Research Output
Articles
Publication Date: 2025
IET Generation, Transmission and Distribution (17518687)19(1)
Peak load management is a pivotal aspect of power generation and distribution, representing one of the primary challenges for power companies. A key feature of smart grids is their capability to manage available resources effectively to mitigate peak load while accounting for the inherent uncertainties in load demand and the generation of all renewable energy sources. Thereby, this paper proposes a two-stage coordination approach that integrates price-based demand response (PBDR) and energy storage systems, encompassing Battery Energy Storage Systems (BESS) and Compressed Air Energy Storage (CAES). This approach integrates CAES with BESSs to optimise the charging and discharging processes while minimising degradation costs. Specifically, it aims to address the substantial degradation expenses of BESSs by strategically utilising CAES as a complementary storage solution. The objective is to minimise operational costs while controlling peak demand load in smart microgrids. Moreover, to simultaneously address the inherent uncertainties associated with the demanded load and the generating power of renewable energy sources, a method incorporating scenario generation and reduction is introduced to improve scheduling accuracy and enhance the reliability of energy management. To tackle this multifaceted challenge, a novel scenario-based Developed Two-Stage Interval Optimisation (DTSIO) model has been proposed to effectively address uncertainty. By employing the scenario generation method in conjunction with the k-means technique to reduce scenarios with low probabilities of occurrence, the analysis process is optimised for better problem-solving efficiency. The proposed model's efficacy is validated through its implementation on a 33 and 69 bus microgrid, showcasing its ability to enhance profitability, manage peak load, reduce reliance on the upstream grid, and lower carbon dioxide emissions. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Publication Date: 2024
IET Generation, Transmission and Distribution (17518687)18(20)pp. 3234-3246
In response to growing reliance on electricity and gas systems, this paper introduces a stochastic bi-level model for the optimized integration of these systems. This integration is achieved through sizing and allocating of power-to-gas (P2G) and gas-to-power (G2P) units. The first level of the model focuses on decisions related to P2G and G2P unit installations, while the second level addresses optimal system operation considering decisions made from first level and stochastic scenarios. The primary aim is to enhance energy-sharing capabilities through coupling devices and mitigate wind generation curtailment. An economic evaluation assesses the model's effectiveness in reducing costs. N − 1 contingency analysis gauges the integrated system's ability to supply load under emergency conditions. Two new indices, performance of the electricity system and performance of the natural gas system, are proposed for N − 1 contingency analysis. These indices quantify the proportion of the supplied load to the total load, thereby illustrating the system's capacity to meet demand. For numerical investigation, the proposed model is applied to a modified IEEE 14-bus power system and a 10-node natural gas system. Numerical results demonstrate a 9.426% reduction in investment costs and a significant 10.6% reduction in wind curtailment costs through proposed planning model. © 2024 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Publication Date: 2024
IEEE Transactions on Transportation Electrification (23327782)10(4)pp. 8235-8245
In this article, a transactive-based scheduling approach is proposed to optimize electric vehicle (EV) charging/discharging scheduling taking into account the technical requirements of EVs with different state-of-charge (SOC) levels and EV owners' preferences. In the proposed approach, an EV aggregator (EVA) solves an optimization problem to determine the charging/discharging schedule of each individual EV in the EV Parking Lot (PL) in which the response curves of individual EVs are used to consider the EV owners' charging/discharging preferences. Then, the EVAs provide their optimum day-ahead bids to the corresponding DSO based on calculated distribution locational marginal prices (DLMPs). The DSO's transactive market-clearing procedure is simulated to iteratively calculate DLMPs in the local distribution area (LDA) nodes. The Monte Carlo (MC) scenarios are used to model the uncertainties associated with the EVs' parameters and the driving behavior of the EV owners. Also, the robust optimization method is used to model the uncertainties associated with LMPs of the transmission network (TN) bus, distributed renewable energy resources (DRERs), and load demand. The proposed model is implemented on the modified IEEE-33 node distribution system and the effectiveness of the model is investigated and presented. © 2024 IEEE.
Publication Date: 2023
IEEE Transactions on Power Systems (08858950)38(2)pp. 1894-1905
In this paper, a robust distribution system expansion planning (DSP) approach is presented to supply the load growth locally and move toward nearly zero energy local distribution areas (LDAs). In the proposed approach, a distribution system operator (DSO) is responsible for secure and optimum operation of LDAs. Therefore, investors on distribution system upgrades use this approach to maximize the profit on investments by determining the installation year of new distribution feeders and energy resources, distributed energy resource (DER) placements and sizes considered by corresponding DSOs. The accurate AC power flow solution is used and mathematical methods are developed to model the DSP as a quadratically constrained programming (QCP) problem. The Benders decomposition is applied to investigate the reliability and the optimality of the proposed plan and correspondingly modify the investment plan as required. The uncertainty of renewable DER forecast errors, locational marginal price (LMP) of an LDA at a transmission bus, and transactive power associated with a load bus is modeled using robust optimization. The proposed transactive DSP (TDSP) approach is implemented on the IEEE 33-bus distribution test system and the results are analyzed and validated. The proposed numerical results show the optimality and the robustness of the proposed approach. © 1969-2012 IEEE.
Publication Date: 2022
IET Renewable Power Generation (17521416)16(15)pp. 3368-3383
The electric industry is developing towards a more efficient, reliable, and resilient electric power network. In this way, utilizing distributed energy resources (DERs), especially renewable DERs (RDERs) are a paradigm change. DERs offer many advantages in power systems, including transmission loss reduction, environmental benefits of RDERs, and enhancement of security, reliability, and resiliency of the network. However, high penetration of DERs increases uncertainty and challenges the efficient and reliable operation of the power system. This paper provides a thorough review of the transactive distribution platform which is essential to address the aforementioned challenges. This distribution platform includes a transactive distribution system operator providing a seamless and coordinated control to dynamically balance supply and demand and follow uncertain generations of RDERs. Indeed, this review paper highlights the capability of transactive energy (TE) by considering its key aspect in providing energy sharing opportunities for the integration of DERs into smart grids (SGs). TE is acombination of economic and control methods that enable optimum, reliable, sustainable, and efficient operation of SGs. Furthermore, a fully transactive framework offers more choices for DERs to control and manage the energy transactions in the retail market, as well as improves the inter operability among various market players. © 2022 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
