Advanced Distribution Network Research Articles

Security in advanced metering infrastructures: Lightweight cryptography

Abstract Smart grids are designed to revolutionize the energy sector by creating a smarter, more efficient and reliable power supply network. The rise of smart grids is a response to the need for a more comprehensive and sophisticated energy system that caters to the needs of homes and businesses. Key features of smart grids include the integration of renewable energy sources, decentralized generation and advanced distribution networks. At the heart of smart grids is a sophisticated metering system, consisting of intelligent electronic devices that measure energy consumption and enable two-way communication between the utility company and the consumer. The information exchanged via these devices is critical, as it includes sensitive data such as energy consumption patterns and billing information. With the rise of the Internet of Things (IoT) and Industry 4.0, the security of this information is of utmost importance. This paper delves into the security challenges faced by advanced metering infrastructure (AMI) and highlights the crucial role played by cryptography in ensuring the secure exchange of sensitive information. Cryptographic protocols such as encryption, authentication and digital signatures are essential components of a secure smart grid infrastructure. These protocols work together to ensure the confidentiality, integrity and authenticity of the information exchanged between the utility company and the consumer.

Dynamic state estimation of distribution networks with mixed measurements based on Cubature Kalman Filter

The integration of synchronized phasor measurement units (μPMU) has introduced diverse data sources into the distribution network. Each has distinct time scales and precision levels. To enable effective decision-making, it is crucial to amalgamate these measurements efficiently, ensuring both speed and accuracy in distribution network state estimation. This study explores a dynamic state estimation approach for distribution networks, employing Cubature Kalman Filtering (CKF) as the central methodology. By deeply fusing μPMU, AMI and SCADA data and incorporating CKF techniques, it improves accuracy and stability, which is validated on the IEEE 37-node system. This research supports advanced distribution network apps and large-scale data analysis.

Combined unbalanced distribution system state and line impedance matrix estimation

To address the challenges that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these strategies require accurate network models to work effectively. However, distribution network data are known to contain errors, and attention has been given to techniques that allow to derive improved network information. This paper presents a novel method to derive line impedance values from smart meter measurement time series, with realistic assumptions in terms of meter accuracy, resolution and penetration. The method is based on unbalanced state estimation and is cast as a non-convex quadratically constrained optimization problem. Both line lengths and impedance matrix models can be estimated based on an exact nonlinear formulation of the steady-state three-phase network physics. The method is evaluated on both the IEEE European Low Voltage Feeder (906 buses) and a real Belgian LV feeder, showing promising results.

Risk Constrained Energy Efficient Optimal Operation of a Converter Governed AC/DC Hybrid Distribution Network With Distributed Energy Resources and Volt-VAR Controlling Devices

Increasing penetration of direct current (dc) based distributed energy resources and dc loads in the conventional alternating current (ac) network necessitate the deployment of ac/dc hybrid distribution networks (HDNs). In view with the development of advanced energy management policy for ac/dc HDNs, unlike previous literatures, this article proposes a risk constrained energy efficient management algorithm by merging load shifting (LS) and conservation voltage reduction (CVR) techniques. The optimization framework aims to simultaneously minimize both true and conditional risk or conditional value at risk values of the expected energy cost under uncertain solar power generation, load demand, and upper grid energy price. In contrast with the available stochastic optimization process, in this article, two-point estimation strategy is employed in place of Monte Carlo simulation for scenario generation from the probability density functions of the uncertain parameters to reduce computational exertion. The proposed centralized optimization framework is initially developed as mixed integer nonconvex programming but to avoid computation complexity, the nonlinear components are replaced by their linear counterparts. Later, a new solution process named successive mixed integer linear programming (s-MILP) is proposed to obtain the optimal decisions for deployment of LS and CVR through smart inverters and volt-VAR controlling devices. Efficacy of the proposed technique is demonstrated on modified IEEE 33 bus ac/dc HDN and the most energy efficient operation is found by merging LS and CVR. Simulation outcomes prove fast and near optimal convergence of the s-MILP compared to conventional second-order conic programming relaxed mixed integer convex programming and piecewise linearization based MILP. Further, to assess the impact of network size on the solution time and optimality, the proposed advanced distribution network management systems strategy is employed on 132 bus ac/dc HDN.

Voltage control approach based on PCPM distributed algorithm in the presence of high PV penetration: a stochastic modeling

The uncertainty of solar generation is a challenging issue for the operation and control of distribution systems. Also, because of the large number of control and operation variables in advanced distribution networks, the centralized optimization technique requires high computational capabilities. In this paper, a distributed voltage regulation approach in a distribution system based on the predictor corrector proximal multiplier (PCPM) algorithm has been presented. This paper focuses on the voltage rise in distribution systems with high penetration of photovoltaics. The proposed method uses a clustering approach to divide the network into partitions based on the coupling degrees among different nodes. The optimal reactive power control strategy is conducted in each partition and integrated using PCPM. The proposed method is tested on 69 and 123 nodes IEEE distribution test systems. The results confirmed that the proposed method achieves the optimal solution with the same accuracy as the centralized method but in a much shorter computation time.

Reliability Assessment on Typical Connection Modes of High Reliability Demonstration Area

In the process of construction and modification of distribution, choosing connection modes is a key link of network construction and plays a pivotal role in distribution network. In this thesis, firstly, the distribution network frame and its present situation in demonstration area is analyzed, and the shortcomings in present network are designed; After using the features of advanced distribution network frame in international metropolis for reference, the experience is concluded and four kinds typical connection modes which suit for demonstration area are designed; The characteristics and the scope of application of every typical connection modes are analyzed, the modification scheme according to the present distribution network frame in demonstration area is illustrated. Finally, the examples are taken as cases to illustrate that these typical connection modes are of high power supply reliability. With the constant rise of the require of power supply reliability, the connection modes in demonstration area will be widely used and extended gradually.

State Forecasting and Operational Planning for Distribution Network Energy Management Systems

This paper describes the application of advanced metering infrastructure data for developing energy forecasting and operational planning services in distribution networks with significant distributed energy resources. This paper describes development of three services designed for use in distribution network energy management systems. These are comprised of a demand forecasting service, an approach for constraint management in distribution networks, and a service for forecasting voltage profiles in the low voltage network. These services could be applied as part of an advanced distribution network management system in order to improve situational awareness and provide early warning of potential network issues. The methodology and its applicability is demonstrated using recorded supervisory control and data acquisition and smart meter data from an existing medium voltage distribution network.

Advanced Network Management Systems: A Risk-Based AC OPF Approach

This paper presents a risk-based advanced distribution network management system (NMS) aimed at maximizing wind energy harvesting while simultaneously managing congestion and voltages. The NMS allows the adoption of multi-minute control cycles so the volume of actions from on-load tap changers (OLTCs) and distributed generation (DG) plants can be reduced while effectively catering for the effects of wind power uncertainties. This work presents the quantification of benefits and impacts from adopting different control cycles as well a comparison with a deterministic approach. The risk-based approach is implemented by adapting and expanding an AC optimal power flow. A risk level is used to determine the extent to which congestion and voltage rise could exist during a control cycle. The proposed NMS is applied to a real-life U.K. MV network from the North West of England to assess its effectiveness in managing high penetration of wind power considering minute-by-minute simulations for one week. The results show that the risk-based approach can effectively manage network constraints better than the deterministic approach, particularly for multi-minute control cycles, reducing also the number of control actions but at the expense of higher levels of curtailment.