Small-scale Grids Research Articles

Decentralized Cooperative Active Power Control for Small-Scale Grids with High Renewable Penetration through VSC-HVDC

Rising renewable penetration has accelerated the volatility and instability of the power grid. A small-scale grid is especially vulnerable. Therefore, flexibility and stability enhancement are required for small-scale grids. The interconnection with the large-scale grid through the voltage sourced converter-high voltage direct current (VSC-HVDC) can be a solution to the aforementioned problems. VSC-HVDC can deliver power bidirectionally and change the direction in a short time. Hereby, the cooperative operation of distributed generations (DG) and a large-scale grid through the VSC-HVDC system is proposed in this paper. The VSC-HVDC will take the role of the main source of the small-scale grid. It determines the grid frequency based on its output power. DGs adjust their output power according to the grid frequency, and then the balance between the demand and the supply is maintained. To verify it, a PSCAD/EMTDC simulation was conducted using actual data from Jeju Island, including transmission lines, loads, and climate. Consequently, by the proposed method, the RE share was improved and the grid was operated stably even though the fault situations.

Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant

Energy production and supply are important challenges for civilisation. Renewable energy sources present an increased share of the energy supply. Under these circumstances, small-scale grids operating in small areas as fully functioning energy systems are becoming an interesting solution. One crucial element to the success of micro-grid structures is the accurate forecasting of energy consumption by large customers, such as factories. This study aimed to develop a universal forecasting tool for energy consumption by end-use consumers. The tool estimates energy use based on real energy-consumption data obtained from a factory or a production machine. This model allows the end-users to be equipped with an energy demand prediction, enabling them to participate more effectively in the smart grid energy market. A single, long short-term memory (LSTM)-layer-based artificial neural network model for short-term energy demand prediction was developed. The model was based on a manufacturing plant’s energy consumption data. The model is characterised by high prediction capability, and it predicted energy consumption, with a mean absolute error value of 0.0464. The developed model was compared with two other methodologies.

Placement and sizing of EESS bundled with uncertainty modeling by two-stage stochastic search based on improved shark smell optimization algorithm in micro-grids

Microgrids are low voltage small-scale grids which are used for supplying the thermal and electric loads in small areas or locations without access to the main grid. A microgrids can be a proper solution for power generation considering the environmental and economic problems of these locations. In this paper, the optimum size and placement of electric energy storage is presented considering the uncertainties associated with renewable energy resources and fault in the microgrid lines as well as impact of responsive loads in a microgrid. The studied hybrid system is composed of wind turbines and fuel cells. Basically, the wind energy is not certainly accessible and the newly suggested method is based on a two-stage stochastic search (TSSS) model for modeling the impact of uncertainty in the power generated by the wind generation resources by considering the wind energy prediction error as the main stochastic parameter in the planning. The standard probability distribution function is used for showing the error variation. With respect to continuity of this function, the prediction error probability function is discretized using a new method and a certain number of scenarios with specific probability are extracted. Finally, the studied problem is converted to an optimization problem and the developed shark smell optimization (SSO) algorithm is used to solve it. Numerous case studies show and approve the performance of the proposed model.

Bias correction and statistical downscaling of earth system models using quantile delta mapping (QDM) and bias correction constructed analogues with quantile mapping reordering (BCCAQ)

Earth System Models (ESM) is a model that can simulate, predict climate change that occurred in the past, present, and create climate change scenarios in the future. ESM output has not been able to represent local scale climate. Statistical Downscaling (SD) is a static downscaling process in which data on a large-scale grids in a certain period and time period is used as a basis for determining data on small-scale grids. SD results still have a sizeable bias, so we need a method that works to reduce the bias. The bias correction method used in this research is Quantile Delta Mapping (QDM) and bias correction constructed analogues with quantile mapping reordering (BCCAQ). This study downscales the rainfall and maximum temperature data generated from Beijing Normal University Earth System Model (BNU-ESM) and ERA-Interim as the proxy of the observation. The skill is verified by means of Taylor Diagram showing the correlation value, the Root Mean Square Error (RMSE), and standard deviation. Based Taylor Diagram the QDM has better performance compared to the BCCAQ method. The performance of downscaling and bias correction during September-October-November (SON) is the best compared to other seasons.

Supply smoothing planning with integrated gasification combined cycle to address renewable fluctuations in small-scale grids

Integrated gasification combined cycle (IGCC) technology is an efficient way to reduce CO2 emissions from fossil fuels, achieving lower emissions than liquid natural gas combined cycle (LNG-CC) plants. In this study, we examine the performance of an IGCC plant for smoothing network-frequency deviations in a stand-alone power-supply network with fluctuating renewable generation by developing a numerical model of the plant’s dynamic response to realistic load changes with meteorological conditions. The IGCC plant with added inertia from a flywheel is very effective for accommodating short-cycle fluctuations associated with photovoltaic generation, but it performs poorly with the long-cycle fluctuations associated with wind generation. The same numerical analysis was used to compare IGCC and LNG-CC plants in equal terms. The LNG-CC plant’s faster load response is more suited for interconnection with windfarms, although the IGCC emits about half as much CO2. Both technologies may prove useful for installation along with renewable generation capacity.

Modeling and simulation of a residential microgrid supplied with PV/batteries in connected/disconnected modes—Case of Morocco

Micro-grids are small-scale grids, equipped with energy management and on-site energy generation. They offer low-cost power to consumers, and combined with green energy, they are a promising solution to build the future smart city. This paper discusses the different components of a micro-grid and their mathematical equations. The model is built in Matlab/Simulink and respects the distribution rules of the Moroccan grid code. The modeled residential group represents the power consumption of an average Moroccan household in the mountainous region of Ifrane. Two simulation scenarios were designed in order to validate the model. The first scenario tests the microgrid during normal operation, while the second scenario introduces a grid fault, requiring disconnection from the utility grid. Simulation results show that the model responds to the specified weather and system conditions as predicted.

Dynamic Simulation-Based Case Study of Fusion on Small-Scale Electrical Grids

This study analyzed adverse effects of fusion plants on the stability of small-scale grids through simulation-based case studies. Major frequency fluctuations were observed in the event of a plant start-up and a sudden interruption of fusion electric output power, as represented by plasma disruption, suggesting difficulties in fusion plant installation to majority of countries in the future. To mitigate the adverse effects, a fusion plant combined with an energy storage was proposed and evaluated. The results indicated that large energy storage like pumped-hydro storage could successfully reduce the frequency deviations to the acceptable range. An innovative offshore pumped-hydro storage, marine inverse dam, is also discussed in this paper.

A New Numerical Method for Two Phase Flow in Heterogeneous Porous Media

For the internodal absolute permeabilities of flow in heterogeneous porous media, the most popular method is harmonic means, but it has been known that the harmonic mean method underestimate the flux of the single phase steady flow, especially for the strong heterogeneities. Based on the power-law behaviour of pressure, a finite analytic numerical method is proposed for two phase incompressible flow in 2D heterogeneous porous media. Numerical example shows that the finite can obtain more accurate value than traditional numerical methods such as the harmonic average method and so on, for different permeability distributions, only with or subdivisions can provide rather accurate solutions while traditional numerical methods need very small scale grids.