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Grid connected electrical energy storage could enable large numbers of intermittent renewable generators to be deployed in the UK. Many studies investigate the revenue which could be achieved through arbitrage assuming perfect foresight of electricity prices. In practice, storage operators will not have perfect foresight and will have to devise operational strategies using price forecasts. This paper investigates the impact of forecast accuracy on the optimality of storage revenue. The optimal revenue available is determined using linear programming and historic electricity prices. The results are compared to those found using dynamic programming and electricity price forecasts with increasing percentage error. A small scale lithium ion battery and a large pumped hydro energy storage (PHES) device are compared. The results show that revenue reduces at an increasing rate with increasing forecast error. The PHES device is more sensitive to forecast accuracy than the lithium ion battery. For both technologies, with a maximum error of 30%, 80% of the optimal revenue can be achieved. With increased capacity and significantly increased power rating, the lithium ion battery becomes more sensitive to price forecast accuracy.

Increasing penetration of distributed generation in distribution networks requires active constraint management to provide greater flexibility and use ofexisting network assets. Curtailing DG output under worse case scenarios to keep the network operating below voltage and thermal limits will play a major role in active network management. While a number of curtailment priority schemes are established there is a need to demonstrate the benefit of different priority schemes for curtailing multiple DGs. Comparing with ‘first in last out’ and other priority methods, this paper proposes and demonstrates that optimalsetting of DG curtailment priority using multi-period OPF is not just technically appropriate but also economically beneficial. By extending this idea into the planning arena, the impact of curtailment management schemes on networkhosting capacity is evaluated.

To be able to integrate significant levels of dispersed renewable energy generation (DG) into rural distribution networks, the conventional ‘fit-and-forget’ approach will have to be evolved into a ‘connect-and-actively-manage’system using Active Network Management techniques. Coordinated scheduling and control of DG and network assets can avoid having to constrain renewable capacity and significantly increase energy production and economicperformance of DG in weak or congested networks. This paper proposes a time-dependent receding-horizon OPF technique for the ‘pseudo-real time’ scheduling of network control set-points to better integrate DG into previouslypassive areas of distribution networks. The methodology demonstrates the potential for real time reconfigurable autonomous network control over windows of previously problematic network power flows. Modelling of the real time controller application is performed through a purpose built pseudo-real time distribution network simulator.