A review of the tools and methods for distribution networks' hosting capacity calculation

Integration of distributed energy resources (DERs) has numerous advantages as well as some disadvantages. To safely integrate DERs into a given distribution network and to maximize their benefits, it is important to thoroughly analyze the impact of DERs on that particular network. The maximum amount of DERs that a given distribution network can accommodate without causing technical problems or without requiring infrastructure modifications is defined as the hosting capacity (HC). In this work, a review of the recent literature regarding the HC is presented. The major limiting factors of HC are found to be voltage deviation, phase unbalance, thermal overload, power losses, power quality, installation location and protection devices’ miscoordination. The studies are found to employ one of four different methods for HC calculation: (i) deterministic, (ii) stochastic, (iii) optimization-based and (iv) streamlined. Commercially available tools for HC calculation are also presented. The review concludes that the choice of tools and methods for HC calculation depends on the data available and the type of study that is to be performed

HVDC transmission : technology review, market trends and future outlook

HVDC systems are playing an increasingly significant role in energy transmission due to their technical and economic superiority over HVAC systems for long distance transmission. HVDC is preferable beyond 300–800 km for overhead point-to-point transmission projects and for the cable based interconnection or the grid integration of remote offshore wind farms beyond 50–100 km. Several HVDC review papers exist in literature but often focus on specific geographic locations or system components. In contrast, this paper presents a detailed, up-to-date, analysis and assessment of HVDC transmission systems on a global scale, targeting expert and general audience alike. The paper covers the following aspects: technical and economic comparison of HVAC and HVDC systems; investigation of international HVDC market size, conditions, geographic sparsity of the technology adoption, as well as the main suppliers landscape; and high-level comparisons and analysis of HVDC system components such as Voltage Source Converters (VSCs) and Line Commutated Converters (LCCs), etc. The presented analysis are supported by practical case studies from existing projects in an effort to reveal the complex technical and economic considerations, factors and rationale involved in the evaluation and selection of transmission system technology for a given project. The contemporary operational challenges such as the ownership of Multi-Terminal DC (MTDC) networks are also discussed. Subsequently, the required development factors, both technically and regulatory, for proper MTDC networks operation are highlighted, including a future outlook of different HVDC system components. Collectively, the role of HVDC transmission in achieving national renewable energy targets in light of the Paris agreement commitments is highlighted with relevant examples of potential HVDC corridors

Reinforcement learning-based school energy management system

Energy efficiency is a key to reduced carbon footprint, savings on energy bills, and sustainability for future generations. For instance, in hot climate countries such as Qatar, buildings are high energy consumers due to air conditioning that resulted from high temperatures and humidity. Optimizing the building energy management system will reduce unnecessary energy consumptions, improve indoor environmental conditions, maximize building occupant's comfort, and limit building greenhouse gas emissions. However, lowering energy consumption cannot be done despite the occupants' comfort. Solutions must take into account these tradeoffs. Conventional Building Energy Management methods suffer from a high dimensional and complex control environment. In recent years, the Deep Reinforcement Learning algorithm, applying neural networks for function approximation, shows promising results in handling such complex problems. In this work, a Deep Reinforcement Learning agent is proposed for controlling and optimizing a school building's energy consumption. It is designed to search for optimal policies to minimize energy consumption, maintain thermal comfort, and reduce indoor contaminant levels in a challenging 21-zone environment. First, the agent is trained with the baseline in a supervised learning framework. After cloning the baseline strategy, the agent learns with proximal policy optimization in an actor-critic framework. The performance is evaluated on a school model simulated environment considering thermal comfort, CO2 levels, and energy consumption. The proposed methodology can achieve a 21% reduction in energy consumption, a 44% better thermal comfort, and healthier CO2 concentrations over a one-year simulation, with reduced training time thanks to the integration of the behavior cloning learning technique. 2020 by the authors. Licensee MDPI, Basel, Switzerland.Acknowledgments: This publication was made possible by the National Priority Research Program (NPRP) grant [NPRP10-1203-160008] from the Qatar National Research Fund (a member of Qatar Foundation) and the co-funding by IBERDROLA QSTP LLC. The findings achieved herein are solely the responsibility of the authors.Scopus2-s2.0-8510663929

On optimal battery sizing for households participating in demand-side management schemes

The smart grid with its two-way communication and bi-directional power layers is a cornerstone in the combat against global warming. It allows for the large scale adoption of distributed (individually-owned) renewable energy resources such as solar photovoltaic systems. Their intermittency poses a threat to the stability of the grid which can be addressed by the introduction of energy storage systems. Determining the optimal capacity of a battery has been an active area of research in recent years. In this research an in-depth analysis of the relation between optimal capacity, and demand and generation patterns is performed for households taking part in a community-wide demand-side management scheme. The scheme is based on a non-cooperative dynamic game approach in which participants compete for the lowest electricity bill by scheduling their energy storage systems. The results are evaluated based on self-consumption, the peak-to-average ratio of the aggregated load, and potential cost reductions. Furthermore, the difference between individually-owned batteries to a centralised community energy storage system serving the whole community is investigated.Comment: 8 pages, 9 figure

Performance evaluation of four grid-forming control techniques with soft black-start capabilities

Grid-Forming Converters (GFC) can be controlled as independent, self-starting, voltage sources. This feature is essential for power converters to achieve successful black-start sequence initiation. Conventional grid-following converters are not capable of self-starting an islanded network. GFC control thus exploits wider grid support and network restart potential. This study analyzes and compares four GFC controllers to assess their generic and soft black-start (ramping voltage) capabilities. The compared techniques are: Droop Control, Power Synchronizing Control (PSC), Virtual Synchronous Machine (VSM), and Matching control. These techniques are selected based on their direct voltage reference control flexibility. Various simulations are performed with common parameters to assess the response of each technique under similar conditions against load, DC voltage and active power reference disturbances, in addition to their soft-start readiness. The results demonstrate the high-level compatibility of these four controllers with soft black-start through successful and timely ramping voltage reference tracking. Moreover, the four considered control techniques achieve satisfactory performance, with VSM demonstrating more flexibility due to its tunable virtual inertia parameter (J)

Innovative energy management system for MVDC networks with black-start capabilities

Medium voltage DC (MVDC) networks are attracting more attention amid increased renewables penetration. The reliability of these DC systems is critical, especially following grid contingencies to maintain critical loads supply and provide ancillary services, such as black-start. This paper proposes an innovative energy management system (EMS) to maintain reliable MVDC network operation under prolonged AC grid contingencies. Similar EMS designs in literature tend to focus on limited operating modes and fall short of covering comprehensive elongated blackout considerations. The proposed EMS in this paper aims to preserve the distribution network functionality of the impacted MVDC system through maintaining a constant DC bus voltage, maximizing critical load supply duration, and maintaining the MVDC system black-start readiness. These objectives are achieved through controlling generation units between Maximum Power Point Tracking (MPPT) and Voltage Regulation (VR) modes, and implementing a smart load shedding and restoration algorithm based on network parameters feedback, such as storage State of Change (SoC) and available resources. Practical design considerations for MVDC network participation in AC network black start, and the following grid synchronization steps are presented and tested as part of the EMS. The proposed system is validated through simulations and scaled lab setup experimental scenarios

Lightweight KPABE Architecture Enabled in Mesh Networked Resource-Constrained IoT Devices

Internet of Things (IoT) environments are widely employed in industrial applications including intelligent transportation systems, healthcare systems, and building energy management systems. For such environments of highly sensitive data, adapting scalable and flexible communication with efficient security is vital. Research investigated wireless Ad-hoc/mesh networking, while Attribute Based Encryption (ABE) schemes have been highly recommended for IoT. However, a combined implementation of both mesh networking and Key-Policy Attribute Based Encryption (KPABE) on resource-constrained devices has been rarely addressed. Hence, in this work, an integrated system that deploys a lightweight KPABE security built on wireless mesh networking is proposed. Implementation results show that the proposed system ensures flexibility and scalability of self-forming and cooperative mesh networking in addition to a fine-grained security access structure for IoT nodes. Moreover, the work introduces a case study of an enabled scenario at a school building for optimizing energy efficiency, in which the proposed integrated system architecture is deployed on IoT sensing and actuating devices. Therefore, the encryption attributes and access policy are well-defined, and can be adopted in relevant IoT applications. 2013 IEEE.This publication was made possible by the National Priority Research Program (NPRP) grant [NPRP10-1203-160008] from the Qatar National Research Fund (a member of Qatar Foundation) and the co-funding by the IBERDROLA QSTP LLC. The publication of this article was funded by the Qatar National Library. The findings achieved herein are solely the responsibility of the authors.Scopus2-s2.0-8509909047

SiC-based improved neutral legs with reduced capacitors for three-phase four-wire EV chargers

An electric vehicle (EV) charger can operate in an autonomous mode to create its own grid by utilizing the EV batteries during grid blackouts. This requires three-phase four-wire inverters as the grid-side ac/dc port of the EV charger to supply unbalanced loads. Although silicon carbide (SiC) MOSFETs can be adopted to increase the power density of these inverters, the second order ripples exhibited on the dc bus caused by unbalanced loads need to be mitigated by a large dc capacitance—increasing the size of inverters. In this paper, an improved neutral leg for three-phase four-wire inverters is presented, which not only provides the neutral current for unbalanced loads like a conventional neutral leg, but also reduces the second order ripples on the dc bus without the need for additional hardware components. Furthermore, it can reduce by 50% the dc capacitance compared to its conventional counterpart. A control strategy featuring power decoupling capability is included for the improved leg. It was built with SiC MOSFETs and experimentally assessed with a three-phase inverter, with results verifying its effectiveness. For completeness, the performance of the improved neutral leg is also evaluated through simulations in PLECS and compared to a conventional neutral leg

Control of A high-Performance Z-Source Inverter for Fuel Cell/ Supercapacitor Hybrid Electric Vehicles

This paper presents a supercapacitor (SC) module connected in parallel with fuel cell (FC) stack to supply a high-performance Z-Source Inverter (HP-ZSI) feeding a three phase induction motor for hybrid electric vehicles applications. The supercapacitor is connected between the input diode and the bidirectional switch of the highperformance ZSI topology. The indirect field-oriented control (IFOC) method is used to control an induction motor speed during motoring and regenerative braking operations to produce the modulation index and a dual loop controller is used to control the Z-network capacitor voltage to produce the shoot-through duty ratio. MATLAB simulation results verified the validity of the proposed control strategy during motoring and regenerative braking operations