Real Grid Research Articles

Distributed Deep Reinforcement Learning for Renewable Energy Accommodation Assessment With Communication Uncertainty in Internet of Energy

Nowadays, microgrids (MG) have attracted much attention, as a key technology of the Internet of Energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this article. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical MG is a hybrid control based on distribution deep reinforcement learning. The temporal difference (TD) generation adversarial network (TD-GAN) is proposed as a value-based method. Compared with the policy-based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the TD error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.

Sparse Measurement-Based Coordination of Electric Vehicle Charging Stations to Manage Congestions in Low Voltage Grids

The increasing use of distributed generation and electric vehicle charging stations provokes violations of the operational limits in low voltage grids. The mitigation of voltage limit violations is addressed by Volt/var control strategies, while thermal overload is avoided by using congestion management. Congestions in low voltage grids can be managed by coordinating the active power contributions of the connected elements. As a prerequisite, the system state must be carefully observed. This study presents and investigates a method for the sparse measurement-based detection of feeder congestions that bypasses the major hurdles of distribution system state estimation. Furthermore, the developed method is used to enable congestion management by the centralized coordination of the distributed electric vehicle charging stations. Different algorithms are presented and tested by conducting load flow simulations on a real urban low voltage grid for several scenarios. Results show that the proposed method reliably detects all congestions, but in some cases, overloads are detected when none are present. A minimal detection accuracy of 73.07% is found across all simulations. The coordination algorithms react to detected congestions by reducing the power consumption of the corresponding charging stations. When properly designed, this strategy avoids congestions reliably but conservatively. Unnecessary reduction of the charging power may occur. In total, the presented solution offers an acceptable performance while requiring low implementation effort; no complex adaptations are required after grid reinforcement and expansion.

Integrated Algorithm for Selecting the Location and Control of Energy Storage Units to Improve the Voltage Level in Distribution Grids

This paper refers to the issue that mainly appears in distribution grids, where renewable energy sources (RES) are widely installed. In such grids, one of the main problems is the coordination of energy production time with demand time, especially if photovoltaic energy sources are present. To face this problem, battery energy storage units (ESU) can be installed. In recent years, more and more attention has been paid to optimizing the use of ESU. This paper contains a simple description of available solutions for the application of ESU as well as an original proposal for selecting the optimal location and control of ESU. The ESU selection method is based on the use of a genetic algorithm and the ESU control method utilizes the fuzzy logic. The combination of the aforementioned methods/algorithms of ESU application is named an integrated algorithm. The performance of the proposed algorithm was validated by multivariate computer simulations with the use of the real low-voltage grid model. The DIgSILENT PowerFactory environment was employed to develop the simulation model of the integrated algorithm. The proposal was utilized to improve the voltage level in the distribution grid and to install the optimal number of ESU. Based on daily load variations for selected load profiles, it was shown that after the ESU application the voltage deviations in the analyzed network were significantly limited. Moreover, the analysis proves that both the location of ESU in the grid and the control of their active and reactive power are important from the point of view of reducing overall costs.

Comparison of Measurement Methods for 2–150-kHz Conducted Emissions in Power Networks

This article presents a comparison of measurement methods for current and voltage distortions in low-voltage networks in the frequency range from 2 to 150 kHz (supraharmonics). The comparison encompasses the methods informatively described in IEC and CISPR international standards, as well as other innovative techniques presented in the literature. This work is carried out within a novel framework that includes advanced and complex synthetic test signals, as well as real grid recordings that allow an accurate comparison of the performance of the tested methods. Specifically designed indices are employed to characterize the accuracy of the tested methods in the frequency and amplitude assessments. In light of that, the strengths and weaknesses of the methods are identified. The results of this article contribute to the ongoing standardization work carried out by the IEC SC77A/WG9 with the purpose of defining a normative measurement method suitable for assessing grid disturbance levels in the range from 2 to 150 kHz.

Estimation of Modal Parameters for Inter-Area Oscillations Analysis by a Machine Learning Approach with Offline Training

An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and also the integration of renewable sources. An interesting countermeasure to prevent the occurrence of such oscillations is to continuously identify their characteristic electromechanical mode parameters, possibly realizing an online monitoring system. In this paper an attempt to develop an online modal parameters identification system is done using machine learning techniques. An approach based on the development of a proper artificial neural network exploiting the frequency measurements coming from actual PMU devices is presented. The specifically developed offline training stage is fully detailed. The output results from the dynamic mode decomposition method are considered as reference in order to validate the machine learning approach. Some results are presented in order to validate the effectiveness of the proposed approach on data coming from recordings of real grid events. The main key points affecting the performance of the proposed technique are discussed by means of proper validation scenarios. This contribution is the first step of a more extended project whose final aim is the development of an artificial neural networks (ANN) architecture able to predict the system behavior (in a given time span) in terms of LFO modal parameters, and to classify the contingencies/disturbances based on an online training that has memory of the passed training samples.

Methodology for Power Quality Measurement Synchronization Based on GPS Pulse-Per-Second Algorithm

Power quality disturbances (PQD) are generated by nonlinear loads and they propagate through the electrical grid to other sensitive devices causing malfunction. To study the propagation of PQD, it is important to perform synchronized and simultaneous measurements at multiple locations in the electrical network. This article proposes a methodology to synchronize measurements of electrical variables at multiple locations in the electrical grid based on the synchronization of the internal time reference of several data loggers with the pulse per second (PPS) reference provided by a global positioning system (GPS) receiver module. As the PPS signal is synchronized to the atomic clocks of the satellites in the GPS, a precise time reference in multiple sites can be obtained simultaneously. The proposed methodology has been tested in two stages. The first stage is a validation experiment that compares the number of samples obtained between three GPS synchronized data loggers and two data loggers with internal synchronization only. The PPS signal has been interrupted in one GPS synchronized data logger to simulate a signal loss and test the resynchronization capability of the methodology. A very low synchronization error has been obtained for the GPS synchronized data loggers, whereas the data loggers with the internal synchronization show greater synchronization errors. The second stage consists in experimental case of study measuring electrical variables in multiple locations of a real electrical grid where the propagation of multiple disturbances is tracked with the GPS synchronized data loggers.

Toward a Retail Market for Distribution Grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). Proper coordination and scheduling of these DERs requires a local retail market which can operate at the distribution grid level. In this paper, we propose a retail market for optimally managing and scheduling DERs, and coordinating ancillary services in a distribution grid. Our proposed retail market leverages a recently proposed distributed proximal atomic coordination (PAC) algorithm which has several advantages over other distributed algorithms, with reduced local computational effort and enhanced privacy. We describe how the market can be implemented using a Distribution System Operator (DSO), whose representatives are located at the primary feeder level and workers are located at the substation level, and how the DSO will interact with the Wholesale Electricity Market. Finally, we extensively validate the performance of the proposed retail market via simulations of three networks: a real distribution grid in Tokyo, a balanced IEEE 123-bus distribution grid, and a modified IEEE 13-bus network. Our results show that the proposed market is practical and can be easily implemented in distribution grids, resulting in optimal real-time scheduling of DERs and compensation in the form of distributed locational marginal prices.

A New Virtual Synchronous Generator Design Based on the SMES System for Frequency Stability of Low-Inertia Power Grids

In light of the challenges of integrating more renewable energy sources (RESs) into the utility grid, the virtual synchronous generator (VSG) will become an indispensable configuration of modern power systems. RESs are gradually replacing the conventional synchronous generators that are responsible for supplying the utility grid with the inertia damping properties, thus renewable power grids are more vulnerable to disruption than traditional power grids. Therefore, the VSG is presented to mimic the behavior of a real synchronous generator in the power grid through the virtual rotor concept (i.e., which emulates the properties of inertia and damping) and virtual primary and secondary controls (i.e., which emulate the conventional frequency control loops). However, inadequate imitation of the inertia power owing to the low and short-term power of the energy storage systems (ESSs) may cause system instability and fail dramatically. To overcome this issue, this paper proposes a VSG based on superconducting magnetic energy storage (SMES) technology to emulate the needed inertia power in a short time and thus stabilizing the system frequency at different disturbances. The proposed VSG based on SMES is applied to improve the frequency stability of a real hybrid power grid, Egyptian Power System (EPS), with high renewables penetration levels, nonlinearities, and uncertainties. The performance superiority of the proposed VSG-based SMES is validated by comparing it with the traditional VSG approach based on battery ESSs. The simulation results demonstrated that the proposed VSG based on the SMES system could significantly promote ultra-low-inertia renewable power systems for several contingencies.

Test Grids for the Integration of RES—A Contribution for the European Context

A long-term sustainable energy transition can only be achieved by technological advancements and new approaches for efficiently integrating renewable energies into the overall energy system. Significantly increasing the share of renewable energy sources (RES) within the overall energy system requires appropriate network models of current transmission and distribution grids, which, as limiting factors of energy infrastructures, confine this share due to capacity constraints. However, especially regarding electrical network models, data (e.g., geographical data, load and generation profiles, etc.) is rarely available since it usually includes user-specific information and is, therefore, subject to data protection. Synthetically obtained electrical networks, on the other hand, may not be representative and may fail to replicate real grid structures due to the heterogeneous properties of currently operated networks. To account for this heterogeneity, this paper offers a contribution for the European electrical energy system and presents the development of four synthetic test networks at different voltage levels which are representative and include non-confidential time-series data. The test network development is based on an extensive literature research on a multitude of different network parameters for grids within the ENTSO-E (European Network of Transmission System Operators for Electricity) interconnected system in Europe. These parameters are then used to design the networks in NEPLAN®. Then, these networks are provided with load and generation profiles for enabling time-series calculations. To validate the representativeness of the test networks, a short-circuit analysis is conducted and the obtained results are compared to short-circuit parameters common for Austrian and German literature values as well as for value ranges for European ENTSO-E grids. The analysis shows that the presented test networks replicate European electrical network behavior accurately and can, therefore, be utilized for various application purposes to assess technological impacts on European ENTSO-E grids.

Adaptive inertia emulation control for high‐speed flywheel energy storage systems

Low-inertia power systems suffer from a high rate of change of frequency (ROCOF) during a sudden imbalance in supply and demand. Inertia emulation techniques using storage systems, such as flywheel energy storage systems (FESSs), can help to reduce the ROCOF by rapidly providing the needed power to balance the grid. In this work, a new adaptive controller for inertia emulation using high-speed FESS is proposed. The controller inertia and damping coefficients vary using a combination of bang–bang control approaches and self-adaptive ones, to simultaneously improve both the ROCOF and the frequency nadir. The performance of the proposed adaptive controller has been initially validated and compared with several existing adaptive controllers by means of offline simulations, and then validated with experimental results. The proposed controller has been implemented on a real 60 kW high-speed FESS, and its performance has been evaluated by means of power hardware-in-the-loop (PHIL) testing of the FESS in realistic grid conditions. Both simulations and PHIL testing results confirm that the proposed inertia emulation control for the FESS outperforms several previously reported controllers, in terms of reducing the maximum ROCOF and improving the frequency nadir during large disturbances.

Influence of the Phenomenon of Spectrum Leakage on the Evaluation Process of Metrological Properties of Power Quality Analyser

Power quality assessment is a complex measurement task, requiring the usage of a system with suitable metrological properties. This complex measurement task in the real power grid is performed with the use of power quality analysers that measure and record the parameters determining the power quality. The paper presents selected research results for a class A power quality analyser in the specially prepared measurement system containing measuring instruments calibrated by the Main Office of Measures. The measurement results were completed with a presentation of the uncertainty budget. The influence of the phenomenon of spectrum leakage on metrological properties of the tested power quality analyser was considered in the paper. To assess this influence, the simulation studies with the use of MATLAB and the experimental studies were carried out for selected test signals. The Metrological interpretation of the research results is presented in the paper.

Study on the Joint Optimization Mode of Electric Energy and Regulation Market

Introduction As the guarantee for the stable of the power system， the marketization of the regulation auxiliary service has been put on the agenda. The energy-regulation joint optimization becomes a mode of market organization. This paper aims to study the combined optimization model of the electric energy-regulation market considering the performance difference of frequency modulation resources in the PJM market background. Method Regulation auxiliary service pricing adjustment model and the joint clearing model of electric energy and regulation market considering the difference of fast and slow regulation resources and regulation performance was proposed in this paper， which were applied to the simulation of a real power grid to compared with domestic electric energy - regulation step clearing market. Two typical scenarios were set up in wet season and dry season. The clearing of regulation resources， the influence on the electricity purchase cost in the spot market and the active degree of regulation market were studied in detail. Result In the stepwise clearing mode， the rapid regulation resource has obvious advantages and causes the increase of electricity purchase cost in the electricity market. In the electricity-regulation joint optimization model， the clearing results of fast and slow regulation resources are affected by the pricing adjustment strategy and the increase rate of electricity purchase costs in the power market is smaller than that in the stepwise model， and the clearing price and fluctuation degree in the regulation market are higher than that in the stepwise clearing mode model. Conclusion The combined optimization mode of electric-regulation modulation with reasonable price adjustment strategy is helpful to realize reasonable competition among units with different regulation performance and stimulate market vitality.

Future-oriented generic grid code regarding wind power plants in Europe

The assessment of compliance with grid codes requirements can be challenging for researchers and (early stage) technology developers. To tackle this problem, a generic grid code is proposed in this article that defines certain requirements aimed for future scenarios with high shares of renewables. The proposed code is not linked to a particular grid of a certain region or country. The requirements are expressed in a mathematical way, in contrast to the legal format of real grid codes, to enable for easier implementation. The parameterised expressions enable for easy customisation for specific use cases. The compliance is assessed through reference under/over voltage test events and a test frequency disturbance, to which desired responses are specified. Detailed dynamic tolerance bands are defined around the desired responses, using linear parameterised functions providing a clear and continuous definition that leaves little room for interpretation. This generic grid code is inspired by the present regulations, especially the European grid code framework (ENTSO-E RfG) and the Irish grid code, which is seen by many as a benchmark regarding wind power integration. However, the generic grid code includes additional requirements that are not commonly part of grid codes today, including requirements covering asymmetric faults and inertia-like response to frequency deviations.

Applying Directly Modified RDFT Method in Active Power Filter for the Power Quality Improvement of the Weak Power Grid

The recursive discrete Fourier transformation (RDFT) method can be used for grid voltage phase-locking and harmonic current detection in a shunt active power filter (SAPF). However, in weak power grids such as microgrids, significant errors might occur in the amplitude and phase detection due to grid frequency deviation. In this study, to resolve this problem, a directly modified RDFT (DMRDFT) method is proposed for SAPF weak grid application. Through theoretical analysis, the errors of phase and amplitude detection were found to consist of fixed error and fluctuating error. The fixed error is only determined by frequency deviation, whereas the fluctuating error is also related to the recursive pointer and the initial phase. The DMRDFT algorithm can obtain the real grid frequency through the calculation of the phase angle difference for two consecutive periods. Then it can employ the grid frequency deviation and the recursive pointer value to directly correct the detection results gathered by the conventional RDFT algorithm. As a result, DMRDFT can yield accurate amplitude and phase information of the grid voltage or current with a simple calculation. Simulation results verify the high precision of the proposed DMRDFT method in both steady and dynamic situations. Experimental results show that the DMRDFT method can significantly increase the SAPF compensation performance when grid frequency shifts.

Experimental validation of feedback optimization in power distribution grids

We consider the problem of controlling the voltage of a distribution feeder using the reactive power capabilities of inverters. On a real distribution grid, we compare the local Volt/VAr droop control recommended in recent grid codes, a centralized dispatch based on optimal power flow (OPF) programming, and a feedback optimization (FO) controller that we propose. The local droop control yields suboptimal regulation, as predicted analytically. The OPF-based dispatch strategy requires an accurate grid model and measurement of all loads on the feeder in order to achieve proper voltage regulation. However, in the experiment, the OPF-based strategy violates voltage constraints due to inevitable model mismatch and uncertainties. Our proposed FO controller, on the other hand, satisfies the constraints and does not require load measurements or any grid state estimation. The only needed model knowledge is the sensitivity of the voltages with respect to reactive power, which can be obtained from data. As we show, an approximation of these sensitivities is also sufficient, which makes the approach essentially model-free, easy to tune, compatible with the current sensing and control infrastructure, and remarkably robust to measurement noise. We expect these properties to be fundamental features of FO for power systems and not specific to Volt/VAr regulation or to distribution grids.

Scheduling electric vehicle charging to minimise carbon emissions and wind curtailment

This paper presents an investigation of the potential for coordinated charging of electric vehicles to i) reduce the CO 2 emissions associated with their charging by selectively charging when grid carbon intensity (gCO2/kWh) is low and ii) absorb excess wind generation in times when it would otherwise be curtailed. A method of scheduling charge events that seeks the minimum carbon intensity of charging while respecting EV and network constraints is presented via a time-coupled linearised optimal power flow formulation, based on plugging-in periods derived from a large travel dataset. Schedules are derived using real half-hourly grid intensity data; if charging in a particular event can be done entirely through use of renewable energy that would otherwise have been curtailed, its carbon intensity is zero. It was found that if ‘dumb’ charged from the current UK mainland (GB) grid, average emissions related to electric vehicle (EV) charging are in the range 35–56 gCO2/km; this can be reduced to 28–40 gCO2/km by controlled charging – approximately 20–30% of the tailpipe emissions of an average new petrol or diesel car sold in Europe. There is potential for EVs to absorb excess wind generation; based on the modelled charging behaviour, 500,000 EVs (20% of Scotland’s current car fleet) could absorb around three quarters of curtailment at Scotland’s largest onshore wind farm. • Electric vehicle (EV) charging is scheduled for minimum carbon intensity. • EVs’ potential to absorb excess renewables by charging selectively is investigated. • Real GB grid carbon intensity (gCO 2 /kWh) and wind curtailment data used. • Scheduling can reduce EV emissions from 35 to 56 gCO 2 /km to 28–40 gCO 2 /km. • 500,000 EVs can absorb 75% of curtailment at GB’s largest onshore wind farm.

Saturated‐core fault current limiters for AC power systems: Towards reliable, economical and better performance application

With the continuous expansion of power grid capacity, the problem of short-circuit current exceeding the standard is becoming increasingly serious. Fault current limiter is a promising solution and is gradually becoming a research hotspot. In this study, fault current limiters are classified into four categories. And saturated-core fault current limiters are emphatically introduced, including its working principle and comparison with the other three categories. Saturated core fault current limiters are divided into four branches according to the ways leading the core saturated. A comprehensive review of the research activities and emerging technologies of saturated-core fault current limiters for AC power systems is presented in this study. The working principle and typical structure of DC-biased-, permanent-magnet-, superconducting- and hybrid-type saturated-core fault current limiters are introduced. The advantages and disadvantages of four types of saturated core fault current limiters are compared in detail from the aspects of current-limiting performance, iron core size and DC magnetomotive force. Real grid application examples of some types of devices are presented, as well as new progress in the techniques, are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or as practical technical guidance.

Smart routing of electric vehicles for load balancing in smart grids

Electric vehicles (EVs) are expected to be a major component of the smart grid. The rapid proliferation of EVs will introduce an unprecedented load on the existing electric grid due to the charging/discharging behavior of the EVs, thus motivating the need for novel approaches for routing EVs across the grid. In this paper, a novel distributed control framework based on noncooperative game theory for routing of EVs within the smart grid is proposed. The goal of this framework is to control and balance the electricity load in a distributed manner across the grid while taking into account the traffic congestion and the waiting time at charging stations. The EV routing problem is formulated as a repeated game, and it is shown that the selfish behavior of EVs will result in a pure-strategy Nash equilibrium with the price of anarchy upper bounded by the ratio of the variance of the ground load to the total number of EVs in the grid. In particular, it is shown that any achieved Nash equilibrium substantially improves the load balance across the grid. Moreover, the results are extended to capture the stochastic nature of induced ground load as well as the subjective behavior of the EV owners using the behavioral framework of prospect theory. Simulation results provide new insights on efficient energy pricing at charging stations and under realistic grid conditions.

An Improved Two-Stage Optimization Procedure for Optimal Power Flow Calculation

The paper focuses on the problem of optimal power flow. Minimized production and transmission costs were considered to design an optimal power flow search method based on the novel two-stage optimization procedure that takes into account the voltage and power constraints. As a result, the efficient optimum search method for the non-convex optimal power flow problem is proposed and implemented. The efficient L-BFGS-B optimization algorithm is employed to allow for the constraints. The stochastic modification of the global optimization algorithm of the Lipschitz function was used in the first stage to improve the L-BFGS-B method. Two illustrative examples on the IEEE 118-bus test scheme and real regional grid demonstrate the efficiency of the proposed two-stage optimization procedure. The possibility of taking into account the renewable sources in the proposed optimal power flow algorithm is briefly discussed

A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment

Health-related limitations prohibit a human from working in hazardous environments, due to which cognitive robots are needed to work there. A robot cannot learn the spatial semantics of the environment or object, which hinders the robot from interacting with the working environment. To overcome this problem, in this work, an agent is computationally devised that mimics the grid and place neuron functionality to learn cognitive maps from the input spatial data of an environment or an object. A novel quadrant-based approach is proposed to model the behavior of the grid neuron, which, like the real grid neuron, is capable of generating periodic hexagonal grid-like output patterns from the input body movement. Furthermore, a cognitive map formation and their learning mechanism are proposed using the place–grid neuron interaction system, which is meant for making predictions of environmental sensations from the body movement. A place sequence learning system is also introduced, which is like an episodic memory of a trip that is forgettable based on their usage frequency and helps in reducing the accumulation of error during a visit to distant places. The model has been deployed and validated in two different spatial data learning applications, one being the 2D object detection by touch, and another is the navigation in an environment. The result analysis shows that the proposed model is significantly associated with the expected outcomes.