Medium Voltage Distribution Grid Research Articles

Maximum load for medium voltage lines in N-1 conditions

This paper proposes a methodology able to assess the maximum value of the load that can be connected to a medium voltage (MV) feeder in addition to the existing one without impairing the N-1 security criterion. A simplified approach is pursued, the original problem is split into several separate subproblems, and the maximum additional load is calculated solving multiple optimization problems. The proposed method is simple and practically applicable by distribution system operators (DSOs) that, after a request by a customer to connect a load, must know if an MV feeder is fit to host the additional demand or if a new infrastructure is needed. The application of the methodology to the whole MV distribution grid of Rome, in Italy, is presented; execution times show that the DSO can apply the model daily, to follow the load growth day by day.

Using Simplified Distflow-Based Mixed-Integer Quadratically Constrained Programming Formulation for Optimum Selection of Conductor Size in Electrical Distribution Networks

Determining conductor size is a subproblem that plays a vital role in the planning process of distribution systems. The problem of selecting the conductor cross-sectional area is usually made a model of mixed-integer non-linear programming (MINLP). It is important to note, however, that the MINLP model is not always capable of ensuring convergence to a solution that is globally optimum. In this research, a mixed-integer quadratically constrained programming (MIQCP) formulation is developed as a method for determining the conductor cross-sectional area in power distribution networks in an optimal manner. The goal function aims at minimizing the lifetime cost of lines, including initial capital cost together with operational expenses. The suggested optimization model’s constraints consist of power balance equations, thermal loading capacity of branches, nodal voltage restrictions, budget limitations for investment, and the need to have the same wire size in the main feeder. By using the simplified DistFlow approach for electrical distribution networks and precisely linearizing the product of a binary variable and a continuous variable, the MIQCP formulation is derived from the MINLP model. It is likely possible to solve the MIQCP formulation efficiently in the GAMS environment by using available commercial solvers like CPLEX. The developed MIQCP model is validated using three medium voltage distribution grids of IEEE 33 buses, IEEE 85 nodes, and the Vietnamese real 102 buses. The calculation results revealed the accuracy along with the efficacy of the proposed optimization procedure.

Reactive power management considering Transmission System Operator and Distribution System Operator coordination

The increasing integration of Distribution Energy Resources (DER) in the distribution system has brought the necessity of a change in grid management and also for better coordination between the Transmission System Operator (TSO) and the Distribution System Operator (DSO). This work proposes a reactive power management model to be used by DSOs, in which reactive power flexibility from DER, and also from On-Load Tap Changer (OLTC) transformers and capacitor banks are used to handle voltage problems that may arise in both transmission and distribution grids due to the uncertain production of Renewable Energy Sources (RES). Besides, it is proposed that the DSO may provide a service to the TSO, in which the latter requests a reactive power setpoint from the first one, in the TSO-DSO boundary. Adaptive robust optimization on an Alternating Current Optimal Power Flow (AC-OPF) is modelled, ensuring that the DSO receives a feasible solution and is able to manage congestion and voltage problems. The proposed model is compared with its stochastic equivalent to assess its strengths and drawbacks. To test and validate the proposed models, a 37-bus Medium Voltage (MV) distribution grid with high RES penetration is used. An important conclusion is that, though the robust model presents a safer solution than the stochastic model, the operator must be aware of the trade-off between the desired level of robustness and the expected operating cost.

Reliability-Centered Maintenance Framework for Distribution Grids Based on Fault-Tree Analysis

Reliability is the key issue in the supply of electrical energy in modern society, which is jeopardized by the failures occurring in different sections of distribution grids. To address this challenge, this paper presents a reliability-centered maintenance framework for transformers, switchgear panels and power cables in medium-voltage distribution grids. First, fault tree models for the different equipment are established in this paper, with which the impacts of different failures and the effects of maintenance actions are analyzed in a quantitative manner. Using the fault tree models, the influences of different maintenance strategies on the reliability indexes of equipments in long-term operations can be estimated, which provides references for the selection and prioritization of preventive maintenance actions. This research work provides a generalized and practical framework for designing reliability-centered maintenance plans for distribution grids.

Optimal sizing and siting of energy storage systems considering curtailable photovoltaic generation in power distribution networks

This work proposes a method for optimal planning (sizing and siting) energy storage systems (ESSs) in power distribution grids while considering the option of curtailing photo-voltaic (PV) generation. More specifically, for a given PV generation capacity to install, this method evaluates whether curtailing PV generation might be more economical than installing ESS. Indeed, while curtailing excess PV generation might be considered a last resort to avoid grid violations during operations, it is typically neglected in the planning phase. The proposed method accounts for the constraints of the power grid (i.e., nodal voltages, lines, and substation transformer limits) modeled by linearized power flow equations to keep the problem formulation tractable. The planning problem minimizes the net investment costs of the ESSs, and the imported and exported electricity costs considering a planning horizon of 20 years. The results are presented for a medium voltage (MV) distribution grid with different levels of installed capacity of PV generation, reflecting future scenarios of PV generation development. The sensitivity of the ESSs’ sizes and investment costs to the electricity prices accounting for variable levels of PV production in the global generation mix is also investigated.

Comparison of Control Techniques for Harmonic Isolation in Series VSC-Based Power Flow Controller in Distribution Grids

The application of power electronics equipment in medium voltage (MV) distribution grids can provide new management solutions for power flow control, load balancing and voltage problems. A series MV VSC-based power flow controller has recently been presented to interconnect two radial distribution feeders performing active and reactive power transfers to improve the flexibility and utilization of these circuits in a controlled and secure way. Although not previously explored, this power flow controller can integrate the functionality of a series power filter, accomplishing independent control of the fundamental power flow while isolating the harmonic content between the two interconnected feeders. This prevents harmonic pollution from one feeder from propagating to the other, improving the voltage quality. To implement the harmonic isolation, several control strategies can be used. Therefore, this paper provides a comparative analysis between two of the main harmonic control techniques found in the literature: the Synchronous Reference Frame (SRF) controller and the Proportional Resonant (PR) controller. Assessments are conducted both through simulations and experimental results in a meshed network at 13.8 kV with different types of non-linear loads. In the simulation cases, both algorithms showed similar results; however, in the experimental cases, the PR-based solution exhibited better performance in isolating the harmonics from one feeder to the other.

Selective Virtual Synthetic Vector Embedding for Full-Range Current Harmonic Suppression of the DC Collector

With the rapid development of rene wable energy (e.g., wind and photovoltaic energy) and energy storage, the dc collector has become an effective conversion link of distributed energy access to medium-voltage power distribution grids. However, considering the inconsistency of dc voltage and the duty cycle of each submodule, the traditional carrier phase-shifting modulation is difficult to achieve the excellent harmonic characteristic in the dc collector, thereby introducing difficulties in the design of passive filters. To solve this problem, this article proposes a selective virtual synthetic vector embedding algorithm to realize full-range current harmonic suppression in the dc collector system. In this scheme, the switching voltage vectors outputted by each submodule are sorted, and the voltage vectors are selectively extracted and synthesized to calculate the embedded virtual voltage vector. Afterward, the voltage vector of each submodule is rotated and calculated online from the perspective of space geometry, and the optimal carrier phase combination in variable-angle carrier phase-shift modulation is obtained. Finally, the experimental results show that the proposed control algorithm can not only be used in the dc collector with any number of modules but also achieve fast and effective harmonic suppression in the whole operating range.

Interpretation and Quantification of the Flexibility Sources Location on the Flexibility Service in the Distribution Grid

The economic and technical requirements of current changes in the distribution system are reflected in the use of all available resources and the activation of mechanisms for local use of flexibility. Local flexibility markets are evolving and face numerous obstacles for which appropriate solutions must be found. The local flexibility market will be complemented by the development of a local flexibility register, which will contain all relevant information about the flexibility assets necessary for the efficient operation of the local flexibility market. In this paper, interpretation and quantification of the flexibility sources location on the flexibility service in the distribution grid is given. The information is derived from power flow simulation results and finally written down in the form of line coefficients, which are determined by applying the least squares method to the power flow results. We have developed a Python-based simulator to perform the methodology to determine the information and test it on a realistic medium voltage distribution grid in Bosnia and Herzegovina. This paper confirms the approximate linearity of the active power changes on the demand side to the line load and to the voltage at the nodes for a given operating condition of the distribution grid.

PMU-based Real-time Distribution System State Estimation Considering Anomaly Detection, Discrimination and Identification

In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.

Economic feasibility of green hydrogen in providing flexibility to medium-voltage distribution grids in the presence of local-heat systems

The recent strong increase in the penetration of renewable energy sources (RESs) in medium-voltage distribution grids (MVDNs) has raised the need for congestion management in such grids, as they were not designed for this new condition. This paper examines to what extent producing green hydrogen through electrolyzers can profitably contribute to congestion alleviation in MVDNs in the presence of high amounts of RES, as well as flexible consumers of electricity and a local heat system. To address this issue, an incentive-based method for improving flexibility in MVDNs is used which is based on a single-leader–multiple-followers game formulated by bi-level mathematical programming. At the upper level, the distribution system operator, who is the leader of this game, determines dynamic prices as incentives at each node based on the levels of generation and load. Next, at the lower level, providers of flexibility, including producers using electrolyzers, price-responsive power consumers, heat consumers, as well as heat producers, respond to these incentives by reshaping their output and consumption patterns. The model is applied to a region in the North of The Netherlands. The obtained results demonstrate that converting power to hydrogen can be an economically efficient way to reduce congestion in MVDNs when there is a high amount of RES. However, the economic value of electrolyzers as providers of flexibility to MVDNs decreases when more other options for flexibility provision exist.

Reactive power control in photovoltaic systems through (explainable) artificial intelligence

Across the world, efforts to support the energy transition and halt climate change have resulted in significant growth of the number of renewable distributed generators (DGs) installed over the last decade, among which photovoltaic (PV) systems are the fastest growing technology. However, high PV penetration in the electricity grid is known to lead to numerous operational problems such as voltage fluctuations and line congestions, which could be eased by utilizing the reactive power capability of PV systems. To this end, we propose to use artificial neural network (ANN) to predict optimal reactive power dispatch in PV systems by learning approximate input–output mappings from AC optimal power flow (ACOPF) solutions in either a centralized or a decentralized manner. In the case of decentralized control, we leverage Shapley Additive Explanations (SHAP), an explainable artificial intelligence (XAI) technique, to identify non-local grid state measurements which significantly influence the optimal dispatch of each individual system. Both centralized and decentralized ANN-based controllers are evaluated through a case study based on the CIGRE medium-voltage distribution grid and compared to baseline control strategies. Results show that both ANN-based controllers exhibit superior performance, hindering voltage problems and line congestions which are encountered with baseline strategies while recording an energy saving of 0.44% compared to fixed power factor control. By leveraging ANN and SHAP, the proposed decentralized controllers for reactive power control are able to achieve ACOPF-level performance while promoting data privacy and reducing computational burden.

Deep learning technology of Internet of Things Blockchain in distribution network faults

AbstractNowadays, the development of human society and daily life are inseparable from the power supply. Therefore, people also put forward higher requirements for the reliability of distribution network, but power companies can only passively deal with distribution network failures, which is a bottleneck for the improvement of distribution network reliability. The Internet of Things (IoT) is the best solution for online equipment status monitoring and basic data sharing for large, widely distributed, relatively fixed, and large numbers of equipment. The construction of the IoT for power distribution equipment faces many important problems, including the selection of networking, equipment selection, and interaction standards. When researching the implementation plan, research on the distribution of IoT market was carried out. Based on the grid, the idea of optimizing the investment selection plan of the power distribution using IoT was discussed, and a result verification model was established. After the completion of the theoretical part, a case study of medium-voltage distribution grid equipment management and medium-voltage distribution network equipment management based on the grid was carried out by taking a real enterprise application situation as an example. Realizing fault diagnosis of distribution network will not only provide decision support for operation and maintenance of distribution network for power companies, but also have great economic and social benefits. Aiming at the shortcomings of single data mining method in distribution network fault diagnosis, hybrid data mining method is proposed. First, rough set theory is used to reduce the original fault data and form a simplified rule set. Because of the non-linearity of distribution network fault and the strong learning ability, adaptability, and robustness of Bayesian network, Bayesian network can be used to classify distribution network faults. Therefore, a simplified fault diagnosis system is established in this paper, and its correctness is confirmed. Then, the learning and training are carried out by using Bayesian network to call the simplest rule set, which has the characteristics of short learning and training time and high diagnostic accuracy.

A Constant-Frequency High-Voltage Gain Resonant Converter Module With Semiactive Phase-Shifted Voltage Multiplier for MVdc Distribution

A constant-frequency step-up LLC resonant converter module that employs a new phase-shifted semiactive voltage multiplier technique is proposed in this article for a medium-voltage dc distribution grid. In the proposed converter, a wide voltage gain for different loading conditions can be realized by adaptively controlling the phase shift of the auxiliary switch in a voltage quadrupler-based semiactive voltage multiplier. Zero-voltage switching turn-on is always achieved for all the primary side switches with a constant-frequency operation. In the proposed semiactive voltage multiplier, zero-current switching (ZCS) turn-on is achieved for the auxiliary switch with ZCS operations provided for all the output diodes over a wide range of voltage gain and loading conditions. The operating principles of the proposed circuit are discussed in this article. Simulation results on a 120-kW, 10-kV output and experimental results on a modular laboratory-scale silicon carbide (SiC)-based 5-kW (per-module), 240–440-V/5-kV prototype are provided to demonstrate the performance of the proposed converter.

Design of High-Power Solid-State Transformers with Grain-Oriented Electrical Steel Cores

The paper proposes a simple structure of high-power solid-state transformers (SSTs) able to control the energy flow in critical lines of the medium-voltage (20 kV) distribution grid. With an increasing number of renewable intermittent sources connected at the nodes of the meshed distribution grid and a reduced number of nodes connected to large power plants, the distribution grid stability is more and more difficult to achieve. Control of the energy flow in critical lines can improve the stability of the distribution grid. This control can be provided by the proposed high-power SSTs operating a 20 kV with powers over 10 MW. This function is difficult to achieve with standard SST technologies that operate at high frequencies. These devices are made with expensive magnetic materials (amorphous or nanocrystalline cores) and a limited power by SST cells. The required total power is reached by assembling many SST cells. On the other hand, existing SST designs are mainly aimed at reducing the equipment’s size and it is difficult to design small objects able to operate at high voltages. The authors propose to use cores made with grain-oriented electrical steel (GOES) thin strips assembled in wound cores. Experimental results obtained, with GOES wound cores, show that the core losses are lower for a square voltage than for a sine one. This counterintuitive result is explained with an analytical calculus of eddy currents and confirmed by a non-linear time-stepping simulation. Therefore, simple converter structures, operating with rectangular voltages and low switching losses, are the best solutions. Experimental results also show that the core losses decrease with temperature. Consequently, high-power SST cells can be made with transformers whose GOES cores are hotter than coils for reducing core losses and keeping copper losses at low levels. The paper proposes an appropriate transformer mechanical structure that avoids any contact between the hot GOES wound core and the winding, with a specific cooling system and thermal insulation of the hot GOES wound core. The proposed design makes it possible to build SST cells over 1MW and full SSTs over 10 MW at moderate costs.

An approach to arranging primary and secondary control of the operating parameters in microgrids featuring inverter-connected generator sets

Various low-power generator sets (GS) are mainly integrated into microgrids by means of inverters coupled with energy storage systems (ESS). ESSs are connected either to the DC busbars of GSs or to the AC busbars of the microgrid, in this case they are equipped with individual inverters. Use of ESS helps balance power in cases of load surge / loadshedding when the microgrid is islanded. This paper overviews approaches to arranging primary and secondary control of the operating parameters of microgrids. It discusses the technical challenges of, and possible solutions for, implementing primary and secondary control in low- and medium-voltage distribution grids featuring inverter-connected GSs. Calculations of electromechanical transients show that inverter-connected GSs have advantages over conventional GSs when used microgrids that are weakly connected to the power system (the external grid) or islanded. In a microgrid featuring inverter-connected GSs, active and reactive power need to be controlled separately as in power systems featuring conventional GSs; implementation of such controls must be adjusted for the parametric and topological characteristics of low- and medium-voltage distribution grids. To address the issues of linked control, this paper proposes a comprehensive approach that adjusts microgrid design for the specifics of inverter-connected GSs, improves inductance, and incorporates algorithmic solutions. It details upon the virtual impedance algorithm, which extends the common principle of droop speed control. Its implementation helps load the GSs and the ESS with adjustments for their installed capacity; it can also simulate the mechanical constant of GS inertia. Primary control has a low margin that mainly comes from the ESS; thus, backups available to secondary control must be used to compensate for the deviations in operating parameters; this is a key challenge of secondary frequency and voltage control in microgrids. When primary and secondary voltage and frequency controls have been duly implemented in a microgrid, its GSs, the ESS, and electricity delivery to the loads within the microgrid can all operate reliably in a variety of operating situations.

Power Flow Control Using Series Voltage Source Converters in Distribution Grids

This study presents an application for a series VSC (voltage source converter) in distribution grids for power flow management. Series devices have been widely studied for FACTS (flexible AC transmission systems), however, more recently these devices have gained increased interest in applications for loading balance in medium voltage distribution grids. As the number of distributed generation (DG) units increases, increasing the capacity and reliability of distribution grids while maximizing the benefits of installed DGs and loading behavior is an ever more important task. In this paper, we describe a test system and control proposals for a practical application of series converters interconnecting two distribution feeders at 13.8 kV under load variation disturbances. This approach provides solutions when installed at the end of distribution lines by interconnecting two feeders, resulting in capacity increases in the feeders without needing grid reconfigurations using a small-rated series VSC.

Architecture of an Artificial Intelligence Model Manager for Event-Driven Component-Based SCADA Systems

This paper analyzes Hat, an open-source framework for developing event-driven component-based SCADA applications, and discusses possibilities to add various analytical tools to such platforms. As a part of the contribution, an open-source component called Artificial Intelligence Model Manager (AIMM) has been developed and integrated into a Hat-based SCADA platform. AIMM is extensible through various plugins, allowing the addition of various models for advanced analytics e.g., machine learning tools, statistical tools, etc. The paper describes AIMM architecture and provides a use case in which state estimation was performed in a medium-voltage distribution grid. This case study demonstrates that it is possible to extend component-based SCADA systems with components for advanced analytics with minimal fundamental system changes.

10 kV SiC MOSFET Based Medium Voltage Power Conditioning System for Asynchronous Microgrids

Distributed energy resources (DERs) and microgrids have seen tremendous growth and research activities in recent years. Flexible DERs and asynchronous microgrids (ASMG) can have many system-level benefits over fixed DERs and conventional microgrids. The key enabler for flexible DERs and ASMG is a power converter based power conditioning system (PCS) as the interface between DERs/microgrids and the medium voltage (MV) distribution grid. High voltage (HV, >3.3 kV) silicon carbide (SiC) based MV converter is now a promising solution for the PCS. This article presents development and testing of a 10 kV SiC MOSFET based MV PCS for 13.8 kV ASMG. MV PCS converter design addressing high dv/dt issue generated by fast switching of the 10 kV SiC MOSFET is presented. The developed PCS is successfully tested at 25 kV dc 13.8 kV ac voltages and 100 kVA power. Grid support functions are also demonstrated with the developed PCS prototype and hardware tests beds, validating HV SiC converter benefits for ASMG.

Optimal Management for Megawatt Level Electric Vehicle Charging Stations With a Grid Interface Based on Modular Multilevel Converter

This paper proposes a strategy to manage an electric vehicle charging station (EVCSs) with a grid-side interface based on a Modular Multilevel Converter (MMC). The MMC topology is studied due to its potential for reducing the footprint and the use of active material in the internal distribution system by allowing for transformer-less connection to the medium voltage distribution grid. However, heterogeneous charging demands and arrival-departure profiles of the electric vehicles (EVs) could lead to significant loading unbalances among the MMC arms and among the modules of a single arm. Nevertheless, the current in the grid interface must be kept balanced and sinusoidal. Furthermore, the voltages of the modules of an arm must be balanced. This work combines a load management (LM) algorithm with a power flow management (PFM) algorithm to achieve the required characteristics of grid current and module voltages under the heterogeneity of the charging demand in MMC-based EVCSs. The PFM algorithm controls the circulating currents to compensate the phase-to-phase, arm-to-arm and intra-arm unbalances of the given loading. To minimize the additional losses resulting from active balancing by the PFM, the LM optimizes the charging schedules and allocations of incoming EVs into charging units in order to minimize phase-to-phase and arm-to-arm unbalances in the system. The performance of the proposed optimization-based LM is compared with a rule-based benchmark LM by simulating the daily operation of an example shopping mall parking with MMC-based grid interface. In scenarios with pronounced unbalance limitations, the optimization-based LM increases the supplied energy significantly. Real-time (RT) simulations demonstrate a balanced and sinusoidal grid current profile and balanced module voltages in MMC arms over the daily scenarios. These results indicate that the proposed strategy combining LM and PFM is applicable for real-world deployments.

Efficient Reduction in the Annual Investment Costs in AC Distribution Networks via Optimal Integration of Solar PV Sources Using the Newton Metaheuristic Algorithm

This research addresses the problem of the optimal placement and sizing of (PV) sources in medium voltage distribution grids through the application of the recently developed Newton metaheuristic optimization algorithm (NMA). The studied problem is formulated through a mixed-integer nonlinear programming model where the binary variables regard the installation of a PV source in a particular node, and the continuous variables are associated with power generations as well as the voltage magnitudes and angles, among others. To improve the performance of the NMA, we propose the implementation of a discrete–continuous codification where the discrete component deals with the location problem and the continuous component works with the sizing problem of the PV sources. The main advantage of the NMA is that it works based on the first and second derivatives of the fitness function considering an evolution formula that contains its current solution (xit) and the best current solution (xbest), where the former one allows location exploitation and the latter allows the global exploration of the solution space. To evaluate the fitness function and its derivatives, the successive approximation power flow method was implemented, which became the proposed solution strategy in a master–slave optimizer, where the master stage is governed by the NMA and the slave stage corresponds to the power flow method. Numerical results in the IEEE 34- and IEEE 85-bus systems show the effectiveness of the proposed optimization approach to minimize the total annual operative costs of the network when compared to the classical Chu and Beasley genetic algorithm and the MINLP solvers available in the general algebraic modeling system with reductions of 26.89% and 27.60% for each test feeder with respect to the benchmark cases.