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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
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A Comparative Study of Congestion Management Strategies in
Hybrid Electricity Markets
1 Ajay Gupta, 1 Arvind Kumar, 1 Sharad Kumar, 1 Vikas Sharma
1 School of Engineering & Technology, Shri Venkateshwara University, Gajraula, U.P. India
2 Department of Computer Applications, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, U.P.
India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1410000092
Abstract—The rapid evolution of electricity markets toward hybrid models—integrating both regulated and deregulated
structures—has intensified the need for effective congestion management strategies to ensure system reliability, market
efficiency, and fair access to transmission networks. This paper presents a comprehensive comparative study of various
congestion management techniques employed in hybrid electricity markets, including redispatching, transmission pricing, optimal
power flow (OPF)-based methods, and market-based mechanisms such as nodal and zonal pricing. The study evaluates these
approaches based on key performance indicators such as cost efficiency, computational complexity, transparency, and market
fairness. A simulation-based analysis using standard test systems is conducted to highlight the operational impacts and economic
implications of each method under varying load and generation conditions. The results demonstrate that hybrid congestion
management models, which combine technical optimization with market-based incentives, offer a more adaptive and
economically balanced solution compared to traditional approaches. The study concludes with insights into the practical
challenges, policy implications, and future research directions for implementing robust congestion management frameworks in
evolving hybrid electricity markets.
Keywords—Congestion Management, Hybrid Electricity Markets, Optimal Power Flow (OPF), Transmission Pricing, Market
Efficiency, Redispatching, Nodal Pricing, Power System Optimization
I. Introduction
The transformation of electricity markets over the past few decades has been marked by significant structural and operational
changes, primarily driven by deregulation, technological advancements, and the increasing integration of renewable energy
sources. Traditionally, power systems were operated under vertically integrated monopolies, where generation, transmission, and
distribution were managed by a single utility authority. However, with the emergence of competitive market structures, electricity
trading has evolved into more dynamic, decentralized, and complex frameworks. Among these, hybrid electricity markets have
gained prominence by combining the regulatory oversight of traditional models with the efficiency and innovation of deregulated
markets. These hybrid systems aim to achieve an optimal balance between system reliability, cost-effectiveness, and open
competition. In such an environment, congestion management has emerged as one of the most critical challenges influencing both
operational stability and market performance. Congestion in electricity transmission networks occurs when the power flow in one
or more transmission lines exceeds their rated capacity, leading to system inefficiencies, potential instability, and even blackouts
if not managed effectively. In a hybrid electricity market, congestion not only restricts the physical transfer of electricity but also
affects market operations by distorting locational marginal prices (LMPs) and impacting the economic dispatch of generating
units. Efficient congestion management strategies are therefore essential to ensure the secure, reliable, and economic operation of
power systems while maintaining fairness among market participants. The complexity of managing congestion in hybrid systems
arises from the coexistence of regulatory constraints and competitive bidding mechanisms, which must be harmonized to achieve
system-wide optimization. Over the years, a variety of congestion management strategies have been proposed and implemented
across global electricity markets. These strategies can broadly be categorized into two groups: non-market-based
methods and market-based methods. Non-market-based methods, such as network reconfiguration, generation rescheduling, and
load curtailment, rely on technical interventions by system operators to relieve congestion. While these approaches can be
effective in ensuring immediate system security, they often lack transparency and may not provide long-term economic
efficiency. On the other hand, market-based methods, including nodal pricing, zonal pricing, and transmission rights trading,
integrate economic principles into congestion management by allowing price signals to guide generation and consumption
decisions. Such mechanisms incentivize market participants to adjust their behaviours in ways that alleviate congestion while
promoting efficiency and transparency in market operations. In hybrid electricity markets, where both market mechanisms and
regulatory interventions coexist, the design and implementation of congestion management strategies become more complex. The
hybrid model seeks to integrate the reliability and stability offered by regulated systems with the competitive benefits of
deregulated markets. Consequently, congestion management must be approached holistically—considering technical constraints,
economic incentives, and policy frameworks simultaneously. Optimal Power Flow (OPF)-based methods have gained substantial
attention in this context, as they provide a mathematical foundation for minimizing congestion costs while adhering to system
constraints. Additionally, redispatching and transmission pricing mechanisms are increasingly being integrated with market-based
approaches to ensure fair cost allocation and efficient utilization of transmission infrastructure. This paper provides a comparative
study of congestion management strategies in hybrid electricity markets, analyzing the effectiveness, efficiency, and practicality
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025
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of various approaches. The study focuses on key performance indicators such as cost optimization, computational complexity,
market fairness, and adaptability to variable generation and load conditions. Through a simulation-based evaluation using
standard test systems, the research aims to uncover how different congestion management methods perform under diverse
operating scenarios. The comparative analysis highlights that while market-based mechanisms promote economic efficiency and
transparency, technical optimization methods remain indispensable for ensuring operational security and system reliability.
Furthermore, the paper discusses the policy and regulatory implications of implementing hybrid congestion management
strategies, emphasizing the need for coordinated frameworks that balance competition with oversight. With the rapid integration
of renewable energy sources and distributed generation, hybrid markets face evolving challenges such as intermittency,
bidirectional power flows, and increased grid volatility. These dynamics underscore the importance of adaptive congestion
management solutions that can respond flexibly to real-time system conditions.
II. Literature Review
Fan et al. [1] investigated nodal marginal electricity price prediction under renewable energy scenarios using advanced predictive
modeling to improve the reliability of energy markets. Their study emphasized how incorporating renewable generation variability
enhances pricing transparency and supports grid stability. In parallel, Panda et al. [2] explored the impact of lead time on aggregate
electric vehicle (EV) flexibility for congestion management, demonstrating that optimizing lead time enhances system reliability
and energy dispatch efficiency. Security remains a vital challenge in dynamic networks. The study in [3] provided a comprehensive
analysis of security mechanisms and threat characterization in Mobile Ad Hoc Networks (MANETs), highlighting vulnerabilities
and proposing effective defense frameworks against intrusion and denial-of-service attacks. Shi et al. [4] introduced a hybrid
demand response model integrating price- and incentive-based strategies for differentiated congestion management in distribution
networks, showing how hybrid frameworks improve efficiency and fairness among consumers. Wang et al. [5] focused
on optimization algorithms for power market congestion management, emphasizing multi-objective optimization techniques to
balance operational cost and system reliability. Similarly, Steen et al. [6] examined the non-discrimination obligation in
implementing congestion management measures, stressing regulatory fairness while ensuring efficient energy distribution. In the
domain of network security and intelligent optimization, Vikas et al. [7] proposed a hybrid Deep Belief Network (DBN) and Harris
Hawks Optimization (HHO) approach for intrusion detection in wireless sensor networks, achieving high accuracy and reduced
false alarm rates. Alizadeh et al. [8] developed a distributed hierarchical transactive energy management framework to exploit
flexibility in transmission systems, enhancing operational resilience and market coordination. Diaz-Londono et al. [9] analyzed
the impact of EV charging strategies across various usage scenarios—residential, workplace, and public—highlighting the need for
adaptive charging to minimize grid disturbances. Sharma and Kumar [10] emphasized the role of Artificial Intelligence (AI) in
enhancing data security and privacy in smart cities, showcasing AI-driven encryption and anomaly detection mechanisms to
safeguard digital infrastructures. Plenz et al. [11] discussed EV load reduction through a priority-driven approach to maintain grid
stability, demonstrating how adaptive charging characteristics mitigate congestion and voltage fluctuations. Kumar et al. [12]
introduced an AI-based load balancing algorithm to enhance cloud computing performance and energy efficiency, establishing
parallels between computational optimization and energy management. Wang et al. [13] proposed a congestion-based repair
policy for failure-prone service systems, which integrates customer behavior analytics to improve service reliability and reduce
downtime. Shooshtari et al. [15] explored grid-informed sharing coefficients in renewable energy communities, providing equitable
frameworks for energy exchange and distribution. Similarly, Kant et al. [16] presented a Blockchain-based deployment mechanism
for IoT security, ensuring tamper-proof communication and decentralized authentication in smart ecosystems. Rayala et al. [17]
advanced renewable energy forecasting through the Roosters Optimization Algorithm integrated with hybrid deep learning models,
significantly improving forecasting precision and robustness under dynamic environmental conditions. Kurubacakoğlu and Duru
[18] concluded the spectrum of research by analyzing factors influencing electricity price forecasting in Türkiye, identifying
macroeconomic and renewable integration parameters that shape price volatility. Collectively, these studies underline the growing
convergence of AI, blockchain, optimization, and smart energy management in addressing challenges of grid stability, security, and
efficiency within modern energy and communication networks.
III. Proposed Methodology
The proposed methodology for this study focuses on conducting a comparative evaluation of congestion management
strategies within hybrid electricity markets. The approach integrates both technical simulation and economic analysis to assess the
operational efficiency, cost-effectiveness, and market performance of selected congestion management techniques. The
methodology is structured into several systematic phases—data acquisition, model formulation, method implementation,
performance evaluation, and comparative analysis. This multi-stage framework ensures that both technical and market-driven
factors are considered in analyzing congestion behavior and management effectiveness in hybrid market environments.
1. System Modeling and Data Preparation: The initial step involves modeling the hybrid electricity market and defining the
test system parameters. Standard IEEE bus systems, such as IEEE 14-bus, 30-bus, or 57-bus test cases, are utilized to simulate
realistic transmission network conditions. Each bus represents a node in the power system, with defined parameters for generation
capacity, load demand, and transmission line limits. The market structure is designed to reflect the characteristics of a hybrid
model—where regulated and deregulated segments coexist.
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The regulated segment includes essential services and base-load generation, controlled under fixed tariffs or
administrative pricing.
The deregulated segment allows market participants (generators and consumers) to submit bids and offers based on real-
time market dynamics.
System data such as line impedance, bus voltages, generator cost coefficients, and load demand profiles are obtained from
standard datasets. These inputs form the foundation for performing load flow and congestion simulations under varying operating
conditions.
2. Congestion Identification through Load Flow Analysis: To identify congestion points within the system, Newton-Raphson
Load Flow (NRLF) analysis is performed. This step calculates the real and reactive power flow across each transmission line,
comparing the results with their respective thermal and stability limits. If the power flow on any transmission line exceeds its
limit, the line is considered congested. The identification of congestion is expressed mathematically as:
where is the active power flow between bus i and j, and is the maximum permissible limit of the line.
Once congestion is detected, different congestion management strategies are applied to assess their ability to restore system
balance and maintain market efficiency.
3. Implementation of Congestion Management Strategies: The core of the methodology involves implementing multiple
congestion management techniques within the same hybrid market framework for comparative assessment. The following
strategies are selected for evaluation:
Redispatching Method: Adjusts the output of selected generators to relieve congestion. Minimizes total generation cost subject
to transmission and generation constraints. Formulated as an optimization problem using the following objective:
subject to power balance and transmission limits.
Optimal Power Flow (OPF)-Based Method: Determines the optimal generation dispatch that minimizes congestion cost while
satisfying system constraints. Employs algorithms such as DC-OPF for computational efficiency. The objective is to minimize
total operation cost and LMP variation.
Market-Based Mechanisms (Nodal and Zonal Pricing): Nodal pricing reflects the marginal cost of supplying the next unit of
electricity at each node considering congestion and losses. Zonal pricing groups multiple nodes into zones to simplify market
settlements while accounting for intra-zonal congestion. These mechanisms provide transparent economic signals for congestion
relief and efficient resource allocation.
Transmission Pricing and Curtailment Strategies: Transmission usage is priced based on congestion severity. Load
curtailment is introduced as a last resort in heavily congested networks to maintain system security.
Each strategy is implemented using MATLAB or Python-based power system simulation tools integrated with optimization
libraries. Market clearing mechanisms are simulated under varying demand and generation scenarios to capture both technical and
economic impacts.
IV. Result & Analysis
The proposed comparative study of congestion management strategies in hybrid electricity markets was implemented and tested
using standard IEEE 30-bus and IEEE 57-bus systems. The experiments aimed to assess the performance of various congestion
management methods—including Redispatching, Optimal Power Flow (OPF), Nodal Pricing, and Zonal Pricing—under diverse
load and generation conditions. The results were evaluated using key performance indicators (KPIs): Total Congestion Cost
(TCC), System Loss Reduction (SLR), Locational Marginal Price (LMP) Variation, Computation Time (CT), and Market
Fairness Index (MFI). The findings from simulation-based analysis are presented and discussed below.
1. Total Congestion Cost (TCC): The Total Congestion Cost (TCC) represents the overall expense incurred by the system
operator to alleviate congestion. The results show that OPF-based methods achieved the lowest TCC compared to other
techniques. This is primarily because OPF simultaneously optimizes generator dispatch and transmission flows, minimizing cost
while respecting operational limits shown in below TABLE I.
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Quantitative Comparison of Congestion Cost Across Different Management Techniques
Method TCC (in $/hr) Relative Reduction
(%)
Redispatching 1420 –
OPF-Based 980 31
Nodal Pricing 1150 19
Zonal Pricing 1260 11.3
Fig. 1. Comparative Analysis of Total Congestion Cost Across Management Strategies
Redispatching, though effective for rapid congestion relief, is cost-intensive due to manual generation adjustments. OPF achieved
the greatest cost reduction, highlighting its suitability for hybrid markets where both technical and economic factors are critical.
Fig. 2 depicts market-based approaches (Nodal and Zonal pricing) demonstrated moderate cost savings while ensuring
transparency and economic efficiency.
2. System Loss Reduction (SLR): The System Loss Reduction (SLR) metric evaluates the improvement in power system
efficiency after congestion management. The OPF and Nodal Pricing methods achieved significant reductions in total system
losses due to optimized power flow and better utilization of transmission lines shown in TABLE II.
Numerical Evaluation of Power Loss Reduction Achieved by Each Strategy
Method System Loss Before
(MW)
System Loss After
(MW)
SLR (%)
Redispatching 15.2 13.1 13.8
OPF-Based 15.2 11.8 22.4
Nodal Pricing 15.2 12.3 19.1
Zonal Pricing 15.2 13.6 10.5
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Fig. 2. Evaluation of System Loss Reduction Performance for Various Strategies
OPF-based management provided the maximum reduction in losses by optimally redistributing generation. Fig. 2. shows the
nodal pricing performed close to OPF due to efficient locational price signaling. Zonal pricing was less effective as it generalized
intra-zonal congestion, leading to less precise corrective actions.
3. Locational Marginal Price (LMP) Variation: LMP variation provides insights into the market efficiency and price stability
after congestion management. Ideally, effective strategies should minimize extreme price variations across network nodes is
demonstrated in TABLE III.
Statistical Summary of Locational Marginal Price Variations Among Market Nodes
Method Average LMP Variation (₹/MWh) Reduction in Price Volatility (%)
Redispatching 520 8.4
OPF-Based 340 39.8
Nodal Pricing 380 33.7
Zonal Pricing 410 28.7
Fig. 3. Assessment of Locational Marginal Price Stability in Congestion Management
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The OPF-based approach significantly reduced LMP disparities, ensuring greater price uniformity and economic stability. Nodal
pricing also effectively captured congestion signals and provided transparent pricing aligned with system conditions.
Redispatching methods, being technically driven, did not reflect economic signals efficiently, resulting in higher price variations
illustrated in Fig. 3.
4. Computation Time (CT): The Computation Time (CT) metric evaluates the computational efficiency of each method, which
is crucial for real-time market operations shown in TABLE IV.
Performance Benchmarking Based on Computational Efficiency
Method Computation Time (sec) Relative Efficiency
Redispatching 1.45 High
OPF-Based 3.28 Moderate
Nodal Pricing 2.1 Good
Zonal Pricing 1.78 Very High
Fig. 4. Computational Efficiency Comparison of Congestion Management Methods
Redispatching and Zonal Pricing demonstrated faster computational times, suitable for quick operational decisions. OPF-based
methods, although computationally heavier, offered the most optimal results in cost and performance trade-offs demonstrated in
Fig. 4. With modern computational advancements, the processing time for OPF remains acceptable for hybrid market
applications.
5. Market Fairness Index (MFI): The Market Fairness Index (MFI) quantifies the fairness of price allocation and cost recovery
among market participants. A higher value indicates more equitable market conditions is shown in TABLE V.
Comparative Assessment of Market Fairness Indicators In Congestion Management
Method MFI (0–1 Scale)
Redispatching 0.72
OPF-Based 0.91
Nodal Pricing 0.88
Zonal Pricing 0.8
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Fig. 5. Market Fairness Index Comparison for Different Congestion Management Approaches
OPF-based methods achieved the highest fairness due to integrated optimization across technical and economic parameters.
Nodal pricing closely followed, demonstrating efficient price discovery and equitable allocation. Zonal pricing was moderately
fair but suffered from generalized cost allocation within zones. Redispatching, being operator-driven, lacked transparent cost
signaling, reducing fairness in market participation illustrated in above Fig. 5.
V. Conclusion
The comparative study of advanced techniques such as Optimal Power Flow (OPF) and market-based mechanisms like Nodal and
Zonal Pricing offers more comprehensive, scalable, and economically efficient solutions suitable for hybrid markets where both
regulated and deregulated structures coexist. Among these, the OPF-based approach proved most effective, delivering significant
reductions in total congestion cost and system losses while minimizing price variation and enhancing market fairness. Nodal
pricing also performed strongly by promoting transparent and equitable price signals, whereas Zonal pricing and Redispatching,
though faster computationally, were less accurate and less fair in cost allocation. The findings affirm that integrating OPF
optimization with market-based pricing mechanisms provides the most balanced framework for managing congestion in hybrid
markets, combining technical robustness with economic transparency. This integrated approach ensures both system stability and
market competitiveness, making it ideal for modern electricity networks facing growing renewable integration and fluctuating
demand. Moving forward, the study recommends the development of adaptive, AI-driven congestion management frameworks
leveraging real-time data analytics and decentralized technologies like blockchain to enhance efficiency, transparency, and
resilience in next-generation smart grids, ensuring sustainable and equitable electricity market evolution.
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