The cost of active network management schemes at distribution level

The growth of wind generation in distribution networks is leading to the development of Active Network Management (ANM) strategies. ANM systems aim to increase the capacity of renewable and distributed generation (DG) that can connect to the network. In addition to DG, ANM schemes can also include storage devices and Demand Side Management (DSM) strategies. Currently ANM schemes are mainly part of network research and development programmes, funded through network innovation schemes. In future, ANM schemes will need to cover the costs of establishing such a scheme through payments from the network owners and the users of the network. This paper discusses the current charging arrangements which account for network upgrades and the access arrangements for wind farms connecting to networks which are close to capacity. The Orkney ANM scheme is used as a case study, where the costs of the implemented ANM scheme are compared to conventional network upgrades. In order to run ANM as a ‘business as usual’ case, there must be a way in which to recover the costs incurred in implementing and operating an ANM scheme on the network. These costs could be recovered through Use of System (UoS) charging, and there is an opportunity for domestic customers participating in an ANM scheme (through Demand Side Management, for example) to further reduce electricity bills by providing ancillary services to the network. ANM may increase the cost of electricity for domestic customers, however this increase can be considered substantially less than the cost incurred for significant network upgrades

Quantification of over-speed risk in wind turbine fleets

The effective life management of large and diverse fleets of wind turbines is a new problem facing power system utilities. More specifically, the minimization of over-speed risk is of high importance due to the related impacts of possible loss of life and economic implications of over-speed, such as a loss of containment event. Meeting the goal of risk minimization is complicated by the large range of turbine types present in a typical fleet. These turbines may have different pitch systems, over-speed detection systems and also different levels of functional redundancy, implying different levels of risk. The purpose of this work is to carry out a quantitative comparison of over-speed risk in different turbine configurations, using a Markov process to model detection of faults and repair actions. In the medium-long term, the risk associated with different assets can used as a decision making aid. For example if the operator is a utility, it may want to avoid purchasing high risk sites in the future, or may need to develop mitigation strategies for turbines at high risk of over speed

Network reinforcement requirements for Scotland and the rest of the UK (RUK) - and possible solutions for this

A novel multi-objective transmission expansion planning (MOTEP) tool has been developed to analyse, on a comprehensive geographical scale, the reinforcements required to a base case electrical transmission network following application of a chosen future energy scenario, and to generate optimal network expansion plans, designed to alleviate these areas of strain, for a range of crucial network planning objectives. Here, we report the application of the MOTEP tool to a base case predicted 2014 GB transmission network (thereby including already planned reinforcements such as the Beauly to Denny line) under heavy strain from three 2020 energy scenarios developed by the two-region UK MARKAL energy system model. Reinforcement requirements for Scotland and the RUK beyond 2014, along with optimal network expansion plan options, are examined

An assessment of principles of access for wind generation curtailment in active network management schemes

The growth of wind generation embedded in distribution networks is leading to the development and implementation of Active Network Management (ANM) strategies. These aim to increase the capacity of Distributed Generation (DG) that can connect to a network. One such ANM strategy is generation curtailment where DG is given a non-firm connection under which the network can instruct a generator to reduce its output under specified conditions. Currently in the UK the Orkney distribution network operates a curtailment scheme for wind and other renewable generation [1]and a similar scheme is being developed for the Shetland Islands [2]. The main objective of this paper is to explore the options for Principles of Access (PoA) for curtailment of wind generation on distribution networks which employ ANM. The PoA define the commercial rules by which a DG unit obtains access to the distribution network and under an ANM curtailment scheme the PoA defines the curtailment instructions that would be sent to different DG units when network constraints occur. The scenarios studied in this paper are based on the Orkney distribution network

Using dynamic optimal power flow to inform the design and operation of active network management schemes

Active Network Management (ANM) schemes are providing the communications and control infrastructure to allow the integration of energy storage and flexible demand in distribution networks. These technologies can be characterised as intertemporal in that their operation at different points in time is linked. This paper provides a discussion of the issues created when optimising an ANM scheme containing intertemporal energy technologies. A technique called Dynamic Optimal Power Flow is discussed and a case study is presented. The requirement to use forecasts of renewable energy resources such as wind power is discussed together with the issues that this creates

The Importance of Revenue Sharing for the Local Economic Impacts of a Renewable Energy Project: A Social Accounting Matrix Approach

As demand for electricity from renewable energy sources grows, there is increasing interest, and public and financial support, for local communities to become involved in the development of renewable energy projects. In the UK, “Community Benefit” payments are the most common financial link between renewable energy projects and local communities. These are “goodwill” payments from the project developer for the community to spend as it wishes. However, if an ownership stake in the renewable energy project were possible, receipts to the local community would potentially be considerably higher. The local economic impacts of these receipts are difficult to quantify using traditional Input-Output techniques, but can be more appropriately handled within a Social Accounting Matrix (SAM) framework where income flows between agents can be traced in detail. We use a SAM for the Shetland Islands to evaluate the potential local economic and employment impact of a large onshore wind energy project proposed for the Islands. Sensitivity analysis is used to show how the local impact varies with: the level of Community Benefit payments; the portion of intermediate inputs being sourced from within the local economy; and the level of any local community ownership of the project. By a substantial margin, local ownership confers the greatest economic impacts for the local community.renewable energy; rural economic impacts; revenue sharing; community ownership

Wind farm capital cost regression model for accurate life cycle cost estimation

Various studies over the last decade have attempted to forecast capital cost of wind power. The main assumption underpinning these models is that cost reductions will accrue indefinitely from technological learning over time. In this paper a regression model is proposed for wind farm capital cost which is based on commodities price and water depth rather than technological learning. With greater simplicity and certainty in the theoretical foundations of such a model, it is possible to gain realistic estimates of wind turbine capital cost. Such pragmatic and well-reasoned output is needed so that wind farm developers can understand their future risk exposure to price fluctuations in capital cost of plant

Electricity network scenarios for 2020

This report presents a set of scenarios for the development of the electricity supply industry in Great Britain in the years to 2020. These scenarios illustrate the varied sets of background circumstances which may influence the industry over the coming years – including political and regulatory factors, the strength of the economy and the level to which environmentally-driven restrictions and opportunities influence policy and investment decisions. Previous work by the authors (Elders et al, 2006) has resulted in a set of six scenarios illustrating possible developments in the electricity industry in the period up to 2050. While such scenarios are valuable in gauging the long-term direction of the electricity industry and its economic and environmental consequences, shorter-range scenarios are useful in assessing the steps necessary to achieve these long-range destinations, and to determine their relationship to current trends, policies and targets. In this chapter, a set of medium-range scenarios focused on the year 2020 is developed and described. These scenarios are designed to be consistent both with the current state of the electricity supply industry in Great Britain, and with the achievement of the ultimate electricity generation, supply and utilisation infrastructure and patterns described in each of the 2050 scenarios. The consequences of these scenarios in terms of the emissions of carbon dioxide are evaluated and compared with other predictions. The SuperGen 2020 scenarios described in this report were developed as a collaborative effort between the SuperGen project team and the ITI-Energy Networks Project team both based at the University of Strathclyde

Dynamic optimal power flow for active distribution networks

Active Network Management is a philosophy for the operation of distribution networks with high penetrations of renewable distributed generation.Technologies such as energy storage and flexible demand are now beginning to be included in Active Network Management (ANM) schemes. Optimizing the operation of these schemes requires consideration of inter-temporal linkages as well as network power flow effects. Network effects are included in Optimal Power Flow (OPF) solutions but this only optimizes for a single point in time. Dynamic Optimal Power Flow (DOPF) is an extension of OPF to cover multiple time periods. This paper reviews the generic formulation of Dynamic Optimal Power Flow before developing a framework for modeling energy technologies with inter-temporal characteristics in an ANM context. The framework includes the optimization of non-firm connected generation, Principles of Access for non-firm generators, energy storage and flexible demand. Two objectives based on maximizing export and revenue are developed and a case study is used to illustrate the technique. Results show that DOPF is able to successfully schedule these energy technologies. DOPF schedules energy storage and flexible demand to reduce generator curtailment significantly in the case study. Finally the role of DOPF in analyzing ANM schemes is discussed with reference to extending the optimization framework to include other technologies and objectives

Electricity Network Scenarios for Great Britain in 2050

The next fifty years are likely to see great developments in the technologies deployed in electricity systems, with consequent changes in the structure and operation of power networks. This paper, which forms a chapter in the forthcoming book Future Electricity Technologies and Systems, develops and presents six possible future electricity industry scenarios for Great Britain, focussed on the year 2050. The paper draws upon discussions of important technologies presented by expert authors in other chapters of the book to consider the impact of different combinations of key influences on the nature of the power system in 2050. For each scenario there is a discussion of the effects of the key parameters, with a description and pictorial illustration. Summary tables identify the role of the technologies presented in other chapters of the book, and list important figures of interest, such as the capacity and energy production of renewable generation technologies