Barry Hayes

Optimal Power Flow for Maximizing Network Benefits From Demand-Side Management

This paper applies optimal power flow (OPF) to evaluate and maximize network benefits of demand-side management (DSM). The benefits are quantified in terms of the ability of demand-responsive loads to relieve upstream network constraints and provide ancillary services, such as operating reserve. The study incorporates detailed information on the load structure and composition, and allows the potential network benefits, which could be obtained through management of different load types, to be quantified and compared. It is demonstrated that the actual network location of demand-manageable load has an important influence on the effectiveness of the applied DSM scheme, since the characteristics of the loads and their interconnecting networks vary from one location to another. Consequently, some network locations are more favorable for implementation of DSM, and OPF can be applied to determine the optimal allocation of demand-side resources. The effectiveness of the presented approach is assessed using a time-sequential OPF applied to typical radial and meshed U.K. distribution networks. The results of the analysis suggest that network operators could not just participate in, but also encourage and add value to the implementation of specific DSM schemes at the optimum network locations in order to maximize the total benefit from DSM.

A Closed-Loop State Estimation Tool for MV Network Monitoring and Operation

This paper discusses the design and simulation of an integrated load forecasting and state estimation tool for distribution system operations. A predictive database is created and applied to forecast future network states in order to allow short-term (e.g., hours/days ahead) planning to be carried out. The predictive database is based on adaptive nonlinear auto-regressive exogenous (NARX) load estimation and forecasting models, which are continuously updated using feedback from the state estimator. This creates a closed-loop information flow designed to continuously monitor and improve the system state estimation performance by updating and retraining models where appropriate. The aim of this methodology is to improve situational awareness and help to provide network operators with early warning of potential issues, in medium voltage (MV) networks where the number of on-line measurements is limited, and state estimation relies heavily on estimates of power injections. The applicability of the approach is demonstrated through simulation using supervisory control and data acquisition (SCADA) and smart meter measurements recorded from an actual MV distribution network.

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.

Short-Term Load Forecasting at the local level using smart meter data

Recent developments in active distribution networks, and the availability of smart meter data has led to much interest in Short-Term Load Forecasting (STLF) of electrical demand at the local level, e.g. estimation of loads at substations, feeders, and individual users. Local demand profiles are volatile and noisy, making STLF difficult as we move towards lower levels of load aggregation. This paper examines in detail the correlations between demand and the variables which influence it, at various levels of load disaggregation. The analysis investigates the forecasting capability of both linear and non-linear STLF approaches for forecasting local demands, and quantifies the forecast uncertainty for each level of load aggregation. The results demonstrate the limitations of several of the most commonly-used STLF approaches in this context. It is shown that, at the local level, standard STLF models may not be effective, and that simple load models created from historical smart meter data can give similar prediction accuracies. The analysis in the paper is carried out using two large smart meter data sets recorded at distribution networks in Denmark and in Ireland.

Residential Demand Management Using Individualized Demand Aware Price Policies

This paper presents a novel approach to demand side management (DSM), using an “individualized” price policy, where each end user receives a separate electricity pricing scheme designed to incentivize demand management in order to optimally manage flexible demands. These pricing schemes have the objective of reducing the peaks in overall system demand in such a way that the average electricity price each individual user receives is non-discriminatory. It is shown in this paper that this approach has a number of advantages and benefits compared to traditional DSM approaches. The “demand aware price policy” approach outlined in this paper exploits the knowledge, or demand-awareness, obtained from advanced metering infrastructure. The presented analysis includes a detailed case study of an existing European distribution network where DSM trial data was available from the residential end-users.

Demand-aware price policy synthesis and verification services for Smart Grids

In management tasks for modern electricity networks the stakeholders face typically two conflicting objectives: maximization of income (increasing demand) and reduction of demand peaks (reducing costs). To improve management of electricity distribution networks, an integrated service-based methodology is presented in this paper. Namely, the proposed approach: i) computes the operational constraints in order to improve utilization of the whole network; ii) enforces those constraints by focusing on each network substation separately; iii) verifies that probability of violating those constraints in nonnominal cases is fairly low. The feasibility of the approach has been tested tested by using a realistic scenario taken from an existing medium voltage Danish distribution network. In such scenario, the proposed method improves the network utilization and offers economic benefits for all the principal participants, i.e. DSOs, retailers and end users.

Distribution network equivalents for reliability analysis. Part 1: Aggregation methodology

This paper, which is part one of a two-part series, presents a general methodology for reducing system complexity by calculating the electrical and reliability equivalent models of low and medium voltage distribution networks. These equivalent models help to reduce calculation times while preserving the accuracy assessment of power system reliability performance. The analysis is applied to typical UK distribution systems, which supply four generic load sectors with different networks and demand compositions (residential, commercial and industrial). This approach allows for a direct correlation between reliability performance and network characteristics, while assessing the most representative aggregate values of failure rates and repair times of power components at each load sector. These are used in the Part 2 paper for assessing the potential benefits of energy storage and demand-side resources on the reliability performance of different generic distribution networks.

A Glimpse of SmartHG Project Test-bed and Communication Infrastructure

The SmartHG project goal is to develop a suite of integrated software services (the SmartHG Platform) aiming at steering residential users energy demand in order to: keep operating conditions of the electrical grid within given healthy bounds, minimize energy costs, and minimize CO2 emissions. This is achieved by exploiting knowledge (demand awareness) of electrical energy prosumption of residential users as gained from SmartHG sensing and communication infrastructure. This paper describes such an infrastructure along with user demand patterns emerging from the data gathered from ~600 sensors installed in ~40 homes participating in SmartHG test-beds.

Distribution network equivalents for reliability analysis. Part 2: Storage and demand-side resources

This paper, which is the second part of a two-part series, considers the influence of distributed energy resource functionalities on reliability performance of active networks. The reliability and network equivalent models defined in the Part 1 paper are used to assess the potential improvements that different demand-side management and energy storage schemes will have on the frequency and duration of customer interruptions. Particular attention is given to energy-related reliability indices which measure the energy and power not supplied to residential and commercial customers. A new theoretical interruption model is also introduced for a more accurate correlation between the different low-voltage and medium-voltage demand profiles and the time when both long and short interruptions are more likely to occur.

All-scale modelling of wind generation and responsive demand in power system studies

This paper presents a general methodology for a more accurate assessment of performance of networks with a high penetration of wind-based energy generation and fully enabled responsive demand capabilities. The presented methodology allows to include in the analysis wind-based generation at all scales of implementation, starting from highly dispersed micro and small-scale units connected at LV, to medium-size wind parks connected at MV, to large-scale wind farms connected at HV. An advanced model of wind energy resources is applied to generate realistic input wind data at all scales of implementation, while newly developed and improved aggregate models are used for the correct representation of micro/small wind generation connected in parallel with demand-responsive system loads. The proposed methodology is specifically intended for the analysis of planning and operation of transmission systems. The approach is illustrated using a case study of an actual section of transmission network, where available measurements at wind parks and other sites are used for the validation of obtained results.