Increasing Photo-Voltaic (PV) penetration and lowcarbon demand can potentially lead to two different flow peaks, generation and load, within distribution networks. This will not only constrain PV penetration but also pose serious threats to network reliability. This paper uses energy storage (ES) to reduce system congestion cost caused by the two peaks by sending cost-reflective economic signals to affect ES operation in responding to network conditions. Firstly, a new charging and discharging (C/D) strategy based on Binary Search Method (BSM) is designed for ES, which responds to system congestion cost over time. Then, a novel pricing method, based on Locational Marginal Pricing (LMP), is designed for ES. The pricing model is derived by evaluating ES impact on the network power flows and congestions from the loss and congestion components in LMP. The impact is then converted into an hourly economic signal to reflect ES operation. The proposed ES C/D strategy and pricing methods are validated on a real local Grid Supply Point (GSP) area. Results show that the proposed LMP-based pricing is efficient to capture the feature of ES and provide signals for affecting its operation. This work can further increase network flexibility and the capability of networks to accommodate increasing PV penetration.

This paper designs a novel dynamic tariff scheme for demand response by considering networks costs through balancing the trade-off between network investment costs and operation costs. The target is to actively engage customers into network planning and operation for reducing network costs and finally their electricity bills. System operation costs are quantified according to generation or load curtailment by assessing their contribution to network congestion. Plus, network investment cost is quantified through examining the needed investment for resolving system congestion. Customers located at various might be facing the same energy signals but they are differentiated by network cost signals. Once customers conduct demand response during system congestion periods, the smaller savings from investment and operation cost are considered as economic singles for rewarding the response. The innovation is that it translates network operation/investment costs into tariffs, where current research is mainly focused on linking customer response to energy prices. Typical UK distribution networks are utilised to illustrate the new approach and results show that the economic signals can effectively benefit end customers for reducing system operation costs and deferring needed network investment.

Energy storage (ES) is playing a vital role in providing multiple services in several electricity markets. However, the benefits and risks vary across markets and time, which justifies the importance to optimise ES capacity share in different markets.In this paper, a novel portfolio theory based approach is proposed for optimally managing ES in various markets to maximise benefits and reduce the risk for ES owners. Three markets are considered, which are: energy arbitrage, ancillary services, and Distributed Network Operator's (DNO's) market. They are modelled based on energy cost, frequency response cost, and system congestion cost. Portfolio theory is utilised to quantify ES capacity allocated to each market over time for various levels of risk aversions. The relation between risks and expected return of different markets are efficiently reflected by portfolio theory, providing implications to storage operation. The extensive demonstration illustrates that the markets that storage can participate in are fundamentally different regarding to its risk aversion. In addition, the optimum portfolio of the markets for storage is on the efficient frontier, providing the maximum return at a certain risk aversion level. This study is particularly useful for guiding market participation and operation of energy storage to gain maximum economic return at minimum risk.

Power system operation faces an increasing level of uncertainties from renewable generation and demand, which may cause large-scale congestion under ineffective operation. This paper applies energy storage (ES) to reduce system peak and congestion by robust optimisation, considering the uncertainties from ES State of Charge (SoC), flexible load, and renewable energy. First, a deterministic operation model for ES, as a benchmark, is designed to reduce the variance of branch power flow based on the least-square concept. Then, a robust model is built to optimise ES operation with the uncertainties in the severest case from load, renewable and ES SoC that are converted into branch flow budgeted uncertainty sets by the cumulant and Gram-Charlier expansion method. The ES SoC uncertainty is modelled as an interval uncertainty set in the robust model, solved by duality theory. These models are demonstrated on a grid supply point (GSP) to illustrate the effectiveness of congestion management technique. Results illustrate that the proposed ES operation significantly improves system performance in reducing system congestion. This robust optimisation based ES operation can further increase system flexibility to facilitate more renewable energy and flexible demand without triggering largescale network investment.

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