Anula Abeygunawardana

A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary non-network solutions (NNSs) such as demand response (DR) and temporary energy storage system/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the “optimal level of demand” for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward-backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach.

An efficient forward-backward algorithm to MSDEPP including batteries and voltage control devices

Electric distribution networks are now in the era of transition from passive to active distribution networks with the integration of energy storage devices. Optimal usage of batteries and voltage control devices along with other upgrades in network needs a distribution expansion planning (DEP) considering inter-temporal dependencies of stages. This paper presents an efficient approach for solving multi-stage distribution expansion planning problems (MSDEPP) based on a forward-backward approach considering energy storage devices such as batteries and voltage control devices such as voltage regulators and capacitors. The proposed algorithm is compared with three other techniques including full dynamic, forward fill-in, backward pull-out from the point of view of their precision and their computational efficiency. The simulation results for the IEEE 13 bus network show the proposed pseudo-dynamic forward-backward approach presents good efficiency in precision and time of optimization.

A conjectural supply function model for the Italian electricity market

This paper presents a detailed electricity market model for analyzing the price development in the Italian electricity market under varying degrees of competitiveness in the market. The proposed model is more realistic than the models proposed for the Italian market by other studies, as it can be used when price elasticity of electricity demand is zero (the short term electricity demand in Italy is almost price inelastic) and it takes into account the Italian large transmission network with its constraints.

Estimating and modeling of distribution network costs for designing cost-reflective network pricing schemes

Electricity prices in Australia have been rising fast, and they are expected to grow further. The price rises are largely driven by increased spending on electricity distribution network. With these developments, distribution network service providers (DNSPs) are under immense pressure to seek new ways to reduce costly investments in networks. Non-network alternatives (NNAs) or distributed energy resources (DERs) such as distributed generators (DGs), energy storage and demand response (DR) are considered as one of the promising option for deferring/avoiding these costly distribution network investments provided that they are implemented in the right places in the right time with the right profile of energy injections. However, NNAs are in very early stage of being adopted due to lack of a proper network tariff/incentive scheme that reflects true network cost/benefits of different loads and generation based on their location and profile of energy injection. Therefore, a cost-reflective network pricing/incentive can play a crucial role in promoting NNAs in right locations in the network and thereby to reduce the cost of electricity from the grid. In this context, this study proposes a methodology for estimating location-specific long run incremental cost/avoided cost of active distribution networks for the purpose of designing a cost-reflective network pricing/ incentive scheme.

Value of concentrated solar power with thermal energy storage in the national electricity market of Australia

The concentrated solar power (CSP) with thermal energy storage (TES) is considered as one of the promising renewable technologies for achieving Australias renewable energy targets. The dispatchability of CSP plants with TES can provide grid flexibility by shifting energy over the time. In this paper the ability of TES to improve the value of CSP plants is examined by production cost simulation (PCS) of the NEM using PLEXOS software. The impacts of variations in the size of solar field (i.e. solar multiple-SM) and the size of TES on the operation benefit of CSP plant are also examined.