Local Grid Research Articles

An initial clustering center optimization method based on neighbourhood density for K-means

The selection of initial clustering centers of traditional K-means algorithm is random and sensitive to outliers, which leads to unstable clustering results and low accuracy. To solve the above problems, the NDK-means algorithm based on neighbourhood density is proposed. Firstly, the grid distribution characteristics of samples are obtained by multi-dimensional grid division. Then, by defining the grid density and the grid neighbourhood density, several local high-density grids are determined. At the same time, the iteration factor is introduced to merge adjacent high-density grids to obtain a candidate set of initial clustering centers. Finally, combined with grid density and distance, K initial clustering centers are obtained by using Max-Min-distance algorithm. Experiments on UCI dataset show that compared with k-means algorithm and literature algorithm, the accuracy of NDK-means algorithm is improved by nearly 11% and 4%, and the iteration speed is improved by 70% and 60% respectively. The algorithm improves the accuracy of clustering and the results have good stability.

Virtual power plant: state of the art providing energy flexibility to local distribution grids

Integrating decentralised energy sources into the traditional distribution networks can result in technical issues impacting the power quality. Innovative ideas are, therefore, needed to promote the transformation of systems to a smart grid. Distribution System operator (DSO) could make use of the flexibility of emerging technologies as a method to address these power quality issues. This study aims to present an overview of a local flexibility market (LFM) which will allow DSO requirements to be fulfilled through the (VPP) as an energy flexibility provider. The required optimization loads, generators and as well as storage units, are undertaken in the general algebraic modeling simulation (GAMS) environment. The aim of the optimization problem is to provide DSOs the opportunity to increase or curtail the local generations and loads in order to satisfy their requirement. The VPP will then be responsible for handling the relevant requests in real time to ensure the correct operating schedule of a resource is applied. The preliminary results of simulation studies presented in this paper have shown that the local market framework for flexibility could have potential for deferring investments in distribution network capacity, minimizing energy costs and improving the hosting capacity of distribution networks.

A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks

Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different preferences on energy trading price and amount. Therefore, in decentralized P2P energy markets, a negotiation between prosumers is needed to obtain a commonly satisfactory set of preferences, i.e., a market-clearing solution. To achieve that, this paper proposes a novel approach in which a decentralized inverse optimization problem is solved by prosumers to cooperatively learn to set their objective function parameters, given their preferential intervals of energy prices and amounts. As such, prosumers’ parameters can be determined in specific intervals computed analytically from the lower and upper bounds of their preferential intervals, if a certain learning condition is satisfied. Next, the structural robustness of prosumer’s cooperative learning against the malicious and Byzantine models of cyberattacks is studied with the weighted-mean-subsequence-reduced (WMSR) resilient consensus algorithm. A novel sufficient robustness condition is then derived. Finally, case studies are conducted on the IEEE European Low Voltage Test Feeder system to validate the effectiveness of the proposed theoretical results.

Performance evaluation of on-grid PV systems in Egypt

ABSTRACT The Egyptian government has set renewable energy targets of 20% of the electricity mix by 2022 and 42% by 2035. The National Water Research Center (NWRC) (Qanatir, Egypt) set up a pilot rooftop 90 kW PV system supporting this approach. This paper analyzes the electrical output signals of 90 kW photovoltaics on-grid system mounted on the roof of the National Water Research Center (NWRC) (Qanatir, Egypt). A previous simulation model using Matlab program is modified to incorporate more accurate information on system configuration became available. Components of the model were modified: four inverters (20 kW rated power) and one (10 kW rated power) and the scenario of tying the system to the local low voltage grid is discarded. Recommendation from previous study was taken to tie system the national high voltage grid to multiply the savings; therefore, it is the only simulated scenario. The simulated output energy of the system, at different values of solar intensity, is compared with the recorded data. A deviation between the simulink model and recorded data is found, due to power failure, surface cleaning of PV panels and weather variations. The results showed that the maximum energy is generated during July (14.5 MWhr) whilst the minimum energy in December and January (5 MWhr).

FCB system analysis and performance test results of 1000MW ultra supercritical coal fired power station

Fast cut back (FCB) function can make the power station restore the external power supply in a very short period of time from the auxiliary power operation conditions through self-help mode. In case of large-scale power grid failure, FCB further ensures the safety of the main equipments and bring great benefits for the rapid recovery of local power grid. Taking Taishan Power Plant 1000 MW unit as a research object, the feasibility scheme of adding FCB function was analyzed and demonstrated on the basis of making full use of the original equipment design margin. The FCB test of TMCR 100% load was completed. The test results and problems during FCB process were discussed, and the corresponding countermeasures were proposed.

High-Accurate Non-Uniform Grids for System-Combined ADI-FDTD Method in Near-Field Scattering With Proper CFL Factor

In this paper, a high-accurate technique with non-uniform grids is introduced into a system-combined alternative-direction-implicit finite-difference time-domain (SC-ADI-FDTD) algorithm, and then successfully used to analyze electromagnetic propagations. To our knowledge, the conventional FDTD with non-uniform grids can be effectively deal with some edges of the three-dimensional cubes and complicated structures of the tiny objects by modulating the local grid scales, which to the extent improves its reliability and accuracy. However, due to existing the finer grids in the local computational region and the inevitable Courant-Friedrichs-Lewy (CFL) limit in the conventional FDTD, the temporal interval must be determined by the minimum fine spatial grid, resulting in much larger temporal sampling density required during the whole computation process. As the advantage of circumventing the repeated variables, the non-uniform SC-ADI-FDTD (NUSC-ADI-FDTD) cannot only break through the CFL limit to implement the high-efficient computation, but also further save more CPU time in the local microstructure cases. Furthermore, the empirical formula between the spatial sampling density and the CFL factors can be obtained from the numerical fitting method after errors analysis. The numerical simulations of the electromagnetic scattering have been executed to illustrate feasibility and validity of our proposed method.

Deep Learning-Aided Sensorless Control Approach for PV Converters in DC Nanogrids

In a microgrid, photovoltaic (PV) systems are broadly preferred with energy storage systems (ESSs) that form small-sized direct current (DC) microgrids. They are also termed local grids i.e., DC nanogrids, which feed the local consumers to some extent in the next decades. Therefore, ESSs enable the DC nanogrids more flexible and stable by preserving the intermittent nature of renewables. Yet still, feeding local consumers smoothly with PV-battery-based systems is exceedingly a considerable theme. In this context, proper control of power electronics converters as the main carrier of the system is essential. Besides, the rise of PV applications challenges possible issues upon integrating the conventional grid. Emerging possible issues in stability, reliability, efficiency and the ways of dealing with them have been developing day by day. Thus, it is inevitable that innovative methods will be put into practice. To achieve this goal, the deep learning aided-sensorless control approach is adopted. To validate the proposed control method, the training phase is presented elaborately with the help of the experimental setup of a DC nanogrid. From the obtained results, it is concluded that the deep learning-based approach reaches very small error values, captures the system dynamics successfully, enables a flexible structure with tunable hyper-parameters, and allows the possibility to apply practically.

Grid-point and time-step requirements for direct numerical simulation and large-eddy simulation

We revisit the grid-point requirement estimates in Choi and Moin [“Grid-point requirements for large eddy simulation: Chapman’s estimates revisited,” Phys. Fluids 24, 011702 (2012)] and establish more general grid-point requirements for direct numerical simulations (DNS) and large-eddy simulations (LES) of a spatially developing turbulent boundary layer. We show that by allowing the local grid spacing to scale with the local Kolmogorov length scale, the grid-point requirement for DNS of a spatially developing turbulent boundary layer is N∼ReLx2.05 rather than N∼ReLx2.64, as suggested by Choi and Moin, where N is the number of grid points and Lx is the length of the plate. In addition to the grid-point requirement, we estimate the time-step requirement for DNS and LES. We show that for a code that treats the convective term explicitly, the time steps required to get converged statistics are Nt∼ReLx/Rex06/7 for wall-modeled LES and Nt∼ReLx/Rex01/7 for wall-resolved LES and DNS (with different prefactors), where Rex0 is the inlet Reynolds number. The grid-point and time-step requirement estimates allow us to estimate the overall cost of DNS and LES. According to the present estimates, the costs of DNS, wall-resolved LES, and wall-modeled LES scale as ReLx2.91, ReLx2.72, and ReLx1.14, respectively.

Hybrid techno-economic and environmental assessment of adiabatic compressed air energy storage system in China-Situation

Adiabatic compressed air energy storage technology is found to reliably stabilize the power load and support renewable energy generation. Comprehensive life cycle techno-economic and environmental optimization analysis for this technology are of great importance to improve system performance. In this study, we first proposed an integrated hybrid life cycle optimization framework to understand trade-offs between the techno-economic and environmental performance of adiabatic compressed air energy storage plant in China, by combining integrated hybrid life cycle analysis with multi-objective optimization. The results show that solutions with investment cost, carbon dioxide emission, water and energy consumption of 1.47 k$/kW, 1.48 kg/kWh, 0.097 m3/kWh and 2.08 × 104 kJ/kWh, respectively, are promising for a practical adiabatic compressed air energy storage system design in China. An analysis of the materials and energy contribution towards the environmental impact at selected optimum points are carried out in detail to elucidate means for reducing the life-cycle emission of the plant; besides, an impact comparison for plant installation in different provinces was performed to provide guidance for policymakers in site selection. As a result, the life-cycle carbon dioxide emission, water and energy consumption of the plant were found to be large in the provinces with a high proportion of thermal power generation — for example, Shandong and Jiangsu — yet the most environmental benefit is achieved in these provinces if the system is combined with the local power grid.

Integration of Wave Power Farms into Power Systems of the Adriatic Islands: Technical Possibilities and Cross-Cutting Aspects

Wave energy is of interest for regions with high wave power potential, as well as for regions with modest wave power potential such as the Adriatic/Mediterranean coastlines and islands. In the present paper, the possibility of integrating a wave power farm with the power system of an island in the Adriatic Sea, combining the wave power with a battery energy storage system (BESS) and solar photovoltaics (PVs) is explored and its impact on the local weak low voltage grid is investigated. The load profile is typical of the demand (consumption) of an Adriatic island, in which the demand substantially increases during summer (the tourist season). The wave power technology is a point-absorbing wave energy converter (WEC) with a direct drive linear permanent-magnet synchronous generator power take-off device. Wave power farms (WPFs) consist of two to ten WECs. In this study, we show that the integration of a WPF consisting of two WECs into the grid is optimal and helps to reach zero grid exchange, and a BESS reduces the intermittency of the power flow into the grid. Since a potential wave power farm is to be installed in a populated recreational area, the technical study is complemented by discussion on cross-cutting aspects such as its environmental and social impact.

Lesson learned – The operation of the pilot scale geothermal power plant 3MW – Kamojang, Indonesia

BPPT has developed and operated a 3 MW Geothermal Power Plant (GPP) of a condensing type technology in Kamojang, West Java - Indonesia, which is the first prototype GPP manufactured by Indonesian companies. The BPPT’s pilot plant was designed based on the geothermal steam field of the KMJ-68 well. The pilot plant becomes an early stage for developing wellhead type GPP in Indonesia. The wellhead GPP can meet with the capability of local manufacturers in the turbine and generator. The local content of BPPT’s pilot plant has reached 63.14 %. This paper discusses the design of Process Flow Diagrams (PFD), performance, and reliability of this plant during continuous synchronous testing to the 20 kV local power grid on November 21–25, 2019. This test is intended to fulfill the requirements to get a Certificate of Operation (SLO). The proposed SLO is for operation at a nominal power of 2 MW considering the limited capacity of the KMJ 68 well. As a technical requirement to obtain the SLO referred to, the plant must be operated continuously for 3 x 24 h in a power range of 1.8–2.2 MW. The utilization efficiency of the GPP was 29 %, which quite good considering dry steam geo-fluids condition. The specific steam consumption (SSC) achieved 14 kg/kWh. The plant still has an opportunity for improving efficiency by reducing the pressure loss in the steam strainer, increasing the condenser vacuum, and improving the turbine design and fabrication. The important aim in this testing was that the plant has operated by following the electricity demand fluctuation in the 20 kV PLN grid. This testing progress becomes a promising stage for the development of GPP’s equipment with local manufactures.

An RBF-Based h-Adaptive Cartesian Grid Refinement Method for Arbitrary Single/Multi-Body Hull Modeling and Reconstruction

Complex single/multi-body structures are generally found in ship and ocean engineering. They have the smooth, sharp, concave, and convex surface features in common. Precise modeling of the structures is the basis of numerical simulation. However, the most widely used explicit modeling method requires considerable manual operations. The result is also difficult to reproduce. Therefore, this paper presents a Radial basis function (RBF) based hierarchical (h-) adaptive Cartesian grid method. The RBF is introduced for arbitrary implicit modeling over the Cartesian framework. Thanks to its natural properties, the RBF is easy to use, highly automated, and only needs a set of scatter points for modeling. The block-based h-adaptive mesh refinement (AMR) combined with the RBF aims to enhance the local grid resolution. It accelerates the calculation of intersecting points compared with the uniform Cartesian grid. The accuracy, efficiency, and robustness of the proposed method are validated by the simulation of the 3D analytical ellipsoidal surface and the unclosed conic surface. To select suitable parameters, we thoroughly investigated the uncertainty factors including sample points, RBF functions, and h-AMR grids. The simulation results of the single/multi-body Wigley hull and KCS hull forms verified the proper selection of the factors and the feasibility of our method to solve practical problems.

Waste heat recovery in low temperature networks versus domestic heat pumps - A techno-economic and environmental analysis

The planning of energy infrastructures in new districts often follows the practice adopted for the rest of the city. In Stockholm, district heating is a common solution for multi-apartment neighborhoods. Recently, because of an average clean electricity mix, heat pumps have gained interest. However, European studies suggest to limit the reliance on electrification to avoid an extreme demand increase. Thus, an effort is required to improve the environmental impact of alternative options. This study proposes waste heat recovery in low temperature networks as a promising solution. By means of a techno-economic and environmental analysis, this option is compared to domestic heat pumps. A new approach is proposed to combine a district level perspective with simulation tools able to capture sector-coupling interactions. Scenarios, for a real neighborhood, assess waste heat recovery potential and electricity grid loading status. Results show that a waste heat recovery capacity equal to 10% of the peak load can reduce fossil fuel use of 40%. Local grid limitations are shown to be a bottleneck for the feasibility of domestic heat pumps. Their heat generation cost is 28% higher than for district heating. The carbon footprint is strongly dependent on the emission factor of the electricity mix (+11%/-24%).

Numerical Simulation of Viscous Flows Around a Surface Combatant Model at Different Drift Angles Using Overset Grids

Viscous flows around ship hull is of great complexity, and when the ship is advancing with drift angles, the flow field can be more complicated. In this paper, the viscous flow field around an obliquely towed surface combatant DTMB 5512 is computed using the unsteady Reynolds-averaged Navier–Stokes (URANS) method. The numerical simulations are carried out by the in-house CFD solver naoe-FOAM-SJTU, which is developed on the open source platform OpenFOAM. To refine local grids and simulate dynamic ship motions, the overset grid approach is applied. Grid convergence study is first performed at straight-ahead towed condition with three sets of grid number and the results show monotonically convergence. Six different drift angles for the DTMB 5512 model at Froude number of 0.28 are simulated and the predicted hydrodynamic forces and flow field are presented. During the simulation, the ship hull is free to trim and sinkage at a free surface environment. As for straight-ahead towed condition, the total resistance coefficient and flow field, i.e., wave pattern and wake region, between this work and the experiment are compared. Both force coefficients and flow field show good agreement with the available experimental data. For oblique towed conditions, the lateral force, yaw moment and the derived hydrodynamic derivatives are also presented and compared with the experimental results. The variation of wave pattern and wake region at different drift angles are presented and analyzed. The results show that the current approach can be an effective tool to predict the viscous flows and hydrodynamic loads for oblique towed ships.

Proton Exchange Membrane Hydrogen Fuel Cell as the Grid Connected Power Generator

In this paper, a proton exchange membrane fuel cell (PEMFC) is implemented as a grid-connected electrical generator that uses hydrogen gas as fuel and air as an oxidant to produce electricity through electrochemical reactions. Analysis demonstrated that the performance of the PEMFC greatly depends on the rate of fuel supply and air supply pressure. Critical fuel and air supply pressures of the PEMFC are analysed to test its feasibility for the grid connection. Air and fuel supply pressures are varied to observe the effects on the PEMFC characteristics, efficiency, fuel supply, and air consumption over time. The PEMFC model is then implemented into an electrical power system with the aid of power electronics applications. Detailed mathematical modelling of the PEMFC is discussed with justification. The PEMFC functions as an electrical generator that is connected to the local grid through a power converter and a transformer. Modulation of the converter is controlled by means of a proportional-integral controller. The two-axis control methodology is applied to the current control of the system. The output voltage waveform and control actions of the controller on the current and frequency of the proposed system are plotted as well. Simulation results show that the PEMFC performs efficiently under certain air and fuel pressures, and it can effectively supply electrical power to the grid.

Application of Phase Change Tracking Approach in Predicting Condensate Blockage in Tight, Low, and High Permeability Reservoirs

Prediction of the timing and location of condensate build-up around the wellbore in gas condensate reservoirs is essential for the selection of appropriate methods for condensate recovery from these challenging reservoirs. The present work focuses on the use of a novel phase change tracking approach in monitoring the formation of condensate blockage in a gas condensate reservoir. The procedure entails the simulation of tight, low and high permeability reservoirs using global and local grid analysis in determining the size and timing of three common regions (Region 1, near wellbore; Region 2, condensate build-up; and Region 3, single-phase gas) associated with single and two-phase gas and immobile and mobile gas condensate. The results show that permeability has a significant influence on the occurrence of the three regions around the well, which in turn affects the productivity of the gas condensate reservoir studied. Predictions of the timing and location of condensate in reservoirs with different permeability levels of 1 mD to 100 mD indicate that local damage enhances condensate formation by 60% and shortens the duration of the immobile phase by 45%. Meanwhile, the global change in permeability increases condensate formation by 80% and reduces the presence of the immobile phase by 60%. Finally, this predictive approach can help in mitigating condensate blockage around the wellbore during production.

Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This article focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon partial observable Markov decision process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, finite-horizon deep deterministic policy gradient (FH-DDPG) and finite-horizon recurrent deterministic policy gradient (FH-RDPG), are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated MG data is performed, where the performance of the proposed algorithms are compared with the other baseline DRL and non-DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated, respectively.

New Challenges in the Design of Microgrid Systems: Communication Networks, Cyberattacks, and Resilience

Microgrids (MGs), referred to as next-generation power systems, are receiving considerable attention from both industry and academia. Integrated with distributed energy resources (DERs), energy storage systems, and a variety of loads, MGs function as a localized power grid that can be operated independently or connected to utility grids. With the rapid development of technology in communication networks, the framework of MGs tends to be more distributed, intelligent, and tightly integrated with networks. Applications of MGs can be found on the Internet of Things (IoT), Industry 4.0, smart cities, and so on.

Analysis of influence of mesh partition on Mike21 calculation in flood impact assessment

In order to study the impact of different grid mesh sizes on the calculation results of the model in the analysis of flood impact assessment, this paper takes the newly constructed bridge between the Niushou and Xiangyang sections of the Hanjiang River as an example. It simulates the influences of bridge construction on river flow under the six schemes of setting the bridge pier as a solid boundary, no internal mesh generation, and not participating in numerical calculation, and dividing the bridge pier local mesh size into 2m, 4m, 6m, 8m, and 10m. The results show that different grid sizes in the project have an effect on the river water level and the range of water level changes. The smaller the local grid size of the project relative to the building size, the greater the maximum water level change value, and the closer it is to the calculated value of the bridge pier as a solid boundary. By adding piers module of the model to generalize the project, the water level change in the model results is significantly smaller. Under the same water level change conditions, the larger the mesh size, the greater the scope of the project. For projects located in important river sections with significant water blocking effects, it is recommended that buildings be used as solid boundaries without participating in generating meshes or modifying local terrain to simulate the effects of water flow. The results are more in line with actual conditions.

Pandapipes: An Open-Source Piping Grid Calculation Package for Multi-Energy Grid Simulations

The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. In this work, we analyze a number of applicable tools and find that most of them do not allow coupling of infrastructures, oversimplify the grid model or are based on inaccessible source code. We introduce the open source piping grid simulation tool pandapipes that—in interaction with pandapower—addresses three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes, we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then, we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as an interaction of a local electricity and district heating grid in a small neighborhood. The second case study analyzes the potential of a power-to-gas device to provide flexibility in a power grid while considering gas grid constraints. These cases show the importance of performing coupled simulations for the design and analysis of future energy infrastructures, as well as why the software should fulfill the three criteria.