An improved energy management for MVDC distribution system based on exponential droop control

Modular multilevel converter (MMC) has been widely used in high-voltage direct current (HVDC) transmission system due to its advantages of modularity, low switching frequency and excellent output voltage waveforms. Proper operation of MMC is based on capacitor voltage balancing. Conventional MMC capacitor voltage balancing control needs a large number of voltage sensors and sorting algorithm which increases computing burden on the processor. In this paper, a capacitor voltage self-balancing MMC with DC fault ride-through capability is proposed. Capacitor voltage balancing control of this proposed MMC is significantly simple due to its self-balancing auxiliary circuit. Sorting algorithm is not needed, each phase leg only needs one voltage sensor for top SM. So control complexity and computing burden are reduced greatly. Furthermore, DC fault ride-through capability is also reserved. Simulation results validates capacitor voltage of this new MMC topology can be well balanced and DC fault ride-through capability is effective.

Integrating non-dispatchable renewable energy sources (RES) such as wave farms into electric power systems presents inherent challenges. Specifically, the variability of the resource and its non-dispatchable) nature introduces power imbalances and power system stability problems in the system over both short and medium terms. This issue is particularly relevant for isolated power systems, such as those found on several European islands, where interconnection with the mainland is lacking. Therefore, although wave energy has significant potential for integration into island power systems due to resource availability, determining the maximum RES penetration that such systems can sustain while maintaining stability is not straightforward. Power system stability is closely tied to frequency. A well-established solution for improving the stability of isolated systems when integrating non-dispatchable RES is to install devices capable of responding to over- or under-frequency events, such as energy storage systems (ESS). The literature extensively discusses ESS control techniques for frequency regulation, with one of the most straightforward approaches being to operate the ESS according to a droop control strategy (similar to a statism). In this method, the power dispatched or stored by the ESS increases proportionally to the deviation from the frequency reference value. This paper presents a case study analyzing the frequency stability of an island power system equipped with an energy storage system (ESS) when integrating a wave farm. Frequency stability is evaluated based on two variables: wave energy penetration and the ESS droop control parameter. The case study is based on the island of El Hierro (Canary Islands, Spain), an isolated electrical system with a very high penetration of RES. The island’s electrical generation system consists of a wind farm, a pumped-storage hydroelectric power plant, and conventional generation from a diesel power plant. The analysis is conducted through simulations in MATLAB Simulink. A wave farm is modeled to evaluate the generated power and its associated oscillations, based on the wave energy resource at various locations along El Hierro’s coast. Additionally, an aggregated inertial dynamic model of the electrical power system is employed to assess the impact of the wave energy farm's generation on the system’s frequency and the aging that the frequency excursions introduce in the elements of the system. The Spanish Grid Code is considered to ensure compliance with frequency regulation mechanisms in isolated systems. Simulations are performed across different scenarios, varying both the nominal power of the wave farm and the ESS droop control parameter, in order to evaluate the ESS capacity. The results identify the optimal droop control parameter for each wave farm size to ensure frequency remains within acceptable thresholds. Furthermore, the minimum ESS capacity required for each wave farm size is determined from the simulations. These findings provide a basis for ESS and wave farm dimensioning, which could be translated into economic terms to maximize revenue (this aspect is beyond the scope of the present work).

The conventional half-bridge sub-module (HB-SM) based modular multilevel converter (MMC) fails to provide the DC fault handling capability. Nevertheless, this drawback can be overcome by replacing the HB-SMs with the full-bridge (FB) SMs since the FB SMs can generate bipolar voltages thus handling the DC fault condition. But the drawback of using FB-SMs arises as it employs double the number of switches used that in case of HB-SM-based MMC, thus increasing the switching losses due to an increase in device count. Then, the Hybrid MMC, which employs HB SMs in addition to FB SMs, came into the picture and mitigated the drawbacks mentioned earlier significantly. This paper presents two cases for 21-level Hybrid MMC, wherein the first case implements the hybridization of 50% conventional SMs and 50% FB-SMs, and case two implements the same with 33.33% conventional SMs and 66.66% FB-SMs. The reduced switching frequency (RSF) capacitor voltage balancing approach is employed to balance the capacitor voltages of HB-SMs and FB-SMs for the first combination (50% HB and 50% FB-SMs); whereas the modified voltage balancing approach has been applied to the second case (33.33% HB and 66.66% FB-SMs). To evaluate their performances, a comparative analysis of 21-level conventional HB-SM-based MMC, FB-SM-based MMC, and Hybrid MMC with both mentioned combinations, is carried out in MATLAB simulation for the corresponding voltage balancing techniques used. The impact of voltage balancing approach on switching transitions is also presented.

Energy storage system utilisation to increase photovoltaic penetration in low voltage distribution feeders

A single-phase-to-ground (SPG) fault in a neutral non-effectively grounded AC distribution network may cause the arc at the fault point and a power supply interruption, permeating the zero-sequence voltage into the DC network and the nonfault AC-side of AC/DC hybrid distribution networks (HDNs). This paper proposes a flexible control method for a modified hybrid modular multilevel converter (HMMC), incorporating an additional full-bridge submodules (FBSM) cluster at its DC side, to deal with SPG faults in HDN. The HMMC includes a modified modular multilevel converter with a zero-sequence current flow path to realize active ground fault arc suppression (AGAS) and an FBSM cluster in its DC side to block the zero-sequence component to the DC grid and the non-fault circuits of HDN. The operation principles of the HMMC that achieves AGAS and zero-sequence voltage transmission suppression (ZVTS) are presented. The HMMC can realize AGAS when there is an SPG fault on the AC side, and ZVTS can be realized on the DC side by utilizing the FBSM cluster. The flexible control method proposed herein can ensure the normal operation of the HMMC under SPG fault conditions, achieve power transfer during fault current suppression, and realize ZVTS during arc suppression. Simulation and experimental results verify the feasibility and validity of the proposed HMMC and its flexible control method.

The second-harmonic injection strategy for modular multilevel converters (MMCs) can effectively suppress the capacitor voltage fluctuation in steady states. However, the capacitor voltage fluctuation under grid-side single-line-to-ground (SLG) faults will be more severe. Moreover, there is no research on the mitigation of capacitor voltage fluctuation under grid-side SLG faults for the hybrid MMC with mixed half-bridge and full-bridge sub-modules. To address this issue for hybrid MMCs, an optimal second-harmonic current injection scheme for mitigating the capacitor voltage fluctuation in the hybrid MMC under grid-side SLG faults is proposed in this paper. The mathematical models of arm power fluctuations of the hybrid MMC are firstly developed under both steady states and grid-side SLG faults. Then, the principle and optimal strategy of the proposed capacitor voltage fluctuation strategy are presented. The capacitor voltage fluctuation under grid-side SLG faults can be reduced by suppressing the three-phase arm power fluctuations. A hybrid MMC model with the proposed scheme has been established in PSCAD/EMTDC to verify its effectiveness and feasibility. Simulation results show that the average voltage fluctuation reduction rate can reach 55.77% with the proposed strategy.

Recently, the development of microgrid system is growing rapidly. Energy such as sun, wind, ocean, wave currents, and geothermal are alternative energy in developing Renewable Energy Sources (RES) in the microgrid system. Most commonly hybrid RES system in the microgrid is a combination between Photovoltaic (PV), ESS and diesel generation. To implement the hybrid RES, the combination strategy control is needed. One of the main motivations using energy management in the microgrid is that they are capable of managing and coordinating diesel generators, storages, RES and loads to optimize the RES for cost efficiency. In this paper, the optimized energy management for the hybrid RES system combined by PV, Li-ion battery ESS, and diesel generator is proposed. To accommodate the diesel generation condition, the developed control strategy is the economic dispatch for fuel cost optimization in diesel generation using combination of the Lagrange multiplier and lambda iteration method and the optimization in energy management operation using an Adaptive Model Predictive Control (AMPC) with the lifetime constraint of batteries. Finally, the proposed control algorithm is verified through the PSiM simulation results.

This article presents a capacitor-switching semi-full-bridge (CS-SFB) submodule (SM) using Si-IGBT and SiC-MOSFET for a modular multilevel converter (MMC). Compared with the conventional hybrid half-/full-bridge MMC, the CS-SFB MMC has the same dc fault ride-through capability and lower power losses. The conduction loss reduces due to the current sharing phenomenon between Si-IGBTs and SiC-MOSFETs. The switching loss reduces in virtue of the fast recovery characteristic of SiC-diode. The derivation process and working principle of the proposed SM are first introduced. Then, the power loss models are established in the switch, SM, and MMC system levels, respectively. Afterward, the piecewise calculation and simulation results reveal the low-power-loss characteristics of the proposed SM and MMC. Finally, these features are verified by experimental results acquired from a downscaled MMC prototype with Si-IGBTs and SiC-MOSFETs.

In contrast to conventional power plants, which are based on large synchronous generators with large inertia capabilities to dampen sudden disturbances, renewable energy sources, such as solar and wind, connected to the grid through power electronics converters, display low system inertia and overload limiting capabilities. Additionally, because they lack primary frequency regulation capabilities, they are unable to actively respond to the frequency response of the system. This research investigates the contribution of droop control strategies to grid resilience by focusing on their ability to maintain frequency stability during grid disturbances. The study employs a simulation-based approach using MATLAB/Simulink to model the Djoum power plant in Cameroun and implement droop control algorithm. The methodology involves designing and analyzing the system's response under sudden load changes using droop and supervisory control strategies. Parameters such as droop coefficients and control bandwidths were systematically varied to analyze their impact on frequency regulation and grid resilience. Results show that droop control maintains system operation by adjusting frequency and voltage under disturbance, while supervisory control acts as a secondary layer to fully restore parameters to their reference values thereby ensuring reliable operation during grid disturbances.

SUMMARY This paper discusses optimal energy management considering battery characteristics for smart grid and microgrid systems. Energy storage systems are very important for energy peak shifting and peak load cutting in microgrid and smart grid systems, but optimal battery formulation and energy management in the use of expensive energy storage system and methods of taking account of battery characteristics are still in the developmental stage. First, the optimal battery placement for a smart grid system with lithium-ion batteries is considered as a means of lifetime extension. The battery choice index for this optimal battery utilization problem of optimal energy management is discussed. Second, a smart grid system with a distributed battery system for grid frequency control with consideration of distributed control and battery characteristics is discussed. Optimal controllers for distributed battery systems to reduce the battery load and to expend the battery lifetime are designed. Simulations are used to demonstrate the effectiveness of the proposed method in comparison with conventional methods.

Residential and commercial buildings are responsible for 35% of the EU energy-related greenhouse gas (GHG) emissions. Reducing their emissions is crucial for meeting the challenging EU objective of the agenda for becoming a net-zero continent by 2050. The diffusion and integration of distributed renewable energy sources (RESs) and energy storage systems (ESSs), as well as the creation of energy communities (ECs), have proven to be crucial aspects in reducing GHG emissions. In this context, this article proposes a multi-agent AI-based control framework to solve the EC’s energy management problem in the presence of distributed RESs and ESSs as well as considering a shared ESS. The objectives of the proposed control framework are to satisfy the EC members’ load demand to maximize self-consumption and to manage ESSs charging and discharging processes, to enforce cooperative behavior among the EC members by adopting fair and personalized strategies and to maximize EC members’ profits. The proposed control procedure is based on three sequential stages, each solved by a dedicated local RL agent exploiting the Q-Learning algorithm. To reduce the computational complexity of the proposed approach, specifically defined state aggregation criteria were defined to map the RL agents’ continuous state spaces into discrete state spaces of limited dimensions. During the training phase, the EC members’ profiles and the ESSs’ and RESs’ characteristics were randomly changed to allow the RL agents to learn the correct policy to follow in any given scenario. Simulations proved the effectiveness of the proposed approach for different costumers’ load demand profiles and different EC configurations. Indeed, the trained RL agents proved to be able to satisfy the EC members’ load demands to maximize self-consumption, to correctly use the distributed and shared ESSs, to charge them according to respective personalized criteria and to sell the energy surplus, prioritizing sales to the EC. The proposed control framework also proved to be a useful tool for understanding EC performance in different configurations and, thus, for properly dimensioning the EC elements.

Numerous promising features of Medium Voltage DC Distribution System (MVDC-DS) make it highly probable that it would be extensively integrated with the future AC power grid to enhance power availability. However, issues may arise during interconnection such as voltage incompatibility between the two grids i.e. MVDC-DS and AC grid. Keeping this in mind, in this paper an Improved Modular Multilevel Converter topology (I-MMC) is considered and a novel voltage balancing algorithm (VBA) is proposed. I-MMC offers several unique features like wide range of voltage boosting capability, increased number of levels in the output voltage, low switch count and enhanced THD compared to the conventional MMC. Thus, the I-MMC ensures the interconnection of the AC and DC grids and also ensures low foot print size, high power density and low infrastructure cost. I-MMC is equally effective for applications like electrical machine drive systems, High Voltage DC (HVDC) systems, and other high voltage, high power applications. I-MMC and VBA are simulated in MATLAB/Simulink environment. Representative waveforms for a wide range of loading conditions are included to validate the theoretical claims.

The Modular Multilevel Converter (MMC) topology is getting popularized for the medium to high voltage applications after its inception. This paper presents a modified topology of the conventional MMC, named as Hybrid MMC (HMMC). This topology has reduced number of switch counts than the conventional MMC. Therefore, it is more efficient and has less complex circuitry than other existing multi-level topologies. In this paper a modified phase-shifted carrier pulse width modulation scheme (PS-PWM) is used for the switching of HMMC. The three-phase circuit is simulated in the Matlab/Simulink for different load conditions and the results are provided to validate the concepts behind the topology.

The electric power grid is undergoing significant changes and updates nowadays, especially on a production and transmission level. Initially, the move towards a distributed generation in contrast to the existing centralized one implies a significant integration of renewable energy sources and electricity storage systems. In addition, environmental awareness and related concerns regarding pollutant emissions have given rise to a high interest in electrical mobility. Advanced power electronics interfacing systems are expected to play a key role in the development of such modern controllable and efficient large-scale grids and associated infrastructures. During the last decade, a global research and development interest has been stimulated in the field of modular multilevel conversion, due to the well-known offered advantages over conventional solutions in the medium- and high-voltage and power range. In the context of battery energy storage systems, the Modular Multilevel Converter (MMC) family exhibits an additional attractive feature, i.e., the capability of embedding such storage elements in a split manner, given the existence of several submodules operating at significantly lower voltages. This thesis deals with several technical challenges associated with Modular Multilevel Converters as well as their enhancement with battery energy storage elements. Initially, the accurate submodule capacitor voltage ripple estimation for a DC/AC MMC is derived analytically, avoiding any strong assumptions. This is beneficial for converter dimensioning purposes as well as for the implementation improvement of several control schemes, which have been proposed in the literature. The impact of unbalanced grid conditions on the operation and design of an MMC is then investigated, drawing important conclusions regarding the choice of line current control and required capacitive storage energy during grid faults. [...]

With the emerging of the smart grid, it has become easier for consumers to control their consumption. The efficient use of the integration of renewable energy sources with electric vehicle (EV) and energy storage systems (ESSs) in the smart home is a popular choice to reduce electricity costs and improve the stability of the grid. Therefore, this study presents optimal energy management based on the Jaya algorithm for controlling energy flow in the smart home that contains photovoltaic generation (PV), integrated with ESS and EV. The objective of the proposed energy management is to reduce electricity cost while meeting the household load demand and energy requirement for the EV trip distance. By using the Jaya algorithm, the modes of home-to-vehicle (H2V) and vehicle-to-home (V2H) are controlled, in addition to controlling the purchase of energy from the grid and sale of the energy to the grid from surplus PV generation and ESS. Before EV participation in the V2H process, the amount of energy stored in the electric vehicle battery will be verified to be more than the energy amount required for the remaining EV trip to ensure that the required energy for the remaining EV trip is satisfied. Simulation results highlight the performance of the optimal energy scheduling to achieve the reduction of the daily electricity cost and meeting of load demand and EV energy required. The simulation results prove that optimal energy management solutions can be found with significant electricity cost savings. In addition, Jaya is compared with the particle swarm optimization (PSO) algorithm in order to evaluate its performance. Jaya outperforms PSO in terms of achieving optimal energy management objectives.