system and its results are presented and discussed.

Photovoltaic Integrated Distributed Energy Generation System for Sustainable Energy Development Considering Reliability Indices and Levelized Cost of Energy

Photovoltaic Integrated Distributed Energy Generation System for Sustainable Energy Development Considering Reliability Indices and Levelized Cost of Energy

Multidisciplinary design optimization of distributed energy generation systems: The trade-offs between life cycle environmental and economic impacts

Distributed energy systems (DES) are the focus of increasing attention because they have the potential to enhance the sustainability performance of energy generation. Previous DES researches evaluated various distributed energy technologies and systems from different aspects. However, there is still a research gap to evaluate and compare the multiple technology combinations and sizes for finding optimal energy solutions under various scenarios. This study aims to determine the best combination of technologies and their corresponding sizes for DES for various building types and climate zones in terms of life cycle environmental and economic impact. We developed parametric models (which considers dynamic hour by hour energy demand) for six commercially available distributed energy technologies and simulated the performance of them under various conditions. Then, we used a novel approach – multidisciplinary design optimization (MDO) to examine the billions of options (e.g., technologies, sizes, climate zone, Etc.) and identified the Pareto front with the optimal environmental and economic impact. According to MDO simulations, the microturbine-solar PVs-lithium ion battery and solid oxide fuel cells-solar PVs-lithium ion battery are two optimal combinations of technologies for three commercial building types for five climate zones. The DES can primarily reduce the environmental impact compared to conventional centralized energy production (CCEP) by 16–61% in all scenarios. However, the life cycle cost of DES is higher than CCEP, especially for SOFC-based DES. The microturbine-based DES is more cost-competitive and economical (about 65%, 32%, and 64% lower than SOFC-based DES for the small, medium, and large office, respectively).

Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest

Short-term load forecast (STLF) determines the power system planning for unit commitment, which is of great significance in power dispatching. A large amount of research work has been carried out on STLF. However, with the increase of load consumption and penetration of beyond the meter distributed energy generation systems, new challenges are brought to power load forecasting. Therefore, the probability density interval prediction which can more accurately reflect the uncertainty of power grid load is particularly important. In this study, a novel new day-ahead (24 h) short-term load probability density forecasting hybrid method with a decomposition-based quantile regression forest is proposed. First, the stationarity analysis is performed, and the load sequence is decomposed into several sub-models by Variational mode decomposition (VMD). Secondly, the influence of relevant factors such as weighted temperature and humidity index (WTHI) and day type is considered and extended to each sub-model sequence. Finally, a multi-step prediction strategy is proposed to predict the result of each sub-model using Quantile Regression Forest (QRF), and the prediction results are reconstructed to obtain the complete prediction probability density by Kernel density estimation (KDE). Specifically, the Bayesian optimization algorithm based on Tree-structured of Parzen Estimators (TPE) is adopted to optimize the hyperparameters. Furthermore, to verify the performance of the proposed method, the day-ahead short-term load forecasting of the proposed method and the contrast methods including decomposition-based methods and non-decomposition based methods were studied by the real load data of Henan Province, China. The probability density prediction obtained by the experiment indicates that the proposed method can acquire the narrowest prediction intervals at different confidence.

Design and Implementation of Multilevel Inverters for Fuel Cell Energy Conversion System

Power converter plays a significant role in Proton Exchange Membrane Fuel Cell (PEMFC) energy generation systems, which is an alternative of distributed energy generation systems. So there creates a demand for high-quality power conditioning used in PEMFC systems. This article proposes a converter topology as a power interface and also introduced a multilevel inverter topology for various levels of operation. The converter steps up the input voltage to the rated voltage and transforms to the DC bus, the multilevel inverter converts the voltage to AC and feeds to AC loads. In this article, we develop an entire unit stack, which can produce an output with positive and zero sequences. The addition of H-bridge to the fundamental unit known to be an advance cascaded H-bridge multilevel inverter resulting in the formation of all sequences like positive, zero and negative levels. The conventional multilevel inverters are compared with the proposed inverters in terms of switch count, DC sources, diodes, through which the lesser requirement of components in a multilevel inverter is possible to observe, which results in the reduction in cost, dv/dt stress, component space of the driver circuit. With this implementation, the better possibility of control, increase the quality of output, reliability of the inverter with a reduced THD, and stress. The converter output is tested and verified in MATLAB, and the respective results of the different levels like five, seven and fifteen of a single-phase cascaded inverter are tested experimentally and in MATLAB Simulink.

Green Parking Lots for Energy plus Human Settlements

Energy is the basic need for any development programme. Growing energy demands are putting the non renewable energy sources at risk of extinction. One of the major energy consuming sector is automobiles, traffic in urban settlements. Now a days battery operated vehicles are on road for public and private transportation which are an alternative to conventional fuel vehicle systems of petrol, diesel, gas oil. But the issue of fuel or we can say charging the battery operated vehicles remains constant and a challenge for urban transportation systems. This paper is focused to design approach of such parking lots and road side parking slots which are not only space for vehicle parking but also charges the vehicle while it is parked. This kind of refueling system will use the idle time of the vehicle and will reduce the requirement of refueling stations. Such system will save the time of the car owner for waiting at refueling or charging station. The methodology of this approach consists of using the renewable sources of energy such as solar, wind energy and distributed energy generation systems so that such systems can be used in designing energy plus human settlements. The distributed energy generation system will reduce the need of major electric substations to handle the generated electricity

Extended Electric System Cascade Analysis (ESCA) for optimal power system targeting considering generation flexibility and heat rate factor

In order to cater for fluctuating energy demand, power plants are designed either as base power plant or peak power plant. The advantage of base power plant is that due to its constant power generation, the power plant has a higher efficiency. To optimize and design a power plant, many previous study has been conducted. Among the studies, Power Pinch tool named Electric System Cascade Analysis (ESCA) was applied to design an optimal power system. ESCA analysis is conducted by assuming that the power plant generates constant power as it is more efficient. However, further analysis using ESCA shows that with a minimal power plant capacity would result in a trade-off that the energy storage system would be larger and leads to higher energy charging and discharging tendency (result in higher energy lost). Considering the time change of heat rate with the corresponding load factor, this study incorporates new algorithm for flexible power generation into the existing ESCA methodology. To validate the new algorithm, an off-grid distributed energy generation system is mathematically modelled and solved. The result from the new algorithm is compared with that of the mathematical model. The comparison of optimal generator capacity shows a difference of 5.71%. The similarity of the result hence validates that the new algorithm is suitable.

Coupling Degree Clustering-Based Distributed Model Predictive Control Network Design

Designing a stabilized distributed model predictive control (DMPC) with constraints is an open and important problem for a class of large-scale distributed systems, which are composed by both weakly and strongly coupled subsystems. This paper proposes a design of DMPC network to stabilize this class of large-scale systems. A coupling degree-based clustering method is first designed to classify subsystems into some middle-scale subsystems (M-subsystem) off-line according to the adjacent matrix, so that these M-subsystems are weakly coupled with each other. Then, each M-subsystem is controlled by a virtual model predictive control (MPC), which is realized by several individual controllers with running iterative cooperative DMPC algorithm, since the solution of cooperative DMPC is able to converge to a fixed point without coupling constraints. Each MPC communicates with the corresponding interacted M-subsystems’ MPCs once in a control period for exchanging future state evolution estimation. All the subsystem-based MPCs are composed of the proposed peer-to-peer DMPC network. In addition, an additional consistency and stabilization constraints are added to guarantee the recursive feasibility and stability of the overall system. The convergence of the iterative DMPC algorithm for each M-subsystem and the stabilization analysis of the overall system are provided. The simulation results show the efficiency of the proposed method. Note to Practitioners —The proposed method is designed for the systems, which are composed of both strong and weak coupling subsystems and are controlled in a distributed structure. The aim of this method is to design a distributed model predictive control (MPC) network for the operational optimization of entire system. The designed control structure and the distributed MPC algorithm with the proposed method could guarantee the recursive feasibility and asymptotic stability of the entire system. To apply this method, the first step is to obtain the models of each subsystem. Then, design the distributed MPC network structure according to the proposed cluster algorithm, which determines the set of subsystems that each virtue MPC controls. Next step is to configure the network information exchanging among these subsystems and construct the optimization problem of each subsystem. Finally, use the existing quadratically constrained quadratic program solvers to calculate the optimal control laws and apply these optimal control laws to the corresponding subsystems. This method can be used in large-scale chemical processes, air conditioner systems for multizone buildings, distributed energy generation systems, and so on.

Low-grade waste heat integration in distributed energy generation systems - An economic optimization approach

This paper proposes a Mixed-Integer Linear Programming (MILP) formulation for the economic optimization of the synthesis, design, and operation of an energy supply system of a manufacturing company. The multi-period approach incorporates both Heat Upgrading Technologies (HUTs) and conventional Distributed Energy Ressources (DER). Temperature requirements of heating and cooling demands are addressed explicitly and fluctuating ambient temperatures are considered, this gives rise to the possibility of temperature dependent modeling of technology efficiencies. The model enables the planner to consider waste heat recovery from hot process streams or from refrigeration cycles via direct heat integration or HUTs, such as mechanical heat pumps. Furthermore, it enables the planner to evaluate the complex interactions of HUTs with Combined Heat and Power (CHP) plants. To illustrate the practicability of the presented modeling approach, it is applied to a real-world case study. Furthermore, we exemplify how the optimal design is adjusted if HUTs and DER are investigated integrally in contrast to an isolated optimization.

The research of the schemes of regional distributed energy system

The distributed energy generation system is one of the main forms of the second-generation energy system currently. Three kinds of viable schemas of distributed energy supply system for nine users of the small region heat of Yangpu area combining with urban heating were proposed in this thesis, in which the gas turbines were selected. By analyzing the heat economy and pollutant emissions, the advantages and disadvantages of each schema were found out and the relatively better one was selected ultimately. Finally, some possible development trends and the prospects of the distributing energy supply system were also related and some complementary proposals were to table for some aspects of the system.

A method for the identification of low frequency oscillation modes in power systems subjected to noise

The high penetration of renewable energy sources and the consequent integration of such distributed energy generation systems into the grid have significantly increased the interaction between power sources, which can result in low frequency oscillations. Different control techniques are proposed in literature to suppress low frequency oscillations, but the first step to attenuate such oscillations is to characterize them in terms of their dominant modes. This paper combines different results from literature to propose a unified two-step methodology for the mode characterization of low frequency oscillations based on the signals acquired by wide area measurement systems. Since the measured signals typically contain noise, the first stage of the proposed method uses the basis pursuit denoising method combined with a Tunable Q-factor wavelet transform to increase the signal-to-noise ratio and improve the identification results. In a second stage, an improved version of the matrix pencil algorithm, as proposed in this paper, is used to identify the parameters of low frequency oscillation dominant modes. Both simulation and experimental results show that the proposed method has better characterization accuracy than traditional methods, especially when Gaussian noise is considered in the measurements. In addition, the processing time has proven to be reasonable for online identification and characterization of low frequency oscillations.