Placement and Optimal Size of DG in the Distribution Network Based on Nodal Pricing Reduction with Nonlinear Load Model using the IABC Algorithm

The growing use of distributed generation (DG) at the distribution level has led to a change in the status of distribution networks from a passive network to an active network such as transmission systems. Therefore, transmission network pricing methods such as nodal pricing could be used in the distribution network. DG connection to the distribution network affects bus nodal pricing. If the DG presence reduces losses and congestion in the distribution network, nodal pricing will also decrease. This paper presents a method for calculating the optimal size and place of DG in the distribution network based on nodal pricing. This planning is doing to maximize the profits of distribution companies that have used DG in their network to meet several advantages. The simulation was performing using the improved artificial bee colony algorithm (IABC). In the IABC algorithm, by exchanging the received information between bees according to Newton and gravity laws, it uses all this algorithm capacity to find the ideal answer by considering the constraints applied to the system. In most DG placement articles, network loads are assuming to constant. Because loads are often sensitive to voltage and frequency, constant load analysis leads to inaccurate results. Therefore, in this paper, the proposed method is implementing on a 38-bus radial distribution system with a model of real loads sensitive to the voltage and frequency of the system, including residential, commercial, and industrial loads.

Financing costs and barriers to entry in Australia’s electricity market

Purpose This paper aims to provide investors’ views on financing costs and barriers to entry into the electricity generation sector, with a focus on investors’ views on potential impacts on cost of capital from adopting nodal pricing and financial transmission rights (FTRs). The implications for policymakers and policy reforms are also discussed in detail. Design/methodology/approach Survey-based data collection of investors and developers in electricity generation, consisting of multiple choice questions from a closed list of discrete choices, binary-choice questions, and questions with free-text/open-ended answers. Findings Across survey respondents, weighted-average cost of capital (WACCs) were broadly unchanged over 2019, with increases for undiversified/non-integrated participants offset by decreases for horizontally integrated participants. Cost of equity has risen, whereas cost of debt has fallen. Nodal pricing-cum-FTRs were estimated to increase WACCs by 150–200 basis points p.a. (15–20%), reflecting concerns around the firmness of FTRs and ability to automatically access intraregional settlement residues. Research limitations/implications These findings have energy policy implications, namely, the need to consider the interaction between economic theory and real-world financing models when designing and implementing fundamental energy sector reforms. Practical implications The need to consider implementation and transitional issues (e.g. grandfathering of existing rights, focusing on reducing the largest barriers to entry) is associated with implementing nodal pricing. Originality/value Unique set of survey questions and insights that have not previously been addressed in an Australian context; what-if analysis not previously done in an Australian context