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 intertemporal 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 nonfirm 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. Index Terms-Energy storage, Flexible demand, Active Network Management, OPF, dynamic optimal power flow I. NOMENCLATURE 1 General DOPF Vector of OPF control variables Vector of OPF fixed parameters Vector of intertemporal variables Vector of OPF derived variables Objective function OPF equality constraints OPF inequality constraints Intertemporal equality constraints Intertemporal inequality constraints This work is partly funded through the in Wind Energy Systems Centre for Doctoral Training at the University of Strathclyde. EPSRC EP/G037728/1.

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The European Single Market aims to promote trade and competition in electricity generation across the EU, with investment signals for new generation capacity and interconnection coming from zonal electricity prices reflecting scarcity value. However, a growing number of EU Member States have implemented national Capacity Mechanisms in order to ensure future security of supply within their own borders, which may distort the cross-border trade of energy. This local view of energy security is in response to internal technical and economic constraints and a perceived inability of cross-border electricity flows to be a reliable source of capacity at times of maximum stress, in favour of self-sufficiency. A number of routes are available to resolve this conflict through permitting cross-border participation of generators in local Capacity Mechanisms, but this requires resolution of a number of complicating factors, not least a means for properly allocating transmission capacity without introducing further distortions to the energy market. Alternative solutions could be enacted at an EU-level, such as through the alignment of Capacity Mechanisms to a common model, or the introduction of an EU-wide single Capacity Mechanism, but the current regulatory focus appears to remain on resolution of such issues at a national level.

The connection of high penetrations of renewable generation such as wind to distribution networks requires new active management techniques. Curtailing distributed generation during periods of network congestion allows for a higher penetration of distributed wind to connect, however, it reduces the potential revenue from these wind turbines. Energy storage can be used to alleviate this and the store can also be used to carry out other tasks such as trading on an electricity spot market, a mode of operation known as arbitrage. The combination of available revenue streams is crucial in the financial viability of energy storage. This study presents a heuristic algorithm for the optimisation of revenue generated by an energy storage unit working with two revenue streams: generation-curtailment reduction and arbitrage. The algorithm is used to demonstrate the ability of storage to generate revenue and to reduce generation curtailment for two case study networks. Studies carried out include a single wind farm and multiple wind farms connected under a 'last-in-first-out' principle of access. The results clearly show that storage using both operating modes increases revenue over either mode individually. Moreover, energy storage is shown to be effective at reducing curtailment while increasing the utilisation of circuits linking the distribution and transmission networks. Finally, renewable subsidies are considered as a potential third revenue stream. It is interesting to note that under current market agreements such subsidies have the potential to perversely encourage the installation of inefficient storage technologies, because of increased losses facilitating greater "utilisation" of renewable generation. reached the maximum capacity of firmly connected generation, for example, in the Orkney Isles [2]. An alternative management philosophy is Active Network Management (ANM) in which generators and other network components are managed in real time to reach specified goals. These techniques have been shown to be successful in managing voltage and thermal limits with increased distributed generation [3, 4]. Two applications of ANM are generation curtailment and the use of Energy storage systems (ESSs). Generation curtailment allows additional distributed generation to connect with the agreement that under specified network conditions the new generator may have to reduce output or disconnect entirely. A generation curtailment scheme is in operation on the Orkney distribution network [2] and manages thermal and voltage limits across the network including an undersea connection to the transmission network. Connections on a generation curtailment scheme are described as non-firm connections as they do not guarantee network access at all times. Generation curtailment and other ANM techniques provide an alternative to reinforcing www.ietdl.org