Probabilistic locational marginal price computation in radial distribution system based on active power loss reduction

Veeramsetty, Venkataramana; Venkaiah, Chintham; Kumar, D. M. Vinod

doi:10.1049/iet-gtd.2019.0952

Cited by 14 publications

(3 citation statements)

References 28 publications

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“…There is an increased interest in tariff models that address the issue of congestion costs in distribution networks. Several methods have been proposed, including methodologies based on dynamic tariffs [6], [7], distribution locational marginal price [8]- [10], and social welfare maximization [11]. Reference [12] proposed a composite method incorporating locational marginal price and either a postage-stamp or marginal participation element.…”

Section: Introductionmentioning

confidence: 99%

Locational Tariff Structure for Radial Network Fixed Costs in a DER Context

Etchebehere

Lima

2022

IEEE Access

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The regulation of Distributed Energy Resources (DERs) has cast doubt on the sustainability of utility infrastructure charging models. The development of feed-in tariffs, net-metering, and network charge rebates for distributed generation (DG) has been questioned because of the cross-subsidies that result between passive consumers and DG investors. Besides DG investors, other new entrants, such as owners of energy storage resources and electrical vehicles, are creating challenges for the regulation of distribution service pricing as a whole. This paper proposes a new approach to dealing with the fixed cost element of service pricing, that enhances economic signaling in the distribution network. We isolate and address the issue from a distribution network point of view, by excluding from our model the random variables associated with the complexity of social, environmental and other externalities. The solution developed is particularly relevant at a time when distribution networks, historically dominated by passive users, struggle to adapt to a dramatic increase in the number of users who are active agents. It considers the principles governing tariff design from the perspective of simplicity, economic signaling, and revenue reconciliation. Results are presented of simulations performed with different arrangements of alternative energy generators and energy storage devices, using an actual feeder from a distribution company in Brazil. An analysis of these results is provided that suggests a combination of locational and time-of-use rates can provide effective economic signals to these new types of system user.

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Section: Introductionmentioning

confidence: 99%

Locational Tariff Structure for Radial Network Fixed Costs in a DER Context

Etchebehere

Lima

2022

IEEE Access

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“…Distributed Generation (DG) integration into the distribution system has been growing very fast due to technical, economical and environmental benefits [1,2] as presented in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Lakshmi

Jayalaxmi

Veeramsetty

2021

Technol Econ Smart Grids Sustain Energy

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Optimal placement of distributed generation (DG) is an essential task for distribution companies in order to operate the distribution network in good operating conditions. Optimal placement of DG units is an optimization problem where minimization of active power losses in the network is considered as an objective function. In this paper, hybrid genetic dragonfly algorithm is used as an optimization technique to find the optimal location and size of distributed generation units. The proposed algorithm is implemented on IEEE 15 and PG & E 69 bus distribution systems in MATLAB environment. Based on the simulation results it has been observed that with proper placement and size of DG units, distribution network can be operated with less active power losses.Keywords Distributed generation • Optimal placement • Hybrid genetic dragonfly algorithm • Active power loss Nomenclature ΔY t+1 Step vector for dragonflies A iAlignment value of dragonfly i C i Cohesion value of dragonfly i E iEnemy source of dragonfly i F iFood source of dragonfly i I k i Current at bus 'i' and iteration 'k' I k LR Current injecting at receiving node of line 'L' at iteration 'k' I l Current flowing through line l I k L Current through line 'L' at iteration 'k' I max l

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“…Among all prediction tasks in power systems, short-term LMP forecasting is more difficult than its competitors, e.g., load forecasting, renewable generation forecasting, and the reasons mainly lie in three aspects. First, LMPs are influenced by much more factors and thus become more volatile [10]. A lower prediction accuracy could often be expected when forecasting LMPs.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting based on Graph Convolution Network and Attention Mechanism

Yang¹,

Tan

Yang³

et al. 2021

Preprint

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In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning and operation. Unlike existing methods that only consider LMPs' temporal features, this paper tailors a spectral graph convolutional network (GCN) to greatly improve the accuracy of short-term LMP forecasting. A three-branch network structure is then designed to match the structure of LMPs' compositions. Such kind of network can extract the spatial-temporal features of LMPs, and provide fast and high-quality predictions for all nodes simultaneously. The attention mechanism is also implemented to assign varying importance weights between different nodes and time slots. Case studies based on the IEEE-118 test system and real-world data from the PJM validate that the proposed model outperforms existing forecasting models in accuracy, and maintains a robust performance by avoiding extreme errors.

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Probabilistic locational marginal price computation in radial distribution system based on active power loss reduction

Cited by 14 publications

References 28 publications

Locational Tariff Structure for Radial Network Fixed Costs in a DER Context

Locational Tariff Structure for Radial Network Fixed Costs in a DER Context

Optimal Placement of Distribution Generation in Radial Distribution System Using Hybrid Genetic Dragonfly Algorithm

Short-Term Electricity Price Forecasting based on Graph Convolution Network and Attention Mechanism

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