Holistic simulation of wind turbines with fully aero-elastic and electrical model

Requirements for the design of wind turbines advance facing the challenges of a high content of renewable energy sources in the public grid. A high percentage of renewable energy weaken the grid and grid faults become more likely, which add additional loads on the wind turbine. Load calculations with aero-elastic models are standard for the design of wind turbines. Components of the electric system are usually roughly modeled in aero-elastic models and therefore the effect of detailed electrical models on the load calculations is unclear. A holistic wind turbine model is obtained, by combining an aero-elastic model and detailed electrical model into one co-simulation. The holistic model, representing a DFIG turbine is compared to a standard aero-elastic model for load calculations. It is shown that a detailed modelling of the electrical components e.g., generator, converter, and grid, have an influence on the results of load calculations. An analysis of low-voltage-ride-trough events during turbulent wind shows massive increase of loads on the drive train and effects the tower loads. Furthermore, the presented holistic model could be used to investigate different control approaches on the wind turbine dynamics and loads. This approach is applicable to the modelling of a holistic wind park to investigate interaction on the electrical level and simultaneously evaluate the loads on the wind turbine.

     

大电网运行控制面临的形势、问题与挑战

多项特高压交直流输变电工程的相继建成投产,标志着我国电网建设已处于国际领先水平。同时,我国当前大电网运行与控制也面临着前所未有的问题与挑战。该文介绍了当前大电网运行面临的形势,即具备新能源占比不断增加,电力跨大区大容量远距离输送等特点,分析了电网运行与控制面临的问题与挑战。以东北电网为例,介绍了应对大电网运行的措施。东北电网通过调峰辅助服务市场、高频紧急控制系统等技术手段促进网源协调,提高了新能源消纳水平。     

Energy Prediction in IoT Systems Using Machine Learning Models

The Internet of Things (IoT) technology has been developed for directing and maintaining the atmosphere in smart buildings in real time. In order to optimise the power generation sector and schedule routine maintenance, it is crucial to predict future energy demand. Electricity demand forecasting is difficult because of the complexity of the available demand patterns. Establishing a perfect prediction of energy consumption at the building’s level is vital and significant to efficiently managing the consumed energy by utilising a strong predictive model. Low forecast accuracy is just one of the reasons why energy consumption and prediction models have failed to advance. Therefore, the purpose of this study is to create an IoT-based energy prediction (IoT-EP) model that can reliably estimate the energy consumption of smart buildings. A real-world test case on power predictions is conducted on a local electricity grid to test the practicality of the approach. The proposed (IoT-EP) model selects the significant features as input neurons, the predictable data is selected as output nodes, and a multi-layer perceptron is constructed along with the features of the Convolution Neural Network (CNN) algorithm. The analysis of the proposed IoT-EP model has higher accuracy of 90%, correlation of 89%, and variance of 16% in less training time of 29.2 s, and with a higher prediction speed of 396 (observation/sec). When compared to existing models, the results showed that the proposed (IoT-EP) model outperforms with a satisfactory level of accuracy in predicting energy consumption in smart buildings.     

电动汽车智能充放电控制与应用综述

随着电动汽车规模的增大,电动汽车接入电网对电力系统运行与控制的影响不容忽视。电动汽车作为一种可控负荷,对其进行充放电控制可以有效削弱充电负荷带来的不利影响,同时还能起到削峰填谷、促进新能源消纳的作用,这将成为电力系统运行控制的一种重要手段。给出了充电负荷建模需要考虑的因素,总结了建立电动汽车负荷预测模型的方法。归纳了电动汽车参与电网调度的可行方法,并分析了不同方法的特点。同时,为了提高电动汽车参与调度的积极性,介绍了用区块链完成电动汽车电力交易的架构与方法。最后,对尚未解决的问题和可能的研究方向进行了讨论。     

Optimizing Decision-Making of A Smart Prosumer Microgrid Using Simulation

Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand. Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources. In energy-efficient data centers, distributed generation can be used to meet the facility’s overall power needs. This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demand-side load management into account. This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from the grid. While diesel generators are categorized as a dispatchable distributed generation with energy storage added to handle solar radiation from the sun and high grid power operating costs in the suggested scenario, wind turbines and solar Photovoltaic (PV) are classified as non-dispatchable distributed generators. The resulting linear math issues are assessed and displayed in MATLAB optimization software using a mixed-integer linear programming (MILP) strategy. According to the simulation results, the suggested energy management strategy reduced the university microgrid’s grid power costs by 38.8%, making it an affordable solution which is somehow greater than the prior case scenario’s 23% savings. The installed solar system capacity’s effects on the economy, society, and finances were also assessed, and it became clear that the best option for the smart microgrid was determined that would be 325 kW of solar PV, 25 kW of wind turbine, and 600 kW of diesel generators, respectively. Given the current situation, university administrators are urged to participate in distributed generators and adopt cutting-edge designs for energy storage technologies due to the significant environmental and financial benefits.     

Blockchain-Based SQKD and IDS in Edge Enabled Smart Grid Network

Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all smart meters are first registered in the blockchain, and then the blockchain generates a secret key. The blockchain verifies the user ID and the secret key during authentication matches the one authorized to access the network. The secret key is shared during transmission through secure quantum key distribution (SQKD). The second phase is the lightweight data encryption, for which we use a lightweight symmetric encryption algorithm, named Camellia. The third phase is the multi-constraint-based edge selection; the data are transmitted to the control center through the edge server, which is also authenticated by blockchain to enhance the security during the data transmission. We proposed a perfect matching algorithm for selecting the optimal edge. The fourth phase is a dual intrusion detection system which acts as a firewall used to drop irrelevant packets, and data packets are classified into normal, physical errors and attacks, which is done by Double Deep Q Network (DDQN). The last phase is optimal user privacy management. In this phase, smart meter updates and revocations are done, for which we proposed Forensic based Investigation Optimization (FBI), which improves the security of the smart grid network. The simulation is performed using network simulator NS3.26, which evaluates the performance in terms of computational complexity, accuracy, false detection, and false alarm rate. The proposed BLIDSE model effectively mitigates cyber-attacks, thereby contributing to improved security in the network.     

Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use. To overcome these limitations, we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use. Experimental results indicate that this architecture could achieve the highest average of recall and accuracy (83.43%) compared to benchmark models. To verify the effectiveness of the architecture, another experimental result shows that energy storage system (ESS) scheme in accordance with the forecast results of the proposed model (LSTM-MATO) in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method. Therefore, the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.     

Combined particle swarm optimization and heuristic fuzzy inference systems for a smart home one-step-ahead load forecasting

This article presents a hybrid approach that combines particle swarm optimization (PSO) and heuristic fuzzy inference system (HFIS) for smart home one-step-ahead load forecasting. Smart home load forecasting is an important issue in the development of smart grids. Generally, the electricity consumption of a household is inherently nonlinear and dynamic and heavily dependent on the habitual nature of power demand, activities of daily living and on holidays or weekends, so it is often difficult to construct an adequate forecasting model for this type of load. To address this problem, a hybrid model, consisting of two phases, is proposed in this article. In the first phase, the popular PSO algorithm is used to determine the locations of fuzzy membership functions. Then, the proposed HFIS technique is used to develop the one-step-ahead load forecasting model in the second phase. Because of the robust nature of the proposed HFIS technique, which does not need to retrain or re-estimate model parameters, it is very suitable for smart home load forecasting. The proposed method was verified using two different households’ load data. Simulation results indicate that the proposed method produces better forecasting accuracy than existing methods.     

An ultrafast electric vehicle charging station as an object of decentralized power engineering

In developing technologies of ultrafast (within 5 min) electric vehicle charging, problems occur related to the power supply. The charging station, an analog of a refueling station, should have an extremely irregular load with a high peak power. It might be located far from the possible point of connection to the power grid and should represent an object of decentralized power generation by means of an electrical energy storage system. We consider and compare an autonomous gas-turbine facility and a lead-acid battery as the possible power supply. We found an averaged statistically optimal relation between the gas turbine power (or the contracted power) and the battery capacity providing for minimum expenses, reduced to the service life, for creation and exploitation of the station of ultrafast charging of a given number of electric vehicles per day.     

智能用电小区——智能小区发展的一个里程碑

智能小区在国内已经走过了20多个年头,近几年,随着智能电网的发展,其技术成果被大量应用于智能小区,催生了智能用电小区。论文结合智能用电小区在国内的发展状况,并以某省级电力公司职工住宅智能用电小区项目（省级科技攻关项目）的规划设计为例,介绍智能用电小区规划设计的内容和要素,包括配电自动化、电动汽车充电、分布式能源（太阳能、风能）应用、无源光网络、远程抄表、安全防范、增值服务等。规划设计中除了应用常规智能小区技术外,还大量应用上述智能电网的技术成果,而且还有相对较成熟的技术规范和标准;以此阐明智能电网技术的发展和应用给智能小区规划设计带来的发展和改变;最后对智能用电小区的主要特点进行了归纳总结,再次阐明融入了智能电网技术成果的智能用电小区是对智能小区内涵的极大丰富和发展,引领着智能小区的发展方向,堪称智能小区发展史上的一个重大里程碑。     

Short-term forecasting of electricity prices in the colombian electricity market

The restructuring of the electricity-generating industry from protected monopoly to an open competitive market has presented producers with a problem scheduling generation: finding the optimal bidding strategy to maximise their profits. In order to solve this scheduling problem, a reliable system capable of forecasting electricity prices is needed. This work evaluates the forecasting capabilities of several modelling techniques for the next-day-prices forecasting problem in the Colombian market, measured in USD/MWh. The models include exogenous variables such as reservoir levels and load demand. Results show that a segmentation of the prices into three intervals, based on load demand behaviour, contribute to an important standard deviation reduction. Regarding the models under analysis, Takagi?Sugeno?Kang models and ARMAX models identified by means of a Kalman filter perform the best forecasting, with an error rate below 6%.     

大容量直流充电桩集群的调峰研究

在新能源政策倡导的发展趋势下,我国新能源汽车的发展已取得了长足的进步,随之增加的是电动汽车的电池能量密度。相应的电动汽车动力电池充电的直流充电桩的功率越来越高,使得直流充电桩的充电功率在配电层总负荷功率中的占比越来越大,这对配电网的规划建设带来新的挑战。根据电动汽车充电过程的随时可中断性,提出在电动汽车的总规模达到一定数量时,可以通过灵活控制电动汽车的总体充电功率,从而达到为电网调峰的目的。通过对电动汽车充电桩的功率和控制模式的分析,得出当直流充电桩的数量及其总功率在配电网负荷中的占比达到一定规模时,在集群控制模式下,更加适合参与配电网层的调峰。     

Implementing factory demand response via onsite renewable energy: a design-of-experiment approach

This paper proposes the implementation of demand response (DR) programmes in large manufacturing facilities featuring distributed wind and solar energy. Manufacturing facilities are high consumers of electric power. For this reason, these facilities usually pay exorbitant utility bills, which could be as much as $10–20 million per year. A high consumption of electricity also means that upstream fossil-fuelled power plants must release thousands of metric tonnes of carbon annually during the generation of electricity. DR contracts offer a lower utility rate in return for a load reduction during contingent events (i.e. peak hours). This paper covers the modelling and implementation of an interruptible/curtailable DR programme participated by a manufacturer that possesses onsite renewable generation units. These complementary energy resources allow the manufacturer to meet the curtailment requirements without causing any major electricity shortage that adversely affects the normal production schedule. We developed a stochastic programming model to determine the capacity of the wind turbine and solar panels that maximise the DR programme savings. The optimal solutions are derived based on central composite design methodology.     

主动配电网规划方法研究

分布式电源接入配电网后对供电可靠性、电压分布、网损均产生一定影响,传统配电网规划方法已无法适用。构建主动配电网（active distribution network,ADN）规划设计体系结构,从电网现状、负荷预测等方面制定主动配电网规划内容,重点研究基于分布式电源风险度出力置信区间的源网荷一体化平衡及网荷协同性规划方法,最后给出主动配电网电气校验方法,验证方案制定的合理性。     

Transactive Demand Response Operation at the Grid Edge using the IEEE 2030.5 Standard

This paper presents a transactive demand response (TDR) scheme for a network of residential customers with generation assets that emphasizes interoperability within a transactive energy architecture. A complete laboratory-based implementation provides the first (to our knowledge) realization of a comprehensive TDR use case that is fully compliant with the Institute of Electrical and Electronics Engineers (IEEE) 2030.5 standard, which addresses interoperability within a cybersecure smart energy profile (SEP) context. Verification is provided by a full system integration with commercial hardware using Internet Protocol (IP)-based (local area network (LAN) and Wi-Fi) communication protocols and transport layer security (TLS) 1.2 cryptographic protocol, and validation is provided by emulation using extensive residential smart meter data. The demand response (DR) scheme is designed to accommodate privacy concerns, allows customers to select their DR compliance level, and provides incentives to maximize their participation. The proposed TDR scheme addresses privacy through the implementation of the SEP 2.0 messaging protocol between a transactive agent (TA) and home energy management system (HEMS) agents. Customer response is handled by a multi-input multi-output (MIMO) fuzzy controller that manages negotiation between the customer agent and the TA. We take a multi-agent system approach to neighborhood coordination, with the TA servicing multiple residences on a common transformer, and use a reward mechanism to maximize customer engagement during the event-based optimization. Based on a set of smart meter data acquired over an extended time period, we engage in multiple TDR scenarios, and demonstrate with a fully-functional IEEE 2030.5-compliant implementation that our scheme can reduce network peak power consumption by 22% under realistic conditions.     

Scheduling a storage-augmented discrete production facility under incentive-based demand response

Demand response (DR) is considered as one of the most important measures for balancing energy supply and demand in the smart grid paradigm. Incentive-based programs, one manifestation of DR, contribute to short-term system stability and prevent critical periods when system stability is at risk by enabling the system operator (SO) to directly change total energy demand. The fact that a third party would be empowered to interfere with internal operations is, however, also one of the major drawbacks of DR that prevents especially industrial consumers from participating with full capacity in such programs. This paper considers an alternative Incentive-based program with application to a discrete manufacturing facility where load reduction curves (LRCs) are generated a priori outlining the potential load reduction in the DR period. The SO uses the LRC to determine the desired level of load reduction for critical periods. To illustrate the generation of the LRC, this paper builds on a flexible flow shop (FFS) formulation for a discrete manufacturing facility and presents a model that includes multiple machine modes and product- and machine-specific energy consumption trajectories. Based on the FFS, a procedure is developed to generate the LRC. The paper also investigates the potential of including a battery energy storage system (BESS) into the production facility and illustrates the effects of the BESS on the LRC.     

Composite load models based on field measurements and their applications in dynamic analysis

The composite load model with an induction motor in parallel with a static load has been studied and applied in analysis of dynamics of power systems for a long time. However, the load parameters from field tests are still very limited. Based on the theoretical results of identifiability and estimation methodology of load parameters that have been achieved in the previous work, a project of load modelling based on field measurements is described. Eight sets of equipment for load modelling were installed in HeNan Electric Power Grid (HNEPG) Corporation, and the dynamics of the load were recorded and a dynamic index is proposed to evaluate the dynamic characteristics of the data measured. The load models derived from the field measurements are applied to stability analysis of HNEPG. The critical clearing time and power transfer capability analyses illustrate the enhancement of the stability analysis of the power grid using the load parameters derived from the field measurements and show the benefits of application of such model parameters in power system dynamic analysis.     

Smart demand for improving short-term voltage control on distribution networks

Smart grids must involve active roles from end users in order to be truly smart. The energy consumption has to be done in a flexible and intelligent manner, in accordance with the current conditions of the power system. Moreover, with the advent of dispersed and renewable generation, increasing customer integration to aid power system performance is almost inevitable. This study introduces a new type of smart demand side technology, denoted demand as voltage controlled reserve (DVR), to improve short-term voltage control, where customers are expected to play a more dynamic role to improve voltage control. The technology can be provided by thermostatically controlled loads as well as other types of load. This technology is proven to be effective in case of distribution systems with a large composition of induction motors, where the voltage presents a slow recovery characteristic due to deceleration of the motors during faults. This study presents detailed models, discussion and simulation tests to demonstrate the technical viability and effectiveness of the DVR technology for short-term voltage control.     

泛在电力物联网中将家电可控负荷转化成智能负荷的方法研究

为了实现负荷控制,传统的需求响应针对居民用户主要采用拉闸限电的调峰策略,用电方式较为被动。泛在电力物联网概念的提出使可控负荷转化成智能负荷成为可能。为了实现可控负荷控制的智能化,研究了泛在电力物联网在家电可控负荷控制中的应用。在智能家电控制算法中增加电网闲时的填谷作用,不仅实现了用电高峰期电网削峰,而且有效利用了用电低谷时期的容量闲置。并且允许用户预先设定负荷需求范围和不同时段家电重要系数,最大限度地满足了用户的舒适度需求。仿真结果验证了改进后智能家电控制算法的合理性。     

极端灾害下基于智能楼宇分布式电源的配电系统负荷恢复力评估分析

作为一类重要的负荷侧资源,智能楼宇中广泛存在的各类分布式电源为极端灾害后电力系统的供电快速恢复及负荷转带提供了新的可能性。为此,提出一种针对智能楼宇负荷恢复力的综合评估框架,用于定量分析和计算极端灾害后智能楼宇末端存活分布式电源对配电系统中重要负荷的转带能力。在对智能楼宇内不同类型物理设备进行建模的基础上,重点考虑多能互补及能量耦合特性,首先,提出了电能转移量、热能转移量、冷能转移量3项定量评价指标,用于精确量化极端灾害后智能楼宇电源对系统负荷恢复的贡献。其次,在此基础上,通过综合利用随机混合整数规划方法,进一步提出了针对上述评价指标的具体计算方法。最后,以某一工业园区负荷为例,对所提评估框架进行有效性验证。仿真结果表明,所提方法在保证智能楼宇正常运行前提下可充分发掘智能楼宇的能源供应潜力,有效提升配电系统在极端灾害下的供能可靠性。