Two-way Energy Flow Research Articles

Analysis and Modeling of Four-Quadrant PWM Rectifier

Different from the traditional AC/DC converters, the four-quadrant converters are widely used in modern AC power transmission systems because of their advantages such as two-way flow of energy, adjustable power factor on the grid side, and stable voltage on the DC side [1]. In this paper, the mathematical model of four-quadrant PWM rectifier is presented, and on this basis, the constant-frequency double closed-loop control strategy is adopted to control the DC voltage and the power factor of the grid side. The determination method of voltage and current PI regulator parameters is analyzed, and the influence of ac side inductance and DC side capacitance on the whole system is pointed out, and the design method is given. Through the establishment of simulation model under MATLAB/ Simulink, it is proved that this model can realize the DC voltage and power factor control at the side of the network, has a good load regulation ability, and can switch between the rectifier mode and the inverter mode, realizing the two-way flow of energy. This paper has some guiding effect on the design of four-quadrant PWM rectifier.

Integrated planning of power-gas systems considering N-1 constraint

With the deepening interdependence between the power system and the natural gas system (NGS), the integrated power-gas system (IGPS) has been a vital part of the energy structure. On one hand, interdependence improves energy utilization. On the other hand, the two-way energy flow brings new challenges to the reliable operation of IPGS. Therefore, the integrated planning model of IPGS considering N-1 constraint is proposed. Firstly, a framework of IPGS is developed to describe the N-1 contingency both in the power system and NGS considering cascading effects. Then an integrated planning model is proposed considering N-1 constraint and cascading effects between the power system and NGS. The reduced disjunctive model (RDM) is utilized to obtain the optimal planning scheme and reduce calculation difficulty. Furthermore, to further verify the effectiveness of the proposed methods, the integrated planning model is performed on an integrated gas and power test system.

Resilience enhancement of interdependent electricity-natural gas system considering gas storage and power to gas technology

Large-scale utilization of natural gas for electrical power generation, and application of gas facilities run by electricity has deepened the interconnection of electricity and natural gas systems. Meanwhile, the influence of extreme events on energy systems has increased, promoting the great significance of developing resilient energy systems. Considering gas-fired power generating units (PGUs), electricity-driven gas compressor stations (GCSs), and power to gas technology (PtG) as coupling facilities, an interdependent electricity-natural gas system (IENS) is presented in this paper. There is a bilateral and looped failure propagation as well as a two-way energy flow between two systems. A tri-level robust optimization framework is proposed for resilience enhancement of the IENS against natural disasters by combining pre-and post-disaster strategies, and the Nested C&CG approach is used to achieve its optimal solution. As a preventive planning strategy, IENS transmission components are hardened to mitigate the impact of natural disasters. Fast-response PGUs, gas storages, and PtGs are also used as emergency response resources to cope with the aftermath of disasters. Moreover, a multi-zone uncertainty set is proposed to model the effect of disasters on IENS considering damage probability of components. Numerical simulations on two IENSs demonstrate the effectiveness of the proposed methodology. The 24bus & 20node IENS results reveal that hardening only five critical components can decrease >40 % of damage to the IENS, and utilizing PtGs and gas storages reduces >20 % of load loss cost for different cases.

Improved hybrid sparrow search algorithm for an extreme learning machine neural network for short‐term photovoltaic power prediction in 5G energy‐routing base stations

Given the advancements in solar power generation and fifth-generation (5G) technologies, it is crucial to reduce energy consumption based on accurate predictions of the photovoltaic power requirements of 5G base stations (BSs) connected to renewable energy sources. To ensure sufficient prediction performance even under complex weather conditions and promote two-way flow of energy and information in the power supply system of the 5G BS, this work first proposes a reference scenario for a 5G BS with an energy router. Then, the comprehensive grey relational analysis is used to determine the optimal photovoltaic input variables. Finally, the improved logistic distribution, Laplacian distribution with inverse incomplete gamma-function weight factor, and nonlinear mutation perturbation strategy are introduced to improve the ability of the sparrow search algorithm to avoid locally optimal solutions for an extreme learning machine of the short-term photovoltaic power prediction model. The proposed model is applied to actual measured data for validation, and the results show that this model has the lowest mean percentage error as well as highest prediction accuracy under different weather conditions. The coefficient of determination for the proposed prediction model is greater than 99%; therefore, it is expected to improve the photovoltaic prediction performances of 5G energy-routing BSs as well as promote sustainable development and sharing of energy and information.

ПРОБЛЕМИ УПРАВЛІННЯ В СИСТЕМАХ SMART GRID УНІВЕРСИТЕТСЬКОГО ХАБА ЕНЕРГОЕФЕКТИВНОСТІ

The article attempts to tackle the issues of enhancing the performance of university energy efficiency management systems. An emphasis is put that in modern realia, alternative and renewable energy sources are becoming increasingly important in the electric power sector, thus contributing to environmental protection and enabling active electricity consumers to have their own sources of energy generation. However, it is observed that the relationships between energy generation sources and electricity consumers are complicated by new demands for setting balancing modes due to certain volatility of energy generation by alternative sources as well as the need to connect additional energy storage facilities. To identify opportunities of using Smart Grid technologies to manage the University energy consumption, a power balance equation was used to determine an active power balance between generated power, generation sources and power consumed by electricity consumers. In addition, the indicators of the total active power loss in the electrical network associated with the technological consumption of energy for its transmission was included into this equation. The study presents the results of an in-depth critical analysis on Smart Grid methodology and provides argument for the relevance of using artificial intelligence techniques in Smart Grid management systems of the University energy efficiency hub, along with suggesting a notion of electricity generating consumer in the concept of intelligent networks with two-way flow of energy and information as subsystems of a different nature. It is argued that the developed conceptual model of the electricity generating consumer for multilevel smart grid management systems and their infrastructure within the University energy efficiency hub allows establishing relationships between its structural elements and objects of different character. The findings reveal that the specifics of the developed method in setting priorities and regulatory standards for optimal management by a generating consumer within the University energy efficiency hub is the possibility of its automatic adaptation to changes in the external environment subject to interactions between electricity generating consumers.

Electrical Energy Flow Algorithm for Household, Street and Battery Charging in Smart Street Development

The world demands a smart and green future in every sector, which directly corresponds to increases in electrical energy demand one way or another. It is unfeasible to attain future energy demand with the present electrical infrastructure. That means more research and development is required. Future energy sources should be intermittent, and, in addition, the energy sector should be more inwards for distributed energy generation with demand side control. In such cases, the smartest and most autonomous system would be essential to deliver an adequate power supply with all electrical properties. A real-time monitoring and control system with a self-healing infrastructure is a forthcoming desideratum. By accepting these challenges, we have designed a smart street. The basic idea of the smart street is presented in this paper as a landing page; the paper is more focused on emphasizing information regarding the electrical energy flow algorithm for the household, street, and street battery storages. This algorithm is helpful for two-way energy flow and the automatic detection of islanding and the grid connection mode. It will be not only helpful for the users but to the utility as well.

Connecting Parking Facilities to the Electric Grid: A Vehicle-to-Grid Feasibility Study in a Railway Station’s Car Park

This study evaluates the impact of energy on the distribution network at the point of connection of an electric plant of a railway car parking facility in which charging points for electric vehicles (EVs) were installed. The objective is to identify a possible load curve of the simulated car park and, based on the principle of vehicle-to-grid (V2G) technology, to develop an appropriate algorithm. Such an algorithm explores the possibility of a two-way energy flow between the connected vehicles and the electricity grid, and performs a peak shaving of the load curve of the plant under examination in order to avoid absorption peaks, which are usually difficult to manage when using the distribution system operator (DSO). The work also presents the coupling with a photovoltaic system designed specifically for the car park. The study results are presented after a summary of the current state of development of electric mobility, describing the various types of EVs, the charging infrastructure, and the possible applications in smart grids (SGs).

A Wireless Vehicle-to-Grid-to-Home Power Interface With an Adaptive DC Link

Electric vehicles (EVs) that offer grid services using the vehicle-to-grid (V2G) concept essentially require an interface that allows for directional power flow. This article presents a wireless bidirectional grid power interface for EVs that facilitate power flow between the grid, EV, and homes with nonlinear loads. The proposed wireless vehicle-to-grid-to-home power interface (WVGH-PI) uses the grid-side low frequency to dc converter, used in typical wireless bidirectional chargers only to facilitate the two-way energy flow, to improve the power quality by compensating for the harmonic power and reactive power of nonlinear household loads. To improve the overall efficiency of the interface, an adaptive dc-link voltage controller is also proposed. Operation of the proposed wireless interface is investigated under dynamic conditions and in different modes. The simulation and experimental results obtained from a 1-kW prototype are presented, benchmarking against conventional control, to demonstrate the validity of the proposed concept.

Demystifying Distributed Ledger Technologies: Limits, Challenges, and Potentials in the Energy Sector

The success of the sustainable transformation of the energy sector, both in terms of planning and operation, relies on new entities, business models, and technologies. The shift from a relatively small number of centralized bulk producers and single direction energy flow to a decentralized multi-actor renewable system with a two-way flow of energy and multi-way flow of information needs to be accompanied by new technological solutions. Blockchain and other Distributed Ledger Technologies (DLT) represent a new technology for the energy sector, creating both opportunities and challenges for different aspects of energy systems, such as energy production, peer-to-peer (P2P) energy markets, green certificate registries, etc. Due to its decentralized nature and no need for intermediaries, DLT can facilitate energy democratization processes and decentralized energy production. In this paper, we present a systematic review of DLT principles, its theoretical background, and the most notable implementations, as well as an in-depth analysis of representative research projects and companies researching DLT use cases in the energy sector, taking into consideration technical aspects of DLT. We provide an insight into the benefits and limitations of DLT and identify technical challenges that need to be solved to enable widespread usage of DLT in energy systems. Additionally, we provide suggestions and guidelines for implementing DLT in different categories of use cases in the energy sector.

On-Site Energy Consumption Technologies and Prosumer Marketing for Distributed Poverty Alleviation Photovoltaic Linked to Agricultural Loads in China

In China, photovoltaic poverty alleviation projects play important role in promoting social and economic development in poverty-stricken areas. They can help stabilizing and increasing incomes of the poor. However, poverty alleviation photovoltaics are mainly connected to the rural distribution network in large numbers, which have a greater impact on the safe and stable operation of the distribution network, making the rural distribution network difficult to solve the problem of collecting and consuming distributed photovoltaic energies on-site. Meanwhile, photovoltaic poverty alleviation projects mainly rely on subsidies from the government and power grids, and the prosumer marketing methods are inadequate. In this paper, an effective on-site consumption technology for photovoltaic power generation linked to agricultural load for poverty alleviation is discussed, together with new energy management and planning technologies for stable two-way energy flow between photovoltaic power generation and rural distribution networks, which can increase the photovoltaic power supply radius, and deal with load fluctuations, and perform precise reactive power control of agricultural load. Furthermore, a prosumer marketing that can simultaneously create maximum economic benefits for power grid and poverty alleviation targets is proposed. The detailed economic benefits are calculated for a demonstration project in Funan county, Anhui province, China.

Secure Edge Electricity Data Aggregation Scheme for Low Voltage Transformer Areas

The smart grid system is the physical power infrastructure integrated with many intelligent electronic devices. It supports the two-way flow of energy as well as information. The smart meters are important intelligent devices, which account for the largest portion among all terminals in the smart grid. In many application scenarios, the master station need high frequency total electricity consumption information of each low voltage transformer area. It requires frequent data interchange between smart meters and master station. However, the electric communication network cannot provide enough bandwidth resource to support such high frequency data interchange. Edge Computing Nodes (ECNs), which can be deployed at different low voltage transformer areas, can achieve the electricity data aggregation at the edge side. The communication resource can be saved since only aggregated electricity data will be uploaded to the master station. Traditional data aggregation scheme requires the plaintext from smart meters. From the viewpoint of security and privacy-preserving, it is not safe to implement the data decryption operation at edge side. Therefore, the data aggregation scheme with ciphertext is required at the edge side. In this paper, we propose a secure edge electricity data aggregation scheme based on homomorphic public key encryption techniques. The ECNs will implement the electricity data aggregation with the ciphertext from different smart meters, and the aggregated data will be transmitted to the master station through communication network. The mater station can use its public key to obtain the total electricity consumption of each low voltage transformer area.

Hawai'i's Grid Architecture for High Renewables: Developing the State's Modernization Strategy

The Hawailan electric companies the United States in private rooftop solar adoption, with 30% of single-family homes pushing electricity onto the grid. The grid of the future must better accommodate this two-way flow of energy, which is why Hawaiian Electric, Maui Electric, and Hawai'i Electric Light developed a comprehensive grid modernization strategy. Modernizing Hawai'i's Grid for Our Customers was published in August 2017 with input from both customers and stakeholders.

Smart Grid Testing Management Platform (SGTMP)

The Smart Grid (SG) is nowadays an essential part of modern society, providing two-way energy flow and smart services between providers and customers. The main drawback is the SG complexity, with an SG composed of multiple layers, with devices and components that have to communicate, integrate, and cooperate as a unified system. Such complexity brings challenges for ensuring proper reliability, resilience, availability, integration, and security of the overall infrastructure. In this paper, we introduce a new smart grid testing management platform (herein called SGTMP) for executing real-time hardware-in-the-loop SG tests and experiments that can simplify the testing process in the context of interconnected SG devices. We discuss the context of usage, the system architecture, the interactive web-based interface, the provided API, and the integration with co-simulations frameworks to provide virtualized environments for testing. Furthermore, we present one main scenario about the stress-testing of SG devices that can showcase the applicability of the platform.

Security risk assessment for SDN-enabled smart grids

The increasing energy dependence of cloud data centers to Internet-of-Things necessitates availability of high reliable power systems. Smart grid enables two-way flow of energy from power to plug to be automated, monitored and controlled. However, IP-based communications in smart grids increase the likelihood of network attacks, such as IP spoofing and Distributed Denial of Service (DDoS) attacks. These attacks cause damages such as wrong smart meter readings, false demands for electricity, and impaired protection devices. Thus, there is a need for cyber resilient smart grid communication network. Software Defined Networking (SDN) which has the ability to redefine operations of a network at runtime presents the most resilience benefits when used as an underlying infrastructure for the smart grid. In this paper, we present a framework to assess security risks within an SDN-enabled smart grid communication network. Specifically, we quantify the security risks for DoS attacks on Intelligent Electronic Devices (IEDs) and the IEC 61850 network. Our security score model incorporates the critical role of each IED and measures impact on the overall smart grid network. We illustrate how SDN relieves our smart grid network of congestion and improves timing performance of IEC 61850 type messages, making them time compliant.

OPTIMIZATION OF THE FAR EAST WIND RESOURCES ENERGY EFFICIENCY ON THE BASIS OF THE SWARM INTELLIGENCE ALGORITHM

The paper shows the necessity of power consumption modes and energy balance optimization of an intelligent network (Smart Grid) with a function of two-way energy flow based on the alternative energy sources. In this regard, the concept of a generating consumer which provides the ability to flexibly regulate energy flows and equalize the load schedule as well as to minimize the financial costs of the consumed energy are introduced for the active consumers. The paper’s key point is the use of self wind resources which are quite large in the coastal zone of the Far East and on the Russky and Popov islands. A new mathematical model of the optimal energy balance has been developed with the participation of generating consumers and an alternative source of energy in the form of a wind resource as an intelligent system with a two-way flow of energy. Moreover, the paper proposes a system for selecting the priority of generation sources which minimizes the material and financial costs of the electric consumer. At the same time, the swarm particle of swarm algorithm is used as a universal method for solving the optimization multi-criterion problem. The new concept of an intelligent network with active consumers and a two-way flow of energy from alternative sources with the function of its accumulation allows significantly increasing the energy efficiency of wind resources using. Considering special status of some territories of the Far East and the shortage of energy resources, using of alternative energy of wind flows can largely solve the energy problem.

Исследование оптимальных режимов интеллектуальных сетей с двухсторонним потоком энергии

Исследование оптимальных режимов интеллектуальных сетей с двухсторонним потоком энергии

Cyber Security in Smart Grid System

Abstract: The smart grid is an evolution of the electrical grid to respond to these challenges. A smart grid is an energy transmission and distribution network enhanced through digital control, monitoring, and telecommunications capabilities. It provides a real-time, two-way flow of energy and information to all stakeholders in the electricity chain, from the generation plant to the commercial, industrial, and residential end user. This evolution is crucial for integrating both renewable and distributed energy resources and to improve the efficiency and sustainability of the electrical grid and associated services. It will also help in other ways, such as enabling:• Smart and positive energy infrastructures• Increased energy density management during peaks• Real-time pricing to customers• Integrated mobility services• New virtual power plants• Micro grid

Wireless Information and Power Transfer Design for Energy Cooperation Distributed Antenna Systems

Distributed antenna systems (DAS) have been widely implemented in state-of-the-art cellular communication systems to cover dead spots. Recent studies have also indicated that DAS have advantages in wireless energy transfer (WET). In this paper, we study simultaneous wireless information and power transfer (SWIPT) for a multiple-input single-output (MISO) DAS in the downlink which consists of arbitrarily distributed remote antenna units (RAUs). In order to save the energy cost, we adopt energy cooperation of energy harvesting (EH) and two-way energy flows to let the RAUs trade their harvested energy through the smart grid network. Under individual EH constraints, per-RAU power constraints and various smart grid considerations, we investigate a power management strategy that determines how to utilize the stochastically spatially distributed harvested energy at the RAUs and how to trade the energy with the smart grid simultaneously to supply maximum wireless information transfer (WIT) with a minimum WET constraint for a receiver adopting power splitting (PS). Our analysis shows that the optimal design can be achieved in two steps. The first step is to maximize a new objective that can simultaneously maximize both WET and WIT, considering both the smart grid profitable and smart grid neutral cases. For the grid-profitable case, we derive the optimal full power strategy and provide a closed-form result to see under what condition this strategy is used. On the other hand, for the grid-neutral case, we illustrate that the optimal power policy has a double-threshold structure and present an optimal allocation strategy. The second step is then to solve the whole problem by obtaining the splitting power ratio based on the minimum WET constraint. Simulation results are provided to evaluate the performance under various settings and characterize the double-threshold structure.

Cost-aware green cellular networks with energy and communication cooperation

Energy cost of cellular networks is ever-increasing to match the surge of wireless data traffic, and the saving of this cost is important to reduce the operational expenditure (OPEX) of wireless operators in future. The recent advancements of renewable energy integration and two-way energy flow in smart grid provide potential new solutions to save the cost. However, they also impose challenges, especially on how to use the stochastically and spatially distributed renewable energy harvested at cellular base stations (BSs) to reliably supply time- and space-varying wireless traffic over cellular networks. To overcome these challenges, in this article we present three approaches, namely, {\emph{energy cooperation, communication cooperation, and joint energy and communication cooperation}}, in which different BSs bidirectionally trade or share energy via the aggregator in smart grid, and/or share wireless resources and shift loads with each other to reduce the total energy cost.

CoMP Meets Smart Grid: A New Communication and Energy Cooperation Paradigm

In this paper, we pursue a unified study on smart grid and coordinated multi-point (CoMP) enabled wireless communication by investigating a new joint communication and energy cooperation approach. We consider a practical CoMP system with clustered multiple-antenna base stations (BSs) cooperatively communicating with multiple single-antenna mobile terminals (MTs), where each BS is equipped with local renewable energy generators to supply power and also a smart meter to enable two-way energy flow with the grid. We propose a new energy cooperation paradigm, where a group of BSs dynamically share their renewable energy for more efficient operation via locally injecting/drawing power to/from an aggregator with a zero effective sum-energy exchanged. Under this new energy cooperation model, we consider the downlink transmission in one CoMP cluster with cooperative zero-forcing (ZF) based precoding at the BSs. We maximize the weighted sum-rate for all MTs by jointly optimizing the transmit power allocations at cooperative BSs and their exchanged energy amounts subject to a new type of power constraints featuring energy cooperation among BSs with practical loss ratios. Our new setup with BSs' energy cooperation generalizes the conventional CoMP transmit optimization under BSs' sum-power or individual-power constraints. Finally, we validate our results by simulations under various practical setups, and show that the proposed joint communication and energy cooperation scheme substantially improves the downlink throughput of CoMP systems powered by smart grid and renewable energy, as compared to other suboptimal designs without communication and/or energy cooperation.