A techno-economic planning model for integrated generation and transmission expansion in modern power systems with renewables and energy storage using hybrid Runge Kutta-gradient-based optimization algorithm

Rawa, Muhyaddin; AlKubaisy, Zenah M.; Alghamdi, Sultan; Refaat, Mohamed M.; Ali, Ziad M.; Aleem, Shady H. E. Abdel

doi:10.1016/j.egyr.2022.04.066

Cited by 17 publications

(12 citation statements)

References 41 publications

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“…Therefore, the total costs of the TSO and the financers have higher values in this case. Moreover, five new transmission lines are required between (1,26), (8,9), (13,15), (20,22), and (21,24) corridors. The average of the LMPs in all scenarios and the total planning horizon are illustrated in Figure 9 for all case studies.…”

Section: Simulation Results Without Considering the Correlationsmentioning

confidence: 99%

“…The uncertainties of the load demand and wind generation have been addressed with the Monte Carlo simulation technique [14]. A GTEP consisting RESs, and Energy Storage Systems (ESSs) to improve the system reliability and reduce the emission cost of the system has been presented in [15]. The uncertainties of the load demand and power production of the RESs have been considered in the proposed model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A static TEP model taking into account the integration of the RESs has been proposed in [8,9]. Besides, [10][11][12][13][14][15][16][17][18][19][20][21][22][23] have focused on the TEP modelling considering the uncertainties with/without integrating the RESs (i.e. Photovoltaic (PV) and wind farms)/Distributed Generation (DGs).…”

Section: Literature Reviewmentioning

confidence: 99%

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Stochastic multi‐stage joint expansion planning of transmission system and energy hubs in the presence of correlated uncertainties

Allahvirdizadeh

Galvani

Shayanfar

2023

IET Renewable Power Gen

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Deployment of Energy Hubs (EHs) across the power grid can alleviate the Transmission System (TS) capacity and substitute the conventional fossil fuel‐based thermal units. Therefore, this paper presents a tri‐level multi‐stage Joint Expansion Planning of the Transmission system and EHs (JEPT&EHs). In this approach, the Cholesky decomposition technique combined with the Nataf transformation is applied to make the uncertain input parameters correlated. Then, the k‐means data‐clustering method is employed to reduce the initial correlated samples. In the first level, the Transmission System Operator (TSO) optimizes the planning and scheduling strategies associated with the TS capacity requirements and operation costs of the conventional generators. In the second level, the financers specify the expansion of the EHs based on the Locational Marginal Prices (LMPs). In the third level, the Direct Current Optimal Power Flow (DCOPF) is determined to update the LMPs by the Independent System Operator (ISO). The optimization problem is an Equilibrium Problem with Equilibrium Constraints (EPEC) since there are multiple financers across the TS. The proposed model is implemented on the IEEE standard 30 bus TS to present the effectiveness of the EHs' deployment and the impact of the correlations in the total costs of the TSO and financers.

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Section: Simulation Results Without Considering the Correlationsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Stochastic multi‐stage joint expansion planning of transmission system and energy hubs in the presence of correlated uncertainties

Allahvirdizadeh

Galvani

Shayanfar

2023

IET Renewable Power Gen

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“…Notable categories of ESS include battery energy storage systems (BESS), vehicle-to-grid (V2G), and pumped hydroelectric storage (PHS) [35]. These systems are efficient [38] and exhibit very high ramping rates compared to other power generation technologies. Thus, ESSs are considered in many proposed GEP models with VRES penetration.…”

Section: B Literature Reviewmentioning

confidence: 99%

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Diewvilai,

Audomvongseree

2024

IEEE Access

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This paper proposes a method for generation expansion planning that incorporates full-year hourly multiple load levels balance constraints, providing sufficient flexibility to address load fluctuations and intermittency associated with variable renewable energy sources. Typically, ensuring that the generation system possesses enough flexibility to manage this intermittency involves considering the operational characteristics of generators within unit commitment constraints. However, to mitigate the substantial computational burden caused by the number and type of variables, various approximation techniques are often employed. Unfortunately, these techniques can introduce unrealistic elements into the problem. Instead of considering the operational characteristics of generators, this approach classifies the system's demand into three levels: base load, intermediate load, and peak load, using the proposed load classification method. The multiple load-level balance constraints are then applied to ensure that the capacity of generation units in each level is sufficient to meet their corresponding demand, with particular emphasis on matching fast-response generation units and their corresponding demand. The resulting generation expansion plan can be obtained with significantly reduced computational effort. The proposed load classification method and generation expansion planning approach have been tested using the latest power development plan of Thailand. Compared to another method that is not taken flexibility into account, 5 Gigawatts of fast-response generation capacity are selected instead of base load generation units. With the improved computational time achieved by the proposed generation expansion planning method, it can account for input data uncertainty by solving multiple generation expansion planning problems with varying input data and distinct individual probabilities. INDEX TERMS power generation planning, power generation reliability, generation expansion planning, renewable energy NOMENCLATURE A. ACRONYMS

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“…The mechanism of the RUN is depicted in Figure 7. The RUN has been employed in many applications, such as (a) reconfiguring the partially shaded PV array to maximize its generated power [53], (b) enhancing the reliability of the power system and increasing renewables penetration [54], and (c) finding the optimal tuning of different power system stabilizer controllers [55].…”

Section: Run Optimization Techniquementioning

confidence: 99%

Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study

Mahmoud

Aleem²,

Abdelaziz³

et al. 2022

Sustainability

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Improving the performance of distribution systems is one of the main objectives of power system operators. This can be done in several ways, such as network reconfiguration, system reinforcement, and the addition of different types of equipment, such as distributed generation (DG) units, shunt capacitor banks (CBs), and voltage regulators (VRs). In addition, the optimal use of renewable and sustainable energy sources (RSESs) has become crucial for meeting the increase in demand for electricity and reducing greenhouse gas emissions. This requires the development of techno-economic planning models that can measure to what extent modern power systems can host RSESs. This article applies a new optimization technique called RUN to increase hosting capacity (HC) for a rural Egyptian radial feeder system called the Egyptian Talla system (ETS). RUN relies on mathematical concepts and principles of the widely known Runge–Kutta (RK) method to get optimal locations and sizes of DGs, CBs, and VRs. Furthermore, this paper presents a cost-benefit analysis that includes fixed and operating costs of the compensators (DGs, CBs, and VRs), the benefits obtained by reducing the power purchased from the utility, and the active power loss. The current requirements of Egyptian electricity distribution companies are met in the formulated optimization problem to improve the HC of this rural system. Uncertain loading conditions are taken into account in this study. The main load demand clusters are obtained using the soft fuzzy C-means clustering approach according to load consumption patterns in this rural area. The introduced RUN optimization algorithm is used to solve the optimal coordination problem between DGs, CBs, and VRs. Excellent outcomes are obtained with a noteworthy reduction in the distribution network power losses, improvement in the system’s minimum voltage, and improvement of the loading capacity. Several case studies are investigated, and the results prove the efficiency of the introduced RUN-based methodology, in which the probabilistic HC of the system reaches 100% when allowing reverse power flow to the utility. In comparison, this becomes 49% when allowing reverse power to flow back to the utility.

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A techno-economic planning model for integrated generation and transmission expansion in modern power systems with renewables and energy storage using hybrid Runge Kutta-gradient-based optimization algorithm

Cited by 17 publications

References 41 publications

Stochastic multi‐stage joint expansion planning of transmission system and energy hubs in the presence of correlated uncertainties

Stochastic multi‐stage joint expansion planning of transmission system and energy hubs in the presence of correlated uncertainties

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Towards Maximizing Hosting Capacity by Optimal Planning of Active and Reactive Power Compensators and Voltage Regulators: Case Study

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