Multi-objective, multi-year dynamic generation and transmission expansion planning- renewable energy sources integration for Iran's National Power Grid

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Financial Transmission Rights and Auction Revenue Rights

Abstract: This paper surveys on two important issues in restructured power systems. One of them is Financial Transmission (FTR). Financial transmission right is a financial instrument which can improve the liquidity of operation in power system from point of view of all decision makers in competitive power systems. Another approach is Auction Revenue Rights (ARR) which ARR allocation consistent with congestion as determined by the FTR Auction. Analysis of these two mechanism and their impacts on long-term operation of power system are considered in this paper. Suppliers and large consumer, therefore, desire to contract in FTR to hedge their long-term risks. The FTR mechanism is based on the after settling market and determination Locational Marginal Price (LMP). In this area, delivery of energy (quantity and price) from the amount of FTRs which supplier is bidding for distinct path, and the price that the supplier is willing to pay for each FTR, are determined. This paper surveys on the longterm conditions of the FTR and mature one

Infrastructure Resource Planning in Modern Power System

Abstract: Generation Expansion Planning (GEP) is one of the most important issues in long-term power system planning. In from past, investigators noticed to GEP and supply of energy. In power system planning, generation expansion planning is performed for 5-yrears planning horizon or more. There are two main objective functions in GEP. First is the minimization of investment cost and another one is the maximization of reliability. GEP use future likeable engineering economics function, in order to drive certain indicator. Supply of fuel problem is one of the most important of effective factors for result. For this reason, Some times GEP and fuel supply center go hand-inhand. In this case, construction and operation cost of transmission network add to power system costs. This paper presents the simultaneous generation expansion planning with Natura

Optimal Planning and Management of Hybrid Vehicles in Smart Grid

Smart grid can be expressed as a combination of power network substructures with an extensive telecommunication network which is able to provide a two-way communication and use of advanced sensors in order improve efficiency, system reliability, transport security, and power consumption. Loads in this network are divided into two groups, linear and non-linear. The majority of these loads on the network, such as rectifiers, electric vehicles are non-linear. The non-linear loads can cause odd harmonics in the network and can damage transformers. In this article, management and planning of hybrid vehicles for total harmonic index reduction and also annual cost reduction has been considered

Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network

Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance–current–power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Energy storage system impact on the operation of a demand response aggregator

In this paper, we consider a demand response (DR) aggregator responsible for participating in the wholesale electricity market on behalf of the end-users who participated in the DR programs. Thus, the DR aggregator can trade its acquired DR within the short-term electricity markets, i.e., the day-ahead and the balancing (real-time) markets. In the proposed framework, the electricity market prices are considered uncertain, and a robust optimization approach is applied to address the uncertainties to maximize the profit of the DR aggregator. A model for analyzing the impact of the energy storage system (ESS) unit on a DR aggregator's performance is developed to provide more flexibility for the consumers. The direct interactions of a DR aggregator with an ESS are neglected in many models. However, this consideration can lead to improvement in the flexibility of the aggregator and also increase the profit of the entity by trading energy in the short-term markets to charge the ESS during the low-price periods and discharge it to the market while the electricity market prices are high. Hence, it is assumed that the DR aggregator owns an ESS unit and can cover a percentage of its traded power through the ESS. An analysis of the impact of the ESS unit on the DR aggregator's performance is applied to study the most appropriate size of the ESS that can maximize the profit of the aggregator. In addition, renewable energy production is employed for end-users through the installation of rooftop photovoltaic (PV) panels. This demand-side renewable generation can provide more flexibility for the participants in DR programs. Various feasible case studies have been applied to demonstrate the model's effectiveness and usefulness, and conclusions are duly drawn. The numerical results indicate that having an ESS seems necessary when the decision-maker desires to protect its profit from the worst-case scenarios and reduces the negative effect of the uncertain parameter, i.e., the wholesale electricity market prices. Thus, it can be shown that having a greater capacity for the ESS has a significant and direct impact on increasing the profit of the aggregator even in the worst-case scenarios, where the profit rises 20 % when the budget of uncertainty in the robust optimization is equal to 12.© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed