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

Detrending and characterizing system frequency oscillations using an adapted Zhou algorithm

Electro-mechanical oscillations between interconnected synchronous generators and oscillations in system frequency are an inherent part of the operation of large power systems. Very Low Frequency (VLF) oscillations are usually classified as oscillations in the 0.01-0.1 Hz range. With the move towards variable renewable energy sources and low-inertia power systems, VLF oscillations are being observed with increasing regularity in many small and island grids. If left undamped, these can present a threat to system stability. However, finding the root cause and source(s) of VLF oscillations is an extremely challenging task for network operators. Recent work has identified a need for improved tools for identifying and characterising VLF oscillations, in order to determine the combination of system conditions that can be used as predictors for VLF events. A suitable small signal model is also required in order to enable verification of the root cause of VLF events and study of mitigation measures. Accordingly, this paper presents a new approach for detrending and characterizing system frequency oscillations using an adapted Zhou algorithm. The paper also describes a method for applying this algorithm for the detection/location of oscillations, and for their detrending and characterization. Finally, an approach for relating detected oscillation events to power system operating conditions for diagnostic purposes is described. The effectiveness of the proposed approach is demonstrated using a single frequency power system model, and using system frequency oscillations recorded from the Irish power system

Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data

This paper develops an approach for household appliance identification and classification of household Activities of Daily Living (ADLs) using residential smart meter data. The process of household appliance identification, i.e. decomposing a mains electricity measurement into each of its constituent individual appliances, is a very challenging classification problem. Recent advances have made deep learning a dominant approach for classification in fields such as image processing and speech recognition. This paper presents a deep learning approach based on multi-layer, feedforward neural networks that can identify common household electrical appliances from a typical household smart meter measurement. The performance of this approach is tested and validated using publicly-available smart meter data sets. The identified appliances are then mapped to household activities, or ADLs. The resulting ADL classifier can provide insights into the behaviour of the household occupants, which has a number of applications in the energy domain and in other fields

PTP-based time synchronisation of smart meter data for state estimation in power distribution networks

This paper develops a novel approach for distribution system monitoring and state estimation, where time synchronisation of smart-meter measurements is carried out via the Precision Time Protocol (PTP). The approach is based on the concept of a Modified Smart Meter (MSM), a distribution system monitoring instrument that enables accurate time synchronisation of smart meter data. The design, application, communication technique and protocols of the MSM are described in detail. The proposed MSM device features PTP-based time synchronisation of smart meter measurements, and the concept of unbundling is applied to collect measurements utilising the existing smart meter sensors. This is expected to reduce the overall implementation cost of an MSM-based distribution network monitoring system compared to a system based on Phasor Measurement Units (PMUs). The problem of requiring open sky access for GPS links can potentially be solved by means of PTP synchronisation. Three-phase state estimation simulations using the IEEE-13 and 123 bus unbalanced test networks are employed to demonstrate the applicability of the MSM, and its performance is compared to standard PMU devices. The results indicate that the MSM may represent a workable monitoring solution for MV and LV distribution networks, with an acceptable trade-off between cost and performance

Non-bias allocation of export capacity for distribution network planning with high distributed energy resource integration

A novel distributed energy resources (DER) allocation method focused on grid constraints that avoids topological bias is proposed for distribution networks. A technology-agnostic approach is used, where a non-bias allocation of export capacity (NAEC) not specific to generation type is calculated. Moreover, the proposed NAEC is extended from an export capacity into a hosting capacity (HC) using a statistical approach. The methods are tested using the IEEE 33-bus distribution system, and two typical Irish distribution feeders -one urban, one rural- as case studies. Using a high-resolution year-long quasi-static time series simulation (QSTS) and three different generation profiles, the proposed NAEC method is validated against current practices and state of the art allocation methods in terms of active balancing, security of supply, interactions between users, operational concerns, and fairness. Results show that an equivalent or higher level of DER penetration is achieved using the proposed methodology. There are no additional constraint violations using the NAEC methodology, moreover, time slots with violations are reduced, improving security of supply. Furthermore, results suggest that avoiding topological bias makes the network accessible for more users, and prioritises self-consumption

Smart meter based two-layer distribution system state estimation in unbalanced MV/LV networks

This article presents a smart meter based two-layer state estimation technique, design to enable integrated monitoring of medium-voltage (MV) and low-voltage (LV) power distribution networks. The main contributions of this work include the development of a novel topology reduction technique for LV networks in order to carry out state estimation with a reduced number of smart meter based measurements, and a linear LV state estimation technique, which reduces convergence problems in LV networks. In addition, a detailed framework for integrated MV/LV network state estimation in realistic, unbalanced three-phase networks is proposed and demonstrated using the IEEE 13 bus and IEEE 906 LV networks. The results suggest that the proposed two-layer state estimation technique is robust and provides improved distribution system state estimation accuracy compared to traditional approaches

Design of an open-source laboratory demonstrator for peer-to-peer trading in local energy markets

There is significant research interest in new energy trading mechanisms based on peer-to-peer or community-based markets, which are more consumer-centric and direct compared to traditional electricity retail markets. Such trading mechanisms require an electricity trading platform that can manage and settle energy transactions from vast numbers of small distributed energy resources, and therefore scalability and interoperability are major challenges. The hardware, communications and software required for implementing local energy trading platforms needs to be designed, tested and demonstrated in a real-time environment. Accordingly, this paper presents a design for an open-source laboratory demonstrator, which allows testing of the hardware and software required for peer-to-peer local energy trading using distributed ledger technology

State of the art in energy communities and sharing economy concepts in the electricity sector

Due to high penetration of Distributed Renewable Energy Resources and their inflexible dispatch nature, modern and future electrical grids are facing technical challenges. New participants can offer the flexibility required by the grid through investments in energy storage and demand response capabilities. This article provides a comprehensive review on the concept of Energy Communities, their technical and economic motivations, available resources in the literature, trading schemes, price negotiation algorithms, and benefits for the grid. Furthermore, this review describes the policy framework in the European Union to make Energy Communities a reality. Moreover, a brief survey of related projects over the world is presented, along with a discussion around benefits, obstacles, and future research opportunities in this area

Distributed double auction for peer to peer energy trade using blockchains

In this paper we use the blockchain technology to develop a peer to peer energy trade platform without a trusted third party. Our main contribution is a novel distributed double auction mechanism which allows any peer to act as an auctioneer and the blockchain mechanism ensures that a peer behaves lawfully while acting as an auctioneer. Using experimental evaluation we show that (1) the distributed auction converges quickly, (2) it minimizes energy loss due to long transmission, (3) computational overhead due to employing a blockchain is negligible, (4) it is efficient and (5) it can implement trade restrictions imposed by the energy distribution network

Real-time grouped management of Electric Vehicle Battery Chargers (EVBCs) for voltage profile improvement in radial distribution networks

Voltage limit violation is one of the main factors that impact large-scale integration of electric vehicles in distribution networks. In order to improve voltage profiles, active charging management techniques can be deployed in real-time, considering voltage sensitivities of customer buses. The study in this paper investigates a real-time charging management approach for electric vehicles, clustered according to voltage sensitivities among the customer buses, in a local network. Constant power (CP) and constant current (CC) models, representing a range of electric vehicle battery chargers (EVBCs), are used in simulations with high-resolution stochastic EV charging and residential demand profiles. The paper quantifies the performance of the proposed management approach in a local network model based on real data and IEEE European Low Voltage (LV) Test Feeder